Unsupervised Learning-Derived Complex Metabolic Signatures Refine Cardiometabolic Risk

Unsupervised Learning-Derived Complex Metabolic Signatures Refine Cardiometabolic Risk

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 profil...

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Bibliographic Details
Main Authors: Yujia Zhou, MD, Boyang Xiang, MD, Xiaoqin Yang, PhD, Yuxin Ren, MD, Xiaosong Gu, PhD, Xiang Zhou, PhD
Format: Article
Language:English
Published: Elsevier 2025-03-01
Series:JACC: Advances
Subjects:
cardiometabolic disease
GWAS
metabolomics
PheWAS
unsupervised learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2772963X25000377
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