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

The gut microbiota plays a crucial role in digestion, metabolism, nutrition, and immune regulation in the human body. In recent years, numerous studies have indicated that the gut microbiota and its metabolites are closely associated with metabolic diseases, including obesity, non-alcoholic fatty liver disease, and diabetes. China has rapidly entered an aging society, where aging is a major risk factor for abnormal glucose metabolism, manifested by decreased pancreatic function and increased insulin resistance. Concurrently, as the body ages, the composition of the gut microbiota also undergoes age-related changes, such as reduced microbial diversity, increased inter-individual variability, and a downregulation of the beneficial/harmful bacteria ratio. Therefore, direct modulation of the gut microbiota could become a potential therapeutic target for age-related metabolic diseases. Currently, some preclinical studies have transplanted fecal microbiota from young mice to aged mice to explore the improvement of age-related phenotypes, such as cognitive impairment, decreased immunity, and chronic inflammation. The regulation of the gut microbiota is susceptible to changes caused by various factors, including age, diet, antibiotics, and psychological stress. Although mice and humans share high genetic homology, differences in diet structure, body size, and metabolic processes can result in significant diversity and compositional differences in their gut microbiota. Research indicates that the core microbiota of the mouse gut consists of 4 genera, while 90% of the European population comprises 9 genera, highlighting the differences in genus or species richness between mouse and human gut microbiota. Preliminary research by our group has shown that transplanting fecal microbiota from young mice to aged mice can increase postprandial plasma insulin levels in aged mice, suggesting that the restoration of gut microbiota diversity may be involved in age-related glucose metabolism abnormalities. However, due to interspecies differences in the gut microbiota, whether the differential microbiota between elderly and young humans can improve age-related glucose metabolism abnormalities remains to be explored. Despite the abundance of human gut microbiota composition data in public databases, differences in sequencing methods, DNA extraction from specimens, and the nationality of subjects prevent standardization and integration of these data. Additionally, traditional 16s-rRNA sequencing methods lack sufficient precision in microbial classification and cannot annotate gene functions. These limitations have resulted in many studies on gut microbiota remaining at the level of exploring correlations with diseases, without establishing causality. The development of metagenomic sequencing technology can extend the definition of the human core gut microbiota to the species level and accurately annotate their gene functions. Combined with metabolomics detection, this technology can provide more comprehensive information on the dialogue between gut microbiota and the host. Therefore, this study aims to use multi-omics approaches (metagenomic sequencing and metabolomics detection) to analyze the differences in fecal microbiota and their metabolites between young and elderly populations under different glucose metabolism states. This will provide potential intervention targets for preventing age-related glucose metabolism abnormalities and offer new theoretical foundations for the molecular mechanisms of age-related metabolic diseases.

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.