Microbiome Features Differentiating Unsupervised-Stratification-Based Clusters of Patients with Abnormal...

Area of Science:

• Microbiome Research
• Metabolic Health
• Type 2 Diabetes (T2D) Pathogenesis

Background:

• Gut microbiota alterations are implicated in abnormal glycometabolism, including type 2 diabetes (T2D) and prediabetes.
• Previous studies show conflicting results regarding specific bacteria associated with T2D, hindering precision medicine.
• Patient stratification based solely on blood glucose levels overlooks variations in insulin resistance and dyslipidemia.

Purpose of the Study:

• To identify specific gut microbiome features associated with abnormal glycometabolism.
• To investigate the impact of stratifying patients using comprehensive clinical parameters (glucose, insulin, lipid levels) on microbiome analysis.
• To develop a microbiome-based classifier for distinguishing glycometabolic states.

Main Methods:

• Unsupervised clustering of 258 participants into three groups based on 16 clinical parameters (blood glucose, insulin, lipid levels).
• 16S rRNA gene V3-V4 sequencing to identify 67 cluster-specific amplicon sequence variants (ASVs).
• Machine learning classifiers were trained and validated on discovery and testing cohorts (n=83) using identified ASVs.

Main Results:

• Identified 67 cluster-specific ASVs, with distinct bacterial enrichments correlating with glycometabolic profiles.
• Cluster 1 (low glucose, high insulin sensitivity) showed enrichment of *Barnesville* and *Alistipes* ASVs.
• Cluster 2 (moderate glucose, insulin resistance, dyslipidemia) was enriched with *Prevotella copri* and *Ruminococcus gnavus* ASVs.
• Cluster 3 (high glucose, insulin deficiency) showed enrichment of *P. copri* and *Bacteroides vulgatus* ASVs.
• Machine learning models accurately distinguished between clusters using the identified ASVs in both cohorts.

Conclusions:

• Stratifying patients by blood glucose, insulin, and lipid levels provides a more robust approach to identifying microbiome features linked to glycometabolism.
• The identified cluster-specific ASVs serve as potential biomarkers for abnormal glycometabolism progression.
• This refined stratification method advances understanding of gut microbiota's role in metabolic disorders and precision medicine.