High microbial age was associated with elevated lipid markers and higher cardiovascular risk, with a random forest model achieving excellent discrimination for CVD risk prediction (AUC=0.95).
Cross-Sectional (n=103)
In populations at high risk for diabetes, advanced microbial age and specific metabolic clusters are associated with elevated atherogenic lipids and increased cardiovascular disease risk.
Effect estimate: AUC 0.95
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 ( R 2 =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.
Xinlin et al. (2026) conducted a cross-sectional in High-risk populations for diabetes (n=103). High microbial age was associated with elevated lipid markers and higher cardiovascular risk, with a random forest model achieving excellent discrimination for CVD risk prediction (AUC=0.95).