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 c

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC11891690 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature