Correlation Between Gut Microbiota and Pancreatic Β-Cell Function in Diabetic Patients

1. Research Objective: This study aims to explore the differences in gut microbiota and their metabolites, as well as gene function differences, between young and elderly individuals under different glucose metabolism states. The goal is to screen for specific bacterial species or metabolites with significant differences and examine their correlation with pancreatic β-cell function. 2. Research Design 2.1 Study Subjects: Young and elderly subjects with different glucose metabolism states will be recruited from the community or the outpatient department of Peking University Third Hospital between September 2024 and December 2025, meeting the following criteria. Inclusion Criteria: 1. Age: Young (18 \< age \< 45 years) and elderly (age ≥ 65 years) subjects; 2. Individuals with different glucose metabolism states, including normal glucose metabolism, pre-diabetes, and diabetes, without gastrointestinal diseases or a history of gastrointestinal surgery (such as active gastrointestinal inflammation or bleeding, inflammatory bowel disease, etc.), without cognitive impairment, without tumors, and without chronic respiratory diseases (such as chronic obstructive pneumonia, asthma, etc.), and not on a special diet (e.g., vegetarians). Exclusion Criteria: 1. Use of antibiotics or health supplements within the last month. 2. Diarrhea within the last 2 weeks: bowel movements ≥3 times/24 hours, with changes in stool consistency. 3. Constipation within the last 2 weeks (based on the Rome III criteria for functional constipation). 4. Acute complications of diabetes, such as diabetic ketoacidosis, within the last 3 months. 5. History of gastrointestinal diseases or gastrointestinal surgery. 6. Cognitive impairment. 7. History of tumors. 8. Special diet (e.g., vegetarians). 2.2 Research Methods: 2.2.1 Fecal Sample Collection: Collect 2 grams of fecal tissue from selected participants and place it in sterile commercial test tubes. A total of 160 samples will be collected (40 from young individuals with normal glucose metabolism, 40 from young diabetic patients, 40 from elderly individuals with normal glucose metabolism, and 40 from elderly diabetic patients). Place the samples on ice immediately and transport them back to the laboratory within 1 hour. Store them at -80°C in a freezer. 2.2.2 Serum Sample Collection: Participants will undergo routine blood and urine tests, as well as blood biochemical and glucose metabolism tests, at the clinical laboratory of Peking University Third Hospital. During blood collection, an additional 5 mL of serum sample will be drawn. After centrifugation at 4°C, the supernatant will be aliquoted into 1 mL EP tubes and stored at -80°C in a freezer. 2.2.3 Metagenomic and Metabolomic Analysis: Metagenomic Sequencing: Extract microbial DNA from fecal samples using commercial kits. Fragment the DNA and prepare libraries for sequencing. Perform data quality control, metagenomic assembly, clustering for redundancy removal, and abundance analysis to obtain final sequencing fragments (Scaftigs). Annotate Scaftigs for species and predict gene functions, followed by standardized analysis across multiple samples, including abundance clustering, principal component analysis, and clustering analysis. Metabolomic Analysis: Preprocess experimental samples to extract metabolites and perform detection on a metabolomics platform to obtain raw data. Use data processing software to convert the raw data into a data matrix suitable for further analysis, including information on metabolite mass-to-charge ratio, retention time, and peak area. Process and statistically analyze the dataset to identify differential metabolites. Finally, identify and screen metabolites associated with aging-related microbiota. 3\. Statistical Analysis: Statistical analysis of the data will be conducted using R 4.2.1. Quantitative data will undergo normality testing and will be expressed as mean ± standard deviation if they conform to a normal distribution. Inter-group comparisons of relevant indicators will be conducted using analysis of variance (ANOVA). Categorical data will be expressed as frequencies (%) and compared between groups using the chi-square test. A p-value \< 0.05 will be considered statistically significant.