Application of reagents for detecting intestinal microorganisms in the preparation of diabetic diagnostic preparations and kits

Abstract

This invention belongs to the technical field of diabetes diagnosis, specifically the use of reagents for detecting intestinal microorganisms in the preparation of diabetic diagnostic preparations and kits. Through metagenomics and metabolomics, key biomarkers, including key differential gut microorganisms and key differential fecal metabolites, will be screened. Its clinical value lies in the eventual identification of a multi-omics classifier that can distinguish adult-onset T1D from healthy controls (HCs) and T2D. This will facilitate the precise diagnosis and typing of adult-onset T1D and provide novel diagnostic and therapeutic targets.

Description

Technical Field

[0001] The present invention belongs to the technical field of diabetes diagnosis, and in particular relates to the application of a reagent for detecting intestinal microorganisms in the preparation of a preparation for diagnosing diabetes, and a kit. Background Art

[0002] Type 1 diabetes (T1D) is caused by autoimmune destruction of pancreatic beta cells, resulting in severe insulin deficiency and the need for insulin therapy. It is generally considered a disease of childhood and adolescents, but recent epidemiological data indicate that more than half of new T1D cases occur in adults worldwide. However, the pathogenesis of adult-onset T1D remains unclear. More than 40% of patients with adult-onset T1D are initially misdiagnosed as having type 2 diabetes (T2D). This misdiagnosis leads to unnecessary insulin therapy, a significant economic burden, and may even result in poor glycemic control, increased risk of ketoacidosis, and potentially life-threatening symptoms. Therefore, accurate diagnosis of adult-onset T1D is crucial, highlighting the need to identify new diagnostic biomarkers. Summary of the Invention

[0003] The primary objective of the present invention is to provide a method for preparing a kit for diagnosing diabetes using a combination of biomarkers for detecting intestinal microorganisms and / or fecal metabolites. The types of diabetes include adult-onset T1D and T2D. The kit can be used to differentiate between adult-onset T1D and HC and / or between adult-onset T1D and T2D.

[0004] The first object of the present invention is to provide a reagent for detecting intestinal microorganisms and / or fecal metabolites for use in preparing a preparation for diagnosing diabetes.

[0005] The types of diabetes include adult-onset T1D and T2D,

[0006] The intestinal microorganisms include one or more of the following:

[0007] Acidaminococcaceae, Bacteroidaceae, Desulfovibrionaceae, Lachnospiraceae, Oscillospiraceae, Tannerellaceae, Agathobaculum, Anaerostipes, Anaerotruncus, Bacteroides, Bilophila, Butyricicoccus, Butyricimonas, Catenibacterium, Eggerthella, Evtepia, Faecalibacterium, Flavonifractor, Intestinimonas, Lawsonibacter, M assilimaliae, Oscillibacter, Parabacteroides, Phascolarctobacterium, Romboutsia, Roseburia, unclassified Lachnospiraceae, unclassified Odoribacteraceae, unclassified Rikenellaceae, unclassified Ruminococcaceae, [Bacteroides]_pectinophilus, [Eubacterium]_rectale, [Ruminococcus]_gnavus_CAG:126, Agathobaculum butyriciproducens, Alistipes finegoldii, Alistipes senegalensis, Alistipes shahii, Alistipes AF14-19 (Alistipes_sp._AF14-19), Alistipes HGB5 (Alistipes_sp._HGB5), Anaerostipes_hadrus, Anaerobic bacteria of the colon (Anaerotruncus_colihominis), Bacteroides_cellulosilyticus, Bacteroides clarus, Bacteroides_eggerthii, Bacteroides_faecis, Bacteroides_fluxus, Bacteroides_intestinalis, Bacteroides_oleiciplenus, Bacteroides_salyersiae, Bacteroides (Bacteroides_sp.), Bacteroides 1_1_14 (Bacteroides_sp. _1_1_14), Bacteroides 4_1_36 (Bacteroides_sp._4_1_36), Bacteroides A1C1 (Bacteroides_sp._A1C1), Bacteroides AF26-10BH (Bacteroides_sp._AF26-10BH), Bacteroides AF34-31BH (Bacteroides_sp._AF34-31BH), Bacteroides AR29 (Bacteroides_sp._AR29), Bacteroides D20 (Bacteroides_sp._D20), Bacteroides faecalis CAG:1 20 (Bacteroides_stercoris_CAG:120), Bacteroides_thetaiotaomicron, Bacteroides_uniformis, Bacteroides_uniformis_CAG:3, Bifidobacterium (Bifidobacterium_bifidum), Bilophila_sp._4_1_30, Bilophila_wads worthia), Blautia sp. AF14-40, Blautia sp. AF32-4BH, Blautia sp. AM23-13AC, Blautia sp. CAG:37, Blautia sp. OF09-25XD, Butyricicoccus sp._AF10-3), Butyricicoccus sp. AM27-36, Butyricicoccus sp. AM28-25, Butyricimonas virosa, Catenibacterium mitsuokai, Clostridiaceae bacterium TF01-6, Clostridiaceae bacterium, Clostridiaceae bacterium 36_14, Clostridiales bacterium Nov_37_41, Clostridium disporicum, Clostridium phoceensis, Clostridium 27_14 (Clostridium_sp._27_14), Clostridium AF28-12 (Clostridium_sp._AF28-12), Clostridium AM25-23AC (Clostridium_sp._AM25-23AC), Clostridium AM51-4 (Clostridium_sp._AM51-4), Clostridium CA G:354 (Clostridium_sp._CAG:354), Clostridium CAG:571 (Clostridium_sp._CAG:571), Clostridium CAG:575 (Clostridium_sp._CAG:575), Clostridium SS2 / 1 (Clostridium_sp._SS2 / 1), Clostridium TF06-15AC (Clostridi um_sp._TF06-15AC), Eggerthella_sp._CAG:298, Enteroclosterasparagiformis, Eubacterium_rectale_CAG:36, Eubacterium_sp., Eubacterium_sp._36_13, Eubacterium_sp._41_20, Eubacterium_sp._CAG:180, Eubacterium_sp._CAG:251, Eubacterium_sp._CAG:86), Evtepia gabavorous, Faecalibacterium prausnitzii, Faecalibacterium sp., Faecalibacterium sp. AF10-46, Faecalibacterium sp. AF27-11BH, Faecalibacterium sp. AF28-13AC, Faecalibacterium sp. AM43-5AT, Faecalibacterium sp. OM 04-11BH (Faecalibacterium_sp._OM04-11BH), Firmicutes AF12-30 (Firmicutes_bacterium_AF12-30), Firmicutes AF16-15 (Firmicutes_bacterium_AF16-15), Firmicutes CAG: 114 (Firmicutes_bacterium_CAG:114), Firmicutes CAG:137 (Firmicutes_bacterium_CAG:137), Firmicutes CAG:341 (Firmicutes_bacterium_CAG:341), Flavonifractor plautii, Intestinimonas butyriciproducens, Lachnospiraceae bacterium 2_1_58FAA, Lachnospiraceae bacterium 5_1_63FAA, Lachnospiraceae bacterium AM26-1LB, Lawsonibacter asaccharolyticus, Massilimalia emassiliensis, Mediterraneibacter butyricigenes, Odoribacter laneus, Oscillibacter sp., Parabacteroides johnsonii, Parabacteroides merdae, Phascolarctobacterium sp.), Phascolarctobacterium succinatutens, Prevotella bivia, Rikenellaceae bacterium, Romboutsia timonensis, Roseburia faecis, Roseburia intestinalis, Roseburia intestinalis CAG:13, Roseburia inulinivorans, Roseburia AM23-20, Roseburia MUC / MUC-530-WT-4D, Roseburia OF03-24, Roseburia TF10-5, Ruminococcus AM45-9BH, Subdoligranulum OF01-18, unclassified Lachnospiraceae, unclassified Odoribacteraceae, unclassified Ruminococcus unclassified_Ruminococcaceae), unclassified_Alistipes, unclassified_Bilophila, unclassified_Faecalibacterium, unclassified_Oscillibacter, unclassified_Roseburia, unclassified_Ruminococcus, uncultured_Bacteroides_sp., uncultured_Butyricicoccus_sp., uncultured_Clostridium_sp., uncultured_Faecalibacterium_sp.), uncultured Oscillibacter sp.

[0008] Furthermore, the application,

[0009] Gut microbiota that are significantly different only in adult-onset T1D can be used to diagnose adult-onset T1D and distinguish it from T2D and HC:

[0010] Acidaminococcaceae, Oscillospiraceae, Anaerotruncus, Butyricimonas, Eggerthella, Evtepia, Intestinimonas, Lawsonibacter, Massilimaliae, Oscillibacter, Phascolarctobacterium, unclassified Odoribacteraceae, unclassified Rikenellaceae, unclassified Ruminococcaceae, [Ruminococcus]_gnavus_CAG:126, Alistipes finegoldii, Alistipes senegalensis, Alistipes shahii, Alistipes sp. AF14-19, Alistipes sp. HGB5, Anaerotruncus colihominis, Bacteroides faecis, Bacteroides sp., Bacteroides AF34-31BH, Bacteroides sp. AF34-31BH, Bacteroides faecis CAG:120 (B acteroides_stercoris_CAG:120), Bacteroides_thetaiotaomicron, Blautia_sp._AF14-40, Blautia_sp._AF32-4BH, Blautia_sp._AM23-13AC, Blautia_sp._AM23-13AC, and Blautia_sp._OF09-25XD._OF09-25XD), Butyricimonas virosa, Clostridiaceae bacterium TF01-6, Clostridiaceae bacterium, Clostridium disporicum, Clostridium phoceensis (Clostridium_phoceensis), Clostridium AF28-12 (Clostridium_sp._AF28-12), Clostridium AM25-23AC (Clostridium_sp._AM25-23AC), Clostridium CAG:354 (Clostridium_sp._CAG:354), Clostridium CAG:571 (Clostridium_sp._CAG:571), Clostridium CAG:575 (Clostridium_sp._CAG:575), Eggerthella CAG:298 (Eggerthella_sp._CAG:298), Enterocloster asparagiformis, Eubacterium CAG:180 (Eubacterium_sp._CAG:180), Eubacterium CAG:251 (Eubacterium_sp._CAG:251), Evtepia gabavorous (Evtepia_gabavorous), Firmicutes CAG:114 (Firmicutes_bacterium_CAG:114), Firmicutes CAG:137 (Firmicutes_bacterium_CAG:137), Firmicutes CAG:341 (Firmicutes_bacterium_CAG:341), Intestinimonas_butyriciproducens, Lachnospiraceae_bacterium_2_1_58FAA), Lawsonibacter_asaccharolyticus, Massilimalia emassiliensis, Oscillibacter_sp., Parabacteroides_johnsonii, Parabacteroides_merdae, Phascolarctobacterium_sp.), Phascolarctobacterium succinatutens, Rikenellaceae bacteria, Roseburia inulinivorans, Ruminococcaceae bacteria, Ruminococcus sp. AM45-9BH, unclassified Odoribacteraceae, unclassified Ruminococcaceae, unclassified Alistipes, unclassified Oscillibacter, unclassified Ruminococcus, uncultured Oscillibacter sp.;.

[0011] Gut microbiota that are significantly different only in T2D can be used to diagnose T2D and distinguish it from adult-onset T1D and normal subjects:

[0012] Bacteroidaceae, Desulfovibrionaceae, Agathobaculum, Bacteroides, Bilophila, Butyricicoccus, Faecalibacterium, Flavonifractor, Roseburia, [Bacteroides]_pectinophilus, Agathobaculumbutyriciproducens, Bacteroides clarus, Bacteroides_eggerthii, Bacteroides oleiciplenus, Bacteroides_salyersiae, Bacteroides_sp._1_1_14, Bilophila_sp._4_1_30, Bilophila_wadsworthia, Butyricicoccus_sp._AF10-3, Butyricicoccus_sp._AM27-36, Butyricicoccus_sp._AM28-25, Clostridiales_bacterium_36_14, Clostridiales_bacterium_Nov_37_41 , Clostridium 27_14 (Clostridium_sp._27_14), Clostridium TF06-15AC (Clostridium_sp._TF06-15AC), Eubacterium (Eubacterium_sp.), Eubacterium 36_13 (Eubacterium_sp._36_13), Eubacterium CAG:86 (Eubacterium_sp._CAG:86), Faecalibacterium_prausnitzii, Faecalibacterium (Faecalibacterium_sp.), Faecalibacterium AF10-46 (Faecalibacterium_sp._AF10-46), Faecalibacterium AF27-11BH (Faecalibacterium_sp._AF27-11BH), Faecalibacterium AF28-13AC (Faecalibacterium_sp._AF28-13AC), Faecalibacterium_sp._AM43-5AT, Faecalibacterium_sp._OM04-11BH, Firmicutes_bacterium_AF16-15, Flavonifractor plautii, Lachnospiraceae bacterium 5_1_63FAA, Mediterranei bacterium butyricigenes, Roseburia faecis, Roseburia sp. AM23-20, Roseburia sp. MUC / MUC-530-WT-4D, Roseburia sp. OF03-24, Roseburia sp. TF10-5 TF10-5), Subdoligranulum_sp._OF01-18, unclassified_Bilophila, unclassified_Faecalibacterium, unclassified_Roseburia, uncultured_Bacteroides_sp., uncultured_Butyricicoccus_sp., uncultured_Clostridium_sp.;.

[0013] The gut microbiota that are significantly different in adult-onset T1D and T2D can be used to diagnose adult-onset T1D and T2D and distinguish them from healthy controls, but cannot distinguish T1D from T2D:

[0014] Lachnospiraceae, Tannerellaceae, Anaerostipes, Catenibacterium, Parabacteroides, Romboutsia, unclassified Lachnospiraceae, [Eubacterium] recale, Anaerostipes hadrus, Bacteroides cellulosilyticus, Bacteroides fluxus, Bacteroides intestinalis, Bacteroides 4_1_36 (Bacteroides_sp._4_1_36), Bacteroides A1C1 (Bacteroides_sp._A1C1), Bacteroides AF26-10BH (Bacteroides_sp._AF26-10BH), Bacteroides AR29 (Bacteroides_sp._AR29), Bacteroides D20 (Bacteroides_sp._D20), Bacteroides uniformis (Bacteroides_uniformis), Bacteroides uniformis CAG:3 (Bacteroides_uniformis_CAG:3), Bifidobacterium (Bifidobacterium_bifidum), Eubacterium sphaeroides CAG:37 (Blautia_sp._CAG:37), Catenibacterium mitsuokai, Clostridium sp. AM51-4, Clostridium sp. SS2 / 1, Eubacterium recale CAG: 36, Eubacterium sp._41_20), Firmicutes_bacterium_AF12-30, Lachnospiraceae_bacterium_AM26-1LB, Odoribacter_laneus, Prevotella bivia, Romboutsia timonensis, Roseburia_intestinalis, Roseburia_intestinalis_CAG:13, unclassified_Lachnospiraceae, uncultured_Faecalibacterium_sp.

[0015] The fecal metabolites include one or more of the following:

[0016] Sarcosine, Creatine, Gallic acid, 4-Hydroxybenzoic acid, Hippuric acid, Ethylmethylacetic acid, Phenylacetic acid, Indole-3-carboxylic acid, N-Acetyltryptophan, Cinnamic acid, N-Phenylacetylphenylalanine, Azelaic acid, Methylmalonic acid, 4-Aminohippuric acid, Rhamnose, 3-(3-Hydroxyphenyl)-3-hydroxypropanoic acid, Isobutyric acid, Isovaleric acid acid), Octanoic acid, Succinic acid, Isocitric acid, Malonic acid, Phenyllactic acid, L-Lysine, Indole-3-methyl acetate, 2-Hydroxycaproic acid, Indole-3-propionic acid, Pyroglutamic acid, alpha-Hydroxyisobutyric acid, Methylglutaric acid, Citric acid, Oxoglutaric acid, Kynurenine, L-Glutamic acid.

[0017] Furthermore, the application,

[0018] Fecal metabolites that were significantly different only in adult-onset T1D could be used to diagnose adult-onset T1D and distinguish it from T2D and normal subjects:

[0019] Sarcosine, Creatine, Gallic acid, 4-Hydroxybenzoic acid, Hippuric acid, Cinnamic acid, Azelaic acid, Rhamnose, Malonic acid.

[0020] Fecal metabolites that are significantly different only in T2D can be used to diagnose T2D and distinguish it from adult-onset T1D and normal subjects:

[0021] L-Lysine, Indole-3-methyl acetate, 2-Hydroxycaproic acid, Indole-3-propionic acid, Pyroglutamic acid, alpha-Hydroxyisobutyric acid, Methylglutaric acid, Citric acid, Oxoglutaric acid, Kynurenine, L-Glutamic acid.

[0022] Fecal metabolites that are significantly different in adult-onset T1D and T2D can be used to diagnose adult-onset T1D and T2D and distinguish them from healthy controls, but cannot distinguish T1D from T2D:

[0023] Ethylmethylacetic acid, Phenylacetic acid, Indole-3-carboxylic acid, N-Acetyltryptophan, N-Phenylacetylphenylalanine, Methylmalonic acid, 4-Aminohippuric acid, 3-(3-Hydroxyphenyl)-3-hydroxypropanoic acid, Isobutyric acid, Isovaleric acid, Octanoic acid, Succinic acid, Isocitric acid, Phenyllactic acid.

[0024] Furthermore, the application,

[0025] Combined with a combination of 6 intestinal microbial markers, including one or more of [Eubacterium]_rectale, Parabacteroides_johnsonii, Clostridium_sp._CAG:575, Flavonifractor plautii, Bacteroides_stercoris_CAG:120, and Firmicutes_bacterium_AF16-15.

[0026] Furthermore, the application,

[0027] Combined with a combination of 6 fecal metabolite markers, including one or more of kynurenine, citric acid, indole-3-propionic acid, L-lysine, N-phenylacetylphenylalanine, and octanoic acid.

[0028] Furthermore,

[0029] A combination of six gut microbial markers and six fecal metabolite markers was used. The gut microbial populations included one or more of Eubacterium recale, Parabacteroides johnsonii, Clostridium sp. CAG:575, Flavonifractor plautii, Bacteroides stercoris CAG:120, and Firmicutes bacterium AF16-15, and the fecal metabolites included one or more of kynurenine, citric acid, indole-3-propionic acid, L-lysine, N-phenylacetylphenylalanine, and octanoic acid.

[0030] The second object of the present invention is to provide a kit for diagnosing diabetes, comprising the above-mentioned reagents required for detecting intestinal microorganisms and / or fecal metabolites.

[0031] Furthermore, the kit includes one or more of a biochemical detection kit, an immunoassay kit, or a molecular detection kit, preferably an immunoassay kit. Further preferably, the kit is one or more of a Western blot kit, an enzyme-linked immunosorbent assay kit, a radioimmunoassay kit, a radial immunodiffusion kit, an ouchterlony immunodiffusion kit, a rocket immunoelectrophoresis kit, an immunohistochemistry staining kit, an immunoprecipitation assay kit, a complement fixation assay kit, a fluorescence-activated cell sorting kit, an aptamer chip kit, a microarray kit, and a protein chip kit.

[0032] The present invention also provides a method for screening intestinal biomarkers:

[0033] 1. Subjects were divided into healthy controls (HC), patients with adult-onset type 1 diabetes (T1D), and patients with type 2 diabetes (T2D).

[0034] 2. Detect the levels of candidate intestinal biomarkers in the subjects, including intestinal microorganisms and fecal metabolites in fecal samples;

[0035] 3. Intestinal biomarker levels are detected by metagenomic sequencing and / or targeted metabolomics.

[0036] 4. Differential intestinal microorganisms were calculated by LefSe (linear discriminant analysis) method, and differential fecal metabolites were calculated by OPLS-DA (orthogonal partial least squares discriminant analysis) method.

[0037] 5. Compare the biomarker levels between the healthy control and the two disease groups, and obtain differential intestinal microorganisms and differential intestinal metabolites using the above methods.

[0038] 6. Perform RDA / CCA analysis (redundancy analysis) on environmental factors (including but not limited to clinical data) to determine the environmental factors that affect the microbiome.

[0039] 7. Use MaAslin (multivariate linear regression model) to adjust the covariates obtained above and screen for the final differential biomarkers.

[0040] 8. The diagnostic rate of biomarkers is ranked by importance score using machine learning model methods such as random forest.

[0041] 9. Screen the biomarkers to be included in the model based on the importance score.

[0042] Metagenomic analysis can help reveal the overall profile of the gut microbiome across different diabetic patients and identify novel diagnostic and therapeutic targets. Therefore, the clinical value of screening key biomarkers through metagenomics and metabolomics, including key differential gut microbes (including bacteria) and key differential fecal metabolites, lies in ultimately identifying a multi-omics classifier that can distinguish adult-onset T1D from healthy controls (HCs) and T2D. This will facilitate the precise diagnosis and classification of adult-onset T1D and provide novel diagnostic and therapeutic targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 The AUC curves for detecting adult-onset T1D and HC in the discovery cohort using the 6 screened microorganisms and / or 6 fecal metabolites are shown;

[0044] Figure 2 The AUC curves for detecting adult-onset T1D and T2D in the discovery cohort using the six screened microorganisms and / or six fecal metabolites are shown;

[0045] Figure 3 The AUC curves for detecting adult-onset T1D and HC in the validation cohort using the 6 screened microorganisms and / or 6 fecal metabolites are shown;

[0046] Figure 4 Figure 2 AUC curves for detecting adult-onset T1D and T2D in the validation cohort using the six screened microorganisms and / or six fecal metabolites. DETAILED DESCRIPTION

[0047] The following examples are intended to further illustrate the present invention, but are not intended to limit the present invention.

[0048] Example 1. Biomarker Screening for Diagnosis / Differential Diagnosis of Adult-Onset T1D

[0049] method:

[0050] 1. Source of clinical samples

[0051] All samples for this study were approved by the institutional Medical Research Ethics Committee and collected with informed consent from patients. The discovery cohort included 36 healthy controls, 51 patients with adult-onset T1D, and 56 patients with T2D; the external validation cohort included 28 healthy controls, 27 patients with adult-onset T1D, and 20 patients with T2D. All patients were matched for sex and age.

[0052] Common inclusion criteria for patients with diabetes (strictly in accordance with the 1999 World Health Organization diagnostic criteria for diabetes): 1. Symptoms of diabetes plus ① plasma glucose ≥ 11.1 mmol / L at any time, ② fasting plasma glucose ≥ 7.0 mmol / L, or ③ plasma glucose ≥ 11.1 mmol / L 2 hours after a meal during an oral glucose tolerance test (OGTT). 2. If there are no symptoms of diabetes, the test should be repeated on another day.

[0053] Patient inclusion criteria:

[0054] (1) Inclusion criteria for adult-onset T1D (in addition to the above criteria, the following criteria must be met): T1D diagnosed according to the 2017 ADA diagnostic criteria; age of onset ≥ 20 years.

[0055] (2) T2D inclusion criteria (in addition to the above criteria, the following criteria must be met): T2D diagnosed according to the ADA's 1999 diagnostic criteria; negative pancreatic islet autoantibodies (GADA, IA-2A, ZnT8A);

[0056] (3) Exclusion criteria:

[0057] 1) Recently drank yogurt or other drinks containing probiotics that affect intestinal flora, or used mouthwash.

[0058] 2) Use of antibiotics or other drugs that may affect intestinal flora in the past 3 months, such as biguanides, alpha glucosidase inhibitors, proton pump inhibitors, glucocorticoids, psychiatric drugs, etc.

[0059] 3) Signs of acute infection or inflammation (such as cold, fever, diarrhea, etc.).

[0060] 4) History of gastrointestinal surgery, malignant tumors, and other serious diseases (serious lesions of the heart, lungs, liver, kidneys, etc.).

[0061] 5) Combined with other autoimmune diseases such as hyperthyroidism, asthma, systemic lupus erythematosus, and rheumatoid arthritis.

[0062] 6) Diabetic ketoacidosis, ketoacidosis or within one week after the resolution of ketosis.

[0063] 7) Pregnancy or breastfeeding.

[0064] 2. Fecal Sample Collection and DNA Extraction

[0065] Fresh stool samples were collected and quickly transferred to -80°C for storage until ready for metagenomic sequencing. Fecal DNA was extracted according to the manufacturer's protocol. DNA concentration and quality were analyzed using a Qubit (Invitrogen, USA) and 1% agarose gel electrophoresis. DNA was fragmented using a Covaris M220 sonicator for paired-end library construction.

[0066] 3. Metagenomic Analysis

[0067] A total of 218 samples were sequenced on the Illumina platform according to standard protocols (paired-end; insert size, 350 bp; read length, 150 bp). Low-quality raw sequence reads (length <50 bp, quality score <20, or N bases) were removed and human-derived reads were filtered out by mapping them to the human genome (HG 19). Clean raw reads were then assembled into contigs using MEGAHIT. MetaProdigal was used to predict open reading frames for each contig (http: / / metagene.cb.ku-tokyo.ac.jp / ). Sequences of predicted genes were clustered using CD-HIT software. High-quality reads were aligned to a non-redundant gene set to determine relative gene abundance in each sample. Genes with a relative abundance of less than 0.01% were removed for subsequent analysis. The non-redundant gene set was aligned to the NR database using BLASTP (BLAST Version 2.2.28+) with an e-value cutoff of 1e-5. Species annotation was obtained based on the NCBI NR database. LefSe (linear discriminant analysis) and MaAslin (multivariate linear regression model) were used to jointly screen for differential gut microbiota (LDA > 2, P < 0.05).

[0068] 4. Fecal Metabolomics Analysis

[0069] Fecal metabolites were extracted according to standard procedures, and targeted metabolomics was performed using an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) system (ACQUITY UPLC-Xevo TQ-S, Waters Corp., Milford, MA, USA). Quality control (QC) was performed by pooling equal aliquots of each sample and injecting them periodically throughout the analytical run (every 14 test samples for LC-MS). UPLC-MS / MS was used to acquire raw data files. Peak integration, alignment, and quantification of each metabolite were performed using MassLynx software (v4.1, Waters, Milford, MA, USA). The resulting data were imported into the Human Metabolome Database (HMDB) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases for further analysis. OPLS-DA (orthogonal partial least squares discriminant analysis) was used to calculate the contribution of each metabolite to the model, i.e., the variable influence on projection (VIP) score. P < 0.05 was considered statistically significant, and FDR < 0.1 was considered a significant difference.

[0070] result:

[0071] 1. Different gut bacteria between adult-onset T1D and healthy controls

[0072] Table 1 Different intestinal bacteria between adult-onset T1D and healthy controls

[0073]

[0074]

[0075]

[0076] 2. Different gut bacteria between T2D and healthy controls

[0077] Table 2 Different intestinal bacteria between T2D and healthy controls

[0078]

[0079]

[0080]

[0081] 3. Differential fecal metabolites between adult-onset T1D and healthy controls

[0082]

[0083] 4. Differential fecal metabolites between T2D and healthy controls

[0084]

[0085] Example 2. Screening of Important Biomarkers Using Random Forest (RF) and Validation of the Random Forest Model

[0086] method:

[0087] 1. Screening of important biomarkers and construction of a differential diagnostic model for adult-onset T1D

[0088] First, a discovery set was constructed, encompassing healthy controls, adult-onset T1D, and T2D. All differential biomarker data were collected from each sample. A training set was constructed based on the discovery set for training a random forest, and an external validation set was used to validate the model. The model was then trained using the training set using ten-fold cross-validation. After training, the importance ranking of each biomarker was output. The discriminatory performance of each biomarker in distinguishing adult-onset T1D from healthy controls and adult-onset T1D from T2D was calculated, and the model's discriminatory performance was assessed by calculating the area under the curve (AUC). Biomarker combinations that performed well in distinguishing adult-onset T1D from healthy controls and adult-onset T1D from T2D were selected as the disclosed combinations. These selected biomarker combinations may include one or more biomarkers from gut bacteria or fecal metabolites, exhibit good discriminatory performance, and can simultaneously distinguish adult-onset T1D from healthy controls and adult-onset T1D from T2D.

[0089] 2. Random Forest Model Validation

[0090] The combined biomarker panel of microorganisms and fecal metabolites has good discriminatory power for adult-onset T1D. A "6+6" combination of microorganisms and metabolites was selected. The six microorganisms included: [Eubacterium]_rectale, Parabacteroides_johnsonii, Clostridium_sp._CAG:575, Flavonifractor plautii, Bacteroides_stercoris_CAG:120, and Firmicutes_bacterium_AF16-15; the six metabolites included: Kynurenine, Citric acid, Indole-3-propionic acid, L-Lysine, N-Phenylacetylphenylalanine, and Octanoic acid.

[0091] For adult-onset T1D and healthy controls,

[0092] Figure 1 and Figure 3 The random forest model analysis combined with the above six microorganisms showed that the discriminant efficacy of the discovery set samples was: AUC = 0.931 [95% CI 0.88-0.98], and the discriminant efficacy of the validation set samples was: AUC = 0.729 [95%CI: 0.59-0.86]; the random forest model analysis combined with the above six fecal metabolite markers showed that the discriminant efficacy of the discovery set samples was: AUC = 0.836 [95% CI 0.74-0.93], and the discriminant efficacy of the validation set samples was: AUC = 0.657 [95% CI 0.50-0.82]. The random forest model analysis of the above-mentioned "6+6" intestinal microorganisms and fecal metabolites combination showed that the discriminant efficiency of the discovery set samples was: AUC = 0.988 (95% CI 0.97-1.00), and the discriminant efficiency of the validation set samples was: AUC = 0.824 (95% CI 0.70-0.95).

[0093] For adult-onset T1D and T2D,

[0094] Figure 2 and Figure 4The results showed that the random forest model analysis of the above six microorganisms combined with the above six types of microorganisms had a discriminant efficiency of AUC = 0.932 [95% CI 0.89-0.98] for the discovery set samples and AUC = 0.784 [95% CI 0.66-0.91] for the validation set samples; the random forest model analysis of the above six fecal metabolite markers combined with the above six types of fecal metabolite markers had a discriminant efficiency of AUC = 0.844 [95% CI 0.76-0.93] for the discovery set samples and AUC = 0.644 [95% CI 0.48-0.81] for the validation set samples; the random forest model analysis of the above "6+6" intestinal microorganisms and fecal metabolites combined with the above six types of fecal metabolites had a discriminant efficiency of AUC = 0.981 (95% CI 0.96-1.00) for the discovery set samples and AUC = 0.812 (95% CI 0.69-0.94).

[0095] These results indicate that the "6+6" marker combination can well differentiate adult-onset T1D from healthy controls and T2D, and has considerable potential for the diagnosis and differential diagnosis of adult-onset T1D.

Claims

1. Use of a reagent for detecting intestinal microorganisms in the preparation of a preparation for diagnosing diabetes, characterized in that: Markers of intestinal microorganisms include Eubacterium recale, Parabacteroides johnsonii, Clostridium sp. CAG:575, Flavonifractor plautii, Bacteroides stercoris CAG:120, and Firmicutes bacterium AF16-15. The types of diabetes include adult-onset T1D and T2D.

2. A kit for diagnosing diabetes, characterized in that: The invention comprises the reagent required for detecting intestinal microorganisms according to claim 1.

3. The kit according to claim 2, wherein The kit includes one or more of a biochemical detection kit, an immunoassay kit, and a molecular detection kit.

4. The kit according to claim 3, wherein The kit is an immunoassay kit.

5. The kit according to claim 4, characterized in that The immunoassay kit is one or more of a Western blot kit, an enzyme-linked immunosorbent assay kit, a radioimmunoassay kit, an ouchterlony immunodiffusion kit, a rocket immunoelectrophoresis kit, an immunoprecipitation assay kit, an aptamer chip kit, and a protein chip kit.

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