A microbial marker combination, method, system, device and medium for diagnosing or predicting type 2 diabetes

Abstract

The present invention discloses a microbial marker combination, method, system, device and medium for diagnosing or predicting type 2 diabetes, and belongs to the field of microbial marker technology. The microbial marker combination of the present invention can be used to accurately diagnose whether a subject suffers from type 2 diabetes or predict whether a subject has a risk of developing type 2 diabetes. Furthermore, based on a specific machine learning model, accurate prediction can be achieved based on the predicted degree of risk of a subject suffering from type 2 diabetes, so that medical intervention can be carried out in a timely manner, which has great clinical application value.

Description

Technical Field

[0001] The present invention belongs to the technical field of microbial markers, and in particular, relates to a microbial marker combination, method, system, device and medium for diagnosing or predicting type 2 diabetes. Background Art

[0002] Diabetes is a chronic disease caused by insufficient secretion or defective utilization of insulin. Type 2 diabetes is the most common type of diabetes, accounting for about 90%, and is more common in adults. It is a group of clinical syndromes caused by genetic and environmental factors, with glucose metabolism disorders as the main manifestation. It is currently believed that the factors affecting the occurrence of diabetes mainly include age, obesity, body fat distribution type, lifestyle, blood sugar level, insulin level, etc. In addition, studies have found that intestinal flora is closely related to type 2 diabetes. Diabetic patients are prone to intestinal flora imbalance, affecting the host's energy metabolism, inflammatory response, etc.

[0003] Bacteria, viruses and single-cell eukaryotes living in the human intestine are usually called intestinal flora, which are dominated by bacteria, with a wide variety and huge number. There are more than 1,000 species of intestinal flora, with a number of up to 100 trillion, which is 10 times the number of human cells. The sum of the genes of the intestinal flora is called the "human metagenome", and the number of genes encoded is at least 150 times that of the human body itself. The results of 16SrRNA sequencing and identification of the intestinal flora showed that more than 90% belonged to the Bacteroidetes and Firmicutes. The two phyla are the dominant flora in the human intestine, including Bacteroidetes, Prevotella, Clostridium, Enterococcus, Lactobacillus, Ruminococcus, etc. Other less common phyla include Actinomycetes, Proteobacteria, Verrucomicrobia and methanogenic archaea. From birth, microorganisms interact and influence each other with the body. Microorganisms and their metabolites promote the maturation of the body's physiological functions, co-evolve and develop with the host, and continue to affect human health.

[0004] As an internal environmental factor, intestinal flora is closely related to type 2 diabetes. There are obvious differences in the intestinal flora between healthy people and people with type 2 diabetes in terms of phyla and species. Studies have found that the overall number of bifidobacteria and lactobacilli in the intestines of patients with type 2 diabetes is significantly reduced; the number of Bacteroidetes and Proteobacteria increases, and the number of Firmicutes decreases relatively. The proportion of β-Proteobacteria is significantly increased in people with diabetes and is positively correlated with the degree of increase in blood sugar concentration. In addition, studies have shown that Roseburia ( Roseburia ), Eubacterium hallii ( Eubacterium halii ), Faecalibacterium prausnitzii ( Faecalibacterium prauznitzii ) reduce, Lactobacillus gasseri ( Lactobacillus gasseri )、Streptococcus mutans( Streptococcus mutans )、Escherichia coli( E.coli ) increases, which can serve as a potential clinical diagnostic marker for type 2 diabetes.

[0005] Therefore, screening intestinal microbial markers and predicting the risk of type 2 diabetes through machine learning models can help humans better prevent type 2 diabetes. At present, there is no good prediction model for predicting type 2 diabetes based on intestinal microbial markers. The characteristic values ​​of some research prediction models have a poor correlation with type 2 diabetes and a low causal relationship, which means that this feature often cannot explain the relationship with type 2 diabetes. Summary of the invention

[0006] In order to solve at least one of the above technical problems, the inventors obtained intestinal microbial markers that are causally related to type 2 diabetes through Mendelian randomization analysis, and further explored their causal relationship with type 2 diabetes. The markers obtained are all intestinal microorganisms that are causally related to type 2 diabetes, which can better perform diagnosis or prediction.

[0007] The first aspect of the present invention provides a microbial marker combination for diagnosing or predicting type 2 diabetes, the microbial marker combination comprising Anaerostipes , Lactococcus , Gordonibacter , Parasutterella , Lachnoclostridium , Parabacteroides , Actinomyces , Desulfovibrio , Dialister , Butyricimonas , Paraprevotella , Holdemanella , Methanobrevibacter , Anaerotruncus , Alloprevotella , Collinsella , Akkermansia , Alistipes , Sutterella , Lactobacillus , Intestinibacter , Flavonifractor , Odoribacter , Veillonella , Barnesiella , Olsenella , Adlercreutzia , Oscillibacter , Streptococcus , Haemophilus and Bacteroides .

[0008] In some preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Intestinibacter , Oscillibacter , Streptococcus , Bacteroides .

[0009] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Intestinibacter , Barnesiella , Oscillibacter , Streptococcus , Bacteroides .

[0010] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Intestinibacter , Flavonifractor , Barnesiella , Oscillibacter , Streptococcus , Bacteroides .

[0011] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Lactobacillus , Intestinibacter , Flavonifractor , Barnesiella , Oscillibacter , Streptococcus , Bacteroides .

[0012] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Parabacteroides , Lactobacillus , Intestinibacter , Flavonifractor , Barnesiella , Oscillibacter , Streptococcus , Bacteroides .

[0013] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Parabacteroides , Anaerotruncus , Lactobacillus , Intestinibacter , Flavonifractor , Barnesiella , Oscillibacter , Streptococcus , Bacteroides .

[0014] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Parabacteroides , Dialister , Anaerotruncus , Lactobacillus , Intestinibacter , Flavonifractor , Barnesiella , Oscillibacter , Streptococcus , Bacteroides .

[0015] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Parabacteroides , Dialister , Anaerotruncus , Alistipes , Lactobacillus , Intestinibacter , Flavonifractor , Barnesiella , Oscillibacter , Streptococcus , Bacteroides .

[0016] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Parabacteroides , Dialister , Anaerotruncus , Alistipes , Lactobacillus , Intestinibacter , Flavonifractor , Barnesiella , Olsenella , Oscillibacter , Streptococcus , Bacteroides .

[0017] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Lachnoclostridium , Parabacteroides , Dialister , Anaerotruncus , Alistipes , Lactobacillus , Intestinibacter , Flavonifractor , Barnesiella , Olsenella , Oscillibacter , Streptococcus , Bacteroides .

[0018] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Lachnoclostridium , Parabacteroides , Dialister , Anaerotruncus , Alistipes , Sutterella , Lactobacillus , Intestinibacter , Flavonifractor , Barnesiella , Olsenella , Oscillibacter , Streptococcus , Bacteroides .

[0019] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Lachnoclostridium , Parabacteroides , Dialister , Anaerotruncus , Collinsella , Alistipes , Sutterella , Lactobacillus , Intestinibacter , Flavonifractor , Barnesiella , Olsenella , Oscillibacter , Streptococcus , Bacteroides .

[0020] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Lachnoclostridium , Parabacteroides , Desulfovibrio , Dialister , Anaerotruncus , Collinsella , Alistipes , Sutterella , Lactobacillus , Intestinibacter , Flavonifractor , Barnesiella , Olsenella , Oscillibacter , Streptococcus , Bacteroides .

[0021] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Lachnoclostridium , Parabacteroides , Desulfovibrio , Dialister , Anaerotruncus , Collinsella , Alistipes , Sutterella , Lactobacillus , Intestinibacter , Flavonifractor , Barnesiella , Olsenella , Oscillibacter , Streptococcus , Haemophilus , Bacteroides .

[0022] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Parasutterella , Lachnoclostridium , Parabacteroides , Desulfovibrio , Dialister , Anaerotruncus , Collinsella , Alistipes , Sutterella , Lactobacillus , Intestinibacter , Flavonifractor , Barnesiella , Olsenella , Oscillibacter , Streptococcus , Haemophilus , Bacteroides .

[0023] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Parasutterella , Lachnoclostridium , Parabacteroides , Desulfovibrio , Dialister , Butyricimonas , Anaerotruncus , Collinsella , Alistipes , Sutterella , Lactobacillus , Intestinibacter , Flavonifractor , Barnesiella , Olsenella , Oscillibacter , Streptococcus , Haemophilus , Bacteroides .

[0024] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Parasutterella , Lachnoclostridium , Parabacteroids , Desulfovibrio , Dialer , Butyricimonas , Anaerotrunc , Collinsella , Akkermansia , Common grebe , Sutterella , Lactobacillus , Intestinal bacteria , Flavonoid , Barnesiella , Olsenella , Oscillibacter , Streptococcus , Haemophilus , Bacteroides .

[0025] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerotypes , Parasutterella , Lachnoclostridium , Parabacteroids , Desulfovibrio , Dialer , Butyricimonas , Anaerotrunc , Collinsella , Akkermansia , Common grebe , Sutterella , Lactobacillus , Intestinal bacteria , Flavonoid , Veillonella , Barnesiella , Olsenella , Oscillibacter , Streptococcus , Haemophilus , Bacteroidea .

[0026] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerotypes , Parasutterella , Lachnoclostridium , Parabacteroids , Desulfovibrio , Dialer , Butyricimonas , Anaerotrunc , Collinsella , Akkermansia , Common grebe , Sutterella , Lactobacillus , Intestinal bacteria , Flavonoid , Odoric bacteria , Veillonella , Barnesiella , Olsenella , Oscillibacter , Streptococcus , Haemophilus , Bacteroides .

[0027] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerotypes , Parasutterella , Lachnoclostridium , Parabacteroids , Desulfovibrio , Dialer , Butyricimonas , Anaerotrunc , Collinsella , Akkermansia , Common grebe , Sutterella , Lactobacillus , Intestinal bacteria , Flavonoid , Odoric bacteria , Veillonella , Barnesiella , Olsenella , Adlercreutzia , Oscillibacter , Streptococcus , Haemophilus , Bacteroides .

[0028] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Parasutterella , Lachnoclostridium , Parabacteroides , Actinomyces , Desulfovibrio , Dialister , Butyricimonas , Anaerotruncus , Collinsella , Akkermansia , Alistipes , Sutterella , Lactobacillus , Intestinibacter , Flavonifractor , Odoribacter , Veillonella , Barnesiella , Olsenella , Adlercreutzia , Oscillibacter , Streptococcus , Haemophilus , Bacteroides .

[0029] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Parasutterella , Lachnoclostridium , Parabacteroides , Actinomyces , Desulfovibrio , Dialister , Butyricimonas , Anaerotruncus , Alloprevotella , Collinsella , Akkermansia , Alistipes , Sutterella , Lactobacillus , Intestinibacter , Flavonifractor , Odoribacter , Veillonella , Barnesiella , Olsenella , Adlercreutzia , Oscillibacter , Streptococcus , Haemophilus , Bacteroides .

[0030] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Parasutterella , Lachnoclostridium , Parabacteroides , Actinomyces , Desulfovibrio , Dialister , Butyricimonas , Paraprevotella , Anaerotruncus , Alloprevotella , Collinsella , Akkermansia , Alistipes , Sutterella , Lactobacillus , Intestinibacter , Flavonifractor , Odoribacter , Veillonella , Barnesiella , Olsenella , Adlercreutzia , Oscillibacter , Streptococcus , Haemophilus , Bacteroides .

[0031] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Gordonibacter , Parasutterella , Lachnoclostridium , Parabacteroides , Actinomyces , Desulfovibrio , Dialister , Butyricimonas , Paraprevotella , Anaerotruncus , Alloprevotella , Collinsella , Akkermansia , Alistipes , Sutterella , Lactobacillus , Intestinibacter , Flavonifractor , Odoribacter , Veillonella , Barnesiella , Olsenella , Adlercreutzia , Oscillibacter , Streptococcus , Haemophilus , Bacteroides .

[0032] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Lactococcus , Gordonibacter 、Parasutterella、 Lachnoclostridium , Parabacteroides , Actinomyces , Desulfovibrio , Dialister , Butyricimonas , Paraprevotella , Anaerotruncus , Alloprevotella , Collinsella , Akkermansia , Alistipes , Sutterella , Lactobacillus , Intestinibacter , Flavonifractor , Odoribacter , Veillonella , Barnesiella , Olsenella , Adlercreutzia , Oscillibacter , Streptococcus , Haemophilus , Bacteroides .

[0033] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Lactococcus , Gordonibacter , Parasutterella , Lachnoclostridium , Parabacteroides , Actinomyces , Desulfovibrio , Dialister , Butyricimonas , Paraprevotella , Holdemanella , Anaerotruncus , Alloprevotella , Collinsella , Akkermansia , Alistipes , Sutterella , Lactobacillus , Intestinibacter , Flavonifractor , Odoribacter , Veillonella , Barnesiella , Olsenella , Adlercreutzia , Oscillibacter , Streptococcus , Haemophilus , Bacteroides .

[0034] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Lactococcus , Gordonibacter , Parasutterella , Slackia , Lachnoclostridium , Parabacteroides , Actinomyces , Desulfovibrio , Dialister , Butyricimonas , Paraprevotella , Holdemanella , Methanobrevibacter , Anaerotruncus , Alloprevotella , Collinsella , Akkermansia , Alistipes , Sutterella , Lactobacillus , Intestinibacter , Flavonifractor , Odoribacter , Veillonella , Barnesiella , Olsenella , Adlercreutzia , Oscillibacter , Streptococcus , Haemophilus , Bacteroides .

[0035] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Lactococcus , Gordonibacter , Parasutterella , Slackia , Lachnoclostridium , Parabacteroides , Actinomyces , Desulfovibrio , Dialister , Anaerofilum , Butyricimonas , Paraprevotella , Holdemanella , Methanobrevibacter , Anaerotruncus , Alloprevotella , Collinsella , Akkermansia , Alistipes , Sutterella , Lactobacillus , Intestinibacter , Flavonifractor , Odoribacter , Veillonella , Barnesiella , Olsenella , Adlercreutzia , Oscillibacter , Streptococcus , Haemophilus , Bacteroides .

[0036] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Lactococcus , Gordonibacter , Parasutterella , Slackia , Lachnoclostridium , Parabacteroides , Actinomyces , Desulfovibrio , Dialister , Anaerofilum , Butyricimonas , Paraprevotella , Holdemanella , Methanobrevibacter , Anaerotruncus , Alloprevotella , Collinsella , Akkermansia , Alistipes , Sutterella , Lactobacillus , Intestinibacter , Flavonifractor , Odoribacter , Veillonella , Coprobacter , Barnesiella , Olsenella , Adlercreutzia , Oscillibacter , Streptococcus , Haemophilus , Bacteroides .

[0037] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Lactococcus , Gordonibacter , Parasutterella , Slackia , Lachnoclostridium , Parabacteroides , Actinomyces , Desulfovibrio , Dialister , Anaerofilum , Butyricimonas , Paraprevotella , Holdemanella , Methanobrevibacter , Anaerotruncus , Alloprevotella , Collinsella , Akkermansia , Alistipes , Sutterella , Lactobacillus , Intestinibacter , Flavonifractor , Odoribacter , Veillonella , Coprobacter , Barnesiella , Olsenella , Adlercreutzia , Oscillibacter , Streptococcus , Marvinbryantia , Haemophilus , Bacteroides .

[0038] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Lactococcus , Gordonibacter , Parasutterella , Slackia , Lachnoclostridium , Parabacteroides , Actinomyces , Desulfovibrio , Dialister , Anaerofilum , Butyricimonas , Paraprevotella , Holdemanella , Methanobrevibacter , Anaerotruncus , Alloprevotella , Collinsella , Akkermansia , Alistipes , Sutterella , Lactobacillus , Intestinibacter , Flavonifractor , Odoribacter , Veillonella , Coprobacter , Victivallis , Barnesiella , Olsenella , Adlercreutzia , Oscillibacter , Streptococcus , Marvinbryantia , Haemophilus , Bacteroides .

[0039] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Lactococcus , Gordonibacter , Parasutterella , Slackia , Lachnoclostridium , Parabacteroides , Actinomyces , Desulfovibrio , Dialister , Anaerofilum , Butyricimonas , Paraprevotella , Holdemanella , Methanobrevibacter , Anaerotruncus , Alloprevotella , Collinsella , Turicibacter , Akkermansia , Alistipes , Sutterella , Lactobacillus , Intestinibacter , Flavonifractor , Odoribacter , Veillonella , Coprobacter , Victivallis , Barnesiella , Olsenella , Adlercreutzia , Oscillibacter , Streptococcus , Marvinbryantia , Haemophilus , Bacteroide s.

[0040] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Lactococcus , Gordonibacter , Parasutterella , Slackia , Lachnoclostridium , Parabacteroides , Actinomyces , Desulfovibrio , Dialister , Anaerofilum , Butyricimonas , Paraprevotella , Holdemanella , Methanobrevibacter , Anaerotruncus , Alloprevotella , Collinsella , Turicibacter , Akkermansia , Alistipes , Hungatella , Sutterella , Lactobacillus , Intestinibacter , Flavonifractor , Odoribacter , Veillonella , Coprobacter , Victivallis , Barnesiella , Olsenella , Adlercreutzia , Oscillibacter , Streptococcus , Marvinbryantia , Haemophilus , Bacteroides .

[0041] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Lactococcus , Gordonibacter , Parasutterella , Slackia , Lachnoclostridium , Parabacteroides , Actinomyces , Desulfovibrio , Dialister , Anaerofilum , Butyricimonas , Paraprevotella , Holdemanella , Methanobrevibacter , Catenibacterium , Anaerotruncus , Alloprevotella , Collinsella , Turicibacter , Akkermansia , Alistipes , Hungatella , Sutterella , Lactobacillus , Intestinibacter , Flavonifractor , Odoribacter , Veillonella , Coprobacter , Victivallis , Barnesiella , Olsenella , Adlercreutzia , Oscillibacter , Streptococcus , Marvinbryantia , Haemophilus , Bacteroides .

[0042] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Lactococcus , Gordonibacter , Parasutterella , Slackia , Lachnoclostridium , Parabacteroides , Actinomyces , Desulfovibrio , Dialister , Anaerofilum , Butyricimonas , Paraprevotella , Holdemanella , Methanobrevibacter , Catenibacterium , Anaerotruncus , Alloprevotella , Collinsella , Turicibacter , Akkermansia , Alistipes , Hungatella , Sutterella , Lactobacillus , Intestinibacter , Flavonifractor , Odoribacter , Veillonella , Romboutsia , Coprobacter , Victivallis , Barnesiella , Olsenella , Adlercreutzia , Oscillibacter , Streptococcus , Marvinbryantia , Haemophilus , Bacteroides .

[0043] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Lactococcus , Gordonibacter , Parasutterella , Slackia , Lachnoclostridium , Parabacteroides , Actinomyces , Desulfovibrio , Dialister , Anaerofilum , Butyricimonas , Paraprevotella , Holdemanella , Methanobrevibacter , Catenibacterium , Anaerotruncus , Alloprevotella , Collinsella , Turicibacter , Akkermansia , Alistipes , Hungatella , Sutterella , Lactobacillus , Butyricicoccus , Intestinibacter , Flavonifractor , Odoribacter , Veillonella , Romboutsia , Coprobacter , Victivallis , Barnesiella , Olsenella , Adlercreutzia , Oscillibacter , Streptococcus , Marvinbryantia , Haemophilus , Bacteroides .

[0044] In some more preferred embodiments of the present invention, the microbial marker combination comprises Anaerostipes , Lactococcus , Gordonibacter , Parasutterella , Slackia , Lachnoclostridium , Parabacteroides , Actinomyces , Desulfovibrio , Dialister , Anaerofilum , Butyricimonas , Paraprevotella , Holdemanella , Methanobrevibacter , Catenibacterium , Anaerotruncus , Alloprevotella , Collinsella , Turicibacter , Akkermansia , Alistipes , Hungatella , Sutterella , Lactobacillus , Butyricicoccus , Intestinibacter , Flavonifractor , Odoribacter , Veillonella , Enterorhabdus , Romboutsia , Coprobacter , Victivallis , Barnesiella , Olsenella , Adlercreutzia , Oscillibacter , Streptococcus , Marvinbryantia , Haemophilus , Bacteroides .

[0045] By detecting each microorganism in the above-mentioned microbial marker combination, its abundance data is obtained, and further by constructing a machine learning model to diagnose whether the subject has type 2 diabetes or predict whether the subject has a risk of developing type 2 diabetes.

[0046] A second aspect of the present invention provides a method for constructing a type 2 diabetes diagnosis or prediction model, comprising the following steps:

[0047] S1, randomly dividing the abundance data of the combination of microbial markers described in the first aspect of the present invention in biological samples of a population into two groups, one group being a training set and the other group being a test set, wherein the population includes patients with type 2 diabetes and subjects without type 2 diabetes;

[0048] S2, using the training set data, constructing the type 2 diabetes diagnosis or prediction model based on the machine learning algorithm, and performing multi-fold cross validation;

[0049] S3, verifying the obtained type 2 diabetes diagnosis or prediction model in a test set.

[0050] It is worth noting that all steps of the method for constructing a type 2 diabetes diagnosis or prediction model of the present invention are implemented by a computer or other device.

[0051] In some embodiments of the present invention, the abundance data of each microbial marker is obtained based on qPCR, 16SrRNA sequencing or metagenomic sequencing.

[0052] In some specific embodiments of the present invention, it refers to the method obtained by 16S rRNA sequencing, specifically including:

[0053] Extracting genomic DNA from the biological sample and performing 16S rRNA sequencing to obtain sequencing data;

[0054] The original reads of the sequencing data are preprocessed, filtered to obtain high-quality reads, compared with the 16SrRNA gene reference database, and chimera sequences are removed. Finally, the filtered sequences are clustered in a certain manner (including but not limited to NanoCLUST) to obtain multiple sequence clustering operational taxonomy units (OTUs), and the OTUs are annotated with taxonomy to obtain the abundance data of each microorganism.

[0055] In some embodiments of the invention, the abundance is a relative abundance.

[0056] In some embodiments of the present invention, the method further comprises the step of drawing a ROC curve to evaluate the type 2 diabetes diagnosis or prediction model.

[0057] In the present invention, the biological sample includes but is not limited to feces, intestinal lavage fluid, anal swab samples, preferably, a feces sample.

[0058] In some embodiments of the present invention, constructing a prediction model comprises the following steps:

[0059] S1, randomly dividing the abundance data of the microbial marker combination in the biological samples of the group into two groups, one group is a training set, and the other group is a test set;

[0060] S2, using the training set data, builds a prediction model based on the machine learning algorithm and performs multi-fold cross validation;

[0061] S3, verify the obtained prediction model in the test set.

[0062] In some embodiments of the present invention, stratified random sampling is used for grouping, and the grouping ratio may be 7:3, 4:1, etc.

[0063] In some embodiments of the present invention, the machine learning algorithm is selected from any one of the following algorithms: logistic regression algorithm, linear regression algorithm, random forest algorithm, neural network algorithm, support vector machine algorithm, Bayesian classification algorithm, gradient boosting algorithm, K nearest neighbor algorithm and decision tree algorithm.

[0064] In some specific embodiments of the present invention, the machine learning algorithm is a random forest algorithm, and the maximum depth of the prediction model obtained based on the random forest algorithm is 8, the maximum number of features is 10, the minimum number of samples required at the leaf node is 40, and the number of base evaluators is 50.

[0065] The third aspect of the present invention provides a computer device, comprising: a memory for storing a computer program; and a processor for implementing the steps of any method described in the second aspect of the present invention when executing the computer program.

[0066] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any method described in the second aspect of the present invention are implemented.

[0067] A fifth aspect of the present invention provides a system for diagnosing or predicting type 2 diabetes, comprising the following modules:

[0068] A data input module, used to input the abundance data of the microbial marker combination described in the first aspect of the present invention in the biological sample of the subject obtained;

[0069] A database storage module, for storing the abundance data of the microbial marker combination in biological samples of a population, the population including type 2 diabetes patients and non-type 2 diabetes subjects;

[0070] A disease prediction module is connected to the data input module and the database storage module, respectively, and is used to construct a prediction model using the abundance data of the microbial marker combination in the biological samples of the population, and to diagnose whether the subject has type 2 diabetes or predict whether the subject has a risk of developing type 2 diabetes based on the abundance data of the microbial marker combination of the subject obtained from the data input module.

[0071] The sixth aspect of the present invention provides use of the abundance detection reagent of the microbial marker combination described in the first aspect of the present invention in the preparation of a kit for diagnosing or predicting type 2 diabetes.

[0072] In some preferred embodiments of the present invention, the abundance detection reagent refers to a high-throughput sequencing reagent, including a nucleic acid extraction, amplification reagent and / or purification reagent.

[0073] In some embodiments of the present invention, the abundance detection reagent includes primers and / or probes. Further, the probes are prepared into a chip.

[0074] Beneficial effects of the present invention

[0075] Compared with the prior art, the present invention has achieved the following beneficial effects:

[0076] By using the microbial markers of the present invention and establishing a machine learning model, the AUC reached 0.9703 in the training set, 0.9190 in the validation set, and 0.8264 in the independent validation set, indicating that the microbial markers of the present invention can accurately diagnose whether a subject has type 2 diabetes or predict whether a subject has a risk of developing type 2 diabetes, achieve accurate prediction, enable timely medical intervention, and have great clinical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 The performance evaluation of the machine learning model in Example 2 of the present invention in the training set and the validation set is shown.

[0078] Figure 2 The performance evaluation of the machine learning model in Example 3 of the present invention in the external validation set is shown. DETAILED DESCRIPTION

[0079] Unless otherwise indicated, implied from the context, or customary in the prior art, all parts and percentages in this application are based on weight, and the tests and characterization methods used are all synchronized with the filing date of this application. Where applicable, the contents of any patent, patent application or disclosure involved in this application are fully incorporated herein by reference, and their equivalent patent families are also introduced as references, especially the definitions of relevant terms in the art disclosed in these documents. If the definition of a specific term disclosed in the prior art is inconsistent with any definition provided in this application, the definition of the term provided in this application shall prevail.

[0080] In order to make the technical problems, technical solutions and beneficial effects solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments.

[0081] The following examples are used to demonstrate preferred embodiments of the present invention. It will be appreciated by those skilled in the art that the techniques disclosed in the following examples represent techniques discovered by the inventors that can be used to implement the present invention and therefore can be considered as preferred embodiments of the present invention. However, it will be appreciated by those skilled in the art based on this specification that many modifications may be made to the specific embodiments disclosed herein and still achieve the same or similar results without departing from the spirit or scope of the present invention.

[0082] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs, and the disclosure and materials cited therein are hereby incorporated by reference.

[0083] Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many technical equivalents to the specific embodiments of the invention described herein. Such equivalents are intended to be encompassed by the claims.

[0084] The experimental methods in the following examples are conventional methods unless otherwise specified. The instruments and equipment used in the following examples are conventional laboratory instruments and equipment unless otherwise specified; the experimental materials used in the following examples are purchased from conventional biochemical reagent stores unless otherwise specified.

[0085] Example 1 Screening of intestinal microbial markers for type 2 diabetes

[0086] This example screens intestinal microbial markers associated with type 2 diabetes, and the specific screening steps are as follows:

[0087] 1. Get the dataset

[0088] Single nucleotide polymorphisms (SNPs) related to gut microbiome (GM) were selected as instrumental variables (IVs) for exposure. The genetic variation data of these SNPs came from the MiBioGen Consortium, which analyzed the 16SrRNA gene sequencing data of 18,340 participants.

[0089] The genetic variation data of type 2 diabetes comes from the IEU OpenGWAS project, which contains 38 data in total. The data numbers are: ebi-a-GCST006867, ebi-a-GCST90018926, ebi-a-GCST010118, ebi-a-GCST90013892, ebi-a-GCST90013942, ebi-a-GCST007515, ebi-a-GCST007516, bbj-a -153, bbj-a-77, ebi-a-GCST90018706, ieu-a-1090, ieu-a-23, ebi-a-GCST005413, ebi-a-GC ST005047, ieu-a-26, ieu-a-976, ebi-a-GCST90093110, ebi-a-GCST90006934, ebi-a-GCST00 8048, ebi-a-GCST90026417, ebi-a-GCST90026416, ebi-a-GCST90026415, ebi-a-GCST900264 13. ebi-a-GCST90026414, ebi-a-GCST90026412, finn-b-E4_DM2, finn-b-E4_DM2COMA, finn- b-E4_DM2KETO, finn-b-E4_DM2NEU, finn-b-E4_DM2OPTH, finn-b-E4_DM2NASCOMP, finn-b-E4_DM2PERIPH, finn-b-E4_DM2REN, finn-b-E4_DM2NOCOMP, finn-b-T2D, finn-b-T2D_INCLAVO, finn-b-E4_DM2_STRICT and finn-b-T2D_WIDE.

[0090] 2. Select instrumental variables

[0091] After excluding 15 unknown genera from the 211 intestinal microorganisms, a total of 196 intestinal microorganisms were included in this example, including 9 phylum-level microorganisms, 16 class-level microorganisms, 20 order-level microorganisms, 32 family-level microorganisms, and 119 genus-level microorganisms.

[0092] Instrumental variable inclusion criteria: The SNP significance threshold was set at P < 1.0 × 10 -5 , the linkage disequilibrium standard is r 2 <0.001, genetic distance 10000 kb, and highly correlated SNPs were removed to ensure that the included SNPs were independent of each other.

[0093] In order to eliminate the influence of weak instrumental variable bias on the estimation of association effect, the F value is used to test the strength of the instrumental variable. The calculation formula is as follows:

[0094] F=[R 2 / (R 2 -1)]×[(NK-1) / K]

[0095] Among them, N represents the sample size of the gut microbiome GWAS study, K represents the number of instrumental variables, and R 2 represents the extent to which the SNP explains the exposure, R 2 The calculation formula is: 2 =2×β 2 × (1-MAF) × MAF, MAF is the minor allele frequency, and β is the effect size of SNP on exposure.

[0096] F>10 is considered to be able to effectively avoid the bias caused by weak instrumental variables, so SNPs with F<10 were eliminated. A total of 1519 SNPs were obtained as instrumental variables.

[0097] 3. Statistical Analysis

[0098] Statistical analysis was performed using the TwoSampleMR and MR-PRESSO packages in R. Inverse-variance weighted analysis (IVW) was used to calculate the OR value and 95% confidence interval (95% CI) to evaluate the potential causal association between intestinal microbial abundance and the risk of type 2 diabetes, combined with MR-Egger regression, weighted median (WME), and weighted mode (WM) as supplementary methods of IVW.

[0099] The IVW method was used to screen the eigenvalues, and P < 0.05 was considered statistically significant.

[0100] 4. Sensitivity and pleiotropy analysis

[0101] In terms of sensitivity, this example calculates Cochran's Q statistic by IVW and MR-Egger regression. If P>0.05, it means there is no obvious heterogeneity. At the same time, the inventors also eliminated the included SNPs one by one by leaving one out method to observe whether it affects the analysis results, and draw a forest map. If P>0.05 is obtained after eliminating a certain SNP, it is considered that the SNP will not have a significant impact on the results.

[0102] In terms of pleiotropy, the inventors simultaneously used the intercept term of MR-Egger regression and Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) to test the horizontal pleiotropy of the included SNPs. In MR-Egger regression, if the intercept tends to 0, it can be considered that there is no horizontal pleiotropy. The horizontal pleiotropy screening retains the characteristic value of P>0.05.

[0103] 5. Summary Analysis

[0104] IVW is considered the standard method for MR summary data, which uses the Wald ratio method to calculate the causal effect estimate for each included instrumental SNP and then performs a weighted summary analysis.

[0105] The results of the inverse variance weighted analysis of the above 38 data in this example are shown in Tables 1 to 38.

[0106] Table 1 IVW analysis results of ebi-a-GCST006867 data

[0107] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 3 0.036 0.8858(0.7908-0.9921) 9 0.0459 1.065(1.0012-1.133) 5 0.0023 0.8333(0.7411-0.9371) 10 0.0182 1.1155(1.0187-1.2213) 11 0.0298 0.9129(0.8408-0.9911) 11 0.0298 1.0762(1.0072-1.1498) 6 0.0366 0.9456(0.8972-0.9965) 9 0.0002 1.2185(1.0978-1.3525) 7 0.0148 1.0973(1.0183-1.1825) 12 0.019 1.1323(1.0206-1.2561)

[0108] As shown in Table 1, in ebi-a-GCST006867, Eubacterium oxidoreducens group (Redox Eubacterium group), Ruminococcus gnavus group (active rumen cocci), RuminococcaceaeUCG010 (Ruminococcaceae UCG 010), Ruminococcus torques group (Ruminococcus contortus group), RuminococcaceaeUCG003 (Ruminococcaceae UCG 003), Oscillibacter (Oscillatory Spirulina), Sellimonas (Selemonas spp.), Lachnoclostridium (Core bacteria in the intestine), Actinomyces (Actinomyces), Streptococcus The causal relationship between Streptococcus and diabetes reached a statistically significant level (P<0.05). Ruminococcus gnavus group , Ruminococcus torques group , Oscillibacter , Lachnoclostridium , Actinomyces , Streptococcus It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; Eubacterium oxidoreducens group , RuminococcaceaeUCG010 , RuminococcaceaeUCG003 and Sellimonas It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0109] Table 2 IVW analysis results of ebi-a-GCST90018926 data

[0110] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 11 0.0206 1.04(1.006-1.0751) 14 0.0415 0.9429(0.8912-0.9977) 14 0.0093 1.0447(1.0108-1.0796) 13 0.0109 1.1078(1.0239-1.1985) 17 0.0476 1.0701(1.0007-1.1443) 10 0.0192 0.9328(0.8801-0.9887) 9 0.0167 0.9093(0.8412-0.9829) 8 0.0003 0.8754(0.8151-0.9402) 11 0.0054 1.0535(1.0155-1.093)

[0111] As can be seen from Table 2, in ebi-a-GCST90018926, Lactococcus (Lactococcus lactis), RuminococcaceaeUCG003 (Ruminococcaceae UCG 003), Gordonibacter (Gordonella parafaciens), Ruminococcus torques group (Ruminococcus contortus group), Parasutterella (Paramagitates), DefluviitaleaceaeUCG011 (Wastewater Bacillus family UCG011), Slackia (Slackia spp.), Oscillospira (Oscillatory Spirulina), Eubacterium nodatum group The causal relationship between Eubacterium and diabetes reached a statistically significant level (P<0.05). Lactococcus , Gordonibacter , Ruminococcus torques group , Parasutterella , Eubacterium nodatum group It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; RuminococcaceaeUCG003 , DefluviitaleaceaeUCG011 , Slackia , Oscillospira It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0112] Table 3 IVW analysis results of ebi-a-GCST010118 data

[0113] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 15 0.0498 0.9448(0.8926-0.9999) 7 0.0115 0.9168(0.857-0.9806) 7 0.0481 1.0992(1.0008-1.2073) 11 0.0311 0.9286(0.868-0.9933) 11 0.0382 1.0508(1.0027-1.1011) 10 0.0217 1.0464(1.0066-1.0877) 11 0.024 0.9121(0.8421-0.9879) 9 0.0282 1.0483(1.0051-1.0934) 13 0.0286 1.0744(1.0076-1.1457)

[0114] As shown in Table 3, in ebi-a-GCST010118, Erysipelatoclostridium (Erysipelothrix spp.), Enterorhabdus (Enterobacter spp.), RuminococcaceaeUCG010 (Ruminococcaceae UCG 010), Romboutsia (Rombus spp.), Coprobacter (Coprococcus), Victivallis (Food Pseudomonas), Ruminococcus1 (Ruminococcus spp.), Eubacterium nodatum group (Eubacterium), Lachnospiraceae NK4A136 group The causal relationship between Lachnospiraceae and diabetes reached a statistically significant level (P<0.05). RuminococcaceaeUCG010 , Coprobacter , Victivallis , Eubacterium nodatum group , Lachnospiraceae NK4A136 group It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; Erysipelatoclostridium , Enterorhabdus , Romboutsia , Ruminococcus1 It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0115] Table 4 IVW analysis results of ebi-a-GCST90013892 data

[0116] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 13 0.0425 1.1412(1.0045-1.2966) 8 0.0006 0.8552(0.7821-0.9352) 6 0.0204 0.8318(0.7118-0.9719) 7 0.0488 1.1152(1.0006-1.243) 12 0.023 0.8641(0.7618-0.98) 12 0.048 0.8617(0.7435-0.9987) 6 0.0063 0.8224(0.7149-0.9462) 10 0.0498 0.8859(0.7849-0.9999) 11 0.0058 0.8639(0.7786-0.9585)

[0117] From Table 4, we can see that in ebi-a-GCST90013892, Lachnoclostridium (Core bacteria in the intestine), LachnospiraceaeNC2004 group (Lachnospiraceae), Parabacteroides (Parabacteroides spp.), Actinomyces (Actinomyces), RuminococcaceaeUCG003 (Ruminococcaceae UCG003), Coprococcus1 (Coprococcus 1), RuminococcaceaeUCG010 (Ruminococcaceae UCG010), Desulfovibrio (Desulfovibrio spp.), Dialister The causal relationship between (Diarlisteria spp.) and diabetes reached a statistically significant level (P<0.05). Lachnoclostridium , Actinomyces It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; LachnospiraceaeNC2004 group , Parabacteroides , RuminococcaceaeUCG003 , Coprococcus1 , RuminococcaceaeUCG01 0. Desulfovibrio , Dialister It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0118] Table 5 IVW analysis results of ebi-a-GCST90013942 data

[0119] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 10 0.0486 0.8856(0.7849-0.9993) 11 0.0061 0.8643(0.7788-0.9592) 6 0.0213 0.8326(0.7124-0.9731) 7 0.0489 1.1152(1.0005-1.243) 12 0.0252 0.8654(0.7624-0.9822) 13 0.0425 1.1412(1.0045-1.2966) 12 0.047 0.8613(0.7432-0.998) 8 0.0006 0.8553(0.7821-0.9354) 6 0.0063 0.8224(0.7149-0.9462)

[0120] From Table 5, we can see that in ebi-a-GCST90013942, Desulfovibrio (Desulfovibrio spp.), Dialister (Diarlistella spp.), Parabacteroides (Parabacteroides spp.), Actinomyces(Actinomyces), RuminococcaceaeUCG003 (Ruminococcaceae UCG003), Lachnoclostridium (Core bacteria in the intestine), Coprococcus1 (Coprococcus 1), L achnospiraceaeNC2004 group (Lachnospiraceae), RuminococcaceaeUCG010 The causal relationship between (Ruminococcaceae UCG010) and diabetes reached a statistically significant level (P<0.05). Lachnoclostridium , Actinomyces It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; Desulfovibrio , Dialister , Parabacteroides , RuminococcaceaeUCG003 , Coprococcus1 , LachnospiraceaeNC2004 group , RuminococcaceaeUCG010 It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0121] Table 6 IVW analysis results of ebi-a-GCST007515 data

[0122] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 3 0.0423 1.0985(1.0033-1.2029)

[0123] From Table 6, we can see that in ebi-a-GCST007515, Victivallis The causal relationship between (food genus) and diabetes reached a statistically significant level (P<0.05). In addition, Victivallis It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes.

[0124] Table 7 IVW analysis results of ebi-a-GCST007516 data

[0125] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 3 0.02 1.12(1.018-1.2322)

[0126] From Table 7, we can see that in ebi-a-GCST007516, Victivallis The causal relationship between (food genus) and diabetes reached a statistically significant level (P<0.05). In addition, Victivallis It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes.

[0127] Table 8 IVW analysis results of bbj-a-153 data

[0128] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 9 0.0489 1.0502(1.0002-1.1026) 10 0.0317 1.1186(1.0099-1.2391) 11 0.0426 0.8959(0.8056-0.9963)

[0129] From Table 8, we can see that in bbj-a-153, Olsenella (Lactobacillus spp.), Anaerostipes (Lachnospiraceae Anaerostipes genus), Ruminiclostridium9The causal relationship between (Ruminal Clostridium 9) and diabetes reached a statistically significant level (P<0.05). Olsenella , Anaerostipes It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; Ruminiclostridium9 It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0130] Table 9 IVW analysis results of bbj-a-77 data

[0131] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 10 0.0145 1.1097(1.0209-1.2063) 11 0.0316 1.0887(1.0075-1.1765) 9 0.0212 1.0628(1.0092-1.1192)

[0132] From Table 9, we can see that in bbj-a-77, Adlercreutzia (Adler's Kreutzeria spp.), Holdemanella (Haldemannia spp.), Olsenella The causal relationship between Lactobacillus (Lactobacillus) and diabetes reached a statistically significant level (P<0.05). Adlercreutzia , Holdemanella , Olsenella It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes.

[0133] Table 10 IVW analysis results of ebi-a-GCST90018706 data

[0134] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 10 0.0342 0.9297(0.869-0.9946) 10 0.0306 1.0639(1.0058-1.1254) 10 0.024 1.0544(1.007-1.1041)

[0135] It can be seen from Table 10 that in ebi-a-GCST90018706, Anaerofilum (Lachnospiraceae Anaerofilum genus), Eubacterium nodatum group (Eubacterium), Lactococcus The causal relationship between Lactococcus lactis and diabetes reached a statistically significant level (P<0.05). Eubacterium nodatum group , Lactococcus It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; Anaerofilum It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0136] Table 11 Results of IVW analysis of IEU-A-1090 data

[0137] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 12 0.0234 0.5626(0.3422-0.925) 11 0.0061 0.732(0.5856-0.915) 19 0.01 1.4241(1.088-1.8639) 11 0.0345 0.7314(0.5473-0.9775) 17 0.0192 1.5727(1.0766-2.2974) 15 0.0368 1.3584(1.019-1.8108)

[0138] It can be seen from Table 11 that in IEU-A-1090, Ruminiclostridium5 (Clostridium rumenicola 5), Lactococcus (Lactococcus lactis), Eubacterium ruminantium group (Eubacterium ruminantum group), Howardella (Howardia spp.), Butyricimonas(Butyricum spp.), Erysipelatoclostridium The causal relationship between Erysipelothrix and diabetes reached a statistically significant level (P<0.05). Eubacterium ruminantium group , Butyricimonas , Erysipelatoclostridium It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; Ruminiclostridium5 , Lactococcus , Howardella It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0139] Table 12 Results of IVW analysis of IEU-A-23 data

[0140] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 4 0.0418 1.2801(1.0092-1.6236) 8 0.0095 0.78(0.6464-0.9412) 3 0.0262 1.295(1.031-1.6266) 9 0.0293 1.1381(1.0131-1.2785) 11 0.0176 0.845(0.7354-0.9711) 4 0.005 1.5051(1.1314-2.0021) 9 0.0031 1.311(1.0958-1.5684) 7 0.0112 0.7904(0.6591-0.948) 7 0.0183 0.8151(0.6878-0.966)

[0141] From Table 12, we can see that in IEU-A-23, Ruminiclostridium9 (Clostridium rumenii 9), Ruminococcaceae NK4A214 group (Ruminococcaceae NK4A214), Holdemanella (Haldemannia spp.), Ruminococcus gnavus group (active rumen cocci), Parasutterella (Paramagitates), Parabacteroides (Parabacteroides spp.), Lachnoclostridium (Core bacteria in the intestine), Marvinbryantia (Marvinberry), Akkermansia The causal relationship between Akkermansia and diabetes reached a statistically significant level (P<0.05). Ruminiclostridium9 , Holdemanella , Ruminococcus gnavus group , Parabacteroides , Lachnoclostridium It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; Ruminococcaceae NK4A214 group , Parasutterella , Marvinbryantia , Akkermansia It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0142] Table 13 IVW analysis results of ebi-a-GCST005413 data

[0143] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 19 0.037 1.1091(1.0062-1.2225) 11 0.0308 1.124(1.0109-1.2498) 15 0.0214 1.1972(1.027-1.3956) 15 0.0166 0.825(0.7049-0.9656)

[0144] It can be seen from Table 13 that in ebi-a-GCST005413, Eubacterium ruminantium group (Eubacterium ruminantum group), Howardella (Howardia spp.), Eubacterium ventriosum group (Eubacterium venosus group), Family XIIIAD3011 groupThe causal relationship with diabetes reached a statistically significant level (P<0.05). Eubacterium ruminantium group , Howardella , Eubacterium ventriosum group It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; Family XIIIAD3011 group It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0145] Table 14 IVW analysis results of ebi-a-GCST005047 data

[0146] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 9 0.0059 1.2889(1.0758-1.5443) 9 0.0154 1.1641(1.0294-1.3164) 3 0.0468 1.2477(1.0031-1.5518) 8 0.0207 0.7909(0.6484-0.9647) 4 0.0052 1.5285(1.1347-2.0588) 7 0.0196 0.8015(0.6656-0.9652) 4 0.0297 1.2862(1.0251-1.6138) 7 0.0279 0.8287(0.701-0.9798) 5 0.0499 0.8608(0.741-0.9999) 6 0.0467 0.7765(0.6051-0.9963) 11 0.017 0.8344(0.7191-0.9682)

[0147] It can be seen from Table 14 that in ebi-a-GCST005047, Lachnoclostridium (Core bacteria in the intestine), Ruminococcus gnavus group (active rumen cocci), Holdemanella (Holdemania), Ruminococcaceae NK4A214 group (Ruminococcaceae NK4A214), Parabacteroides (Parabacteroides spp.), Marvinbryantia (Marvinberry), Ruminiclostridium9 (Clostridium rumenii 9), Akkermansia (Akkermansia), Haemophilus (Haemophilus spp.), Bacteroides (Bacteroides), Parasutterella The causal relationship between (Paramastigotes) and diabetes reached a statistically significant level (P<0.05). Lachnoclostridium , Ruminococcus gnavus group , Holdemanella , Parabacteroides , Ruminiclostridium9 It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; Ruminococcaceae NK4A214 group , Marvinbryantia , Akkermansia , Haemophilus , Bacteroides , Parasutterella It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0148] Table 15 Results of IVW analysis of IEU-A-26 data

[0149] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 11 0.038 1.3162(1.0153-1.7062) 8 0.0258 0.7488(0.5806-0.9657) 3 0.0321 0.7311(0.549-0.9737) 8 0.0071 1.4231(1.1006-1.84)

[0150] From Table 15, we can see that in IEU-A-26, Streptococcus (Streptococcus), Ruminococcaceae NK4A214 group (Ruminococcaceae NK4A214), Actinomyces (Actinomyces), LachnoclostridiumThe causal relationship between (core intestinal bacteria) and diabetes reached a statistically significant level (P<0.05). Streptococcus , Lachnoclostridium It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; Ruminococcaceae NK4A214 group , Actinomyces It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0151] Table 16 Results of IVW analysis of IEU-A-976 data

[0152] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 7 0.0265 0.8337(0.71-0.979) 7 0.0367 0.7252(0.5364-0.9803)

[0153] From Table 16, we can see that in IEU-A-976, Howardella (Howardia spp.), Akkermansia The causal relationship between Akkermansia and diabetes reached a statistically significant level (P<0.05). Howardella , Akkermansia It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0154] Table 17 IVW analysis results of ebi-a-GCST90093110 data

[0155] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 13 0.0064 1.2926(1.0747-1.5547)

[0156] It can be seen from Table 17 that in ebi-a-GCST90093110, Anaerostipes (Lachnospiraceae Anaerostipes The causal relationship between genus and diabetes reached a statistically significant level (P<0.05). It was positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes.

[0157] Table 18 IVW analysis results of ebi-a-GCST90006934 data

[0158] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 24 0.0495 0.8586(0.7375-0.9997) 14 0.0228 0.8637(0.7613-0.9798) 12 0.0272 1.1227(1.0131-1.2442) 6 0.0069 1.2747(1.0687-1.5203) 9 0.019 0.8613(0.7602-0.9758) 16 0.0025 0.7611(0.6377-0.9083) 8 0.008 0.7484(0.6041-0.9272)

[0159] It can be seen from Table 18 that in ebi-a-GCST90006934, RuminococcaceaeUCG002 (Ruminococcaceae UCG002), Coprobacter (Coprococcus), RikenellaceaeRC9 gut group (RIKEN Bacteria RC9 intestinal group), Eubacterium oxidoreducens group (Redox Eubacterium group), Eubacterium fissicatena group (Eubacterium spp.), Barnesiella (Barnesella spp.), Flavonifractor The causal relationship between (Flavonobacterium) and diabetes reached a statistically significant level (P<0.05). RikenellaceaeRC9 gut group , Eubacterium oxidoreducens group It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; RuminococcaceaeUCG002 , Coprobacter , Eubacterium fissicatena group , Barnesiella , Flavonifractor It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0160] Table 19 IVW analysis results of ebi-a-GCST008048 data

[0161] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 12 0.0208 1.1485(1.0213-1.2917) 24 0.0094 0.847(0.7472-0.9601) 13 0.0238 1.224(1.0272-1.4584)

[0162] It can be seen from Table 19 that in ebi-a-GCST008048, Eisenbergiella (Eisenbergia spp.), RuminococcaceaeUCG002 (Ruminococcaceae UCG002), Lachnoclostridium The causal relationship between (core intestinal bacteria) and diabetes reached a statistically significant level (P<0.05). Eisenbergiella , Lachnoclostridium It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; RuminococcaceaeUCG002 It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0163] Table 20 IVW analysis results of ebi-a-GCST90026417 data

[0164] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 17 0.0406 0.7234(0.5306-0.9863)

[0165] It can be seen from Table 20 that in ebi-a-GCST90026417, Fusicatenibacter The causal relationship between (Spindle-like rods) and diabetes reached a statistically significant level (P<0.05). It was negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0166] Table 21 IVW analysis results of ebi-a-GCST90026416 data

[0167] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 18 0.0356 0.6731(0.4653-0.9736) 15 0.0289 1.6297(1.0516-2.5258) 15 0.021 0.668(0.4742-0.9409)

[0168] It can be seen from Table 21 that in ebi-a-GCST90026416, Fusicatenibacter (Spindle chain rod), FamilyXIIIAD3011 group , Escherichia.Shigella The causal relationship between Escherichia coli and diabetes reached a statistically significant level (P<0.05). FamilyXIIIAD3011group It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; Fusicatenibacter , Escherichia.ShigellaIt is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0169] Table 22 IVW analysis results of ebi-a-GCST90026415 data

[0170] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 16 0.0184 0.6337(0.4338-0.9259) 18 0.0387 0.5791(0.3451-0.972) 12 0.0388 0.7004(0.4997-0.9818) 14 0.0261 0.6939(0.5029-0.9575) 8 0.0298 0.7009(0.5086-0.9658)

[0171] It can be seen from Table 22 that in ebi-a-GCST90026415, Butyricimonas (Butyricum spp.), Fusicatenibacter (Spindle chain rod), Paraprevotella (Escherichia), Holdemanella (Haldemannia spp.), Methanobrevibacter The causal relationship between (Methanobacterium) and diabetes reached a statistically significant level (P<0.05). It was negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0172] Table 23 IVW analysis results of ebi-a-GCST90026413 data

[0173] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 5 0.0485 0.6745(0.4561-0.9975)

[0174] It can be seen from Table 23 that in ebi-a-GCST90026413, Catenibacterium The causal relationship between (Streptobacillus) and diabetes reached a statistically significant level (P<0.05). It was negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0175] Table 24 IVW analysis results of ebi-a-GCST90026414 data

[0176] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 14 0.0421 1.7001(1.0192-2.8358) 9 0.0196 0.5122(0.292-0.8985) 13 0.0048 2.3272(1.2933-4.1876)

[0177] It can be seen from Table 24 that in ebi-a-GCST90026414, Coprococcus1 (Coprococcus 1), Coprococcus2 (Coprococcus 2), Anaerotruncus ( Anaerotruncus The causal relationship between diabetes and diabetes reached a statistically significant level (P<0.05). Coprococcus1 , Anaerotruncus It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; Coprococcus2 It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0178] Table 25 IVW analysis results of ebi-a-GCST90026412 data

[0179] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 7 0.0462 1.6134(1.0081-2.5819) 11 0.0438 0.4961(0.251-0.9806) 11 0.0472 0.4354(0.1915-0.9897) 12 0.0287 2.0588(1.0778-3.9326) 12 0.0376 2.0116(1.041-3.8874)

[0180] It can be seen from Table 25 that in ebi-a-GCST90026412, Alloprevotella (Pseudo-Prevotella), RuminococcaceaeUCG004 (Ruminococcaceae UCG004), Collinsella (Collinsella spp.), Turicibacter (Zurichobacterium spp.), Akkermansia The causal relationship between Akkermansia and diabetes reached a statistically significant level (P<0.05). Alloprevotella , Turicibacter , Akkermansia It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; RuminococcaceaeUCG004 , Collinsella It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0181] Table 26 IVW analysis results of finn-b-E4_DM2 data

[0182] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 16 0.025 1.0926(1.0112-1.1806) 12 0.0326 0.9133(0.8403-0.9925) 8 0.0027 0.8369(0.7449-0.9403) 14 0.0363 0.9044(0.8232-0.9936)

[0183] It can be seen from Table 26 that in finn-b-E4_DM2, Parasutterella (Paramagitates), Turicibacter (Zurichobacterium spp.), RuminococcaceaeUCG010 (Ruminococcaceae UCG010), Ruminiclostridium5 The causal relationship between (Ruminal Clostridium 5) and diabetes reached a statistically significant level (P<0.05). Parasutterella It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; Turicibacter , RuminococcaceaeUCG010 , Ruminiclostridium5 It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0184] Table 27 finn-b-E4_DM2COMA data IVW analysis results

[0185] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 17 0.0221 0.7338(0.563-0.9565) 13 0.0194 1.4152(1.0578-1.8935) 14 0.0156 0.703(0.5284-0.9354) 4 0.0002 1.6915(1.2847-2.2272) 19 0.0324 0.8237(0.6896-0.9839) 13 0.0351 1.4562(1.0265-2.0657) 6 0.0241 1.3811(1.0431-1.8284)

[0186] From Table 27, we can see that in finn-b-E4_DM2COMA, ErysipelotrichaceaeUCG003 (Erysipelothrixaceae UCG 003), Ruminococcus gauvreauii group (Ruminococcus glaucii group), RuminococcaceaeUCG00 3 (Ruminococcaceae UCG003), Catenibacterium (Streptobacillus), Eubacterium ruminantium group (Eubacterium ruminantum group), LachnospiraceaeUCG004 (Lachnospirillum UCG004), Eubacterium oxidoreducens groupThe causal relationship between (Redox Eubacterium group) and diabetes reached a statistically significant level (P<0.05). Ruminococcus gauvreauii group , Catenibacterium , LachnospiraceaeUCG004 , Eubacterium oxidoreducens group It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; ErysipelotrichaceaeUCG003 , RuminococcaceaeUCG003 , Eubacterium ruminantium group It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0187] Table 28 finn-b-E4_DM2KETO data IVW analysis results

[0188] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 8 0.0211 2.2132(1.1265-4.3484) 17 0.0033 0.3242(0.1528-0.6875) 12 0.0165 1.5999(1.0898-2.3487) 12 0.0431 1.9535(1.0211-3.7373) 13 0.0066 3.9525(1.4664-10.6535) 5 0.0236 2.4919(1.1305-5.4928) 14 0.0154 1.8136(1.1206-2.935) 8 0.0342 0.6056(0.3807-0.9634)

[0189] It can be seen from Table 28 that in finn-b-E4_DM2KETO, Actinomyces (Actinomyces), RuminococcaceaeUCG014 (Ruminococcaceae UCG014), RikenellaceaeRC9 gut group (RIKEN Bacteria RC9 intestinal group), Eisenbergiella (Eisenbergia spp.), Alistipes (Another mycobacterium), Hungatella (Clostridium family Hungatella genus), Holdemanella (Haldemannia spp.), RuminococcaceaeUCG011 The causal relationship between (Ruminococcaceae UCG011) and diabetes reached a statistically significant level (P<0.05). Actinomyces , RikenellaceaeRC9 gut group , Eisenbergiella , Alistipes , Hungatella , Holdemanella It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; RuminococcaceaeUCG014 , RuminococcaceaeUCG011 It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0190] Table 29 finn-b-E4_DM2NEU data IVW analysis results

[0191] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 12 0.0331 0.5976(0.3722-0.9595)

[0192] It can be seen from Table 29 that in finn-b-E4_DM2NEU, Sutterella The causal relationship between (Sutterella) and diabetes reached a statistically significant level (P<0.05). It was negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0193] Table 30 finn-b-E4_DM2OPTH data IVW analysis results

[0194] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 9 0.0191 1.3278(1.0476-1.6829) 8 0.0259 1.3887(1.0403-1.8538) 14 0.0135 0.8129(0.6898-0.9581)

[0195] From Table 30, we can see that in finn-b-E4_DM2OPTH, Lactobacillus (Lactobacillus), Actinomyces (Actinomyces), Gordonibacter The causal relationship between (Gordonella parafaciens) and diabetes reached a statistically significant level (P<0.05). Lactobacillus , Actinomyces It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; Gordonibacter It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0196] Table 31 finn-b-E4_DM2NASCOMP data IVW analysis results

[0197] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 9 0.0055 0.8247(0.7197-0.9449) 10 0.0302 0.8886(0.7986-0.9887) 8 0.0007 0.7977(0.6996-0.9094)

[0198] From Table 31, we can see that in finn-b-E4_DM2NASCOMP, Butyricicoccus (Butyric acid bacteria), Desulfovibrio (Desulfovibrio spp.), RuminococcaceaeUCG010 The causal relationship between (Ruminococcaceae UCG010) and diabetes reached a statistically significant level (P<0.05). It was negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0199] Table 32 finn-b-E4_DM2PERIPH data IVW analysis results

[0200] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 9 0.0244 0.5784(0.359-0.9318) 9 0.0018 1.6913(1.2166-2.3512) 15 0.0272 0.6567(0.4521-0.9538) 12 0.034 0.7374(0.5564-0.9773) 13 0.0031 2.0699(1.2773-3.3541) 12 0.0225 0.5871(0.3716-0.9275) 10 0.0195 0.5716(0.3575-0.9139)

[0201] From Table 32, we can see that in finn-b-E4_DM2PERIPH, Coprococcus2 (Coprococcus 2), Lactobacillus (Lactobacillus), Intestinibacter ( Intestinibacter genus), Anaerofilum (Lachnospiraceae Anaerofilum genus), Lachnoclostridium (Core bacteria in the intestine), LachnospiraceaeUCG010 (Lachnospirillum UCG010), FamilyXIIIUCG001 The causal relationship with diabetes reached a statistically significant level (P<0.05). Lactobacillus , Lachnoclostridium It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; Coprococcus2 , Intestinibacter , Anaerofilum , LachnospiraceaeUCG010, FamilyXIIIUCG001 It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0202] Table 33 finn-b-E4_DM2REN data IVW analysis results

[0203] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 12 0.0041 1.7174(1.1866-2.4856) 8 0.031 1.5905(1.0434-2.4244) 12 0.0223 0.6291(0.4228-0.936)

[0204] From Table 33, we can see that in finn-b-E4_DM2REN, Akkermansia (Akkermansia), Flavonifractor (Flavone-degrading bacteria), Sutterella The causal relationship between Sutterella and diabetes reached a statistically significant level (P<0.05). Akkermansia , Flavonifractor It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; Sutterella It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0205] Table 34 finn-b-E4_DM2NOCOMP data IVW analysis results

[0206] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 15 0.0444 1.1785(1.0041-1.3831) 15 0.0169 1.1729(1.029-1.3369) 8 0.0356 1.2089(1.0129-1.4429) 9 0.0276 0.8272(0.6987-0.9793) 8 0.0033 0.7901(0.675-0.9247) 8 0.016 1.2939(1.0492-1.5956)

[0207] From Table 34, we can see that in finn-b-E4_DM2NOCOMP, Anaerostipes (Lachnospiraceae Anaerostipes genus), Escherichia.Shigella (Escherichia), Odoribacter ( Odoribacter genus), Butyricicoccus (Butyric acid bacteria), RuminococcaceaeUCG010 (Ruminococcaceae UCG010), Parabacteroides The causal relationship between (Parabacteroides) and diabetes reached a statistically significant level (P<0.05). Anaerostipes , Escherichia.Shigella , Odoribacter , Parabacteroides It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; Butyricicoccus , RuminococcaceaeUCG010 It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0208] Table 35 IVW analysis results of finn-b-T2D data

[0209] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 12 0.0277 0.9072(0.8318-0.9894) 11 0.0491 1.0629(1.0003-1.1295) 8 0.019 0.865(0.7663-0.9764) 9 0.0287 0.8692(0.7667-0.9855) 8 0.0341 1.1278(1.0091-1.2606) 16 0.028 1.1055(1.0109-1.209)

[0210] As shown in Table 35, in finn-b-T2D, Turicibacter (Zurichobacterium spp.), Clostridium innocuum group (Innocent Clostridium group), RuminococcaceaeUCG010 (Ruminococcaceae UCG010), Butyricicoccus (Butyric acid bacteria), Actinomyces (Actinomyces), Parasutterella The causal relationship between (Paramastigotes) and diabetes reached a statistically significant level (P<0.05). Clostridium innocuum group , Actinomyces , Parasutterella It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; Turicibacter , RuminococcaceaeUCG010 , Butyricicoccus It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0211] Table 36 IVW analysis results of finn-b-T2D_INCLAVO data

[0212] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 16 0.0129 1.12(1.0243-1.2246) 8 0.033 1.1405(1.0107-1.287)

[0213] From Table 36, we can see that in finn-b-T2D_INCLAVO, Actinomyces (Actinomyces), Parasutterella The causal relationship between Paramastigotes and diabetes reached a statistically significant level (P<0.05). It was positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes.

[0214] Table 37 finn-b-E4_DM2_STRICT data IVW analysis results

[0215] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 8 0.0379 1.1282(1.0067-1.2643) 12 0.0319 0.9094(0.8338-0.9918) 8 0.0146 0.8599(0.7618-0.9707) 16 0.0266 1.1072(1.0119-1.2115) 11 0.0453 1.064(1.0013-1.1307) 9 0.0315 0.8713(0.7685-0.9879)

[0216] From Table 37, we can see that in finn-b-E4_DM2_STRICT, Actinomyces (Actinomyces), Turicibacter (Zurichobacterium spp.), RuminococcaceaeUCG010 (Ruminococcaceae UCG010), Parasutterella (Paramagitates), Clostridium innocuum group (Innocent Clostridium group), Butyricicoccus The causal relationship between Butyric acid bacteria and diabetes reached a statistically significant level (P<0.05). Actinomyces , Parasutterella , Clostridium innocuum group It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; Turicibacter , RuminococcaceaeUCG010 , Butyricicoccus It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0217] Table 38 IVW analysis results of finn-b-T2D_WIDE data

[0218] Name of intestinal microorganism Number of SNPs P OR (95% confidence interval) 4 0.0328 1.1487(1.0114-1.3046) 3 0.0426 1.3493(1.0101-1.8026) 9 0.0057 1.1967(1.0536-1.3591) 8 0.0158 1.2248(1.0388-1.4441) 8 0.0019 0.7691(0.6517-0.9078) 15 0.0369 1.1256(1.0072-1.258)

[0219] From Table 38, we can see that in finn-b-T2D_WIDE, Catenibacterium (Streptobacillus), LachnospiraceaeND3007group (Lachnospira ND3007 group), Veillonella (Veillonella spp.), Odoribacter ( Odoribacter genus), RuminococcaceaeUCG010 (Ruminococcaceae UCG010), Escherichia.Shigella The causal relationship between Escherichia coli and diabetes reached a statistically significant level (P<0.05). Catenibacterium , LachnospiraceaeND3007group , Veillonella , Odoribacter , Escherichia.Shigella It is positively correlated with the occurrence of diabetes, suggesting that it is a risk factor for diabetes; RuminococcaceaeUCG010 It is negatively correlated with the occurrence of diabetes, suggesting that it is a protective factor for diabetes.

[0220] Example 2 Machine Learning Model for Predicting Type 2 Diabetes

[0221] This embodiment uses the RandomForestClassifier function in the Python sklearn.ensemble module to perform random forest algorithm modeling on the data.

[0222] This example constructs a prediction model for calculating the risk of type 2 diabetes in a host based on the abundance values ​​of intestinal microbial markers screened in Example 1. The specific steps are as follows:

[0223] S1, Dataset acquisition: 888 sample data were downloaded from the MicroPhenoDB database, including 500 cases in the control group and 388 cases in the case group.

[0224] S2, the StandardScaler function of the klearn.preprocessing module standardizes the data, and then uses the recursive feature elimination method for feature selection. The recursive feature elimination method uses a base model for multiple rounds of training. After each round of training, several unimportant features are eliminated, and then the next round of training is based on the new feature set. It uses model accuracy to identify which attribute combinations contribute the most to the prediction of the target attribute, and then eliminates useless features. Sklearn provides two recursive feature elimination methods, namely, recursive feature elimination (RFE) and cross recursive feature elimination (RFECV). The present invention uses the RFE recursive feature elimination method for feature value screening, and determines the optimal feature set according to the AUC value of the model test set. The optimal feature set has 31 features, which are: Anaerostipes , Lactococcus , Gordonibacter , Parasutterella , Lachnoclostridium , Parabacteroides , Actinomyces , Desulfovibrio , Dialister , Butyricimonas , Paraprevotella , Holdemanella , Methanobrevibacter , Anaerotruncus , Alloprevotella , Collinsella , Akkermansia , Alistipes , Sutterella , Lactobacillus , Intestinibacter , Flavonifractor , Odoribacter , Veillonella , Barnesiella , Olsenella , Adlercreutzia , Oscillibacter , Streptococcus , Haemophilus and Bacteroides The 31 intestinal microbial characteristics will be used for subsequent model construction and prediction.

[0225] S3, the train_test_split function divides the data set into a training set and an internal validation set. In this embodiment, 25% of the samples are used as the internal validation set.

[0226] S4, a random forest model is established by cross-validation, and the model is continuously optimized. In this embodiment, the most suitable parameter combination is selected by parameter grid search. When max_depth=8, max_features=10, n_estimators=50, and min_samples_leaf=40, the model prediction effect is the best.

[0227] The ROC curves of the training set and the validation set are as follows: Figure 1 As shown, the accuracy of the training set is 91.59%, AUC=0.9703, and the accuracy of the validation set is 83.78%, AUC=0.919.

[0228] Example 3: Evaluation of model performance in independent validation set

[0229] The inventors applied the type 2 diabetes prediction model constructed using Example 2 to an independent validation set.

[0230] The independent validation set mainly comes from real physical examination sample data. The sample type is feces, with a total of 40 samples, including 15 cases with type 2 diabetes and 25 healthy controls. Nanopore 16S rRNA sequencing analysis was used. The specific analysis steps are as follows:

[0231] (1) Sequencing

[0232] DNA extraction: DNA was extracted using a fecal sample bacterial genome extraction kit, and the extracted DNA concentration and purity were tested using Nanodrop;

[0233] 16S rRNA amplification: Targeted amplification of the V4 region of the bacterial 16S rRNA gene region was performed using a 16S sequencing library construction kit for 30 cycles. The reaction conditions were: 95°C, 1 min, 94°C, 30 s, 62°C, 30 s, 62°C, 2 min, 62°C, 5 min; then maintained at 4°C.

[0234] Recover PCR products: Purify the target fragments using AMPure XP beads and quantify using Qubit;

[0235] Adapter connection: Mix the above barcoded libraries according to the concentration ratio into a 10μL system, so that the total amount of DNA after mixing is 200ng. Add 1μL rapid sequencing library adapter (RAP, Rapid sequencing adapter) to the mixed sample, blow gently with the tip of the pipette to mix, and react at room temperature for 5min.

[0236] Finally, the constructed library was sequenced using the GridION nanopore sequencer.

[0237] (2) Bioinformatics analysis

[0238] First, NanoStat software was used to quality control, filter, and remove sequencing adapters of the raw data to obtain high-quality reads. Minimap2 was used for global alignment to find chimeras, and then yacrd was used to remove chimera sequences. Filtlong software was then used to filter data of specific length and Q value. Finally, the filtered sequences were clustered in a certain way (NanoCLUST in this embodiment) to obtain multiple sequence clustering operational taxonomy units (OTUs), and the OTUs were annotated with taxonomy to obtain species abundance information.

[0239] (3) Model prediction

[0240] The abundance information of intestinal microbial markers in the case group and the control group was collated, and then model prediction and risk level classification were performed.

[0241] The model constructed in Example 2 was used to perform the prediction analysis of the above independent validation set, and the results were as follows: Figure 2 As shown, AUC=0.8264, accuracy rate 76%. The above results show that the model constructed by using Example 2 can distinguish type 2 diabetes patients from healthy people, and can be used clinically to assist in diagnosing whether a subject has type 2 diabetes.

[0242] All documents mentioned in the present invention are cited as references in this application, just as each document is cited as reference individually. In addition, it should be understood that after reading the above teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the claims attached to this application.

Claims

1. A combination of microbial markers for diagnosing or predicting type 2 diabetes, characterized in that: The microbial marker combination includes Anaerostipes , Lactococcus , Gordonibacter , Parasutterella , Lachnoclostridium , Parabacteroides , Actinomyces , Desulfovibrio , Dialister , Butyricimonas , Paraprevotella , Holdemanella , Methanobrevibacter , Anaerotruncus , Alloprevotella , Collinsella , Akkermansia , Alistipes , Sutterella , Lactobacillus , Intestinibacter , Flavonifractor , Odoribacter , Veillonella , Barnesiella , Olsenella , Adlercreutzia , Oscillibacter , Streptococcus , Haemophilus and Bacteroides .

2. A method for constructing a type 2 diabetes diagnosis or prediction model, characterized in that: The following steps are involved: S1, randomly dividing the abundance data of the combination of microbial markers described in claim 1 in biological samples of a population into two groups, one group being a training set and the other group being a test set, wherein the population includes patients with type 2 diabetes and subjects without type 2 diabetes; S2, using the training set data, constructing the type 2 diabetes diagnosis or prediction model based on the machine learning algorithm, and performing multi-fold cross validation; S3, verifying the obtained type 2 diabetes diagnosis or prediction model in the test set, Among them, the machine learning algorithm is a random forest algorithm. The maximum depth of the prediction model obtained based on the random forest algorithm is 8, the maximum number of features is 10, the minimum number of samples required at the leaf node is 40, and the number of base evaluators is 50.

3. The method according to claim 2, characterized in that The abundance data of the microbial marker combination is obtained based on qPCR, 16S rRNA sequencing or metagenomic sequencing.

4. The method according to claim 2, characterized in that: The method further comprises the step of drawing a ROC curve to evaluate the type 2 diabetes diagnosis or prediction model.

5. A computer device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the method according to any one of claims 2 to 4 when executing the computer program.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 2 to 4 are implemented.

7. A system for diagnosing or predicting type 2 diabetes, characterized in that: Includes the following modules: A data input module, used to input the abundance data of the microbial marker combination according to claim 1 obtained in the subject's biological sample; A database storage module, for storing the abundance data of the microbial marker combination in biological samples of a population, the population including type 2 diabetes patients and non-type 2 diabetes subjects; A disease prediction module is connected to the data input module and the database storage module, respectively, and is used to construct a prediction model using the abundance data of the microbial marker combination in the biological samples of the population, and to diagnose whether the subject has type 2 diabetes or predict whether the subject has a risk of developing type 2 diabetes based on the abundance data of the microbial marker combination of the subject obtained from the data input module.

8. Use of the abundance detection reagent of the microbial marker combination according to claim 1 in the preparation of a kit for diagnosing or predicting type 2 diabetes.

Images