Method for determining whether a subject is at risk for developing a mental and / or behavioral disorder

Abstract

A method for determining whether a subject is at risk of developing a mental and / or behavioral disorder is disclosed. The method may include determining a quantitative value of at least one of 24 blood biomarkers, such as albumin, glycoprotein acetyl, docosahexaenoic acid to total fatty acid ratio, fatty acid unsaturation, docosahexaenoic acid, linoleic acid, monounsaturated fatty acid and / or oleic acid, omega-3 fatty acid, omega-6 fatty acid, in a biological sample obtained from the subject, and comparing the quantitative value of the at least one biomarker with a control sample or control value, where an increase or decrease in the quantitative value of the at least one biomarker compared to the control sample or control value indicates that the subject is at high risk of developing the mental and / or behavioral disorder. The at least one biomarker includes or is glycoprotein acetyl.

Description

[Technical Field]

[0001] The present disclosure relates generally to methods for determining whether a subject is at risk for developing a mental and / or behavioral disorder. [Background technology]

[0002] Mental and behavioral disorders are patterns of behavioral and / or psychological symptoms that affect various areas of life. These disorders cause great distress to patients experiencing symptoms and their families. Common mental disorders include anxiety, depression, bipolar disorder, and schizophrenia. Fortunately, effective strategies exist for preventing and treating many mental disorders. Early identification of individuals at high risk for developing such disorders is important to provide early access to medical and social services and prevent the onset of more serious conditions.

[0003] Various blood biomarkers may be useful in predicting whether an individual is at increased risk for developing various mental and / or behavioral disorders, such as psychiatric disorders due to known physiological conditions, mood disorders, anxiety, dissociative, stress-related, somatoform, and other non-psychotic disorders, delirium, major depressive disorder, anxiety disorders, and other symptoms and signs related to cognitive function and awareness. Biomarkers that predict the development of these disorders would help enable more effective screening and better targeted early treatment and prevention. Such biomarkers may be measured from a biological sample, for example, a blood sample or related biological fluid. Summary of the Invention

[0004] A method for determining whether a subject is at risk for developing a mental and / or behavioral disorder is disclosed, the method comprising detecting in a biological sample obtained from the subject: ·albumin, Glycoprotein acetyl Ratio of docosahexaenoic acid to total fatty acids, the ratio of linoleic acid to total fatty acids, the ratio of monounsaturated fatty acids and / or oleic acid to total fatty acids, the ratio of omega-3 fatty acids to total fatty acids, the ratio of omega-6 fatty acids to total fatty acids; -Ratio of saturated fatty acids to total fatty acids, ·Fatty acid unsaturation, Docosahexaenoic acid, Linoleic acid, monounsaturated fatty acids and / or oleic acid, ·ω-3 fatty acids, ·ω-6 fatty acids, ·Saturated fatty acids, Triglycerides in high-density lipoproteins (HDL), · Triglycerides in low-density lipoproteins (LDL), High-density lipoprotein (HDL) particle size, Low-density lipoprotein (LDL) particle size, Very low density lipoprotein (VLDL) particle size, Acetate, citrate, ·glutamine, Histidine determining a quantitative value of at least one biomarker among comparing said quantitative value of said at least one biomarker with a control sample or control value; may include An increase or decrease in the quantitative value of the at least one biomarker when compared to the control sample or control value indicates that the subject is at increased risk of developing the mental and / or behavioral disorder. [Brief explanation of the drawings]

[0005] The accompanying drawings, which are included to provide a further understanding of the embodiments and constitute a part of this specification, illustrate various embodiments.

[0006] [Figure 1a]The relationship between baseline concentrations of 24 blood biomarkers and future development of any mental and / or behavioral disorder (defined as the combined endpoint of any ICD-10 diagnosis within F00-F99, T36-T50, X60-X84, referred to herein as "any mental and / or behavioral disorder") is shown when biomarker concentrations are analyzed by absolute concentration and quintiles of biomarker concentration. Results are based on plasma samples from approximately 115,000 generally healthy individuals from the UK Biobank. [Figure 1b] Cumulative risk of "any mental and / or behavioral disorder" during follow-up for the lowest, middle, and highest quantiles of 24 blood biomarker concentrations is shown. [Figure 2a] The relationship between baseline concentrations of 24 blood biomarkers and future onset of six different categories of mental and / or behavioral disorders (defined by ICD-10 subchapters) is shown in the form of a heat map. The results demonstrate that all six different subgroups of mental and / or behavioral disorders have very similar associations with the 24 biomarkers measured by nuclear magnetic resonance (NMR) spectroscopy of plasma samples from generally healthy humans. [Figure 2b] The consistency of biomarker associations with six different categories of mental and / or behavioral disorders defined by ICD-10 subchapters is shown compared with the direction of the corresponding biomarker association with "any mental and / or behavioral disorder." [Figure 3a] The relationship between baseline biomarker levels and future onset of 14 specific mental and / or behavioral disorders (defined by ICD-10 three-letter diagnoses) is shown in the form of a heat map. The results demonstrate that the specific mental and / or behavioral disorders defined by three-letter ICD-10 codes all have very similar associations with a broad biomarker panel measured by NMR spectroscopy in plasma samples from generally healthy humans. [Figure 3b]The consistency of biomarker associations with specific mental and / or behavioral disorders (defined by three-letter ICD-10 diagnoses) is shown compared with the direction of association with "any mental and / or behavioral disorder." [Figure 4a] The relationship between baseline biomarker levels and future development of six different mental and / or behavioral disorder categories (defined by ICD-10 subchapters) is shown in the form of a forest plot of hazard ratios for disease development. [Figure 4b] The relationship between baseline biomarker levels and future development of six different mental and / or behavioral disorder categories (defined by ICD-10 subchapters) is shown in the form of a forest plot of hazard ratios for disease development. [Figure 4c] The relationship between baseline biomarker levels and future development of six different mental and / or behavioral disorder categories (defined by ICD-10 subchapters) is shown in the form of a forest plot of hazard ratios for disease development. [Figure 5a] The relationship between baseline biomarker levels and future development of 14 different mental and / or behavioral disorders (defined by ICD-10 3-digit diagnoses) is shown in the form of a forest plot of hazard ratios for disease development. [Figure 5b] The relationship between baseline biomarker levels and future development of 14 different mental and / or behavioral disorders (defined by ICD-10 3-digit diagnoses) is shown in the form of a forest plot of hazard ratios for disease development. [Figure 5c] The relationship between baseline biomarker levels and future development of 14 different mental and / or behavioral disorders (defined by ICD-10 3-digit diagnoses) is shown in the form of a forest plot of hazard ratios for disease development. [Figure 5d] The relationship between baseline biomarker levels and future development of 14 different mental and / or behavioral disorders (defined by ICD-10 3-digit diagnoses) is shown in the form of a forest plot of hazard ratios for disease development. [Figure 5e] The relationship between baseline biomarker levels and future development of 14 different mental and / or behavioral disorders (defined by ICD-10 3-digit diagnoses) is shown in the form of a forest plot of hazard ratios for disease development. [Figure 5f] The relationship between baseline biomarker levels and future development of 14 different mental and / or behavioral disorders (defined by ICD-10 3-digit diagnoses) is shown in the form of a forest plot of hazard ratios for disease development. [Figure 5g] The relationship between baseline biomarker levels and future development of 14 different mental and / or behavioral disorders (defined by ICD-10 3-digit diagnoses) is shown in the form of a forest plot of hazard ratios for disease development. [Figure 6] An example of the relationship between a multi-biomarker score and the risk of "mental and / or behavioral disorders" is shown. Selected examples of multi-biomarker scores are shown to illustrate the improved prediction achieved by multi-biomarker scores compared to individual biomarkers. [Figure 7a] We first demonstrate the intended use case of a multi-biomarker score to predict risk of developing a psychiatric disorder due to a known physiological condition among healthy humans. [Figure 7b] We show that predicting risk of developing a psychiatric disorder according to known physiological conditions works effectively for different populations and people with different risk factor profiles. [Figure 8a] We first present the intended use case of a multi-biomarker score to predict risk of developing mood affective disorders among healthy humans. [Figure 8b] We show that prediction of risk for developing mood disorders works effectively for different populations and people with different risk factor profiles. [Figure 9a] We first present the intended use case of the multi-biomarker score to predict risk of developing anxiety, dissociative, stress-related, somatoform, and other non-psychiatric disorders among healthy humans. [Figure 9b] We show that prediction of risk for developing anxiety, dissociative, stress-related, somatoform, and other non-psychotic disorders works effectively for different populations and people with risk factor profiles. [Figure 10a] We first present the intended use case of the multi-biomarker score to predict risk of developing delirium due to known physiological conditions among healthy humans. [Figure 10b] We show that predicting risk of developing delirium according to known physiological conditions works effectively for people with different populations and risk factor profiles. [Figure 11a] We first demonstrate the intended use case of a multi-biomarker score to predict the risk of developing major depressive disorder (single episode) among healthy humans. [Figure 11b] We show that prediction of risk of developing major depressive disorder (single episode) works effectively for different populations and people with risk factor profiles. [Figure 12a] We first demonstrate the intended use case of a multi-biomarker score to predict risk of developing an anxiety disorder among healthy humans. [Figure 12b] We show that prediction of risk for developing anxiety disorders works effectively for different populations and people with different risk factor profiles. [Figure 13a] We first present the intended use case of a multi-biomarker score to predict risk of developing symptoms and signs related to cognitive function and consciousness among healthy humans. [Figure 13b] We show that prediction of risk for developing symptoms and signs related to cognitive function and awareness works effectively for different populations and people with different risk factor profiles. DETAILED DESCRIPTION OF THE INVENTION

[0007] Methods for determining whether a subject is at risk for developing a mental and / or behavioral disorder are disclosed.

[0008] The method comprises detecting in a biological sample obtained from the subject: ·albumin, Glycoprotein acetyl Ratio of docosahexaenoic acid to total fatty acids, the ratio of linoleic acid to total fatty acids, the ratio of monounsaturated fatty acids and / or oleic acid to total fatty acids, the ratio of omega-3 fatty acids to total fatty acids, the ratio of omega-6 fatty acids to total fatty acids; -Ratio of saturated fatty acids to total fatty acids, ·Fatty acid unsaturation, Docosahexaenoic acid, Linoleic acid, monounsaturated fatty acids and / or oleic acid, ·ω-3 fatty acids, ·ω-6 fatty acids, ·Saturated fatty acids, Triglycerides in high-density lipoproteins (HDL), · Triglycerides in low-density lipoproteins (LDL), High-density lipoprotein (HDL) particle size, Low-density lipoprotein (LDL) particle size, Very low density lipoprotein (VLDL) particle size, Acetate, citrate, ·glutamine, Histidine determining a quantitative value of at least one biomarker among comparing said quantitative value of said at least one biomarker with a control sample or control value; may include An increase or decrease in the quantitative value of the at least one biomarker when compared to the control sample or control value indicates that the subject is at increased risk of developing the mental and / or behavioral disorder.

[0009] Various blood biomarkers may be useful in predicting whether an individual is at increased risk for developing a wide range of mental and / or behavioral disorders. Such biomarkers may be measured from a biological sample, for example, a blood sample or related biological fluid.

[0010] Biomarkers that predict mental and / or behavioral disorders may help to enable more effective screening and more targeted preventative treatment.

[0011] In one embodiment, the method includes determining a quantitative value of albumin.

[0012] In one embodiment, the method comprises determining a quantitative value of glycoprotein acetyl.

[0013] In one embodiment, the method comprises determining a quantitative value for the ratio of docosahexaenoic acid to total fatty acids.

[0014] In one embodiment, the method comprises determining a quantitative value for the ratio of linoleic acid to total fatty acids.

[0015] In one embodiment, the method comprises determining a quantitative value for the ratio of monounsaturated fatty acids and / or oleic acid to total fatty acids.

[0016] In one embodiment, the method comprises determining a quantitative value for the ratio of omega-3 fatty acids to total fatty acids.

[0017] In one embodiment, the method comprises determining a quantitative value for the ratio of omega-6 fatty acids to total fatty acids.

[0018] In one embodiment, the method comprises determining a quantitative value for the ratio of saturated fatty acids to total fatty acids.

[0019] In one embodiment, the method includes determining a quantitative value of fatty acid unsaturation.

[0020] In one embodiment, the method includes determining a quantitative value of docosahexaenoic acid.

[0021] In one embodiment, the method includes determining a quantitative value of linoleic acid.

[0022] In one embodiment, the method comprises determining a quantitative value of monounsaturated fatty acids and / or oleic acid.

[0023] In one embodiment, the method includes determining a quantitative value of omega-3 fatty acids.

[0024] In one embodiment, the method includes determining a quantitative value of omega-6 fatty acids.

[0025] In one embodiment, the method includes determining a quantitative value of saturated fatty acids.

[0026] In one embodiment, the method comprises determining a quantitative value of triglycerides in high density lipoprotein (HDL).

[0027] In one embodiment, the method comprises determining a quantitative value of triglycerides in low density lipoprotein (LDL).

[0028] In one embodiment, the method includes determining a quantitative value of high density lipoprotein (HDL) particle size.

[0029] In one embodiment, the method includes determining a quantitative value of low density lipoprotein (LDL) particle size.

[0030] In one embodiment, the method includes determining a quantitative value of very low density lipoprotein (VLDL) particle size.

[0031] In one embodiment, the method includes determining a quantitative value of acetate.

[0032] In one embodiment, the method includes determining a quantitative value of citrate.

[0033] In one embodiment, the method includes determining a quantitative value of glutamine.

[0034] In one embodiment, the method includes determining a quantitative value of histidine.

[0035] The metabolic biomarkers described herein have been found to be significantly different, i.e., their quantitative values ​​(such as amounts and / or concentrations) have been found to be significantly higher or lower for subjects who subsequently develop a mental and / or behavioral disorder. The biomarkers may be detected and quantified from blood, serum or plasma, dried blood spots, or other suitable biological samples, and may be used alone or in combination with other biomarkers to determine the risk of developing a mental and / or behavioral disorder.

[0036] Furthermore, biomarkers can significantly improve the likelihood of identifying subjects at risk for mental and / or behavioral disorders, even when combined with and / or taking into account established risk factors that can currently be used for screening and risk prediction, such as age, sex, smoking status, alcohol and / or recreational drug use, body mass index (BMI), ongoing medical conditions, traumatic experiences, living situations and conflict, social isolation, socioeconomic factors, genetic risk, and / or personal and / or family history of mental and / or behavioral disorders and / or other comorbidities. The biomarkers described herein, alone or as risk scores (such as multi-biomarker scores), hazard ratios, odds ratios, and / or predicted absolute or relative risks, or in combination with other risk factors and tests, may improve prediction or even replace the need for other tests or measurements. This may include improving predictive accuracy by complementing predictive information from other risk factors or by replacing the need for other analyses, such as physical examinations, psychological evaluations, and / or thyroid function checks, and / or clinical tests such as alcohol and / or drug use. Thus, biomarkers or risk scores, hazard ratios, odds ratios, and / or predicted absolute or relative risks according to one or more embodiments described herein may enable efficient assessment of risk for future development of mental and / or behavioral disorders, even in situations where other risk factor measures are less feasible.

[0037] In one embodiment, the method is a method for determining whether a subject is at risk for developing a mental and / or behavioral disorder.

[0038] The methods may include determining quantitative values ​​of a plurality of biomarkers in a biological sample, e.g., two, three, four, five, or more biomarkers. For example, the plurality of biomarkers may include two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, or twenty-four of the biomarkers (i.e., at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, or all of the biomarkers). Thus, the term "plurality of biomarkers" may be understood herein to mean any number (more than one) of biomarkers. Thus, the term "multiple biomarkers" may be understood to mean any number (more than one) and / or combination or subset of the biomarkers described herein. Determining multiple biomarkers may increase the accuracy of the prediction of whether a subject is at risk for developing a mental and / or behavioral disorder. Generally, the greater the number of biomarkers, the more accurate or predictive the method may be. However, even a single biomarker described herein may enable or aid in determining whether a subject is at risk for developing a mental and / or behavioral disorder. Multiple biomarkers may be measured from the same biological sample or separate biological samples, using the same or different analytical methods. In one embodiment, the multiple biomarkers may be a panel of multiple biomarkers.

[0039] In the context of this specification, the phrase "comparing the quantitative values ​​of biomarkers to a control sample or control value" will be understood by those skilled in the art to mean comparing one or more quantitative values ​​of one or more biomarkers to the control sample or control value, either individually or as multiple biomarkers (e.g., when calculating a risk score from quantitative values ​​of multiple biomarkers), e.g., depending on whether a quantitative value of a single (individual) biomarker or multiple biomarkers is determined.

[0040] In one embodiment, the method comprises detecting in a biological sample obtained from a subject: ·albumin, Glycoprotein acetyl Ratio of docosahexaenoic acid to total fatty acids, the ratio of linoleic acid to total fatty acids, the ratio of monounsaturated fatty acids and / or oleic acid to total fatty acids, the ratio of omega-3 fatty acids to total fatty acids, the ratio of omega-6 fatty acids to total fatty acids; -Ratio of saturated fatty acids to total fatty acids, ·Fatty acid unsaturation, Docosahexaenoic acid, Linoleic acid, monounsaturated fatty acids and / or oleic acid, ·ω-3 fatty acids, ·ω-6 fatty acids, ·Saturated fatty acids, Triglycerides in high-density lipoproteins (HDL), · Triglycerides in low-density lipoproteins (LDL), High-density lipoprotein (HDL) particle size, Low-density lipoprotein (LDL) particle size, Very low density lipoprotein (VLDL) particle size, Acetate, citrate, ·glutamine, Histidine determining a quantitative value of one or more biomarkers among comparing said quantitative values ​​of said biomarkers to a control sample or control value; may include An increase or decrease in the quantitative value of the biomarker when compared to the control sample or control value indicates that the subject is at increased risk for developing the mental and / or behavioral disorder.

[0041] In one embodiment, the at least one biomarker comprises or is glycoprotein acetyl. The method may further comprise determining a quantitative value of at least one of the other biomarkers described herein.

[0042] The subject may be a human. The human may be healthy or may have an existing disease, e.g., an existing mental and / or behavioral disorder. Specifically, the human may have an existing form of a mental and / or behavioral disorder, and the risk of developing a more severe form of this disorder and / or another mental and / or behavioral disorder, or other mental and / or behavioral disorder, may be determined and / or calculated. The subject may additionally or alternatively be an animal, such as a mammal, e.g., a non-human primate, dog, cat, horse, or rodent.

[0043] In the context of this specification, the term "biomarker" may refer to a biomarker, e.g., a chemical or molecular marker, that may be found to be associated with a disease or condition or the risk of suffering from or developing the same. Biomarker does not necessarily mean a biomarker that has been thoroughly statistically validated as having particular validity in a clinical setting. A biomarker may be a metabolite, compound, lipid, protein, moiety, functional group, composition, combination of two or more metabolites and / or compounds, their (measurable or measured) amounts, their ratios, or other values, or in principle, any measurement that reflects a chemical and / or biological component that may be found to be associated with a disease or condition or the risk of having or developing it. Biomarkers and any combinations thereof, optionally in combination with further analyses and / or measurements, may be used to measure biological processes that indicate the risk of developing mental and / or behavioral disorders, such as mood affective disorders, anxiety, dissociative, stress-related, somatoform, and other non-psychotic disorders, delirium, major depressive disorder, anxiety disorders, and other symptoms and signs related to cognitive function and consciousness.

[0044] Disorder may refer to a category of mental and / or behavioral disorders or a specific disorder within this category. In the context of this specification, the term "mental and / or behavioral disorder" may be understood to mean a disease, disorder, and / or condition with behavioral and / or psychological symptoms. The disorder may be an acute, occasional, or chronic condition, which in the context of this specification may be understood as a disease whose effects are persistent or otherwise long-lasting and / or develop over time. Signs and symptoms of mental and / or behavioral disorders may vary from mild to severe or disabling, depending on factors such as the age and / or overall health of the subject.

[0045] The associations of biomarkers may be similar for different mental and / or behavioral disorders. Thus, the same individual biomarkers and combinations of biomarkers may be extended to predict risk for specific mental and / or behavioral disorders. Examples of such specific mental and / or behavioral disorders may include mood disorders, anxiety, dissociative, stress-related, somatoform, and other non-psychotic disorders, delirium, major depressive disorder, anxiety disorders, and other symptoms and signs related to cognitive function and consciousness.

[0046] The mental and / or behavioral disorders described herein may be classified as follows: "ICD-10" may be understood to mean the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) (2019 edition by WHO). Similar disorders classified or diagnosed by disease classification systems other than ICD-10, such as ICD-9 or ICD-11, may also apply.

[0047] The term "any mental and / or behavioral disorder" may be understood to mean any mental and / or behavioral disease, disorder, or condition. Any mental and / or behavioral disorder (or "mental and / or behavioral disorder") may be understood to mean any incident occurrence of ICD-10 diagnoses F00-F99, T36-T50, and / or X60-X84.

[0048] The subgroup of mental and / or behavioral disorders may be understood to mean diseases and / or conditions classified within the ICD-10 subchapter diagnoses of mental and / or behavioral disorders (F01-F09, F30-F29, F30-F39, F40-F48, T36-T50, X60-X84).

[0049] Specific mental and / or behavioral disorders may be understood to mean diseases and / or disorders classified within the ICD-10 three-digit diagnoses of mental and / or behavioral disorders (F05, F06, F20, F31, F32, F33, F40, F41, F43, R41, T39, T40, T42, T43).

[0050] In one embodiment, the mental and / or behavioral disorder is a disease classified within a subgroup of mental and / or behavioral disorders as defined by one or more of the ICD-10 subchapters described herein.

[0051] In one embodiment, the mental and / or behavioral disorder is a specific disease, eg, a specific disease or disorder, as defined by an ICD-10 three-digit code diagnosis.

[0052] In one embodiment, the mental and / or behavioral disorder is a disease classified in one or more of the following subgroups of mental and / or behavioral disorders: F01-F09: Mental disorders due to known physiological conditions F20-F29: Schizophrenia, schizophrenic, delusional, and other non-mood psychotic disorders F30-F39: Mood [emotional] disorders F40-F48: Anxiety, dissociative, stress-related, somatoform, and other non-psychotic disorders T36-T50: Poisoning, adverse effects, and underdosing due to drugs, medicines, and biological substances X60-X84: Intentional self-harm

[0053] In one embodiment, the mental and / or behavioral disorder is one of the following ICD10 three digit diagnoses or is selected from the following ICD10 three digit diagnoses disorders: F05: Delirium due to a known physiological condition F06: Other mental disorders caused by known physiological conditions F20: Schizophrenia F31: Bipolar disorder F32: Major depressive disorder, single episode F33: Major depressive disorder, recurrent F40: Phobic anxiety disorder F41: Other anxiety disorders F43: Response to severe stress and adjustment disorders R41: Other symptoms and signs related to cognitive function and consciousness T39: Poisoning, adverse effects, and underdosing with non-opioid analgesics, antipyretics, and antirheumatic drugs T40: Narcotic and psychotropic drug (hallucinogenic) intoxication, adverse effects, and underdosing T42: Toxicity, adverse effects, and underdosage of antiepileptic, sedative-hypnotic, and antiparkinsonian drugs T43: Psychotropic drug intoxication, adverse effects, and underdosage, not elsewhere classified

[0054] In one embodiment, the mental and / or behavioral disorder may include, or may result in death from, a mental and / or behavioral disorder such as those represented by the ICD-10 codes listed above, including addiction and intentional self-harm.

[0055] In one embodiment, the mental and / or behavioral disorders may include or be mental disorders due to known physiological conditions (F01-F09), schizophrenia, schizophrenic, delusional, and other non-mood psychotic disorders (F20-F29), mood [affective] disorders (F30-F39), anxiety, dissociative, stress-related, somatoform, and other non-psychotic disorders (F40-F48), drug, medicinal, and biological intoxication, adverse effects, and underdosage (T36-T50), and / or intentional acts of self-harm (X60-X84).

[0056] In one embodiment, the mental and / or behavioral disorder is selected from the group consisting of delirium due to a known physiological condition (F05), other mental disorders due to a known physiological condition (F06), schizophrenia (F20), bipolar disorder (F31), major depressive disorder, single episode (F32), major depressive disorder, recurrent (F33), phobic anxiety disorder (F40), other anxiety disorders (F41), response to severe stress and adjustment disorder (F43), other symptoms and signs related to cognitive function and consciousness (F44), and other symptoms and signs related to cognitive function and consciousness (F45). Poisoning, adverse effects, and underdosage by antiepileptic, sedative / hypnotic, and antiparkinsonian drugs (T42); and / or poisoning, adverse effects, and underdosage by psychotropic drugs, not elsewhere classified (T43).

[0057] In one embodiment, the mental and / or behavioral disorders may include or be mood disorders, anxiety, dissociative, stress-related, somatoform, and other non-psychotic disorders, delirium, major depressive disorder, anxiety disorders, and / or other symptoms and signs involving cognitive function and / or consciousness.

[0058] The method may further comprise determining whether the subject is at risk of developing a mental and / or behavioral disorder using a calculated risk score, hazard ratio, and / or predicted absolute or relative risk based on the quantitative values ​​of at least one biomarker or multiple biomarkers.

[0059] An increase or decrease in the risk score, hazard ratio, and / or predicted absolute and / or relative risk may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder.

[0060] A risk score and / or hazard ratio, and / or predicted absolute or relative risk may be calculated based on any multiple, combination, or subset of the biomarkers described herein.

[0061] Risk scores and / or hazard ratios, and / or predicted absolute or relative risks may be calculated, for example, as shown in the following examples. For example, using an appropriate method, multiple biomarkers measured using, for example, NMR spectroscopy, may be combined using a regression algorithm and multivariate analysis, and / or machine learning analysis. Prior to regression analysis or machine learning, any missing values ​​in the biomarkers may be imputed with the average value of each biomarker for the data set. Several biomarkers, for example, five, that may be most associated with the development of a disease or condition may be selected for use in the predictive model. Other modeling approaches may be used to calculate risk scores and / or hazard ratios, and / or predicted absolute or relative risks based on combinations or subsets of individual biomarkers, i.e., multiple biomarkers.

[0062] The risk score may be calculated, for example, as a weighted sum of individual biomarkers, i.e., multiple biomarkers. The weighted sum may be, for example,

number

[0063] For example, the risk score can be defined as β1* concentration (glycoprotein acetyl) + β2* concentration (ratio of monounsaturated fatty acids to total fatty acids) + β3* concentration (albumin) + β0, where β1, β2, β3 are multipliers of each biomarker by the magnitude of association with the risk of mental and / or behavioral disorders, and β0 is the intercept term. As one skilled in the art will appreciate, the biomarkers mentioned in this example may be replaced by any other biomarker described herein. In general, the more biomarkers included in the risk score, the stronger the predictive performance can be. When additional biomarkers are included in the risk score, β i The weights may be varied for all biomarkers according to the optimal combination for the prediction of mental and / or behavioral disorders.

[0064] The risk score, hazard ratio, odds ratio, and / or predicted relative risk and / or absolute risk may be calculated based on at least one additional measure, e.g., a subject's characteristic. Such characteristics may be determined before, simultaneously with, or after obtaining a biological sample from the subject. As will be understood by those skilled in the art, some of the characteristics may be information collected, for example, using a questionnaire, or previously collected clinical data. Some of the characteristics may be (or may have been) determined by biochemical or clinical diagnostic measurements and / or medical diagnoses. Such characteristics may include, for example, one or more of age, height, weight, body mass index, race or ethnicity, smoking, and / or family history of mental and / or behavioral disorders.

[0065] The method may further include treating a subject at risk of developing a mental and / or behavioral disorder by administering the treatment to the subject to prevent or treat the disease in the subject. Predicting the risk of a mental and / or behavioral disorder based on one or more of the biomarkers can be used to guide preventative efforts, such as psychotherapy, alcohol and smoking awareness, a healthy diet, adequate sleep, physical activity, and / or determining the frequency of clinical screenings and / or pharmacological treatments. For example, information about future risk of a mental and / or behavioral disorder can be used to guide psychological care, psychosocial interventions, psychiatric treatments, or treatments with, for example, cholinesterase inhibitors, antidepressants, psychosomatic medicine, and / or mood stabilizers and stimulants.

[0066] In the context of this specification, the term "albumin" may be understood to mean serum albumin (often referred to as blood albumin). This is the albumin found in the blood of vertebrates. Albumin is a globular, water-soluble, non-glycosylated serum protein with a molecular weight of approximately 65,000 daltons. Measurement of albumin using NMR is described, for example, in the publications Kettunen et al., 2012, Nature Genetics 44, 269-276 and Soininen et al., 2015, Circulation: Cardiovascular Genetics 8, 212-206 (DOI: 10.1161 / CIRCGENETICS.114.000216). Albumin may also be measured by various other methods, for example, by clinical chemistry analyzers. Examples of such methods may include dye-binding methods such as bromocresol green and bromocresol purple.

[0067] In the context of this specification, the terms "glycoprotein acetyl," "glycoprotein acetylation," or "GlycA" may refer to the abundance of circulating glycosylated proteins and / or nuclear magnetic resonance spectroscopy (NMR) signals representing the abundance of circulating glycosylated proteins, i.e., N-acetylated glycoproteins. Glycoprotein acetyl may include signals from multiple different glycoproteins, including, for example, alpha-1-acid glycoprotein, alpha-1-antitrypsin, haptoglobin, transferrin, and / or alpha-1-antichymotrypsin. In the scientific literature on cardiac metabolic biomarkers, the terms "glycoprotein acetyl" or "GlycA" typically refer to the NMR signals of circulating glycated proteins (e.g., Ritchie et al., Cell Systems 2015 1(4):293-301 and Connelly et al., J Transl Med. 2017;15(1):219). Glycoprotein acetyltransferases and methods for measuring them are described, for example, in Kettunen et al., 2018, Circ Genom Precis Med. 11:e002234 and Soininen et al., 2009, Analyst 134, 1781-1785. Using glycoprotein acetyltransferase NMR signals for risk prediction may offer advantages over measuring the individual proteins that contribute to the NMR signal. For example, improved analytical accuracy and stability over time may be achieved, assay costs may be reduced, and the NMR signal may be measured simultaneously with many other biomarkers.

[0068] In the present context, the term "omega-3 fatty acids" may refer to total omega-3 fatty acids, i.e., the amount and / or concentration of total omega-3 fatty acids, i.e., the sum of different omega-3 fatty acids. Omega-3 fatty acids are polyunsaturated fatty acids. In omega-3 fatty acids, the last double bond of the fatty acid chain is the third bond counting from the methyl end. Docosahexaenoic acid is an example of an omega-3 fatty acid.

[0069] In the present context, the term "omega-6 fatty acids" may refer to total omega-6 fatty acids, i.e., the amount and / or concentration of total omega-6 fatty acids, i.e., the sum of the amounts and / or concentrations of different omega-6 fatty acids. Omega-6 fatty acids are polyunsaturated fatty acids. In omega-6 fatty acids, the last double bond of the fatty acid chain is the sixth bond counting from the methyl end.

[0070] In one embodiment, the omega-6 fatty acid may be linoleic acid. Linoleic acid (18:2 omega-6) is the most abundant type of omega-6 fatty acid and may therefore be considered a good approximation of total omega-6 fatty acids for predicting risk of mental and / or behavioral disorders.

[0071] In the context of this specification, the term "monounsaturated fatty acids" (MUFA) may refer to total monounsaturated fatty acids, i.e., the amount and / or concentration of total MUFA. Alternatively, monounsaturated fatty acids may refer to oleic acid, which is the most abundant monounsaturated fatty acid in human serum. Monounsaturated fatty acids have one double bond in their fatty acid chain. Monounsaturated fatty acids may include omega-9 and omega-7 fatty acids. Oleic acid (18:1 omega-9), palmitoleic acid (16:1 omega-7), and cis-vaccenic acid (18:1 omega-7) are examples of common monounsaturated fatty acids in human serum.

[0072] In one embodiment, the monounsaturated fatty acid may be oleic acid, which is the most abundant monounsaturated fatty acid and may therefore be considered a good approximation of total monounsaturated fatty acids for predicting risk of mental and / or behavioral disorders.

[0073] In the present context, the term "saturated fatty acids" (SFA) may refer to all saturated fatty acids. Saturated fatty acids may be or may include fatty acids that do not have double bonds in their structure. Palmitic acid (16:0) and stearic acid (18:0) are examples of abundant SFAs in human serum.

[0074] For all fatty acid measures, including omega-6, docosahexaenoic, linoleic, monounsaturated, and / or saturated fatty acids, the fatty acid measure may include blood (or serum / plasma) free fatty acids, bound fatty acids, and esterified fatty acids. Esterified fatty acids may be esterified to glycerol, for example, as triglycerides, diglycerides, monoglycerides, or phosphoglycerides, or to cholesterol, for example, as cholesterol esters.

[0075] In the context of this specification, the term "fatty acid unsaturation" or "unsaturation" may be understood to mean the number of double bonds in all fatty acids, e.g., the average number of double bonds in all fatty acids.

[0076] In the present context, the term "HDL" means high density lipoprotein.

[0077] In the present context, the term "LDL" means low density lipoprotein.

[0078] In the present context, the term "VLDL" means very low density lipoprotein.

[0079] In the context of this specification, "low density lipoprotein (LDL) triglycerides", "high density lipoprotein (HDL) triglycerides", "triglycerides in HDL (high density lipoprotein)", or "triglycerides in LDL (low density lipoprotein)" may be understood to mean the total triglyceride concentration in said lipoprotein class or subfraction.

[0080] In the context of this specification, the terms "high density lipoprotein (HDL) particle size", "low density lipoprotein (LDL) particle size" or "very low density lipoprotein (VLDL) particle size" may be understood to mean the mean diameter of particles in said lipoprotein class or subfraction.

[0081] In the context of this specification, the term "acetate" may refer to, for example, acetate molecules and / or acetic acid in blood, plasma or serum or related biological fluids.

[0082] In the context of this specification, the term "citrate" may refer to, for example, the citrate molecule and / or citric acid in blood, plasma or serum or related biological fluids.

[0083] In the present context, the term "glutamine" may refer to, for example, the glutamine amino acid in blood, plasma or serum or related biological fluids.

[0084] In the present context, the term "histidine" may refer to, for example, the histidine amino acid in blood, plasma or serum or related biological fluids.

[0085] In the present context, the term "quantitative value" may refer to any quantitative value that characterizes the amount and / or concentration of a biomarker. For example, it may be the amount or concentration of a biomarker in a biological sample, or it may be a signal derived from nuclear magnetic resonance spectroscopy (NMR) or other methods suitable for quantitatively detecting a biomarker. Such a signal may indicate or correlate with the amount or concentration of a biomarker. It may also be a quantitative value calculated from one or more signals derived from an NMR or other measurement. A quantitative value may additionally or alternatively be measured using various techniques. Such methods may include mass spectrometry (MS), gas chromatography combined with MS, high-performance liquid chromatography alone or combined with MS, immunoturbidimetry, ultracentrifugation, ion mobility, enzymatic analysis, colorimetric or fluorometric analysis, immunoblot analysis, immunohistochemical methods (e.g., in situ methods based on antibody detection of metabolites), and immunoassays (e.g., ELISA). Examples of various methods are provided below. The method used to determine the quantitative value in a subject may be the same method used to determine the quantitative value in a control subject or control sample.

[0086] The quantitative or initial quantitative value of at least one biomarker or multiple biomarkers may be measured using nuclear magnetic resonance (NMR) spectroscopy, such as 1H-NMR. At least one additional biomarker or multiple additional biomarkers may be measured using NMR. NMR can provide a particularly efficient and rapid method for measuring biomarkers, including multiple biomarkers simultaneously, and can provide quantitative values ​​for them. NMR also typically requires little sample pretreatment or preparation. Biomarkers measured by NMR can be effectively measured on large numbers of samples using blood (serum or plasma) NMR metabolomics assays previously published by Soininen et al., 2015, Circulation: Cardiovascular Genetics 8, 212-206 (DOI: 10.1161 / CIRCGENETICS.114.000216); Soininen et al., 2009, Analyst 134, 1781-1785; and Wuertz et al., 2017, American Journal of Epidemiology 186 (9), 1084-1096 (DOI: 10.1093 / aje / kwx016). This provides data on up to 250 biomarkers per sample, as described in detail in the above scientific papers.

[0087] In one embodiment, the (initial) quantitative value of at least one biomarker is determined using nuclear magnetic resonance spectroscopy.

[0088] However, quantification of the various biomarkers described herein may also be performed by techniques other than NMR, for example, mass spectrometry (MS), enzymatic methods, antibody-based detection methods, or other biochemical or chemical methods may be contemplated, depending on the biomarker.

[0089] For example, glycoprotein acetylation can be measured or estimated by immunoturbidimetric assays of alpha-1-acid glycoprotein, haptoglobin, alpha-1-antitrypsin, and transferrin (e.g., as described in Ritchie et al., 2015, Cell Syst. 28;1(4):293-301).

[0090] For example, monounsaturated fatty acids, saturated fatty acids, and omega-6 fatty acids can be quantified (i.e., their quantitative values ​​can be determined) by serum total fatty acid composition using gas chromatography (e.g., as described in Jula et al., 2005, Arterioscler Thromb Vasc Biol 25, 2152-2159).

[0091] In the context of this specification, the term "sample" or "biological sample" may refer to any biological sample obtained from a subject or from a group or population of subjects. Samples may be fresh, frozen, or dried.

[0092] The biological sample may include or be, for example, a blood sample, a plasma sample, a serum sample, or a sample derived therefrom. The biological sample may be, for example, a fasting blood sample, a fasting plasma sample, a fasting serum sample, or a fraction obtainable therefrom. However, the biological sample does not necessarily have to be a fasting sample. The blood sample may be a venous blood sample.

[0093] The blood sample may be a dried blood sample. The dried blood sample may be a dried whole blood sample, a dried plasma sample, a dried serum sample, or a dried sample derived therefrom.

[0094] The method may include obtaining a biological sample from a subject before determining the quantitative value of at least one biomarker. Collecting blood or tissue samples from a subject or patient is part of normal clinical practice. The collected blood or tissue sample can be prepared to separate serum or plasma using techniques well known to those skilled in the art. Methods for separating one or more fractions from a biological sample, such as a blood sample or tissue sample, are also available to those skilled in the art. The term "fraction," in the context of this specification, may also refer to a portion or component of a biological sample separated according to one or more physical properties, such as solubility, hydrophilicity or hydrophobicity, density, or molecular size.

[0095] In the present context, the term "control sample" may refer to a sample obtained from a subject known to be free of a disease or condition or known not to be at risk of having or developing a disease or condition. This control sample may be matched. In one embodiment, the control sample may be a biological sample derived from a healthy individual or a generalized population of healthy individuals. The term "control value" may be understood as a value obtainable from a control sample and / or a quantitative value derivable therefrom. For example, it may be possible to calculate a threshold value from the control sample and / or the control value, above or below which the risk of developing the disease or condition increases. In other words, a value higher or lower than the threshold (depending on the biomarker, risk score, hazard ratio, and / or predicted absolute or relative risk) may indicate that the subject is at increased risk of developing the disease or condition.

[0096] An increase or decrease in the quantitative value of at least one biomarker or plurality of biomarkers when compared to a control sample or control value may indicate that the subject has or is at increased risk for developing a disease or condition. Whether an increase or decrease indicates that the subject is at increased risk for developing a disease or condition may depend on the biomarker.

[0097] A 1.2-fold, 1.5-fold, or, for example, 2-fold or 3-fold increase or decrease in the quantitative value of at least one biomarker (or an individual biomarker of a plurality of biomarkers) when compared to a control sample or control value may indicate that the subject is at increased risk for developing the disease or condition.

[0098] In one embodiment, a decrease in the quantified value of albumin when compared to a control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder, such as mood affective disorder, anxiety, dissociative, stress-related, somatoform, and other non-psychiatric disorders, delirium, major depressive disorder, anxiety disorder, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0099] In one embodiment, an increase in the quantification of glycoprotein acetyl when compared to a control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder, such as mood disorders, anxiety, dissociative, stress-related, somatoform, and other non-psychiatric disorders, delirium, major depressive disorder, anxiety disorders, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0100] In one embodiment, a decrease in the quantitative value of the ratio of docosahexaenoic acid to total fatty acids when compared to a control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder, such as mood affective disorder, anxiety, dissociative, stress-related, somatoform, and other non-psychiatric disorders, delirium, major depressive disorder, anxiety disorder, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0101] In one embodiment, a decrease in the quantitative value of the ratio of linoleic acid to total fatty acids when compared to a control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder, such as mood affective disorder, anxiety, dissociative, stress-related, somatoform, and other non-psychiatric disorders, delirium, major depressive disorder, anxiety disorder, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0102] In one embodiment, an increase in the quantitative value of the ratio of monounsaturated fatty acids and / or oleic acid to total fatty acids when compared to a control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder, such as mood affective disorder, anxiety, dissociative, stress-related, somatoform, and other non-psychotic disorders, delirium, major depressive disorder, anxiety disorder, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0103] In one embodiment, a decrease in the quantitative value of the ratio of omega-3 fatty acids to total fatty acids when compared to a control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder, such as mood affective disorder, anxiety, dissociative, stress-related, somatoform, and other non-psychiatric disorders, delirium, major depressive disorder, anxiety disorder, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0104] In one embodiment, a decrease in the quantitative value of the ratio of omega-6 fatty acids to total fatty acids when compared to a control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder, such as mood affective disorder, anxiety, dissociative, stress-related, somatoform, and other non-psychiatric disorders, delirium, major depressive disorder, anxiety disorder, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0105] In one embodiment, an increase in the quantitative value of the ratio of saturated fatty acids to total fatty acids when compared to a control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder, such as mood affective disorder, anxiety, dissociative, stress-related, somatoform, and other non-psychiatric disorders, delirium, major depressive disorder, anxiety disorder, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0106] In one embodiment, a decrease in the quantitative value of fatty acid unsaturation when compared to a control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder, such as mood affective disorder, anxiety, dissociative, stress-related, somatoform, and other non-psychiatric disorders, delirium, major depressive disorder, anxiety disorder, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0107] In one embodiment, a decrease in the quantified value of docosahexaene compared to a control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder, such as a mood disorder, anxiety, dissociative, stress-related, somatoform, and other non-psychotic disorders, delirium, major depressive disorder, anxiety disorder, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0108] In one embodiment, a decrease in the quantified value of linoleic acid compared to a control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder, such as mood disorders, anxiety, dissociative, stress-related, somatoform, and other non-psychiatric disorders, delirium, major depressive disorder, anxiety disorders, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0109] In one embodiment, an increase in the quantified values ​​of monounsaturated fatty acids and / or oleic acid when compared to a control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder, such as mood affective disorders, anxiety, dissociative, stress-related, somatoform, and other non-psychotic disorders, delirium, major depressive disorder, anxiety disorders, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0110] In one embodiment, a decrease in the quantified value of omega-3 fatty acids when compared to a control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder, such as mood disorder, anxiety, dissociative, stress-related, somatoform, and other non-psychiatric disorders, delirium, major depressive disorder, anxiety disorder, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0111] In one embodiment, a decrease in the quantified value of omega-6 fatty acids when compared to a control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder, such as mood affective disorders, anxiety, dissociative, stress-related, somatoform, and other non-psychiatric disorders, delirium, major depressive disorder, anxiety disorders, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0112] In one embodiment, an increase in the quantitative value of saturated fatty acids when compared to a control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder, such as mood disorders, anxiety, dissociative, stress-related, somatoform, and other non-psychiatric disorders, delirium, major depressive disorder, anxiety disorders, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0113] In one embodiment, an increase in the quantification of triglyceride acids in high density lipoprotein (HDL) when compared to a control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder, such as mood affective disorder, anxiety, dissociative, stress-related, somatoform, and other non-psychotic disorders, delirium, major depressive disorder, anxiety disorder, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0114] In one embodiment, an increase in the quantification of triglyceride acids in low density lipoprotein (LDL) when compared to a control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder, such as mood affective disorders, anxiety, dissociative, stress-related, somatoform, and other non-psychotic disorders, delirium, major depressive disorder, anxiety disorders, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0115] In one embodiment, a decrease in the quantitative value of high density lipoprotein (HDL) particle size when compared to a control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder, such as mood affective disorder, anxiety, dissociative, stress-related, somatoform, and other non-psychiatric disorders, delirium, major depressive disorder, anxiety disorder, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0116] In one embodiment, a decrease in the quantitative value of low-density lipoprotein (LDL) particle size when compared to a control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder, such as mood affective disorder, anxiety, dissociative, stress-related, somatoform, and other non-psychiatric disorders, delirium, major depressive disorder, anxiety disorder, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0117] In one embodiment, an increase in the quantification of very low density lipoprotein (VLDL) particle size when compared to a control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder, such as mood affective disorder, anxiety, dissociative, stress-related, somatoform, and other non-psychiatric disorders, delirium, major depressive disorder, anxiety disorder, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0118] In one embodiment, a decrease in the quantified value of acetate when compared to a control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder, such as mood disorders, anxiety, dissociative, stress-related, somatoform, and other non-psychiatric disorders, delirium, major depressive disorder, anxiety disorders, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0119] In one embodiment, a decrease in the quantified value of citrate when compared to a control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder, such as mood affective disorder, anxiety, dissociative, stress-related, somatoform, and other non-psychiatric disorders, delirium, major depressive disorder, anxiety disorder, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0120] In one embodiment, a decrease in the quantitative value of glutamine when compared to a control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder, such as mood disorders, anxiety, dissociative, stress-related, somatoform, and other non-psychiatric disorders, delirium, major depressive disorder, anxiety disorders, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0121] In one embodiment, a decrease in the quantified value of histidine when compared to a control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder, such as mood affective disorder, anxiety, dissociative, stress-related, somatoform, and other non-psychotic disorders, delirium, major depressive disorder, anxiety disorder, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0122] In one embodiment, a risk score is defined as β0 + β1 *concentration(glycoprotein acetyl) + β2 *concentration(albumin), where β0 is the intercept term, β1 is a weighting coefficient attributable to the concentration of glycoprotein acetyl, and β2 is a weighting coefficient attributable to the concentration of albumin, and this risk score may indicate that the subject is at increased risk of developing a mental and / or behavioral disorder, such as a mood disorder, anxiety, dissociative, stress-related, somatoform, and other non-psychotic disorders, delirium, major depressive disorder, anxiety disorder, and / or other symptoms and / or signs involving cognitive function and / or consciousness.

[0123] In one embodiment, a risk score may be defined as β0 + β1 *concentration (glycoprotein acetyl) + β2 *concentration (fatty acid measure), where β0 is an intercept term, β1 is a weighting factor attributable to glycoprotein acetyl concentration, and β2 is a weighting factor attributable to the fatty acid measure, and this risk score may indicate that a subject is at increased risk of developing a mental and / or behavioral disorder, such as mood affective disorder, anxiety, dissociative, stress-related, somatoform, and other non-psychotic disorders, delirium, major depressive disorder, anxiety disorder, and / or other symptoms and / or signs related to cognitive function and / or consciousness. The fatty acid measure may be one or more of fatty acids, such as docosahexaenoic acid, linoleic acid, omega-3 fatty acids, omega-6 fatty acids, monounsaturated fatty acids, saturated fatty acids, or their ratios to total fatty acids, and / or degree of fatty acid unsaturation.

[0124] In one embodiment, a risk score may be defined as β0 + β1 *concentration(glycoprotein acetyl) + β2 *concentration(albumin) + β3 *(fatty acid measure) + β0, where β0 is the intercept term, β1 is a weighting factor attributable to the concentration of glycoprotein acetyl, β2 is a weighting factor attributable to the concentration of albumin, and β3 is a weighting factor attributable to the concentration of the fatty acid measure, and this risk score may indicate that a subject is at high risk for developing a mental and / or behavioral disorder, such as a mood disorder, anxiety, dissociative, stress-related, somatoform, and other non-psychotic disorders, delirium, major depressive disorder, anxiety disorder, and / or other symptoms and / or signs related to cognitive function and / or consciousness. The fatty acid measure may be one or more of the following fatty acids: docosahexaenoic acid, linoleic acid, omega-3 fatty acids, omega-6 fatty acids, monounsaturated fatty acids, saturated fatty acids, or their ratios to total fatty acids, and / or the degree of fatty acid unsaturation.

[0125] The term "combination" may be understood to mean that, at least in some embodiments, the method includes using a risk score, hazard ratio, odds ratio, and / or predicted absolute or relative risk calculated based on the quantitative values ​​of the biomarkers. For example, if quantitative values ​​of both glycoprotein acetylcholine and albumin are determined, the quantitative values ​​of both biomarkers may be compared separately to a control sample or control value, or a risk score, hazard ratio, odds ratio, and / or predicted absolute or relative risk may be calculated based on the quantitative values ​​of both biomarkers, and these risk scores, odds ratios, and / or predicted absolute or relative risks may be compared to a control sample or control value.

[0126] In one embodiment, the method comprises detecting in a biological sample obtained from a subject: Glycoprotein acetyl ·albumin determining the quantitative value of the biomarker; comparing said quantitative values ​​and / or combinations thereof of said biomarkers to a control sample or control value; may include An increase or decrease in the quantitative value and / or combination of the biomarkers when compared to the control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder. An increase in the quantitative value of glycoprotein acetyl and a decrease in the quantitative value of albumin when compared to the control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder.

[0127] In one embodiment, the method comprises detecting in a biological sample obtained from a subject: Glycoprotein acetyl At least one fatty acid measure of fatty acids, such as docosahexaenoic acid, linoleic acid, omega-3 fatty acids, omega-6 fatty acids, monounsaturated fatty acids, saturated fatty acids, or their ratio to total fatty acids, and / or degree of fatty acid unsaturation. determining the quantitative value of the biomarker; comparing said quantitative values ​​and / or combinations thereof of said biomarkers to a control sample or control value; may include An increase or decrease in the quantitative values ​​and / or combinations of the biomarkers compared to the control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder. An increase in the quantitative value of glycoprotein acetyl, a decrease in the quantitative value of docosahexaenoic acid and / or linoleic acid and / or omega-3 fatty acids and / or omega-6 fatty acids and / or fatty acid unsaturation and / or their ratio to total fatty acids, and / or an increase in the quantitative value of monounsaturated fatty acids and / or saturated fatty acids and / or their ratio to total fatty acids compared to the control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder.

[0128] In one embodiment, the method comprises detecting in a biological sample obtained from a subject: ·albumin, At least one fatty acid measure of fatty acids, such as docosahexaenoic acid, linoleic acid, omega-3 fatty acids, omega-6 fatty acids, monounsaturated fatty acids, saturated fatty acids, or their ratio to total fatty acids, and / or degree of fatty acid unsaturation. determining the quantitative value of the biomarker; comparing said quantitative values ​​and / or combinations thereof of said biomarkers to a control sample or control value; may include An increase or decrease in the quantitative values ​​and / or combinations thereof of the biomarkers when compared to the control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder. A decrease in the quantitative value of albumin, a decrease in the quantitative value of docosahexaenoic acid and / or linoleic acid and / or omega-3 fatty acids and / or omega-6 fatty acids and / or fatty acid unsaturation and / or their ratio to total fatty acids, and / or an increase in the quantitative value of monounsaturated fatty acids and / or saturated fatty acids and / or their ratio to total fatty acids when compared to the control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder.

[0129] In one embodiment, the method comprises detecting in a biological sample obtained from a subject the following biomarkers: Glycoprotein acetyl ·albumin, At least one fatty acid measure of fatty acids, such as docosahexaenoic acid, linoleic acid, omega-3 fatty acids, omega-6 fatty acids, monounsaturated fatty acids, saturated fatty acids, or their ratio to total fatty acids, and / or degree of fatty acid unsaturation. determining the quantitative value of the biomarker; comparing said quantitative values ​​and / or combinations thereof of said biomarkers to a control sample or control value; may include An increase or decrease in the quantitative values ​​and / or combinations thereof of the biomarkers when compared to the control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder. An increase in the quantitative value of glycoprotein acetyl, a decrease in the quantitative value of albumin, a decrease in the quantitative value of docosahexaenoic acid and / or linoleic acid and / or omega-3 fatty acids and / or omega-6 fatty acids and / or fatty acid unsaturation and / or their ratio to total fatty acids, and / or an increase in the quantitative value of monounsaturated fatty acids and / or saturated fatty acids and / or their ratio to total fatty acids when compared to the control sample or control value may indicate that the subject is at increased risk for developing a mental and / or behavioral disorder.

[0130] The following embodiments are disclosed.

[0131] (Embodiment 1) 1. A method for determining whether a subject is at risk for developing a mental and / or behavioral disorder, comprising: The method includes, in a biological sample obtained from the subject, detecting the following in the biological sample: ·albumin, Glycoprotein acetyl Ratio of docosahexaenoic acid to total fatty acids, the ratio of linoleic acid to total fatty acids, the ratio of monounsaturated fatty acids and / or oleic acid to total fatty acids, the ratio of omega-3 fatty acids to total fatty acids, the ratio of omega-6 fatty acids to total fatty acids; -Ratio of saturated fatty acids to total fatty acids, ·Fatty acid unsaturation, Docosahexaenoic acid, Linoleic acid, monounsaturated fatty acids and / or oleic acid, ·ω-3 fatty acids, ·ω-6 fatty acids, ·Saturated fatty acids, Triglycerides in high-density lipoproteins (HDL), · Triglycerides in low-density lipoproteins (LDL), High-density lipoprotein (HDL) particle size, Low-density lipoprotein (LDL) particle size, Very low density lipoprotein (VLDL) particle size, Acetate, citrate, ·glutamine, Histidine determining a quantitative value of at least one biomarker among comparing said quantitative value of said at least one biomarker with a control sample or control value; Including, wherein an increase or decrease in the quantitative value of the at least one biomarker when compared to the control sample or control value indicates that the subject is at high risk of developing the mental and / or behavioral disorder.

[0132] (Embodiment 2) 2. The method of embodiment 1, wherein the method comprises determining quantitative values ​​of a plurality of said biomarkers, e.g., two, three, four, five or more biomarkers, in said biological sample.

[0133] (Embodiment 3) 3. The method of embodiment 1 or 2, wherein said at least one biomarker comprises or is glycoprotein acetyl.

[0134] (Embodiment 4) The method comprises detecting in the biological sample obtained from the subject: Glycoprotein acetyl ·albumin determining the quantitative value of the biomarker; comparing said quantitative values ​​of said biomarkers to a control sample or control value; may include 4. The method of any one of embodiments 1 to 3, wherein an increase or decrease in said quantitative value of said biomarker when compared to said control sample or said control value indicates that said subject is at increased risk for developing a mental and / or behavioral disorder.

[0135] (Embodiment 5) The method comprises detecting in the biological sample obtained from the subject: Glycoprotein acetyl At least one fatty acid measure of the following: ratio of docosahexaenoic acid to total fatty acids, docosahexaenoic acid, ratio of linoleic acid to total fatty acids, linoleic acid, ratio of monounsaturated fatty acids and / or oleic acid to total fatty acids, ratio of omega-6 fatty acids to total fatty acids, omega-6 fatty acids, ratio of saturated fatty acids to total fatty acids, saturated fatty acids, and degree of fatty acid unsaturation determining the quantitative value of the biomarker; comparing said quantitative values ​​of said biomarkers to a control sample or control value; Including, 5. The method of any one of embodiments 1 to 4, wherein an increase or decrease in said quantitative value of said biomarker when compared to said control sample or said control value indicates that said subject is at increased risk for developing a mental and / or behavioral disorder.

[0136] (Embodiment 6) 6. The method of any one of embodiments 1 to 5, wherein the mental and / or behavioral disorders include or are: mental disorders due to known physiological conditions (F01-F09), schizophrenia, schizophrenic, delusional, and / or other non-mood psychotic disorders (F20-F29), mood [affective] disorders (F30-F39), anxiety, dissociative, stress-related, somatoform, and / or other non-psychotic disorders (F40-F48), drug, medicinal, and / or biological intoxication, adverse effects, and / or underdosage (T36-T50), intentional acts of self-harm (X60-X84).

[0137] (Embodiment 7) The mental and / or behavioral disorders are: delirium due to a known physiological condition (F05), other mental disorders due to a known physiological condition (F06), schizophrenia (F20), bipolar disorder (F31), major depressive disorder, single episode (F32), major depressive disorder, recurrent (F33), phobic anxiety disorder (F40), other anxiety disorder (F41), reaction to severe stress and / or adjustment disorder (F43), other symptoms and / or signs related to cognitive function and / or consciousness (R41), non-opioid analgesics, 7. The method of any one of embodiments 1 to 6, including or being antipyretic and / or antirheumatic drug poisoning, adverse effects, and / or underdosing (T39), narcotic and / or psychotropic drug poisoning, adverse effects, and / or underdosing (T40), antiepileptic, sedative / hypnotic, and / or antiparkinsonian drug poisoning, adverse effects, and / or underdosing (T42), and / or psychotropic drug poisoning, adverse effects, and / or underdosing, not elsewhere classified (T43).

[0138] (Embodiment 8) 8. The method of any one of embodiments 1 to 7, wherein the mental and / or behavioral disorder comprises or is a mood disorder, anxiety, dissociative, stress-related, somatoform, and / or other non-psychotic disorder, delirium, major depressive disorder, anxiety disorder, and / or other symptoms and / or signs related to cognitive function and / or consciousness.

[0139] (Embodiment 9) 9. The method of any one of embodiments 1 to 8, wherein said quantitative value of said at least one biomarker is measured using nuclear magnetic resonance spectroscopy.

[0140] (Embodiment 10) 10. The method of any one of embodiments 1 to 9, wherein the method further comprises determining whether the subject is at risk of developing a mental and / or behavioral disorder using a risk score, hazard ratio, odds ratio, and / or predicted absolute or relative risk calculated based on the quantitative values ​​of the at least one biomarker or the plurality of biomarkers. [Example]

[0141] Reference will now be made in detail to various embodiments, examples of which are illustrated in the accompanying drawings. The following description discloses certain embodiments in detail to enable those skilled in the art to utilize the embodiments based on the present disclosure. Because many of the steps or features of the embodiments will be apparent to those skilled in the art based on this specification, not all steps or features will be discussed in detail.

[0142] (Abbreviations used in the figure) DHA%: Ratio of docosahexaenoic acid to total fatty acids; LA%: Ratio of linoleic acid to total fatty acids MUF%: Ratio of monounsaturated fatty acids to total fatty acids ω-3%: Ratio of ω-3 fatty acids to total fatty acids ω-6%: Ratio of ω-6 fatty acids to total fatty acids SFA%: Ratio of saturated fatty acids to total fatty acids DHA: Docosahexaenoic acid LA: Linoleic acid MUFA: Monounsaturated fatty acids ω-6: ω-6 fatty acids ω-3:ω-3 fatty acids SFA: Saturated fatty acids Unsaturation: degree of fatty acid unsaturation HDL: high-density lipoprotein LDL: low-density lipoprotein VLDL: very low density lipoprotein HDL-TG: Triglycerides in high-density lipoprotein (HDL) LDL-TG: low-density lipoprotein (HDL) particle size CI: confidence interval SD: standard deviation BMI: Body Mass Index

[0143] Example 1 Biomarker measures quantified by nuclear magnetic resonance (NMR) were investigated for their ability to predict mental and / or behavioral disorders, including mood-affective disorders, anxiety, dissociative, stress-related, somatoform, and other nonpsychiatric disorders, delirium, major depressive disorder, anxiety disorders, and other symptoms and signs related to cognitive function and consciousness. All analyses were conducted using the UK Biobank, with approximately 115,000 study participants for whom blood biomarker data from NMR spectroscopy were available.

[0144] (Study population) Details of the UK Biobank design were reported by Sudlow et al. in 2015, PLoS Med. 2015;12(3):e1001779. Briefly, the UK Biobank recruited 502,639 participants aged 37 to 73 years at 22 assessment centers across the UK. All participants provided written informed consent, and ethical approval was obtained from the North West Multi-Center Research Ethics Committee. Blood samples were collected at baseline between 2007 and 2010. No selection criteria were applied to sampling.

[0145] (biomarker profiling) A random subset of baseline plasma samples from 118,466 individuals from the entire UK Biobank population was measured using the Nightingale NMR biomarker platform (Nightingale Health Ltd, Finland). This blood analysis method provides simultaneous quantification of many blood biomarkers, including lipoprotein lipids, circulating fatty acids, and various low-molecular-weight metabolites, including amino acids, ketone bodies, and gluconeogenesis-related metabolites, in molar concentrations. Technical details and epidemiological applications have been reviewed (Soininen et al., 2015, Circ Cardiovasc Genet; 2015;8:192-206; Wurtz et al., 2017, Am J Epidemiol 2017;186:1084-1096). Values ​​outside the four interquartile ranges from the median were considered outliers and were excluded.

[0146] (Epidemiological analysis of the relationship of biomarkers with the risk of mental and / or behavioral disorders) Association of blood biomarkers with risk of mental and / or behavioral disorders was conducted based on UK Biobank data. Analysis focused on the relationship between biomarkers and the occurrence of mental and / or behavioral disorders after blood samples were collected to determine whether individual biomarkers were associated with the risk of future development of mental and / or behavioral disorders. Examples using multi-biomarker scores in the form of weighted sums of biomarkers were also investigated to see whether they could be significantly more predictive than each individual biomarker.

[0147] Information on disease events occurring after blood sampling for all study participants was recorded from UK Hospital Episode Statistics data and the death registry. All analyses were based on the first occurrence of a diagnosis, such that individuals with a recorded diagnosis of a given disease before blood sampling were excluded from statistical analysis. A composite endpoint of any mental and / or behavioral disorder was defined based on any incident occurrence of ICD-10 diagnoses F00-F99, T36-T50, or X60-X84. More refined subtypes of mental and / or behavioral disorders were defined according to the ICD-10 diagnoses listed in Table 1.

[0148] Registry-based follow-up was performed using blood sampling (approximately 1.1 million person-years) from 2007–2010 to 2020. Specific diseases with fewer than 100 recorded disease events during follow-up were excluded.

[0149] For biomarker association studies, Cox proportional hazards regression models adjusted for age, sex, and UK Biobank assessment center were used. Results were plotted as the magnitude per standard deviation of each biomarker measure, allowing direct comparison of the magnitude of associations.

[0150] (Summary of results) Baseline characteristics of the study population for biomarker analysis versus future risk of mental and / or behavioral disorders are shown in Table 1. The number of disease events occurring after blood sampling is listed for all conditions analyzed.

[0151] Table 1: Clinical characteristics of study participants and number of disease events analyzed [Table 1(1)] [Table 1(2)]

[0152] Figure 1a shows the hazard ratios for 24 blood biomarkers associated with future risk of any mental and / or behavioral disorder ICD-10 code (F00-F99, T36-T50, or X60-X84). The left side of the figure shows the hazard ratios when the biomarkers were analyzed by absolute concentration and scaled to the standard deviation of the study population. The right side shows the corresponding hazard ratios when comparing individuals in the highest quintile of biomarker concentration with those in the lowest quintile. Results are based on a statistical analysis of over 115,000 individuals from the UK Biobank, of whom 9,710 developed a mental and / or behavioral disorder (defined as a diagnosis F00-F99, T36-T50, or X60-X84 in the hospital registry or death register) during approximately 10 years of follow-up. The analysis was adjusted for age, sex, and UK Biobank assessment center in a Cox proportional hazards regression model. P values ​​were <0.0001 (corresponding to multiple testing correction) for all associations. These results demonstrate that 24 individual biomarkers predict risk of mental and / or behavioral disorders in a general population setting.

[0153] Figure 1b shows a Kaplan-Meier plot of the cumulative risk of mental and / or behavioral disorders for each of the 24 blood biomarkers by lowest, middle, and highest quintile of biomarker concentration. The results are based on a statistical analysis of over 115,000 individuals from the UK Biobank, of which 9,716 developed a mental and / or behavioral disorder. These results further demonstrate that the 24 individual biomarkers predict risk of mental and / or behavioral disorders in a general population setting.

[0154] Figure 2a shows the hazard ratios of 24 blood biomarkers for future onset of six subgroups of mental and / or behavioral disorders defined by the subchapters of ICD-10. The results show that the patterns of biomarker associations are highly consistent across the six different subtypes of mental and / or behavioral disorders.

[0155] Figure 2b shows the consistency of biomarker associations with the six mental and / or behavioral disorder subgroups (defined by ICD-10 subchapters) compared to the definition of "any mental and / or behavioral disorder." The biomarker associations were all in the same direction of association as "any mental and / or behavioral disorder," or were statistically insignificant in the discrepant direction. Therefore, any biomarker combination that strongly predicts "any mental and / or behavioral disorder" will also predict all listed mental and / or behavioral disorder subgroups.

[0156] Figure 3a shows the hazard ratios of 24 blood biomarkers for future development of 14 specific mental and / or behavioral disorders defined by 3-digit ICD-10 diagnostic codes. The results show that the pattern of biomarker associations is highly consistent across all 14 specific disorders.

[0157] Figure 3b shows the consistency of biomarker associations with 14 specific mental and / or behavioral disorders (defined by 3-digit ICD-10 diagnostic codes) compared to the definition of "any mental and / or behavioral disorder." Generally, the biomarker associations are all in the same direction of association as "any mental and / or behavioral disorder," or are statistically insignificant in the direction of discordance. Thus, any combination of biomarkers that strongly predicts "any mental and / or behavioral disorder" will also predict all specific mental and / or behavioral disorders listed.

[0158] Figures 4a-c show the hazard ratios of 24 blood biomarkers for future onset of each of the six mental and / or behavioral disorder subgroups (defined by ICD-10 subchapters) studied here. Hazard ratios are shown as absolute concentrations scaled to the standard deviation of each biomarker. Results are based on statistical analysis of over 115,000 individuals from the UK Biobank; the number of individuals who developed the disorder over approximately 10 years of follow-up is shown at the top of each plot. Filled circles indicate a P value for association of P<0.0001 (corresponding to multiple testing correction), whereas open circles indicate a P value for association of P≥0.0001. Analyses were performed using Cox proportional hazards regression models, adjusting for age, sex, and UK Biobank assessment center.

[0159] Figures 5a–g show the hazard ratios of 24 blood biomarkers for the future onset of each of the 14 mental and / or behavioral disorder subgroups (defined by ICD-10 subchapters) studied here. Hazard ratios are shown as absolute concentrations scaled to the standard deviation of each biomarker. Results are based on statistical analysis of over 115,000 individuals from the UK Biobank; the number of individuals who developed a particular disorder during approximately 10 years of follow-up is shown at the top of each plot. Filled circles indicate a P value for association of P<0.0001 (corresponding to multiple testing correction), whereas open circles indicate a P value for association of P≥0.0001. Analyses were performed using Cox proportional hazards regression models, adjusting for age, sex, and UK Biobank assessment center.

[0160] Figure 6 shows examples where the results of the association with any mental and / or behavioral disorder are stronger when two or more biomarkers are combined. Hazard ratios for future risk of mental and / or behavioral disorder (a composite endpoint of ICD-10 codes F00-F99, T36-T50, or X60-X84) are shown for selected combinations of biomarker pairs and example biomarker scores. Results were similar for many other combinations, particularly including measurements of different fatty acids in addition to albumin and glycoprotein acetylcholine. Biomarker scores were also

number

[0161] (Intended Use Description: Biomarker Score for Risk Prediction of Mental and / or Behavioral Disorders) Further epidemiological analyses are provided below to illustrate intended uses related to predicting mental and / or behavioral disorders. These uses are exemplified for predicting risk of mood-affective disorders, anxiety, dissociative, stress-related, somatoform, and other non-psychotic disorders, delirium, major depressive disorder, anxiety disorders, and other symptoms and signs related to cognitive function and consciousness. Similar results apply to other mental and / or behavioral disorders listed in Table 1. Results are presented for a biomarker score combining the 24 biomarkers characterized in Figures 1-6. Similar results, albeit slightly weaker, are obtained with a combination of only two or three individual biomarkers.

[0162] Figure 7a shows that the risk of psychiatric disorders due to known physiological conditions (ICD-10 subchapters F01-F09) increases with increasing levels of the multi-biomarker score, which consists of a weighted sum of 24 biomarkers. On the left, the risk increase is plotted in the form of a gradient percentile plot, showing the proportion of individuals who developed a psychiatric disorder due to a known physiological condition during follow-up when individuals are binned into percentiles of biomarker score level. Each dot corresponds to approximately 500 individuals. The Kaplan-Meier plot on the right shows the cumulative risk of psychiatric disorders due to known physiological conditions during follow-up for selected quintiles of the multi-biomarker score. Both plots help demonstrate that risk increases nonlinearly at the upper end of the distribution of the multi-biomarker score. The plots are shown for the validation set portion of the study population, i.e., the 50% not included in the derivation of the multi-biomarker score (n = 58,751 individuals).

[0163] Figure 7b shows the hazard ratios for the same multi-biomarker score with future onset of psychiatric disorders (ICD-10 subchapters F01-F09) according to known physiological conditions when accounting for the relevant risk factor characteristics of study participants. The first panel demonstrates that risk prediction works well for men and women. The second panel shows that risk prediction also works for people of different ages at the time of blood sampling, with stronger results for younger individuals. The third panel shows that the magnitude of the hazard ratio is only slightly attenuated when taking body mass index and smoking status into account in the statistical modeling. The final panel shows that the hazard ratios are similar for both short-term and long-term risk prediction.

[0164] Figure 8a shows that the risk of mood-affective disorders (ICD-10 subchapters F30-F39) increases with increasing levels of the multi-biomarker score, which consists of a weighted sum of 24 biomarkers. On the left, the risk increase is plotted in the form of a gradient percentile plot, showing the proportion of individuals who developed a mood-affective disorder during follow-up when individuals are binned into percentiles of biomarker score level. Each dot corresponds to approximately 500 individuals. The Kaplan-Meier plot on the right shows the cumulative risk of mood-affective disorders during follow-up for selected quintiles of the multi-biomarker score. Both plots help demonstrate that risk increases nonlinearly at the upper end of the multi-biomarker score distribution. The plots are shown for the validation set portion of the study population, i.e., the 50% not included in the derivation of the multi-biomarker score (n = 57,643 individuals).

[0165] Figure 8b shows the hazard ratios of the same multi-biomarker score to future onset of mood and affective disorders (ICD-10 subchapters F30-F39) when accounting for the relevant risk factor characteristics of study participants. The first and second panels demonstrate that risk prediction works well for men and women, and for people of different ages at the time of blood sampling. The third panel shows that the magnitude of the hazard ratio is only slightly attenuated when taking body mass index and smoking status into account in the statistical modeling. The final panel demonstrates that the hazard ratios are significantly enhanced when focusing on short-term risk prediction.

[0166] Figure 9a shows that the risk of anxiety, dissociative, stress-related, somatoform, and / or other nonpsychotic disorders (ICD-10 subchapters F40-F48) increases with increasing levels of a multi-biomarker score, consisting of a weighted sum of 24 biomarkers. On the left, the increased risk is plotted in the form of a gradient percentile plot, showing the proportion of individuals who developed anxiety, dissociative, stress-related, somatoform, and / or other nonpsychotic disorders during follow-up when binned into percentiles of biomarker score levels. Each dot corresponds to approximately 500 individuals. On the right, a Kaplan-Meier plot shows the cumulative risk of anxiety, dissociative, stress-related, somatoform, and / or other nonpsychotic disorders during follow-up for selected quintiles of the multi-biomarker score. Both plots help demonstrate that risk increases nonlinearly at the upper end of the multi-biomarker score distribution. Plots are shown for the validation set portion of the study population, i.e., the 50% that were not included in the derivation of the multi-biomarker score (n=58236 individuals).

[0167] Figure 9b shows the hazard ratios for the same multi-biomarker score with future onset of anxiety, dissociative, stress-related, somatoform, and / or other nonpsychotic disorders (ICD-10 subchapters F40-F48) when accounting for the relevant risk factor characteristics of study participants. The first two panels demonstrate that risk prediction works well for men and women and for people of different ages at the time of blood sampling, with stronger results obtained for younger individuals. The third panel shows that the magnitude of the hazard ratio is only slightly attenuated when taking body mass index and smoking status into account in the statistical modeling. The final panel demonstrates that the hazard ratios are significantly enhanced when focusing on short-term risk prediction.

[0168] Figure 10a shows that the risk of delirium due to a known physiological condition (ICD-10 code F05) increases with increasing levels of the multi-biomarker score, which is composed of a weighted sum of 24 biomarkers. On the left, the risk increase is plotted in the form of a gradient percentile plot, showing the proportion of individuals who developed delirium due to a known physiological condition during follow-up when individuals are binned into percentiles of biomarker score level. Each dot corresponds to approximately 500 individuals. The Kaplan-Meier plot on the right shows the cumulative risk of delirium due to a known physiological condition during follow-up for selected quintiles of the multi-biomarker score. Both plots help demonstrate that risk increases nonlinearly at the upper end of the multi-biomarker score distribution. The plots are shown for the validation set portion of the study population, i.e., the 50% not included in the derivation of the multi-biomarker score (n = 58,793 individuals).

[0169] Figure 10b shows the hazard ratios for the same multi-biomarker score with future onset of a psychiatric disorder (ICD-10 code F05) due to known physiological conditions when accounting for the relevant risk factor characteristics of study participants. The first panel demonstrates that risk prediction works well for men and women. The second panel shows that risk prediction also works for people of different ages at the time of blood sampling, with stronger results for younger individuals. The third panel shows that the magnitude of the hazard ratio is only slightly attenuated when taking body mass index and smoking status into account in the statistical modeling. The final panel demonstrates that the hazard ratios are significantly enhanced when focusing on short-term risk prediction.

[0170] Figure 11a shows that the risk of major depressive disorder, single episode (ICD-10 code F32) increases with increasing levels of the multi-biomarker score, which is composed of a weighted sum of 24 biomarkers. On the left, the risk increase is plotted in the form of a gradient percentile plot, showing the proportion of individuals who developed major depressive disorder, single episode, during follow-up when individuals are binned into percentiles of biomarker score level. Each dot corresponds to approximately 500 individuals. On the right, the Kaplan-Meier plot shows the cumulative risk of major depressive disorder, single episode, during follow-up for selected quintiles of the multi-biomarker score. Both plots help demonstrate that risk increases nonlinearly at the upper end of the multi-biomarker score distribution. The plots are shown for the validation set portion of the study population, i.e., the 50% not included in the derivation of the multi-biomarker score (n = 57,822 individuals).

[0171] Figure 11b shows the hazard ratios for the same multi-biomarker score with future onset of major depressive disorder, single episode (ICD-10 code F32) when accounting for the relevant risk factor characteristics of study participants. The first two panels demonstrate that risk prediction works well for men and women, and for people of different ages at the time of blood sampling. The third panel shows that the magnitude of the hazard ratio is only slightly attenuated when taking body mass index and smoking status into account in the statistical modeling. The final panel demonstrates that the hazard ratios are significantly enhanced when focusing on short-term risk prediction.

[0172] Figure 12a shows that the risk of anxiety disorders (ICD-10 code F41) increases with increasing levels of the multi-biomarker score, which is a weighted sum of 24 biomarkers. On the left, the risk increase is plotted in the form of a gradient percentile plot, showing the proportion of individuals who developed an anxiety disorder during follow-up when binned into percentiles of biomarker score levels. Each dot corresponds to approximately 500 individuals. The Kaplan-Meier plot on the right shows the cumulative risk of anxiety disorders during follow-up for selected quintiles of the multi-biomarker score. Both plots help demonstrate that risk increases nonlinearly at the upper end of the multi-biomarker score distribution. The plots are shown for the validation set portion of the study population, i.e., the 50% not included in the derivation of the multi-biomarker score (n = 58,451 individuals).

[0173] Figure 12b shows the hazard ratios for the same multi-biomarker score with future onset of anxiety disorder (ICD-10 code F41) when accounting for the relevant risk factor characteristics of study participants. The first panel demonstrates that risk prediction works well for men and women. The second panel shows that risk prediction also works for people of different ages at the time of blood sampling, with stronger results for younger individuals. The third panel shows that the magnitude of the hazard ratio is only slightly attenuated when taking body mass index and smoking status into account in the statistical modeling. The final panel demonstrates that the hazard ratios are significantly enhanced when focusing on short-term risk prediction.

[0174] Figure 13a shows that the risk of cognitive symptoms and signs (ICD-10 code R41) increases with increasing levels of the multi-biomarker score, which is a weighted sum of 24 biomarkers. On the left, the risk increase is plotted in the form of a gradient percentile plot, showing the proportion of individuals who developed cognitive symptoms and signs during follow-up when binned into percentiles of biomarker score levels. Each dot corresponds to approximately 500 individuals. On the right, the Kaplan-Meier plot shows the cumulative risk of cognitive symptoms and signs during follow-up for selected quintiles of the multi-biomarker score. Both plots help demonstrate that risk increases nonlinearly at the upper end of the multi-biomarker score distribution. The plots are shown for the validation set portion of the study population, i.e., the 50% not included in the derivation of the multi-biomarker score (n = 58,575 individuals).

[0175] Figure 13b shows the hazard ratios for the same multi-biomarker score with future onset of symptoms and signs related to cognitive function and consciousness (ICD-10 code R41) when accounting for the relevant risk factor profiles of study participants. The first panel demonstrates that risk prediction works well for men and women. The second panel shows that risk prediction also works for people of different ages at the time of blood sampling, with stronger results for younger individuals. The third panel shows that the magnitude of the hazard ratio is only slightly attenuated when taking body mass index and smoking status into account in the statistical modeling. The final panel demonstrates that the hazard ratios are significantly enhanced when focusing on short-term risk prediction.

[0176] It is obvious to those skilled in the art that with the advancement of technology, the basic idea can be implemented in various ways, so the embodiments are not limited to the above examples, but instead may vary within the scope of the claims.

[0177] The above-described embodiments may be used in any combination with each other. Some of the embodiments may be combined together to form further embodiments. The methods disclosed herein may include at least one of the above-described embodiments. It will be understood that the benefits and advantages described above may relate to one embodiment or to several embodiments. The embodiments are not limited to those that solve any or all of the stated problems or have any or all of the stated benefits and advantages. It will be further understood that reference to "an" or "an" item means one or more of those items. The term "comprising" is used herein to mean including the features or acts that follow the phrase without excluding the presence of one or more additional features or acts.

Claims

1. 1. A method for determining whether a subject is at risk for developing a psychiatric disorder, comprising: The method includes, in a biological sample obtained from the subject, determining the following of the biological sample: -glycoprotein acetyl, ·albumin, - the ratio of docosahexaenoic acid to total fatty acids, - the ratio of linoleic acid to total fatty acids, the ratio of monounsaturated fatty acids and / or oleic acid to total fatty acids; - the ratio of omega-3 fatty acids to total fatty acids; the ratio of omega-6 fatty acids to total fatty acids; - the ratio of saturated fatty acids to total fatty acids; ・Fatty acid unsaturation degree, ・docosahexaenoic acid, Linoleic acid, monounsaturated fatty acids and / or oleic acid, ・ω-3 fatty acids, ・ω-6 fatty acids, ・Saturated fatty acids, - Triglycerides in high density lipoproteins (HDL), - triglycerides in low-density lipoproteins (LDL), High density lipoprotein (HDL) particle size, - low density lipoprotein (LDL) particle size, - very low density lipoprotein (VLDL) particle size, Acetate, citrate, ·glutamine, Histidine determining a quantitative value of at least one biomarker among comparing the quantitative value of the at least one biomarker to a control sample or control value; Including, an increase or decrease in the quantitative value of the at least one biomarker when compared to the control sample or control value indicates that the subject is at increased risk for developing a psychiatric disorder; the at least one biomarker comprises or is glycoprotein acetyl, where glycoprotein acetyl refers to a nuclear magnetic resonance spectroscopy signal indicative of the abundance of circulating glycated proteins; The method, wherein said psychiatric disorder is an anxiety disorder.

2. 10. The method of claim 1, wherein the method comprises determining quantitative values ​​of a plurality of the biomarkers, e.g., two, three, four, five or more biomarkers, in the biological sample.

3. The method comprises detecting in the biological sample obtained from the subject: -glycoprotein acetyl, ·albumin determining the quantitative value of the biomarker; comparing the quantitative values ​​of the biomarkers with control samples or control values; Including, 3. The method of claim 1 or 2, wherein an increase or decrease in the quantitative value of the biomarker when compared to the control sample or control value indicates that the subject is at increased risk of developing a psychiatric disorder.

4. The method comprises detecting in the biological sample obtained from the subject: -glycoprotein acetyl, At least one fatty acid measure of the following: ratio of docosahexaenoic acid to total fatty acids, docosahexaenoic acid, ratio of linoleic acid to total fatty acids, linoleic acid, ratio of monounsaturated fatty acids and / or oleic acid to total fatty acids, ratio of omega-6 fatty acids to total fatty acids, omega-6 fatty acids, ratio of saturated fatty acids to total fatty acids, saturated fatty acids, degree of fatty acid unsaturation determining the quantitative value of the biomarker; comparing the quantitative values ​​of the biomarkers with control samples or control values; Including, 4. The method of any one of claims 1 to 3, wherein an increase or decrease in the quantitative value of the biomarker when compared to the control sample or control value indicates that the subject is at increased risk of developing the psychiatric disorder.

5. The method according to any one of claims 1 to 4, wherein the psychiatric disorder comprises or is a phobic anxiety disorder (F40) and / or other anxiety disorder (F41).

6. The method of any one of claims 1 to 5, wherein the quantitative value of the at least one biomarker is determined using nuclear magnetic resonance spectroscopy.

7. 7. The method of any one of claims 1 to 6, wherein the method further comprises determining whether the subject is at risk of developing a psychiatric disorder using a risk score, hazard ratio, odds ratio, and / or predicted absolute or relative risk calculated based on the quantitative values ​​of the at least one biomarker or the plurality of biomarkers.

8. 8. The method of claim 7, wherein the risk score, hazard ratio, odds ratio, and / or predicted absolute or relative risk is calculated based on at least one additional measure, e.g., a characteristic of the subject.

9. 9. The method of claim 8, wherein the characteristics of the subject include one or more of age, height, weight, body mass index, race or ethnicity, smoking, and / or family history of mental and / or behavioral disorders.

10. The method comprises detecting in the biological sample obtained from the subject: -glycoprotein acetyl, ·albumin, - the ratio of docosahexaenoic acid to total fatty acids, - the ratio of linoleic acid to total fatty acids, the ratio of monounsaturated fatty acids and / or oleic acid to total fatty acids; - the ratio of omega-3 fatty acids to total fatty acids; the ratio of omega-6 fatty acids to total fatty acids; - the ratio of saturated fatty acids to total fatty acids; ・Fatty acid unsaturation degree, ・docosahexaenoic acid, Linoleic acid, monounsaturated fatty acids and / or oleic acid, ・ω-3 fatty acids, ・ω-6 fatty acids, ・Saturated fatty acids, - Triglycerides in high density lipoproteins (HDL), - triglycerides in low-density lipoproteins (LDL), High density lipoprotein (HDL) particle size, - low density lipoprotein (LDL) particle size, - very low density lipoprotein (VLDL) particle size, Acetate, citrate, ·glutamine, Histidine determining a quantitative value of one or more biomarkers among comparing the quantitative values ​​of the biomarkers with control samples or control values; Including, 10. The method of any one of claims 1 to 9, wherein an increase or decrease in the quantitative value of the biomarker when compared to the control sample or control value indicates that the subject is at increased risk for developing the psychiatric disorder.

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