Methods of small molecule biochemical profiling of individual subjects for disease diagnosis and health assessment

Abstract

A method of small molecule biochemical profiling of an individual subject for diagnosis of a disease or disorder, a method for facilitating diagnosis of a disease or disorder, and / or a method for identifying an increased risk of developing a disease or disorder in an individual subject.SOLUTION: An aberrant level of a small molecule present in a sample from the individual subject is identified, and diagnostic information associated with the individual subject is obtained based on the identified aberrant level. The obtained diagnostic information includes one or more of an identification of at least one biochemical pathway associated with the identified subset of small molecules having abnormal levels, an identification of at least one disease or disorder associated with the identified subset of small molecules having abnormal levels, and an identification of at least one recommended follow-up test associated with the identified subset of small molecules having abnormal levels.SELECTED DRAWING: None

Description

[Technical Field]

[0001] Related Applications This application claims priority to U.S. Provisional Patent Application No. 61 / 976,886, filed August 8, 2014, U.S. Provisional Patent Application No. 62 / 037,422, filed August 14, 2014, and U.S. Provisional Patent Application No. 62 / 118,338, filed February 19, 2015, the contents of all of which are incorporated herein by reference in their entireties. [Background technology]

[0002] background Metabolomics is a rapidly developing field that aims to measure all small molecules (metabolites) present in biological samples. Metabolites represent intermediate biological processes that act as a bridge between gene function, environmental influences, and health / disease endpoints (Suhre, K. & Gieger, C. Genetic variation in metabolic phenotypes: study designs and applications. Nat Rev Genet 13, 759-769 (2012)). Metabolic phenotyping and genomics can provide new insights into gene function, disease pathology, and biomarkers for disease diagnosis and prognosis (Suhre, K. et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature 477, 54-60 (2011); Milburn, M.V. & Lawton, K.A. Application of metabolomics to the diagnosis of insulin resistance. Annu Rev Med 64, 291-305 (2013)).

[0003] Since the beginning of this century, metabolomics has found a place in scientific research and discovery. This approach has been widely and successfully used to identify biomarkers for various indications. However, metabolomics has not been widely used to diagnose disease in individual patients.

[0004] Currently, clinical diagnostic tests measure from one compound (e.g., urinary organic acids) to up to 100 biochemical compounds, typically only one biochemical, necessitating the running of many diagnostic tests. Typically, tests are run one after the other, requiring a sufficient amount of sample (e.g., at least 0.5 mL per test), and in some cases, invasive methods (e.g., tissue biopsy) to obtain the sample. [Prior art documents] [Non-patent literature]

[0005] [Non-Patent Document 1] Suhre, K. & Gieger, C. Genetic variation in metabolic phenotypes: study designs and applications. Nat Rev Genet 13, 759-769(2012) [Non-patent document 2] Suhre, K. et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature 477, 54-60 (2011) [Non-patent document 3] Milburn, MV & Lawton, KA Application of metabolomics to diagnosis of insulin resistance. Annu Rev Med 64, 291-305(2013) Summary of the Invention

[0006] overview Some embodiments described herein include systems, methods, and devices that use metabolic profiling of symptomatic and asymptomatic individuals with unknown disease (or, as a control, known disease) in a clinical setting. In some embodiments, metabolomic profiling of symptomatic individuals aids in the diagnosis of the disease or disorder responsible for the symptoms observed in these individuals. In some embodiments, metabolomic profiling of symptomatic individuals determines the severity of the individual's disease. In some embodiments, metabolomic profiling of asymptomatic individuals reveals health risks, allowing for therapeutic intervention before symptoms and disease onset or at an early stage of disease. In the examples described herein, biological samples from individuals were examined for changes (increases or decreases, presence or absence) in biochemicals and associated biochemical pathways, and changes in endogenous, dietary, microbial, and xenobiotic small molecules (i.e., metabolites, biochemicals, chemicals, and compounds) indicative of perturbations in biochemical subpathways and pathways associated with diseases and disorders were detected, and individuals in a population of individuals were identified as having or at risk for certain diseases or disorders. In this example, for each individual in a population of individuals, one biological sample from the individual was simultaneously examined for the presence, absence, or abnormal levels of endogenous, dietary, microbial, and xenobiotic small molecules. The simultaneous examination of biological samples included automatically generating biochemical profiles of the biological samples and then automatically performing statistical analysis of the biochemical profile data to identify small molecules that were statistical outliers. This example demonstrates that generated biochemical profiles of samples from symptomatic individuals and automated statistical analysis of the generated biochemical profiles reveal perturbed or abnormal biochemical pathways and / or abnormal levels of small molecules that aid in the diagnosis of the symptomatic individual. In some embodiments, the results of the automated statistical analysis of the generated biochemical profiles may be visualized to aid in the identification of perturbed or abnormal biochemical pathways and / or abnormal levels of small molecules.In some embodiments, a list of one or more potential diagnoses associated with the identified perturbed or abnormal biochemical pathway and / or abnormal levels of small molecules is displayed. In some embodiments, a list of additional diagnostic tests and methods associated with the identified perturbed or abnormal biochemical pathway and / or abnormal levels of small molecules is displayed. In some embodiments, the systems, methods, and devices of the present invention expand the ability to diagnose disease in individuals and improve clinical assessment of an individual's health status.

[0007] In one aspect, a method is provided for aiding in the diagnosis of a symptomatic individual.

[0008] In one embodiment, the symptomatic individual is a newborn.

[0009] In one embodiment, a method is provided for identifying at-risk individuals (ie, asymptomatic individuals) within a "healthy" clinical cohort.

[0010] In one embodiment, the asymptomatic individual is a newborn.

[0011] In one embodiment, the disease being diagnosed is a rare disease.

[0012] An embodiment includes a method for facilitating the diagnosis of a disease or disorder in an individual subject. The method includes obtaining a sample from the individual subject and creating a small molecule profile of the sample containing information regarding the presence or absence and level of each of a plurality of small molecules in the sample. The method also includes comparing the small molecule profile of the sample to a reference small molecule profile containing a normal range of levels of each of the plurality of small molecules, and identifying a subset of small molecules in the sample each having an abnormal level. An abnormal level of a small molecule in the sample is a level that is outside the normal range of the small molecule. The comparison and identification are performed using an analysis facility running on a processor of a computing device. The method further includes obtaining diagnostic information from a database based on the abnormal levels of the identified subset of small molecules. The database holds, for each of a plurality of diseases and disorders, information relating to the disease or disorder to an abnormal level of one or more small molecules of the plurality of small molecules. The method also includes storing the obtained diagnostic information. The stored diagnostic information includes one or more of the following: identification of at least one biochemical pathway associated with the identified subset of small molecules having abnormal levels; identification of at least one disease or disorder associated with the identified subset of small molecules having abnormal levels; and identification of at least one recommended follow-up test associated with the identified subset of small molecules having abnormal levels.

[0013] In one embodiment, the disease or disorder to be diagnosed is any of a number of diseases and disorders, including, but not limited to, urea cycle disorders, organic acidemias, disorders of amino acid metabolism and transport, fatty acid oxidation disorders, disorders of carbohydrate metabolism, defects in nucleoside synthesis and recycling, disorders of metal transport, disorders of neurotransmitters and small peptides, disorders of mitochondrial energy metabolism, lysosomal storage diseases, disorders of protein glycosylation, disorders of pentose phosphate metabolism, disorders of glucose transport, disorders of ketogenesis and ketone breakdown, disorders of vitamin / cofactor synthesis and recycling, disorders of lipid metabolism and bile acid metabolism, and human inherited metabolic disorders, including peroxisomal diseases.

[0014] Another embodiment includes a method for screening an individual subject for multiple diseases or disorders. The method includes obtaining a sample from the individual subject and creating a small molecule profile for the sample containing information regarding the presence or absence and levels of each of a plurality of small molecules in the sample. The method further includes comparing the small molecule profile of the sample to a reference small molecule profile containing a standard range of levels for each of the plurality of small molecules to determine whether any of the plurality of small molecules has an abnormal level in the sample. An abnormal level of a small molecule in the sample is a level that is outside the standard range for the small molecule. For a small molecule profile having an abnormal level of any of the plurality of small molecules in the sample, a subset of small molecules is identified, each of which has an abnormal level in the sample. The comparison and identification are performed using an analytical function running on a processor of a computing device. For a small molecule profile having an abnormal level of any of the plurality of small molecules in the sample, diagnostic information is obtained from a database based on the abnormal levels of the identified subset of small molecules. The database contains, for each of a plurality of diseases and disorders, information relating an abnormal level of one or more of the plurality of small molecules to information regarding the disease or disorder. For small molecule profiles having abnormal levels of any of a plurality of small molecules in the sample, the obtained diagnostic information is stored. The stored diagnostic information includes one or more of the following: identification of at least one biochemical pathway associated with the identified subset of small molecules having abnormal levels, identification of at least one disease or disorder associated with the identified subset of small molecules having abnormal levels, and identification of at least one recommended follow-up test associated with the identified subset of small molecules having abnormal levels. For small molecule profiles not having abnormal levels of any of a plurality of small molecules in the sample, information indicating that no abnormal levels were detected is stored.

[0015] An embodiment includes a method for screening an individual subject for a high risk of developing multiple diseases or disorders. The method includes obtaining a sample from the individual subject and creating a small molecule profile of the sample containing information regarding the presence or absence and level of each of a plurality of small molecules present in the sample. The method also includes comparing the small molecule profile of the sample to a reference small molecule profile containing a standard range of levels of each of a plurality of small molecules to determine whether any of the plurality of small molecules has an abnormal level in the sample. An abnormal level of a small molecule in the sample is a level that is outside the standard range of the small molecule. For a small molecule profile having an abnormal level of any of a plurality of small molecules present in the sample, a subset of small molecules is identified, each of which has an abnormal level in the sample, where the comparison and identification are performed using an analytical function running on a processor of a computing device. For a small molecule profile having an abnormal level of any of a plurality of small molecules present in the sample, diagnostic information is obtained from a database based on the abnormal levels of the identified subset of small molecules. The database contains, for each of a plurality of diseases and disorders, information correlating abnormal levels of one or more of the plurality of small molecules with information about the disease or disorder. For small molecule profiles having abnormal levels of any of the plurality of small molecules in the sample, obtained diagnostic information is stored. The stored diagnostic information includes an identification of an elevated risk for developing a disease or disorder associated with a specified subset of small molecules having abnormal levels. For small molecule profiles having no abnormal levels of any of the plurality of small molecules in the sample, information indicating that no abnormal levels were detected is stored. [Brief explanation of the drawings]

[0016] [Figure 1] FIG. 1 is a block diagram of a method for facilitating diagnosis of a disease or disorder in an individual subject, according to an embodiment. [Figure 2]FIG. 1 is a block diagram of a method for screening an individual subject for multiple diseases or disorders, according to an embodiment. [Figure 3] FIG. 1 is a block diagram of a method for screening an individual subject for increased risk of developing a disease or disorder, according to an embodiment. [Figure 4] Figure 1 illustrates eight biochemical super-pathways and a visual representation of the classification status of each pathway across 113 patients. [Figure 5] 1 is an exemplary visual representation of the status of all biochemical subpathways for patient 113. FIG. [Figure 6] A visual representation of the abnormal biochemical sub-pathways for patient 113 is illustrated. [Figure 7] 1 is an exemplary visual representation of individual abnormal biochemicals for a patient 113. [Figure 8] 1 is an example of a visual display of individual abnormal biochemicals for a patient 113 based on Z-score analysis to diagnose a disorder or biochemically correlate with diagnostic biochemicals. [Figure 9] 9 is an example of a visual display of individual abnormal biochemicals for patient 101. In FIG. 9, "1-pentadecanoylglycerophosphocholin" refers to 1-pentadecanoylglycerophosphocholine (15:0), "15-methylpalmitate-isobar-with-2-meth" refers to 15-methylpalmitate (isobaric with 2-methylpalmitate), and "3-carboxy-4-methyl-5-propyl-2-furanpro" refers to 3-carboxy-4-methyl-5-propyl-2-furanpropanoate (CMPF). [Figure 10]Figure 10 depicts a visual representation of the biochemical sub-pathways identified as abnormal for patient 101. In Figure 10, "Drug" refers to xenobiotic drug metabolites. "Fatty.Acid.Metabolism.Also.BCAA.Meta..." refers to fatty acid metabolism (also BCAA metabolism), and "Pyrimidine.Metabolism.Thymine.Conta..." refers to pyrimidine metabolism (thymine containing). [Figure 11] This is a visual representation of the individual abnormal biochemicals in patient 139. In Figure 11, "1-eicosapentaenoylglycerophosphoethanolamine..." refers to 1-eicosapentaenoylglycerophosphoethanolamine, and "2-arachidonoylglycerophosphoethanol..." refers to 2-arachidonoylglycerophosphoethanolamine. [Figure 12] This is an example of a visual display of individual abnormal biochemicals for patient 120844. In Figure 12, "5-acetylamino-6-formylamino-3-methyl..." refers to 5-acetylamino-6-formylamino-3-methyluracil, and "5α-androstane-3α,17β-diol..." refers to 5α-androstane-3α,17β-diol disulfate. [Figure 13] 1 is an exemplary visual representation of individual abnormal biochemicals for patient 135109. [Figure 14] This is a visual representation of the individual abnormal biochemicals in patient 135308. In Figure 14, "3-carboxy-4-methyl-5-propyl-2-furanpro..." refers to 3-carboxy-4-methyl-5-propyl-2-furanpropanoate (CMPF). [Figure 15] An example of a visual representation of the branched-chain amino acid (BCAA) biochemical pathway with abnormal metabolites from 51 patients and the BCAA metabolic disorders mapped to the pathway. Disorders are designated by numbers 1-7. Diamonds indicate known biochemical pathway disruptions for a particular disorder. [Figure 16]16A and 16B are plots of an exemplary visual representation of metabolites that changed as a result of patient replacement therapy in Example 4. Figure 16A is a plot of the change in trimethylamine N-oxide levels measured in patients treated with supplemental carnitine, separated or "binned" by the patient's IEM disorder. As shown by the plot, supplemental carnitine increases trimethylamine N-oxide levels. Figure 16B is a plot of the change in phenylactylglutamine levels in patients treated with ammonia-scavenging phenylbutyrate therapy, binned by the patient's IEM disorder. As shown by the plot, ammonia-scavenging phenylbutyrate therapy increases phenylacetylglutamine. Figure 16C is a plot of the change in creatine levels measured in patients treated with supplemental creatine, binned by the patient's IEM disorder. As shown by the plot, creatine can be measured directly in plasma. "Other" refers to the remaining patients with IEM that do not fall into one of the binned classes shown. [Figure 17] Figure 1 shows an exemplary visual representation of biochemical pathways associated with argininemia and the abnormal metabolites mapped to the pathway. Patients are indicated by numbers 1–4. Diamonds indicate the location of the defective enzyme in argininemia. Values ​​are z-scores (y-axis), and gray dashed lines indicate z-scores of >2 or <−2. Pathway intermediates are indicated by numbered circles: (1) arginine, (2) argininosuccinate, (3) citrulline, (4) ornithine, (5) carbamoylphosphate, (6) orotate, (7) uridine, (8) uracil, (9) dihydrouracil, (10) 3-ureidopropionate, (11) homoarginine, (12) homoargininosuccinate, and (13) homocitrulline. [Figure 18] Graphical representation of the total number of metabolite outliers for each subject, ranked. [Figure 19]An exemplary visual representation of dot plots showing data distribution within the cohort for acetaminophen metabolites, four primary bile acids, and glutathione is shown. Black dots indicate metabolite levels of 3976 or 3958. White dots indicate data distribution for the remainder of the cohort. The box represents the middle 50% of the distribution, and the left and right "whiskers" represent the overall spread of the data. The vertical line indicates the median, and the plus sign represents the mean. [Figure 20] Figure 1 shows an exemplary visual representation of a simplified metabolic scheme for energy metabolism and a dot plot illustrating the data distribution of key metabolites known to be associated with type 2 diabetes in the cohort. Black dots indicate metabolite levels for specific subjects indicated adjacent to the plot. White dots indicate the data distribution for the remainder of the cohort. The box represents the middle 50% of the distribution, and the left and right "whiskers" represent the overall spread of the data. The vertical line indicates the median, and the plus sign represents the mean. [Figure 21] A diagram of the bile acid cycle and dot plots showing data distribution within the cohort for four primary bile acids are shown. Black dots indicate metabolite levels of 3917 or 3952. White dots indicate data distribution for the remainder of the cohort. The box represents the middle 50% of the distribution, and the left and right "whiskers" represent the overall spread of the data. The vertical line indicates the median, and the plus sign represents the mean. [Figure 22] FIG. 1 is a block diagram of a computing device for use in performing some steps of some example methods, according to some aspects. [Figure 23] 1 is an exemplary report showing analysis results for an individual and reporting the individual's clinical status, including a disease risk score, according to an embodiment. [Figure 24] 1 shows a diagram of a visual display of scores for an exemplary disease- or disorder-specific composite scoring method to aid in the diagnosis of isovaleric acidemia in a subject. Composite scores for 200 patient samples are shown. Composite scores for two isovaleric acidemia subjects are shown as black boxes. [Figure 25]1 shows a diagram of a visual display of scores for an exemplary disease- or disorder-specific composite scoring method to aid in the diagnosis of lysinuric protein intolerance in a subject. Composite scores for 200 patient samples are shown. The composite scores for two lysinuric protein intolerant subjects are shown as black boxes. DETAILED DESCRIPTION OF THE INVENTION

[0017] Detailed Description To diagnose or aid in the diagnosis of disease in an individual subject, it is desirable to have a single test that can measure hundreds of biochemicals in multiple biochemical classes and pathways using a small sample volume (e.g., 100 μl or less). Such a test could also identify disease earlier than current clinical diagnostic tests, thereby allowing for earlier treatment and / or intervention. Such a test could be useful for early detection of metabolic indications of disease in healthy or asymptomatic individuals or as a health assessment. Such a test could be useful in symptomatic individuals who have not yet been diagnosed. Such a test could be particularly useful in newborns, where it may be difficult to obtain multiple large samples.

[0018] To control ever-increasing healthcare costs and maintain quality of life, it is important to incorporate more advanced and informative technologies into clinical practice to guide disease prevention and early diagnosis. Some aspects of the technology described herein use an efficient and effective approach to obtain a comprehensive metabolic phenotype of an individual. This approach can be useful for assessing an individual's health status and aiding in the diagnosis of disease based on small sample sizes.

[0019] definition "Sample" or "biological sample" or "specimen" refers to biological material isolated from a subject. A biological sample may contain any biological material suitable for detecting a desired biomarker and may include cellular and / or non-cellular material derived from a subject. A sample can be isolated from any suitable biological tissue or fluid, such as, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample. A sample can also be a dried blood spot, for example, where a blood sample is blotted onto filter paper and allowed to dry.

[0020] "Subject" means any animal, but is preferably a mammal, such as, for example, a human, monkey, non-human primate, rat, mouse, cow, dog, cat, pig, horse, or rabbit. The subject may be symptomatic (i.e., may have one or more characteristics suggestive of the presence of or predisposition to a disease, condition, or disorder, including genetic indicators of the disease, condition, or disorder) or asymptomatic (i.e., may lack the characteristics). The subject may be a newborn. The subject may be an infant. The subject may be a child or juvenile. The subject may be an adult.

[0021] "Human infant" means a young human child in the earliest period of life, especially before they can walk, typically from birth to one year of age.

[0022] "Neonatal human" means the period beginning at birth and continuing up to 28 days after birth.

[0023] The "level" of one or more biomarkers refers to the absolute or relative amount or concentration of the biomarker in a sample.

[0024] "Small molecules," "metabolites," and "biochemicals" refer to organic and inorganic molecules present in cells. The terms do not include large macromolecules, such as large proteins (e.g., proteins with molecular weights greater than 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, or 10,000), large nucleic acids (e.g., nucleic acids with molecular weights greater than 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, or 10,000), or large polysaccharides (e.g., polysaccharides with molecular weights greater than 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, or 10,000). Cellular small molecules are generally found in a dissolved and free state in the cytoplasm or other organelles such as mitochondria. In these locations, cellular small molecules form pools of intermediates that are further metabolized or used to generate larger molecules called macromolecules. The term "small molecule" includes signaling molecules and intermediates in chemical reactions that convert food-derived energy into usable forms. Non-limiting examples of small molecules include sugars, fatty acids, amino acids, nucleotides, intermediates formed during cellular processes, and other small molecules found within cells.

[0025] By "endogenous small molecule" is meant a small molecule that originates from or is produced within a human subject.

[0026] "Dietary small molecules" refers to small molecules that originate from the diet, typically derived from plants and not endogenously produced in humans, such as essential amino acids and essential fatty acids.

[0027] "Xenobiotic small molecule" or "xenobiotic" refers to a small molecule found in an organism that is not normally produced in nature or expected to be present in the organism. As used herein, xenobiotic small molecule refers to a chemical substance such as a drug (e.g., antibiotic), toxin, poison, or pollutant (e.g., dioxin, polychlorinated biphenyls).

[0028] By "small microbial molecule" is meant a small molecule that originates from a microorganism and is not produced in humans (eg, 3-indoxyl sulfate).

[0029] "Abnormal" or "abnormal metabolite" or "abnormal level" refers to a metabolite or level of said metabolite above or below a defined standard range. For example, for some metabolites, a level of at least 1.5 * Log-transformed levels outside the IQR (interquartile range) are abnormal. * Log-transformed levels outside the IQR are identified as abnormal. In some cases, levels of at least 1.5 * Data were analyzed assuming that log-transformed levels outside the IQR were abnormal, and in some cases, levels of at least 3.0 * Data was analyzed assuming that log-transformed levels outside the IQR were abnormal. In another example, for some metabolites, metabolites with a Z-score of >1 or <-1 at the log-transformed level are abnormal. In some embodiments, for some metabolites, metabolites with a Z-score of >1.5 or <-1.5 at the log-transformed level are abnormal. In some embodiments, for some metabolites, metabolites with a Z-score of >2.0 or <-2.0 at the log-transformed level are abnormal. In some embodiments, different ranges of Z-scores are used for different metabolites. In some embodiments, the defined normal range may be based on the IQR of the level instead of the IQR of the log-transformed level. In some embodiments, the defined normal range may be based on the Z-score of the level instead of the Z-score of the log-transformed level. Abnormal metabolites may also include rare metabolites and / or missing metabolites, as defined below. Abnormal metabolites can be ascertained using any statistical method.

[0030] An "outlier" or "outlier value" refers to any biochemical that has a level above or below a defined normal range. Any statistical method can be used to determine an outlier value. As non-limiting examples, the following tests may be used to identify outliers: t-test, Z-score, modified Z-score, Grubbs test, Tietjen-Moore test, and Generalized Extreme Studentized Deviate (ESD), which can be performed on transformed (e.g., log-transformed) or untransformed data.

[0031] "Pathway" is a commonly used term to define a series of interconnected steps or reactions. For example, a biochemical pathway in which the product of one reaction serves as a substrate for a subsequent reaction. Biochemical reactions are not necessarily linear. More precisely, the term biochemical pathway is understood to include a network of interrelated biochemical reactions involved in metabolism, including biosynthetic and catabolic reactions. An unqualified "pathway" can refer to a "superpathway" and / or a "subpathway." A "superpathway" refers to a broad category of metabolism. A "subpathway" refers to any subset within an even broader pathway. For example, glutamate metabolism is a subpathway of the amino acid metabolism biochemical superpathway. An "abnormal pathway" refers to a pathway in which one or more abnormal biochemicals are mapped, or in which the biochemical distance of the pathway in an individual is long when compared to the expected biochemical distance of the pathway in a population (e.g., the biochemical distance of the pathway in an individual is in the top 10% when compared to a population of samples derived from reference subjects for a particular pathway).

[0032] A "rare disease" or "orphan disease" is any disease that affects a small percentage of the population, typically fewer than 200,000 Americans at any given time, according to the National Institutes of Health.

[0033] "Test sample" means a sample derived from an individual subject to be analyzed.

[0034] "Reference sample" refers to a sample used to determine a standard range of small molecule levels. "Reference sample" may refer to an individual sample derived from an individual reference subject (e.g., a normal (healthy) reference subject or a disease reference subject), which may be selected to closely resemble the test subject in terms of age and sex. "Reference sample" may also refer to a sample containing pooled aliquots derived from the reference samples of individual reference subjects.

[0035] "Anchor sample" refers to a sample that is run in multiple analyses and may be used to compare results from any analyses in which the same anchor sample was run. In some cases, a reference sample may also serve as an anchor sample.

[0036] "Matrix samples" refer to pooled samples designed to most closely resemble the general population in terms of age and gender. They are prepared by taking aliquots from appropriate age- and gender-matched samples and combining them into a single sample pool. These matrix samples are injected throughout the platform operation and serve as technical replicates, allowing for the variability of quantification of all consistently detected biochemicals and the monitoring of overall process variability and platform performance.

[0037] A "rare metabolite" or "rare biochemical" refers to a biochemical that is present in a test sample but is rarely observed in a reference sample. A "rare" metabolite is absent in the reference sample, is below the detection limit, or is present in only a small portion of the reference sample, or is detected in only a small portion of the reference sample (e.g., is detected in less than 15% of the reference samples, or is detected in less than 10% of the reference samples).

[0038] A "missing metabolite" or "missing biochemical" refers to a biochemical that is not detected in the profile of a test sample but is detected in the profile of a reference sample or is detected in more than 90% of the reference samples.

[0039] "Human inherited metabolic disorders" include urea cycle disorders (e.g., argininosuccinate lyase deficiency, argininemia, citrullinemia, guanidinoacetate methyltransferase deficiency, ornithine transcarbamylase deficiency, hyperornithine-homocitrulline-hyperammonemia, citrin deficiency), organic acidemias (e.g., glutaric aciduria, methylmalonic acidemia, isovaleric acidemia, propionic acidemia), disorders of amino acid metabolism and transport (e.g., HMG Co lyase deficiency, maple syrup urine disease, PKU, cobalamin deficiency, Cbl A, CblC, 3-methylcrotonyl-CoA carboxylase deficiency, homocystinuria, molybdenum cofactor, tyrosinemia, aromatic amino acid decarboxylase deficiency, 3-methylglutaconic aciduria, urocanase deficiency, 3-hydroxyisobutyryl-CoA hydrolase deficiency), fatty acid oxidation disorders (e.g., CPTII, MCAD deficiency, very long-chain acyl-CoA dehydrogenase deficiency, SCAD deficiency, LCHAD deficiency, carnitine transport defects, carnitine-acylcarnitine translocase deficiency, trimethyllysine hydroxylase ε deficiency, primary carnitine deficiency, γ-butyrobetaine hydroxylase deficiency), carbohydrate metabolism disorders (e.g., galactosemia), nucleoside synthesis and recycling defects (e.g., dihydropyrimidine dehydrogenase deficiency, xanthinuria, thymidine phosphorylase deficiency, adenosine deaminase deficiency, succinyladenosine lyase deficiency), gold Disorders of mitochondrial transport (e.g., atransferrinemia, aceruloplasminemia), disorders related to central carbon metabolism (e.g., pyruvate dehydrogenase deficiency, citrate transporter deficiency, GLUT1 deficiency, hyperoxaluria), disorders related to GABA metabolism (e.g., succinic semialdehyde dehydrogenase deficiency, 4-aminobutyrate aminotransferase deficiency), disorders of mitochondrial energy metabolism (e.g., cytochrome c oxidase deficiency), lysosomal storage diseases (e.g., cystinosis), disorders of protein glycosylation (e.g., congenital glycosylation disorders), disorders of pentose phosphate metabolism (e.g., ribose-5-phosphate isomerase deficiency, transaldolase deficiency), disorders of glucose transport (e.g., GLUT1 deficiency syndrome), disorders of ketogenesis and ketone breakdown (e.g., mitochondrial 3-hydroxy-3-methylglutaryl-CoA synthetase deficiency, succinyl-CoARefers to inborn errors of metabolism, including disorders of vitamin / cofactor synthesis and recycling (e.g., 3-oxoacid CoA transferase deficiency, mitochondrial acetoacetyl-CoA thiolase deficiency), disorders of lipid and bile acid metabolism (e.g., cholesterol 7α-hydroxylase, sterol 27-hydroxylase), peroxisomal disorders (e.g., Smith-Lemli-Opitz syndrome, Zellweger syndrome), and sarcosinemia.

[0040] Improvements over the technology and / or current clinical practice The overall nature and breadth of analysis obtained from a single small sample to simultaneously interrogate over 7,000 compounds is a major advancement and an improvement over current clinical practice. In the methods described in the Examples, reports generated from the analysis of individual test samples in individual subjects contained over 700 designated metabolites. Interrogation and measurement of these metabolites allows for simultaneous interrogation of metabolites across at least eight biochemical superpathways and 41 biochemical subpathways.

[0041] The small sample volume required to obtain a large amount of biochemical information is a significant improvement over current clinical practice. The sample volume typically used in this method is less than 100 μL of bodily fluid (e.g., blood, plasma, serum, urine, cerebrospinal fluid, saliva, gingival fluid, etc.). In some embodiments, the method allows for the use of small, non-invasive biological samples, allowing for the simultaneous evaluation of many biochemicals, sub-pathways, and super-pathways, resulting in savings in time and cost. In some embodiments, the ability to analyze biochemicals in relation to super-pathways and sub-pathways is an advantage over current clinical tests. One output of the method, according to some embodiments, is a list of recommended clinical diagnostic tests to be performed. Currently, clinical diagnostic tests measure up to 100 biochemical compounds from a single class of compounds (e.g., urinary organic acids) and typically measure only one biochemical. This limits the diagnosis of diseases or disorders affecting that biochemical or class and requires the performance of numerous diagnostic tests. Tests are typically performed sequentially, and therefore, test results can take days to weeks to obtain, which can delay diagnosis. Furthermore, tests require a fairly large sample volume (0.5 mL), and in some cases, invasive methods may be required to obtain the sample (e.g., tissue biopsy). Current clinical diagnostic tests typically only report on one biochemical or one class of compound, so diagnosis is necessarily limited to diseases or disorders in which biochemicals from one biochemical or class of compound are altered. Some embodiments of the present methods simultaneously interrogate a single, small, non-invasive or minimally invasive biological sample for thousands of small molecules from multiple biochemical classes and biochemical pathways, aiding diagnosis by automatically providing a "short list" of the number of diagnostic tests required for follow-up. In some embodiments, the methods described herein represent an improvement over current clinical practice by reducing the number of tests required to confirm a diagnosis.In current clinical practice, achieving the same diagnostic utility as the methods described herein would require multiple different tests, including targeted panel assays for amino acids, acylcarnitines, organic acids, purines, pyrimidines, acylglycines, bile acids, orotic acid, and carnitine biosynthesis intermediates. For example, even within a single disease or disorder, such as cobalamin synthesis disorders or propionic acidemia, multiple different tests are required to distinguish these disorders from each other and determine treatment regimens. In some embodiments, the methods described herein improve upon current clinical practice by analyzing additional metabolites not currently available using current diagnostic panels. In a single evaluation, clinical biochemistry and genetics laboratories were unable to analyze the more than 400 endogenous human analytes positively identified in the examples described herein, even when using all available tests, including urinary organic acid analysis, plasma amino acid analysis, or any of the myriad other smaller, targeted test panels. In some embodiments, investigating the presence, absence, or altered levels of thousands of small molecules in a single, small, non-invasive sample can aid in the diagnosis of dozens, hundreds, thousands, or tens of thousands of diseases or disorders, an improvement over current clinical practice, which involves obtaining multiple, large, or invasive samples from a subject and performing multiple tests. Furthermore, based on the recommendations provided in some exemplary methods described herein, several follow-up diagnostic tests can be performed in parallel, thereby saving diagnostic time. Furthermore, in some embodiments, the method relies on a single sample type. Current clinical diagnostic methods measure several metabolites (e.g., homocysteine) in serum, plasma, or blood, while other diagnostic methods measure metabolites (e.g., organic acids) in urine, and other diagnostic methods measure other biochemicals (e.g., γ-aminobutyric acid) in CSF. The methods described herein involve the use of a reference sample. Statistical analysis of the small molecule profile of the reference sample provides a standard range for the level of each small molecule.

[0042] In some embodiments, the reference sample is designed to closely resemble the test sample in terms of age and gender. For example, an ethylenediaminetetraacetic acid-plasma (EDTA-plasma) test sample from a symptomatic female newborn is compared to a reference sample made from a pool of EDTA-plasma samples from female newborns.

[0043] In some embodiments, reference sample can be operated simultaneously with test sample.In these embodiments, one or more of reference samples can also serve as anchor sample.In some embodiments, test sample can also serve as one of reference samples.In some embodiments, reference sample is analyzed in a separate operation from test sample.In these embodiments, a separate anchor sample can be used that is analyzed in the same operation together with test sample, in order to link the data from the operation using test sample with the data from the operation using reference sample.

[0044] In some embodiments, the methods described herein also include the use of an anchor sample that is run simultaneously (i.e., in the same run) with the test sample. The experimental techniques used in some embodiments (e.g., LC / MS, GC / MS) provide relative values, rather than absolute values, for the levels of small molecules in a sample, which vary between runs. The anchor sample analyzed in the same experimental run can be used to adjust for inter-run variability. For example, after a reference sample is run once, an aliquot of the reference sample can then be run together with a new sample as an anchor sample. This allows for inter-run variability to be corrected, so that the results of the subsequent analysis can be compared with the results of the previous analysis. In another example, a test sample is analyzed once, and the results are reported. Then, the same test sample is analyzed again as an anchor sample together with a new test sample. This allows for inter-run variability to be corrected, so that the results of the second analysis can be compared with the results of the first analysis.

[0045] In the embodiment where the reference sample is operated separately from the test sample, when comparing the test sample with the reference small molecule profile that is generated from the statistical analysis of the reference sample, one or more additional anchor samples are used to correct for the variability between operations.For example, in some embodiments, one or more anchor samples are generated from the pooled aliquots of the reference samples that are derived from reference subjects, and are simultaneously operated with the test sample.Using one or more anchor samples that are generated from the pooled reference samples allows for the variability between operations to be corrected for the comparison of the test sample data with the previous statistical analysis of the reference samples that are derived from multiple reference subjects.The results of the statistical analysis of the reference sample can be stored in a database for comparison.In some embodiments, the reference sample can be operated before the test sample.In some embodiments, the reference sample can be operated after the test sample.

[0046] In some embodiments, the data from the reference samples from multiple reference subjects are statistically analyzed to obtain reference small molecule profile, which comprises the standard range of the level of each small molecule.In some embodiments, one or more anchor samples are created from the pooled aliquot of reference samples, and are run simultaneously with test sample.The one or more anchor samples created from pooled reference samples can be used to correct the variation between runs, so as to compare test sample data with the previous statistical analysis of the samples from multiple reference subjects.

[0047] Abnormal or outlier small molecules are identified by comparing the test sample with a reference small molecule profile, after correcting for run-to-run variability as needed. In some embodiments, for each individual, abnormal or "outlier" metabolites, super-pathways, and sub-pathways may be automatically identified, automatically reported, and automatically visualized by automated statistical analysis based on Euclidean distance. In some embodiments, outlier metabolites are those with a distance of at least 1.5 *Outlier metabolites are defined as metabolites with log-transformed levels of IQR. Outlier metabolites may also be defined as metabolites with a selected log-transformed Z-score range (e.g., a Z-score of >1 or <-1, a Z-score of >1.5 or <-1.5, or a Z-score of >2.0 or <-2.0). In some embodiments, the Z-score range used to define outlier metabolite levels may be different for some metabolites than for other metabolites (e.g., a Z-score of >1 or <-1 may be used for some metabolites, and a Z-score of >1.5 or <-1.5 may be used for other metabolites). In some embodiments, a report is automatically generated, listing all rarely observed metabolites and common metabolites missing from the profile. In some embodiments, metabolites are automatically sorted into sub-pathways and super-pathways. In some embodiments, an automated visualization of the results is generated showing super-pathways, sub-pathways, and individual small molecules. In some embodiments, abnormal biochemical pathways (i.e., abnormal superpathways and / or abnormal subpathways) are identified. Thus, in some embodiments, the methods analyze an entire biochemical profile and report information derived therefrom to identify abnormal metabolites, biochemical superpathways, and biochemical subpathways (e.g., normal and / or abnormal pathways) to confirm or aid in the diagnosis of a disease or disorder and assess the health status of an individual without relying on a specific diagnostic test comprised of a single biomarker or set of biomarkers (e.g., a biomarker panel) for a particular disease.

[0048] Furthermore, in one embodiment involving report generation, a report is provided for an individual, rather than a group of individuals. This is in contrast to current biochemical profiling or metabolomics-based disease diagnosis methods. Typically, metabolomics / biochemical profiling experiments classify groups of subjects, and subjects are classified only based on their group membership, not individually. This is particularly true for individuals with biochemical profiles or measurements that are considered "outliers." These outliers are typically ignored, removed from the dataset, or considered incorrectly classified in typical metabolomics biochemical profiling experiments. In contrast, the method embodiments presented herein seek to identify outliers and use this information to diagnose or aid in the diagnosis of disease.

[0049] Aspects In one aspect, a method is provided for diagnosing or aiding in the diagnosis of a disease or disorder in a symptomatic subject by examining a biological sample from the symptomatic subject for the presence, absence, or abnormal levels of endogenous, dietary, microbial, and xenobiotic small molecules by statistical analysis to identify outlier values ​​for said small molecules that indicate said small molecules are abnormal in said sample, and to enumerate abnormal small molecules.

[0050] In a further embodiment, the abnormal small molecules are visually displayed. The visualization can display each pathway (e.g., a super-pathway or sub-pathway) with an indication of whether each of the pathways is normal / typical or abnormal, including an indication of rare or missing values ​​for the pathway. Any number of pathways can be listed in the display, and any number of diagrams can be used to present the results. In one embodiment, the diagram displays each abnormal biochemical, categorized according to the level of the biochemical present in the sample. Any number of biochemicals can be displayed in the diagram. For example, all biochemicals measured in the sample may be displayed, or only biochemicals that meet a certain statistical cutoff may be displayed. The biochemicals may be ordered in any way within the display, such as alphabetically, by significance, association with disease, biochemical super-pathways and / or sub-pathways, etc.

[0051] In one embodiment, a method is provided for diagnosing or assisting in the diagnosis of a disease or disorder in a symptomatic subject by examining biological samples from the symptomatic subject for the presence, absence, or abnormal levels of endogenous, dietary, microbial, and xenobiotic small molecules by statistical analysis, identifying outlier values ​​for the small molecules that indicate the small molecules are abnormal in the sample, visualizing and enumerating the list of abnormal small molecules, and mapping the abnormal small molecules to pathways (e.g., biochemical superpathways and / or biochemical subpathways) to diagnose or assist in the diagnosis of the disease or disorder. Small molecules can be classified in any manner, including by association with disease, biochemical relationships, chemical structure, etc. Any number of categories can be used. For example, 1 or more, 2 or more, 3 or more, 5 or more, 10 or more, 15 or more, 20 or more, 50 or more, 100 or more categories can be used to classify small molecules. Any number of subcategories can be used. For example, 1 or more, 2 or more, 3 or more, 5 or more, 10 or more, 15 or more, 20 or more, 50 or more, 100 or more can be used to classify small molecules into subcategories. In one embodiment, abnormal small molecules may be classified by one or more biochemical superpathways, including amino acids; peptides; carbohydrates; energy; lipids; complex lipids; nucleotides; cofactors and vitamins; and xenobiotics. In one embodiment, the abnormal small molecule may be mapped to one or more biochemical sub-pathways, including glycine metabolism, serine metabolism, and threonine metabolism; alanine metabolism and aspartate metabolism; glutamate metabolism; histidine metabolism; lysine metabolism; phenylalanine metabolism and tyrosine metabolism; tryptophan metabolism; leucine metabolism, isoleucine metabolism, and valine metabolism; methionine metabolism, cysteine ​​metabolism, SAM metabolism, and taurine metabolism; urea cycle; arginine metabolism and proline metabolism; creatine metabolism; polyamine metabolism; guanidino metabolism and acetamide metabolism; glutathione metabolism; felinine metabolism; gamma-glutamyl amino acids; dipeptide derivatives; dipeptides; polypeptides; fibrinogen cleavage peptides;Glycolysis, gluconeogenesis, and pyruvate metabolism; glycolysis, gluconeogenesis, and pyruvate metabolism; pentose phosphate pathway; pentose metabolism; glycogen metabolism; disaccharides and oligosaccharides; fructose metabolism, mannose metabolism, and galactose metabolism; nucleotide sugars; amino sugar metabolism; advanced glycation end products; TCA cycle; oxidative phosphorylation; short-chain fatty acids; medium-chain fatty acids; long-chain fatty acids; polyunsaturated fatty acids (n3 and n6); quantitative free fatty acids Acid; Fatty acids, branched; Fatty acids, dicarboxylic; Fatty acids, methyl esters; Fatty acids, esters; Fatty acids, amides; Fatty acids, keto; Fatty alcohols, long chain; Fatty acid synthesis; Fatty acid metabolism; Fatty acid metabolism (also BCAA metabolism); Fatty acid metabolism (acylglycines); Fatty acid metabolism (acylcarnitines); Carnitine metabolism; Ketone bodies; Neurotransmitters; Fatty acids, monohydroxy; Fatty acids, dihydroxy; Fatty acids, oxidized; Eicosanoids; Endocannabinoids; Inositol metabolism; Phospholipid metabolism; Lysolipids; Glycerolipid metabolism; Monoacylglycerol; Diacylglycerol; Sphingolipid metabolism; Mevalonate metabolism; Sterols; Steroids; Primary bile acid metabolism; Secondary bile acid metabolism; Diacylglycerol; Triacylglycerol; Lysophosphatidylcholine; Phosphatidylcholine; Phosphatidylethanolamine; Phosphatidylserine; Sphingomyelin; Sphingolipid metabolism; Ka These include: rgiolipin; cholesterol esters; phospholipids; purine metabolism, (hypo)xanthine / inosine-containing; purine metabolism, adenine-containing; purine metabolism, guanine-containing; pyrimidine metabolism, orotate-containing; pyrimidine metabolism, uracil-containing; pyrimidine metabolism, cytidine-containing; pyrimidine metabolism, thymine-containing; purine and pyrimidine metabolism; nicotinic acid and nicotinamide metabolism; riboflavin metabolism; pantothenic acid and CoA metabolism; ascorbic acid and aldarate metabolism; tocopherol metabolism; biotin metabolism; folate metabolism; tetrahydrobiopterin metabolism; pterin metabolism; hemoglobin and porphyrin metabolism; lipoic acid metabolism; thiamine metabolism; vitamin K metabolism; vitamin A metabolism; vitamin B12 metabolism; vitamin B6 metabolism; benzoic acid metabolism; xanthine metabolism; tobacco metabolites; food ingredients / plants; bacteria; drugs; phthalates; and chemicals.

[0052] In one aspect, a method is provided for diagnosing or aiding in the diagnosis of a disease or disorder in a symptomatic subject by examining a biological sample from the symptomatic subject for the presence, absence, or abnormal levels of endogenous, dietary, microbial, and xenobiotic small molecules by statistical analysis, identifying outlier values ​​for the small molecules that indicate the small molecules are abnormal in the sample, listing the abnormal small molecules, mapping the abnormal small molecules to biochemical pathways (e.g., biochemical superpathways and / or biochemical subpathways), visualizing the map, and providing a list of recommended follow-on clinical diagnostic tests.

[0053] In one aspect, a method is provided for diagnosing or aiding in the diagnosis of a disease or disorder in a symptomatic subject by examining a biological sample from the symptomatic subject for the presence, absence, or abnormal levels of endogenous, dietary, microbial, and xenobiotic small molecules by statistical analysis to identify outlier values ​​for said small molecules that indicate said small molecules are abnormal in said sample, enumerate the abnormal small molecules, map the abnormal small molecules to pathways (e.g., biochemical super-pathways and / or biochemical sub-pathways), visualize the map, and provide a list of possible / possible diseases or disorders.

[0054] In one aspect, a method is provided for diagnosing or aiding in the diagnosis of a disease or disorder in a symptomatic subject by examining a biological sample from the symptomatic subject by statistical analysis for the presence, absence, or abnormal levels of endogenous, dietary, microbial, and xenobiotic small molecules, identifying outlier values ​​for the small molecules that indicate the small molecules are abnormal in the sample, enumerating the abnormal small molecules, mapping the abnormal small molecules to pathways (e.g., biochemical superpathways and / or biochemical subpathways), visualizing the map, providing a list of possible / possible diseases or disorders, and providing a list of recommended follow-on clinical diagnostic tests.

[0055] FIG. 1 is a block diagram of a method 10 for diagnosing a disease or harm in an individual subject, or for facilitating the diagnosis of a disease or disorder, according to some embodiments. A sample is obtained from the individual subject (step 12). A small molecule profile of the sample is created (step 14), containing information about the presence, absence, and levels of each of a plurality of small molecules present in the sample. The small molecule profile of the sample is compared to a reference small molecule profile containing a standard range of levels of each of the plurality of small molecules, and a subset of small molecules present in the sample having abnormal levels is identified (step 16). Diagnostic information is obtained from a database based on the abnormal levels of the identified subset of small molecules (step 18). In some embodiments, obtaining diagnostic information based on the abnormal levels of the identified subset of small molecules includes identifying one or more abnormal biochemical pathways (step 19). The database contains, for each of a plurality of diseases and disorders, information correlating abnormal levels of one or more small molecules of a plurality of small molecules with information about the disease or disorder. In some embodiments, the method includes creating a disease- or disorder-specific composite score based on a weighted combination of data from one or more subsets of small molecules identified as having abnormal levels, and obtaining diagnostic information from a database based on the abnormal levels of the identified subset of small molecules includes obtaining diagnostic information from the database based on the created disease- or disorder-specific composite score. The resulting diagnostic information is stored (step 20). The stored diagnostic information includes one or more of: identification of at least one biochemical pathway associated with the identified subset of small molecules having abnormal levels; identification of at least one disease or disorder associated with the identified subset of small molecules having abnormal levels; and identification of at least one recommended follow-up test associated with the identified subset of small molecules having abnormal levels. Thus, the stored diagnostic information facilitates diagnosis of a disease or disorder in an individual subject. In some embodiments, the method further includes storing a graphical representation of the resulting diagnostic information (step 22).The dashed lines surrounding the boxes for steps 19 and 22 in FIG. 1 indicate that they need not be present in all embodiments.

[0056] In some embodiments of the methods described herein, the individual subject shows one or more symptoms of a plurality of diseases and disorders.In some embodiments, method 10 is a method for diagnosing a disease or disorder in a symptomatic individual subject, or a method for facilitating the diagnosis of a disease or disorder.

[0057] In some embodiments of the methods described herein, individual subjects do not show all symptoms of multiple diseases and disorders.In some embodiments, method 10 is a method for diagnosing disease or disorder in asymptomatic individual subjects, or a method for facilitating the diagnosis of disease or disorder.In some embodiments of the methods described herein, individual subjects are human newborns.In some embodiments of the methods described herein, individual subjects are human infants.

[0058] In step 12, the sample obtained from the individual subject is a biological sample. As explained in the definition section, in various embodiments, the biological sample can be any biological material isolated from a subject. The biological sample can contain any biological material suitable for detecting a desired biomarker, and can include cellular and / or non-cellular material from the subject, and can be isolated from any suitable biological tissue or biological fluid. In some embodiments, the individual subject is a human newborn, and the biological sample is blood or urine. In some embodiments, the individual is a human newborn or human infant, and the biological sample is urine obtained from a diaper.

[0059] In some embodiments, the volume of a sample obtained from an individual subject is less than 500 μL, less than 400 μL, less than 300 μL, less than 200 μL, less than 100 μL, less than 90 μL, less than 80 μL, less than 70 μL, less than 60 μL, less than 50 μL, less than 40 μL, or less than 30 μL. In some embodiments, the volume of a sample obtained from an individual subject is in the range of 20-500 μL, 20-400 μL, 20-300 μL, 20-200 μL, 20-100 μL, 20-90 μL, 20-80 μL, 20-70 μL, 20-60 μL, 20-50 μL, 20-40 μL, or 15 μL-30 μL. As explained above, small biological sample sizes can be particularly beneficial when testing biological samples obtained from human newborns.

[0060] Creating a small molecule profile of the sample (step 14) involves generating information about the presence, absence, and levels of each of a plurality of small molecules. This may also be referred to herein as interrogating the biological sample. A small molecule profile of the sample is generated (step 14) that includes information about the presence, absence, and levels of each of a plurality of small molecules in the sample.

[0061] In some embodiments, the plurality of small molecules comprises two or more of endogenous small molecules, dietary small molecules, microbial small molecules, and xenobiotic small molecules (e.g., endogenous small molecules and microbial small molecules; endogenous small molecules and xenobiotic small molecules; endogenous small molecules and dietary small molecules). In some embodiments, the plurality of small molecules comprises three or more of endogenous small molecules, dietary small molecules, microbial small molecules, and xenobiotic small molecules (e.g., endogenous small molecules, microbial small molecules, and dietary small molecules; endogenous small molecules, microbial small molecules, and xenobiotic small molecules). In some embodiments, the plurality of small molecules comprises endogenous small molecules, dietary small molecules, microbial small molecules, and xenobiotic small molecules.

[0062] The plurality of small molecules includes small molecules from various classes or superpathways, such as amino acids, peptides, carbohydrates, energy, lipids, complex lipids, nucleotides, cofactors and vitamins, and xenobiotics. In some embodiments, the plurality of small molecules includes two or more of sugars, fatty acids, amino acids, nucleotides, and catabolic products. In some embodiments, the plurality of small molecules includes three or more of sugars, fatty acids, amino acids, nucleotides, and catabolic products. In some embodiments, the plurality of small molecules includes four or more of sugars, fatty acids, amino acids, nucleotides, and catabolic products. In some embodiments, the plurality of small molecules includes sugars, fatty acids, amino acids, nucleotides, and catabolic products.

[0063] In some embodiments, the plurality of small molecules comprises more than 25 small molecules, more than 50 small molecules, more than 100 small molecules, more than 200 small molecules, more than 300 small molecules, more than 400 small molecules, more than 500 small molecules, more than 600 small molecules, more than 700 small molecules, more than 800 small molecules, more than 900 small molecules, more than 1000 small molecules, more than 1100 small molecules, more than 1200 small molecules, more than 1300 small molecules, more than 1400 small molecules, more than 1500 small molecules, more than 2000 small molecules, more than 3000 small molecules, more than 4000 small molecules, more than 5000 small molecules, more than 6000 small molecules, or more than 7000 small molecules.

[0064] In some embodiments, the plurality of small molecules includes 25 to 2500 types of small molecules, 50 to 2500 types of small molecules, 100 to 2500 types of small molecules, 200 to 2500 types of small molecules, 300 to 2500 types of small molecules, 400 to 2500 types of small molecules, 500 to 2500 types of small molecules, 600 to 2500 types of small molecules, 700 to 2500 types of small molecules, 800 to 2500 types of small molecules, 900 to 2500 types of small molecules, or 1000 to 2500 types of small molecules.

[0065] In some embodiments, the plurality of small molecules comprises 25 to 25,000 small molecules, 50 to 25,000 small molecules, 100 to 25,000 small molecules, 200 to 25,000 small molecules, 300 to 25,000 small molecules, 400 to 25,000 small molecules, 500 to 25,000 small molecules, 600 to 25,000 small molecules, 700 to 25,000 small molecules, 800 to 25,000 small molecules, 900 to 25,000 small molecules, 1000 to 25,000 small molecules, 1100 to 25,000 small molecules, 1200 to 25,000 small molecules, or 1300 to 25,000 small molecules.

[0066] The generation of a small molecule profile of a sample requires the analysis of its constituent small biochemical molecules, which may include extracting at least a portion of the plurality of small molecules from the sample. Analysis can be performed using one or more different analytical methods known in the art, such as liquid chromatography (LC), high performance liquid chromatography (HPLC) (see Kristal, et al. Anal. Biochem. 263:18-25 (1998)), gas chromatography (GC), thin layer chromatography (TLC), electrochemical separation (see WO99 / 27361, WO92 / 13273, US5,290,420, US5,284,567, US5,104,639, US4,863,873, and USRE32,920), refractive index spectroscopy (RI), ultraviolet spectroscopy (UV), fluorescence spectroscopy, radiochemical analysis, near-infrared spectroscopy (Near-IR), nuclear magnetic resonance spectroscopy (NMR), light scattering spectroscopy (LS), mass spectroscopy (MS), tandem mass spectroscopy (MS / MS), or the like. 2 ), as well as combined methods such as gas chromatography / mass spectrometry (GC-MS), liquid chromatography / mass spectrometry (LC-MS), ultra-high performance liquid chromatography / tandem mass spectrometry (UHLC / MS / MS 2 ), and gas chromatography / tandem mass spectrometry (GC / MS / MS 2 ) may be used.

[0067] U.S. Patent No. 7,884,318 discloses a system, method, and computer-readable medium for determining the composition of chemical constituents in a complex mixture, which can be used to generate a small molecule profile of the sample, according to some embodiments. U.S. Patent No. 7,884,318 is incorporated herein by reference in its entirety. As described in U.S. Patent No. 7,884,318, separation data and mass spectroscopy data of the sample are generated, and the generated separation data and mass spectroscopy data are compared with a chemical information library ("compound library") to determine the small molecule chemical constituents of the sample.

[0068] U.S. Patent No. 7,561,975 and U.S. Patent No. 7,949,475, each of which is incorporated herein by reference in its entirety, provide further disclosure regarding assays for identifying small molecule components of biological samples for multiple biological samples.

[0069] In some embodiments, multiple aliquots (e.g., 2, 3, 4, 5, 6, etc.) of a single test sample may be tested. In some embodiments, data analysis to generate a small molecule profile is fully automated, and the information obtained from each analysis of the test sample is recombined after appropriate quality control (QC). In some embodiments, one or more additional control or standard samples (e.g., anchor samples, quality control samples) are analyzed in the same run as one or more aliquots from a single test sample. In some embodiments, aliquots from test samples from multiple different individuals are analyzed in the same run. In one embodiment, the test sample may be run twice, three times, four times, etc., as separate samples.

[0070] In some embodiments, the data analysis to generate the small molecule profile is partially automated (e.g., a human manually verifies the identification of some or all of the small molecules based on the generated data). In some embodiments, the data analysis to generate the small molecule profile is fully automated.

[0071] The small molecule profile of a sample contains information about the presence, absence, and levels of many different small molecules. For some small molecules, the information may indicate a specific chemical composition or specific chemical structure of the small molecule (see the discussion of "named" small molecules below). For some small molecules, the information may indicate a property or behavior of the small molecule resulting from an analytical method, without indicating a specific chemical composition or specific chemical structure of the small molecule (see the discussion of "unnamed" small molecules below).

[0072] In some embodiments, the small molecule profile clearly identifies the small molecules that are confirmed to be present in the sample, the associated levels of each small molecule present, and the small molecules that are confirmed to be absent from the sample.The small molecule profile does not need to clearly identify the small molecules that are confirmed to be absent from the sample, as long as information about the small molecules that are confirmed to be absent from the sample can be inferred.For example, in some embodiments, the small molecule profile identifies the small molecules that are present in the sample and the level of each small molecule present in the sample.If present, the small molecules that are confirmed to be absent can be inferred by comparing with the list of small molecules that are detected based on the experimental technique used.

[0073] The small molecule profile is compared to a reference small molecule profile to identify a subset of the plurality of small molecules, each of which has an abnormal level in the sample (step 16). The reference small molecule profile includes a standard range for the level of each of the plurality of small molecules. An abnormal level of a small molecule in the sample is a level that is outside the standard range for the small molecule. Abnormal levels of a small molecule in the sample include small molecules that are absent in the sample (e.g., missing biochemicals) when the standard range indicates that the small molecule should be present, small molecules that are present in the sample (e.g., rare biochemicals) when the standard range indicates that the small molecule should not be present, and small molecules that are present in the sample at concentrations that are outside the concentrations predicted by the standard range (e.g., outliers).

[0074] In some embodiments, reference small molecule profile is determined at least in part from the statistical analysis of the small molecule profile that is made for reference sample from reference subject population.As mentioned above, in some embodiments, reference sample is selected to be similar to test sample (i.e., sample from individual subject) in terms of age and / or gender.In some embodiments, reference sample is selected to not show symptoms of multiple diseases and disorders.In some embodiments, reference sample is not selected based on whether asymptomatic or symptomatic.

[0075] Like the test samples, each reference control may be divided into multiple aliquots for analysis, which may be analyzed in parallel, simultaneously, or sequentially in various runs, and the resulting data may be combined after appropriate quality control.

[0076] As mentioned above, in some embodiments, the reference sample is analyzed in the same experimental run as the test sample, which serves as the anchor sample. In some embodiments, the reference sample is analyzed in a separate run from the test sample, and an additional anchor sample is used to correct for variations between runs. In some embodiments, the anchor sample is a pooled aliquot of the reference sample or a pooled aliquot of the test sample from a previous run.

[0077] Statistical analysis of the reference sample is used to generate a reference small molecule profile that includes a standard range for each of a plurality of small molecules. In some embodiments, the standard range for a small molecule is based on the interquartile range (IQR) of the log-transformed concentration data of the small molecule in the reference sample (e.g., <1.5). * In some embodiments, the normal range of a small molecule is based on the number of standard deviations from the mean (i.e., Z-score) of the log-transformed concentration data of the small molecule in the reference sample.

[0078] Diagnostic information is obtained from a database based on abnormal levels of the identified subset of small molecules (step 18). The database contains, for each of a plurality of diseases and disorders, information correlating abnormal levels of one or more of the plurality of small molecules with information about the disease or disorder. In methods in which a disease- or disorder-specific composite score is generated based on a weighted combination of data from one or more of the subsets of small molecules identified as having abnormal levels, the database further contains information correlating a range of disease- or disorder-specific composite scores for one or more of the diseases or disorders with information about the disease or disorder.

[0079] In some embodiments, the diagnostic information in the database associates abnormal levels of small molecules with one or more biochemical pathways, and the obtained diagnostic information identifies one or more biochemical pathways associated with the subset of small molecules with abnormal levels. In some embodiments, the diagnostic information in the database associates abnormal levels of small molecules with biochemical superpathways and biochemical subpathways, and the obtained diagnostic information identifies the biochemical superpathways and biochemical subpathways associated with the subset of small molecules with abnormal levels. As used herein, obtaining information about the biochemical pathways (e.g., biochemical superpathways and / or biochemical subpathways) associated with the subset of small molecules with abnormal levels in a sample is referred to as mapping small molecules with abnormal levels to pathways. Information indicating the biochemical pathways associated with the subset of small molecules with abnormal levels is considered diagnostic information because it indicates biochemical pathways (abnormal pathways) that may not be functioning properly in an individual subject. This diagnostic information facilitates diagnosis.

[0080] In some embodiments, pathways associated with a subset of small molecules having abnormal levels are identified as abnormal pathways. In some embodiments, one or more abnormal biochemical pathways are identified by determining that the biochemical distance of the pathway in an individual is long compared to the expected biochemical distance of the pathway in a reference population (e.g., the biochemical distance of the pathway in an individual is in the top 10% compared to a population of samples derived from reference subjects for a particular pathway). The biochemical distance may be calculated from the Euclidean distance from the geometric mean of each small molecule in the pathway. In some embodiments, a standard range of biochemical distances may be determined for each pathway from a reference sample population, and abnormal pathways may be identified by comparing the biochemical distance of each pathway in the sample with the corresponding standard range of biochemical distances for that pathway (e.g., the standard range of biochemical distances may be a range of biochemical distances that includes 90% of the reference samples, such that distances that are outside the standard range or in the top 10% of the reference samples identify abnormal pathways). In some embodiments, a combination of the above is used to identify aberrant pathways (e.g., a pathway is aberrant if it is associated with either a subset or small molecule that has abnormal levels, or if the biochemical distance of the pathway is long compared to the expected biochemical distance of that pathway in the population).

[0081] In some embodiments, the information in the database associates small molecules with abnormal levels with a disease or disorder identification, and the obtained information includes the identification of at least one disease or disorder associated with the identified subset of small molecules with abnormal levels. In some embodiments, the information in the database associates a range of disease-specific or disorder-specific composite scores with the identification of at least one disease or disorder. In some embodiments, the identification is a possible or probable diagnosis of one or more diseases or disorders associated with the identified subset of small molecules with abnormal levels. In some embodiments, the identification is a list of one or more possible diseases or diagnoses associated with the identified subset of small molecules with abnormal levels.

[0082] In some embodiments, the diagnostic information in the database associates small molecules having abnormal levels with the identification of at least one follow-up test, and the obtained information includes the identification of at least one follow-up test associated with the identified subset of small molecules having abnormal levels.

[0083] In some embodiments, the diagnostic information in the database includes information correlating abnormal levels of small molecules with one or more biochemical pathways, information correlating abnormal levels of small molecules with the identification of at least one disease or disorder, and information correlating abnormal levels of small molecules with the identification of at least one follow-up test. In some embodiments, the diagnostic information may be stored in multiple databases (e.g., pathway information may be stored in one database, and information regarding the identification of a disease or disorder and information regarding follow-up tests may be stored in another database).

[0084] The obtained diagnostic information is stored (step 20). The stored diagnostic information includes one or more of: identification of at least one biochemical pathway associated with the identified subset of small molecules having abnormal levels; identification of at least one disease or disorder associated with the identified subset of small molecules having abnormal levels; and identification of at least one follow-up test associated with the identified subset of small molecules having abnormal levels. In some embodiments, the stored diagnostic information includes one or more of: identification of at least one biochemical pathway associated with the identified subset of small molecules having abnormal levels; and identification of at least one disease or disorder associated with the identified subset of small molecules having abnormal levels; and identification of at least one follow-up test associated with the identified subset of small molecules having abnormal levels.

[0085] The abnormal level of one or more small molecules in the sample, which indicates the existence of disease or disorder, is referred to herein as the signature of disease or disorder.The signature can be specific to the method used to test the sample.In some embodiments, the database comprises the signatures of multiple diseases or disorders.

[0086] In some embodiments of the methods described herein, the plurality of diseases and disorders includes more than 2 diseases or disorders, more than 10 diseases or disorders, more than 20 diseases or disorders, more than 30 diseases or disorders, more than 50 diseases or disorders, more than 100 diseases or disorders, more than 200 diseases or disorders, more than 300 diseases or disorders, more than 400 diseases or disorders, more than 500 diseases or disorders, more than 600 diseases or disorders, more than 700 diseases or disorders, more than 800 diseases or disorders, more than 900 diseases or disorders, more than 1000 diseases or disorders, more than 1100 diseases or disorders, more than 1200 diseases or disorders, more than 1300 diseases or disorders, more than 1400 diseases or disorders, more than 1500 diseases or disorders, more than 2000 diseases or disorders, more than 3000 diseases or disorders, more than 4000 diseases or disorders, more than 5000 diseases or disorders, more than 6000 diseases or disorders, or more than 7000 diseases or disorders.

[0087] In some embodiments of the methods described herein, the plurality of diseases and disorders includes between 2 and 10,000 diseases or disorders, between 10 and 10,000 diseases or disorders, between 20 and 10,000 diseases or disorders, between 30 and 10,000 diseases or disorders, between 50 and 10,000 diseases or disorders, between 100 and 10,000 diseases or disorders, between 200 and 10,000 diseases or disorders, between 300 and 10,000 diseases or disorders, between 400 and 10,000 diseases or disorders, between 500 and 10,000 diseases or disorders, between 600 and 10,000 diseases or disorders, between 700 and 10,000 diseases or disorders, between 800 and 10,000 diseases or disorders, between 90 ...100 and 10,000 diseases or disorders, between 100 and 10,000 diseases or disorders, between 100 and 10,000 diseases or disorders, between 100 and 10,000 diseases or disorders, between 100 and 10,000 diseases or disorders, between 100 and 1 disorders, 900 to 10,000 diseases or disorders, 1000 to 10,000 diseases or disorders, 1100 to 10,000 diseases or disorders, 1200 to 10,000 diseases or disorders, 1300 to 10,000 diseases or disorders, 1400 to 10,000 diseases or disorders, 1500 to 10,000 diseases or disorders, 2000 to 10,000 diseases or disorders, 3000 to 10,000 diseases or disorders, 4000 to 10,000 diseases or disorders, 5000 to 15,000 diseases or disorders, 6000 to 15,000 diseases or disorders, or 7000 to 15,000 diseases or disorders.

[0088] Some embodiments include a step of storing a graphical representation of some or all of the obtained diagnostic information (step 22). In some embodiments, a graphical representation of one or more biochemical pathways associated with the identified subset of small molecules having abnormal levels is stored. In some embodiments, the graphical representation includes a graphical representation of a superpathway and an indication that the superpathway is associated with the identified subset of small molecules having abnormal levels (see, e.g., Figure 4 and related description in the Examples section below). In some embodiments, the graphical representation includes a graphical representation of a subpathway associated with the identified subset of small molecules having abnormal levels (see, e.g., Figure 6 and related description in the Examples section below). In some embodiments, the graphical representation includes a graphical representation of all biochemical subpathways and a graphical representation of a biochemical subpathway associated with the identified subset of small molecules having abnormal levels (see, e.g., Figure 5 and related description in the Examples section below). In some embodiments, the graphical representation further comprises a graphical representation of the identified subset of small molecules having abnormal levels, and a graphical representation of the respective levels (see, e.g., Figures 7-9, 11-14, and 19-21, and the associated discussion in the Examples section below). As used herein, creating, storing, and / or displaying a graphical representation of some or all of the obtained diagnostic information is also referred to as information visualization or result visualization.

[0089] The graphical representation can be stored in any known format for electronically storing images (e.g., JPEG / JFIF, JPEG2000, Exif, TIFF, RAW, GIF, BMP, PNG, PPM, PGM, PNM, WEBP, CGM, SVG, etc.) In some embodiments, the graphical representation may be incorporated into a document and stored in a document file format (e.g., PDF, .ps, .doc, .docx, .ppt, .odt, .htm, .html, etc.).

[0090] In some embodiments, multiple types of diagnostic information for an individual subject may be compiled and compiled into a single document, herein referred to as a report.The report may include any or all of the following information: the total number of different small biochemicals detected; the identification of the biochemical pathways (e.g., superpathways and / or subpathways) associated with the small molecules with abnormal levels; the identification of which small molecules are present at abnormal levels within the identified subpathways; the identification and level of the small biochemical molecules present at abnormal levels; the identification of rare biochemicals; the identification of missing biochemicals; the indication of whether each outlier biochemical is present at a level higher or lower than the standard range; a list of possible or probable diagnoses; a list of recommended confirmatory tests; and a list of recommended follow-up tests.The report may be stored in a suitable electronic file format.In some embodiments, the report of results is provided to healthcare providers and / or individual subjects in any suitable electronic or non-electronic form (e.g., paper).

[0091] 2 is a block diagram of a method 20 for screening an individual subject for multiple diseases or disorders, according to an embodiment. A sample is obtained from an individual subject (step 32). A small molecule profile of the sample is created (step 34), containing information about the presence, absence, and levels of each of a plurality of small molecules in the sample. The small molecule profile of the sample is compared to a reference small molecule profile containing standard ranges for the levels of each of the plurality of small molecules to determine whether any of the small molecules in the profile have abnormal levels in the sample (step 36). As explained above, abnormal levels of small molecules in the sample include small molecules that are absent in the sample (e.g., missing biochemicals) when the standard range indicates that the small molecule should be present, small molecules that are present in the sample (e.g., rare biochemicals) when the standard range indicates that the small molecule should be absent, and small molecules that are present in the sample at concentrations outside the concentrations predicted by the standard range (e.g., outliers).

[0092] If the sample does not have abnormal levels of any small molecules in its small molecule profile, information indicating that no abnormal levels were detected is stored (step 37).

[0093] If abnormal levels are detected, a subset of the plurality of small molecules having abnormal levels is identified (step 38). Diagnostic information is obtained from a database based on the identified subset of small molecules (step 40). In some embodiments, the method includes creating a disease- or disorder-specific composite score based on a weighted combination of data from one or more of the subsets of small molecules identified as having abnormal levels, and obtaining diagnostic information from the database based on the abnormal levels of the identified subset of small molecules includes obtaining diagnostic information from the database based on the created disease- or disorder-specific composite score. In some embodiments, obtaining diagnostic information based on the abnormal levels of the identified subset of small molecules includes identifying one or more abnormal biochemical pathways (step 41). The database contains, for each of a plurality of diseases or disorders, information relating to the disease or disorder with respect to the disease or disorder. The obtained diagnostic information is stored (step 42). The stored diagnostic information includes one or more of: identification of at least one biochemical pathway associated with the identified subset of small molecules having abnormal levels; identification of at least one disease or disorder associated with the identified subset of small molecules having abnormal levels; and identification of at least one follow-up test associated with the identified subset of small molecules having abnormal levels. In some embodiments, the method also includes a step of storing a graphical representation of the obtained diagnostic information (step 44). The dashed lines surrounding the boxes for steps 41 and 44 in Figure 2 indicate that these steps need not be present in all embodiments.

[0094] In some embodiments, the individual subject does not exhibit symptoms of multiple diseases and disorders, and method 30 is a method for screening an individual asymptomatic subject for multiple diseases or disorders.

[0095] In some embodiments, the individual subject is symptomatic and method 30 is a method for screening the individual symptomatic subject for multiple diseases or disorders.

[0096] In some embodiments, the individual subject is a human newborn and method 30 is a method for screening human newborns for multiple diseases or disorders. In some embodiments, the individual subject is a human infant and method 30 is a method for screening human infants for multiple diseases or disorders.

[0097] 3 is a block diagram of a method 50 for screening an individual subject for an increased risk of developing one of a plurality of diseases or disorders, according to an embodiment. A sample is obtained from an individual subject (step 52). A small molecule profile of the sample is created (step 54), which includes information regarding the presence, absence, and levels of each of a plurality of small molecules in the sample. The small molecule profile of the sample is compared to a reference small molecule profile, which includes a standard range of levels of each of the plurality of small molecules, to determine whether any of the small molecules in the profile have abnormal levels in the sample (step 56). If no small molecules in the small molecule profile of the sample have abnormal levels, information indicating that no abnormal levels were detected is stored (step 57).

[0098] If abnormal levels are detected, a subset of the plurality of small molecules having abnormal levels is identified (step 58). Diagnostic information is obtained from a database based on the identified subset of small molecules (step 60). In some embodiments, obtaining diagnostic information based on abnormal levels of the identified subset of small molecules includes identifying one or more abnormal biochemical pathways (step 61). The database contains information correlating abnormal levels of one or more of the plurality of small molecules with information about the disease or disorder for each of a plurality of diseases or disorders. The obtained diagnostic information, including an identification of an elevated risk for developing a disease or disorder associated with the identified subset of small molecules having abnormal levels, is stored (step 62). In some embodiments, the method also includes storing a graphical representation of the obtained diagnostic information (step 64).

[0099] Abnormal levels of one or more small molecules in a sample that indicate an increased risk for developing a disease or disorder are referred to herein as a signature of increased risk for a disease or disorder. In method 50, the database includes multiple disease or disorder signatures. The dashed lines surrounding the boxes for steps 61 and 64 in Figure 3 indicate that they need not be present in all embodiments.

[0100] In a further aspect, a method is provided for diagnosing or aiding in the diagnosis of a disease or disorder in a newborn or infant.

[0101] In a further aspect, a method for diagnosing a disease or disorder in an asymptomatic subject or for aiding in the diagnosis of a disease or disorder is provided.

[0102] In one embodiment, a method for diagnosing or aiding in the diagnosis of a disease or disorder in a symptomatic subject is provided, which may include determining diagnostic information including a list of one or more possible diseases and / or one or more recommended further tests.

[0103] In one embodiment, detection of rare metabolites may be used to diagnose a disease or disorder or to aid in the diagnosis of a disease or disorder.

[0104] In one embodiment, the identification of defective metabolites may be used to diagnose a disease or disorder or to aid in the diagnosis of a disease or disorder.

[0105] In one embodiment, the biological sample is a urine sample, which may be obtained from materials such as diapers.

[0106] In one embodiment, generating a small molecule profile of a sample involves the use of a matrix sample to assist in the identification of biochemicals present in the test sample.

[0107] The steps of generating a small molecule profile of the sample, comparing the small molecule profile of the sample to a reference small molecule profile to determine whether small molecules in the sample have abnormal levels, identifying a subset of a plurality of small molecules having abnormal levels, obtaining diagnostic information from a database based on the abnormal levels of the identified subset of small molecules, storing an indication that no abnormal levels were found, storing the obtained diagnostic information, and storing a graphical representation of the obtained diagnostic information may be performed in part or in whole using instructions executing on one or more processors of one or more computing systems.

[0108] Some aspects include a storage device that holds computer-executable code comprising instructions for performing various steps of the methods described herein.

[0109] 22 is a block diagram of an example computing device 100 that may be used to perform various steps of example methods described herein. The computing device 100 includes one or more non-transitory computer-readable media for storing one or more computer-executable instructions or software for performing example embodiments. The non-transitory computer-readable media may include, but are not limited to, one or more types of hardware memory, non-transitory tangible media (e.g., one or more magnetic storage disks, one or more optical disks, one or more flash drives), etc. For example, memory 106 included in the computing device 100 may store computer-readable and computer-executable instructions or software for performing all or a portion of the steps or methods described herein. Computing device 100 also includes a configurable and / or programmable processor 102 and associated cores 104, and optionally one or more additional configurable and / or programmable processors 102′ and associated cores 104′ (e.g., in the case of a computer system having multiple processors / cores) for executing computer-readable and computer-executable instructions or software stored in memory 106 and other programs for controlling the system hardware. Processor 102 and processor 102′ may each be a single core processor or a multiple core (104 and 104′) processor.

[0110] Virtualization may be used in computing device 100 so that infrastructure and resources can be dynamically shared within the computing device. Virtual machines 114 may be provided to handle processes running on multiple processors so that the processes appear to be using only one computing resource rather than multiple computing resources. Multiple virtual machines may be used with a single processor.

[0111] Memory 106 may comprise computer system memory or random access memory, such as DRAM, SRAM, EDO RAM, etc. Memory 106 may also comprise other types of memory, or combinations thereof.

[0112] A user may interact with computing device 100 through a visual display device 118, such as a computer monitor. Visual display device 118 may display one or more graphical user interfaces, which may be provided according to exemplary embodiments. Computing device 100 may also include other I / O devices for receiving input from a user, such as a keyboard or any suitable multi-point touch interface 108, a pointing device 110 (e.g., a mouse), a microphone 128, and / or an image capture device 132 (e.g., a camera or scanner). Multi-point touch interface 108 and pointing device 110 may be coupled to visual display device 118. Computing device 100 may also include other suitable conventional I / O peripheral devices.

[0113] The computing device 100 may also include one or more storage devices 124, such as a hard drive, CD-ROM, or other computer-readable medium for storing data, as well as computer-readable instructions and / or software for carrying out the exemplary embodiments described herein. The exemplary storage device 124 may also store one or more databases for storing any appropriate information necessary to carry out the exemplary embodiments. For example, the exemplary storage device 124 may store one or more databases 126 including any or all of: a compound library; a reference small molecule profile; a small molecule profile of one or more reference samples; a small molecule profile of one or more test samples; diagnostic information associated with one or more abnormal small molecules for a plurality of diseases and disorders; information associating pathways with one or more abnormal small molecules; information associating a disease or disorder with one or more abnormal small molecule levels for a plurality of diseases or disorders; and identification of at least one recommended follow-up test associated with one or more abnormal small molecule levels. In other embodiments, different databases for different processes may be associated with different computing devices.

[0114] One or more storage devices 124 may be used to store any or all of the diagnostic information obtained, graphical representations of the diagnostic information obtained, individual subject reports, and the like.

[0115] The one or more storage devices 124 and / or memory 106 may hold software 125 executable on the processor 102 for performing analytical functions such as comparing the small molecule profile of a sample to a reference small molecule profile comprising standard ranges of levels for each of a plurality of small molecules, and identifying a subset of small molecules in the sample that each have abnormal levels.

[0116] Computing device 100 may include a network interface 112 configured to interface with one or more networks, e.g., a local area network (LAN), a wide area network (WAN), or the Internet, via one or more network devices 120 through various connections, including, but not limited to, a standard telephone line, a LAN or WAN link (e.g., 802.11, T1, T3, 56 kb, X.25), a broadband connection (e.g., ISDN, Frame Relay, ATM), a wireless connection, a Controller Area Network (CAN), or some combination or all of the above. In an exemplary embodiment, computing device 100 may include one or more antennas 130 that facilitate wireless communication between computing device 100 and the network (e.g., via the network interface). Network interface 112 may include a built-in network adapter, a network interface card, a PCMCIA network card, a card bus network adapter, a wireless network adapter, a USB network adapter, a modem, or any other device suitable for interfacing computing device 100 to any type of network capable of communicating and performing the operations described herein. Furthermore, computing device 100 may be any computer system, such as a workstation, desktop computer, server, laptop, handheld computer, tablet computer (e.g., iPad™ tablet computer), mobile computing or communication device (e.g., iPhone™ communication device), or other type of computing or telecommunication device capable of communicating and having sufficient processing power and memory capacity to perform the operations described herein. In some embodiments, computing device 100 may communicate over a network with one or more computing devices used to obtain experimental data (e.g., a computing device associated with an LC / MS system, a computing device associated with a GC / MS system).

[0117] Computing device 100 may run any operating system 116, such as any version of the Microsoft® Windows® operating system, various releases of Unix and Linux operating systems, any version of MacOS® for Macintosh computers, any embedded operating system, any real-time operating system, any open source operating system, any intellectual property protected operating system, or any other operating system capable of running on a computing device and performing the operations described herein. In an exemplary embodiment, operating system 116 may run in native mode or in emulated mode. In an exemplary embodiment, operating system 116 may run on one or more cloud machine instances.

[0118] In describing the exemplary embodiments, specific terminology is used to ensure ease of understanding. For purposes of description, each specific term is intended to include, at a minimum, all technical and functional equivalents that operate in a similar manner to serve a similar purpose. Furthermore, in some cases where a particular exemplary embodiment includes multiple system elements, device components, or method steps, these elements, components, or steps may be replaced with a single element, component, or step. Similarly, a single element, component, or step may be replaced with multiple elements, components, or steps that serve the same purpose. Furthermore, while exemplary embodiments have been shown and described with respect to certain embodiments thereof, those skilled in the art will understand that various substitutions and changes in form and detail may be made therein without departing from the scope of the invention. Still further, other aspects, features, and advantages are within the scope of the invention.

[0119] The exemplary block diagrams / flow charts are presented herein for illustrative purposes and are non-limiting examples of methods. Those skilled in the art will appreciate that the exemplary methods may include more or fewer steps than those depicted in the exemplary block diagrams / flow charts, and that the steps in the exemplary block diagrams / flow charts may be performed in a different order than that depicted in the exemplary block diagrams / flow charts.

[0120] In some embodiments, the results of the methods described herein may be combined with other methods (e.g., whole exome sequencing).

[0121] Biomarkers that indicate disease status The metabolomic profiling technique described herein has been used to identify novel biomarkers specific to specific disorders.Generally, the metabolomic profiles of biological samples obtained from subjects diagnosed with one or more disorders and other subjects (e.g., healthy control subjects) who have not been diagnosed with the disorders are determined.The metabolomic profiles of biological samples from subjects with a specific disorder are compared with those of biological samples from healthy control subjects.Molecules that are differently present (e.g., at a statistically significant level) in the metabolomic profiles of samples from subjects with a specific disorder compared to healthy control subjects are identified as biomarkers that distinguish these groups.Detecting the level of one or more biomarkers thus identified can be used to diagnose or facilitate the diagnosis of individual subjects.The biomarkers can be detected in subjects individually or in combination with other biomarkers (e.g., as part of a profile).

[0122] Biological samples suitable for detecting biomarkers include biological material isolated from a subject. In exemplary embodiments, the sample can be isolated from any suitable biological source, such as blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0123] Any suitable method can be used to analyze a biological sample to determine the level of one or more biomarkers in the sample. Suitable methods include, but are not limited to, chromatography (e.g., HPLC, gas chromatography, liquid chromatography), mass spectrometry (e.g., MS, MS-MS), enzyme-linked immunosorbent assay (ELISA), antibody binding, other immunochemical techniques, and combinations thereof. Additionally, the level of one or more biomarkers can be measured indirectly, for example, by using an assay that measures the level of a compound that correlates with the level of the biomarker desired to be measured. The level of one or more biomarkers can also be determined as part of a biomarker profile, for example, a metabolomic profile, using, for example, the methods provided herein.

[0124] After the level of one or more biomarkers in a biological sample obtained from a subject is ascertained, the level is compared to a reference level characteristic of subjects with the disorder (i.e., "positive" for the disorder) and / or a reference level characteristic of subjects without the disorder (i.e., "negative" for the disorder). A level of one or more biomarkers that matches the reference level characteristic of a subject positive for a particular disorder (e.g., a level the same as the reference level, a level substantially the same as the reference level, a level not statistically different from the reference level, a level within a defined range of the reference level) indicates a positive diagnosis of the disorder in the subject. A level of one or more biomarkers that matches the reference level characteristic of a subject negative for a particular disorder (e.g., a level the same as the reference level, a level substantially the same as the reference level, a level not statistically different from the reference level, a level within a defined range of the reference level, etc.) indicates a negative diagnosis of the disorder in the subject. Furthermore, a level of one or more biomarkers that is different (e.g., at a statistically significant level) in a sample obtained from a subject compared to a reference level characteristic of a subject positive for a particular disorder indicates a negative diagnosis of the disorder in the subject. A level of one or more biomarkers that is differentially present (e.g., at a statistically significant level) in a sample obtained from a subject compared to a reference level characteristic of subjects negative for a particular disorder indicates a positive diagnosis of the disorder in the subject.

[0125] The level of one or more biomarkers may be compared to the disorder-positive reference level and / or the disorder-negative reference level using a variety of techniques, including a simple comparison of the level of one or more biomarkers in a biological sample to the disorder-positive reference level and / or the disorder-negative reference level. The level of one or more biomarkers in a biological sample may also be compared to the disorder-positive reference level and / or the disorder-negative reference level using one or more statistical analyses (e.g., t-test, Welch T-test, Wilcoxon rank sum test, random forest). In various embodiments, such comparisons may be performed manually, by an automated system, or by an automated system and manual confirmation.

[0126] A "reference level" of a biomarker refers to a level of the biomarker that is indicative of a particular disease state, phenotype, or lack thereof, as well as combinations of disease states, phenotypes, or lack thereof. A "positive" reference level of a biomarker refers to a level that is indicative of a particular disease state or phenotype. A "negative" reference level of a biomarker refers to a level that is indicative of the absence of a particular disease state or phenotype. A "reference level" of a biomarker can be the absolute or relative amount or concentration of the biomarker, the presence or absence of the biomarker, a range of amounts or concentrations of the biomarker, a minimum and / or maximum amount or concentration of the biomarker, an average amount or concentration of the biomarker, and / or a median amount or concentration of the biomarker. Furthermore, a "reference level" of a combination of biomarkers can also be a ratio of the absolute or relative amounts or concentrations of two or more biomarkers compared to each other. Positive and negative reference levels of biomarkers appropriate for a particular disease state, phenotype, or lack thereof may be determined by measuring the level of the desired biomarker in one or more appropriate subjects, and such reference levels may be tailored to a particular subject population (e.g., the reference levels may be age-adjusted so that a comparison can be made between the biomarker level in a sample from a subject of a particular age and the reference level for a particular disease state, phenotype, or lack thereof in a particular age group). Such reference levels may also be tailored to the particular technique (e.g., LC-MS, GC-MS, etc.) used to measure the level of the biomarker in a biological sample. In this case, the level of the biomarker may vary depending on the particular technique used.

[0127] In one embodiment, a novel biomarker specific to a particular disorder may be biochemically related to a currently used diagnostic metabolite. In another embodiment, the novel biomarker is unrelated to any currently used diagnostic metabolite for a particular disorder. The novel biomarkers are listed in the last column of Table 2 and the last column of Table 3. The levels of one or more biomarkers listed in Tables 2 and 3 may be used to diagnose or facilitate the diagnosis of a disorder in an individual subject. For example, in such methods, the levels of one biomarker, two or more biomarkers, three or more biomarkers, four or more biomarkers, five or more biomarkers, six or more biomarkers, seven or more biomarkers, eight or more biomarkers, nine or more biomarkers, ten or more biomarkers, etc., including all of the biomarkers in Tables 2 and / or 3, or any subset thereof, may be determined and used. Ascertaining the levels of a combination of biomarkers may increase the sensitivity and specificity of a particular disorder and may allow for better differentiation from other disorders. In one embodiment, the levels of one or more novel biomarkers specific to a particular disorder may be assessed in combination with the levels of one or more currently used diagnostic metabolites indicative of the disorder. Currently used diagnostic metabolites indicative of particular disorders are listed in column 5 of Table 2 and column 5 of Table 3. Some biomarkers may be currently used diagnostic metabolites for one type of sample (e.g., plasma) but novel diagnostic metabolites for another type of sample (e.g., urine).

[0128] Identifying new metabolic biomarkers for particular disorders also allows for the treatment of the disorders. Thus, in exemplary embodiments, an effective amount of a therapeutic agent can be administered to a subject diagnosed with a particular disease or disorder using the biomarkers provided herein.

[0129] Biomarkers that can be used to diagnose or facilitate the diagnosis of particular disorders are described below.

[0130] 3-Methylcrotonyl-CoA carboxylase deficiency Novel metabolic biomarkers indicative of 3-methylcrotonyl-CoA carboxylase deficiency include β-hydroxyisovaleroylcarnitine, glutarylcarnitine (C5), 3-methylcrotonylglycine, adipate, glutarate (pentanedioate), octadecanedioate (C18), hexadecanedioate (C16), tetradecanedioate (C14), dodecanedioate (C12), isovalerate, leucine, isovalerylglycine, α-hydroxyisovalerate, succinylcarnitine, 3-methylglutarylcarnitine, isovalerylcarnitine, alanylalanine, pyroglutamylvaline, ethylmalonate, N-acetylleucine, X-12007, X-12814, and combinations thereof. Therefore, a diagnosis of 3-methylcrotonyl-CoA carboxylase deficiency in a subject may be made by analyzing a biological sample obtained from the subject for the presence of β-hydroxyisovaleroylcarnitine, glutarylcarnitine (C5), 3-methylcrotonylglycine, adipate, glutarate (pentanedioate), octadecandioate (C18), hexadecanedioate (C16), tetradecandioate (C14), dodecandioate (C12), isovalerate, leucine, isovalerylglycine, α-hydroxyisovalerate, succinylglycerol, α-hydroxyisovalerate ... The method can be performed or facilitated by ascertaining the level of one or more metabolites, including 3-methylcrotonyl-CoA carboxylase deficiency positive reference level and / or 3-methylcrotonyl-CoA carboxylase deficiency negative reference level, and comparing the level of the metabolite in the sample with a 3-methylcrotonyl-CoA carboxylase deficiency positive reference level and / or a 3-methylcrotonyl-CoA carboxylase deficiency negative reference level of the metabolite.

[0131] Optionally, the diagnosis of 3-methylcrotonyl-CoA carboxylase deficiency in a subject can be performed or facilitated by analyzing the levels of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.Currently used diagnostic metabolites indicating 3-methylcrotonyl-CoA carboxylase deficiency include 3-methylcrotonylglycine and β-hydroxyisovalerate.Therefore, a method for diagnosing or facilitating the diagnosis of 3-methylcrotonyl-CoA carboxylase deficiency may further include analyzing a biological sample obtained from the subject to determine the levels of one or more additional metabolites, including 3-methylcrotonylglycine, β-hydroxyisovalerate, and combinations thereof.

[0132] In one embodiment, one or more biomarkers selected from the group consisting of β-hydroxyisovaleroylcarnitine, β-hydroxyisovalerate, isovalerylglycine, leucine, 3-methylcrotonylglycine, isovalerate, and 3-hydroxyisovalerate may be used in diagnosing or aiding in the diagnosis of 3-methylcrotonyl-CoA carboxylase deficiency in a subject.

[0133] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0134] In some embodiments, diagnosis of 3-methylcrotonyl-CoA carboxylase deficiency in a subject can be made or facilitated by analyzing a biological sample derived from the subject's urine to ascertain levels of one or more metabolites, including ethyl malonate, N-acetylleucine, and any combination thereof.

[0135] Adenosine deaminase deficiency Novel metabolic biomarkers indicative of adenosine deaminase deficiency include 2'-deoxyinosine, adenine, N2-methylguanosine, 2'-deoxyguanosine, urate, N1-methyladenosine, adenosine, allantoin, xanthine, guanosine, hypoxanthine, N2,N2-dimethylguanosine, 7-methylguanosine, and combinations thereof. Thus, diagnosis of adenosine deaminase deficiency in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to ascertain the levels of one or more metabolites, including 2'-deoxyinosine, adenine, N2-methylguanosine, 2'-deoxyguanosine, urate, N1-methyladenosine, adenosine, allantoin, xanthine, guanosine, hypoxanthine, N2,N2-dimethylguanosine, and 7-methylguanosine, and comparing the level of the metabolite in the sample to an adenosine deaminase deficiency-positive reference level and / or an adenosine deaminase deficiency-negative reference level of the metabolite.

[0136] Optionally, the diagnosis of adenosine deaminase deficiency in a subject can be carried out or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.The currently used diagnostic metabolites that indicate adenosine deaminase deficiency include deoxyadenosine and S-adenosylhomocysteine.Therefore, the method for diagnosing adenosine deaminase deficiency or the method for facilitating the diagnosis of adenosine deaminase deficiency can further comprise the step of analyzing biological samples obtained from a subject to determine the level of one or more additional metabolites, including deoxyadenosine and S-adenosylhomocysteine.

[0137] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0138] In some embodiments, diagnosing adenosine deaminase deficiency in a subject can be made or facilitated by analyzing a biological sample derived from the subject's urine to ascertain levels of one or more metabolites, including 2'-deoxyinosine, adenine, N2-methylguanosine, 2'-deoxyguanosine, urate, N1-methyladenosine, adenosine, allantoin, xanthine, guanosine, hypoxanthine, N2,N2-dimethylguanosine, 7-methylguanosine, and any combination thereof.

[0139] Argininosuccinate lyase deficiency Novel metabolic biomarkers indicative of argininosuccinate lyase deficiency include N-δ-acetylornithine, uracil, arginine, aspartate, sorbose, fructose, citrulline, methyl-4-hydroxybenzoate, isoleucyl aspartate, ornithine, uridine, homocitrulline, orotate, homoarginine, O-sulfo-L-tyrosine, palmitoylsphingomyelin, X-13507, X-15245, X-15664, X-15454, and combinations thereof. Thus, diagnosis of argininosuccinate lyase deficiency in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to ascertain the level of one or more metabolites, including N-δ-acetylornithine, uracil, arginine, aspartate, sorbose, fructose, citrulline, methyl-4-hydroxybenzoate; isoleucyl aspartate, ornithine, uridine, homocitrulline, orotate, homoarginine, O-sulfo-L-tyrosine, palmitoylsphingomyelin, X-13507, X-15245, X-15664, and X-15454, and comparing the level of the metabolite in the sample to an argininosuccinate lyase deficiency-positive reference level and / or an argininosuccinate lyase deficiency-negative reference level of the metabolite.

[0140] Optionally, the diagnosis of argininosuccinate lyase deficiency in a subject can be carried out or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.The currently used diagnostic metabolites that indicate argininosuccinate lyase deficiency include argininosuccinate.Therefore, the method for diagnosing argininosuccinate lyase deficiency or the method for facilitating the diagnosis of argininosuccinate lyase deficiency can further comprise the step of analyzing biological samples obtained from a subject to determine the level of one or more additional metabolites, including argininosuccinate.

[0141] In one embodiment, one or more biomarkers selected from the group consisting of argininosuccinate, citrulline, uracil, arginine, and aspartate may be used in diagnosing or aiding in the diagnosis of argininosuccinate lyase deficiency in a subject.

[0142] Argininemia Novel metabolic biomarkers indicative of argininemia include homoarginine, N-acetylarginine, ornithine, urea, homocitrulline, uracil, aspartate, argininosuccinate, proline, orotate, creatinine, uridine, 3-ureidopropionate, creatine, betaine, leucine, isoleucine, gamma-glutamylleucine, X-12339, X-12681, and combinations thereof. Thus, diagnosis of argininemia in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to ascertain the level of one or more metabolites, including homoarginine, N-acetylarginine, ornithine, urea, homocitrulline, uracil, aspartate, argininosuccinate, proline, orotate, creatinine, uridine, 3-ureidopropionate, creatine, betaine, leucine, isoleucine, γ-glutamylleucine, X-12339, and X-12681, and comparing the level of the metabolite in the sample to an argininemia-positive reference level and / or an argininemia-negative reference level for the metabolite.

[0143] Optionally, the diagnosis of argininemia in a subject can be performed or facilitated by analyzing the levels of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.Currently used diagnostic metabolites indicating argininemia include arginine and 4-guanidinobutanoate.Therefore, a method for diagnosing or facilitating the diagnosis of argininemia may further include analyzing a biological sample obtained from the subject to determine the levels of one or more additional metabolites, including arginine, 4-guanidinobutanoate, and combinations thereof.

[0144] In one embodiment, one or more biomarkers selected from the group consisting of argininosuccinate, 4-guanidinobutanoate, uridine, arginine, homocitrulline, N-acetylarginine, orotate, uracil, aspartate, creatinine, urea, proline, ornithine, X-12681, and X-12339 may be used in diagnosing or assisting in the diagnosis of argininemia in a subject.

[0145] Biotinidase deficiency Novel metabolic biomarkers indicative of biotinidase deficiency include biotin, xylitol, 3-methylcrotonylglycine, propionylcarnitine (C3), and combinations thereof. Thus, diagnosing biotinidase deficiency in a subject can be performed or facilitated by analyzing a biological sample obtained from the subject to determine the levels of one or more metabolites, including xylitol, biotin, 3-methylcrotonylglycine, and propionylcarnitine (C3), and comparing the levels of the metabolites in the sample with a biotinidase deficiency-positive reference level and / or a biotinidase deficiency-negative reference level of the metabolite.

[0146] Cbl (cobalamin deficiency) Novel metabolic biomarkers indicative of cobalamin deficiency include 2-methylmalonylcarnitine, propionylcarnitine, X-12749, and combinations thereof. Thus, diagnosing cobalamin deficiency in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to determine the levels of one or more metabolites, including 2-methylmalonylcarnitine, propionylcarnitine, and X-12749, and comparing the levels of the metabolites in the sample with a cobalamin deficiency-positive reference level and / or a cobalamin deficiency-negative reference level of the metabolite.

[0147] Optionally, the diagnosis of cobalamin deficiency in a subject can be performed or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.Currently used diagnostic metabolites that indicate cobalamin deficiency include methylmalonic acid, homocysteine, 2-methylcitrate, and cystathionine.Therefore, the method for diagnosing or facilitating the diagnosis of CBL (cobalamin deficiency) can further comprise analyzing biological samples obtained from a subject to determine the level of one or more additional metabolites, including methylmalonic acid, homocysteine, 2-methylcitrate, cystathionine, and combinations thereof.

[0148] In one embodiment, one or more biomarkers selected from the group consisting of 2-methylmalonylcarnitine, tiglylcarnitine, 2-methylbutyrylcarnitine, and 2-methylcitrate may be used in diagnosing or aiding in the diagnosis of cobalamin deficiency in a subject.

[0149] Cbl A Novel metabolic biomarkers indicative of Cbl A include 2-methylmalonylcarnitine, tiglylcarnitine, 2-methylbutyrylcarnitine, propionylcarnitine, X-12749, and combinations thereof. Thus, diagnosis of Cbl A in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to ascertain the levels of one or more metabolites, including 2-methylmalonylcarnitine and tiglylcarnitine, 2-methylbutyrylcarnitine, propionylcarnitine, and X-12749, and comparing the levels of the metabolites in the sample to a Cbl A-positive reference level and / or a Cbl A-negative reference level of the metabolite.

[0150] Optionally, diagnosis of Cbl A in a subject can be made or facilitated by analyzing the levels of one or more currently used diagnostic metabolites in combination with the above-described novel metabolic biomarkers. Currently used diagnostic metabolites indicative of Cbl A include methylmalonic acid, homocysteine, 2-methylcitrate, and cystathionine. Thus, a method for diagnosing or facilitating the diagnosis of Cbl A may further comprise analyzing a biological sample obtained from the subject to ascertain the levels of one or more additional metabolites, including methylmalonic acid, homocysteine, 2-methylcitrate, cystathionine, and combinations thereof.

[0151] Cbl C Novel metabolic biomarkers indicative of Cbl C include 2-methylmalonylcarnitine, propionylcarnitine, X-17677, X-12749, and combinations thereof. Thus, diagnosis of Cbl C in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to ascertain levels of one or more metabolites, including 2-methylmalonylcarnitine, propionylcarnitine, X-12749, and X-17677, and comparing the levels of the metabolites in the sample to a Cbl C-positive reference level and / or a Cbl C-negative reference level of the metabolite.

[0152] Optionally, diagnosis of Cbl C in a subject can be made or facilitated by analyzing the levels of one or more currently used diagnostic metabolites in combination with the above-described novel metabolic biomarkers. Currently used diagnostic metabolites indicative of Cbl C include methylmalonic acid, homocysteine, 2-methylcitrate, and cystathionine. Thus, a method for diagnosing or facilitating the diagnosis of Cbl C may further comprise analyzing a biological sample obtained from the subject to ascertain the levels of one or more additional metabolites, including methylmalonic acid, homocysteine, 2-methylcitrate, cystathionine, and combinations thereof.

[0153] Citrullinemia Novel metabolic biomarkers indicative of citrullinemia include homocitrulline, 3-ureidopropionate, N-acetylalanine, phenylacetylglutamine, phenylacetate, 4-ureidobutyrate, N-carbamoyl aspartate, guanidinoacetate, urea, 4-guanidinobutanoate, N-acetylarginine, hippurate, ornithine, 2-methylhippurate, phenylacetylglycine, and 4-phenylbutyrate. , creatinine, orotate, 3,4-dihydroxyphenylacetate, homoarginine, guanidinosuccinate, gamma-glutamylphenylalanine, gamma-glutamylisoleucine, tryptophan, 1,5-anhydroglucitol (1,5-AG), N-acetyl-citrulline (formerly X-12386), X-19684, X-12681, X-20598, X-18446, X-20588, and combinations thereof. Thus, the diagnosis of citrullinemia in a subject may be determined by analyzing a biological sample obtained from the subject to determine the presence or absence of homocitrulline, 3-ureidopropionate, N-acetylalanine, phenylacetylglutamine, phenylacetate, 4-ureidobutyrate, N-carbamoyl aspartate, guanidinoacetate, urea, 4-guanidinobutanoate, N-acetylarginine, hippurate, ornithine, 2-methylhippurate, phenylacetylglycine, 4-phenylbutyrate, creatinine, orotate, 3,4-dihydroxyphenylacetate, homoarginine, This can be performed or facilitated by ascertaining the level of one or more metabolites, including guanidinosuccinate, gamma-glutamylphenylalanine, gamma-glutamylisoleucine, tryptophan, 1,5-anhydroglucitol (1,5-AG), N-acetyl-citrulline (formerly X-12386), X-19684, X-12681, X-20598, X-18446, and X-20588, and comparing the level of the metabolite in the sample to a citrullinemia-positive reference level and / or a citrullinemia-negative reference level of the metabolite.

[0154] Optionally, the diagnosis of citrullinemia in a subject can be performed or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.The currently used diagnostic metabolites that indicate citrullinemia include citrulline and argininosuccinic acid.Therefore, the method for diagnosing or facilitating the diagnosis of citrullinemia can further comprise analyzing the biological sample obtained from the subject to determine the level of one or more additional metabolites, including citrulline, argininosuccinic acid, and combinations thereof.

[0155] In one embodiment, one or more biomarkers selected from the group consisting of 3-ureidopropionate, homocitrulline, citrulline, and N-acetyl-citrulline (formerly X-12386) may be used in diagnosing or assisting in the diagnosis of citrullinemia in a subject.

[0156] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0157] In some embodiments, diagnosis of citrullinemia in a subject can be made or facilitated by analyzing a biological sample derived from the subject's urine to ascertain levels of one or more metabolites, including 4-ureidobutyrate, N-carbamoyl aspartate, guanidinoacetate, 4-guanidinobutanoate, N-acetylarginine, hippurate, ornithine, 2-methylhippurate, 4-phenylbutyrate, creatinine, orotate, 3,4-dihydroxyphenylacetate, and any combination thereof.

[0158] Carnitine palmitoyltransferase 2 deficiency (CPTII) Novel metabolic biomarkers indicative of carnitine palmitoyltransferase 2 deficiency (CPTII) include N-octanoylglycine (C8 ester), sebacate (C8), caprate (C10), caprylate (C8), octanoylcarnitine, hexanoylcarnitine, and combinations thereof. Thus, diagnosing CPTII in a subject can be performed or facilitated by analyzing a biological sample obtained from the subject to determine the levels of one or more metabolites, including N-octanoylglycine (C8 ester), sebacate (C8), caprate (C10), caprylate (C8), octanoylcarnitine, and hexanoylcarnitine, and comparing the levels of the metabolites in the sample with a CPTII-positive reference level and / or a CPTII-negative reference level of the metabolite.

[0159] Optionally, the diagnosis of CPTII in a subject can be carried out or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.The currently used diagnostic metabolites that indicate CPTII include carnitine and acylcarnitine.Therefore, the method for diagnosing or facilitating the diagnosis of CPTII can further comprise the step of analyzing biological samples obtained from a subject to determine the level of one or more additional metabolites, including carnitine, acylcarnitine, and combinations thereof.

[0160] In one embodiment, one or more biomarkers selected from the group consisting of sebacate (decandioate), decanoylcarnitine, caprylate, caprate, octanoylcarnitine, hexanoylcarnitine, and N-octanoylglycine may be used in diagnosing or assisting in the diagnosis of CPTII in a subject.

[0161] Cystinosis Novel metabolic biomarkers indicative of cystinosis include oxidized cys-gly, 1,5-anhydroglucitol (1,5-AG), glycocholesterol sulfate, 4-acetylphenol sulfate, cresol glucuronide (formerly X-11837), erythritol, vanillylmandelate, N2,N2-dimethyl-guanosine, phenylacetylglutamine, X-12846, X-12303, X-19145, X-12216, X-17717, X-15667, X-12119, X-11315, X-12731, X-12705, X-17685, X-18371, and combinations thereof. Therefore, the diagnosis of cystinosis in a subject may be determined by analyzing a biological sample obtained from the subject for the following: cys-gly (oxidized), 1,5-anhydroglucitol (1,5-AG), glycocholate sulfate, 4-acetylphenol sulfate, cresol glucuronide (formerly X-11837), erythritol, vanillylmandelate, N2,N2-dimethyl-guanosine, phenylacetylglutamine, X-12846, X-123 X-12705, X-17685, and X-18371, and comparing the level of the metabolite in the sample to a cystinosis-positive reference level and / or a cystinosis-negative reference level for the metabolite.

[0162] Optionally, the diagnosis of cystinosis in a subject can be made or facilitated by analyzing the levels of one or more currently used diagnostic metabolites in combination with the above-described novel metabolic biomarkers. Currently used diagnostic metabolites indicative of cystinosis include cystine. Thus, a method for diagnosing or facilitating the diagnosis of cystinosis may further comprise analyzing a biological sample obtained from the subject to determine the levels of one or more additional metabolites, including cystine.

[0163] Dihydropyrimidine dehydrogenase deficiency New metabolic biomarkers that indicate dihydropyrimidine dehydrogenase deficiency include cytidine, 5,6-dihydrouracil, 4-ureidobutyrate, 3-ureidopropionate, uridine, orotate, N-carbamoyl aspartate, and combinations thereof.Therefore, dihydropyrimidine dehydrogenase deficiency in a subject can be diagnosed or facilitated by analyzing a biological sample obtained from the subject to determine the level of one or more metabolites, including cytidine, 5,6-dihydrouracil, 4-ureidobutyrate, 3-ureidopropionate, uridine, orotate, and N-carbamoyl aspartate, and comparing the level of the metabolite in the sample with a dihydropyrimidine dehydrogenase deficiency positive reference level and / or a dihydropyrimidine dehydrogenase deficiency negative reference level of the metabolite.

[0164] Optionally, the diagnosis of dihydropyrimidine dehydrogenase deficiency in a subject can be performed or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.The currently used diagnostic metabolites that indicate dihydropyrimidine dehydrogenase deficiency include uracil and thymine.Therefore, the method for dihydropyrimidine dehydrogenase deficiency or the method for facilitating the diagnosis of dihydropyrimidine dehydrogenase deficiency can further comprise analyzing the biological sample obtained from the subject to determine the level of one or more additional metabolites, including uracil, thymine, and combinations thereof.

[0165] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0166] In some embodiments, diagnosing dihydropyrimidine dehydrogenase deficiency in a subject can be made or facilitated by analyzing a biological sample derived from the subject's urine.

[0167] Glutaric aciduria type 1 Novel metabolic biomarkers indicative of glutaric aciduria type 1 include 3-methylglutarylcarnitine, 2-aminoadipate, X-12364, X-15674, and combinations thereof. Thus, diagnosis of glutaric aciduria type 1 in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to ascertain the levels of one or more metabolites, including 3-methylglutarylcarnitine, 2-aminoadipate, X-12364, and X-15674, and comparing the levels of the metabolites in the sample to a glutaric aciduria type 1 positive reference level and / or a glutaric aciduria type 1 negative reference level of the metabolite.

[0168] Optionally, the diagnosis of glutaric aciduria type 1 in a subject can be made or facilitated by analyzing the levels of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.Currently used diagnostic metabolites indicative of glutaric aciduria type 1 include glutarate (pentanedioate), glutarylcarnitine (C5), 3-hydroxyglutarate, and glutaconate.Therefore, a method for diagnosing or facilitating the diagnosis of glutaric aciduria type 1 can further include analyzing a biological sample obtained from the subject to determine the levels of one or more additional metabolites, including glutarate (pentanedioate), glutarylcarnitine (C5), 3-hydroxyglutarate, glutaconate, and combinations thereof.

[0169] In one embodiment, one or more biomarkers selected from the group consisting of glutarylcarnitine and glutarate (pentanedioate) may be used in diagnosing or aiding in the diagnosis of glutaric aciduria type 1 in a subject.

[0170] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0171] In some embodiments, diagnosis of glutaric aciduria type 1 in a subject can be made or facilitated by analyzing a biological sample derived from the subject's urine to ascertain levels of one or more metabolites, including one or more of 3-methylglutarylcarnitine, 2-aminoadipate, and combinations thereof.

[0172] Guanidinoacetate methyltransferase (GAMT) deficiency Novel metabolic biomarkers indicative of guanidinoacetate methyltransferase deficiency include creatine, 3-(4-hydroxyphenyl)lactate, 1,3-dipalmitoylglycerol, guanidinoacetate, creatinine, cysteine ​​S-sulfate, X-19602, X-12906, X-13007, X-10458, and combinations thereof. Therefore, a diagnosis of guanidinoacetate methyltransferase deficiency in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to determine the levels of one or more metabolites, including creatine, 3-(4-hydroxyphenyl)lactate, 1,3-dipalmitoylglycerol, guanidinoacetate, creatinine, cysteine ​​S-sulfate, X-19602, X-12906, X-13007, and X-10458, and comparing the levels of the metabolites in the sample with a guanidinoacetate methyltransferase deficiency-positive reference level and / or a guanidinoacetate methyltransferase deficiency-negative reference level of the metabolite. As explained above, a biological sample from a subject can be isolated from any suitable biological source, such as blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0173] In some embodiments, diagnosis of guanidinoacetate methyltransferase deficiency in a subject can be made or facilitated by analyzing a biological sample derived from the subject's urine to ascertain levels of one or more metabolites, including guanidinoacetate.

[0174] 3-Hydroxy-3-methylglutaric aciduria (HMG CoA lyase deficiency) Novel metabolic biomarkers indicative of 3-hydroxy-3-methylglutaric aciduria (HMG CoA lyase deficiency) include β-hydroxyisovalerate, β-hydroxyisovaleroylcarnitine, glutarylcarnitine (C5), arginylproline, 1-stearoylglycerophosphoethanolamine, o-cresol sulfate, 3-methylcrotonylglycine, adipate, glutarate (pentanedioate), octadecanedioate (C18), hexadecanedioate ( X-17001, X-17715, X-12741, X-16134, X-10593, X-12688, and combinations thereof. The diagnosis of CoA lyase deficiency is made by analyzing a biological sample obtained from a subject for the presence of β-hydroxyisovalerate, β-hydroxyisovaleroylcarnitine, glutarylcarnitine (C5), arginylproline, 1-stearoylglycerophosphoethanolamine, o-cresol sulfate, 3-methylcrotonylglycine, adipate, glutarate (pentanedioate), octadecanedioate (C18), hexadecanedioate (C16), and hydroxybenzoates (C18). X-17715, X-12741, X-16134, X-10593, and X-12688, and comparing the level of the metabolite in the sample to an HMG CoA lyase deficiency positive reference level and / or an HMG CoA lyase deficiency negative reference level for the metabolite.

[0175] Optionally, the diagnosis of HMG CoA lyase deficiency in a subject can be performed or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.Currently used diagnostic metabolites that indicate HMG CoA lyase deficiency include 3-methylglutarylcarnitine (C6), 3-hydroxy-3-methyl-glutarate, 3-methylglutarate, and 3-hydroxyisovalerate.Therefore, the method for diagnosing or facilitating the diagnosis of HMG CoA lyase deficiency can further include analyzing a biological sample obtained from a subject to determine the level of one or more additional metabolites, including 3-methylglutarylcarnitine (C6), 3-hydroxy-3-methyl-glutarate, 3-methylglutarate, and 3-hydroxyisovalerate, and combinations thereof.

[0176] In one embodiment, one or more biomarkers selected from the group consisting of glutarylcarnitine, β-hydroxyisovalerylcarnitine, β-hydroxyisovalerate, 3-methylglutarylcarnitine, glutarate, hexadecanedioate, tetradecanedioate, octadecanedioate, dodecanedioate, 3-methylcrotonylglycine, 3-methylglutarylcarnitine, and adipate may be used in diagnosing or aiding in the diagnosis of HMG CoA lyase deficiency in a subject.

[0177] Holocarboxylase Novel metabolic biomarkers indicative of holocarboxylase include 3-methylcrotonylglycine, β-hydroxyisovaleroylcarnitine (C5), propionylglycine (C3), 3-hydroxypropanoate, tigloylglycine, succinylcarnitine, 2-methylcitrate, 3-hydroxyisobutyrate, lactate, 3-hydroxy-2-ethylpropionate, isobutyrylglycine, α-hydroxyisovaleroylcarnitine, and 3-methyl-2-oxo butyrate, 3-methyl-2-oxovalerate, 3-hydroxy-2-methylbutyrate, 4-methyl-2-oxopentanoate, malonylcarnitine, alpha-hydroxyisovalerate, 2-hydroxyl-3-methylvalerate, propionylcarnitine, tiglylcarnitine, isovalerylcarnitine, hydroxybutyrylcarnitine, succinate, 2-methylmalonylcarnitine, alpha-hydroxyisocaproate, biotin, and combinations thereof. Therefore, the diagnosis of holocarboxylase in a subject may be accomplished by analyzing a biological sample obtained from the subject to determine the presence of 3-methylcrotonylglycine, β-hydroxyisovaleroylcarnitine (C5), propionylglycine (C3), 3-hydroxypropanoate, tigloylglycine, succinylcarnitine, 2-methylcitrate, 3-hydroxyisobutyrate, lactate, 3-hydroxy-2-ethylpropionate, isobutyrylglycine, α-hydroxyisovaleroylcarnitine, 3-methyl-2-oxobutyrate, 3-methyl-2-oxovalerate, 3-hydroxy-2-methylbutyrate, 4-methyl In some embodiments, the method may be performed or facilitated by ascertaining the level of one or more metabolites, including 2-hydroxyl-2-oxopentanoate, malonylcarnitine, α-hydroxyisovalerate, 2-hydroxyl-3-methylvalerate, propionylcarnitine, tiglylcarnitine, isovalerylcarnitine, hydroxybutyrylcarnitine, succinate, 2-methylmalonylcarnitine, and α-hydroxyisocaproate, biotin, and comparing the level of the metabolite in the sample with a holocarboxylase-positive reference level and / or a holocarboxylase-negative reference level of the metabolite.

[0178] Optionally, diagnosing holocarboxylase in a subject can be performed or facilitated by analyzing the levels of one or more currently used diagnostic metabolites in combination with the above-mentioned novel metabolic biomarkers. Currently used diagnostic metabolites indicative of holocarboxylase include β-hydroxyisovalerate. Thus, a method for diagnosing or facilitating the diagnosis of holocarboxylase may further comprise analyzing a biological sample obtained from the subject to determine the levels of one or more additional metabolites, including β-hydroxyisovalerate.

[0179] In one embodiment, one or more biomarkers selected from the group consisting of β-hydroxyisovalerylcarnitine, β-hydroxyisovalerate, 3-hydroxypropanoate, propionylglycine, tigloylglycine, 3-methylcrotonylglycine, succinylcarnitine may be used in diagnosing or aiding in the diagnosis of holocarboxylase in a subject.

[0180] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0181] In some embodiments, diagnosing holocarboxylase in a subject can be made or facilitated by analyzing a biological sample from the subject's urine to ascertain levels of one or more metabolites including 2-methylcitrate, 3-hydroxyisobutyrate, lactate, 3-hydroxy-2-ethylpropionate, isobutyrylglycine, α-hydroxyisovaleroylcarnitine, 3-methyl-2-oxobutyrate, 3-methyl-2-oxovalerate, 3-hydroxy-2-methylbutyrate, 4-methyl-2-oxopentanoate, malonylcarnitine, α-hydroxyisovalerate, 2-hydroxyl-3-methylvalerate, propionylcarnitine, tiglylcarnitine, isovalerylcarnitine, hydroxybutyrylcarnitine, succinate, 2-methylmalonylcarnitine, α-hydroxyisocaproate, and any combination thereof.

[0182] Homocystinuria Novel metabolic biomarkers indicative of homocystinuria include γ-glutamylmethionine, 5-methylthioadenosine (MTA), S-adenosylhomocysteine ​​(SAH), N1-methyladenosine, glycylproline, 1-eicosenoylglycerophosphoethanolamine (20:1n9), 1-methylnicotinamide, N-acetyl-aspartyl-glutamate (NAAG), pyridoxal, 2-hydroxyisobutyrate, acisoga, carnosine, 3-methoxytyrosine, 2-hydroxydecanoate, and δ-tocopherol. X-19350, X-18965, X-15649, X-17303, X-18897, X-11564, X-18891, X-12748, X-18918, X-18905, X-18606, X-16574, X-18895, X-18907, X-19455, X-18909, X-19574, X-12110, X-20676, X-11360, X-18920, and combinations thereof.Therefore, the diagnosis of homocystinuria in a subject may be made by analyzing a biological sample obtained from the subject for the presence of γ-glutamylmethionine, 5-methylthioadenosine (MTA), S-adenosylhomocysteine ​​(SAH), N1-methyladenosine, glycylproline, 1-eicosenoylglycerophosphoethanolamine (20:1n9), 1-methylnicotinamide, N-acetyl-aspartyl-glutamate (NAAG), pyridoxal, 2-hydroxyisobutyrate, asisoga, carnosine, 3-methoxytyrosine, 2-hydroxydecanoate, δ-tocopherol, α-CEHC (2,5,7,8-tetramethyl-2-(2'-carboxyethyl)-6-hydroxychroman) sulfate (formerly X-1). X-19455, X-18909, X-19574, X-12110, X-20676, X-11360, and X-18920, and comparing the level of the metabolite in the sample to a homocystinuria-positive reference level and / or a homocystinuria-negative reference level for the metabolite.

[0183] Optionally, the diagnosis of homocystinuria in a subject can be made or facilitated by analyzing the levels of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.Currently used diagnostic metabolites that indicate homocystinuria include homocysteine, cysteine, methionine, and other amino acids.Therefore, a method for diagnosing or facilitating the diagnosis of homocystinuria may further include analyzing a biological sample obtained from the subject to determine the levels of one or more additional metabolites, including homocysteine, cysteine, methionine, other amino acids, and combinations thereof.

[0184] In one embodiment, one or more biomarkers selected from the group consisting of methionine, S-adenosylhomocysteine ​​(SAH), γ-glutamylmethionine, 5-methylthioadenosine (MTA), and N1-methyladenosine may be used in diagnosing or assisting in the diagnosis of homocystinuria in a subject.

[0185] Isovaleric acidemia Novel metabolic biomarkers indicative of isovaleric acidemia include isovalerylglycine, isovalerylcarnitine (C5), valerate, valerylcarnitine, β-hydroxyisovalerate, phenylcarnitine, β-hydroxybutyrate, 3-methylcrotonylglycine, α-hydroxybutyrate, X-16577, X-14331, and combinations thereof. Thus, a diagnosis of isovaleric acidemia in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to ascertain the level of one or more metabolites, including isovalerylglycine, isovalerylcarnitine (C5), valerate, valerylcarnitine, β-hydroxyisovalerate, phenylcarnitine, β-hydroxybutyrate, 3-methylcrotonylglycine, α-hydroxybutyrate, X-16577, and X-14331, and comparing the level of the metabolite in the sample to an isovaleric acidemia-positive reference level and / or an isovaleric acidemia-negative reference level for the metabolite.

[0186] Optionally, the diagnosis of isovaleric acidemia in a subject can be made or facilitated by analyzing the levels of one or more currently used diagnostic metabolites in combination with the above-described novel metabolic biomarkers.Currently used diagnostic metabolites indicative of isovaleric acidemia include isovalerate (C5).Accordingly, a method for diagnosing or facilitating the diagnosis of isovaleric acidemia may further include analyzing a biological sample obtained from the subject to determine the levels of one or more additional metabolites, including isovalerate (C5).

[0187] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0188] In one embodiment, one or more biomarkers selected from the group consisting of isovalerylglycine, isovalerylcarnitine, isovalerate, lactate, 3-methylglutaroylcarnitine, glutaroylcarnitine, and β-hydroxyisovalerate may be used in diagnosing or assisting in the diagnosis of isovaleric acidemia in a subject.

[0189] In some embodiments, a weighted combination of results from one or more abnormal small molecules measured in a sample in the form of a disease-specific composite score may be used to aid in the diagnosis of isovaleric acidemia in a subject. In one embodiment, the weighted combination, or composite score, includes results for the metabolites 3-hydroxyisovalerate, isovalerylcarnitine, and isovalerate.

[0190] In some embodiments, diagnosis of isovaleric acidemia in a subject can be made or facilitated by analyzing a biological sample derived from the subject's urine to ascertain levels of one or more metabolites, including beta-hydroxybutyrate and alpha-hydroxybutyrate.

[0191] Lysinuric protein intolerance Novel metabolic biomarkers indicative of lysinuric protein intolerance include asparagine, N6-acetyllysine, glutamine, N2-acetyllysine, N-acetylarginine, gamma-glutamylglutamine, proline, S-methylcysteine, 2-hydroxydecanoate, 1-methylimidazole acetate, 2-aminoheptanoate, 3-methylglutarylcarnitine, glutarylcarnitine, N6-trimethyllysine, 5-(galactosylhydroxy)-L-lysine, X-15636, 17654, X-12193, X-12425, and combinations thereof. Thus, diagnosis of lysinuric protein intolerance in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to ascertain the levels of one or more metabolites, including asparagine, N6-acetyllysine, glutamine, N2-acetyllysine, N-acetylarginine, γ-glutamylglutamine, proline, S-methylcysteine, 2-hydroxydecanoate, 1-methylimidazole acetate, -aminoheptanoate, 3-methylglutarylcarnitine, glutarylcarnitine, N6-trimethyllysine, 5-(galactosylhydroxy)-L-lysine, X-15636, 17654, X-12193, and X-12425, and comparing the level of the metabolite in the sample to a lysinuric protein intolerance-positive reference level and / or a lysinuric protein intolerance-negative reference level of the metabolite.

[0192] Optionally, the diagnosis of lysinuric protein intolerance in a subject can be performed or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.The currently used diagnostic metabolites that indicate lysinuric protein intolerance include ornithine, arginine, and lysine.Therefore, the method for diagnosing or facilitating the diagnosis of lysinuric protein intolerance can further comprise the step of analyzing biological samples obtained from a subject to determine the level of one or more additional metabolites, including ornithine, arginine, lysine, and combinations thereof.

[0193] In one embodiment, one or more biomarkers selected from the group consisting of N6-acetyllysine, glutamine, asparagine, arginine, ornithine, and lysine may be used in diagnosing or aiding in the diagnosis of lysinuric protein intolerance in a subject.

[0194] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0195] In some embodiments, diagnosing lysinuric protein intolerance in a subject can be made or facilitated by analyzing a biological sample derived from the subject's urine to ascertain levels of one or more metabolites, including 2-aminoheptanoate, 3-methylglutarylcarnitine, glutarylcarnitine, N6-trimethyllysine, 5-(galactosylhydroxy)-L-lysine, and any combination thereof.

[0196] Medium-chain acyl-CoA dehydrogenase deficiency Novel metabolic biomarkers indicative of medium-chain acyl-CoA dehydrogenase deficiency include N-octanoylglycine, caproate (6:0), caprylate (8:0), heptanoate (7:0), dodecanedioate, O-methylcatechol sulfate, 1-stearoylglycerophosphocholine (18:0), and 1-margalloylglycerophosphocholine. (17:0), 1-docosapentaenoylglycerophosphocholine (22:5n3), N-palmitoyl taurine, pelargonate (9:0), deoxycarnitine, heptanoylglycine, 3-methyl adipate, 2-hydroxyglutarate, α-CEHC (2,5,7,8-tetramethyl-2-(2'-carboxyethyl)-6-hydroxychroman) sulfate (formerly X-12435), methylhexanoylglutamine (formerly X-12637), X-11521 (possible empirical formula: C 15 H 27NO4 and structure: 2-octenoylcarnitine), X-15646, X-12802, X-11478, X-11440 (possible hydroxypregnene-diol disulfate or pregnanolone-diol disulfate), X-15486, X-18913, X-13837, X-18946, X-11861, X-18888, X-18922, X-17438, X-18916, X-16674, X-12824, and combinations thereof. Therefore, a diagnosis of medium-chain acyl-CoA dehydrogenase deficiency in a subject may be made by analyzing a biological sample obtained from the subject for the presence of N-octanoylglycine, caproate (6:0), caprylate (8:0), heptanoate (7:0), dodecanedioate, O-methylcatechol sulfate, 1-stearoylglycerophosphocholine (18:0), 1-margalloylglycerophosphocholine (17:0), 1-docosapentaenoylglycerophosphocholine (18:0), 1-docosapentaenoylglycerophosphocholine (17:0), 1-octanoylglycerol, ... X-11521 (possible empirical formula: C) 15 H 27 X-17438, X-18916, X-16674, and X-12824, and comparing the level of the metabolite in the sample to a medium-chain acyl-CoA dehydrogenase deficiency-positive reference level and / or a medium-chain acyl-CoA dehydrogenase deficiency-negative reference level of the metabolite.

[0197] Optionally, the diagnosis of medium-chain acyl-CoA dehydrogenase deficiency in a subject can be performed or facilitated by analyzing the levels of one or more currently used diagnostic metabolites in combination with the novel metabolic biomarkers described above.Currently used diagnostic metabolites indicative of medium-chain acyl-CoA dehydrogenase deficiency include acylcarnitine, carnitine, 4-octenedioate, adipate, and organic acids (e.g., hexanoylglycine (C6), octanoylcarnitine (C8), hexanoylcarnitine (C6), cis-4-decenoylcarnitine, 5-hydroxyhexanoate, suberate (octanedioate), sebacate (decanedioate), decanoylcarnitine, and 3-hydroxydecanoate). Thus, a method for diagnosing or facilitating the diagnosis of medium-chain acyl-CoA dehydrogenase deficiency may further comprise analyzing a biological sample obtained from the subject to ascertain levels of one or more additional metabolites, including acylcarnitines, carnitine, 4-octenedioate, adipate, organic acids, and combinations thereof.

[0198] In one embodiment, one or more biomarkers selected from the group consisting of caproate, hexanoylglycine, octanoycarnitine, hexanoycarnitine, N-octanoyglycine, cis-4-decenoylcarnitine, deoxycarnitine, caprylate, 5-hydroxyhexanoate, decanoylcarnitine, suberate (octanedioate), N-palmitoyltaurine, and heptanoate may be used in diagnosing medium-chain acyl-CoA dehydrogenase deficiency or aiding in the diagnosis of medium-chain acyl-CoA dehydrogenase deficiency in a subject.

[0199] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0200] In some embodiments, diagnosing medium-chain acyl-CoA dehydrogenase deficiency in a subject can be made or facilitated by analyzing a biological sample derived from the subject's urine to ascertain levels of one or more metabolites, including heptanoylglycine, 3-methyladipate, 2-hydroxyglutarate, and any combination thereof.

[0201] Methylmalonic acidemia Novel metabolic biomarkers indicative of methylmalonic acidemia include 2-methylmalonylcarnitine, propionylcarnitine (C3), tiglylcarnitine, 2-methylbutyrylcarnitine (C5), 2-methylcitrate, succinylcarnitine, propionylglycine, 3-hydroxypropanoate, valerylcarnitine, isovalerylcarnitine, succinate, tigloylglycine, β-hydroxyisovaleroylcarnitine, β-hydroxyisovalerate, isobutyrylcarnitine, 3-hydroxy-2-ethylpropionate, butyrylcarnitine, 3-methylglutarylcarnitine, methylsuccinate, 3-methyl-2-oxovalerate, 3-methyl-2-oxobutyrate, X-12749, X-17564, X-12114, and combinations thereof. Therefore, the diagnosis of methylmalonic acidemia in a subject may be determined by analyzing a biological sample obtained from the subject for the presence of 2-methylmalonylcarnitine, propionylcarnitine (C3), tiglylcarnitine, 2-methylbutyrylcarnitine (C5), 2-methylcitrate, succinylcarnitine, propionylglycine, 3-hydroxypropanoate, valerylcarnitine, isovalerylcarnitine, succinate, tigloylglycine, β-hydroxyisovaleroylcarnitine, β-hydroxyisovalerate, and isobutyrylcarnitine. , 3-hydroxy-2-ethylpropionate, butyrylcarnitine, 3-methylglutarylcarnitine, methylsuccinate, 3-methyl-2-oxovalerate, 3-methyl-2-oxobutyrate, X-12749, X-17564, and X-12114, and comparing the level of the metabolite in the sample to a methylmalonic acidemia-positive reference level and / or a methylmalonic acidemia-negative reference level for the metabolite.

[0202] Optionally, the diagnosis of methylmalonic acidemia in a subject can be performed or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.Currently used diagnostic metabolites that indicate methylmalonic acidemia include methylmalonate and methylmalonyl-CoA.Therefore, the method for diagnosing or facilitating the diagnosis of methylmalonic acidemia can further include analyzing a biological sample obtained from the subject to determine the level of one or more additional metabolites, including methylmalonate, methylmalonyl-CoA, and combinations thereof.

[0203] In one embodiment, one or more biomarkers selected from the group consisting of propionylcarnitine, 2-methylmalonylcarnitine, 2-methylbutyrylcarnitine, valerylcarnitine, propionylglycine, 3-hydroxypropanoate, tigloylglycine, tiglylcarnitine, succinylcarnitine, succinate, isovalerylcarnitine, and 2-methylcitrate may be used in diagnosing or aiding in the diagnosis of methylmalonic acidemia in a subject.

[0204] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0205] In some embodiments, diagnosis of methylmalonic acidemia in a subject can be made or facilitated by analyzing a biological sample derived from the subject's urine to ascertain levels of one or more metabolites, including 3-methylglutarylcarnitine, methylsuccinate, and any combination thereof.

[0206] Molybdenum cofactor deficiency or sulfite oxidase deficiency Novel metabolic biomarkers indicative of molybdenum cofactor deficiency or sulfite oxidase deficiency include 5-HETE, leukotriene B4, 13-HODE+9-HODE, 12-HETE, urate, and combinations thereof. Thus, diagnosing molybdenum cofactor deficiency or sulfite oxidase deficiency in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to determine the levels of one or more metabolites, including 5-HETE, leukotriene B4, 13-HODE+9-HODE, 12-HETE, and urate, and comparing the levels of the metabolites in the sample with a molybdenum cofactor deficiency or sulfite oxidase deficiency-positive reference level and / or a molybdenum cofactor deficiency or sulfite oxidase deficiency-negative reference level of the metabolite.

[0207] Optionally, the diagnosis of molybdenum cofactor deficiency or sulfite oxidase deficiency in a subject can be performed or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.Currently used diagnostic metabolites that indicate molybdenum cofactor deficiency or sulfite oxidase deficiency include xanthine, S-sulfocysteine, and thiosulfate.Therefore, the method for diagnosing or facilitating the diagnosis of molybdenum cofactor deficiency or sulfite oxidase deficiency can further comprise analyzing a biological sample obtained from a subject to determine the level of one or more additional metabolites, including xanthine, S-sulfocysteine, thiosulfate, and combinations thereof.

[0208] In one embodiment, one or more biomarkers selected from the group consisting of 5-HETE, leukotriene B4, 13-HODE+9-HODE, and 12-HETE may be used in diagnosing or aiding in the diagnosis of molybdenum cofactor deficiency or sulfite oxidase deficiency in a subject.

[0209] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0210] In some embodiments, diagnosis of molybdenum cofactor deficiency or sulfite oxidase deficiency in a subject can be made or facilitated by analyzing a biological sample derived from the subject's urine to ascertain levels of one or more metabolites, including urate, xanthine, S-sulfocysteine, and any combination thereof.

[0211] Maple syrup urine disease Novel metabolic biomarkers indicative of maple syrup urine disease include 2-hydroxy-3-methylvalerate, α-hydroxyisovalerate, isovalerylcarnitine, 2-aminoheptanoate, 4-methyl-2-oxopentanoate, 1-linolenoylglycerophosphocholine (18:3n3), 2-linolenoylglycerophosphocholine (18:3n3), 1-myristoylglycerophosphocholine (14:0), 2-myristoylglycerophosphocholine, 5α-androstane-3α,17β-diol disulfate, and 3-methyl-2-oxobutyrate. , isovalerate, isobutyrylcarnitine, 3-hydroxyisobutyrate, 2-methylbutyrylcarnitine, β-hydroxyisovaleroylcarnitine, allo-isoleucine, 3-methyl-2-oxovalerate, β-hydroxyisovalerate, succinate, acetylcarnitine, 2-methylcitrate, tigloylglycine, tiglylcarnitine, hydroxybutyrylcarnitine, α-hydroxyisocaproate, X-13581, X-17690, X-13689 (glucuronide conjugate), and combinations thereof.Therefore, a diagnosis of maple syrup urine disease in a subject may be made by analyzing a biological sample obtained from the subject for the presence of 2-hydroxy-3-methylvalerate, α-hydroxyisovalerate, isovalerylcarnitine, 2-aminoheptanoate, 4-methyl-2-oxopentanoate, 1-linolenoylglycerophosphocholine (18:3n3), 2-linolenoylglycerophosphocholine (18:3n3), 1-myristoylglycerophosphocholine (14:0), 2-myristoylglycerophosphocholine, 5α-androstane-3α,17β-diol disulfate, 3-methyl-2-oxobutyrate, isovalerate, isobutyrylcarnitine, 3-hydroxyisobutyrate, 2-methylbutyrylcarnitine. This may be accomplished or facilitated by ascertaining the level of one or more metabolites, including lunitine, β-hydroxyisovaleroylcarnitine, allo-isoleucine, 3-methyl-2-oxovalerate, β-hydroxyisovalerate, succinate, acetylcarnitine, 2-methylcitrate, tigloylglycine, tiglylcarnitine, hydroxybutyrylcarnitine, α-hydroxyisocaproate, X-13581, X-17690, and X-13689 (glucuronide conjugates), and comparing the level of the metabolite in the sample to a maple syrup urine disease positive reference level and / or a maple syrup urine disease negative reference level for the metabolite.

[0212] Optionally, the diagnosis of maple syrup urine disease in a subject can be made or facilitated by analyzing the levels of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.Currently used diagnostic metabolites indicative of maple syrup urine disease include leucine, isoleucine, and valine.Accordingly, a method for diagnosing maple syrup urine disease or a method for facilitating the diagnosis of maple syrup urine disease may further comprise analyzing a biological sample obtained from the subject to determine the levels of one or more additional metabolites, including leucine, isoleucine, valine, and combinations thereof.

[0213] In one embodiment, one or more biomarkers selected from the group consisting of allo-isoleucine, a-hydroxyisovalerate, 2-hydroxy-3-methylvalerate, 4-methyl-2-oxopentanoate, leucine, 3-methyl-2-oxovalerate, isoleucine, isovalerate, 3-methyl-2-oxobutyrate, valine, 3-hydroxyisobutyrate, isobutyrylcarnitine, isovalerylcarnitine, β-hydroxyisovaleroylcarnitine, and 2-methylbutyrylcarnitine may be used in diagnosing or assisting in the diagnosis of maple syrup urine disease in a subject.

[0214] Ornithine transcarbamylase deficiency (OTC deficiency) Novel metabolic biomarkers indicative of OTC deficiency include phenylacetylglutamine, stearidonate (18:4n3), 3-ureidopropionate, 2-methylhippurate, 2-hydroxyphenylacetate, phenylcarnitine, hippurate, phenylpropionylglycine, benzoate, methyl-4-hydroxybenzoate, 4-phenylbutyrate, 2-pentanamido-3-phenylpropanoic acid, phenylacetate, phenylacetylglycine, trans-4-hydroxyproline, pro-hydroxy-proline, urea, phenyllactate (PLA), guanidinosuccinate, ornithine, X-20598, X-20588, and combinations thereof. Thus, diagnosis of OTC deficiency in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to ascertain the level of one or more metabolites, including phenylacetylglutamine, stearidonate (18:4n3), 3-ureidopropionate, 2-methylhippurate, 2-hydroxyphenylacetate, phenylcarnitine, hippurate, phenylpropionylglycine, benzoate, methyl-4-hydroxybenzoate, 4-phenylbutyrate, 2-pentanamido-3-phenylpropanoic acid, phenylacetate, phenylacetylglycine, trans-4-hydroxyproline, pro-hydroxy-pro, urea, phenyllactate (PLA), guanidinosuccinate, ornithine, X-20598, and X-20588, and comparing the level of the metabolite in the sample to an OTC deficiency-positive reference level and / or an OTC deficiency-negative reference level of the metabolite.

[0215] Optionally, the diagnosis of OTC deficiency in a subject can be carried out or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.The currently used diagnostic metabolites that indicate OTC deficiency include orotate, citrulline, and arginine.Therefore, the method for diagnosing or facilitating the diagnosis of OTC deficiency can further comprise the step of analyzing biological samples obtained from a subject to determine the level of one or more additional metabolites, including orotate, citrulline, arginine, and combinations thereof.

[0216] In one embodiment, one or more biomarkers selected from the group consisting of phenylacetylglutamine, phenylcarnitine, phenylacetate, hippurate, glutamine, phenylacetylglycine, orotate, creatinine, and urea may be used in diagnosing or aiding in the diagnosis of OTC deficiency in a subject.

[0217] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0218] In some embodiments, diagnosis of OTC deficiency in a subject can be made or facilitated by analyzing a biological sample derived from the subject's urine to ascertain levels of one or more metabolites, including 2-methylhippurate, 2-hydroxyphenylacetate, phenylpropionylglycine, benzoate, methyl-4-hydroxybenzoate, 4-phenylbutyrate, 2-pentanamido-3-phenylpropanoic acid, and any combination thereof.

[0219] Propionic acidemia Novel metabolic biomarkers indicative of propionic acidemia include propionylglycine (C3), 2-methylcitrate, 3-hydroxypropanoate, propionylcarnitine (C3), 1-pentadecanoylglycerophosphocholine (15:0), tigloylglycine, succinylcarnitine, glutarylcarnitine (C5), 3-methylglutarylcarnitine (C6), tiglylcarnitine, butyrylcarnitine, 2-methylmalonylcarnitine, β-hydroxyisovalerate, X-12819, and combinations thereof. Thus, diagnosis of propionic acidemia in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to ascertain the levels of one or more metabolites, including propionylglycine (C3), 2-methylcitrate, 3-hydroxypropanoate, propionylcarnitine (C3), 1-pentadecanoylglycerophosphocholine (15:0), tigloylglycine, succinylcarnitine, glutarylcarnitine (C5), 3-methylglutarylcarnitine (C6), tiglylcarnitine, butyrylcarnitine, 2-methylmalonylcarnitine, β-hydroxyisovalerate, and X-12819, and comparing the level of the metabolite in the sample to a propionic acidemia-positive reference level and / or a propionic acidemia-negative reference level for the metabolite.

[0220] Optionally, the diagnosis of propionic acidemia in a subject can be made or facilitated by analyzing the levels of one or more currently used diagnostic metabolites in combination with the novel metabolic biomarkers described above. Currently used diagnostic metabolites indicative of propionic acidemia include propionate. Thus, a method for diagnosing or facilitating the diagnosis of propionic acidemia may further comprise analyzing a biological sample obtained from the subject to determine the levels of one or more additional metabolites, including propionate.

[0221] In one embodiment, one or more biomarkers selected from the group consisting of 2-methylcitrate, 3-hydroxypropanoate, propionylcarnitine, propionylglycine, tigloylglycine, and succinylcarnitine may be used in diagnosing or aiding in the diagnosis of propionic acidemia in a subject.

[0222] Phenylketonuria (PKU) Novel metabolic biomarkers indicative of PKU include gamma-glutamylphenylalanine, phenyllactate, N-acetylphenylalanine, phenylpyruvate, gamma-glutamyltyrosine, 3-methoxytyrosine, 4-hydroxyphenylpyruvate, p-cresol sulfate, catechol sulfate, phenylacetylglutamine, o-cresol sulfate, phenylacetylglycine, phenylalanine-containing dipeptides (e.g., phenylalanylarginine, valylphenylalanine, histidylphenylalanine, phenylalanylserine, leucylphenylalanine, threonylphenylalanine, phenylalanylalanine, phenylalanylglycine, phenylalanylglutamate, phenylalanylphenylalanine, aspartylphenylalanine, tryptophylphenylalanine, phenylalanylisoleucine, glycylphenylalanine, phenylalanylleucine, phenylalanylaspartate), X-16283, X-15497, and combinations thereof.Thus, the diagnosis of PKU in a subject may be determined by analyzing a biological sample obtained from the subject for the presence of γ-glutamylphenylalanine, phenyllactate, N-acetylphenylalanine, phenylpyruvate, γ-glutamyltyrosine, 3-methoxytyrosine, 4-hydroxyphenylpyruvate, p-cresol sulfate, catechol sulfate, phenylacetylglutamine, o-cresol sulfate, phenylacetylglycine, phenylalanine-containing dipeptides (e.g., phenylalanylarginine, valylphenylalanine, histidylphenylalanine, phenylalanylserine, leucylphenylalanine, threon ... The determination of the level of one or more metabolites, including tryptophylphenylalanine, phenylalanylalanine, phenylalanylglycine, phenylalanylglutamate, phenylalanylphenylalanine, aspartylphenylalanine, tryptophylphenylalanine, phenylalanylisoleucine, glycylphenylalanine, phenylalanylleucine, phenylalanylaspartate, X-16283, and X-15497, and comparing the level of the metabolite in the sample to a PKU-positive reference level and / or a PKU-negative reference level of the metabolite.

[0223] Optionally, the diagnosis of PKU in a subject can be performed or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.The currently used diagnostic metabolites that indicate PKU include phenylalanine.Therefore, the method for diagnosing or facilitating the diagnosis of PKU can further comprise the step of analyzing biological samples obtained from a subject to determine the level of one or more additional metabolites, including phenylalanine.

[0224] In one embodiment, one or more biomarkers selected from the group consisting of phenylalanine, phenyllactate, gamma-glutamylphenylalanine, N-acetylphenylalanine, phenylpyruvate, phenylalanine-containing dipeptides, 4-hydroxyphenylpyruvate, 3-methoxytyrosine, cyclo(L-phe-L-pro), gamma-glutamyltyrosine, cyclo(L-phe-D-pro), p-cresol sulfate, and catechol sulfate may be used in diagnosing or assisting in the diagnosis of PKU in a subject.

[0225] Succinic semialdehyde dehydrogenase deficiency The new metabolic biomarkers that indicate succinic semialdehyde dehydrogenase deficiency include succinimide.Therefore, the diagnosis of succinic semialdehyde dehydrogenase deficiency in a subject can be made or facilitated by analyzing the biological sample obtained from the subject to determine the level of one or more metabolites, including succinimide, and comparing the level of the metabolite in the sample with the succinic semialdehyde dehydrogenase deficiency positive reference level and / or the succinic semialdehyde dehydrogenase deficiency negative reference level of the metabolite.

[0226] Optionally, the diagnosis of succinic semialdehyde dehydrogenase deficiency in a subject can be performed or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.Currently used diagnostic metabolites that indicate succinic semialdehyde dehydrogenase deficiency include γ-aminobutyrate (GABA).Therefore, the method for diagnosing succinic semialdehyde dehydrogenase deficiency or the method for facilitating the diagnosis of succinic semialdehyde dehydrogenase deficiency can further comprise analyzing a biological sample obtained from a subject to determine the level of one or more additional metabolites, including γ-aminobutyrate (GABA).

[0227] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0228] In some embodiments, diagnosing succinic semialdehyde dehydrogenase deficiency in a subject can be made or facilitated by analyzing a biological sample derived from the subject's urine.

[0229] Succinyladenosine lyase deficiency New metabolic biomarkers that indicate succinyladenosine lyase deficiency include xanthosine, 2'-deoxyguanosine, 2'-deoxyinosine, adenine, and combinations thereof.Therefore, the diagnosis of succinyladenosine lyase deficiency in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to determine the level of one or more metabolites, including xanthosine, 2'-deoxyguanosine, 2'-deoxyinosine, and adenine, and comparing the level of the metabolite in the sample with the succinyladenosine lyase deficiency positive reference level and / or the succinyladenosine lyase deficiency negative reference level of the metabolite.

[0230] Optionally, the diagnosis of succinyladenosine lyase deficiency in a subject can be performed or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.The currently used diagnostic metabolites that indicate succinyladenosine lyase deficiency include N6-succinyladenosine.Therefore, the method for diagnosing succinyladenosine lyase deficiency or the method for facilitating the diagnosis of succinyladenosine lyase deficiency can further comprise the step of analyzing biological samples obtained from a subject to determine the level of one or more additional metabolites, including N6-succinyladenosine.

[0231] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0232] In some embodiments, diagnosing succinyladenosine lyase deficiency in a subject can be made or facilitated by analyzing a biological sample derived from the subject's urine.

[0233] Thymidine phosphorylase deficiency Novel metabolic biomarkers indicative of thymidine phosphorylase deficiency include 2'-deoxyuridine; 5,6-dihydrothymine, 5-methyluridine (ribothymidine), hippurate, 2-linoleoylglycerophosphocholine, 4-methylcatechol sulfate, 1-arachidoylglycerophosphocholine (20:0), taurolithocholate 3-sulfate, glycolithocholate sulfate, imidazole propionate, X-13862, X-19330, X-20620, X-12170, and combinations thereof. Thus, diagnosis of thymidine phosphorylase deficiency in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to ascertain the level of one or more metabolites, including 2'-deoxyuridine, 5,6-dihydrothymine, 5-methyluridine (ribothymidine), hippurate, 2-linoleoylglycerophosphocholine, 4-methylcatechol sulfate, 1-arachidoylglycerophosphocholine (20:0), taurolithocholate 3-sulfate, glycolitocholate sulfate, imidazole propionate, X-13862, X-19330, X-20620, and X-12170, and comparing the level of the metabolite in the sample to a thymidine phosphorylase deficiency-positive reference level and / or a thymidine phosphorylase deficiency-negative reference level of the metabolite.

[0234] Optionally, the diagnosis of thymidine phosphorylase deficiency in a subject can be carried out or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.The currently used diagnostic metabolites that indicate thymidine phosphorylase deficiency include thymidine.Therefore, the method for diagnosing or facilitating the diagnosis of thymidine phosphorylase deficiency can further comprise the step of analyzing biological samples obtained from a subject to determine the level of one or more additional metabolites, including thymidine.

[0235] In one embodiment, one or more biomarkers selected from the group consisting of thymidine, 2'-deoxyuridine, and thymine may be used in diagnosing or aiding in the diagnosis of thymidine phosphorylase deficiency in a subject.

[0236] Trimethyllysine hydroxylase epsilon deficiency Novel metabolic biomarkers indicative of trimethyllysine hydroxylase epsilon deficiency include 1-arachidonoylglyercophosphate, X-16574, X-12822, X-15136, and combinations thereof. Thus, diagnosing trimethyllysine hydroxylase epsilon deficiency in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to ascertain levels of one or more metabolites, including 1-arachidonoylglyercophosphate, X-16574, X-12822, and X-15136, and comparing the levels of the metabolites in the sample with a trimethyllysine hydroxylase epsilon deficiency-positive reference level and / or a trimethyllysine hydroxylase epsilon deficiency-negative reference level of the metabolite.

[0237] Optionally, the diagnosis of trimethyllysine hydroxylase epsilon deficiency in a subject can be made or facilitated by analyzing the levels of one or more currently used diagnostic metabolites in combination with the above-mentioned novel metabolic biomarkers.Currently used diagnostic metabolites indicating trimethyllysine hydroxylase epsilon deficiency include N-6-trimethyllysine.Therefore, the method for diagnosing or facilitating the diagnosis of trimethyllysine hydroxylase epsilon deficiency may further comprise analyzing a biological sample obtained from the subject to determine the levels of one or more additional metabolites, including N-6-trimethyllysine.

[0238] In one embodiment, the biomarker N6-trimethyllysine may be used in diagnosing or aiding in the diagnosis of trimethyllysine hydroxylase epsilon deficiency in a subject.

[0239] Tyrosinemia Novel metabolic biomarkers indicative of tyrosinemia include 3-(4-hydroxyphenyl)lactate, 4-hydroxyphenylpyruvate, 3-(3-hydroxyphenyl)propionate, 4-hydroxyphenylacetate, phenyllactate (PLA), X-13581, and combinations thereof. Accordingly, diagnosis of tyrosinemia in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to ascertain the levels of one or more metabolites, including 3-(4-hydroxyphenyl)lactate, 4-hydroxyphenylpyruvate, 3-(3-hydroxyphenyl)propionate, 4-hydroxyphenylacetate, phenyllactate (PLA), and X-13581, and comparing the levels of the metabolites in the sample to a tyrosinemia-positive reference level and / or a tyrosinemia-negative reference level of the metabolite.

[0240] Optionally, the diagnosis of tyrosinemia in a subject can be made or facilitated by analyzing the levels of one or more currently used diagnostic metabolites in combination with the above-mentioned novel metabolic biomarkers. Currently used diagnostic metabolites indicative of tyrosinemia include tyrosine. Thus, a method for diagnosing or facilitating the diagnosis of tyrosinemia may further comprise analyzing a biological sample obtained from the subject to determine the levels of one or more additional metabolites, including tyrosine.

[0241] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0242] In some embodiments, diagnosing tyrosinemia in a subject can be made or facilitated by analyzing a biological sample derived from the subject's urine.

[0243] Very long-chain acyl-CoA dehydrogenase deficiency Novel metabolic biomarkers indicative of very long-chain acyl-CoA dehydrogenase deficiency include 9-methyluric acid, xylulose, arachidonate (20:4n6), docosahexaenoate (DHA; 22:6n3), eicosapentanoic acid (EPA), 5,8-tetradecadienoic acid (formerly X-12442), 1-docosahexaenoyl-GPC (22:6; DHA-GPC), X-18739 (a possible isomer of 2-tetradecenoylcarnitine), and combinations thereof. Thus, diagnosis of very long-chain acyl-CoA dehydrogenase deficiency in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to ascertain the levels of one or more metabolites, including 9-methyluric acid, xylulose, arachidonate (20:4n6), docosahexaenoate (DHA; 22:6n3), eicosapentanoic acid (EPA), 5,8-tetradecadienoic acid (formerly X-12442), 1-docosahexaenoyl-GPC (22:6; DHA-GPC), and X-18739 (a possible isomer of 2-tetradecenoylcarnitine), and comparing the level of the metabolite in the sample to a very long-chain acyl-CoA dehydrogenase deficiency-positive reference level and / or a very long-chain acyl-CoA dehydrogenase deficiency-negative reference level for the metabolite.

[0244] Optionally, diagnosing very long-chain acyl-CoA dehydrogenase deficiency in a subject can be performed or facilitated by analyzing the levels of one or more currently used diagnostic metabolites in combination with the novel metabolic biomarkers described above.Currently used diagnostic metabolites indicative of very long-chain acyl-CoA dehydrogenase deficiency include myristoylcarnitine, stearoylcarnitine (C18), palmitoylcarnitine (C16), oleoylcarnitine (C18), myristoleate (14:1n5), myristoleoylcarnitine, and linoleoylcarnitine. Thus, a method of diagnosing or facilitating the diagnosis of very long-chain acyl-CoA dehydrogenase deficiency may further comprise analyzing a biological sample obtained from the subject to ascertain levels of one or more additional metabolites, including myristoylcarnitine, stearoylcarnitine (C18), palmitoylcarnitine (C16), oleoylcarnitine (C18), myristoleate (14:1n5), myristoleoylcarnitine, linoleoylcarnitine, and combinations thereof.

[0245] In one embodiment, one or more biomarkers selected from the group consisting of myristoylcarnitine, myristoleate, stearoylcarnitine, palmitoylcarnitine, laurylcarnitine, eicosapentaenoate, arachidonate, oleoylcarnitine, docosahexaenoate, docosapentaenoate, and X-12442 may be used in diagnosing or aiding in the diagnosis of very long-chain acyl-CoA dehydrogenase deficiency in a subject.

[0246] Xanthinuria Novel metabolic biomarkers indicative of xanthinuria include hypoxanthine, xanthosine, 2'-deoxyinosine, inosine, N2-methylguanosine, creatinine, and combinations thereof. Thus, diagnosing xanthinuria in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to determine the levels of one or more metabolites, including hypoxanthine, xanthosine, 2'-deoxyinosine, inosine, N2-methylguanosine, and creatinine, and comparing the levels of the metabolites in the sample with a xanthinuria-positive reference level and / or a xanthinuria-negative reference level of the metabolite.

[0247] Optionally, the diagnosis of xanthinuria in a subject can be made or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.Currently used diagnostic metabolites that indicate xanthinuria include xanthine, urate, and creatine.Therefore, the method for diagnosing or facilitating the diagnosis of xanthinuria can further comprise analyzing a biological sample obtained from a subject to determine the level of one or more additional metabolites, including xanthine, urate, and creatine.

[0248] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0249] In some embodiments, diagnosing xanthinuria in a subject can be made or facilitated by analyzing a biological sample derived from the subject's urine.

[0250] X-linked creatine transporter Novel metabolic biomarkers indicative of the X-linked creatine transporter include glycylleucine, 2-hydroxyoctanoate, 1,6-anhydroglucose, X-11483, X-18943, X-17422, X-17761, X-17335, and combinations thereof. Thus, diagnosing X-linked creatine transporter in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to ascertain the levels of one or more metabolites, including glycylleucine, 2-hydroxyoctanoate, 1,6-anhydroglucose, X-11483, X-18943, X-17422, X-17761, and X-17335, and comparing the levels of the metabolites in the sample to an X-linked creatine transporter-positive reference level and / or an X-linked creatine transporter-negative reference level of the metabolite.

[0251] Optionally, the diagnosis of X-linked creatine transporter in a subject can be performed or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.Currently used diagnostic metabolites that indicate X-linked creatine transporter include creatine.Therefore, the method for diagnosing or facilitating the diagnosis of X-linked creatine transporter can further comprise analyzing a biological sample obtained from the subject to determine the level of one or more additional metabolites, including creatine.

[0252] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0253] In some embodiments, diagnosing an X-linked creatine transporter in a subject can be performed or facilitated by analyzing a biological sample derived from the subject's urine.

[0254] Sarcosinemia Novel metabolic biomarkers indicative of sarcosinemia include choline, betaine, glycine, dimethylglycine, and combinations thereof. Thus, diagnosing sarcosinemia in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to ascertain the levels of one or more metabolites, including choline, betaine, glycine, and dimethylglycine, and comparing the levels of the metabolites in the sample with a sarcosinemia-positive reference level and / or a sarcosinemia-negative reference level of the metabolite.

[0255] Optionally, the diagnosis of sarcosinemia in a subject can be made or facilitated by analyzing the levels of one or more currently used diagnostic metabolites in combination with the above-mentioned novel metabolic biomarkers. Currently used diagnostic metabolites indicative of sarcosinemia include sarcosine. Thus, a method for diagnosing or facilitating the diagnosis of sarcosinemia may further comprise analyzing a biological sample obtained from the subject to ascertain the levels of one or more additional metabolites, including sarcosine.

[0256] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0257] Citrate transporter deficiency The new metabolic biomarkers that indicate citrate transporter deficiency include α-ketoglutarate, succinate, fumarate, malate, glutamate, and their combinations.Therefore, the diagnosis of citrate transporter deficiency in subject can be carried out or facilitated by analyzing the biological sample obtained from subject to determine the level of one or more metabolites, including α-ketoglutarate, succinate, fumarate, malate, and glutamate, and comparing the level of the metabolite in the sample with the citrate transporter deficiency positive reference level and / or the citrate transporter deficiency negative reference level of the metabolite.

[0258] Optionally, the diagnosis of citrate transporter deficiency in subject can be carried out or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.The currently used diagnostic metabolites that indicate citrate transporter deficiency include citrate.Therefore, the method for diagnosing citrate transporter deficiency or the method for facilitating the diagnosis of citrate transporter deficiency can further comprise the step of analyzing the biological sample obtained from subject to determine the level of one or more additional metabolites, including citrate.

[0259] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0260] In some embodiments, diagnosing a citrate transporter deficiency in a subject can be made or facilitated by analyzing a biological sample derived from the subject's plasma, CSF, or urine.

[0261] Pyruvate dehydrogenase deficiency The diagnosis of pyruvate dehydrogenase deficiency in a subject can be performed or facilitated by analyzing the level of one or more currently used diagnostic metabolites.The currently used diagnostic metabolites that indicate pyruvate dehydrogenase deficiency include pyruvate and lactate.Therefore, the method for diagnosing pyruvate dehydrogenase deficiency or the method for facilitating the diagnosis of pyruvate dehydrogenase deficiency can further comprise the step of analyzing biological samples obtained from the subject to determine the level of one or more additional metabolites, including pyruvate and lactate.

[0262] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0263] Hyperornithine, homocitrulline, and hyperammonemia Novel metabolic biomarkers indicative of hyperornithine, homocitrulline, and hyperammonemia include uracil, 3-ureidopropionate, orotate, glutamine, N-acetyl-β-alanine, uridine, N-acetylaspartate (NAA), dimethylarginine (SDMA + ADMA), 5-methylthioadenosine (MTA), β-alanine, 4-ureidobutyrate, and combinations thereof. Thus, a diagnosis of hyperornithine-homocitrulline-hyperammonemia in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to ascertain levels of one or more metabolites, including uracil, 3-ureidopropionate, orotate, glutamine, N-acetyl-β-alanine, uridine, N-acetylaspartate (NAA), dimethylarginine (SDMA+ADMA), 5-methylthioadenosine (MTA), β-alanine, and 4-ureidobutyrate, and comparing the levels of the metabolites in the sample to a high ornithine-homocitrulline-hyperammonemia-positive reference level and / or a high ornithine-homocitrulline-hyperammonemia-negative reference level for the metabolite.

[0264] Optionally, the diagnosis of hyperornithine-homocitrulline-hyperammonemia in a subject can be made or facilitated by analyzing the levels of one or more currently used diagnostic metabolites in combination with the novel metabolic biomarkers described above. Currently used diagnostic metabolites indicative of hyperornithine-homocitrulline-hyperammonemia include ornithine, homocitrulline, spermine, and spermidine. Thus, a method for diagnosing or facilitating the diagnosis of hyperornithine-homocitrulline-hyperammonemia may further include analyzing a biological sample obtained from the subject to determine the levels of one or more additional metabolites, including ornithine, homocitrulline, spermine, and spermidine.

[0265] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0266] Aromatic amino acid decarboxylase (AAAD) deficiency The new metabolic biomarkers that indicate aromatic amino acid decarboxylase (AAAD) deficiency include tyrosine, phenylalanine, tryptophan, and combinations thereof.Therefore, the diagnosis of AAAD deficiency in a subject can be made or facilitated by analyzing the biological sample obtained from the subject to determine the level of one or more metabolites, including tyrosine, phenylalanine, and tryptophan, and comparing the level of the metabolite in the sample with the AAAD deficiency positive reference level and / or the AAAD deficiency negative reference level of the metabolite.

[0267] Optionally, the diagnosis of AAAD deficiency in subject can be carried out or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.The currently used diagnostic metabolites that indicate AAAD deficiency include L-dopa, 3-methoxytyrosine, 5-hydroxytryptophan, homovanillate, 5-hydroxyindoleacetate and vanillactic acid.Therefore, the method for diagnosing AAAD deficiency or the method for facilitating the diagnosis of AAAD deficiency can further comprise analyzing the biological sample obtained from subject to determine the level of one or more additional metabolites, including L-dopa, 3-methoxytyrosine, 5-hydroxytryptophan, homovanillate, 5-hydroxyindoleacetate and vanillactic acid.

[0268] As described above, the biological sample from subject can be separated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate or cell sample.In some embodiments, the diagnosis of AAAD deficiency in subject can be carried out or facilitated by analyzing the biological sample from subject's plasma.

[0269] Smith-Lemli-Opitz syndrome Novel metabolic biomarkers indicative of Smith-Lemli-Opitz syndrome include cholestanol. Accordingly, diagnosis of Smith-Lemli-Opitz syndrome in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to ascertain levels of one or more metabolites, including cholestanol, and comparing the levels of the metabolites in the sample to a Smith-Lemli-Opitz syndrome-positive reference level and / or a Smith-Lemli-Opitz syndrome-negative reference level for the metabolite.

[0270] Optionally, diagnosis of Smith-Lemli-Opitz syndrome in a subject can be made or facilitated by analyzing the levels of one or more currently used diagnostic metabolites in combination with the novel metabolic biomarkers described above. Currently used diagnostic metabolites indicative of Smith-Lemli-Opitz syndrome include 7-dehydrocholesterol. Thus, a method for diagnosing or facilitating the diagnosis of Smith-Lemli-Opitz syndrome may further include analyzing a biological sample obtained from the subject to determine the levels of one or more additional metabolites, including 7-dehydrocholesterol.

[0271] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0272] Primary carnitine deficiency A novel metabolic biomarker that indicates primary carnitine deficiency includes N6-trimethyllysine.Therefore, the diagnosis of primary carnitine deficiency in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to determine the level of one or more metabolites, including N6-trimethyllysine, and comparing the level of the metabolite in the sample with a primary carnitine deficiency-positive reference level and / or a primary carnitine deficiency-negative reference level of the metabolite.

[0273] Optionally, the diagnosis of primary carnitine deficiency in a subject can be carried out or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with the above-mentioned new metabolic biomarkers.The currently used diagnostic metabolites that indicate primary carnitine deficiency include carnitine and acylcarnitine.Therefore, the method for diagnosing or facilitating the diagnosis of primary carnitine deficiency can further comprise the step of analyzing biological samples obtained from a subject to determine the level of one or more additional metabolites, including carnitine and acylcarnitine.

[0274] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0275] Citrin deficiency Novel metabolic biomarkers indicative of citrin deficiency include orotate. Thus, diagnosing citrin deficiency in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to ascertain the levels of one or more metabolites, including orotate, and comparing the levels of the metabolites in the sample with a citrin deficiency-positive reference level and / or a citrin deficiency-negative reference level of the metabolite.

[0276] Optionally, the diagnosis of citrin deficiency in a subject can be performed or facilitated by analyzing the levels of one or more currently used diagnostic metabolites in combination with a new metabolic biomarker.Currently used diagnostic metabolites that indicate citrin deficiency include citrulline, arginine, threonine, serine, methionine, phenylalanine, and tyrosine.Therefore, a method for diagnosing or facilitating the diagnosis of citrin deficiency may further comprise analyzing a biological sample obtained from a subject to determine the levels of one or more additional metabolites, including citrulline, arginine, threonine, serine, methionine, phenylalanine, and tyrosine.

[0277] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0278] 4-aminobutyrate aminotransferase (ABAT) deficiency Novel metabolic biomarkers indicative of ABAT deficiency include 2-pyrrolidone. Thus, diagnosing ABAT deficiency in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to determine the level of one or more metabolites, including 2-pyrrolidone, and comparing the level of the metabolite in the sample with an ABAT deficiency-positive reference level and / or an ABAT deficiency-negative reference level of the metabolite.

[0279] Optionally, the diagnosis of ABAT deficiency in subject can be carried out or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with new metabolic biomarkers.The currently used diagnostic metabolites that indicate ABAT deficiency include GABA.Therefore, the method for diagnosing or facilitating the diagnosis of ABAT deficiency can further comprise the step of analyzing the biological sample obtained from subject to determine the level of one or more additional metabolites, including GABA.

[0280] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0281] In some embodiments, diagnosis of ABAT deficiency in a subject can be made or facilitated by analyzing a biological sample derived from the subject's plasma or urine.

[0282] GLUT1 deficiency (SLC2A1 deficiency) Novel metabolic biomarkers indicative of GLUT1 deficiency include fructose, mannose, prolylhydroxyproline, hydroxyproline, glycylproline, N-acetylneuraminate, dimethylarginine (ADMA+SDMA), glutamine, gamma-glutamylglutamine, N-acetyl-aspartyl-glutamate (NAAG), N-acetylglutamine, ethylmalonate, creatine, and combinations thereof. Thus, diagnosis of GLUT1 deficiency in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to ascertain the levels of one or more metabolites, including fructose, mannose, prolylhydroxyproline, hydroxyproline, glycylproline, N-acetylneuraminate, dimethylarginine (ADMA+SDMA), glutamine, γ-glutamylglutamine, N-acetyl-aspartyl-glutamate (NAAG), N-acetylglutamine, ethylmalonate, and creatine, and comparing the level of the metabolite in the sample to a GLUT1 deficiency-positive reference level and / or a GLUT1 deficiency-negative reference level of the metabolite.

[0283] Optionally, the diagnosis of GLUT1 deficiency in a subject can be performed or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with a new metabolic biomarker.Currently used diagnostic metabolites that indicate GLUT1 deficiency include glucose.Therefore, the method for diagnosing or facilitating the diagnosis of GLUT1 deficiency can further comprise analyzing a biological sample obtained from the subject to determine the level of one or more additional metabolites, including glucose.

[0284] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0285] In some embodiments, diagnosing GLUT1 deficiency in a subject can be made or facilitated by analyzing a biological sample derived from the subject's CSF.

[0286] 3-Methylglutaconic aciduria (MGA) New metabolic biomarkers that indicate MGA include 3-methylglutarylcarnitine.Therefore, the diagnosis of MGA in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to determine the level of one or more metabolites, including 3-methylglutarylcarnitine, and comparing the level of the metabolite in the sample with an MGA-positive reference level and / or an MGA-negative reference level of the metabolite.

[0287] Optionally, the diagnosis of MGA in a subject can be performed or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with new metabolic biomarkers.Currently used diagnostic metabolites that indicate MGA include 3-methylglutaconic acid and 3-methylglutaric acid.Therefore, the method for diagnosing or facilitating the diagnosis of MGA can further comprise analyzing biological samples obtained from a subject to determine the level of one or more additional metabolites, including 3-methylglutaconic acid and 3-methylglutaric acid.

[0288] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0289] In some embodiments, diagnosing MGA in a subject can be made or facilitated by analyzing a biological sample derived from the subject's plasma.

[0290] Short-chain acyl-CoA decarboxylase (SCAD) deficiency The new metabolic biomarkers that indicate SCAD deficiency include butyrylglycine.Therefore, the diagnosis of SCAD deficiency in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to determine the level of one or more metabolites, including butyrylglycine, and comparing the level of the metabolite in the sample with the SCAD deficiency positive reference level and / or the SCAD deficiency negative reference level of the metabolite.

[0291] Optionally, the diagnosis of SCAD deficiency in subject can be carried out or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with new metabolic biomarkers.The currently used diagnostic metabolites that indicate SCAD deficiency include ethyl malonate, butyryl carnitine and methyl succinate.Therefore, the method for diagnosing SCAD deficiency or the method for facilitating the diagnosis of SCAD deficiency can further comprise the step of analyzing the biological sample obtained from subject to determine the level of one or more additional metabolites, including ethyl malonate, butyryl carnitine and methyl succinate.

[0292] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0293] Urocanase deficiency Diagnosis of urocanase deficiency in a subject can be performed or facilitated by analyzing the levels of one or more currently used diagnostic metabolites in combination with new metabolic biomarkers.Currently used diagnostic metabolites that indicate urocanase deficiency include cis-urocanate, trans-urocanate, and imidazole propionate.Therefore, a method for diagnosing or facilitating the diagnosis of urocanase deficiency can further include analyzing a biological sample obtained from a subject to determine the levels of one or more additional metabolites, including cis-urocanate, trans-urocanate, and imidazole propionate.

[0294] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0295] In some embodiments, diagnosing urocanase deficiency in a subject can be made or facilitated by analyzing a biological sample derived from the subject's plasma.

[0296] 3-Hydroxyisobutyryl-CoA hydrolase deficiency New metabolic biomarkers that indicate 3-hydroxyisobutyryl-CoA hydrolase deficiency include 3-hydroxyisobutyrate, isobutyrylglycine, and combinations thereof.Therefore, the diagnosis of 3-hydroxyisobutyryl-CoA hydrolase deficiency in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to determine the level of one or more metabolites, including 3-hydroxyisobutyrate, isobutyrylglycine, and combinations thereof, and comparing the level of the metabolite in the sample with the 3-hydroxyisobutyryl-CoA hydrolase deficiency positive reference level and / or the 3-hydroxyisobutyryl-CoA hydrolase deficiency negative reference level of the metabolite.

[0297] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0298] Hyperoxaluria The diagnosis of hyperoxaluria in a subject can be carried out or facilitated by analyzing the level of one or more currently used diagnostic metabolites in combination with new metabolic biomarkers.The currently used diagnostic metabolites that indicate hyperoxaluria include oxalate and glycolate.Therefore, the method for diagnosing or facilitating the diagnosis of hyperoxaluria can further comprise the step of analyzing biological samples obtained from a subject to determine the level of one or more additional metabolites, including oxalate and glycolate.

[0299] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0300] In some embodiments, diagnosing hyperoxaluria in a subject can be made or facilitated by analyzing a biological sample derived from the subject's urine.

[0301] γ-Butyrobetaine hydroxylase deficiency (BBOX deficiency) Novel metabolic biomarkers indicative of BBOX deficiency include hexadecandioate (C16), docosadioate, eicosanodioate, octadecandioate (C18), dodecandioate (C12), 2-aminooctanoate, 2-aminoheptanoate, α-hydroxyisocaproate, isovalerate (C5), decanoylcarnitine (C10), cis-4-decenoylcarnitine, palmitoylcarnitine (C16), oleoylcarnitine (C18), laurylcarnitine (C12), myristoleoylcarnitine, myristoylcarnitine, glycerol, 3-hydroxymyristate, 2-hydroxydecanoate, 3-hydroxylaurate, 3-hydroxysebacate, 3-hydroxyoctanoate, 3-hydroxydecanoate, pelargonate (9:0), caproate (6:0), and combinations thereof. Therefore, a diagnosis of BBOX deficiency in a subject may be made by analyzing a biological sample obtained from the subject for the detection of hexadecanedioate (C16), docosadioate, eicosanoate, octadecanedioate (C18), dodecanedioate (C12), 2-aminooctanoate, 2-aminoheptanoate, α-hydroxyisocaproate, isovalerate (C5), decanoylcarnitine (C10), cis-4-decenoylcarnitine, palmitoylcarnitine (C16), oleoylcarnitine (C18), laurylcarnitine (C12), myristoleoylcarnitine, or a combination thereof. This can be performed or facilitated by ascertaining the level of one or more metabolites, including nitine, myristoylcarnitine, glycerol, 3-hydroxymyristate, 2-hydroxydecanoate, 3-hydroxylaurate, 3-hydroxysebacate, 3-hydroxyoctanoate, 3-hydroxydecanoate, pelargonate (9:0), caproate (6:0), and combinations thereof, and comparing the level of the metabolite in the sample with a BBOX deficiency-positive reference level and / or a BBOX deficiency-negative reference level of the metabolite.

[0302] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0303] Disorders of amino acid metabolism and transport (including urea cycle disorders and organic acidemias) Metabolic biomarkers indicative of disorders of amino acid metabolism and transport (including urea cycle disorders and organic acidemias) include N-acetylalanine, aspartate, glutarate (pentanedioate), glutarylcarnitine (C5), 3-hydroxyglutarate, glutaconate, phenylalanine, N-acetylphenylalanine, phenylpyruvate, phenyllactate (PLA), phenylacetate, phenylacetylglycine, phenylacetylglutamine, 4-hydroxyphenylpyruvate, 3-(4-hydroxyphenyl)lactate, and 4-hydroxyphenyllactate. Tartrate, p-cresol sulfate, o-cresol sulfate, 3-methoxytyrosine, leucine, 4-methyl-2-oxopentanoate, isovalerate, isovalerylglycine, isovalerylcarnitine (C5), 3-methylcrotonylglycine, β-hydroxyisovalerate, β-hydroxyisovaleroylcarnitine, 3-methylglutarylcarnitine (C6), α-hydroxyisovalerate, isoleucine, allo-isoleucine, 3-methyl-2-oxovalerate, 2-methylbutyrylcarnitine (C5), tiglyl Carnitine, tigloylglycine, 2-hydroxy-3-methylvalerate, 3-hydroxy-2-ethylpropionate, valine, 3-methyl-2-oxobutyrate, isobutyrylcarnitine, 3-hydroxyisobutyrate, α-hydroxyisocaproate, homocysteine, cystathionine, arginine, urea, ornithine, proline, citrulline, argininosuccinate, homoarginine, homocitrulline, N-acetylarginine, N-δ-acetylornithine, trans-4-hydroxyproline, pro-hydroxy- pro, creatine, creatinine, 4-guanidinobutanoate, guanidinosuccinate, gamma-glutamylphenylalanine, gamma-glutamyltyrosine, alanylalanine, arginylproline aspartylphenylalanine, glycylphenylalanine, histidylphenylalanine, isoleucyl aspartate, leucylphenylalanine, phenylalanylalanine, phenylalanylarginine, phenylalanyl aspartate, phenylalanyl glutamate, phenylalanylglycine, phenylalanylisoleucine,Phenylanyl-leucine, phenylalanyl-phenylalanine, phenylalanyl-serine, pyroglutamyl-valine, threonyl-phenylalanine, tryptophyl-phenylalanine, valyl-phenylalanine, fructose, sorbose, succinyl-carnitine, succinate, 2-methyl citrate, valerate, stearidonate (18:4n3), adipate, dodecanedioate (C12), tetradecanedioate (C14), hexadecanedioate (C16), octadecandioate (C18), 2-aminoheptanoate, 2-linoleate Hexanoylglycerophosphocholine (18:3n3), 2-methylmalonylcarnitine, butyrylcarnitine, propionylcarnitine (C3), propionylglycine (C3), methylmalonyl CoA, methylmalonic acid, acetylcarnitine, hydroxybutyrylcarnitine, valerylcarnitine, hexanoylcarnitine, myristoylcarnitine, palmitoylcarnitine (C16), hexenedioylcarnitine (formerly X-17001), 3-hydroxypropanoate, 1-myristoylglycerophosphocholine (14:0), 2-myristoylglycerol Cerophosphocholine, 1-pentadecanoylglycerophosphocholine (15:0), 1-linolenoylglycerophosphocholine (18:3n3), 1-stearoylglycerophosphoethanolamine, 1,3-dipalmitoylglycerol, 5α-androstane-3α,17β-diol disulfate, orotate, uridine, uracil, 3-ureidopropionate, hippurate, catechol sulfate, phenylcarnitine, propionate, 5-hydroxytryptophan, 5-methylthioadenosine (MTA), β-alanine, dimethylarginine (SD) MA+ADMA), glutamine, N-acetylaspartate (NAA), N-acetyl-β-alanine, tryptophan, tyrosine, 2-pyrrolidone, γ-glutamylleucine, O-sulfo-L-tyrosine, palmitoyl-sphingomyelin, γ-glutamylisoleucine, cysteine ​​s-sulfate, 2-aminoadipate, phenylacetylglutamine, 3,4-dihydroxyphenylacetate, phenylpropionylglycine, 2-pentanamido-3-phenylpropanoic acid, 2-hydroxyphenylacetate, N-acetylleucine,These include methyl succinate, ethyl malonate, guanidinoacetate, β-hydroxybutyrate, N-carbamoyl aspartate, 4-ureidobutyrate, hippurate, 2-methyl hippurate, benzoate, methyl-4-hydroxybenzoate, 4-phenylbutyrate, and combinations thereof. Thus, the diagnosis of a disorder of amino acid metabolism and transport (including urea cycle disorders and organic acidemias) in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to determine the level of one or more of the metabolites listed above, and comparing the level of the metabolite in the sample with a disease-positive or disorder-positive reference level and / or a disease-negative or disorder-negative reference level of the metabolite.

[0304] Optionally, diagnosis of disorders of amino acid metabolism and transport (including urea cycle disorders and organic acidemias) in a subject can be made or facilitated by analyzing the levels of one or more currently used diagnostic metabolites in combination with the metabolic biomarkers described above.

[0305] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0306] In some embodiments, diagnosis of an amino acid metabolism and transport disorder in a subject can be made or facilitated by analyzing a biological sample derived from the subject's urine to ascertain levels of one or more metabolites, including 2-aminoadipate, phenylacetylglutamine, 3,4-dihydroxyphenylacetate, phenylpropionylglycine, 2-pentanamido-3-phenylpropanoic acid, 2-hydroxyphenylacetate, N-acetylleucine, methylsuccinate, ethylmalonate, guanidinoacetate, β-hydroxybutyrate, N-carbamoylaspartate, 4-ureidobutyrate, hippurate, 2-methylhippurate, benzoate, methyl-4-hydroxybenzoate, 4-phenylbutyrate, and any combination thereof.

[0307] Fatty acid oxidation disorders Metabolic biomarkers indicative of impaired fatty acid oxidation include xylulose, caproate (6:0), heptanoate (7:0), caprylate (C8), pelargonate (9:0), caprate (C10), myristoleate (14:1n5), eicosapentaenoic acid (EPA), docosahexaenoate (DHA; 22:6n3), arachidonate (20:4n6), suberate (octanedioate), sebacate (C8), and dodecane. Decanoyl carnitine (C12), hexanoyl glycine (C6), N-octanoyl glycine, hexanoyl carnitine, octanoyl carnitine, decanoyl carnitine, cis-4-decenoyl carnitine, myristoyl carnitine, palmitoyl carnitine (C16), stearoyl carnitine (C18), oleoyl carnitine (C18), deoxycarnitine, carnitine, 3-hydroxydecanoate, 5-hydroxyhexanoate Sanoate, N-palmitoyl taurine, 1-margalloylglycerophosphocholine (17:0), 1-stearoylglycerophosphocholine (18:0), 1-docosapentaenoylglycerophosphocholine (22:5n3), 9-methyluric acid, α-CEHC (2,5,7,8-tetramethyl-2-(2'-carboxyethyl)-6-hydroxychroman) sulfate (formerly X-12435), O-methylcatechol sulfur These include glutamate, 5,8-tetradecadienoic acid (formerly X-12442), methylhexanoylglutamine (formerly X-12637), 7-dehydrocholesterol, cholestanol, N6-trimethyllysine, butyrylglycine, 1-docosahexaenoyl-GPC (22:6; DHA-GPC), 2-hydroxyglutarate, 3-methyladipate, 4-octenedioate, heptanoylglycine, and combinations thereof. Thus, diagnosing a fatty acid oxidation disorder in a subject can be made or facilitated by analyzing a biological sample obtained from the subject to ascertain the levels of one or more of the aforementioned metabolic biomarkers and comparing the level of the metabolite in the sample to a disease-positive or disorder-positive reference level and / or a disease-negative or disorder-negative reference level of the metabolite.

[0308] Optionally, diagnosis of a fatty acid oxidation disorder in a subject can be made or facilitated by analyzing the levels of one or more currently used diagnostic metabolites in combination with the metabolic biomarkers described above.

[0309] As explained above, a biological sample from a subject can be isolated from any suitable biological source, for example, blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

[0310] In some embodiments, diagnosis of an amino acid metabolism and transport disorder in a subject can be made or facilitated by analyzing a biological sample derived from the subject's urine to ascertain levels of one or more metabolites, including 2-hydroxyglutarate, 3-methyladipate, 4-octenedioate, heptanoylglycine, and any combination thereof. [Example]

[0311] I. General method A. Metabolomic Profiling The metabolome platform consists of three independent methods: ultra-high performance liquid chromatography / tandem mass spectrometry (UHLC / MS / MS) optimized for basic species; 2 ) and UHLC / MS / MS optimized for acidic species 2 and gas chromatography / mass spectrometry (GC / MS).

[0312] B. Sample Preparation Plasma and urine samples were stored at -80°C until needed and then thawed on ice immediately prior to extraction. Extraction was performed using an automated liquid-handling robot (MicroLab Star, Hamilton Robotics, Reno, NV). In this case, 450 μl of methanol was added to 100 μl of each sample to precipitate proteins. To confirm extraction efficiency, the methanol contained four recovery standards. Each solution was then mixed at 675 strokes / min in a Geno / Grinder 2000 (Glen Mills Inc., Clifton, NJ) and then centrifuged at 2000 rpm for 5 minutes. Four 110 μl aliquots of the supernatant from each sample were taken, dried under nitrogen, and then dried overnight under vacuum. The next day, one aliquot was reconstituted with 50 μL of 6.5 mM ammonium bicarbonate dissolved in water (pH 8). One aliquot was reconstituted with 50 μL of 0.1% formic acid dissolved in water. Both reconstituted solvents contained a set of internal instrument standards to record LC retention indices and assess LC-MS instrument performance. A third 110 μL aliquot was derivatized by treatment with 50 μL of a mixture of N,O-bistrimethylsilyltrifluoroacetamide and 1% trimethylchlorosilane + 5% triethylamine dissolved in cyclohexane:dichloromethane:acetonitrile (5:4:1), to which an internal standard was added to record GC retention indices and assess recovery from the derivatization process. This mixture was then dried overnight under vacuum, and the dried extract was then capped, shaken for 5 minutes, and heated at 60°C for 1 hour. The sample was cooled and briefly spun to pellet the residue before analysis by GC-MS. The remaining aliquot was dried, sealed, and stored at -80°C for use as a backup sample if necessary. The extracts were analyzed by three separate mass spectrometers: one UPLC-MS system using ultra-high performance liquid chromatography-mass spectrometry to detect cations, one UPLC-MS system to detect anions, and one Trace GC Ultra Gas Chromatograph-DSQ gas chromatography-mass spectrometry (GC-MS) system (Thermo Scientific, Waltham, MA).

[0313] C.UPLC method All reconstituted aliquots analyzed by LC-MS were separated using a Waters Acquity UPLC (Waters Corp., Milford, MA). For the aliquots reconstituted with 0.1% formic acid, the mobile phase consisted of 0.1% formic acid in water (A) and 0.1% formic acid in methanol (B). For the aliquots reconstituted with 6.5 mM ammonium bicarbonate, the mobile phase consisted of 6.5 mM ammonium bicarbonate in water at pH 8 (A) and 6.5 mM ammonium bicarbonate in 95 / 5 methanol / water. The gradient profile used for both the formic acid-reconstituted and ammonium bicarbonate-reconstituted extracts was 0.5% B to 70% B in 4 min, 70% B to 98% B in 0.5 min, held at 98% B for 0.9 min, and then returned to 0.5% B in 0.2 min. The flow rate was 350 μL / min. The sample injection volume was 5 μL with a 2× needle loop overfill. Liquid chromatographic separations were performed at 40° C. on separate 2.1 mm×100 mm Waters BEH C18 1.7 μm particle size columns for acids or bases.

[0314] D. UPLC-MS method In Example 1, a linear trap quadrupole mass spectrometer (LTQ, Thermo Scientific, Waltham, MA) was used. In Example 4, a Q-Exactive high resolution / accurate mass orbitrap mass spectrometer (Q-Exactive, ThermoFisher Scientific, Waltham, MA) operating at 35,000 mass resolution was used. In all other examples, an OrbitrapElite (OrbiElite Thermo Scientific, Waltham, MA) mass spectrometer was used. The Q-Exactive and OrbiElite mass spectrometers utilized a HESI-II source with sheath gas set to 80, auxiliary gas set to 12, and voltage set to 4.2 kV for positive mode. For negative mode, the settings were sheath gas at 75, auxiliary gas at 15, and voltage set to 2.75 kV. The source heater temperature for both modes was 430°C, and the capillary temperature was 350°C. The mass range was 99–1000 m / z, and the scan rate was 4.6 full scans / s, alternating one full scan and one MS / MS scan. The resolution was set to 30,000. The Fourier Transform Mass Spectroscopy (FTMS) full scan automatic gain control (AGC) target was set to 5×10. 5 The cutoff time was set to 500 ms. The AGC target for the ion trap MS / MS was 3x10 3The maximum fill time was 100 ms. The normalized collision energy in positive mode was set to 32 arbitrary units and in negative mode to 30. For both methods, the activation Q was 0.35 and the activation time was 30 ms, again with an isolation mass window of 3 m / z. The OrbiElite allowed for dynamic exclusion, set at a duration of 3.5 seconds. Calibration was performed weekly using injections of Pierce™ LTQ Velos electrospray ionization (ESI) positive ion calibration solution or Pierce™ ESI negative ion calibration solution.

[0315] E.GC-MS method Derivatized samples were analyzed by GC-MS. A 1.0 μl sample volume was injected into a diphenyldimethylpolysiloxane stationary phase thin-film fused silica column, Crossbond RTX-5Sil, 0.18 mm id x 20 m, 20 μm thin-film thickness (Restek, Bellefonte, PA) in split mode with a 20:1 split ratio. Compounds were eluted with helium as the carrier gas. The temperature gradient consisted of an initial temperature hold at 60 °C for 1 min, then increased to 220 °C at a rate of 17.1 °C / min, followed by an increase to 340 °C at a rate of 30 °C / min and held at this temperature for 3.67 min. The temperature was then decreased and stabilized at 60 °C for the next injection. The mass spectrometer was operated with a scan range of 50 to 750 mass units, 4 scans / sec, and electron impact ionization at 3077 amu / sec. The dual stage quadrupole (DSQ) was set with an ion source temperature of 290°C and a multiplier voltage of 1865 V. The MS transfer line was maintained at 300°C. The DSQ was adjusted and calibrated daily to ensure optimal performance.

[0316] F. Data Processing and Analysis For each biological matrix dataset on each instrument, the relative standard deviation (RSD) of the peak area was calculated for each internal standard to verify extraction efficiency, instrument performance, column integrity, chromatography, and mass calibration. Some of these internal standards served as retention index (RI) markers and were subjected to retention time and alignment checks. A modified version of the software provided with the UPLC-MS and GC-MS systems was used for peak detection and integration. The output of this process generated a list of m / z ratios, retention times, and area under the curve values. The software specified criteria for peak detection, including signal-to-noise ratio, height, and width thresholds.

[0317] Biological datasets, including QC samples, were chromatographically aligned based on retention indices using internal standards assigned fixed RI values. The RI of experimental peaks was determined by considering a linear fit between adjacent RI markers whose values ​​did not change. The advantage of RI is that it corrects for retention time drift caused by systematic errors such as sample pH and column age. The RI of each compound was assigned based on its elution relationship with two flanking retention markers. Using an in-house software package, integrated and aligned peaks were matched against reference standards and an in-house library of routinely detected unknown compounds (compound libraries). This library was specific to the positive, negative, or GC-MS data acquisition method used. For LTQ and DSQ data, matches were based on retention index values ​​within a specified 150 RI units and experimental precursor mass matches within 0.4 m / z of the library reference standards. Experimental MS / MS spectra were compared to the library spectra of the reference standards, and forward and reverse scores were assigned. A perfect forward score indicates that all ions in the experimental spectrum were found in the reference standard library in the correct ratio. A perfect reverse score indicates that all reference standard library ions were present in the experimental spectrum in the correct ratio. The forward and reverse scores were compared, and an MS / MS fragmentation spectrum score was presented to suggest a match. All matches were then manually reviewed by an analyst, who accepted or rejected each call based on the criteria described above. However, manual review by an analyst is not essential. In some embodiments, the matching process is fully automated.

[0318] Further details regarding compound libraries, methods for matching integrated and aligned peaks to identify named compounds and routinely detected unknown compounds, and computer readable code for identifying small molecules in a sample may be found in U.S. Pat. No. 7,561,975, which is incorporated herein by reference in its entirety.

[0319] G. Quality control From the plasma or urine samples, technical replicates were created by combining aliquots of each individual sample and extracted as described above. To assess process variability, this pooled plasma or urine sample extract was injected six times for each data set on each instrument. As an additional quality control, five aliquots of water were also extracted as part of the sample set on each instrument to serve as process blanks to identify artifacts. Internal instrument standards were included in all QC samples to assess extraction efficiency and instrument performance and to serve as retention index markers for ion identification. Standards were isotopically labeled or labeled with other exogenous molecules selected so as not to interfere with the detection of endogenous ions.

[0320] H. Statistical analysis The goal of the statistical analysis was to identify "extreme" values ​​(outliers) for each metabolite detected in the samples. A two-step process was performed based on percent fill (the percentage of samples in which a value was detected for that metabolite). Samples with detected values ​​were flagged when fill was less than or equal to 10%. When fill was greater than 10%, missing values ​​were imputed using a random normal variable with a mean equal to the minimum observed value and a standard deviation equal to 1. The data were then log-transformed, and the interquartile range (IQR) was calculated, defined as the difference between the third and first quartiles. A value 1.5 above the third quartile was then calculated. * IQR more or 1.5 above the 1st quartile *The IQR less values ​​were flagged. Log-transformed data were analyzed to calculate the Z-score for each metabolite in each individual. The Z-score for an individual's metabolite is the number of standard deviations above the mean for a particular metabolite. A positive Z-score means the metabolite level is above the mean, and a negative Z-score means the metabolite level is below the mean.

[0321] As described in the following examples, the results obtained were useful for diagnosing and / or assisting in the diagnosis of more than 30 disorders using a single small sample from a subject and without prior knowledge of the disorder or disease. Indeed, in some cases, individuals were being treated for genetic disorders, but the metabolomic data shown was not penetrant, and therefore, the patients did not require treatment. In other cases, genetic mutations revealed by exome sequencing were not considered significant, but metabolomic analysis revealed the presence of a disorder. Additional types of diagnostic results were obtained using the methods described below.

[0322] For some subjects, clinical diagnostic biomarkers were confirmed to be abnormal, and additional abnormal biochemicals were also shown to be abnormal, and some of the additional abnormal biochemicals were biochemically related to clinical diagnostic metabolites. For some subjects, clinical diagnostic metabolites were not detected. However, novel biochemicals were shown to be abnormal, and at least some of the novel biochemicals were related to diagnostic metabolites, thereby enabling a diagnosis. For some subjects, clinical diagnostic metabolites were confirmed to be abnormal in sample types different from those currently used in clinical practice (e.g., urinary markers were detected in plasma), thereby enabling a diagnosis using novel sample types. For certain disorders, clinical diagnostic metabolites were not available, but this analysis revealed novel biochemicals useful for clinical diagnosis.

[0323] Example 1: Evaluation of diseased individuals in a pilot study To evaluate the method's ability to diagnose or aid in the diagnosis of disease, we performed it on a cohort of 100 symptomatic individuals. Of these, 56 had a known diagnosis (the method's positive "control," N=56) and 44 had an indeterminate diagnosis ("test" individuals, N=44). Plasma samples were obtained from each individual, and small molecules were extracted from plasma sample aliquots (typically 50-100 μL). Each sample aliquot was interrogated across a biochemical library of over 7,000 biochemicals to detect biochemicals present in the sample. All samples were blinded to diagnosis during analysis. A total of 923 biochemicals were detected, including 523 designated biochemicals and 400 undesignated biochemicals. Designated biochemicals are molecules for which reference chemical standards are available, have been run on a comparable platform, and whose "ion fragmentation signatures" have been identified and captured within an in-house compound library. Unnamed biochemicals are entities for which the "ion fragmentation signature" is known, but for which no known standard is available in a compound library. Unnamed biochemicals have been sufficiently characterized by analytical methods to be uniquely identified. Unnamed biochemicals are represented herein by the term "X-" followed by a specific five-digit number. The analytical information for identifying unnamed small biochemical molecules is provided in Table 4 below.

[0324] For each of the 100 symptomatic individuals, data were automatically statistically analyzed using IQR to identify outliers for the biochemicals investigated. Rare and missing biochemicals were also analyzed for each individual. Metabolites in the samples were automatically mapped to sub- and super-pathways, and data visualizations were created. The results of the analysis of the 100 symptomatic subjects are summarized in Table 1. Diagnosis for each individual subject, 1.5 * IQR, 3.0 * The number of metabolites confirmed to be outliers based on IQR, as well as the number of metabolites confirmed to be outliers (i.e., 1.5 * For IQR and 3.0 * The total number of patients (in the case of IQR) is shown.

[0325] During analyses performed using the methods described herein, analysts were blinded to the diagnosis. After analysis, clinical diagnoses could be used to compare findings and determine whether the results were consistent with the diagnosis. Abnormal metabolites were identified in samples from subjects based on IQR data and missing metabolite data, and this information was used to propose diagnoses for five subjects. When the diagnostic information was unblinded, diagnoses based on abnormal metabolites were shown to be consistent with clinical diagnoses. For example, the abnormal metabolites phenylalanine and phenyllactate were used to diagnose phenylketonuria (PKU) in an individual. In another example, the abnormal metabolites sebacate (decandioate), 2-hydroxyglutarate, azelate (nonanedioate), caprylate (8:0), hexanoylcarnitine, and octanoylcarnitine were used to diagnose medium-chain acyl-CoA dehydrogenase deficiency (MCAD deficiency) in an individual. In another example, the abnormal metabolites isoleucine, valine, 3-methyl-2-oxobutyrate, 3-methyl-2-oxovalerate, and α-hydroxyisovalerate were used to diagnose maple syrup urine disease in an individual. In another example, the abnormal metabolite isovalerylcarnitine was used to diagnose isovaleric acidemia in an individual. In another example, a subject was diagnosed with thymidine phosphorylase deficiency. Thymidine was identified in a sample from the subject as a rare biochemical (a biochemical not normally detected in reference samples) consistent with a diagnosis of thymidine phosphorylase deficiency.

[0326] Table 1. Experimental results identifying abnormal biochemicals for initial analysis of disease or disorder diagnosis. TIFF2026016498000001.tif119154TIFF2026016498000002.tif245154TIFF2026016498000003.tif196154

[0327] Example 2: Clinical evaluation of diseased individuals in an expanded study In another example, plasma samples from 200 individuals were evaluated using the methods described herein. The 200 samples consisted of the 100-sample cohort described in Example 1 and 100 samples from a new cohort of symptomatic individuals. All 200 samples entered into this study had previously been analyzed using one or more targeted analyte panels in quantitative assays performed in clinical diagnostic laboratories. The most common targeted tests completed were amino acid or acylcarnitine panel assays (108 and 26 patients, respectively). Of the 200 subjects in the combined cohort, 132 subjects were diagnosed as positive for inborn errors of metabolism (IEM) by their treating physicians. A clinical diagnosis could not be confirmed for 68 subjects.

[0328] The LC-MS analysis method used an OrbiElite mass spectrometer to interrogate each sample across a biochemical library of over 7,000 compounds and measure the biochemicals detected in that sample. Aliquots were removed from each sample, and small molecules (biochemicals) were extracted and analyzed. Biochemicals detected in the samples were identified, abnormal levels were determined, diagnostic information (e.g., Table 2) was obtained, and stored in a database. A total of 1,292 biochemicals were detected, including 706 designated biochemicals and 586 undesignated biochemicals. These biochemicals included small peptides (e.g., di- and tri-peptides) and small molecules representing many classes of biomarkers, including amino acids (140), peptides (94), carbohydrates (26), lipids (243), cofactors (29), energy metabolites (11), nucleotides (32), and xenobiotics (131).

[0329] For each of the 200 symptomatic individuals, data were automatically statistically analyzed using IQR and Z-scores to identify outliers for the investigated biochemicals. Rare and missing biochemicals were also analyzed for each individual. Unlike targeted diagnostic analysis, metabolomic diagnostic discovery is not limited to a single analyte. Instead, metabolites from multiple biochemical pathways, including amino acids, nucleotides, and lipids, as well as carnitine and glycine conjugate adducts, were detected and used in the analysis. Metabolites detected in the samples were automatically mapped to sub- and super-pathways. All detected biochemicals were automatically classified into 41 biochemical sub-pathways. Automatic visualizations of the data were created, including the abnormal biochemicals and the abnormal biochemical super- and sub-pathways to which the abnormal biochemicals were mapped. Examples of visualization charts are shown in Figures 4–14 and are described in detail below.

[0330] Abnormal biochemicals were identified and abnormal pathways were confirmed as follows. Abnormal biochemicals included missing biochemicals, rare biochemicals, and outliers. Using log-transformed data, 1.5 * IQR or 3.0 * Abnormal biochemicals were identified as outliers based on either the IQR. To accommodate missing values, compounds present in ≤10% of all samples were automatically reported as rare compounds. Missing values ​​were imputed using a random normal variable centered on the minimum observed value for compounds present in >10% of all samples. Abnormal biochemicals were also identified by determining the Z-score for each metabolite. In this example, metabolites with a Z-score <-1.5 or >1.5 were confirmed as abnormal.

[0331] To identify aberrant pathways, data for each biochemical were log-transformed, centered (i.e., the mean was set to mean = 0), and normalized (sd = 1). Observed values ​​were then squared and then summed across all compounds in that pathway. Missing values ​​were imputed using the minimum observed value before transformation. Pathways were classified as either typical or aberrant for a given population. An aberrant pathway means that an individual's biochemical distance for that pathway is in the top 10% of all samples for a given pathway. Aberrant classification was calculated using the Euclidean distance from the geometric mean of each pathway. Each superpathway is composed of multiple subpathways, as described below.

[0332] All diagnoses were blinded to the analysts before analysis. After analysis, the clinical diagnoses could be used to compare with clinical findings and confirm whether the analytical results were consistent with the clinical diagnoses. Furthermore, the 100 previously analyzed samples from Example 1 served as anchor samples, allowing for comparison of data from the subjects described and analyzed in Example 1, data obtained in the repeat analysis, and data from 100 new subjects. In runs using samples from the 100 new subjects, the inclusion of previously analyzed samples as anchor samples allowed for correction of run-to-run variability. To assess data reproducibility, the analytical results of the 100 anchor samples from the 100 previously analyzed symptomatic subjects were compared with the results obtained from the first run in Example 1. Importantly, the metabolites identified as abnormal (including rare or missing biochemicals) were consistent between the two runs.

[0333] Clinicians evaluated the data and visualizations to verify whether the results obtained using the described methods were consistent with clinically confirmed diagnoses. The diagnostic results based on the analysis and clinical diagnosis are summarized in Table 2. Column 1 of Table 2 lists the disorders, including their synonyms. Column 2 indicates the number of individuals with each diagnosis, column 3 lists the sensitivity of the diagnosis of the disorder, and column 4 lists the specificity of the diagnosis of the disorder (Note: Sensitivity and specificity are based on the analytical methods described herein). Column 5 lists metabolites currently used in clinics to diagnose disorders. The analytical results are shown in columns 6 and 7. Column 6 lists clinically used metabolites that were found to be abnormal using the methods disclosed herein. Column 7, labeled "Analysis Results: Novel Metabolites Abnormal in Diagnosed Subjects," lists metabolites not currently used in clinical diagnosis that were identified as abnormal in clinically diagnosed subjects based on the results of the methods used herein. Some of these metabolites are biochemically related to clinically used diagnostic metabolites.

[0334] In some individuals, no clinically diagnostic metabolites were observed. However, other metabolites were abnormal, and at least some of the other metabolites were biochemically related to the diagnostic metabolites, supporting the diagnosis of the disorder. Thus, the methods herein supported the diagnosis of disorders not diagnosed using current clinical methods. These novel results are described in the Examples below and shown for the disorders summarized in Table 2 above. It should be emphasized that metabolites were measured in each sample, and each sample was simultaneously assayed for all reported diseases. Thus, multiple metabolites were measured in a single small (≦100 μl) sample, and abnormal metabolites were identified and automatically mapped to biochemical pathways to support the diagnosis of all of the diseases and disorders listed below.

[0335] As described above, analysis of the data of 200 subjects identified additional abnormal metabolites that may be useful for diagnosing or assisting in the diagnosis of disease. These metabolites can serve as biomarkers to supplement currently used clinical diagnostic assays, provide surrogate biomarkers, and / or provide additional pathway information that may inform or assist in diagnosis. Such markers can be combined with current clinically used metabolites to create a metabolite panel that forms a metabolic signature of a disease or disorder. Such markers can be used alone or in combination with current clinically used metabolites to assist in the diagnosis of disease. Results may be displayed visually. In some embodiments, disease-specific or disorder-specific predictive composite scores based on one or more related metabolites can be used to assist in the diagnosis of disease. Furthermore, such markers can assist in diagnosis when clinically used metabolites are not detected or the measured levels of the metabolites are unclear. The signatures can be stored in a database and used to diagnose or assist in the diagnosis of a disease or disorder.

[0336] Table 2: Laboratory-based diagnosis of disease or disorder TIFF2026016498000004.tif220170TIFF2026016498000005.tif238170TIFF202 6016498000006.tif234170TIFF2026016498000007.tif226170TIFF2026016498 000008.tif228170TIFF2026016498000009.tif187170TIFF2026016498000010. tif220170TIFF2026016498000011.tif162170TIFF2026016498000012.tif92170

[0337] A non-limiting example of an automated visualization diagram of biochemical superpathways is shown in FIG. 4, biochemical subpathways are shown in FIGS. 5, 6, and 10, and biochemicals are shown in FIGS. 7-9 and 11-14. An example of a visualization diagram of biochemical superpathways for a patient is shown in FIG. 4. Biochemical superpathways are listed on the left. Black dots indicate that the pathway is typical or abnormal for that subject. Using the methods described herein, this patient's energy pathways were confirmed to be abnormal. An example visualization of this patient's biochemical subpathways is shown in FIG. 5. The following biochemical subpathways were shown to be abnormal: ascorbic acid metabolism and aldaric acid metabolism; eicosanoid metabolism; fatty acid metabolism (branched-chain amino acid (BCAA) metabolism); fatty acid synthesis; folate metabolism; glycogen metabolism; pentose metabolism; and the TCA (tricarboxylic acid) cycle. An example of a visualization of abnormal subpathways is shown in FIG. 6.

[0338] Examples of automated visual displays of abnormal biochemicals ascertained by IQR analysis or Z-score analysis are shown graphically in Figures 7 and 8, respectively. These results are consistent with the clinical diagnosis of this patient. Using these methods, further non-limiting examples were performed below to obtain results for the following indicated disorders from plasma samples collected from 200 participants:

[0339] Propionic acidemia Propionylglycine levels in samples from patients with propionic acidemia were 3.0 * The IQR was high. From the automated visual display of biochemicals, it was also apparent that other metabolites related to propionylglycine (e.g., 2-methylcitrate, 3-hydroxypropanoate, and propionylcarnitine) were abnormal. The visual display is illustrated in Figure 7. The ability to identify these related metabolites increased confidence in the diagnosis of propionic acidemia. As shown in the display in Figure 7, the biochemical folate was a rare biochemical, and the biochemicals 3-hydroxy-2-ethylpropionate and arabinose were absent. 2-Methylmalonyl-carnitine and 3-methyl-2-oxobutyrate were at least 3.0 *Carnitine and glutarylcarnitine (C5) were present at levels at least 1.5 * 1,2-propanediol, 1-palmitoylglycerophosphocholine (16:0), and 1-pentadecanoylglycerophosphocholine (15:0) were present at levels at least 1.5 q / s. * 2-Methyl citrate, 3-hydroxypropanoate, N-octanoylglycine, hexanoylglycine, propionylcarnitine, propionylglycine, and sucralose were present at levels at least 3.0 IQR. * The data were also statistically analyzed by calculating Z-scores for each metabolite to identify statistically abnormal biochemicals. An example of Z-score visualization is shown in Figure 8. Z-scores for all metabolites were calculated for nine patients diagnosed with propionic acidemia (PAA). Propionylglycine and related metabolites (e.g., 2-methylcitrate, 3-hydroxypropanoate, and propionylcarnitine) all had Z-score values ​​greater than 2. In another patient with propionic acidemia, the levels of tigloylglycine, propionylcarnitine (C3), propionylglycine, 2-methylcitrate, and 3-hydroxypropanoate were 3.0. * The IQR was higher for 2-methylmalonylcarnitine (C3) and succinylcarnitine than for 3.0. *The IQR was low. An automated visual display of the data is illustrated in Figure 9. The abnormal biochemicals were automatically mapped to biochemical pathways. An example of an automated visual display of the abnormal biochemicals mapped to biochemical pathways is shown in Figure 10. In another example, the clinically diagnostic metabolite propionate was not detected in some patients with propionic acidemia. However, in all nine patents, the biochemically related metabolites 2-methylcitrate, 3-hydroxypropanoate, and propionylcarnitine (C3) were elevated, and the Z-score values ​​for the metabolites were at least 2. Additional metabolites, propionylglycine (C3), 1-pentadecanoylglycerophosphocholine (15:0), tigloylglycine, succinylcarnitine, glutarylcarnitine (C5), 3-methylglutarylcarnitine (C6), tiglylcarnitine, butyrylcarnitine, 2-methylmalonylcarnitine, β-hydroxyisovalerate, and X-12819, were abnormal based on Z-scores. The observed abnormal levels of these metabolites aided in diagnosis and demonstrated the utility of these compounds as novel biomarkers of propionic acidemia.

[0340] 3-Methylcrotonyl-CoA carboxylase deficiency 3-Methylcrotonylglycine levels measured in patients with 3-methylcrotonyl-CoA carboxylase deficiency were 3.0 *The IQR was high. From the visual display of the biochemicals, it was clear that not only were 3-methylcrotonylglycine levels abnormal, but additional metabolites were also abnormal. A graphical representation of the data is shown in Figure 11. In a further example, in a patient diagnosed with 3-methylcrotonyl-CoA carboxylase deficiency, the Z-score for the clinical diagnostic metabolite 3-methylcrotonylglycine, β-hydroxyisovalerate, was greater than 2. Additionally, the metabolites β-hydroxyisovaleroylcarnitine, glutarylcarnitine (C5), 3-methylcrotonylglycine, adipate, glutarate (pentanedioate), octadecanedioate (C18), hexadecanedioate (C16), tetradecanedioate (C14), dodecanedioate (C12), isovalerylglycine, leucine, isovalerate, alanylalanine, pyroglutamylvaline, leucine, isovalerate, alanylalanine, α-hydroxyisovalerate, succinylcarnitine, 3-methylglutarylcarnitine, isovalerylcarnitine, X-12007, and X-12814 were abnormal based on Z-score analysis.

[0341] Thymidine phosphorylase deficiency Thymidine is rare in plasma samples. Automated visualization of the data indicated that thymidine was observed in patients confirmed to have thymidine phosphorylase deficiency. These results for thymidine are indicated by a black dot in the "Rare" column in the example visualization chart for one patient shown in Figure 12. Identifying thymidine as a biochemical present in plasma samples from this patient aided in the diagnosis of thymidine phosphorylase deficiency.

[0342] In another example, the clinical metabolite thymidine was identified as a rare compound in a patient with thymidine phosphorylase deficiency. Additionally, the biochemicals 2'-deoxyuridine, 5-methyluridine (ribothymidine), 5,6-dihydrothymine, hippurate, 2-linoleoylglycerophosphocholine, 4-methylcatechol sulfate, 1-arachidoylglycerophosphocholine (20:0), taurolithocholate 3-sulfate, glycolitocholate sulfate, imidazole propionate, X-13862, X-19330, X-20620, and X-12170 were abnormal in both patients based on Z-score analysis.

[0343] Phenylketonuria (PKU) Phenylanine levels in samples from PKU patients were 3.0 *The IQR was high. Additional metabolites were also abnormal. A graphical representation of the automated visual display of the biochemical statistical analysis for one patient is shown in Figure 13. The visual display shows that not only was phenylalanine abnormal, but other biochemicals related to phenylalanine (e.g., γ-glutamylphenylalanine and phenyllactate (PLA)) were also abnormal. In another example, the Z-score of the clinical diagnostic metabolite phenylalanine was greater than 2 in a patient diagnosed with PKU. Additionally, the biochemically related metabolites phenyllactate, γ-glutamylphenylalanine, N-acetylphenylalanine, phenylpyruvate, γ-glutamyltyrosine, 3-methoxytyrosine, 4-hydroxyphenylpyruvate, p-cresol sulfate, catechol sulfate, o-cresol sulfate, phenylacetylglycine, as well as additional metabolites phenylacetylglutamine, phenylalanine-containing dipeptides (e.g., phenylalanylarginine, valylphenylalanine, histidylphenylalanine, phenylalanylserine, leucylphenylalanine, threonylphenylalanine, phenylalanylalanine, phenylalanylglycine, phenylalanylglutamate, phenylalanylphenylalanine, aspartylphenylalanine, tryptophylphenylalanine, phenylalanylisoleucine, glycylphenylalanine, phenylalanylleucine, phenylalanylaspartate), X-15497, and X-16283 were abnormal based on Z-scores.

[0344] Argininemia Arginine, 4-guanidinobutanoate, and homoarginine levels were 1.5 * The IQR was higher. In patients with argininemia, the N-acetylarginine level was 3.0 *The IQR was high. Automated visual display of biochemicals revealed that not only were arginine, homoarginine, and N-acetylarginine levels abnormal, but additional metabolites were also abnormal. An example of a graphical representation of the IQR data is shown in Figure 14. Further analysis, based on Z-scores, showed that the clinical metabolites arginine and 4-guanidinobutanoate, as well as the additional metabolites homoarginine, N-acetylarginine, ornithine, urea, homocitrulline, uracil, aspartate, argininosuccinate, proline, orotate, creatinine, uridine, 3-ureidopropionate, creatine, betaine, leucine, isoleucine, γ-glutamylleucine, X-12339, and X-12681 were abnormal.

[0345] BCAA metabolism Current clinical practice for diagnosing BCAA metabolism disorders is by extracting several organic acids, including 3-methyl-2-oxobutyrate, 3-methyl-2-oxovalerate, β-hydroxyisovalerate, and tiglyglycine, from urine samples. However, using the described method, these disorders may also be diagnosed in plasma samples, facilitating multiplex screening for multiple disorders simultaneously from a single sample type. For example, using the plasma samples described in Example 2, abnormal metabolites detected in samples from 51 patients with BCAA metabolism disorders (e.g., methylmalonic acidemia, cobalamin deficiency, propionic acidemia, HMG CoA lyase deficiency, 3-methylcrotonyl-CoA carboxylase deficiency, isovaleric acidemia, and maple syrup urine disease) were automatically mapped to the BCAA metabolism biochemical pathway. Results indicated that the valine pathway, leucine pathway, and isoleucine pathway were abnormal in subjects with the indicated disorders. The levels of abnormal metabolites measured in the valine, isoleucine, and leucine (BCAA metabolism) biochemical pathways that were abnormal are illustrated in FIG.

[0346] Methylmalonic acidemia In one example, the clinical diagnostic metabolites methylmalonate and methylmalonyl-CoA were not detected in some patients diagnosed with methylmalonic acidemia. However, the biochemically related metabolites 2-methylmalonylcarnitine, propionylcarnitine (C3), tiglylcarnitine, 2-methylbutyrylcarnitine (C5), 2-methylcitrate, succinylcarnitine, propionylglycine, 3-hydroxypropanoate, valerylcarnitine, isovalerylcarnitine, succinate, tigloylglycine, and additional metabolites β-hydroxyisovaleroylcarnitine, β-hydroxyisovalerate, isobutyrylcarnitine, 3-hydroxy-2-ethylpropionate, butyrylcarnitine, 3-methyl-2-oxovalerate, 3-methyl-2-oxobutyrate, X-12749, X-17564, and X-12114 were abnormal based on Z-score analysis.

[0347] Biotinidase deficiency In another example, there are no clinical metabolites useful for diagnosing biotinidase deficiency. However, analysis of samples collected from these subjects revealed that the metabolites 3-methylcrotonylglycine, propionylcarnitine (C3), biotin, and xylitol were abnormal based on Z-score analysis. Thus, a biochemical signature of biotinidase deficiency was observed in plasma samples using the methods described herein.

[0348] Cystinosis In another example, the clinical metabolite cystine was not detected in a patient diagnosed with cystinosis. No pathogenomic abnormalities have been described in nonreduced plasma for cystinosis, and the disorder is usually diagnosed using leukocyte lysates. However, the biochemicals cys-gly (oxidized), 1,5-anhydroglucitol (1,5-AG), glycocholate sulfate, 4-acetylphenol sulfate, cresol glucuronide (formerly X-11837), erythritol, vanillylmandelate, N2,N2-dimethyl-guanosine, phenylacetylglutamine, X-12846, X-12303, X-19145, X-12216, X-17717, X-15667, X-12119, X-11315, X-12731, X-12705, X-17685, and X-18371 were abnormal based on Z-score analysis. Thus, a biochemical signature was observed in plasma samples using the methods described herein.

[0349] Guanidinoacetate methyltransferase (GAMT) deficiency In another example, there are no clinical metabolites useful for diagnosing guanidinoacetate methyltransferase (GAMT) deficiency. However, the metabolites creatine, 3-(4-hydroxyphenyl)lactate, 1,3-dipalmitoylglycerol, guanidinoacetate, cysteine ​​s-sulfate, X-19602, X-12906, X-13007, and X-10458 were abnormal based on Z-score analysis. The observed abnormal levels of these metabolites indicate that these compounds may be useful as novel biomarkers for GAMT deficiency.

[0350] Molybdenum cofactor deficiency In another example, no pathogenomic abnormalities have been described in nonreduced plasma for molybdenum cofactor deficiency, and this disorder is typically diagnosed using reducing agent-treated plasma. In patients diagnosed with molybdenum cofactor deficiency (MOCD), clinical metabolites observed in reducing agent-treated plasma: xanthine; thiosulfate; and S-sulfocysteine, were not detected in plasma. Furthermore, the biochemicals 5-HETE, leukotriene B4, 13-HODE + 9-HODE, and 12-HETE were abnormal based on Z-score analysis. The observed abnormal levels of these metabolites suggest that these compounds may be useful as novel biomarkers for molybdenum cofactor deficiency.

[0351] X-linked creatine transporter In another example, no pathogenomic abnormalities have been described in non-reduced plasma for the X-linked creatine transporter, and this disorder is usually diagnosed using urine. In our analysis of plasma samples from patients with X-linked creatine transporter, the clinical metabolite creatine was not detected in urine. However, the biochemicals glycylleucine, 2-hydroxyoctanoate, 1,6-anhydroglucose, X-11483, X-18943, X-17422, X-17761, and X-17335 were abnormal in plasma based on Z-score analysis. Although no clinical diagnostic metabolites have been described in plasma for this disorder, the observed abnormal levels of these metabolites indicate that these compounds may be useful as novel biomarkers of the X-linked creatine transporter in plasma.

[0352] Argininosuccinate lyase deficiency In another example, in a patient diagnosed with argininosuccinate lyase deficiency, the clinical metabolite argininosuccinate was an abnormal biochemical (i.e., a rare compound). Additional metabolites, including uracil, arginine, aspartate, N-δ-acetylornithine, citrulline, isoleucyl aspartate, ornithine, uridine, homocitrulline, orotate, homoarginine, sorbose, fructose, methyl-4-hydroxybenzoate, O-sulfo-L-tyrosine, palmitoylsphingomyelin, X-13507, X-15245, X-15664, and X-15454, were also abnormal based on Z-score analysis. The observed abnormal levels of these metabolites indicate their potential as novel biomarkers for argininosuccinate lyase deficiency.

[0353] Cobalamin deficiency In another example, in a patient diagnosed with cobalamin deficiency, the clinical metabolites methylmalonic acid, homocysteine, 2-methylcitrate, and cystathionine were not detected, but the additional metabolites 2-methylmalonylcarnitine, propionylcarnitine, and X-12749 were abnormal based on Z-score analysis.

[0354] Cbl a In another example, in a patient diagnosed with Cbl a, the biochemical 2-methylcitrate, a diagnostically useful clinical metabolite, was abnormal. Additional metabolites, 2-methylmalonylcarnitine, tiglylcarnitine, 2-methylbutyrylcarnitine, propionylcarnitine, and X-12749, were also abnormal based on Z-score analysis.

[0355] Cbl c In another example, in a patient diagnosed with Cbl c, the clinical metabolites methylmalonic acid, homocysteine, 2-methylcitrate, and cystathionine were not detected, but the additional metabolites 2-methylmalonylcarnitine, propionylcarnitine, X-12749, and X-17677 were abnormal based on Z-score analysis.

[0356] Citrullinemia In another example, in a patient diagnosed with citrullinemia, the clinically used diagnostic metabolite citrulline was abnormal. Additional metabolites, homocitrulline, 3-ureidopropionate, N-acetylalanine, phenylacetylglutamine, phenylacetate, phenylacetylglycine, homoarginine, urea, guanidinosuccinate, γ-glutamylphenylalanine, γ-glutamylisoleucine, tryptophan, 1,5-anhydroglucitol (1,5-AG), N-acetyl-citrulline (formerly X-12386), X-19684, X-12681, X-20598, X-18446, and X-20588, were abnormal based on Z-score analysis.

[0357] CPTII In another example, in a patient with CPTII, the level of the clinically used diagnostic metabolite decanoylcarnitine (C10) was abnormal. The levels of sebacate (C8), caprate (C10), caprylate (C8), and N-octanoylglycine (C8 ester), octanoylcarnitine, and hexanoylcarnitine were abnormal based on Z-score analysis.

[0358] Glutaric aciduria type 1 In another example, in a patient with glutaric aciduria type 1, the clinical metabolites glutarate (pentanedioate) and glutarylcarnitine (C5) and the biochemicals X-12364 and X-15674 were abnormal based on Z-score analysis.

[0359] HMG CoA lyase deficiency In another example, in a patient with HMG CoA lyase deficiency, the clinical metabolites 3-methylglutarylcarnitine, 3-hydroxy-3-methyl-glutarate, 3-methylglutarate, and 3-hydroxyisovalerate were abnormal. The biochemicals β-hydroxyisovalerate, β-hydroxyisovaleroylcarnitine, glutarylcarnitine (C5), arginylproline, 1-stearoylglycerophosphoethanolamine, o-cresol sulfate, 3-methylcrotonylglycine, adipate, glutarate (pentanedioate), octadecanedioate (C18), hexadecanedioate (C16), tetradecanedioate (C14), dodecanedioate (C12), isovalerate, acetylcarnitine, palmitoylcarnitine, hexanoylcarnitine, myristoylcarnitine, hexenedioylcarnitine (formerly X-17001), X-17715, X-12741, X-16134, X-10593, and X-12688 were also abnormal in both patients based on Z-score analysis.

[0360] Holocarboxylase synthetase deficiency In another example, in a patient diagnosed with holocarboxylase synthetase deficiency, levels of the clinical metabolite β-hydroxyisovalerate were abnormal. The biochemicals 3-methylcrotonylglycine, β-hydroxyisovaleroylcarnitine (C5), propionylglycine (C3), 3-hydroxypropanoate, tigloylglycine, succinylcarnitine, and biotin were also abnormal based on Z-score analysis.

[0361] Homocystinuria In another example, the clinical metabolite methionine was abnormal in a patient diagnosed with homocystinuria. The biochemicals γ-glutamylmethionine, 5-methylthioadenosine (MTA), S-adenosylhomocysteine ​​(SAH), N1-methyladenosine, glycylproline, 1-eicosenoylglycerophosphoethanolamine (20:1n9), 1-methylnicotinamide, N-acetyl-aspartyl-glutamate (NAAG), pyridoxal, 2-hydroxyisobutyrate, asisoga, carnosine, 3-methoxytyrosine, 2-hydroxydecanoate, δ-tocopherol, α-CEHC (2,5,7,8-tetramethyl-2-(2'-carboxyethyl)amino)-2-hydroxybenzoate, δ-tocopherol, and α-CEHC (2,5,7,8-tetramethyl-2-(2'-carboxyethyl)amino)-2-hydroxybenzoate were also abnormal. )-6-hydroxychroman)sulfate (formerly X-12435), N-acetyltryptophan, adenine, cortisol, X-19350, X-18965, X-15649, X-17303, X-18897, X-11564, X-18891, X-12748, X-18918, X-18905, X-18606, X-16574, X-18895, X-18907, X-19455, X-18909, X-19574, X-12110, X-20676, X-11360, and X-18920 were also abnormal in both subjects based on Z-score analysis.

[0362] Isovaleric acidemia In another example, the clinical metabolite isovalerate was abnormal in a patient with isovaleric acidemia. The biochemicals isovalerylglycine, isovalerylcarnitine (C5), valerate, phenylcarnitine, valerylcarnitine, β-hydroxyisovalerate, 3-methylcrotonylglycine, X-16577, and X-14331 were also abnormal in both patients based on Z-score analysis.

[0363] Lysinuric protein intolerance In another example, in a patient with lysinuric protein intolerance, the clinical metabolites ornithine, arginine, and lysine were abnormal. The biochemicals asparagine, N6-acetyllysine, glutamine, N2-acetyllysine, N-acetylarginine, γ-glutamylglutamine, proline, S-methylcysteine, 2-hydroxydecanoate, 1-methylimidazole acetate, X-15636, X-17654, X-12193, and X-12425 were also abnormal based on Z-score analysis.

[0364] Medium-chain acyl-CoA dehydrogenase (MCAD) deficiency In another example, in a patient with medium-chain acyl-CoA dehydrogenase (MCAD) deficiency, the clinical metabolites hexanoylglycine (C6), octanoylcarnitine (C8), hexanoylcarnitine (C6), cis-4-decenoylcarnitine, 5-hydroxyhexanoate, suberate (octanedioate), sebacate (decanedioate), decanoylcarnitine, and 3-hydroxydecanoate were abnormal. Biochemicals N-octanoylglycine, caproate (6:0), caprylate (8:0), heptanoate (7:0), dodecanedioate, 1-docosapentaenoylglycerophosphocholine (22:5n3), O-methylcatechol sulfate, 1-stearoylglycerophosphocholine (18:0), 1-margalloylglycerophosphocholine (17:0), N-palmitoyltaurine, pelargonate (9:0), deoxycarnitine, α-CEHC (2,5,7,8-tetramethyl-2-(2'-carboxyethyl)-6-hydroxychroman) sulfate (formerly X-12435), X-11521 (possible empirical formula: C 15 H 27NO4 and structure: 2-octenoylcarnitine), X-11440 (possible hydroxypregnene-diol disulfate or pregnanolone-diol disulfate), methylhexanoylglutamine (formerly X-12637), X-15646, X-12802, X-11478, X-15486, X-18913, X-13837, X-18946, X-11861, X-18888, X-18922, X-17438, X-18916, X-16674, and X-12824 were also anomalous based on Z-score analysis.

[0365] Maple syrup urine disease In another example, in patients with maple syrup urine disease, the clinical metabolites isoleucine and leucine were abnormal, and the biochemicals 2-hydroxy-3-methylvalerate, α-hydroxyisovalerate, isovalerylcarnitine, 2-aminoheptanoate, 4-methyl-2-oxopentanoate, 1-linolenoylglycerophosphocholine (18:3n3), 2-linolenoylglycerophosphocholine (18:3n3), 1-myristoylglycerophosphocholine (14:0), 2-myristoylglycerophosphocholine, 5α-androstane-3α,17β-diol disulfate, valine, and 3-methyl-2-oxobutyrate were abnormal. Carnitine, isovalerate, isobutyrylcarnitine, 3-hydroxyisobutyrate, 2-methylbutyrylcarnitine, β-hydroxyisovaleroylcarnitine, allo-isoleucine, 3-methyl-2-oxovalerate, β-hydroxyisovalerate, succinate, acetylcarnitine, 2-methylcitrate, tigloylglycine, tiglylcarnitine, hydroxybutyrylcarnitine, α-hydroxyisocaproate, X-13689 (glucuronide conjugate), X-13581, and X-17690 were abnormal in at least 10 of 18 patients based on Z-score analysis.

[0366] Ornithine transcarbamylase deficiency In another example, in a patient with ornithine transcarbamylase deficiency, the clinical metabolites citrulline and arginine were undetectable, but the clinical metabolite orotate and the biochemicals phenylacetylglutamine, stearidonate (18:4n3), 3-ureidopropionate, phenylacetate, phenylcarnitine, phenylacetylglycine, trans-4-hydroxyproline, pro-hydroxy-pro, urea, phenyllactate (PLA), guanidinosuccinate, hippurate, ornithine, X-20598, and X-20588 were abnormal based on Z-score analysis.

[0367] Trimethyllysine hydroxylase epsilon deficiency In another example, in patients with trimethyllysine hydroxylase epsilon deficiency, the clinical metabolite N-6-trimethyllysine was abnormal. The biochemicals 1-arachidonoylglycerol phosphate, X-16574, X-12822, and X-15136 were abnormal in at least three of four patients based on Z-score analysis.

[0368] Very long-chain acyl-CoA dehydrogenase deficiency In another example, in a patient with very-long-chain acyl-CoA dehydrogenase deficiency, the clinical metabolites myristoylcarnitine, stearoylcarnitine (C18), palmitoylcarnitine (C16), oleoylcarnitine (C18), myristoleate (14:1n5), and linoleoylcarnitine were abnormal. The biochemicals 9-methyluric acid, xylulose, arachidonate (20:4n6), docosahexaenoate (DHA; 22:6n3), eicosapentanoic acid (EPA), 5,8-tetradecadienoic acid (formerly X-12442), 1-docosahexaenoyl-GPC (22:6; DHA-GPC), and X-18739 (a possible isomer of 2-tetradecenoylcarnitine) were also abnormal based on Z-score analysis.

[0369] Sarcosinemia In another example, in a patient with sarcosinemia, the clinical metabolite sarcosine was abnormal. The biochemicals dimethylglycine, betaine, choline, and glycine were also abnormal based on Z-score analysis.

[0370] Citrate transporter deficiency In another example, in patients with citrate transporter deficiency, the clinical metabolite citrate was abnormal.The biochemicals α-ketoglutarate, succinate, fumarate, and malate were also abnormal based on Z-score analysis.Currently, there is no diagnostic test available for citrate transporter deficiency, and therefore there is no preferred sample type for diagnosis.As described herein, abnormal metabolites in citrate transporter deficiency have been identified in plasma, urine, and CSF samples.

[0371] Hyperornithine-Homocitrulline-Hyperammonemia (HHH) In another example, in a patient with hyperornithine-homocitrulline-hyperammonemia, the clinical metabolites homocitrulline and ornithine were abnormal. The biochemicals uracil, 3-ureidopropionate, orotate, glutamine, N-acetyl-β-alanine, uridine, N-acetylaspartate (NAA), dimethylarginine (SDMA + ADMA), 5-methylthioadenosine (MTA), β-alanine, and 4-ureidobutyrate were also abnormal based on Z-score analysis.

[0372] Aromatic amino acid decarboxylase deficiency In another example, in a patient diagnosed with aromatic amino acid decarboxylase deficiency, the clinical metabolites L-dopa, homovanillate, 5-hydroxyindoleacetate, and vanillactic acid were not detected. In the case of aromatic amino acid decarboxylase deficiency, this disorder is usually detected using CSF samples. However, in plasma samples, the clinical metabolites 3-methoxytyrosine and 5-hydroxytryptophan and the biochemicals tyrosine, phenylalanine, and tryptophan were abnormal based on Z-score analysis. Thus, a biochemical signature was observed in plasma samples using the methods described herein.

[0373] Smith-Lemli-Opitz syndrome In another example, in patients with Smith-Lemli-Opitz syndrome, the clinical metabolite 7-dehydrocholesterol was abnormal, and the biochemical cholestanol was also abnormal based on Z-score analysis.

[0374] Primary carnitine deficiency In another example, in a patient with primary carnitine deficiency, the clinical metabolite carnitine was abnormal, and the biochemical N6-trimethyllysine was also abnormal based on Z-score analysis.

[0375] Citrin deficiency In another example, in a patient with citrin deficiency, the clinical metabolite citrulline was abnormal. The biochemical orotate was also abnormal based on Z-score analysis.

[0376] ABAT deficiency In another example, in patients with ABAT deficiency, the clinical metabolite GABA was abnormal, and the biochemical 2-pyrrolidone was also abnormal based on Z-score analysis.

[0377] 3-Methylglutaconic aciduria (MGA) In another example, the clinical metabolites 3-methylglutaconic acid and 3-methylglutaric acid were not detected in patients with MGA. In the case of MGA, the disorder is usually detected using urine samples. However, the biochemical butyrylglycine was abnormal in plasma samples based on Z-score analysis.

[0378] SCAD deficiency In another example, in patients with SCAD deficiency, the clinical metabolites ethylmalonate, butyrylcarnitine, and methylsuccinate were abnormal. The biochemical butyrylglycine was also abnormal based on Z-score analysis.

[0379] Urocanase deficiency In another example, in a patient with urocanase deficiency, the clinical metabolites cis-urocanate, trans-urocanate, and imidazole propionate, which are normally detected in urine, were also abnormal in plasma samples based on Z-score analysis.

[0380] 3-Hydroxyisobutyryl-CoA hydrolase deficiency In another example, no clinical metabolites are useful for diagnosing 3-hydroxyisobutyryl-CoA hydrolase deficiency. 3-hydroxyisobutyryl-CoA hydroxylase deficiency in this subject is caused by mutations in the HIBCH gene, which encodes an enzyme that hydrolyzes hydroxyisobutyryl-CoA and hydroxypropionyl-CoA. This enzyme is important for valine metabolism. In one of two subjects diagnosed with 3-hydroxyisobutyryl-CoA hydroxylase deficiency, two metabolites in valine metabolism, specifically 3-hydroxyisobutyrate and isobutyrylglycine, were abnormal based on Z-score analysis. Both individuals also showed abnormal levels of several metabolites in leucine metabolism, a pathway not directly affected by the mutation. This is likely the result of medication or nutritional intervention. Therefore, both individuals likely received treatment for 3-hydroxyisobutyryl-CoA hydroxylase deficiency. This may have masked the metabolic signature of this disease in one patient. However, the observed abnormal levels of the metabolites 3-hydroxyisobutyrate and isobutyrylglycine indicate that these compounds may be useful as novel biomarkers for 3-hydroxyisobutyryl-CoA hydrolase deficiency.

[0381] γ-Butyrobetaine hydroxylase deficiency (BBOX deficiency) In another example, there are no clinical metabolites useful for diagnosing BBOX deficiency. However, the metabolites hexadecandioate (C16), docosadioate, eicosanodioate, octadecandioate (C18), dodecandioate (C12), 2-aminooctanoate, 2-aminoheptanoate, α-hydroxyisocaproate, isovalerate (C5), decanoylcarnitine (C10), cis-4-decenoylcarnitine, palmitoylcarnitine (C16), oleoylcarnitine (C18), laurylcarnitine (C12), myristoleoylcarnitine, myristoylcarnitine, glycerol, 3-hydroxymyristate, 2-hydroxydecanoate, 3-hydroxylaurate, 3-hydroxysebacate, 3-hydroxyoctanoate, 3-hydroxydecanoate, pelargonate (9:0), and caproate (6:0) were abnormal based on Z-score analysis. The observed abnormal levels of these metabolites indicate that these compounds may be useful as novel biomarkers for BBOX deficiency.

[0382] Disorders of amino acid metabolism and transport (including urea cycle disorders and organic acidemias) In another example, in patients with disorders of amino acid metabolism and transport (including urea cycle disorders and organic acidemias), the metabolites N-acetylalanine, aspartate, glutarate (pentanedioate), glutarylcarnitine (C5), 3-hydroxyglutarate, glutaconate, phenylalanine, N-acetylphenylalanine, phenylpyruvate, phenyllactate (PLA), phenylacetate, phenylacetylglycine, phenylacetylglutamine, 4-hydroxyphenylpyruvate, 3-(4-hydroxyphenyl)-2-pyruvate, 4-hydroxyphenyl ) lactate, p-cresol sulfate, o-cresol sulfate, 3-methoxytyrosine, leucine, 4-methyl-2-oxopentanoate, isovalerate, isovalerylglycine, isovalerylcarnitine (C5), 3-methylcrotonylglycine, β-hydroxyisovalerate, β-hydroxyisovaleroylcarnitine, 3-methylglutarylcarnitine (C6), α-hydroxyisovalerate, isoleucine, allo-isoleucine, 3-methyl-2-oxovalerate, 2-methylbutyrylcarnitine (C5), thig Isobutyrylcarnitine, tigloylglycine, 2-hydroxy-3-methylvalerate, 3-hydroxy-2-ethylpropionate, valine, 3-methyl-2-oxobutyrate, isobutyrylcarnitine, 3-hydroxyisobutyrate, α-hydroxyisocaproate, homocysteine, cystathionine, arginine, urea, ornithine, proline, citrulline, argininosuccinate, homoarginine, homocitrulline, N-acetylarginine, N-δ-acetylornithine, trans-4-hydroxyproline, pro-hydroxy -pro, creatine, creatinine, 4-guanidinobutanoate, guanidinosuccinate, gamma-glutamylphenylalanine, gamma-glutamyltyrosine, alanylalanine, arginylproline aspartylphenylalanine, glycylphenylalanine, histidylphenylalanine, isoleucyl aspartate, leucylphenylalanine, phenylalanylalanine, phenylalanylarginine, phenylalanyl aspartate, phenylalanyl glutamate, phenylalanylglycine, phenylalanylisoleucine,Phenylalanyl-leucine, phenylalanyl-phenylalanine, phenylalanyl-serine, pyroglutamyl-valine, threonyl-phenylalanine, tryptophyl-phenylalanine, valyl-phenylalanine, fructose, sorbose, succinyl-carnitine, succinate, 2-methyl citrate, valerate, stearidonate (18:4n3), adipate, dodecanedioate (C12), tetradecanedioate (C14), hexadecanedioate (C16), octadecanediate ( C18), 2-aminoheptanoate, 2-linolenoylglycerophosphocholine (18:3n3), 2-methylmalonylcarnitine, butyrylcarnitine, propionylcarnitine (C3), propionylglycine (C3), methylmalonyl CoA, methylmalonic acid, acetylcarnitine, hydroxybutyrylcarnitine, valerylcarnitine, hexanoylcarnitine, myristoylcarnitine, palmitoylcarnitine (C16), hexenedioylcarnitine (formerly X-17001), 3-hydroxycarnitine hydroxypropanoate, 1-myristoylglycerophosphocholine (14:0), 2-myristoylglycerophosphocholine, 1-pentadecanoylglycerophosphocholine (15:0), 1-linolenoylglycerophosphocholine (18:3n3), 1-stearoylglycerophosphoethanolamine, 1,3-dipalmitoylglycerol, 5α-androstane-3α,17β-diol disulfate, orotate, uridine, uracil, 3-ureidopropionate, hippurate, catechol sulfate, phenanthrene ... The following amino acids were abnormal based on Z-score analysis: phenylcarnitine, propionate, 5-hydroxytryptophan, 5-methylthioadenosine (MTA), β-alanine, dimethylarginine (SDMA + ADMA), glutamine, N-acetylaspartate (NAA), N-acetyl-β-alanine, tryptophan, tyrosine, 2-pyrrolidone, γ-glutamylleucine, O-sulfo-L-tyrosine, palmitoyl-sphingomyelin, γ-glutamylisoleucine, and cysteine ​​s-sulfate.

[0383] Fatty acid oxidation disorders In another example, in patients with fatty acid oxidation disorders, the metabolites xylulose, caproate (6:0), heptanoate (7:0), caprylate (C8), pelargonate (9:0), caprate (C10), myristoleate (14:1n5), eicosapentaenoic acid (EPA), docosahexaenoate (DHA; 22:6n3), arachidonate (20:4n6), suberate (octanedioate), and sevaca were found to be significantly increased in patients with fatty acid oxidation disorders. Carnitine, 3-(4-decenoyl)-3-hydroxybenzoate (C18), octanoyl glycine (C8), dodecanedioate (C12), hexanoyl glycine (C6), N-octanoyl glycine, hexanoyl carnitine, octanoyl carnitine, decanoyl carnitine, cis-4-decenoyl carnitine, myristoyl carnitine, palmitoyl carnitine (C16), stearoyl carnitine (C18), oleoyl carnitine (C18), deoxycarnitine, carnitine, 3- Hydroxydecanoate, 5-hydroxyhexanoate, N-palmitoyltaurine, 1-margalloylglycerophosphocholine (17:0), 1-stearoylglycerophosphocholine (18:0), 1-docosapentaenoylglycerophosphocholine (22:5n3), 9-methyluric acid, α-CEHC (2,5,7,8-tetramethyl-2-(2'-carboxyethyl)-6-hydroxychroman) sulfate (formerly X-12435), O-methylcatechol sulfate, 5,8-tetradecadienoic acid (formerly X-12442), methylhexanoylglutamine (formerly X-12637), 7-dehydrocholesterol, cholestanol, N6-trimethyllysine, butyrylglycine, and 1-docosahexaenoyl-GPC (22:6; DHA-GPC) were abnormal based on Z-score analysis.

[0384] Example 3: Clinical evaluation of diseased individuals (urine) In another example, urine samples from 100 symptomatic individuals were analyzed as described in Example 2 to diagnose or aid in the diagnosis of disease. All samples entered into this study were analyzed using one or more targeted analyte panels in a quantitative assay performed in a clinical diagnostic laboratory. Of the 100 subjects in the cohort, 33 subjects were diagnosed as positive by their treating physicians. A clinical diagnosis could not be confirmed for 67 subjects. During analysis, the analysts were blinded to diagnostic information associated with the samples. A total of 1,201 biochemicals were detected, including 663 designated biochemicals and 538 undesignated biochemicals. As with the plasma samples described in Example 2, clinicians evaluated the resulting data and visualizations after sample analysis to determine whether the results obtained using the method of the present invention were consistent with clinically confirmed diagnoses. The diagnostic results of the analysis are summarized in Table 3. Column 1 lists the disorder, including synonyms; column 2 lists the number of subjects diagnosed; column 3 lists the sensitivity of diagnosing the disorder; and column 4 lists the specificity of diagnosing the disorder. Column 5 lists metabolites currently used in clinics to diagnose the disorder. Column 6 lists clinically used metabolites that were found to be abnormal using the methods disclosed herein. Column 7 lists metabolites not currently used in clinical diagnosis that were identified as abnormal in clinically diagnosed subjects based on the results of the methods used herein. Some of these metabolites are biochemically related to clinically used diagnostic metabolites. In some individuals, no clinical diagnostic metabolites were observed. However, other biochemically related metabolites were abnormal, which aided in the diagnosis of the disorder. These results are shown for the disorders described in the Examples below.

[0385] Table 3: Laboratory-based diagnosis of disease or disorder (urine) TIFF2026016498000013.tif220168TIFF2026016498000014.tif220168TIFF2026016498000015.tif231168TIFF2026016498000016.tif38168

[0386] Adenosine deaminase deficiency In one example, in a patient diagnosed with adenosine deaminase deficiency, the clinically diagnostic metabolite 2'-deoxyadenosine had a Z-score greater than 2. Furthermore, the biochemically related metabolites 2'-deoxyinosine, adenine, N2-methylguanosine, 2'-deoxyguanosine, urate, N1-methyladenosine, adenosine, allantoin, xanthine, guanosine, hypoxanthine, N2,N2-dimethylguanosine, and 7-methylguanosine were abnormal based on Z-score. The observed abnormal levels of these metabolites indicate the utility of these compounds as novel biomarkers for adenosine deaminase deficiency.

[0387] Dihydropyrimidine dehydrogenase deficiency In another example, in a patient diagnosed with dihydropyrimidine dehydrogenase deficiency, the clinical metabolites uracil and thymine and the additional metabolites cytidine, 5,6-dihydrouracil, 4-ureidobutyrate, 3-ureidopropionate, uridine, orotate, and N-carbamoyl aspartate were abnormal based on Z-score analysis.

[0388] Succinic semialdehyde dehydrogenase deficiency In another example, in a patient with succinic semialdehyde dehydrogenase deficiency, the clinical metabolite gamma-aminobutyrate (GABA) and the biochemically related metabolite succinimide were abnormal based on Z-score analysis.

[0389] Succinyladenosine lyase deficiency In another example, in a patient with succinyladenosine lyase deficiency, the clinical metabolite N6-succinyladenosine and the biochemically related metabolites xanthosine, 2'-deoxyguanosine, 2'-deoxyinosine, and adenine were abnormal based on Z-score analysis.

[0390] Tyrosinemia In another example, in a patient with tyrosinemia, the clinical metabolite tyrosine and the additional metabolites 3-(4-hydroxyphenyl)lactate, 4-hydroxyphenylpyruvate, 3-(3-hydroxyphenyl)propionate, 4-hydroxyphenylacetate, phenyllactate (PLA), and X-13581 were abnormal based on Z-score analysis.

[0391] Xanthinuria In another example, in a patient with xanthinuria, the clinical metabolite xanthine was not detected, but the clinical metabolites urate and creatine and the biochemically related metabolites hypoxanthine, xanthosine, 2'-deoxyinosine, inosine, N2-methylguanosine, and creatinine were abnormal based on Z-score analysis.

[0392] 3-Methylcrotonyl-CoA carboxylase deficiency In another example, in a patient diagnosed with 3-methylcrotonyl-CoA carboxylase deficiency, the clinical diagnostic metabolites 3-methylcrotonylglycine and β-hydroxyisovalerate were abnormal. Additionally, the metabolites β-hydroxyisovaleroylcarnitine, ethylmalonate, and N-acetylleucine were abnormal based on Z-score analysis.

[0393] Citrullinemia In another example, in a patient diagnosed with citrullinemia, the clinically used diagnostic metabolite citrulline was abnormal. Additional metabolites 4-ureidobutyrate, homocitrulline, 3-ureidopropionate, phenylacetylglutamine, N-carbamoylaspartate, guanidinoacetate, urea, 4-guanidinobutanoate, N-acetylarginine, hippurate, ornithine, 2-methylhippurate, phenylacetylglycine, 4-phenylbutyrate, creatinine, orotate, and 3,4-dihydroxyphenylacetate were abnormal based on Z-score analysis.

[0394] Glutaric aciduria type 1 In another example, in a patient with glutaric aciduria type 1, the clinical metabolites glutarate (pentanedioate) and glutarylcarnitine (C5) and the biochemicals 3-methylglutarylcarnitine and 2-aminoadipate were abnormal based on Z-score analysis.

[0395] Guanidinoacetate methyltransferase (GAMT) deficiency In another example, there are no clinical metabolites useful for diagnosing guanidinoacetate methyltransferase (GAMT) deficiency. However, in patients with GAMT deficiency, the biochemicals creatinine and guanidinoacetate were abnormal based on Z-score analysis. The observed abnormal levels of these metabolites indicate that these compounds may be useful as novel biomarkers for GAMT deficiency.

[0396] Holocarboxylase synthetase deficiency In another example, in a patient diagnosed with holocarboxylase synthetase deficiency, levels of the clinical metabolite β-hydroxyisovalerate were abnormal. The biochemicals 3-methylcrotonylglycine, β-hydroxyisovaleroylcarnitine, propionylglycine (C3), 3-hydroxypropanoate, 2-methylcitrate, 3-hydroxyisobutyrate, lactate, 3-hydroxy-2-ethylpropionate, isobutyrylglycine, α-hydroxyisovaleroylcarnitine, 3-methyl-2-oxobutyrate, 3-methyl-2-oxovalerate, 3-hydroxy-2-methylbutyrate, 4-methyl-2-oxopentanoate, succinylcarnitine, malonylcarnitine, α-hydroxyisovalerate, 2-hydroxy-3-methylvalerate, propionylcarnitine, tiglylcarnitine, isovalerylcarnitine, hydroxybutyrylcarnitine, succinate, 2-methylmalonylcarnitine, and α-hydroxyisocaproate were also abnormal based on Z-score analysis.

[0397] Isovaleric acidemia In another example, the clinical metabolite isovalerate was abnormal in a patient with isovaleric acidemia. The biochemicals isovalerylglycine, isovalerylcarnitine (C5), β-hydroxybutyrate, and α-hydroxybutyrate were also abnormal in both patients based on Z-score analysis.

[0398] Lysinuric protein intolerance In another example, in a patient with lysinuric protein intolerance, the clinical metabolites ornithine, arginine, and lysine were not detected, whereas the biochemicals 2-aminoheptanoate, N6-acetyllysine, N2-acetyllysine, 3-methylglutarylcarnitine, glutarylcarnitine, N6-trimethyllysine, and 5-(galactosylhydroxy)-L-lysine were abnormal based on Z-score analysis.

[0399] Medium-chain acyl-CoA dehydrogenase (MCAD) deficiency In another example, in a patient with medium-chain acyl-CoA dehydrogenase (MCAD) deficiency, the clinical metabolites hexanoylglycine (C6), 5-hydroxyhexanoate, octanoylcarnitine (C8), suberate (octanedioate), 4-octenedioate, adipate, hexanoylcarnitine (C6), and decanoylcarnitine were abnormal. The biochemicals N-octanoylglycine, heptanoylglycine, 3-methyladipate, and 2-hydroxyglutarate were also abnormal based on Z-score analysis.

[0400] Methylmalonic acidemia In another example, the clinical diagnostic metabolite methylmalonyl-CoA was not detected in a patient diagnosed with methylmalonic acidemia. However, the clinical metabolite methylmalonate and additional metabolites 2-methylmalonylcarnitine, 2-methylcitrate, 3-methylglutarylcarnitine, propionylcarnitine (C3), succinylcarnitine, propionylglycine, β-hydroxyisovaleroylcarnitine, β-hydroxyisovalerate, 3-hydroxy-2-ethylpropionate, and methylsuccinate were abnormal based on Z-score analysis.

[0401] Molybdenum cofactor deficiency In another example, no pathogenomic abnormalities have been described for molybdenum cofactor deficiency in urine, a disorder typically diagnosed using reducing agent-treated plasma. In patients diagnosed with molybdenum cofactor deficiency (MOCD), the clinical metabolites xanthine and S-sulfocysteine, observed in reducing agent-treated plasma, were abnormal in urine samples. The clinical metabolite thiosulfate was not detected. Additionally, the biochemical urate was abnormal based on Z-score analysis. The observed abnormal levels of these metabolites indicate the utility of these compounds as novel biomarkers for molybdenum cofactor deficiency.

[0402] Ornithine transcarbamylase deficiency In another example, in a patient with ornithine transcarbamylase deficiency, the clinical metabolites citrulline and arginine were undetectable, but the clinical metabolite orotate and the biochemicals phenylacetylglutamine, 2-methylhippurate, 2-hydroxyphenylacetate, phenylcarnitine, hippurate, phenylpropionylglycine, benzoate, methyl-4-hydroxybenzoate, 4-phenylbutyrate, and 2-pentamido-3-phenylpropanoic acid were abnormal based on Z-score analysis.

[0403] Citrate transporter deficiency In another example, in a patient with citrate transporter deficiency, the clinical metabolite citrate was abnormal based on Z-score analysis.

[0404] ABAT deficiency In another example, in patients with ABAT deficiency, the clinical metabolite GABA and the biochemical 2-pyrrolidone were abnormal based on Z-score analysis.

[0405] Hyperoxaluria In another example, in a patient with hyperoxaluria, the clinical metabolites oxalate and glycolate were abnormal based on Z-score analysis.

[0406] In another example, in patients with disorders of amino acid metabolism and transport (including urea cycle disorders and organic acidemias), the metabolites 2-aminoadipate, phenylacetylglutamine, 3,4-dihydroxyphenylacetate, phenylpropionylglycine, 2-pentanamido-3-phenylpropanoic acid, 2-hydroxyphenylacetate, N-acetylleucine, methylsuccinate, ethyl malonate, guanidinoacetate, β-hydroxybutyrate, N-carbamoyl aspartate, 4-ureidobutyrate, hippurate, 2-methylhippurate, benzoate, methyl-4-hydroxybenzoate, 4-phenylbutyrate, glutarate (pentanedioate), glutarylcarnitine (C5), 3-hydroxyglutarate, glutaconate, phenylacetyl The following glycine, isovalerate, isovalerylglycine, isovalerylcarnitine (C5), 3-methylcrotonylglycine, β-hydroxyisovalerate, β-hydroxyisovaleroylcarnitine, 3-methylglutarylcarnitine (C6), 3-hydroxy-2-ethylpropionate, arginine, urea, ornithine, citrulline, argininosuccinate, homocitrulline, N-acetylarginine, creatine, 4-guanidinobutanoate, succinylcarnitine, 2-methylcitrate, 2-methylmalonylcarnitine, propionylcarnitine (C3), propionylglycine (C3), methylmalonyl CoA, methylmalonic acid, orotate, 3-ureidopropionate, phenylcarnitine, and 2-pyrrolidone were abnormal based on Z-score analysis.

[0407] Fatty acid oxidation disorders In another example, in a patient with a fatty acid oxidation disorder, the metabolites 2-hydroxyglutarate, 3-methyladipate, 4-octenedioate, heptanoylglycine, adipate, suberate (octanedioate), hexanoylglycine (C6), N-octanoylglycine, hexanoylcarnitine, octanoylcarnitine, decanoylcarnitine, carnitine, and 5-hydroxyhexanoate were abnormal based on Z-score analysis.

[0408] Example 4: Clinical evaluation of diseased individuals: confirmation and monitoring of treatment efficacy and / or disease severity In another example, to confirm treatment efficacy and treatment compliance, the results of the methods described herein on samples from Example 2 were used to evaluate the effectiveness of therapeutic intervention and biochemical changes in response to clinical management of disease in diagnosed individuals to aid in the clinical monitoring of patients. For example, treatment effects were observed in patients with very long-chain acyl-CoA dehydrogenase deficiency (VLCAD) treated with a medium-chain triglyceride formulation. Z-score analysis showed that levels of short- and medium-chain length fatty acids were abnormal in the patient's plasma samples, and the biochemical 7-hydroxyoctanoate was abnormal as an orphan biochemical (a metabolite rarely detected in plasma samples). In another example, in several patients with ornithine transcarbamoylase (OTC) deficiency, the combination of dietary intervention and phenylbutyrate treatment masked the metabolic disorders associated with OTC deficiency (i.e., diagnostic metabolites were at normal levels). As shown in Figure 16B, the phenylbutyrate metabolite phenylacetylglutamine was elevated in patients with OTC deficiency, citrullinemia, and MSUD who were undergoing phenylbutyrate clinical trials. In another example, as shown in Figure 16A, the metabolite trimethylamine N-oxide was elevated in patients receiving supplemental carnitine to treat isovaleric acidemia, propionic acidemia (PAA), and methylmalonic acidemia (MMA). In another example, as shown in Figure 16C, supplemental creatine can be directly measured in plasma collected from patients diagnosed with argininosuccinate lyase deficiency (AL) or GAMT. In another example, in a patient diagnosed with pyruvate dehydrogenase E1α deficiency, the diagnostic metabolites lactate and pyruvate were identified in the sample, which was not unusual because the patient was receiving medical and nutritional treatment. In another example, a patient diagnosed with Smith-Lemli-Opitz syndrome was receiving cholesterol replacement therapy. Although the subject did not exhibit abnormal cholesterol levels, the biomarker cholestanol, a reduced form of cholesterol, was identified as a rare metabolite in the sample.

[0409] In another example, the methods described herein were used to ascertain disease severity. The methods described herein can be used to monitor changes in disease severity. For example, arginemia is caused by an autosomal recessive mutation affecting the urea cycle enzyme arginase, and the disorder is clinically diagnosed based on measurements of plasma arginine levels. The abnormal biochemicals identified in four arginemia patients were automatically mapped to disease-related biochemical pathways. Abnormally high levels of arginine were observed, as well as abnormal levels of several analytes more distal to the primary enzymatic defect (e.g., orotate, uridine, uracil, 3-ureidopropionate, urea, ornithine, N-acetylarginine, 4-guanidinobutanoate, creatinine, and homoarginine). A diagram of the biochemical pathways overlaid with data showing biochemical levels in each patient is shown in Figure 17. Patient 1 had higher levels of several metabolites compared to other arginemia cases, suggesting a more severe clinical condition in Patient 1. Indeed, 3 months prior to sampling, patient 1 was hospitalized three times for acute decompensation related to hyperammonemic events. Conversely, patients 2–4 had no acute events leading to hospitalization, either in the months prior to or immediately following sample submission. Importantly, arginine levels were similarly elevated in all argininemic patients, and differences in clinical status could only be predicted by examining novel diagnostic biomarkers. In Figure 17, patients are numbered 1–4 for convenience only; these numbers do not correspond to the patient numbers used in Table 1.

[0410] Example 5: Clinical evaluation of individuals within the control cohort In another example, abnormal metabolites were revealed in a subset of 68 control samples (i.e., samples from patients with normal biochemical genetic testing) from Example 2 analyzed using the methods described herein, indicating the presence of the disorder in currently asymptomatic patients. For example, trimethyllysine was identified as abnormal in patient 1165, with levels higher than any of the other control samples, and these elevated levels were consistent with those in other patients with confirmed trimethyllysine hydroxylase epsilon deficiency. Therefore, trimethyllysine hydroxylase epsilon deficiency was suspected based on the analysis results, and follow-up testing was recommended.

[0411] In another example, in patient 1169, clinical acylcarnitine analysis revealed slight elevations in C14, C14.1, and C14.2, raising suspicion of VLCAD. Further clinical molecular testing (DNA sequencing) of ACADVL did not detect any deleterious mutations in the gene sequence. However, analysis using the methods described herein revealed (i) abnormally high levels of C14 / C16 fatty acids, (ii) abnormally high levels of acetoacetate, and (iii) normal levels of C14 / C16 acylcarnitine conjugates. The high levels of ketone production and relatively normal acylcarnitine levels despite the extremely elevated C14 / C16 fatty acids strongly suggested that VLCAD deficiency was highly unlikely. This more complete metabolic profile, obtained using the methods described herein, supported the diagnosis of this individual. Furthermore, abnormal metabolite levels and pathway mapping analysis suggested that fasting most likely explained the metabolic changes in patient 1169. With this information in hand, sequencing of the ACADVL gene would not have been necessary.

[0412] In another example, the methods used herein were useful in confirming the significance of base pair changes detected using whole exome sequencing (WES) and aided in diagnosis (i.e., "ruling in" or "ruling out" a disorder). For example, the results of the methods described herein ruled out the possibility of a disorder in a patient for whom WES reported a variant of unknown significance (VUS). In one example, a VUS [c.673G>T (p.G225W)] was reported in GLYCTK, a gene affected in glycerinuria. However, using the methods described herein, this patient's glycerate levels were confirmed to be normal. In another example, a patient with a VUS [c.730G>A (p.G244R)] in SLC25A15, a gene affected in hyperornithine-hyperammonia-homocitrullinemia syndrome, had normal levels of ornithine, glutamine, and homocitrulline, thereby ruling out the possibility of a disorder. In another example, the results of the methods described herein helped to confirm the pathogenicity of molecular findings. For example, WES results for patient 1186 revealed a heterozygous variant of mutation (VUS) [c.455G>A(p.G152D)] in SARDH, the gene deleted in sarcosinemia. Using the methods described herein, significant elevations of choline, betaine, dimethylglycine, and sarcosine were confirmed. These elevated levels are consistent with sarcosinemia, a metabolic disorder whose clinical presentation has been discussed.

[0413] Example 6: mPROFILE Clinical Assessment of Individuals within a Healthy Cohort Using a metabolomics platform consisting of three independent mass spectrometry methods, we interrogated plasma samples from 80 subjects for biochemicals within eight pathways and their associated 41 subpathways, measuring a total of 575 metabolites with known structures. We found that individual metabolites across the 80 subjects exhibited a wide range...

Claims

1. 1. A method for facilitating diagnosis of a disease or disorder in an individual subject, comprising the steps of: obtaining a sample from an individual subject; generating a small molecule profile of the sample containing information regarding the presence or absence and level of each of a plurality of small molecules in the sample; comparing the small molecule profile of the sample to a reference small molecule profile comprising a standard range of levels for each of a plurality of small molecules to identify a subset of small molecules in the sample having abnormal levels for each small molecule, wherein an abnormal level of a small molecule in the sample is a level outside the standard range for that small molecule, and wherein the comparison and identification are performed using analytical functionality running on a processor of a computing device; obtaining diagnostic information from a database based on the abnormal levels of the identified subset of small molecules, the database containing, for each of a plurality of diseases and disorders, information relating to the disease or disorder to an abnormal level of one or more small molecules of the plurality of small molecules; and storing the obtained diagnostic information, wherein the stored diagnostic information includes one or more of the following: identification of at least one biochemical pathway associated with the identified subset of small molecules having abnormal levels; identification of at least one disease or disorder associated with the identified subset of small molecules having abnormal levels; and identification of at least one recommended follow-up test associated with the identified subset of small molecules having abnormal levels. Including, thereby facilitating the diagnosis of a disease or disorder in an individual subject.

2. 1. A method of screening an individual subject for multiple diseases or disorders, comprising the steps of: obtaining a sample from an individual subject; generating a small molecule profile of the sample containing information regarding the presence or absence and level of each of a plurality of small molecules in the sample; comparing the small molecule profile of the sample with a reference small molecule profile comprising a standard range of levels of each of a plurality of small molecules to determine whether any of the plurality of small molecules has an abnormal level in the sample, wherein an abnormal level of a small molecule in the sample is a level that is outside the standard range for that small molecule; For a small molecule profile having abnormal levels of any of a plurality of small molecules in a sample, identifying a subset of small molecules, each of which has an abnormal level in the sample; wherein said comparing and identifying is performed using an analytical function running on a processor of a computing device; obtaining diagnostic information from a database based on the abnormal levels of the identified subset of small molecules for a small molecule profile having abnormal levels of any of a plurality of small molecules in the sample, the database including, for each of a plurality of diseases and disorders, information relating to the disease or disorder with the abnormal levels of one or more small molecules among the plurality of small molecules; storing diagnostic information obtained for a small molecule profile having abnormal levels of any of a plurality of small molecules in the sample, the stored diagnostic information including one or more of the following: identification of at least one biochemical pathway associated with the identified subset of small molecules having abnormal levels; identification of at least one disease or disorder associated with the identified subset of small molecules having abnormal levels; and identification of at least one recommended follow-up test associated with the identified subset of small molecules having abnormal levels; and storing information indicating that no abnormal levels were detected for a small molecule profile that does not have abnormal levels of any of the plurality of small molecules present in the sample; Including, A method whereby individual subjects are screened for multiple diseases and disorders.

3. 3. The method of claim 1 or claim 2, further comprising creating a disease- or disorder-specific composite score based on a weighted combination of data from one or more of the subsets of small molecules identified as having abnormal levels, wherein obtaining diagnostic information from a database based on the abnormal levels of the identified subset of small molecules comprises obtaining diagnostic information from a database based on the created disease- or disorder-specific composite score.

4. abnormal levels of one or more small molecules selected from the group consisting of 3-methylcrotonylglycine, β-hydroxyisovalerate, β-hydroxyisovaleroylcarnitine, glutarylcarnitine (C5), 3-methylcrotonylglycine, adipate, glutarate (pentanedioate), octadecanedioate (C18), hexadecanedioate (C16), tetradecanedioate (C14), dodecanedioate (C12), isovalerate, leucine, isovalerylglycine, α-hydroxyisovalerate, succinylcarnitine, 3-methylglutarylcarnitine, isovalerylcarnitine, alanylalanine, pyroglutamylvaline, X-12007, X-12814, ethylmalonate, N-acetylleucine, and combinations thereof, are indicative of 3-methylcrotonyl-CoA carboxylase deficiency; abnormal levels of one or more small molecules selected from the group consisting of 2'-deoxyadenosine, 2'-deoxyinosine, adenine, N2-methylguanosine, 2'-deoxyguanosine, urate, N1-methyladenosine, adenosine, allantoin, xanthine, guanosine, hypoxanthine, N2,N2-dimethylguanosine, 7-methylguanosine, and combinations thereof, indicative of adenosine deaminase deficiency; abnormal levels of one or more small molecules selected from the group consisting of argininosuccinate, N-δ-acetylornithine, sorbose, fructose, citrulline, methyl-4-hydroxybenzoate, uracil, arginine, aspartate, isoleucyl aspartate, ornithine, uridine, homocitrulline, orotate, homoarginine, O-sulfo-L-tyrosine, palmitoylsphingomyelin, X-13507, X-15245, X-15664, X-15454, and combinations thereof, are indicative of argininosuccinate lyase deficiency; abnormal levels of one or more small molecules selected from the group consisting of arginine, 4-guanidinobutanoate, homoarginine, N-acetylarginine, ornithine, urea, homocitrulline, uracil, aspartate, argininosuccinate, proline, orotate, creatinine, uridine, 3-ureidopropionate, creatine, betaine, leucine, isoleucine, gamma-glutamylleucine, X-12339, X-12681, and combinations thereof, are indicative of argininemia; abnormal levels of one or more small molecules selected from the group consisting of xylitol, biotin, 3-methylcrotonylglycine, propionylcarnitine (C3), and combinations thereof, indicative of biotinidase deficiency; abnormal levels of one or more small molecules selected from the group consisting of methylmalonic acid, homocysteine, 2-methylcitrate, cystathionine, 2-methylmalonylcarnitine, propionylcarnitine, X-12749, and combinations thereof, indicative of Cbl (cobalamin deficiency); abnormal levels of one or more small molecules selected from the group consisting of methylmalonic acid, homocysteine, 2-methylcitrate, cystathionine, 2-methylmalonylcarnitine, tiglylcarnitine, 2-methylbutyrylcarnitine, propionylcarnitine, X-12749, and combinations thereof, indicative of Cbl A; abnormal levels of one or more small molecules selected from the group consisting of methylmalonic acid, homocysteine, 2-methylcitrate, cystathionine, 2-methylmalonylcarnitine, propionylcarnitine, X-12749, X-17677, and combinations thereof, indicative of Cbl C; Citrulline, argininosuccinic acid, homocitrulline, 3-ureidopropionate, N-acetylalanine, phenylacetate, phenylacetylglutamine, 4-ureidobutyrate, N-carbamoyl aspartate, guanidinoacetate, urea, 4-guanidinobutanoate, N-acetylarginine, hippurate, ornithine, 2-methylhippurate, phenylacetylglycine, 4-phenylbutyrate, creatinine, orotate, 3,4- abnormal levels of one or more small molecules selected from the group consisting of dihydroxyphenylacetate, homoarginine, guanidinosuccinate, gamma-glutamylphenylalanine, gamma-glutamylisoleucine, tryptophan, 1,5-anhydroglucitol (1,5-AG), N-acetyl-citrulline, X-19684, X-12681, X-20598, X-18446, X-20588, and combinations thereof, indicative of citrullinemia; abnormal levels of one or more small molecules selected from the group consisting of carnitine, acylcarnitines, N-octanoylglycine (C8 ester), sebacate (C8), caprate (C10), caprylate (C8), octanoylcarnitine, hexanoylcarnitine, and combinations thereof, indicative of carnitine palmitoyltransferase 2 deficiency (CPTII); abnormal levels of one or more small molecules selected from the group consisting of cysteine, cys-gly (oxidized), X-12846, X-12303, X-19145, 1,5-anhydroglucitol (1,5-AG), glycocholate sulfate, 4-acetylphenol sulfate, cresol glucuronide, erythritol, vanillylmandelate, N2,N2-dimethyl-guanosine, phenylacetylglutamine, X-12216, X-17717, X-15667, X-12119, X-11315, X-12731, X-12705, X-17685, X-18371, and combinations thereof, indicative of cystinosis; abnormal levels of one or more small molecules selected from the group consisting of cytidine, 5,6-dihydrouracil, 4-ureidobutyrate, 3-ureidopropionate, uridine, orotate, N-carbamoyl aspartate, uracil, thymine, and combinations thereof, indicative of dihydropyrimidine dehydrogenase deficiency; abnormal levels of one or more small molecules selected from the group consisting of glutarate (pentanedioate), glutarylcarnitine (C5), 3-hydroxyglutarate, glutaconate, 3-methylglutarylcarnitine, 2-aminoadipate, X-12364, X-15674, and combinations thereof, indicative of glutaric aciduria type 1; abnormal levels of one or more small molecules selected from the group consisting of creatine, 3-(4-hydroxyphenyl) lactate, 1,3-dipalmitoylglycerol, guanidinoacetate, creatinine, cysteine ​​s-sulfate, X-19602, X-12906, X-13007, X-10458, and combinations thereof, indicative of guanidinoacetate methyltransferase (GAMT) deficiency; 3-Methylglutarylcarnitine (C6), β-hydroxyisovalerate, β-hydroxyisovaleroylcarnitine, glutarylcarnitine (C5), arginylproline, 1-stearoylglycerophosphoethanolamine, o-cresol sulfate, 3-methylcrotonylglycine, adipate, glutarate (pentanedioate), octadecanedioate (C18), hexadecanedioate (C16), tetradecanedioate abnormal levels of one or more small molecules selected from the group consisting of acetylcholinesterase (C14), dodecanedioate (C12), isovalerate, acetylcarnitine, palmitoylcarnitine, hexanoylcarnitine, myristoylcarnitine, hexenedioylcarnitine, X-17715, X-12741, X-16134, X-10593, X-12688, and combinations thereof, indicative of 3-hydroxy-3-methylglutaric aciduria (HMG CoA lyase deficiency); β-Hydroxyisovalerate, 3-methylcrotonylglycine, β-hydroxyisovaleroylcarnitine (C5), propionylglycine (C3), 3-hydroxypropanoate, tigloylglycine, succinylcarnitine, 2-methylcitrate, 3-hydroxyisobutyrate, lactate, 3-hydroxy-2-ethylpropionate, isobutyrylglycine, α-hydroxyisovaleroylcarnitine, 3-methyl-2-oxobutyrate, 3-methyl-2-oxovalerate, 3-hydroxy- abnormal levels of one or more small molecules selected from the group consisting of 2-methylbutyrate, 4-methyl-2-oxopentanoate, malonylcarnitine, α-hydroxyisovalerate, 2-hydroxy-3-methylvalerate, propionylcarnitine, tiglylcarnitine, isovalerylcarnitine, hydroxybutyrylcarnitine, succinate, 2-methylmalonylcarnitine, α-hydroxyisocaproate, biotin, and combinations thereof, are indicative of holocarboxylase synthetase deficiency; Homocysteine, cysteine, methionine, other amino acids, gamma-glutamylmethionine, 5-methylthioadenosine (MTA), S-adenosylhomocysteine ​​(SAH), N1-methyladenosine, glycylproline, 1-eicosenoylglycerophosphoethanolamine (20:1n9), 1-methylnicotinamide, N-acetyl-aspartyl-glutamate (NAAG), pyridoxal, 2-hydroxyisobutyrate, asisoga, carnosine, 3-methoxytyrosine, 2-hydroxydecanoate, delta-tocopherol, alpha-CEHC (2,5,7,8-tetramethyl-2-(2'-caprylylsilane)-2-hydroxybenzoate). abnormal levels of one or more small molecules selected from the group consisting of (carboxyethyl)-6-hydroxychroman)sulfate), N-acetyltryptophan, adenine, cortisol, X-18965, X-15649, X-17303, X-18897, X-11564, X-18891, X-12748, X-18918, X-18905, X-18606, X-16574, X-18895, X-18907, X-19455, X-18909, X-19574, X-12110, X-20676, X-11360, X-18920, and combinations thereof, are indicative of homocystinuria; abnormal levels of one or more small molecules selected from the group consisting of isovalerate (C5), isovalerylglycine, isovalerylcarnitine (C5), valerate; valerylcarnitine, β-hydroxyisovalerate, phenylcarnitine, β-hydroxybutyrate, 3-methylcrotonylglycine, α-hydroxybutyrate, X-16577, X-14331, and combinations thereof, indicative of isovaleric acidemia; abnormal levels of one or more small molecules selected from the group consisting of ornithine, arginine, lysine, asparagine, N6-acetyllysine, glutamine, N2-acetyllysine, N-acetylarginine, gamma-glutamylglutamine, proline, S-methylcysteine, 2-hydroxydecanoate, 1-methylimidazole acetate, 2-aminoheptanoate, 3-methylglutarylcarnitine, glutarylcarnitine, N6-trimethyllysine, 5-(galactosylhydroxy)-L-lysine, X-15636, 17654, X-12193, X-12425, and combinations thereof, are indicative of lysinuric protein intolerance; Acylcarnitines, carnitine, organic acids, N-octanoylglycine, caproate (6:0), caprylate (8:0), heptanoate (7:0), dodecanedioate, decanoylcarnitine, N-palmitoyltaurine, pelargonate (9:0), deoxycarnitine, O-methylcatechol sulfate, 1-stearoylglycerophosphocholine (18:0), 1-margalloylglycerophosphocholine (17:0), 1-docosapentaenoylglycerophosphocholine (22:5n3), 4-octenedioate, adipate, heptanoylglycine, 3-methyladipate, 2-hydroxyglutarate, α-CEHC (2,5,7,8-tetramethyl-2-(2'-carboxyethyl)-6-hydroxychroman) sulfate, X-11521 (possible empirical formula: C) 15 H 27 NO 4 and structure: 2-octenoylcarnitine), X-15646, X-12802, X-11478, X-11440 (possibly hydroxypregnene-diol disulfate or pregnanolone-diol disulfate), X-15486, X-18913, methylhexanoylglutamine, X-13837, X-18946, X-11861, X-18888, X-18922, X-17438, X-18916, X-16674, X-12824, indicating medium-chain acyl-CoA dehydrogenase deficiency; Methylmalonate, methylmalonyl CoA, 2-methylmalonylcarnitine, propionylcarnitine (C3), tiglylcarnitine, 2-methylbutyrylcarnitine (C5), 2-methylcitrate, succinylcarnitine, propionylglycine, 3-hydroxypropanoate, valerylcarnitine, isovalerylcarnitine, succinate, tigloylglycine, β-hydroxyisovaleroylcarnitine, β-hydroxy abnormal levels of one or more small molecules selected from the group consisting of diisovalerate, isobutyrylcarnitine, 3-hydroxy-2-ethylpropionate, butyrylcarnitine, 3-methylglutarylcarnitine, methylsuccinate, 3-methyl-2-oxovalerate, 3-methyl-2-oxobutyrate, X-12749, X-17564, X-12114, and combinations thereof, indicative of methylmalonic acidemia; abnormal levels of one or more small molecules selected from the group consisting of xanthine, S-sulfocysteine, thiosulfate, 5-HETE, leukotriene B4, 13-HODE+9-HODE, 12-HETE, urate, and combinations thereof, indicative of molybdenum cofactor deficiency or sulfite oxidase deficiency; Leucine, isoleucine, valine, 2-hydroxy-3-methylvalerate, α-hydroxyisovalerate, isovalerylcarnitine, 2-aminoheptanoate, 4-methyl-2-oxopentanoate, 1-linolenoylglycerophosphocholine (18:3n3), 2-linolenoylglycerophosphocholine (18:3n3), 1-myristoylglycerophosphocholine (14:0), 2-myristoylglycerophosphocholine, 5α-androstane-3α,17β-diol disulfate, valine, 3-methyl-2-oxobutyrate, isovalerate, isobutyrylcarnitine, 3-hydroxy abnormal levels of one or more small molecules selected from the group consisting of tigloylglycine, tiglylcarnitine, hydroxybutyrylcarnitine, alpha-hydroxyisocaproate, X-13581, X-17690, X-13689 (glucuronide conjugate), and combinations thereof, indicative of maple syrup urine disease; abnormal levels of one or more small molecules selected from the group consisting of orotate, citrulline, arginine, phenylacetylglutamine, stearidonate (18:4n3), 3-ureidopropionate, 2-methylhippurate, 2-hydroxyphenylacetate, phenylcarnitine, hippurate, phenylpropionylglycine, benzoate, methyl-4-hydroxybenzoate, 4-phenylbutyrate, 2-pentanamido-3-phenylpropanoic acid, phenylacetate, phenylacetylglycine, trans-4-hydroxyproline, pro-hydroxy-pro, urea, phenyllactate (PLA), guanidinosuccinate, ornithine, X-20598, X-20588, and combinations thereof, are indicative of ornithine transcarbamylase deficiency; abnormal levels of one or more small molecules selected from the group consisting of propionate, propionylglycine (C3), 2-methylcitrate, 3-hydroxypropanoate, propionylcarnitine (C3), 1-pentadecanoylglycerophosphocholine (15:0), tigloylglycine, succinylcarnitine, glutarylcarnitine (C5), 3-methylglutarylcarnitine (C6), tiglylcarnitine, butyrylcarnitine, 2-methylmalonylcarnitine, β-hydroxyisovalerate, X-12819, and combinations thereof, indicative of propionic acidemia; abnormal levels of one or more small molecules selected from the group consisting of phenylalanine, gamma-glutamylphenylalanine, phenyllactate, N-acetylphenylalanine, phenylpyruvate, gamma-glutamyltyrosine, 3-methoxytyrosine, 4-hydroxyphenylpyruvate, p-cresol sulfate, catechol sulfate, phenylacetylglutamine, phenylalanylarginine, valylphenylalanine, histidylphenylalanine, phenylalanylserine, leucylphenylalanine, threonylphenylalanine, phenylalanylalanine, phenylalanylglycine, phenylalanylglutamate, phenylalanylphenylalanine, aspartylphenylalanine, tryptophylphenylalanine, phenylalanylisoleucine, glycylphenylalanine, phenylalanylleucine, phenylalanylaspartate, o-cresol sulfate, phenylacetylglycine, X-16283, X-15497, and combinations thereof, are indicative of phenylketonuria (PKU); abnormal levels of one or more small molecules selected from the group consisting of gamma-aminobutyrate (GABA), succinimide, and combinations thereof, indicative of succinic semialdehyde dehydrogenase deficiency; abnormal levels of one or more small molecules selected from the group consisting of N6-succinyladenosine, xanthosine, 2'-deoxyguanosine, 2'-deoxyinosine, adenine, and combinations thereof, indicative of succinyladenosine lyase deficiency; abnormal levels of one or more small molecules selected from the group consisting of thymidine, 2'-deoxyuridine, 5,6-dihydrothymine, 5-methyluridine (ribothymidine); hippurate, 2-linoleoylglycerophosphocholine, 4-methylcatechol sulfate, 1-arachidoylglycerophosphocholine (20:0), taurolithocholate 3-sulfate, glycolitocholate sulfate, imidazole propionate, X-13862, X-19330, X-20620, X-12170, and combinations thereof, are indicative of thymidine phosphorylase deficiency; abnormal levels of one or more small molecules selected from the group consisting of N-6-trimethyllysine, 1-arachidonoyl glyercophosphate, X-16574, X-12822, X-15136, and combinations thereof, indicative of trimethyllysine hydroxylase; abnormal levels of one or more small molecules selected from the group consisting of tyrosine, 3-(4-hydroxyphenyl)lactate, 4-hydroxyphenylpyruvate, 3-(3-hydroxyphenyl)propionate, 4-hydroxyphenylacetate, phenyllactate (PLA), X-13581, and combinations thereof, indicative of tyrosinemia; Abnormal levels of one or more small molecules selected from the group consisting of myristoylcarnitine, stearoylcarnitine (C18), palmitoylcarnitine (C16), oleoylcarnitine (C18), myristoleate (14:1n5), linoleoylcarnitine, 9-methyluric acid, xylulose, arachidonate (20:4n6), docosahexaenoate (DHA; 22:6n3), eicosapentanoic acid (EPA), 5,8-tetradecadienoic acid, 1-docosahexaenoyl-GPC (22:6; DHA-GPC), X-18739 (a possible isomer of 2-tetradecenoylcarnitine), and combinations thereof, are indicative of very long-chain acyl-CoA dehydrogenase deficiency; abnormal levels of one or more small molecules selected from the group consisting of urate, creatine, hypoxanthine, xanthosine, 2'-deoxyinosine, inosine, N2-methylguanosine, creatinine, and combinations thereof, indicative of xanthinuria; abnormal levels of one or more small molecules selected from the group consisting of creatine, glycylleucine, 2-hydroxyoctanoate, X-11483, X-18943, X-17422, X-17761, X-17335, 1,6-anhydroglucose, and combinations thereof, indicative of an X-linked creatine transporter; abnormal levels of one or more small molecules selected from the group consisting of 2-hydroxyacetaminophen sulfate, 2-methoxyacetaminophen sulfate, 3-(cystein-S-yl)acetaminophen, 4-acetaminophen sulfate, 4-acetamidophenol, p-acetamidophenyl glucuronide, glycohyocholate, glycochenodeoxycholate, taurocholate, taurochenodeoxycholate, glutathione, and combinations thereof, are indicative of acetaminophen-induced toxicity; Xylulose, caproate (6:0), heptanoate (7:0), caprylate (C8), pelargonate (9:0), caprate (C10), myristoleate (14:1n5), eicosapentaenoic acid (EPA), docosahexaenoate (DHA; 22:6n3), arachidonate (20:4n6), suberate (octanedioate), sebacate (C8), dodeca N-octanoylglycine, hexanoylcarnitine, octanoylcarnitine, decanoylcarnitine, cis-4-decenoylcarnitine, myristoylcarnitine, palmitoylcarnitine (C16), stearoylcarnitine (C18), oleoylcarnitine (C18), deoxycarnitine, carnitine abnormal levels of one or more small molecules selected from the group consisting of 3-hydroxydecanoate, 5-hydroxyhexanoate, N-palmitoyltaurine, 1-margalloylglycerophosphocholine (17:0), 1-stearoylglycerophosphocholine (18:0), 1-docosapentaenoylglycerophosphocholine (22:5n3), 9-methyluric acid, α-CEHC (2,5,7,8-tetramethyl-2-(2'-carboxyethyl)-6-hydroxychroman) sulfate, O-methylcatechol sulfate, 5,8-tetradecadienoic acid, methylhexanoylglutamine, 2-hydroxyglutarate, 3-methyladipate, 4-octenedioate, heptanoylglycine, and combinations thereof, are indicative of a fatty acid oxidation disorder; abnormal levels of one or more small molecules selected from the group consisting of 1,5-anhydroglucitol (1,5-AG), 2-hydroxybutyrate (AHB), 3-hydroxybutyrate, glucose, glycine, isoleucine, leucine, valine, and combinations thereof, are indicative of diabetes; and an abnormal level of one or more small molecules selected from the group consisting of glycocholate, glycochenodeoxycholate, taurocholate, taurochenodeoxycholate, and combinations thereof, indicative of liver dysfunction; abnormal levels of one or more small molecules selected from the group consisting of dimethylglycine, betaine, choline, glycine, sarcosine, and combinations thereof, indicative of sarcosinemia; abnormal levels of one or more small molecules selected from the group consisting of α-ketoglutarate, succinate, fumarate, malate, glutamate, citrate, and combinations thereof, indicative of a citrate transporter deficiency; abnormal levels of one or more small molecules selected from the group consisting of pyruvate and lactate are indicative of pyruvate dehydrogenase deficiency; Abnormal levels of one or more small molecules selected from the group consisting of uracil, 3-ureidopropionate, orotate, glutamine, N-acetyl-β-alanine, uridine, N-acetylaspartate (NAA), dimethylarginine (SDMA+ADMA), 5-methylthioadenosine (MTA), β-alanine, 4-ureidobutyrate, homocitrulline, ornithine, and combinations thereof, indicating hyperornithine-homocitrulline-hyperammonemia; abnormal levels of one or more small molecules selected from the group consisting of tyrosine, phenylalanine, tryptophan, 3-methoxytyrosine, 5-hydroxytryptophan, L-dopa, homovanillate, 5-hydroxyindoleacetate, vanillactic acid, and combinations thereof, indicative of aromatic amino acid decarboxylase deficiency; abnormal levels of one or more small molecules selected from the group consisting of cholestanol, 7-dehydrocholesterol, and combinations thereof, indicative of Smith-Lemli-Opitz syndrome; Abnormal levels of one or more small molecules selected from the group consisting of N6-trimethyllysine, carnitine, and combinations thereof indicate primary carnitine deficiency; and Abnormal levels of one or more small molecules selected from the group consisting of orotate, citrulline, and combinations thereof are indicative of citrin deficiency; and an abnormal level of one or more small molecules selected from the group consisting of 2-pyrrolidone, GABA, and combinations thereof, is indicative of ABAT deficiency; and abnormal levels of one or more small molecules selected from the group consisting of fructose, mannose, prolylhydroxyproline, hydroxyproline, glycylproline, N-acetylneuraminate, dimethylarginine (ADMA+SDMA), glutamine, gamma-glutamylglutamine, N-acetyl-aspartyl-glutamate (NAAG), N-acetylglutamine, ethylmalonate, creatine, glucose, and combinations thereof, are indicative of GLUT1 deficiency; and Abnormal levels of one or more small molecules selected from the group consisting of 3-methylglutarylcarnitine, 3-methylglutaconic acid, 3-methylglutaric acid, and combinations thereof are indicative of MGA; and an abnormal level of one or more small molecules selected from the group consisting of butyrylglycine, ethylmalonate, butyrylcarnitine, methylsuccinate, and combinations thereof, is indicative of SCAD deficiency; and an abnormal level of one or more small molecules selected from the group consisting of cis-urocanate, trans-urocanate, imidazole propionate, and combinations thereof, is indicative of urocanase deficiency; and an abnormal level of one or more small molecules selected from the group consisting of 3-hydroxyisobutyrate, isobutyrylglycine, and combinations thereof, is indicative of 3-hydroxyisobutyryl-CoA hydrolase deficiency; and abnormal levels of one or more small molecules selected from the group consisting of oxalate, glycolate, and combinations thereof, are indicative of hyperoxaluria; and Hexadecanedioate (C16), docosanoate, eicosanoate, octadecanedioate (C18), dodecanedioate (C12), 2-aminooctanoate, 2-aminoheptanoate, α-hydroxyisocaproate, isovalerate (C5), decanoylcarnitine (C10), cis-4-decenoylcarnitine, palmitoylcarnitine (C16), oleoylcarnitine (C18), laurylcarnitine (C12), myristylcarnitine The method of claim 1 or claim 2, wherein an abnormal level of one or more small molecules selected from the group consisting of leoylcarnitine, myristoylcarnitine, glycerol, 3-hydroxymyristate, 2-hydroxydecanoate, 3-hydroxylaurate, 3-hydroxysebacate, 3-hydroxyoctanoate, 3-hydroxydecanoate, pelargonate (9:0), caproate (6:0), and combinations thereof is indicative of a BBOX deficiency.

5. 3. The method of claim 1 or claim 2, wherein the step of creating a small molecule profile of the sample containing information regarding the presence or absence and level of each of a plurality of small molecules in the sample comprises extracting at least a portion of the plurality of small molecules from the sample.

6. obtaining diagnostic information from a database based on abnormal levels of the identified subset of small molecules, the database containing, for each of a plurality of diseases and disorders, information relating to the disease or disorder to an abnormal level of one or more small molecules of the plurality of small molecules; identifying one or more aberrant biochemical pathways based on a comparison of the small molecule profile of the sample with a reference small molecule profile, wherein the stored diagnostic information includes the identification of the one or more aberrant biochemical pathways.

3. The method of claim 1 or claim 2, comprising:

7. identifying one or more aberrant biochemical pathways based on a comparison of the small molecule profile of the sample with a reference small molecule profile; identifying a plurality of biochemical pathways, each biochemical pathway being associated with one or more of the small molecules in the generated small molecule profile for the sample; calculating a biochemical distance for each of a plurality of biochemical pathways, comparing the calculated biochemical distance with a standard range of biochemical distances for the pathway, and identifying the biochemical pathway as abnormal if the calculated biochemical distance is outside the standard range; 7. The method of claim 6, comprising:

8. 8. The method of claim 7, wherein a biochemical pathway is identified as abnormal if the biochemical pathway is associated with any of the small molecules identified as abnormal.

9. 8. The method of claim 7, further comprising displaying information about pathways identified as anomalous.

10. 3. The method of claim 1 or claim 2, wherein the individual subject exhibits symptoms of one or more of a plurality of diseases and disorders.

11. 3. The method of claim 1 or claim 2, wherein the individual subject does not exhibit symptoms of multiple diseases and disorders.

12. 3. The method of claim 1 or claim 2, wherein the individual subject is a human newborn or human infant.

13. 3. The method of claim 1 or claim 2, wherein the stored diagnostic information associated with the identified subset of small molecules having abnormal levels comprises a suggested diagnosis of at least one disease or disorder.

14. The method of claim 1 or claim 2, wherein the stored diagnostic information associated with the identified subset of small molecules having abnormal levels includes identification of at least one recommended diagnostic test or method to support a diagnosis of the disease or disorder.

15. 3. The method of claim 1 or claim 2, wherein the plurality of diseases and disorders comprises a rare disease or disorder.

16. 3. The method of claim 1 or claim 2, wherein the volume of the sample obtained from an individual subject is less than 100 μL (0.100 mL).

17. 3. The method of claim 1 or claim 2, wherein the volume of the sample obtained from an individual subject is between 25 μL and 100 μL (0.025 mL and 0.100 mL).

18. 3. The method of claim 1 or claim 2, wherein the small molecule profile of the sample comprises information about endogenous and microbial small molecules present in the sample.

19. 20. The method of claim 18, wherein the small molecule profile of the sample further comprises information about small xenobiotic molecules present in the sample.

20. 20. The method of claim 18, wherein the small molecule profile of the sample further comprises information about dietary small molecules present in the sample.

21. 21. The method of claim 20, wherein the small molecule profile of the sample further comprises information about small xenobiotic molecules present in the sample.

22. 3. The method of claim 1, further comprising displaying information representing one or more biochemical pathways associated with the identified subset of small molecules having abnormal levels.

23. displaying information representing one or more biochemical pathways associated with the identified subset of small molecules having abnormal levels, displaying information representing superpathways associated with the identified subset of small molecules having abnormal levels; and displaying information representing subpathways associated with the identified subset of small molecules having abnormal levels.

23. The method of claim 22, comprising:

24. 3. The method of claim 1 or claim 2, further comprising displaying a graphical representation of a plurality of pathways associated with a plurality of small molecules, and a graphical indication of which pathways among the plurality of pathways are associated with the identified subset of small molecules having abnormal levels.

25. 3. The method of claim 1 or claim 2, wherein the identified subset of small molecules having abnormal levels comprises designated biochemicals.

26. 26. The method of claim 25, further comprising, for each designated biochemical within the identified subset of small molecules having abnormal levels, displaying information representing a biochemical pathway associated with the designated biochemical.

27. 3. The method of claim 1 or claim 2, further comprising storing data comprising a graphical representation of a plurality of pathways associated with a plurality of small molecules, and a graphical indication of which pathways among the plurality of pathways are associated with the identified subset of small molecules having abnormal levels.

28. 3. The method of claim 1 or claim 2, further comprising displaying a graphical representation of the identified subset of small molecules having abnormal levels.

29. 3. The method of claim 1 or claim 2, further comprising storing data comprising a graphical representation of the identified subset of small molecules having abnormal levels.

30. 3. The method of claim 1 or claim 2, wherein the abnormal levels of small molecules in the sample comprise small molecules that are present in the reference small molecule profile and that are absent in the small molecule profile of the sample derived from the individual subject.

31. 3. The method of claim 1 or claim 2, wherein the abnormal levels of small molecules in the sample comprise small molecules that are present in the small molecule profile of the sample from the individual subject and that are rare in the reference small molecule profile.

32. generating a small molecule profile of a sample from an individual subject, generating experimental data from samples derived from individual subjects during an experimental procedure; and generating experimental data from at least one anchor sample during the experimental run; 3. The method of claim 1 or claim 2, comprising:

33. 33. The method of claim 32, wherein at least one anchor sample comprises a pooled aliquot of samples from multiple subjects.

34. 34. The method of claim 33, wherein the standard range of reference levels for each small molecule is based on statistical analysis of small molecule profiles generated from samples from multiple subjects.

35. The method of claim 1 or claim 2, wherein the standard range of reference levels of each small molecule is based on statistical analysis of small molecule profiles generated from samples derived from multiple reference subjects.

36. 36. The method of claim 35, wherein the standard range of the reference level of each small molecule is based on the interquartile range in a statistical analysis of small molecule profiles generated from samples derived from multiple reference subjects.

37. The method of claim 36, wherein the standard range of reference levels of each small molecule is based on the range of standard scores (Z-scores) in statistical analysis of small molecule profiles created from samples from multiple reference subjects.

38. obtaining a reference sample from each reference subject among the plurality of reference subjects; generating a reference sample small molecule profile for each reference subject, the small molecule profile comprising, for each reference sample, information regarding the presence or absence and level of each of a plurality of small molecules present in the reference sample; and performing statistical analysis of the small molecule profiles for the plurality of reference subjects to determine a standard range of levels for each of the plurality of small molecules, thereby generating a reference small molecule profile; 3. The method of claim 1 or claim 2, further comprising:

39. 3. The method of claim 1 or claim 2, wherein the sample from an individual subject comprises blood, plasma, or serum.

40. 3. The method of claim 1 or claim 2, wherein the sample from an individual subject comprises urine.

41. 3. The method of claim 1 or claim 2, wherein the sample from an individual subject comprises blood, plasma, serum, skin, epidermal tissue, adipose tissue, aortic tissue, liver tissue, urine, cerebrospinal fluid, gingival exudate, or a cell sample.

42. 3. The method of claim 1 or claim 2, wherein the plurality of small molecules comprises at least 200 small molecules.

43. 3. The method of claim 1 or claim 2, wherein the plurality of small molecules comprises at least 500 small molecules.

44. 3. The method of claim 1 or claim 2, wherein the plurality of types of small molecules comprises 500 to 1300 types of small molecules.

45. 3. The method of claim 1 or claim 2, wherein the plurality of small molecules comprises 500 to 25,000 small molecules.

46. Multiple diseases and disorders are associated with 3-methylcrotonyl-CoA carboxylase deficiency, argininosuccinate lyase deficiency, adenosine deaminase deficiency, argininemia, biotinidase deficiency, cobalamin deficiency (Cbl), Cbl A, Cbl C, citrullinemia, carnitine palmitoyltransferase 2 deficiency (CPTII), cystinosis, dihydropyrimidine dehydrogenase deficiency, glutaric aciduria, and 3-hydroxy-3-methylglutaric aciduria (HMG). CoA lyase deficiency), holocarboxylase, homocystinuria, lysinuric protein intolerance, isovaleric acidemia, medium-chain acyl-CoA dehydrogenase deficiency, methylmalonic acidemia, molybdenum cofactor deficiency or sulfite oxidase deficiency, maple syrup urine disease, ornithine transcarbamylase deficiency, propionic acidemia, phenylketonuria (PKU), succinic semialdehyde dehydrogenase deficiency, succinyl lyase deficiency, thymidine phosphorylase deficiency, trimethyllysine hydroxylase epsilon deficiency, tyrosinemia, very long-chain acyl-CoA dehydrogenase deficiency, xanthan 3. The method of claim 1, wherein the deficiency includes two or more of the following: oxaluria, X-linked creatine transporter, sarcosinemia, citrate transporter deficiency, pyruvate dehydrogenase deficiency, hyperornithine-homocitrulline-hyperammonemia, aromatic amino acid decarboxylase deficiency, Smith-Lemli-Opitz syndrome, primary carnitine deficiency, citrin deficiency, ABAT deficiency, GLUT1 deficiency, MGA, SCAD deficiency, urocanase deficiency, 3-hydroxyisobutyryl-CoA hydrolase deficiency, hyperoxaluria, BBOX deficiency, acetaminophen-induced toxicity, diabetes, and hepatic insufficiency.

47. Multiple diseases and disorders are associated with 3-methylcrotonyl-CoA carboxylase deficiency, argininosuccinate lyase deficiency, adenosine deaminase deficiency, argininemia, biotinidase deficiency, cobalamin deficiency (Cbl), Cbl A, Cbl C, citrullinemia, carnitine palmitoyltransferase 2 deficiency (CPTII), cystinosis, dihydropyrimidine dehydrogenase deficiency, glutaric aciduria, and 3-hydroxy-3-methylglutaric aciduria (HMG). CoA lyase deficiency), holocarboxylase, homocystinuria, isovaleric acidemia, lysinuric protein intolerance, medium-chain acyl-CoA dehydrogenase deficiency, methylmalonic acidemia, molybdenum cofactor deficiency or sulfite oxidase deficiency, maple syrup urine disease, ornithine transcarbamylase deficiency, propionic acidemia, phenylketonuria (PKU), succinic semialdehyde dehydrogenase deficiency, succinyl lyase deficiency, thymidine phosphorylase deficiency, trimethyllysine hydroxylase epsilon deficiency, tyrosinemia, very long-chain acyl-CoA dehydrogenase deficiency, xanthan 3. The method of claim 1, wherein the deficiency includes five or more of the following: oxaluria, X-linked creatine transporter, sarcosinemia, citrate transporter deficiency, pyruvate dehydrogenase deficiency, hyperornithine-homocitrulline-hyperammonemia, aromatic amino acid decarboxylase deficiency, Smith-Lemli-Opitz syndrome, primary carnitine deficiency, citrin deficiency, ABAT deficiency, GLUT1 deficiency, MGA, SCAD deficiency, urocanase deficiency, 3-hydroxyisobutyryl-CoA hydrolase deficiency, hyperoxaluria, BBOX deficiency, acetaminophen-induced toxicity, diabetes, and hepatic insufficiency.

48. Multiple diseases and disorders are associated with 3-methylcrotonyl-CoA carboxylase deficiency, argininosuccinate lyase deficiency, adenosine deaminase deficiency, argininemia, biotinidase deficiency, cobalamin deficiency (Cbl), Cbl A, Cbl C, citrullinemia, carnitine palmitoyltransferase 2 deficiency (CPTII), cystinosis, dihydropyrimidine dehydrogenase deficiency, glutaric aciduria, and 3-hydroxy-3-methylglutaric aciduria (HMG). CoA lyase deficiency), holocarboxylase, homocystinuria, isovaleric acidemia, lysinuric protein intolerance, medium-chain acyl-CoA dehydrogenase deficiency, methylmalonic acidemia, molybdenum cofactor deficiency or sulfite oxidase deficiency, maple syrup urine disease, ornithine transcarbamylase deficiency, propionic acidemia, phenylketonuria (PKU), succinic semialdehyde dehydrogenase deficiency, succinyl lyase deficiency, thymidine phosphorylase deficiency, trimethyllysine hydroxylase epsilon deficiency, tyrosinemia, very long-chain acyl-CoA dehydrogenase deficiency, xanthine The method of claim 1 or 2, wherein the deficiency includes 10 or more of the following: oxaluria, X-linked creatine transporter, sarcosinemia, citrate transporter deficiency, pyruvate dehydrogenase deficiency, hyperornithine-homocitrulline-hyperammonemia, aromatic amino acid decarboxylase deficiency, Smith-Lemli-Opitz syndrome, primary carnitine deficiency, citrin deficiency, ABAT deficiency, GLUT1 deficiency, MGA, SCAD deficiency, urocanase deficiency, 3-hydroxyisobutyryl-CoA hydrolase deficiency, hyperoxaluria, BBOX deficiency, acetaminophen-induced toxicity, diabetes, and hepatic insufficiency.

49. 3. The method of claim 1 or claim 2, wherein the stored diagnostic information comprises an identification of a disorder associated with the identified subset of small molecules having abnormal levels.

50. The identified disorder is 3-methylcrotonyl CoA carboxylase deficiency, and the identified subset of small molecules with abnormal levels are 3-methylcrotonylglycine, β-hydroxyisovalerate, β-hydroxyisovaleroylcarnitine, glutarylcarnitine (C5), 3-methylcrotonylglycine, adipate, glutarate (pentanedioate), octadecanedioate (C18), hexadecanedioate (C16), and tetradecanedioate (C18).

50. The method of claim 49, wherein the compound comprises one or more metabolites selected from the group consisting of ester (C14), dodecanedioate (C12), isovalerate, leucine, isovalerylglycine, alpha-hydroxyisovalerate, succinylcarnitine, 3-methylglutarylcarnitine, isovalerylcarnitine, alanylalanine, pyroglutamylvaline, X-12007, X-12814, ethyl malonate, N-acetylleucine, and combinations thereof.

51. 50. The method of claim 49, wherein the identified disorder is adenosine deaminase deficiency and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of deoxyadenosine, S-adenosylhomocysteine, 2'-deoxyinosine, adenine, N2-methylguanosine, 2'-deoxyguanosine, urate, N1-methyladenosine, adenosine, allantoin, xanthine, guanosine, hypoxanthine, N2,N2-dimethylguanosine, 7-methylguanosine, and combinations thereof.

52. 50. The method of claim 49, wherein the identified disorder is argininosuccinate lyase deficiency and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of argininosuccinate, N-δ-acetylornithine, sorbose, fructose, citrulline, methyl-4-hydroxybenzoate, uracil, arginine, aspartate, isoleucyl aspartate, ornithine, uridine, homocitrulline, orotate, homoarginine, O-sulfo-L-tyrosine, palmitoylsphingomyelin, X-13507, X-15245, X-15664, X-15454, and combinations thereof.

53. 50. The method of claim 49, wherein the identified disorder is argininemia and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of arginine, 4-guanidinobutanoate, homoarginine, N-acetylarginine, ornithine, urea, homocitrulline, uracil, aspartate, argininosuccinate, proline, orotate, creatinine, uridine, 3-ureidopropionate, creatine, betaine, leucine, isoleucine, gamma-glutamylleucine, X-12339, X-12681, and combinations thereof.

54. 50. The method of claim 49, wherein the identified disorder is biotinidase deficiency and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of xylitol, biotin, 3-methylcrotonylglycine, propionylcarnitine (C3), and combinations thereof.

55. 50. The method of claim 49, wherein the identified disorder is Cbl (cobalamin deficiency) and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of methylmalonic acid, homocysteine, 2-methylcitrate, cystathionine, 2-methylmalonylcarnitine, propionylcarnitine, X-12749, and combinations thereof.

56. 50. The method of claim 49, wherein the identified disorder is Cbl A and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of methylmalonic acid, homocysteine, 2-methylcitrate, cystathionine, 2-methylmalonylcarnitine, tiglylcarnitine, 2-methylbutyrylcarnitine, propionylcarnitine, X-12749, and combinations thereof.

57. 50. The method of claim 49, wherein the identified disorder is Cbl C and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of methylmalonic acid, homocysteine, 2-methylcitrate, cystathionine, 2-methylmalonylcarnitine, propionylcarnitine, X-12749, X-17677, and combinations thereof.

58. The identified disorder is citrullinemia, and the identified subset of small molecules with abnormal levels are citrulline, argininosuccinate, homocitrulline, 3-ureidopropionate, N-acetylalanine, phenylacetate, phenylacetylglutamine, 4-ureidobutyrate, N-carbamoyl aspartate, guanidinoacetate, urea, 4-guanidinobutanoate, N-acetylarginine, hippurate, ornithine, 2-methylhippurate, phenylacetylglycine, and 4-phenylalanine.

50. The method of claim 49, wherein the metabolically active agent comprises one or more metabolites selected from the group consisting of 1,5-anhydroglucitol (1,5-AG), N-acetyl-citrulline, X-12681, X-19684, X-20598, X-18446, X-20588, and combinations thereof.

59. 50. The method of claim 49, wherein the identified disorder is carnitine palmitoyltransferase 2 deficiency (CPTII) and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of carnitine, acylcarnitines, N-octanoylglycine (C8 ester), sebacate (C8), caprate (C10), caprylate (C8), octanoylcarnitine, hexanoylcarnitine, and combinations thereof.

60. 50. The method of claim 49, wherein the identified disorder is cystinosis and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of cysteine, cys-gly (oxidized), 1,5-anhydroglucitol (1,5-AG), glycocholate sulfate, 4-acetylphenol sulfate, cresol glucuronide, erythritol, vanillylmandelate, N2,N2-dimethyl-guanosine, phenylacetylglutamine, X-12846, X-12303, X-19145, X-12216, X-17717, X-15667, X-12119, X-11315, X-12731, X-12705, X-17685, X-18371, and combinations thereof.

61. 50. The method of claim 49, wherein the identified disorder is dihydropyrimidine dehydrogenase deficiency and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of uracil, thymine, cytidine, 5,6-dihydrouracil, 4-ureidobutyrate, 3-ureidopropionate, uridine, orotate, N-carbamoyl aspartate, and combinations thereof.

62. 50. The method of claim 49, wherein the identified disorder is glutaric aciduria type 1 and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of glutarate (pentanedioate), glutarylcarnitine (C5), 3-hydroxyglutarate, glutaconate, 3-methylglutarylcarnitine, 2-aminoadipate, X-12364, X-15674, and combinations thereof.

63. 50. The method of claim 49, wherein the identified disorder is guanidinoacetate methyltransferase (GAMT) deficiency and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of creatine, 3-(4-hydroxyphenyl)lactate, 1,3-dipalmitoylglycerol, guanidinoacetate, creatinine, cysteine ​​s-sulfate, X-19602, X-12906, X-13007, X-10458, and combinations thereof.

64. The identified disorder is 3-hydroxy-3-methylglutaric aciduria (HMG CoA lyase deficiency), and the identified subset of small molecules with abnormal levels are 3-methylglutarylcarnitine (C6), β-hydroxyisovalerate, β-hydroxyisovaleroylcarnitine, glutarylcarnitine (C5), arginylproline, 1-stearoylglycerophosphoethanolamine, o-cresol sulfate, 3-methylcrotonylglycine, adipate, glutarate (pentanedioate), octadecanediol (C 50. The method of claim 49, wherein the compound comprises one or more metabolites selected from the group consisting of acetylcarnitine, palmitoylcarnitine, hexanoylcarnitine, myristoylcarnitine, hexenedioylcarnitine, X-17715, X-12741, X-16134, X-10593, X-12688, and combinations thereof.

65. The identified disorder is holocarboxylase synthetase deficiency, and the identified subset of small molecules with abnormal levels are β-hydroxyisovalerate, 3-methylcrotonylglycine, β-hydroxyisovaleroylcarnitine (C5), propionylglycine (C3), 3-hydroxypropanoate, tigloylglycine, succinylcarnitine, 2-methylcitrate, 3-hydroxyisobutyrate, lactate, 3-hydroxy-2-ethylpropionate, isobutyrylglycine, α-hydroxyisovaleroylcarnitine, 3-methyl-2-propionate, and α-hydroxyisovaleroylcarnitine.

50. The method of claim 49, wherein the compound comprises one or more metabolites selected from the group consisting of alpha-oxobutyrate, 3-methyl-2-oxovalerate, 3-hydroxy-2-methylbutyrate, 4-methyl-2-oxopentanoate, malonylcarnitine, alpha-hydroxyisovalerate, 2-hydroxy-3-methylvalerate, propionylcarnitine, tiglylcarnitine, isovalerylcarnitine, hydroxybutyrylcarnitine, succinate, 2-methylmalonylcarnitine, alpha-hydroxyisocaproate, biotin, and combinations thereof.

66. The identified disorder is homocystinuria, and the identified subset of small molecules with abnormal levels are homocysteine, cysteine, methionine, other amino acids, γ-glutamylmethionine, 5-methylthioadenosine (MTA), S-adenosylhomocysteine ​​(SAH), N1-methyladenosine, glycylproline, 1-eicosenoylglycerophosphoethanolamine (20:1n9), 1-methylnicotinamide, N-acetyl-aspartyl-glutamate (NAAG), pyridoxal, 2-hydroxyisobutyrate, asisoga, carnosine, 3-methoxytyrosine, 2-hydroxydecanoate, δ-tocopherol, α-CEHC ( 50. The method of claim 49, wherein the hydroxybenzoates comprise one or more metabolites selected from the group consisting of 2,5,7,8-tetramethyl-2-(2'-carboxyethyl)-6-hydroxychroman)sulfate, N-acetyltryptophan, adenine, cortisol, X-19350, X-18965, X-15649, X-17303, X-18897, X-11564, X-18891, X-12748, X-18918, X-18905, X-18606, X-16574, X-18895, X-18907, X-19455, X-18909, X-19574, X-12110, X-20676, X-11360, X-18920, and combinations thereof.

67. 50. The method of claim 49, wherein the identified disorder is isovaleric acidemia and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of isovalerate (C5), isovalerylglycine, isovalerylcarnitine (C5), valerate; valerylcarnitine, β-hydroxyisovalerate, phenylcarnitine, β-hydroxybutyrate, 3-methylcrotonylglycine, α-hydroxybutyrate, X-16577, X-14331, and combinations thereof.

68. 50. The method of claim 49, wherein the identified disorder is lysinuric protein intolerance and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of ornithine, arginine, lysine, asparagine, N6-acetyllysine, glutamine, N2-acetyllysine, N-acetylarginine, γ-glutamylglutamine, proline, S-methylcysteine, 2-hydroxydecanoate, 1-methylimidazole acetate, 2-aminoheptanoate, 3-methylglutarylcarnitine, glutarylcarnitine, N6-trimethyllysine, 5-(galactosylhydroxy)-L-lysine, X-15636, 17654, X-12193, X-12425, and combinations thereof.

69. The identified disorder is medium-chain acyl-CoA dehydrogenase deficiency, and the identified subset of small molecules with abnormal levels are acylcarnitines, carnitine, organic acids, N-octanoylglycine, caproate (6:0), caprylate (8:0), heptanoate (7:0), dodecanedioate, decanoylcarnitine, N-palmitoyltaurine, pelargonate (9:0), deoxycarnitine, O-methylcatechol sulfate, and 1-stearate. 1-Margalloylglycerophosphocholine (18:0), 1-Margalloylglycerophosphocholine (17:0), 1-Docosapentaenoylglycerophosphocholine (22:5n3), 4-Octenedioate, Adipate, Heptanoylglycine, 3-Methyladipate, 2-Hydroxyglutarate, α-CEHC (2,5,7,8-tetramethyl-2-(2'-carboxyethyl)-6-hydroxychroman) sulfate, X-11521 (possible empirical formula: C 15 H 27 NO 4 and structure: 2-octenoylcarnitine), X-15646, X-12802, X-11478, X-11440 (possibly hydroxypregnene-diol disulfate or pregnanolone-diol disulfate), X-15486, X-18913, methylhexanoylglutamine, X-13837, X-18946, X-11861, X-18888, X-18922, X-17438, X-18916, X-16674, X-12824, and combinations thereof.

70. The identified disorder is methylmalonic acidemia, and the identified subset of small molecules with abnormal levels are methylmalonate, methylmalonyl CoA, 2-methylmalonylcarnitine, propionylcarnitine (C3), tiglylcarnitine, 2-methylbutyrylcarnitine (C5), 2-methylcitrate, succinylcarnitine, propionylglycine, 3-hydroxypropanoate, valerylcarnitine, isovalerylcarnitine, succinate, and tigloylglycine. , β-hydroxyisovaleroylcarnitine, β-hydroxyisovalerate, isobutyrylcarnitine, 3-hydroxy-2-ethylpropionate, butyrylcarnitine, 3-methylglutarylcarnitine, methylsuccinate, 3-methyl-2-oxovalerate, 3-methyl-2-oxobutyrate, X-12749, X-17564, X-12114, and combinations thereof.

71. 50. The method of claim 49, wherein the identified disorder is molybdenum cofactor deficiency or sulfite oxidase deficiency, and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of xanthine, S-sulfocysteine, thiosulfate, 5-HETE, leukotriene B4, 13-HODE+9-HODE, 12-HETE, urate, and combinations thereof.

72. The identified disorder is maple syrup urine disease, and the identified subset of small molecules with abnormal levels are leucine, isoleucine, valine, 2-hydroxy-3-methylvalerate, α-hydroxyisovalerate, isovalerylcarnitine, 2-aminoheptanoate, 4-methyl-2-oxopentanoate, 1-linolenoylglycerophosphocholine (18:3n3), 2-linolenoylglycerophosphocholine (18:3n3), 1-myristoylglycerophosphocholine (14:0), 2-myristoylglycerophosphocholine, 5α-androstane-3α,17β-diol disulfate, valine, 3-methyl-2-oxobutyrate ...1-myristoylglycerophosphocholine (14:0), 2-myristoylglycerophosphocholine, 5α-androstane-3α,17β-diol disulfate, valine, 3-methyl-2-oxobutyrate, 1 50. The method of claim 49, wherein the compound comprises one or more metabolites selected from the group consisting of acetylcarnitine, 2-methylcitrate, tigloylglycine, tiglylcarnitine, hydroxybutyrylcarnitine, α-hydroxyisocaproate, X-13581, X-17690, X-13689 (glucuronide conjugate), and combinations thereof.

73. 50. The method of claim 49, wherein the identified disorder is ornithine transcarbamylase deficiency and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of orotate, citrulline, arginine, phenylacetylglutamine, stearidonate (18:4n3), 3-ureidopropionate, 2-methylhippurate, 2-hydroxyphenylacetate, phenylcarnitine, hippurate, phenylpropionylglycine, benzoate, methyl-4-hydroxybenzoate, 4-phenylbutyrate, 2-pentanamido-3-phenylpropanoic acid, phenylacetate, phenylacetylglycine, trans-4-hydroxyproline, pro-hydroxy-pro, urea, phenyllactate (PLA), guanidinosuccinate, ornithine, X-20598, X-20588, and combinations thereof.

74. 50. The method of claim 49, wherein the identified disorder is propionic acidemia and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of propionate, propionylglycine (C3), 2-methylcitrate, 3-hydroxypropanoate, propionylcarnitine (C3), 1-pentadecanoylglycerophosphocholine (15:0), tigloylglycine, succinylcarnitine, glutarylcarnitine (C5), 3-methylglutarylcarnitine (C6), tiglylcarnitine, butyrylcarnitine, 2-methylmalonylcarnitine, β-hydroxyisovalerate, X-12819, and combinations thereof.

75. The identified disorder is phenylketonuria (PKU), and the identified subset of small molecules with abnormal levels are phenylalanine, gamma-glutamylphenylalanine, phenyllactate, N-acetylphenylalanine, phenylpyruvate, gamma-glutamyltyrosine, 3-methoxytyrosine, 4-hydroxyphenylpyruvate, p-cresol sulfate, catechol sulfate, phenylacetylglutamine, phenylalanylarginine, valylphenylalanine, histidylphenylalanine, phenylalanylserine, and leucylphenylalanine.

50. The method of claim 49, wherein the compound comprises one or more metabolites selected from the group consisting of phenylalanine, threonylphenylalanine, phenylalanylalanine, phenylalanylglycine, phenylalanylglutamate, phenylalanylphenylalanine, aspartylphenylalanine, tryptophylphenylalanine, phenylalanylisoleucine, glycylphenylalanine, phenylalanylleucine, phenylalanylaspartate, o-cresol sulfate, phenylacetylglycine, X-16283, X-15497, and combinations thereof.

76. 50. The method of claim 49, wherein the identified disorder is succinic semialdehyde dehydrogenase deficiency and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of gamma-aminobutyrate (GABA), succinimide, and combinations thereof.

77. 50. The method of claim 49, wherein the identified disorder is succinyladenosine lyase deficiency and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of N6-succinyladenosine, xanthosine, 2'-deoxyguanosine, 2'-deoxyinosine, adenine, and combinations thereof.

78. 50. The method of claim 49, wherein the identified disorder is thymidine phosphorylase deficiency and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of thymidine, 2'-deoxyuridine, 5,6-dihydrothymine, 5-methyluridine (ribothymidine); hippurate, 2-linoleoylglycerophosphocholine, 4-methylcatechol sulfate, 1-arachidoylglycerophosphocholine (20:0), taurolithocholate 3-sulfate, glycolithocholate sulfate, imidazole propionate, X-13862, X-19330, X-20620, X-12170, and combinations thereof, and combinations thereof.

79. 50. The method of claim 49, wherein the identified disorder is trimethyllysine hydroxylase epsilon deficiency and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of N-6-trimethyllysine, 1-arachidonoyl glucuronide, X-16574, X-12822, X-15136, and combinations thereof.

80. 50. The method of claim 49, wherein the identified disorder is tyrosinemia and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of tyrosine, 3-(4-hydroxyphenyl)lactate, 4-hydroxyphenylpyruvate, 3-(3-hydroxyphenyl)propionate, 4-hydroxyphenylacetate, phenyllactate (PLA), X-13581, and combinations thereof.

81. 50. The method of claim 49, wherein the identified disorder is very long-chain acyl-CoA dehydrogenase deficiency and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of myristoylcarnitine, stearoylcarnitine (C18), palmitoylcarnitine (C16), oleoylcarnitine (C18), myristoleate (14:1n5), linoleoylcarnitine, 9-methyluric acid, xylulose, arachidonate (20:4n6), docosahexaenoate (DHA; 22:6n3), eicosapentanoic acid (EPA), 5,8-tetradecadienoic acid, 1-docosahexaenoyl-GPC (22:6; DHA-GPC), X-18739 (a possible isomer of 2-tetradecenoylcarnitine), and combinations thereof.

82. 50. The method of claim 49, wherein the identified disorder is xanthinuria and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of xanthine, urate, creatine, hypoxanthine, xanthosine, 2'-deoxyinosine, inosine, N2-methylguanosine, creatinine, and combinations thereof.

83. 50. The method of claim 49, wherein the identified disorder is X-linked creatine transporter and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of glycylleucine, 2-hydroxyoctanoate, 1,6-anhydroglucose, creatine, X-11483, X-18943, X-17422, X-17761, X-17335, and combinations thereof.

84. The identified disorder is a disorder of amino acid metabolism and transport, and the identified subset of small molecules with abnormal levels are N-acetylalanine, aspartate, glutarate (pentanedioate), glutarylcarnitine (C5), 3-hydroxyglutarate, glutaconate, phenylalanine, N-acetylphenylalanine, phenylpyruvate, phenyllactate (PLA), phenylacetate, phenylacetylglycine, phenylacetylglutamine, 4-hydroxyphenylpyruvate, 3-(4-hydroxyphenyl)- Lactate, p-cresol sulfate, o-cresol sulfate, 3-methoxytyrosine, leucine, 4-methyl-2-oxopentanoate, isovalerate, isovalerylglycine, isovalerylcarnitine (C5), 3-methylcrotonylglycine, β-hydroxyisovalerate, β-hydroxyisovaleroylcarnitine, 3-methylglutarylcarnitine (C6), α-hydroxyisovalerate, isoleucine, allo-isoleucine, 3-methyl-2-oxovalerate, 2-methylbutyrylcarnitine (C5), tiglycine Carnitine, tigloylglycine, 2-hydroxy-3-methylvalerate, 3-hydroxy-2-ethylpropionate, valine, 3-methyl-2-oxobutyrate, isobutyrylcarnitine, 3-hydroxyisobutyrate, α-hydroxyisocaproate, homocysteine, cystathionine, arginine, urea, ornithine, proline, citrulline, argininosuccinate, homoarginine, homocitrulline, N-acetylarginine, N-δ-acetylornithine, trans-4-hydroxyproline, pro-hydroxy -pro, creatine, creatinine, 4-guanidinobutanoate, guanidinosuccinate, gamma-glutamylphenylalanine, gamma-glutamyltyrosine, alanylalanine, arginylproline aspartylphenylalanine, glycylphenylalanine, histidylphenylalanine, isoleucyl aspartate, leucylphenylalanine, phenylalanylalanine, phenylalanylarginine, phenylalanyl aspartate, phenylalanyl glutamate, phenylalanylglycine, phenylalanylisoleucine,Phenylalanyl-leucine, phenylalanyl-phenylalanine, phenylalanyl-serine, pyroglutamyl-valine, threonyl-phenylalanine, tryptophyl-phenylalanine, valyl-phenylalanine, fructose, sorbose, succinyl-carnitine, succinate, 2-methyl citrate, valerate, stearidonate (18:4n3), adipate, dodecanedioate (C12), tetradecanedioate (C14), hexadecanedioate (C16), octadecandioate (C18), 2-aminoheptanoate, 2-linoleate 1-Myristoylglycerophosphocholine (18:3n3), 2-Methylmalonylcarnitine, Butyrylcarnitine, Propionylcarnitine (C3), Propionylglycine (C3), Methylmalonyl CoA, Methylmalonic acid, Acetylcarnitine, Hydroxybutyrylcarnitine, Valerylcarnitine, Hexanoylcarnitine, Myristoylcarnitine, Palmitoylcarnitine (C16), Hexenedioylcarnitine, 3-Hydroxypropanoate, 1-Myristoylglycerophosphocholine (14:0), 2-Myristoylglycerophosphocholine, 1- Pentadecanoylglycerophosphocholine (15:0), 1-linolenoylglycerophosphocholine (18:3n3), 1-stearoylglycerophosphoethanolamine, 1,3-dipalmitoylglycerol, 5α-androstane-3α,17β-diol disulfate, orotate, uridine, uracil, 3-ureidopropionate, hippurate, catechol sulfate, phenylcarnitine, propionate, 5-hydroxytryptophan, 5-methylthioadenosine (MTA), β-alanine, dimethylarginine (SDMA + ADMA), glucuronide, glycerin, glycerol, glycerol esters ... glutamine, N-acetylaspartate (NAA), N-acetyl-β-alanine, tryptophan, tyrosine, 2-pyrrolidone, γ-glutamylleucine, O-sulfo-L-tyrosine, palmitoyl-sphingomyelin, γ-glutamylisoleucine, cysteine ​​s-sulfate, 2-aminoadipate, phenylacetylglutamine, 3,4-dihydroxyphenylacetate, phenylpropionylglycine, 2-pentanamido-3-phenylpropanoic acid, 2-hydroxyphenylacetate, N-acetylleucine, methylsuccinate,50. The method of claim 49, comprising one or more metabolites selected from the group consisting of ethyl malonate, guanidinoacetate, beta-hydroxybutyrate, N-carbamoyl aspartate, 4-ureidobutyrate, hippurate, 2-methyl hippurate, benzoate, methyl-4-hydroxybenzoate, 4-phenylbutyrate, and combinations thereof.

85. The identified disorder is a fatty acid oxidation disorder, and the identified subset of small molecules with abnormal levels are xylulose, caproate (6:0), heptanoate (7:0), caprylate (C8), pelargonate (9:0), caprate (C10), myristoleate (14:1n5), eicosapentaenoic acid (EPA), docosahexaenoate (DHA; 22:6n3), arachidonate (20:4n6), suberate (octanedioic acid), and octanedioic acid (OCTA). ester), sebacate (C8), dodecanedioate (C12), hexanoylglycine (C6), N-octanoylglycine, hexanoylcarnitine, octanoylcarnitine, decanoylcarnitine, cis-4-decenoylcarnitine, myristoylcarnitine, palmitoylcarnitine (C16), stearoylcarnitine (C18), oleoylcarnitine (C18), deoxycarnitine, carnitine, 3-hydroxydecanoate ate, 5-hydroxyhexanoate, N-palmitoyl taurine, 1-margalloylglycerophosphocholine (17:0), 1-stearoylglycerophosphocholine (18:0), 1-docosapentaenoylglycerophosphocholine (22:5n3), 9-methyluric acid, α-CEHC (2,5,7,8-tetramethyl-2-(2'-carboxyethyl)-6-hydroxychroman) sulfate, O-methylcatechol sulfate, 5,8 50. The method of claim 49, comprising one or more metabolites selected from the group consisting of tetradecadienoic acid, methylhexanoylglutamine, 7-dehydrocholesterol, cholestanol, N6-trimethyllysine, butyrylglycine, 1-docosahexaenoyl-GPC (22:6DHA-GPC), 2-hydroxyglutarate, 3-methyladipate, 4-octenedioate, heptanoylglycine, and combinations thereof.

86. 50. The method of claim 49, wherein the identified disorder is acetaminophen-induced toxicity and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of 2-hydroxyacetaminophen sulfate, 2-methoxyacetaminophen sulfate, 3-(cysteine-S-yl)acetaminophen, 4-acetaminophen sulfate, 4-acetamidophenol, p-acetamidophenyl glucuronide, glycohyocholate, glycochenodeoxycholate, taurocholate, taurochenodeoxycholate, glutathione, and combinations thereof.

87. 50. The method of claim 49, wherein the identified disorder is diabetes and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of 1,5-anhydroglucitol (1,5-AG), 2-hydroxybutyrate (AHB), 3-hydroxybutyrate, glucose, glycine, isoleucine, leucine, valine, and combinations thereof.

88. 50. The method of claim 49, wherein the identified disorder is liver dysfunction and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of glycocholate, glycochenodeoxycholate, taurocholate, taurochenodeoxycholate, and combinations thereof.

89. 50. The method of claim 49, wherein the identified disorder is sarcosinemia and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of dimethylglycine, betaine, choline, glycine, sarcosine, and combinations thereof.

90. 50. The method of claim 49, wherein the identified disorder is citrate transporter deficiency and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of alpha-ketoglutarate, succinate, fumarate, malate, glutamate, citrate, and combinations thereof.

91. 50. The method of claim 49, wherein the identified disorder is hyperornithine-homocitrulline-hyperammonemia and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of uracil, 3-ureidopropionate, orotate, glutamine, N-acetyl-β-alanine, uridine, N-acetylaspartate (NAA), dimethylarginine (SDMA+ADMA), 5-methylthioadenosine (MTA), β-alanine, 4-ureidobutyrate, homocitrulline, ornithine, and combinations thereof.

92. 50. The method of claim 49, wherein the identified disorder is aromatic amino acid decarboxylase deficiency and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of tyrosine, phenylalanine, tryptophan, 3-methoxytyrosine, 5-hydroxytryptophan, L-dopa, homovanillate, 5-hydroxyindoleacetate, vanilactate, and combinations thereof.

93. 50. The method of claim 49, wherein the identified disorder is Smith-Lemli-Opitz syndrome and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of cholestanol, 7-dehydrocholesterol, and combinations thereof.

94. 50. The method of claim 49, wherein the identified disorder is primary carnitine deficiency and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of N6-trimethyllysine, carnitine, and combinations thereof.

95. 50. The method of claim 49, wherein the identified disorder is citrin deficiency and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of orotate, citrulline, and combinations thereof.

96. 50. The method of claim 49, wherein the identified disorder is ABAT deficiency and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of 2-pyrrolidone, GABA, and combinations thereof.

97. 50. The method of claim 49, wherein the identified disorder is GLUT1 deficiency and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of fructose, mannose, prolylhydroxyproline, hydroxyproline, glycylproline, N-acetylneuraminate, dimethylarginine (ADMA+SDMA), glutamine, gamma-glutamylglutamine, N-acetyl-aspartyl-glutamate (NAAG), N-acetylglutamine, ethylmalonate, creatine, glucose, and combinations thereof.

98. 50. The method of claim 49, wherein the identified disorder is MGA and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of 3-methylglutarylcarnitine, 3-methylglutaconic acid, 3-methylglutaric acid, and combinations thereof.

99. 50. The method of claim 49, wherein the identified disorder is SCAD deficiency and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of butyrylglycine, ethylmalonate, butyrylcarnitine, methylsuccinate, and combinations thereof.

100. 50. The method of claim 49, wherein the identified disorder is urocanase deficiency and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of cis-urocanate, trans-urocanate, imidazole propionate, and combinations thereof.

101. 50. The method of claim 49, wherein the identified disorder is 3-hydroxyisobutyryl-CoA hydrolase deficiency and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of 3-hydroxyisobutyrate, isobutyrylglycine, and combinations thereof.

102. 50. The method of claim 49, wherein the identified disorder is hyperoxaluria and the identified subset of small molecules having abnormal levels comprises one or more metabolites selected from the group consisting of oxalate, glycolate, and combinations thereof.

103. The identified disorder is BBOX deficiency, and the identified subset of small molecules with abnormal levels are hexadecandioate (C16), docosadioate, eicosanoate, octadecandioate (C18), dodecandioate (C12), 2-aminooctanoate, 2-aminoheptanoate, α-hydroxyisocaproate, isovalerate (C5), decanoylcarnitine (C10), cis-4-decenoylcarnitine, palmitoylcarnitine (C16), and oleic acid.

50. The method of claim 49, wherein the hydroxybenzoate comprises one or more metabolites selected from the group consisting of oleylcarnitine (C18), laurylcarnitine (C12), myristoleoylcarnitine, myristoylcarnitine, glycerol, 3-hydroxymyristate, 2-hydroxydecanoate, 3-hydroxylaurate, 3-hydroxysebacate, 3-hydroxyoctanoate, 3-hydroxydecanoate, pelargonate (9:0), caproate (6:0), and combinations thereof.

104. 1. A method for screening an individual subject for a plurality of diseases or disorders, whether the individual subject is at increased risk for developing a disease or disorder, comprising the steps of: obtaining a sample from an individual subject; generating a small molecule profile of the sample containing information regarding the presence or absence and level of each of a plurality of small molecules in the sample; comparing the small molecule profile of the sample with a reference small molecule profile comprising a standard range of levels of each of a plurality of small molecules to determine whether any of the plurality of small molecules has an abnormal level in the sample, wherein an abnormal level of a small molecule in the sample is a level that is outside the standard range for that small molecule; For a small molecule profile having abnormal levels of any of a plurality of small molecules in a sample, identifying a subset of small molecules, each of which has an abnormal level in the sample; wherein said comparing and identifying is performed using an analytical function running on a processor of a computing device; obtaining diagnostic information from a database for a small molecule profile having abnormal levels of any of a plurality of small molecules present in a sample based on the abnormal levels of an identified subset of the small molecules, the database including, for each of a plurality of diseases and disorders, information relating to the disease or disorder with the abnormal level of one or more small molecules among the plurality of small molecules; storing the diagnostic information obtained for a small molecule profile having abnormal levels of any of a plurality of small molecules in the sample, the stored diagnostic information including an identification of an elevated risk for developing a disease or disorder associated with the identified subset of small molecules having abnormal levels; and storing information indicating that no abnormal levels were detected for a small molecule profile that does not have abnormal levels of any of the plurality of small molecules present in the sample; Including, A method whereby individual subjects are screened for a number of diseases and disorders to determine whether they are at increased risk of developing a disease or disorder.

105. 105. The method of claim 104, wherein the multiple diseases or disorders comprise acetaminophen overdose and diabetes.

106. The method of claim 104, wherein the plurality of diseases or disorders includes acetaminophen overdose, and wherein abnormal levels of one or more small molecules selected from the group consisting of 2-hydroxyacetaminophen sulfate, 2-methyacetaminophen sulfate, 2-(cysteine-S-yl)acetaminophen, 4-acetaminophen sulfate, 4-acetamidolphenol, p-acetamidophenyl glucuronide, and combinations thereof, and abnormal levels of one or more small molecules selected from the group consisting of glycohyocholate, glycochenodeoxycholate, taurocholate, tauorchenodeoxycholate, and combinations thereof, indicate an increased risk of acetaminophen overdose.

107. 105. The method of claim 104, wherein the plurality of diseases or disorders comprises diabetes, and wherein abnormal levels of one or more small molecules selected from the group consisting of 2-hydroxybutyrate, 3-hydroxybutyrate, 1,5-anhydroglucitol, glycine, isoleucine, leucine, valine, and combinations thereof indicate an increased risk of developing diabetes.

108. 105. The method of claim 104, wherein the plurality of diseases or disorders comprises liver disease, and wherein abnormal levels of one or more small molecules selected from the group consisting of glycocholate, glycochenodeoxycholate, taurocholate, taurochenodeoxycholate, and combinations thereof indicate impaired liver function and an increased risk of developing liver disease.

109. 105. The method of claim 104, wherein the plurality of diseases or disorders comprises a metabolic disease, and wherein abnormal levels of one or more small molecules selected from the group consisting of 1,5-androglucitol (1,5AG), 1-linoleoylglycerophosphocholine (18:2n6, LGCP), 2-hydroxybutyrate (AHB), 3-hydroxybutyrate (BHBA), 3-hydroxyisobutyrate, 3-methyl-2-oxobutyrate, 3-methyl-2-oxovalerate, 4-methyl-2-oxopentanoate, alanine, fructose, glucose, glutamate, glycine, isoleucine, leucine, mannose, oleate (18:1n9), palmitoylcarnitine, phenylalanine, serine, tyrosine, valine, and combinations thereof, indicate risk of the metabolic disease.

110. 105. The method of claim 104, wherein the plurality of diseases or disorders comprises cardiovascular dysfunction, and wherein abnormal levels of one or more small molecules selected from the group consisting of adenosine, azelate, colcetrol, creatinine, dodecanedioate, hexadecanedioate, histidine, octadecanedioate, phenylalanine, sebacate, tetradecanedioate, urate, and combinations thereof, indicate risk of cardiovascular dysfunction.

111. 105. The method of claim 104, wherein the plurality of diseases or disorders comprises renal dysfunction, and wherein abnormal levels of one or more small molecules selected from the group consisting of 3-indoxyl sulfate, 3-methylhistidine, 4-acetamidobutanoate, arabitol, C-glycosyltryptophan, creatinine, erythritol, indoleacetate, kynurenine, lathosterol, myoinositol, N2,N2-dimethylguanosine, N-acetylalanine, N-acetylserine, N-acetylthreonine, N-formylmethionine, propylacetylglutamine, pseudouridine, succinylcarnitine, trans-4-hydroxyproline, tryptophan, urea, and combinations thereof indicate a risk of renal dysfunction.

112. 105. The method of claim 104, wherein the plurality of diseases or disorders comprises pulmonary dysfunction, and wherein abnormal levels of one or more small molecules selected from the group consisting of: carboxy-4-methyl-5-propyl-2-furanopropanoate, 3-filpropionate (hydrocinnamate), α-tocopherol, asparagine, benzoate, bilirubin (Z,Z), butyrylcarnitine, γ-glutamylvaline, glycerate, glycine, indopropionate, N-acetylglycine, proline, pseudouridine, pyridoxate, serine, succinylcarnitine, threonate, and combinations thereof, indicate a risk of pulmonary dysfunction.

113. 105. The method of claim 104, wherein the plurality of diseases or disorders comprises inflammation, and wherein abnormal levels of one or more small molecules selected from the group consisting of 13-HODE+9-HODE, arachidonate (20:4n6), cortisol, docosahexaenoate (DHA, 22:6n3), eicosapentaenoate (EPA, 20:5n3), kynurenate, kynurenine, quinolinate, and combinations thereof, indicate inflammation.

114. Multiple diseases or disorders involving hormone imbalances include pregnenolone sulfate, 21-hydroxypregnenolone monosulfate, 21-hydroxypregnenolone disulfate, 5-pregnen-3b,17-diol-20-one 3-sulfate, 5α-pregnane-3β,20α-diol monosulfate, 5α-pregnane-3β,20α-diol disulfate, pregnanediol-3-glucuronide, cortisol, corticosterone, 11-dehydrocorticosterone, cortisone, dehydroisoandrosterone sulfate (DHEA-S), and 16α-hydroxyDHEA.

105. The method of claim 104, wherein an abnormal level of one or more small molecules selected from the group consisting of testosterone 3-sulfate, epiandrosterone sulfate, androsterone sulfate, 5α-androstane-3α,17α-diol monosulfate, 5α-androstane-3α,17α-diol disulfate, etiocholanolong glucuronide, 11-ketoetiocholanolong glucuronide, 4-androstene-3β,17β-diol monosulfate, 4-androstene-3β,17β-diol disulfate, testosterone sulfate, 5α-androstane-3α,17β-diol monosulfate, 5α-androstane-3α,17β-diol disulfate, 5α-androstane-3β,17β-diol monosulfate, 5α-androstane-3β,17β-diol disulfate, estrone 3-sulfate, thyroxine, and combinations thereof indicates a risk of hormone imbalance.

115. 105. The method of claim 104, wherein the plurality of diseases or disorders comprises oxidative stress, and wherein abnormal levels of one or more small molecules selected from the group consisting of 13-HODE+9-HODE, 5-oxoproline, allantoin, alpha-tocopherol, anserine, bilirubin (Z,Z), biliverdin, cys-gly, oxidized, cysteine-glutathione disulfide, cysteine, gamma-tocopherol, hypoxanthine, methionine sulfone, methionine sulfoxide, threonate, urate, and combinations thereof, indicate risk of oxidative stress.

116. Multiple diseases or disorders involve mitochondrial dysfunction, and the following compounds are listed: homocitrulline, leucine, 4-methyl-2-oxopentanoate, isovalerate, isovalerylglycine, isovalerylcarnitine, β-hydroxyisovalerate, β-hydroxyisovaleroylcarnitine, α-hydroxyisovaleroylcarnitine, 3-methylglutaconate, α-hydroxyisovalerate, methylsuccinate, isoleucine, 3-methyl-2-oxovalerate, 2-methylbutyrylcarnitine (C5), tiglylcarnitine, tigloylglycine, 2-hydroxy-3-methylvalerate, 3-hydroxy-2-ethylpropionate, ethylmalonate, valine, 3-methyl-2-oxobutyrate , isobutyrylcarnitine, isobutyrylglycine, 3-hydroxyisobutyrate, α-hydroxyisocaproate, azelate (nonanedioate), sebacate (decanedioate), dodecanedioate, tetradecanedioate, hexadecanedioate, octadecanedioate, formylmethionine, alanine, lactate, citrate, α-ketoglutarate, succinylcarnitine, succinate, fumarate, malate, palmitoylcarnitine, stearoylcarnitine, oleoylcarnitine, BHBA, and combinations thereof, wherein an abnormal level of one or more small molecules selected from the group consisting of: methionine, isobutyrylcarnitine, isobutyrylglycine, 3-hydroxyisobutyrate, α-hydroxyisocaproate, azelate (nonanedioate), sebacate (decanedioate), dodecanedioate, tetradecanedioate, hexadecanedioate, octadecanedioate, formylmethionine, alanine, lactate, citrate, α-ketoglutarate, succinylcarnitine, succinate, fumarate, malate, palmitoylcarnitine, stearoylcarnitine, oleoylcarnitine, BHBA, and combinations thereof indicates a risk of mitochondrial dysfunction.

117. 105. The method of claim 104, wherein the plurality of diseases or disorders comprises a stress response and abnormal levels of one or more small molecules selected from the group consisting of cortisol, cortisone, glucose, thyroxine, and combinations thereof indicate increased stress.

118. 105. The method of claim 104, wherein the plurality of diseases or disorders comprises metabolic aging, and wherein abnormal levels of one or more small molecules selected from the group consisting of carboxy-4-methyl-5-propyl-2-furanoproanoate, C-glycosyltryptophan, citrate, citrulline, creatinine, dehydroisoandrosterone sulfate (DHEA-S), eicosapentaenoate (EPA, 20:5n3), erythritol, glucose, glutamate, glycine, leucine, myoinositol, palmitoleoylsphingomyelin, p-cresol sulfate, phenylacetylglutamine, pseudouridine, and combinations thereof, indicate increased risk of metabolic aging.

119. Multiple diseases or disorders involve microbiome imbalances and include 3-(3-hydroxyphenyl)propionate, 3-(4-hydroxyphenyl)lactate, 3-(4-hydroxyphenyl)propionate, 3-(3-(sulfooxy)phenyl)propanoic acid, 3-hydroxyhippurate, 3-indoxyl sulfate, 3-phenylpropionate (hydrocinnamate), 4-ethylphenyl sulfate, 4-hydroxyhippurate, 4-hydroxyphenylpyruvate, 4-vinylphenol sulfate, 5-hydroxyindole acetate, chenodeoxycholate, cholate, deoxycholate, glycocholate sulfate, glucodeoxycholate, glycocholate, glycolitocholate, glycolitocholate sulfa.

105. The method of claim 104, wherein an abnormal level of one or more small molecules selected from the group consisting of taurocholate, glycoursodeoxycholate, hippurate, hyodeoxycholate, indole acetate, indoleacetylglutamine, indole butyrate, indole lactate, indole propylonate, p-creosol sulfate, o-creosol sulfate, p-cresol-glucuronide, phenol sulfate, phenylacetate, phenylacetylglutamine, phenyllactate (PLA), taurocholate sulfate, taurodeoxycholate, taurolithocholate 3-sulfate, tauroursodeoxycholate, ursodeoxycholate, and combinations thereof indicates a risk of microbiome imbalance.

120. 105. The method of claim 104, wherein the plurality of diseases or disorders comprises macronutrient imbalance, and wherein abnormal levels of one or more small molecules selected from the group consisting of histidine, isoleucine, leucine, valine, lysine, methionine, phenylalanine, tryptophan, threonine, alanine, arginine, aspartate, cysteine, glutamate, glutamine, glycine, proline, serine, tyrosine, asparagine, choline, linolenate [alpha or gamma; (18:3n3 or 6)], linoleate (18:2n6), and combinations thereof, indicate a risk of macronutrient imbalance.

121. 105. The method of claim 104, wherein the plurality of diseases or disorders comprises air pollution exposure, and wherein abnormal levels of one or more small molecules selected from the group consisting of alpha-tocopherol, asparagine, benzoate, glycerate, glycine, N-acetylglycine, serine, threonate, and combinations thereof indicate exposure to air pollution.

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