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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
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Microbiome Features Differentiating Unsupervised-Stratification-Based Clusters of Patients with Abnormal

Ting Xu1, Xuejiao Wang2, Yu Chen1

  • 1State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

Mbio
|January 18, 2023
PubMed
Summary

This study reveals distinct gut microbiome features associated with abnormal glycometabolism progression. Stratifying patients by glucose, insulin, and lipid levels improves identification of specific bacteria linked to metabolic health.

Keywords:
glycometabolisminsulin resistancemicrobiome featuresunsupervised stratification

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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.