Application of microbial marker in diagnosis of hyperglycemia-related diseases

Abstract

The invention discloses an application of a microbial marker in the diagnosis of hyperglycemia related diseases, and the microbial marker comprises the following components: Prevotella, Eubacteria recalcaeCAG.36, Dorealonggena, Butyvibrio, Klebsiella, Bacteroidella, Roseburiaintanalia, Saccharomyces cerevisiae, Saccharomyces cerevisiae, Saccharomyces cerevisiae, Saccharomyces cerevisiae, Saccharomyces cerevisiae, Saccharomyces cerevisiae, Saccharomyces cerevisiae, Saccharomyces cerevisiae, Saccharomyces cerevisiae, Saccharomyces cerevisiae, Saccharomyces cerevisiae, Saccharomyces cerevisiae, Saccharomyces cerevisiae Whether a subject suffers from hyperglycemia related diseases or the risk of suffering from the hyperglycemia related diseases can be predicted or diagnosed by detecting the content or abundance of the microbial marker in a sample of the subject or the base mutation frequency of the genome. The method has a good application prospect in the aspect of diagnosis of the hyperglycemia related diseases.

Description

Technical Field

[0001] The invention belongs to the field of biotechnology and relates to the application of microbial markers in the diagnosis of hyperglycemia-related diseases. Background Art

[0002] When the blood sugar value is higher than the normal range, it is called hyperglycemia. Hyperglycemia is also one of the "three highs" that people usually refer to. The other "two highs" are hypertension and hyperlipidemia. The normal value of fasting blood sugar is below 6.1mmol / L, and the normal value of blood sugar two hours after a meal is below 7.8mmol / L. If it is higher than this range, it is called hyperglycemia. Under normal circumstances, the human body can ensure the balance between the source and destination of blood sugar through the two major regulatory systems of hormone regulation and neural regulation, so that blood sugar is maintained at a certain level. However, under the combined effects of genetic factors (such as a family history of diabetes) and environmental factors (such as unreasonable diet, obesity, etc.), the two major regulatory functions are disturbed, and the blood sugar level will increase.

[0003] Increased blood sugar and increased urine sugar can trigger osmotic diuresis, which can cause symptoms of polyuria; increased blood sugar and large amounts of water loss will increase blood osmotic pressure accordingly, and high blood osmotic pressure can stimulate the thirst center of the hypothalamus, causing symptoms of thirst and polydipsia; due to the relative or absolute lack of insulin, glucose in the body cannot be utilized, and protein and fat consumption increase, causing fatigue and weight loss; in order to compensate for the lost sugar and maintain body activity, more food is needed; this forms the typical "three more and one less" symptom. The symptoms of polydipsia and polyuria in diabetic patients are directly proportional to the severity of the disease. In addition, it is worth noting that the more the patient eats, the higher the blood sugar, the more sugar is lost in the urine, and the more hungry he is, which eventually leads to a vicious cycle. Therefore, it is very important to detect and accurately determine whether there are diseases related to high blood sugar in a timely manner. Summary of the invention

[0004] In order to solve the above problems existing in the prior art, the present invention provides the following technical solutions:

[0005] The present invention provides an application of a reagent for detecting microbial markers in a sample in the preparation of a product for diagnosing hyperglycemia-related diseases. The microbial markers include Prevotella_stercorea, Eubacterium_rectale_CAG.36, Dorea_longicatena, Butyrivibrio_crossotus, Klebsiella_pneumoniae, Bacteroides_caccae, and Roseburia_intestinalis.

[0006] Further, the microbial markers include a combination of Prevotella_stercorea, Eubacterium_rectale_CAG.36, Dorea_longicatena, Butyrivibrio_crossotus, Klebsiella_pneumoniae, Bacteroides_caccae, and Roseburia_intestinalis.

[0007] Furthermore, the hyperglycemia-related diseases include diabetes, obesity, diabetic nephropathy, diabetic ketoacidosis, cerebral infarction, myocardial infarction, coronary heart disease, diabetic neuropathy, and diabetic retinopathy.

[0008] Furthermore, the microbial markers include a mutation at base position 2558 of the Prevotella_stercorea genome, a mutation at base position 221366 of the Eubacterium_rectale_CAG.36 genome, a mutation at base position 130251 of the Dorea_longicatena genome, a mutation at base position 14353 of the Butyrivibrio_crossotus genome, a mutation at base position 24638 of the Klebsiella_pneumoniae genome, a mutation at base position 3454642 of the Bacteroides_caccae genome, and a mutation at base position 106181 of the Roseburia_intestinalis genome. The genomic mutations of the microbial markers can also be referred to as SNP sites.

[0009] Furthermore, the microbial markers include a combination of mutations at base position 2558 of the Prevotella_stercorea genome, mutations at base position 221366 of the Eubacterium_rectale_CAG.36 genome, mutations at base position 130251 of the Dorea_longicatena genome, mutations at base position 14353 of the Butyrivibrio_crossotus genome, mutations at base position 24638 of the Klebsiella_pneumoniae genome, mutations at base position 3454642 of the Bacteroides_caccae genome, and mutations at base position 106181 of the Roseburia_intestinalis genome.

[0010] In the present invention, the reference genome of the Prevotella_stercorea strain used is GenBank ID: QRNO01000118.1, and its genome link is https: / / www.ncbi.nlm.nih.gov / nuccore / QRNO01000118.1. Among them, the 2558th base in the reference genome is an A base, and when the A base at this site mutates to a T base, the risk of hyperglycemia is low.

[0011] In the present invention, the reference genome of the Eubacterium_rectale_CAG.36 strain used is GenBank ID: FR891016.1, and its genome link is https: / / www.ncbi.nlm.nih.gov / nuccore / FR891016.1. Among them, the 221366th base in the reference genome is a C base, and the mutation of the C base at this site to the A base has a low risk of hyperglycemia.

[0012] In the present invention, the reference genome of the Dorea_longicatena strain used is GenBank ID: RCYC01000004.1, and its genome link is https: / / www.ncbi.nlm.nih.gov / nuccore / RCYC01000004.1. Among them, the 130251st base is an A base in the reference genome, and the mutation of the A base at this site to the G base has a low risk of hyperglycemia.

[0013] In the present invention, the reference genome of the Butyrivibrio_crossotus strain used is GenBank ID: RKEB01000030.1, and its genome link is https: / / www.ncbi.nlm.nih.gov / nuccore / RKEB01000030.1. Among them, the 14353rd base in the reference genome is a C base, and the mutation of the C base at this site to an A or T base has a low risk of hyperglycemia.

[0014] In the present invention, the reference genome of Klebsiella_pneumoniae strain used is GenBank ID: CP091979.1, and its genome link is https: / / www.ncbi.nlm.nih.gov / nuccore / CP091979.1. Among them, the 24638th base in the reference genome is a G base, and the mutation of the G base at this site to the A base has a high risk of hyperglycemia.

[0015] In the present invention, the reference genome of the Bacteroides_caccae strain used is GenBank ID: CP081920.1, and its genome link is https: / / www.ncbi.nlm.nih.gov / nuccore / CP081920.1. Among them, the 3454642nd base is a C base in the reference genome, and the mutation of the C base at this site to the A base has a low risk of hyperglycemia.

[0016] In the present invention, the reference genome of the Roseburia_intestinalis strain used is GenBank ID: WNAJ01000013.1, and its genome link is https: / / www.ncbi.nlm.nih.gov / nuccore / WNAJ01000013.1. Among them, the 106181st base in the reference genome is a G base, and the mutation of the G base at this site to the A base has a high risk of hyperglycemia.

[0017] Furthermore, the product is a reagent, kit, primer, chip, test paper, nucleic acid membrane strip, probe, risk prediction model or risk assessment system for diagnosing hyperglycemia-related diseases.

[0018] Furthermore, the reagent for detecting microbial markers in a sample is a reagent for detecting the abundance of the aforementioned microbial markers in the sample.

[0019] Furthermore, the reagent for detecting the microbial marker in the sample includes a reagent for detecting the base type of the mutation site of the microbial marker gene mentioned above in the sample.

[0020] In some embodiments, the base type of the mutation site of the microbial marker gene refers to the 2558th base of the Prevotella_stercorea genome (GenBank ID: QRNO01000118.1), the 221366th base of the Eubacterium_rectale_CAG.36 genome (GenBank ID: FR891016.1), the 130251st base of the Dorea_longicatena genome (GenBank ID: RCYC01000004.1), the 14353rd base of the Butyrivibrio_crossotus genome (GenBank ID: RKEB01000030.1), the 24638th base of the Klebsiella_pneumoniae genome (GenBank ID: CP091979.1), the 24638th base of the Bacteroides_caccae genome (GenBank ID: ID: CP081920.1) and the type of base 106181 of the Roseburia_intestinalis genome (GenBank ID: WNAJ01000013.1).

[0021] Further, the sample includes bone marrow, blood, blood cells, serum, peripheral blood, ascites, tissue or fine needle biopsy samples, cell-containing body fluids, free-floating nucleic acids, sputum, saliva, urine, cerebrospinal fluid, peritoneal fluid, pleural fluid, stool, lymph, gynecological fluid, skin swab, vaginal swab, oral swab, nasal swab, washing fluid, lavage fluid, aspirate, scrape, bone marrow sample, tissue biopsy sample, surgical sample, secretion and / or excretion.

[0022] Furthermore, the sample is a stool sample.

[0023] Furthermore, the microbial marker abundance includes a reagent that specifically detects the abundance of a microbial marker gene, or a reagent that specifically binds to the abundance of a protein encoded by a microbial marker gene.

[0024] Furthermore, the microbial marker abundance is a reagent for specifically detecting the abundance of microbial marker genes.

[0025] The terms "marker" and "biological marker" are synonymous and refer to measurable indicators of the biological state of an individual. Such biomarkers can be any substances in an individual as long as they are related to the specific biological state (for example, disease) of the individual being tested. The meaning of nucleic acid markers is not limited to existing genes that can be expressed as biologically active proteins, but also includes any nucleic acid fragments, which can be DNA or RNA, modified DNA or RNA, or unmodified DNA or RNA, as well as a collection of them. In this article, nucleic acid markers are sometimes also referred to as characteristic fragments. In the present invention, biomarkers can also be represented by "intestinal markers."

[0026] The term "reagent" refers to any conventional reagent in the art, as long as it can detect the abundance of a microbial marker.

[0027] The term "abundance" refers to a measure of the number of target microorganisms in a biological sample. "Abundance" is also called "load". The quantification of the abundance of a target nucleic acid sequence in a biological sample may be absolute or relative. "Relative quantification" is usually based on one or more internal reference genes, i.e., the 16S rRNA gene from a reference strain, such as using universal primers and expressing the abundance of the target nucleic acid sequence as a percentage of total bacterial 16S rRNA gene copies or total bacteria measured by normalization of Escherichia coli 16S rRNA gene copies. "Absolute quantification" gives the exact number of target molecules by comparison with a DNA standard or by normalization of DNA concentration.

[0028] The terms "subject" and "individual" as used herein in the context of the aforementioned methods relate to animals, such as mammals. In one embodiment of the invention, the individual is a human. In one embodiment, the term "diabetes" refers to type 1 diabetes. In another embodiment, the term "diabetes" refers to type 2 diabetes. The terms "type 1 diabetes" and "type 2 diabetes" are well known in the art. In type 2 diabetes, insulin secretion is insufficient. Insulin levels are usually high, especially in the early stages of the disease, but peripheral insulin resistance and increased liver glucose production make insulin levels insufficient to normalize plasma glucose levels. Then insulin production decreases, further exacerbating hyperglycemia.

[0029] The term "obesity" is defined as a BMI equal to or greater than 30 kg / m2 in adults of European descent. 2 According to the WHO definition, the term obesity can be classified as follows: The term "class I obesity" is when the BMI is equal to or greater than 30 kg / m 2 But less than 35kg / m 2 The term "Class II obesity" is a condition in which the BMI is equal to or greater than 35 kg / m2 But less than 40kg / m 2 The term "Class III obesity" is a condition in which the BMI is equal to or greater than 40 kg / m 2 For Asian subjects, the term “obesity” was defined as a BMI equal to or greater than 25 kg / m 2 Obesity in Asians can be further categorized as follows: The term "Class I obesity" is when the BMI is equal to or greater than 25 kg / m 2 But less than 30kg / m 2 The term "Class II obesity" is a condition in which the BMI is equal to or greater than 30 kg / m 2 disease.

[0030] The present invention provides a product for diagnosing whether a subject suffers from a hyperglycemia-related disease or has a risk of suffering from a hyperglycemia-related disease, wherein the product comprises the reagent used in the above application.

[0031] Furthermore, the product includes a reagent for detecting a mutation site in the genome of any of the microbial markers described above or a base site in a specific gene of a microbial marker.

[0032] Further, the base sites of the specific gene include the 2558th base of the Prevotella_stercorea genome (GenBank ID: QRNO01000118.1), the 221366th base of the Eubacterium_rectale_CAG.36 genome (GenBank ID: FR891016.1), the 130251st base of the Dorea_longicatena genome (GenBank ID: RCYC01000004.1), the 14353rd base of the Butyrivibrio_crossotus genome (GenBank ID: RKEB01000030.1), the 24638th base of the Klebsiella_pneumoniae genome (GenBank ID: CP091979.1), the 24638th base of the Bacteroides_caccae genome (GenBank ID: ID: CP081920.1), and base 106181 of the Roseburia_intestinalis genome (GenBank ID: WNAJ01000013.1).

[0033] In some embodiments, the mutation site of the genome of the microbial marker refers to the mutation of the 2558th base of the Prevotella_stercorea genome (GenBank ID: QRNO01000118.1), the mutation of the 221366th base of the Eubacterium_rectale_CAG.36 genome (GenBank ID: FR891016.1), the mutation of the 130251st base of the Dorea_longicatena genome (GenBank ID: RCYC01000004.1), the mutation of the 14353rd base of the Butyrivibrio_crossotus genome (GenBank ID: RKEB01000030.1), the mutation of the 24638th base of the Klebsiella_pneumoniae genome (GenBank ID: CP091979.1), the mutation of the 221366th base of the Bacteroides_caccae ...30251st base of the Dorea_long ID: CP081920.1), and the mutation of base 106181 in the Roseburia_intestinalis genome (GenBank ID: WNAJ01000013.1).

[0034] Furthermore, the products include test kits, primers, test strips, nucleic acid membrane strips, probes, and chips.

[0035] Furthermore, the product includes reagents for detecting the presence / absence or amount of microbial marker genes, functional fragments, or proteins in a sample.

[0036] Further, the reagents include reagents for detecting the presence, absence and / or amount of microbial marker genes, functional fragments, proteins in a sample by PCR reaction, RT-PCR derivatization reaction, 3SR amplification, LCR, SDA, NASBA, TMA, SYBR Green, TaqMan probe, molecular beacon, double hybridization probe, composite probe, ISH, microarray, Southern blot, Northern blot, multi-analyte spectrum test, enzyme-linked immunosorbent assay (ELISA), radioimmunoassay, immunofluorescence assay, enzyme immunoassay, immunoprecipitation assay, chemiluminescence assay, immunohistochemistry assay, dot blot assay or slot blot assay.

[0037] In certain specific embodiments, the diagnosis varies with the sample. In a specific embodiment, the diagnostic sample uses a stool sample, where the microbial markers increase in abundance with high blood sugar levels and show significant differences.

[0038] Furthermore, the product also includes a product instruction sheet recording the above-mentioned diagnosis of whether the subject suffers from a hyperglycemia-related disease or has a risk of suffering from a hyperglycemia-related disease, and the instruction sheet includes the following steps:

[0039] 1) The nucleic acid from the sample is contacted with a reagent for detecting the abundance of a microbial marker or a reagent for detecting the base type of a mutation site in the genome of a microbial marker;

[0040] 2) Determine the abundance of microbial markers or determine the base type of the mutation site of the microbial marker genome;

[0041] 3) judging whether the subject suffers from a hyperglycemia-related disease or has a risk of suffering from a hyperglycemia-related disease based on the abundance or base type of the microbial marker.

[0042] Furthermore, the base types of the microbial markers are: the 2558th base of the Prevotella_stercorea genome (GenBank ID: QRNO01000118.1) is A, the 221366th base of the Eubacterium_rectale_CAG.36 genome (GenBank ID: FR891016.1) is C, the 130251st base of the Dorea_longicatena genome (GenBank ID: RCYC01000004.1) is A, the 14353rd base of the Butyrivibrio_crossotus genome (GenBank ID: RKEB01000030.1) is C, the 24638th base of the Klebsiella_pneumoniae genome (GenBank ID: CP091979.1) is A, and the 221366th base of the Eubacterium_rectale_CAG.36 genome (GenBank ID: FR891016.1) is C. When the base type of the microbial marker is that the 3454642nd base of the genome of R. longicatena (GenBank ID: RCYC01000004.1) is C, and the 106181st base of the genome of Roseburia intestinalis (GenBank ID: WNAJ01000013.1) is A, the subject suffers from a hyperglycemia-related disease or has an increased risk of suffering from a hyperglycemia-related disease; when the base type of the microbial marker is that the 2558th base of the genome of Prevotella stercorea (GenBank ID: QRNO01000118.1) is T, the 221366th base of the genome of Eubacterium rectale_CAG.36 (GenBank ID: FR891016.1) is A, the 130251st base of the genome of Dorea longicatena (GenBank ID: RCYC01000004.1) is G, and the 130252nd base of the genome of Butyrivibrio crossotus (GenBank ID: When the 14353rd base of the Klebsiella_pneumoniae genome (GenBank ID: CP091979.1) is T or A, the 24638th base of the Bacteroides_caccae genome (GenBank ID: CP081920.1) is A, and the 106181st base of the Roseburia_intestinalis genome (GenBank ID: WNAJ01000013.1) is G, the subject suffers from a hyperglycemia-related disease or has a reduced risk of suffering from a hyperglycemia-related disease.

[0043] The present invention provides the use of microbial markers in constructing a computer model for diagnosing hyperglycemia-related diseases, wherein the microbial markers include any of the microbial markers described above.

[0044] The term "computer model" used in the present invention refers to a model for predicting that a patient suffers from a hyperglycemia-related disease based on the obtained abundance detection data of microbial markers.

[0045] Furthermore, the hyperglycemia-related diseases include diabetes, obesity, diabetic nephropathy, diabetic ketoacidosis, cerebral infarction, myocardial infarction, coronary heart disease, diabetic neuropathy, and diabetic retinopathy.

[0046] The present invention provides a method for constructing a risk prediction model for hyperglycemia-related diseases based on microbial markers, the method comprising using a machine learning method to perform model training to construct a risk prediction model for hyperglycemia-related diseases.

[0047] Furthermore, the construction method includes the steps of collecting and processing microbial specimens.

[0048] Furthermore, the construction method includes the steps of detecting and analyzing microbial specimens to screen out microbial markers, wherein the microbial markers include any of the microbial markers described above.

[0049] Furthermore, the microbial specimens are from samples of patients with hyperglycemia-related diseases and samples of healthy people.

[0050] Further, the sample includes bone marrow, blood, blood cells, serum, peripheral blood, ascites, tissue or fine needle biopsy samples, cell-containing body fluids, free-floating nucleic acids, sputum, saliva, urine, cerebrospinal fluid, peritoneal fluid, pleural fluid, stool, lymph, gynecological fluid, skin swab, vaginal swab, oral swab, nasal swab, washing fluid, lavage fluid, aspirate, scrape, bone marrow sample, tissue biopsy sample, surgical sample, secretion and / or excretion.

[0051] Furthermore, the sample is a stool sample.

[0052] Furthermore, the machine learning method includes a decision tree model, a random forest model, a K-nearest neighbor algorithm model, a naive Bayes model, a support vector machine model, and a neural network model.

[0053] Further, the machine learning method includes using the presence and abundance information of microbial markers as features.

[0054] The present invention provides a computer diagnosis and treatment system for diagnosing hyperglycemia-related diseases using microbial markers, characterized in that the computer diagnosis and treatment system comprises:

[0055] An input module, used to input detection data of microbial markers, wherein the microbial markers are the microbial markers described above;

[0056] A result determination module, used to compare the detection data of the microbial marker with the set data;

[0057] An output module, used for outputting the result of the result determination module;

[0058] Furthermore, the base types of the microbial markers are: the 2558th base of the Prevotella_stercorea genome (GenBank ID: QRNO01000118.1) is A, the 221366th base of the Eubacterium_rectale_CAG.36 genome (GenBank ID: FR891016.1) is C, the 130251st base of the Dorea_longicatena genome (GenBank ID: RCYC01000004.1) is A, the 14353rd base of the Butyrivibrio_crossotus genome (GenBank ID: RKEB01000030.1) is C, the 24638th base of the Klebsiella_pneumoniae genome (GenBank ID: CP091979.1) is A, and the 221366th base of the Eubacterium_rectale_CAG.36 genome (GenBank ID: FR891016.1) is C. The computer determines that the subject suffers from a hyperglycemia-related disease or has an increased risk of suffering from a hyperglycemia-related disease when the 3454642nd base of the genome of R. intestinalis (GenBank ID: WNAJ01000013.1) is C and the 106181st base of the genome of Roseburia intestinalis (GenBank ID: WNAJ01000013.1) is A, and the computer determines that the subject suffers from a hyperglycemia-related disease or has an increased risk of suffering from a hyperglycemia-related disease; when the base type of the microbial marker is that the 2558th base of the genome of Prevotella stercorea (GenBank ID: QRNO01000118.1) is T, the 221366th base of the genome of Eubacterium rectale_CAG.36 (GenBank ID: FR891016.1) is A, the 130251st base of the genome of Dorea longicatena (GenBank ID: RCYC01000004.1) is G, and the 130252nd base of the genome of Butyrivibrio crossotus (GenBank ID: When the 14353rd base of the Klebsiella_pneumoniae genome (GenBank ID: CP091979.1) is T or A, the 24638th base of the Klebsiella_pneumoniae genome (GenBank ID: CP091979.1) is G, the 3454642nd base of the Bacteroides_caccae genome (GenBank ID: CP081920.1) is A, and the 106181st base of the Roseburia_intestinalis genome (GenBank ID: WNAJ01000013.1) is G, the computer determines that the subject suffers from a hyperglycemia-related disease or has a reduced risk of suffering from a hyperglycemia-related disease.

[0059] In some embodiments, the method for detecting microbial markers is a sequencing method, and the sequencing method includes but is not limited to a second-generation sequencing method or a third-generation sequencing method. The means for sequencing is not particularly limited, and sequencing by a second-generation or third-generation sequencing method can achieve fast and efficient sequencing. As a specific embodiment, the sequencing method is performed by at least one selected from Hiseq2000, SOLiD, 454, and a single-molecule sequencing device. Thus, the high-throughput and deep sequencing characteristics of these sequencing devices can be utilized, which is conducive to the analysis of subsequent sequencing data, especially the precision and accuracy when performing statistical tests.

[0060] The present invention provides an intestinal flora scoring device for evaluating the risk of hyperglycemia-related diseases, wherein the intestinal flora scoring device comprises the following units:

[0061] Detection unit: detecting the abundance of microbial markers in the sample or the base type of gene mutation of microbial markers in the application as described above;

[0062] Analysis unit: using the detected abundance of microbial markers or the base type of the gene mutation of the microbial marker as an input variable and inputting it into the risk model described above for analysis;

[0063] Evaluation unit: outputs the risk value of the individual suffering from hyperglycemia-related diseases corresponding to the sample.

[0064] The present invention provides the use of a microbial marker in the preparation of a drug for treating a hyperglycemia-related disease, wherein the microbial marker includes any of the microbial markers described above.

[0065] The present invention provides the use of microbial markers in screening candidate drugs for treating hyperglycemia-related diseases, wherein the microbial markers include any of the microbial markers described above.

[0066] As used herein, the term "biological sample" is synonymous with "sample" and typically refers to a sample obtained or derived from a biological source of interest as described herein (e.g., a tissue or organism or cell culture). In some embodiments, the source of interest includes an organism, such as an animal or a human. In some embodiments, the biological sample is or comprises a biological tissue or fluid. In some embodiments, the biological sample may be or comprise bone marrow; blood; blood cells; ascites; tissue or fine needle biopsy samples; cell-containing body fluids; free-floating nucleic acids; sputum; saliva; urine; cerebrospinal fluid, peritoneal fluid; pleural fluid; feces; lymph; gynecological fluid; skin swab; vaginal swab; oral swab; nasal swab; washing fluid or lavage fluid, such as catheter lavage fluid or bronchoalveolar lavage fluid; aspirate; scraping; bone marrow sample; tissue biopsy sample; surgical sample; feces, other body fluids, secretions and / or excretions; and / or cells from them, etc. In some embodiments, the biological sample is or comprises cells obtained from an individual. In some embodiments, the cells obtained are or include cells from the individual from which the sample is obtained. In some embodiments, the sample is a "primary sample" obtained directly from a source of interest by any appropriate means. For example, in some embodiments, the primary biological sample is obtained by a method selected from the group consisting of: biopsy (e.g., fine needle aspiration or tissue biopsy), surgery, body fluid (e.g., blood, lymph, feces, etc.) collection, etc. In some embodiments, as will be clear from the context, the term "sample" refers to a preparation obtained by processing a primary sample (e.g., by removing one or more components of the primary sample and / or by adding one or more agents to the primary sample).

[0067] The term "primer" refers to a 7 to 50 nucleic acid sequence that can form a complementary base pair with a template chain and acts as a starting point for replicating the template chain. Primers are usually synthesized, but naturally occurring nucleic acids can also be used. The sequence of the primer does not necessarily need to be exactly the same as the sequence of the template, as long as it is sufficiently complementary and can hybridize with the template. Additional features that do not change the basic properties of the primer can be mixed in. Examples of additional features that can be mixed in include methylation, capping, replacement of more than one nucleic acid by a homolog, and modification between nucleic acids, but are not limited to these.

[0068] The term "chip" may refer to a solid substrate having a generally planar surface to which an adsorbent is attached. The surface of a biochip may contain a plurality of addressable locations, each of which may be associated with an adsorbent. A biochip may be adapted to engage a probe interface and thus be used as a probe. Protein biochips are suitable for capturing polypeptides and may contain a surface to which a chromatographic or biospecific adsorbent is attached at addressable locations. Microarray chips are generally used for DNA and RNA gene expression detection.

[0069] The term "probe" refers to a molecule that can bind to a specific sequence or subsequence or other portion of another molecule. Unless otherwise indicated, the term "probe" generally refers to a polynucleotide probe that can bind to another polynucleotide (often referred to as a "target polynucleotide") through complementary base pairing. Depending on the stringency of the hybridization conditions, the probe can bind to a target polynucleotide that lacks complete sequence complementarity with the probe. The probe can be labeled directly or indirectly, including primers. Hybridization methods include, but are not limited to: solution phase, solid phase, mixed phase or in situ hybridization assays.

[0070] The term "ROC curve" is the receiver operating characteristic curve, which is a coordinate graph composed of the false positive probability (1-specificity) as the horizontal axis and the true positive probability (sensitivity) as the vertical axis, and the curve drawn by the different results obtained by the subjects under specific stimulation conditions due to different judgment criteria. Select the best diagnostic limit value. The closer the ROC curve is to the upper left corner, the higher the accuracy of the test. The point of the ROC curve closest to the upper left corner is the best threshold with the least error, and its total number of false positives and false negatives is the least. Comparison of the ability of two or more different diagnostic tests to identify diseases. When comparing two or more diagnostic methods for the same disease, the ROC curves of each test can be plotted on the same coordinate to intuitively identify the advantages and disadvantages. The ROC curves close to the upper left corner represent the most accurate test. It can also be compared by calculating the area under the ROC curve (AUC) of each test separately. The test with the largest AUC has the best diagnostic value. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is the ROC curve of the test set using microbial markers for diagnosis of hyperglycemia;

[0072] Figure 2 This is the ROC curve of the validation set using microbial markers for diagnosing abnormal hyperglycemia.

[0073] Figure 3 It is a flowchart of a computerized diagnosis and treatment system for diagnosing hyperglycemia-related diseases using microbial markers;

[0074] Figure 4 This is a schematic diagram of the flow chart of the intestinal flora scoring device for assessing the risk of hyperglycemia-related diseases. DETAILED DESCRIPTION

[0075] Example 1 Screening and testing of intestinal flora associated with hyperglycemia

[0076] 1. Research subjects and sample collection

[0077] A total of 258 samples were collected, including blood, serum and stool samples from 130 patients with hyperglycemia and 128 healthy people.

[0078] 2. Experimental methods

[0079] 1) Stool sample collection and DNA extraction

[0080] After collecting stool samples from the above-mentioned population, DNA was extracted using a kit to obtain extracted DNA samples.

[0081] 2) Metagenomic high-throughput sequencing and analysis

[0082] The microorganisms Prevotella_stercorea (Genome GenBank ID: QRNO01000118.1), Eubacterium_rectale_CAG.36 (Genome GenBank ID: FR891016.1), Dorea_longicatena (Genome GenBank ID: RCYC01000004.1), Butyrivibrio_crossotus (Genome GenBank ID: RKEB01000030.1), Klebsiella_pneumoniae (Genome GenBank ID: CP091979.1), Bacteroides_caccae (Genome GenBank ID: CP081920.1), and Roseburia_intestinalis (Genome GenBank ID: RKEB01000030.1) were downloaded from the NCBI database. ID: WNAJ01000013.1) was used as the reference genome for a total of 7 microbial species. First, the sequences obtained from the original sequencing were quality controlled. The sequenced sequences after quality control were compared with the 7 genomes using BWA software to obtain the sequence of each genome. SAMTools software (parameter: "-vmO zV indels) and VCF tools software (parameters: 1 / d=10 / a=4 / Q=15 / q=10 / .) were used to find SNP sites and screen out high-quality SNP sites. In order to reduce the false positive results, VarScan2 software (parameters: "--min-coverage10--min-reads24--min-var-freq0.2--p-value 0.05.) was also used to find and screen SNPs. The SNP results finally used for downstream analysis took the intersection of the screening results of the two software.

[0083] 3) Diagnostic efficacy analysis

[0084] The R package "pROC" was used to draw the receiver operating curve (ROC) and analyze the AUC value, sensitivity, and specificity of the microbial markers in the test set that showed significant differences in mutation levels between healthy subjects and hyperglycemic subjects to determine their diagnostic efficacy for hyperglycemia-related diseases.

[0085] Among them, the mutation of the microbial marker is used for evaluation and analysis, and the point corresponding to the maximum Youden index is selected as its cutoff value, that is, the optimal division threshold is determined by the point with the largest Youden index.

[0086] 3. Experimental results

[0087] The BLINK software and FarmCPU software in GAPIT software were used to perform genome-wide association analysis on the SNPs of the seven bacterial genomes and the fasting blood glucose phenotype. The results showed that the genomic variations of the seven microorganisms were significantly correlated with the fasting blood glucose level. The BLINK software found a mutation in the 2558th base of the Prevotella_stercorea genome (genome GenBank ID: QRNO01000118.1), a mutation in the 221366th base of the Eubacterium_rectale_CAG.36 genome (genome GenBank ID: FR891016.1), a mutation in the 130251st base of the Dorea_longicatena genome (genome GenBank ID: RCYC01000004.1), a mutation in the 14353rd base of the Butyrivibrio_crossotus genome (genome GenBank ID: RKEB01000030.1), and a mutation in the 14353rd base of the Klebsiella_pneumoniae genome (genome GenBank ID: The mutation of base 24638 in the Bacteroides_caccae genome (GenBank ID: CP091979.1), the mutation of base 3454642 in the Bacteroides_caccae genome (GenBank ID: CP081920.1), and the mutation of base 106181 in the Roseburia_intestinalis genome (GenBank ID: WNAJ01000013.1) were significantly correlated with fasting blood glucose levels.

[0088] The results are shown in Table 1. The results show that the mutation frequencies of specific bases of specific genes of 7 microbial markers are different in hyperglycemia population and normal population, with significant differences. The OR values ​​all show that the seven sites are influencing factors of hyperglycemia, including the 2558th base of the Prevotella_stercorea genome (genome GenBank ID: QRNO01000118.1), the 221366th base of the Eubacterium_rectale_CAG.36 genome (genome GenBank ID: FR891016.1), the 130251st base of the Dorea_longicatena genome (genome GenBank ID: RCYC01000004.1), the 14353rd base of the Butyrivibrio_crossotus genome (genome GenBank ID: RKEB01000030.1), the 1565th base of the Bacteroides_caccae genome (genome GenBank ID: The mutation of base 3454642 in the genome of Klebsiella_pneumoniae (GenBank ID: CP091979.1) and base 106181 in the genome of Roseburia_intestinalis (GenBank ID: WNAJ01000013.1) are risk factors.

[0089] Table 1

[0090]

[0091]

[0092] The combination of microbial markers (combination of mutation sites) was used as the detection variable to draw the ROC curve and calculate its AUC value. The results are as follows: Figure 1 As shown, the AUC value was 0.8029, indicating that the combination of microbial markers as a detection variable has high specificity and sensitivity.

[0093] Example 2 Verification of the ability of microbial markers to diagnose hyperglycemia-related diseases

[0094] 1. Research subjects and sample collection

[0095] Blood, serum and stool samples were collected from 50 sample populations, including 25 patients with hyperglycemia and 25 healthy people.

[0096] 2. Experimental methods

[0097] Refer to the experimental method in Example 1 and make adaptive adjustments.

[0098] 3. Experimental results

[0099] ROC curve analysis of microbial markers in diagnosing hyperglycemia-related diseases

[0100] The combination of microbial markers (combination of mutation sites) was used as the detection variable to draw the ROC curve and calculate its AUC value. The results are as follows: Figure 2 As shown, the AUC value was 0.7639, indicating that the combination of microbial markers can be used as microbial markers for diagnosing hyperglycemia-related diseases.

[0101] The description of the above embodiments is only used to understand the method and core idea of ​​the present invention. It should be pointed out that, for those skilled in the art, several improvements and modifications can be made to the present invention without departing from the principle of the present invention, and these improvements and modifications will also fall within the scope of protection of the claims of the present invention.

Claims

1. Use of a reagent for detecting microbial markers in a sample in the preparation of a product for diagnosing hyperglycemia-related diseases, characterized in that: The microbial markers include Prevotella_stercorea, Eubacterium_rectale_CAG.36, Dorea_longicatena, Butyrivibrio_crossotus, Klebsiella_pneumoniae, Bacteroides_caccae, and Roseburia_intestinalis.

2. The use according to claim 1, characterized in that: The microbial markers include a combination of Prevotella_stercorea, Eubacterium_rectale_CAG.36, Dorea_longicatena, Butyrivibrio_crossotus, Klebsiella_pneumoniae, Bacteroides_caccae, and Roseburia_intestinalis; Preferably, the hyperglycemia-related diseases include diabetes, obesity, diabetic nephropathy, diabetic ketoacidosis, cerebral infarction, myocardial infarction, coronary heart disease, diabetic neuropathy, and diabetic retinopathy; Preferably, the microbial markers include a mutation at base 2558 of the Prevotella_stercorea genome, a mutation at base 221366 of the Eubacterium_rectale_CAG.36 genome, a mutation at base 130251 of the Dorea_longicatena genome, a mutation at base 14353 of the Butyrivibrio_crossotus genome, a mutation at base 24638 of the Klebsiella_pneumoniae genome, a mutation at base 3454642 of the Bacteroides_caccae genome, and a mutation at base 106181 of the Roseburia_intestinalis genome; Preferably, the microbial markers include a combination of a mutation at base position 2558 of the Prevotella_stercorea genome, a mutation at base position 221366 of the Eubacterium_rectale_CAG.36 genome, a mutation at base position 130251 of the Dorea_longicatena genome, a mutation at base position 14353 of the Butyrivibrio_crossotus genome, a mutation at base position 24638 of the Klebsiella_pneumoniae genome, a mutation at base position 3454642 of the Bacteroides_caccae genome, and a mutation at base position 106181 of the Roseburia_intestinalis genome.

3. The use according to claim 1 or 2, characterized in that: The product is a reagent, kit, primer, chip, test paper, nucleic acid membrane strip, probe, risk prediction model or risk assessment system for diagnosing hyperglycemia-related diseases; Preferably, the reagent for detecting microbial markers in a sample includes a reagent for detecting the abundance of the microbial markers according to claim 1 in a sample; Preferably, the reagent for detecting the microbial marker in the sample includes a reagent for detecting the base type of the mutation site of the microbial marker gene according to claim 1 in the sample; Preferably, the sample comprises bone marrow, blood, blood cells, serum, peripheral blood, ascites, tissue or fine needle biopsy sample, cell-containing body fluid, free floating nucleic acid, sputum, saliva, urine, cerebrospinal fluid, peritoneal fluid, pleural fluid, stool, lymph, gynecological fluid, skin swab, vaginal swab, oral swab, nasal swab, wash, lavage, aspirate, scrape, bone marrow sample, tissue biopsy sample, surgical sample, secretion and / or excretion; Preferably, the sample is a stool sample; Preferably, the microbial marker abundance includes a reagent that specifically detects the abundance of a microbial marker gene, or a reagent that specifically binds to a protein encoded by a microbial marker gene; Preferably, the microbial marker abundance is a reagent for specifically detecting the abundance of microbial marker genes.

4. A product for diagnosing whether a subject suffers from a hyperglycemia-related disease or has a risk of suffering from a hyperglycemia-related disease, characterized in that: The product comprises the reagent used in the application according to any one of claims 1 to 3; Preferably, the product comprises a reagent for detecting a mutation site in the genome of a microbial marker or a base site in a specific gene of a microbial marker according to any one of claims 1 to 3; Preferably, the base sites of the specific gene include base 2558 of the Prevotella_stercorea genome, base 221366 of the Eubacterium_rectale_CAG.36 genome, base 130251 of the Dorea_longicatena genome, base 14353 of the Butyrivibrio_crossotus genome, base 24638 of the Klebsiella_pneumoniae genome, base 3454642 of the Bacteroides_caccae genome, and base 106181 of the Roseburia_intestinalis genome; Preferably, the product includes a kit, a primer, a test paper, a nucleic acid membrane strip, a probe, and a chip; Preferably, the product further comprises a product instruction sheet recording the method of diagnosing whether a subject suffers from a hyperglycemia-related disease or has a risk of suffering from a hyperglycemia-related disease as described in claim 4, wherein the instruction sheet comprises the following steps: 1) The nucleic acid from the sample is contacted with a reagent for detecting the abundance of a microbial marker or a reagent for detecting the base type of a mutation site in the genome of a microbial marker; 2) Determine the abundance of microbial markers or determine the base type of the mutation site of the microbial marker genome; 3) judging that the subject suffers from a hyperglycemia-related disease or has a risk of suffering from a hyperglycemia-related disease based on the abundance or base type of the microbial marker; Preferably, when the base type of the microbial marker is that the 2558th base of the Prevotella_stercorea genome is A, the 221366th base of the Eubacterium_rectale_CAG.36 genome is C, the 130251st base of the Dorea_longicatena genome is A, the 14353rd base of the Butyrivibrio_crossotus genome is C, the 24638th base of the Klebsiella_pneumoniae genome is A, the 3454642nd base of the Bacteroides_caccae genome is C, and the 106181st base of the Roseburia_intestinalis genome is A, the subject suffers from a hyperglycemia-related disease or has an increased risk of suffering from a hyperglycemia-related disease large; when the base type of the microbial marker is T at base 2558 of the Prevotella_stercorea genome, A at base 221366 of the Eubacterium_rectale_CAG.36 genome, G at base 130251 of the Dorea_longicatena genome, T or A at base 14353 of the Butyrivibrio_crossotus genome, G at base 24638 of the Klebsiella_pneumoniae genome, A at base 3454642 of the Bacteroides_caccae genome, and G at base 106181 of the Roseburia_intestinalis genome, the subject suffers from a hyperglycemia-related disease or has a reduced risk of suffering from a hyperglycemia-related disease.

5. The use of microbial markers in constructing a computer model for diagnosing hyperglycemia-related diseases, characterized in that: The microbial marker comprises the microbial marker according to any one of claims 1 to 3; Preferably, the hyperglycemia-related diseases include diabetes, obesity, diabetic nephropathy, diabetic ketoacidosis, cerebral infarction, myocardial infarction, coronary heart disease, diabetic neuropathy, and diabetic retinopathy.

6. A method for constructing a risk prediction model for hyperglycemia-related diseases based on microbial markers, characterized in that: The construction method includes using a machine learning method to perform model training to construct a hyperglycemia-related disease risk prediction model; Preferably, the construction method includes the steps of collecting and processing microbial specimens; Preferably, the construction method includes the steps of detecting and analyzing microbial specimens to screen out microbial markers, wherein the microbial markers include the microbial markers according to any one of claims 1 to 3; Preferably, the microbial specimens are samples from patients with hyperglycemia-related diseases and healthy subjects; Preferably, the sample comprises bone marrow, blood, blood cells, serum, peripheral blood, ascites, tissue or fine needle biopsy sample, cell-containing body fluid, free floating nucleic acid, sputum, saliva, urine, cerebrospinal fluid, peritoneal fluid, pleural fluid, stool, lymph, gynecological fluid, skin swab, vaginal swab, oral swab, nasal swab, wash, lavage, aspirate, scrape, bone marrow sample, tissue biopsy sample, surgical sample, secretion and / or excretion; Preferably, the sample is a stool sample; Preferably, the machine learning method includes a decision tree model, a random forest model, a K-nearest neighbor algorithm model, a naive Bayes model, a support vector machine model, and a neural network model; Preferably, the machine learning method comprises using the presence and abundance information of microbial markers as features.

7. A computer diagnosis and treatment system for diagnosing hyperglycemia-related diseases using microbial markers, characterized in that: The computer diagnosis and treatment system comprises: An input module, used to input detection data of a microbial marker, wherein the microbial marker is a microbial marker according to claims 1 to 3; A result determination module, used to compare the detection data of the microbial marker with the set data; An output module, used for outputting the result of the result determination module; Preferably, when the base types of the microbial markers are: the 2558th base of the Prevotella_stercorea genome is A, the 221366th base of the Eubacterium_rectale_CAG.36 genome is C, the 130251st base of the Dorea_longicatena genome is A, the 14353rd base of the Butyrivibrio_crossotus genome is C, the 24638th base of the Klebsiella_pneumoniae genome is A, the 3454642nd base of the Bacteroides_caccae genome is C, and the 106181st base of the Roseburia_intestinalis genome is A, the computer determines that the subject suffers from a hyperglycemia-related disease or has an increased risk of suffering from a hyperglycemia-related disease. large; when the base type of the microbial marker is T at the 2558th base of the Prevotella_stercorea genome, A at the 221366th base of the Eubacterium_rectale_CAG.36 genome, G at the 130251st base of the Dorea_longicatena genome, T or A at the 14353rd base of the Butyrivibrio_crossotus genome, G at the 24638th base of the Klebsiella_pneumoniae genome, A at the 3454642nd base of the Bacteroides_caccae genome, and G at the 106181st base of the Roseburia_intestinalis genome, the computer determines that the subject suffers from a hyperglycemia-related disease or has a reduced risk of suffering from a hyperglycemia-related disease.

8. A device for scoring intestinal flora for assessing the risk of hyperglycemia-related diseases, characterized in that: The intestinal flora scoring device comprises the following units: Detection unit: detecting the abundance of a microbial marker in the application according to any one of claims 1 to 3 or the base type of a gene mutation of a microbial marker in a sample; Analysis unit: using the detected abundance of microbial markers or the base type of gene mutation of microbial markers as input variables and inputting them into the risk model as described in claim 6 for analysis; Evaluation unit: outputs the risk value of the individual suffering from hyperglycemia-related diseases corresponding to the sample.

9. Use of microbial markers in the preparation of drugs for treating hyperglycemia-related diseases, characterized in that: The microbial marker comprises the microbial marker according to any one of claims 1 to 3.

10. The use of microbial markers in screening candidate drugs for treating hyperglycemia-related diseases, characterized in that: The microbial marker comprises the microbial marker according to any one of claims 1 to 3.

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