Using unsupervised machine learning methods to cluster cardio-metabolic profile of the middle-aged and elderly Chinese with general and central obesity.

ABSTRACT:

Background

Obesity is a disease with high heterogeneity. Both overall obesity and central obesity are associated with increased risks of having cardio-metabolic co-morbidities. This study is aimed to examine the cardio-metabolic characteristics and comorbidity profile of the middle-aged and elderly Chinese with general and central obesity by clustering them into different subgroups, which would lead to a deepened understanding of their distinct medical needs.

Methods

Adopting an unsupervised machine learning approach, we conducted a clustering analysis of the adiposity and cardio-metabolic profiles of the middle-aged and elderly Chinese with general obesity and central obesity. The data was obtained from the China Health and Retirement Longitudinal Study (CHARLS). The subgro

SUBMITTER: Xue Y 

PROVIDER: S-EPMC12557893 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature