Gut Microbiome-Based Diagnostic Model to Predict Diabetes Mellitus

Area of Science:

• Microbiology
• Metabolic disorders
• Human health

Background:

• The gut microbiota is increasingly recognized for its role in metabolic diseases.
• Understanding the specific microbial alterations in diabetes is crucial for developing novel diagnostic and therapeutic strategies.

Purpose of the Study:

• To investigate the diversity of intestinal microflora in diabetic patients compared to healthy controls.
• To explore the correlation between gut microbiota composition and clinical parameters in diabetes.
• To assess the diagnostic potential of intestinal flora in diabetes management.

Main Methods:

• 16S rRNA sequencing of fecal samples from 44 diabetic patients and 47 healthy controls.
• Analysis of biochemical indicators and clinical parameters.
• Correlation analysis between microbial abundance and clinical data.
• Development of a random forest model for diabetes diagnosis using microbial markers.

Main Results:

• Diabetic patients displayed significantly different intestinal flora compared to healthy subjects.
• Specific genera like Faecalibacterium, Prevotella, and Roseburia were more abundant, while Shigella and Bifidobacterium were less abundant in diabetics.
• Microbial markers achieved an area under the curve (AUC) of 84.1% in a diagnostic model, outperforming fasting blood glucose alone.
• Intestinal flora composition significantly improved diabetes diagnosis when combined with clinical indicators.

Conclusions:

• Intestinal flora is fundamentally linked to the occurrence and progression of diabetes.
• Gut microbiota alterations are as important as blood glucose levels in the context of diabetes.
• Microbiota-based diagnostics hold promise for enhancing diabetes detection and management.