Observational studyJMIR medical informatics2026
Association Between Metabolic Clusters and Microbial Age in High-Risk Populations for Diabetes and Their Potential Impact on Cardiovascular Disease Risk: Cross-Sectional Observational Study.
Observational study in JMIR medical informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
5 authors.
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Abstract
Background: Metabolic multimorbidity is prevalent in high-risk populations for diabetes and is linked to cardiovascular disease (CVD) and gut microbiota composition. The relationship between metabolic clusters (MCs), microbial age (MA), and metabolic markers remains poorly understood. Objective: This study aimed to investigate 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, we performed metabolomic and microbiomic analyses. K-means clustering identified MCs, and redundancy analysis examined the relationship between metabolic variables and microbiota. A random forest (RF) model predicted MA and CVD risk, while the linear discriminant analysis effect size identified microbial species associated with MCs and MA. Co-occurrence network analysis explored microbial interactions. Results: We included 103 high-risk individuals (56/103, 54.4% female, mean age 50.6, SD 54.6 years). In total, 3 MCs were identified: MC1 (high glucose or blood urea nitrogen), MC2 (relatively healthy), and MC3 (lipid dysregulation). Age explained 3% of gut microbiota variation (R2=0.03; P=.006). The RF model predicting microbial age showed a strong correlation with chronological age in training data (ρ=0.97, root mean square error=3.33; P<.001) and moderate correlation in test data (ρ=0.35; P<.001). High microbial age was associated with elevated lipid markers (low-density lipoprotein and triglycerides; P<.001) and higher cardiovascular risk. The RF model for CVD risk prediction achieved excellent discrimination (area under the curve=0.95 for the low-risk and 0.95 for the high-risk groups). 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.
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