Predicting host health status through an integrated machine learning framework: insights from healthy gut microbiome aging trajectory.

ABSTRACT: The gut microbiome, recognized as a critical component in the development of chronic diseases and aging processes, constitutes a promising approach for predicting host health status. Previous research has underscored the potential of microbiome-based predictions, and the rapid advancements of machine learning techniques have introduced new opportunities for exploiting microbiome data. To predict various host nonhealthy conditions, this study proposed an integrated machine learning-based estimation pipeline of Gut Age Index (GAI) by establishing a health aging baseline with the gut microbiome data from healthy individuals. We assessed the performance of GAI pipeline on two extensive cohorts - the Guangdong Gut Microbiome Project (GGMP) and the American Gut Project (AGP). In the GGMP cohort,

SUBMITTER: Bao Z 

PROVIDER: S-EPMC11682237 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature