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, for 20 common chronic diseases such as metabolic syndrome, obesity, and cardiovascular diseases, the proposed GAI achieved a balanced accuracy, ranging from 66 to 75%, with the prediction performance for atherosclerosis being the highest. In the AGP cohort, the balanced accuracy of GAI ranged from 58 to 72% for 10 diseases. Based on the results from these two datasets, we conclude that our proposed approach in this study can be used to predict individual health status, which offers the potential for scalable, cost-effective, and personalized health insights.