JACC: Advances (Mar 2025)
Unsupervised Learning-Derived Complex Metabolic Signatures Refine Cardiometabolic Risk
Abstract
Background: Cardiometabolic diseases have become a leading cause of morbidity and mortality globally. Nuclear magnetic resonance metabolomics represents a precise tool for assessing metabolic individuality. Objectives: This study aimed to use unsupervised learning to decode plasma metabolomic profiles, providing new insights into the etiology of cardiometabolic diseases. Methods: We applied unsupervised learning to generate robust metabolic signatures from the plasma profiles of 118,001 UK Biobank participants. Phenome-wide and genome-wide association studies were conducted to reveal their phenomic and genetic architectures. Integrated prospective cohort analyses and Mendelian randomization clarified their roles in cardiometabolic risks. Results: Eleven metabolic clusters were identified, linked to 101 loci and 445 phenotypes, mostly cardiometabolic diseases. These novel signatures partially outperformed traditional lipids in cardiometabolic risk prediction. Triglyceride-rich lipoproteins demonstrated superior predictive power for ischemic heart disease, type 2 diabetes, and hypertension, compared with apolipoprotein B and lipoprotein(a). Non-high-density lipoprotein cholesterol was found to increase the risk of hyperlipidemia and ischemic heart disease while offering a protective effect against type 2 diabetes. Furthermore, different high-density lipoprotein clusters showed heterogeneous associations with cardiometabolic diseases, with high-density lipoprotein subpopulations enriched in free cholesterol or triglyceride increasing risk, and those enriched in cholesterol esters providing protection. Conclusions: These metabolic signatures extract comprehensive information from the metabolomic profile while maintaining clarity and interpretability, facilitating clinical translation. The findings emphasize the crucial roles of lipid subpopulations in cardiometabolic risks, encouraging clinicians to take a more nuanced approach to managing blood lipids and balancing different disease risks.
