Machine Learning to Identify Metabolic Subtypes of Obesity: A Multi-Center Study.

ABSTRACT:

Background and objective

Clinical characteristics of obesity are heterogenous, but current classification for diagnosis is simply based on BMI or metabolic healthiness. The purpose of this study was to use machine learning to explore a more precise classification of obesity subgroups towards informing individualized therapy.

Subjects and methods

In a multi-center study (n=2495), we used unsupervised machine learning to cluster patients with obesity from Shanghai Tenth People's hospital (n=882, main cohort) based on three clinical variables (AUCs of glucose and of insulin during OGTT, and uric acid). Verification of the clustering was performed in three independent cohorts from external hospitals in China (n = 130, 137, and 289, respectively). Statistics of a healthy normal-w

SUBMITTER: Lin Z 

PROVIDER: S-EPMC8317220 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature