The influence of gut microbes on aging has been reported in several studies, but the mediating pathways of gut microbiota, whether there is a causal relationship between the two, and biomarker screening and validation have not been fully discussed. In this study, Mendelian Randomization (MR) and Linkage Disequilibrium Score Regression (LDSC) are used to systematically investigate the associations between gut microbiota, three aging indicators, and 14 age-related diseases. Additionally, this study integrates machine learning algorithms to explore the potential of MR and LDSC methods for biomarker screening. Gut microbiota is found to be a potential risk factor for 14 age-related diseases. The causal effects of gut microbiota on chronic kidney disease, cirrhosis, and heart failure are partially mediated by aging indicators. Additionally, gut microbiota identified through MR and LDSC methods exhibit biomarker properties for disease prediction (average AUC = 0.731). These methods can serve as auxiliary tools for conventional biomarker screening, effectively enhancing the performance of disease models (average AUC increased from 0.808 to 0.832). This study provides evidence that supports the association between the gut microbiota and aging and highlights the potential of genetic correlation and causal relationship analysis in biomarker discovery. These findings may help to develop new approaches for healthy aging detection and intervention.
Keywords: Mendelian randomization; aging; gut microbiota; machine learning.
© 2025 The Author(s). Aging Cell published by Anatomical Society and John Wiley & Sons Ltd.
