Obesity metabolomics signature in children: associations with metabolic abnormalities and potential biomarkers - Wan, Jiayi; Luo, Shiyun; Zhong, Wanzhen; Tao, Guixian; Guo, Jiaying; Zeng, Chunzi; Peng, Yujie; Zhang, Weiwei; Zhang, Zhoubin; Gu, Jing; Huang, Jie; Li, Yan (Frontiers in Endocrinology, 2025)
BackgroundThe 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.MethodsThis 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 (RF-RFE) was employed to identify robust obesity-associated serum metabolites independent of metabolic abnormalities and logistic regression was employed to construct the obesity metabolomics signature (OB-MS) model. Multivariable logistic regression was used to assess the associations between the OB-MS and metabolic abnormalities and their components, including hyperglycemia, hypertension, hypertriglyceridemia, and reduced HDL-C.ResultsAmong 934 detected metabolites, 10 core metabolites were selected to construct the OB-MS, which showed high discriminative power, with an ROC-AUC of 0.986 in the testing set. Elevated OB-MS scores were significantly associated with increased risks of metabolic abnormalities, particularly hypertension and hypertriglyceridemia. Additionally, six key metabolites, including oxidative stress markers and dipeptides, were independently associated with metabolic abnormalities.ConclusionsThis study established a pediatric obesity-specific metabolomics signature (OB-MS), implicating oxidative stress, protein catabolism, and glucocorticoid metabolism in obesity-related metabolic abnormalities. These finding illustrate the metabolic mechanisms underlying the relationship between childhood obesity and metabolic abnormalities and provide new scientific support for early and precise prevention of metabolic abnormalities in children. Further longitudinal studies and experimental validation are warranted to elucidate its biological mechanisms and clinical utility.