Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Feb 25, 2025
Date Accepted: Sep 15, 2025
The Association between Metabolic Clusters and Microbial Age in High-Risk Populations for Diabetes and Their Potential Impact on Cardiovascular Disease Risk
ABSTRACT
Background:
Metabolic multimorbidity (MC) is prevalent in high-risk populations for diabetes and is linked to cardiovascular disease (CVD) and gut microbiota composition. The relationship between MCs, microbial age (MA), and metabolic markers remains poorly understood.
Objective:
This study investigates the characteristics of MCs and MA in high-risk diabetic populations, focusing on their associations with gut microbiota, metabolic dysregulation, and CVD risk.
Methods:
Using data from the NIH Integrative Human Microbiome Project (iHMP), we performed metabolomic and microbiomic analyses. K-means clustering identified MCs, and redundancy analysis (RDA) examined the relationship between metabolic variables and microbiota. A random forest (RF) model predicted MA and CVD risk, while linear discriminant analysis effect size (LEfSe) identified microbial species associated with MCs and MA. Co-occurrence network analysis explored microbial interactions.
Results:
Three MCs were identified, each linked to distinct metabolic dysfunctions (glucose, lipid, or homeostasis). RDA revealed age as a major factor in gut microbiota composition (R²=3%). The RF model showed strong MA-age correlation. LEfSe analysis identified Akkermansia and Clostridium IV as key differentially abundant microbes. The high MA group showed greater lipid metabolism dysfunction and CVD risk.
Conclusions:
This study highlights the relationship between MCs, MA, and gut microbiota, providing insights for early intervention and personalized treatment strategies for diabetes and related metabolic disorders. Clinical Trial: Not appliable.
Citation
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