Application of Microbial Biomarkers in the Diagnosis of Hyperglycemia-Related Diseases

Abstract

The present invention discloses the application of microbial markers in the diagnosis of hyperglycemia-related diseases. The microbial markers include Prevotella_stercorea, Eubacterium_rectale_CAG.36, Dorea_longicatena, Butyrivibrio_crossotus, Klebsiella_pneumoniae, Bacteroides_caccae, and Roseburia_intestinalis. By detecting the content, abundance, or genomic base mutation frequency of the microbial markers in a subject's sample, it is possible to predict or diagnose whether the subject has a hyperglycemia-related disease or the risk of developing a hyperglycemia-related disease. The present invention has good application prospects in the diagnosis of 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 the use 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] Further, the hyperglycemia-related diseases include diabetes, obesity, diabetic nephropathy, diabetic ketoacidosis, cerebral infarction, myocardial infarction, coronary heart disease, diabetic neuropathy, and diabetic retinopathy.

[0008] Further, the microbial markers include mutations at the 2558th base of the Prevotella_stercorea genome, the 221366th base of the Eubacterium_rectale_CAG.36 genome, the 130251st base of the Dorea_longicatena genome, the 14353rd base of the Butyrivibrio_crossotus genome, the 24638th base of the Klebsiella_pneumoniae genome, the 3454642nd base of the Bacteroides_caccae genome, and the 106181st base of the Roseburia_intestinalis genome. Mutations in the genomes of the microbial markers can also be referred to as SNP sites.

[0009] Further, the microbial markers include a combination of mutations at the 2558th base of the Prevotella_stercorea genome, the 221366th base of the Eubacterium_rectale_CAG.36 genome, the 130251st base of the Dorea_longicatena genome, the 14353rd base of the Butyrivibrio_crossotus genome, the 24638th base of the Klebsiella_pneumoniae genome, the 3454642nd base of the Bacteroides_caccae genome, and the 106181st base 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 is A in the reference genome, 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 is C in the reference genome, and when the C base at this site mutates to an A base, the risk of hyperglycemia is low.

[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 A in the reference genome, and when the A base at this site mutates to a G base, the risk of hyperglycemia is low.

[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 is C in the reference genome, and when the C base at this site mutates to an A or T base, the risk of hyperglycemia is low.

[0014] In the present invention, the reference genome of the 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 is G in the reference genome, and when the G base at this site mutates to an A base, the risk of hyperglycemia is high.

[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 base at position 3454642 in the reference genome is C, and the mutation of the C base at this site to an A base results in 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 base at position 106181 in the reference genome is G, and the mutation of the G base at this site to an A base results in a high risk of hyperglycemia.

[0017] Furthermore, the product is a reagent, kit, primer, chip, test strip, 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 the test sample is a reagent for detecting the abundance of the aforementioned microbial markers in the test sample.

[0019] Furthermore, the reagent for detecting microbial markers in the test sample includes a reagent for detecting the base type of the mutation site of the gene of the aforementioned microbial markers in the test sample.

[0020] In some embodiments, the base type of the mutation site of the microbial marker gene refers to the type of the base at position 2558 of the Prevotella_stercorea genome (GenBank ID: QRNO01000118.1), the base at position 221366 of the Eubacterium_rectale_CAG.36 genome (GenBank ID: FR891016.1), the base at position 130251 of the Dorea_longicatena genome (GenBank ID: RCYC01000004.1), the base at position 14353 of the Butyrivibrio_crossotus genome (GenBank ID: RKEB01000030.1), the base at position 24638 of the Klebsiella_pneumoniae genome (GenBank ID: CP091979.1), the base at position 3454642 of the Bacteroides_caccae genome (GenBank ID: CP081920.1), and the base at position 106181 of the Roseburia_intestinalis genome (GenBank ID: WNAJ01000013.1).

[0021] Furthermore, the sample includes 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, feces, lymph, gynecological fluid, skin swab, vaginal swab, oral swab, nasal swab, rinse fluid, lavage fluid, aspirate, scrape, bone marrow sample, tissue biopsy sample, surgical sample, secretion and / or excrement.

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

[0023] Furthermore, the microbial marker abundance includes a reagent for specifically detecting the abundance of the microbial marker gene, or a reagent for specifically binding to the abundance of the protein encoded by the microbial marker gene.

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

[0025] The terms "marker" and "biomarker" are synonymous and refer to a measurable biological state indicator of an individual. Such a biomarker can be any substance in an individual, as long as it is related to a specific biological state (e.g., disease) of the individual being tested. Among them, the meaning of nucleic acid biomarker is not limited to genes that can be expressed as biologically active proteins, but also includes any nucleic acid fragment, which can be DNA or RNA, can be modified DNA or RNA, or unmodified DNA or RNA, as well as a collection composed of them. In this article, nucleic acid biomarker can sometimes also be called characteristic fragment. In the present invention, the biomarker can also be represented by "intestinal biomarker".

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

[0027] The term "abundance" refers to a measure of the quantity of the target microorganism in a biological sample. "Abundance" is also referred to as "load". The quantification of the abundance of the 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, that is, the 16S rRNA gene from a reference strain. For example, using universal primers and expressing the abundance of the target nucleic acid sequence as a percentage of the total bacterial 16S rRNA gene copies or the total bacteria determined by normalizing to the Escherichia coli 16S rRNA gene copies. "Absolute quantification" gives the exact number of target molecules by comparison with a DNA standard or by normalizing to the DNA concentration.

[0028] As used herein, the terms "subject" and "individual" in the context of the foregoing methods refer to animals, such as mammals. In one embodiment of the present 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 hepatic glucose production make the insulin levels insufficient to normalize plasma glucose levels. Then insulin production decreases, worsening hyperglycemia.

[0029] The term "obesity" is defined as a condition in which the BMI of an adult of European descent is equal to or greater than 30 kg / m 2 According to the WHO definition, the term obesity can be classified as follows: The term "class I obesity" is a condition in which the BMI is equal to or greater than 30 kg / m 2 but less than 35 kg / 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 40 kg / m 2 of the disease; the term "class III obesity" is a disease in which the BMI is equal to or greater than 40 kg / m 2 of the disease. For Asian subjects, the term "obesity" is defined as a disease in which the BMI of an adult individual is equal to or greater than 25 kg / m 2 of the disease. Obesity in Asians can be further classified as follows: the term "class I obesity" is a disease in which the BMI is equal to or greater than 25 kg / m 2 but less than 30 kg / m 2 of the disease; the term "class II obesity" is a disease in which the BMI is equal to or greater than 30 kg / m 2 of the disease.

[0030] The present invention provides a product for diagnosing whether a subject has a hyperglycemic-related disease or is at risk of having a hyperglycemic-related disease, and the product includes the reagents used in the aforementioned applications.

[0031] Furthermore, the product includes reagents for detecting mutation sites in the genome of the microbial marker described in any one of the foregoing or base sites of specific genes of the microbial marker.

[0032] Furthermore, the base sites of the specific genes 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 3454642nd base of the Bacteroides_caccae genome (GenBank ID: CP081920.1), and the 106181st base of the Roseburia_intestinalis genome (GenBank ID: WNAJ01000013.1).

[0033] In some embodiments, the mutation sites in the genomes of the microbial markers refer 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 3454642nd base of the Bacteroides_caccae genome (GenBank ID: CP081920.1), the mutation of the 106181st base of the Roseburia_intestinalis genome (GenBank ID: WNAJ01000013.1).

[0034] Furthermore, the product includes a kit, primers, test strips, nucleic acid membrane strips, probes, and chips.

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

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

[0037] In certain specific embodiments, the criteria for the diagnosis vary with the sample. In one specific embodiment, a fecal sample is used as the diagnostic sample, and in this case, the abundance of the microbial marker increases continuously with the increasing blood glucose level and shows significant differences.

[0038] Further, the product also includes a product instruction manual that records the above-mentioned products for diagnosing whether a subject has a hyperglycemic-related disease or is at risk of developing a hyperglycemic-related disease. The instruction manual includes the following steps:

[0039] 1) The nucleic acid from the sample is contacted with a reagent for detecting the abundance of a microbial biomarker or a reagent for detecting the base types of genomic mutation sites of a microbial biomarker;

[0040] 2) Determine the abundance of the microbial biomarker or determine the base types of the genomic mutation sites of the microbial biomarker;

[0041] 3) Based on the abundance or base type of the microbial biomarker, determine whether the subject has a hyperglycemic-related disease or is at risk of developing a hyperglycemic-related disease.

[0042] Further, when 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, the 3454642nd base of the Bacteroides_caccae genome (GenBank ID: CP081920.1) is C, and the 106181st base of the Roseburia_intestinalis genome (GenBank ID: WNAJ01000013.1) is A, the subject has a hyperglycemic-related disease or an increased risk of developing a hyperglycemic-related disease; when the base types of the microbial markers are: the 2558th base of the Prevotella_stercorea genome (GenBank ID: QRNO01000118.1) is T, the 221366th base of the Eubacterium_rectale_CAG.36 genome (GenBank ID: FR891016.1) is A, the 130251st base of the Dorea_longicatena genome (GenBank ID: RCYC01000004.1) is G, the 14353rd base of the Butyrivibrio_crossotus genome (GenBank ID: RKEB01000030.1) 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 subject has a hyperglycemic-related disease or a reduced risk of developing a hyperglycemic-related disease.

[0043] The present invention provides an application of a microbial biomarker in constructing a computer model for diagnosing hyperglycemia-related diseases, and the microbial biomarker includes the microbial biomarker described in any one of the preceding items.

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

[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 biomarkers. The construction method includes using a machine learning method for model training to construct a risk prediction model for hyperglycemia-related diseases.

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

[0048] Furthermore, the construction method includes the steps of detection and analysis of microbial specimens to screen out microbial biomarkers, and the microbial biomarker includes the microbial biomarker described in any one of the preceding items.

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

[0050] Furthermore, the samples include 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, feces, lymph, gynecological fluids, skin swabs, vaginal swabs, oral swabs, nasal swabs, irrigation fluids, lavage fluids, aspirates, scrapings, bone marrow samples, tissue biopsy samples, surgical samples, secretions and / or excretions.

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

[0052] Furthermore, the machine learning methods include decision tree models, random forest models, K-nearest neighbor algorithm models, naive Bayes models, support vector machine models, and neural network models.

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

[0054] The present invention provides a computer diagnosis and treatment system for diagnosing hyperglycemia-related diseases using microbial biomarkers. The computer diagnosis and treatment system is characterized in that it includes:

[0055] An input module for inputting detection data of microbial markers, where the microbial markers are the aforementioned microbial markers;

[0056] A result determination module for comparing the detection data of microbial markers with set data;

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

[0058] Further, when 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, the 3454642nd base of the Bacteroides_caccae genome (GenBank ID: CP081920.1) is C, and the 106181st base of the Roseburia_intestinalis genome (GenBank ID: WNAJ01000013.1) is A, the computer determines that the subject has a hyperglycemic-related disease or an increased risk of developing a hyperglycemic-related disease; when the base types of the microbial markers are: the 2558th base of the Prevotella_stercorea genome (GenBank ID: QRNO01000118.1) is T, the 221366th base of the Eubacterium_rectale_CAG.36 genome (GenBank ID: FR891016.1) is A, the 130251st base of the Dorea_longicatena genome (GenBank ID: RCYC01000004.1) is G, the 14353rd base of the Butyrivibrio_crossotus genome (GenBank ID: RKEB01000030.1) 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 has a hyperglycemic-related disease or a decreased risk of developing a hyperglycemic-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, second-generation sequencing methods or third-generation sequencing methods. The means for performing sequencing is not particularly limited. By performing sequencing using second-generation or third-generation sequencing methods, rapid and efficient sequencing can be achieved. As a specific implementation, the sequencing method is performed using at least one selected from Hiseq2000, SOLiD, 454, and single-molecule sequencing devices. Thereby, the high-throughput and deep-sequencing characteristics of these sequencing devices can be utilized, which is beneficial for analyzing subsequent sequencing data, especially the accuracy and precision during statistical testing.

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

[0061] Detection unit: detecting the abundance of microbial markers or the base types of gene mutations of microbial markers in the sample as described in the previous applications;

[0062] Analysis unit: taking the detected abundance of microbial markers or the base types of gene mutations of microbial markers as input variables and inputting them into the risk model as described in the previous applications for analysis;

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

[0064] The present invention provides the application of microbial markers in the preparation of drugs for treating hyperglycemia-related diseases, and the microbial markers include the microbial markers described in any one of the previous items.

[0065] The present invention provides the application of microbial markers in screening candidate drugs for treating hyperglycemia-related diseases, and the microbial markers include the microbial markers described in any one of the previous items.

[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., 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 can 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 fluids; skin swabs; vaginal swabs; oral swabs; nasal swabs; flush or lavage fluids, such as catheter lavage fluid or bronchoalveolar lavage fluid; aspirates; scrapings; bone marrow samples; tissue biopsy samples; surgical samples; feces, other body fluids, secretions and / or excretions; and / or cells therefrom, etc. In some embodiments, the biological sample is or comprises cells obtained from an individual. In some embodiments, the obtained cells are or include cells from the individual from whom the sample was obtained. In some embodiments, the sample is a "primary sample" obtained directly from the source of interest by any suitable 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 the 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 nucleic acid sequence of 7 to 50 nucleotides that is capable of forming base pairs complementary to a template strand and serves as a starting point for replicating the template strand. 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 that of the template, as long as it is sufficiently complementary to hybridize with the template. Additional features that do not change the basic properties of the primer can be incorporated. Examples of additional features that can be incorporated include methylation, capping, replacement of one or more nucleic acids with homologs, and modifications between nucleic acids, but are not limited thereto.

[0068] The term "chip" can refer to a solid substrate having a generally planar surface to which an adsorbent is attached. The surface of the biochip can contain a plurality of addressable locations, each of which can be bound with an adsorbent. The biochip can be adapted to engage a probe interface and thus be used as a probe. Protein biochips are suitable for capturing polypeptides and can comprise 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, subsequence, or other part 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 the "target polynucleotide") through complementary base pairing. Depending on the stringency of the hybridization conditions, a probe can bind to a target polynucleotide that lacks complete sequence complementarity to the probe. Probes can be directly or indirectly labeled and include 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" refers to the Receiver Operating Characteristic curve, which is a coordinate graph with the false positive probability (1 - specificity) on the x-axis and the true positive probability (sensitivity) on the y-axis, and is a curve drawn from different results obtained by a subject under specific stimulus conditions due to different judgment criteria. Select the optimal diagnostic cutoff value. The closer the ROC curve is to the upper left corner, the higher the accuracy of the test. The point on the ROC curve closest to the upper left corner is the best threshold with the fewest errors and the smallest total number of false positives and false negatives. Comparison of the disease recognition capabilities of two or more different diagnostic tests. 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 visually distinguish the advantages and disadvantages. The ROC curve closest to the upper left corner represents the most accurate subject work. 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. Description of the Drawings

[0071] Figure 1 It is a ROC curve graph for diagnosing hyperglycemia using microbial markers in the test set;

[0072] Figure 2 It is a ROC curve graph for diagnosing abnormal hyperglycemia using microbial markers in the validation set.

[0073] Figure 3 It is a schematic flow diagram of a computer diagnosis and treatment system for diagnosing hyperglycemia-related diseases using microbial markers;

[0074] Figure 4 It is a schematic flow diagram of an intestinal flora scoring device for assessing the risk of hyperglycemia-related diseases. Detailed Description of the Invention

[0075] Example 1 Screening and Testing of Intestinal Flora Related to Hyperglycemia

[0076] 1. Research Subjects and Sample Collection

[0077] Collect blood, serum samples, and fecal samples from 258 sample populations, including 130 hyperglycemia patients and 128 healthy individuals.

[0078] 2. Experimental methods

[0079] 1) Collection of fecal samples and DNA extraction

[0080] After collecting the fecal samples of the above-mentioned population, a kit was used for DNA extraction to obtain the extracted DNA samples.

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

[0082] Reference genomes of 7 microbial species, namely Prevotella_stercorea (GenBank ID: QRNO01000118.1), Eubacterium_rectale_CAG.36 (GenBank ID: FR891016.1), Dorea_longicatena (GenBank ID: RCYC01000004.1), Butyrivibrio_crossotus (GenBank ID: RKEB01000030.1), Klebsiella_pneumoniae (GenBank ID: CP091979.1), Bacteroides_caccae (GenBank ID: CP081920.1), and Roseburia_intestinalis (GenBank ID: WNAJ01000013.1), were downloaded from the NCBI database. First, quality control was performed on the sequences obtained from the original sequencing. The quality-controlled sequencing sequences were aligned with the 7 genomes using the BWA software to obtain the sequences of each genome. The SAMTools software (parameters: "-vmO z -V indels") and the 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. To reduce false positives in the results, the VarScan2 software (parameters: "--min-coverage 10 --min-reads 24 --min-var-freq 0.2 --p-value 0.05.") was also used to find and screen SNPs. Finally, the SNP results for downstream analysis were the intersection of the screening results of the two software.

[0083] 3) Diagnostic efficacy analysis

[0084] The receiver operating characteristic (ROC) curve was plotted using the R package "pROC" to analyze the AUC value, sensitivity, and specificity of the microbial markers with significantly different mutation levels between healthy individuals and hyperglycemic subjects in the test set, so as to judge their diagnostic efficacy for hyperglycemic-related diseases.

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

[0086] 3. Experimental results

[0087] Genome-wide association analysis of SNPs of 7 bacterial genomes and fasting blood glucose phenotypes was performed using the BLINK software and FarmCPU software in GAPIT software. The results showed that the genomic variations of 7 microorganisms were significantly correlated with fasting blood glucose levels. The BLINK software found mutations at 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 3454642nd base of the Bacteroides_caccae genome (GenBank ID: CP081920.1), and the 106181st base of the Roseburia_intestinalis genome (GenBank ID: WNAJ01000013.1). These 7 loci were significantly correlated with fasting blood glucose levels.

[0088] The results are shown in Table 1. The results indicate that the mutation frequencies of specific bases of specific genes of seven microbial markers are different between hyperglycemic and normal populations, with significant differences. The OR values all show that the seven loci are influencing factors for hyperglycemia. Among them, the mutation of the 2558th base of the Prevotella_stercorea genome (GenBank ID of the genome: QRNO01000118.1), the 221366th base of the Eubacterium_rectale_CAG.36 genome (GenBank ID of the genome: FR891016.1), the 130251st base of the Dorea_longicatena genome (GenBank ID of the genome: RCYC01000004.1), the 14353rd base of the Butyrivibrio_crossotus genome (GenBank ID of the genome: RKEB01000030.1), and the 3454642nd base of the Bacteroides_caccae genome (GenBank ID of the genome: CP081920.1) are protective factors; the mutation of the 24638th base of the Klebsiella_pneumoniae genome (GenBank ID of the genome: CP091979.1) and the 106181st base of the Roseburia_intestinalis genome (GenBank ID: WNAJ01000013.1) are risk factors.

[0089] Table 1

[0090]

[0091]

[0092] Using the combination of microbial markers (combination of mutation sites) as the detection variable, an ROC curve was plotted and its AUC value was calculated. The results are as Figure 1 shown, indicating that the AUC value is 0.8029, suggesting that using the combination of microbial markers as the detection variable has high specificity and sensitivity.

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

[0094] 1. Research objects and sample collection

[0095] Fifty samples of people were collected, including blood, serum samples, and fecal samples from 25 hyperglycemic patients 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 for diagnosing hyperglycemia-related diseases

[0100] Using the combination of microbial markers (combination of mutation sites) as the detection variable, draw the ROC curve and calculate its AUC value. The results are as Figure 2 shown, with the AUC value being 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 for understanding the method of the present invention and its core idea. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications will also fall within the protection scope of the claims of the present invention.

Claims

1. Use of a reagent for detecting a combination of microbial markers in a sample in the preparation of a product for diagnosing whether a patient has symptoms of hyperglycemia, characterized in that, The microbial biomarker combination is a combination of a base mutation at position 2558 of the Prevotella_stercorea genome, a base mutation at position 221366 of the Eubacterium_rectale_CAG.36 genome, a base mutation at position 130251 of the Dorea_longicatena genome, a base mutation at position 14353 of the Butyrivibrio_crossotus genome, a base mutation at position 24638 of the Klebsiella_pneumoniae genome, a base mutation at position 3454642 of the Bacteroides_caccae genome, and a base mutation at position 106181 of the Roseburia_intestinalis genome; When the base at position 2558 of the Prevotella_stercorea genome in the microbial biomarker combination is A, the base at position 221366 of the Eubacterium_rectale_CAG.36 genome is C, the base at position 130251 of the Dorea_longicatena genome is A, the base at position 14353 of the Butyrivibrio_crossotus genome is C, the base at position 24638 of the Klebsiella_pneumoniae genome is A, the base at position 3454642 of the Bacteroides_caccae genome is C, and the base at position 106181 of the Roseburia_intestinalis genome is A, the subject has hyperglycemic symptoms or an increased risk of having hyperglycemic symptoms; when the base at position 2558 of the Prevotella_stercorea genome in the microbial biomarker combination is T, the base at position 221366 of the Eubacterium_rectale_CAG.36 genome is A, the base at position 130251 of the Dorea_longicatena genome is G, the base at position 14353 of the Butyrivibrio_crossotus genome is T or A, the base at position 24638 of the Klebsiella_pneumoniae genome is G, the base at position 3454642 of the Bacteroides_caccae genome is A, and the base at position 106181 of the Roseburia_intestinalis genome is G, the subject has a reduced risk of having hyperglycemic symptoms or having hyperglycemic symptoms; The GenBank ID of the Prevotella_stercorea genome is QRNO01000118.1, the GenBank ID of the Eubacterium_rectale_CAG.36 genome is FR891016.1, the GenBank ID of the Dorea_longicatena genome is RCYC01000004.1, the GenBank ID of the Butyrivibrio_crossotus genome is RKEB01000030.1, the GenBank ID of the Klebsiella_pneumoniae genome is CP091979.1, the GenBank ID of the Bacteroides_caccae genome is CP081920.1, and the GenBank ID of the Roseburia_intestinalis genome is WNAJ01000013.

1.

2. The application according to claim 1, characterized in that, The product is a reagent for diagnosing whether a patient has symptoms of hyperglycemia.

3. The application according to claim 1, wherein The sample is a fecal sample.

4. Use of a microbial biomarker combination in constructing a computer model for diagnosing whether a patient has hyperglycemic symptoms, characterized in that, The microbial biomarker combination is the microbial biomarker combination described in claim 1.

5. A computer diagnosis and treatment system for diagnosing whether a patient has hyperglycemic symptoms by using a combination of microbial markers, characterized in that, The computer diagnosis and treatment system includes: An input module for inputting the detection data of microbial biomarkers, where the microbial biomarkers are the microbial biomarker combination described in claim 1; A result determination module for comparing the detection data of the microbial biomarker combination with the set data; An output module for outputting the result of the result determination module.

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