Obesity metabolomics signature in children: associations with metabolic abnormalities and potential biomarkers.

ABSTRACT:

Background

The global rise in childhood obesity has heightened its recognition as a major public health concern, with obesity being an independent risk factor for metabolic abnormalities. However, the metabolomics mechanisms linking pediatric obesity to metabolic abnormalities remain unclear.

Methods

This case-control study utilized data from a 2023 cross-sectional survey of children aged 9-18 years in Guangzhou, China. A total of 246 participants were included, with 123 obese and 123 normal-weight participants matched for age and sex. Serum metabolomics profiling was performed via LC-MS. A dual machine learning approach combining penalized multivariable Least Absolute Shrinkage and Selection Operator (LASSO) regression and random forest with recursive feature elimination (R

SUBMITTER: Wan J 

PROVIDER: S-EPMC12507617 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature