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Life sciences · Journal article
Scientific Reports · November 2, 2026
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This study aimed to identify novel metabolic subtypes of central obesity using nuclear magnetic resonance (NMR) metabolomics data and evaluate their prospective associations with the incidence of four classic musculoskeletal disorders including sarcopenia, intervertebral disc disease, osteoporosis, and rheumatoid arthritis. A total of 247,107 community-dwelling participants from the UK Biobank were included, with a median follow-up of 13.8 years. The Categorical Boosting (Catboost) algorithm was employed to select metabolic features with the highest discriminatory power for central obesity from 251 plasma NMR metabolites. Through Shapley value ranking, 13 key metabolites were identified. Subsequently, principal component analysis and K-means clustering were applied to construct obesity metabolic subtypes. Cox proportional hazards models were utilized to assess the associations between these subtypes and the risk of incident musculoskeletal diseases. Four stable metabolic subtypes of central obesity were identified. Subtype III was characterized by significantly elevated levels of glycoprotein acetyls, valine, and phospholipids/lipids in large HDL, whereas Subtype II exhibited markedly lower levels of albumin and glutamine. After multivariable adjustment, Subtypes II and III demonstrated significantly higher risks of developing sarcopenia (Hazard Ratio, HR = 1.43, 95% Confidence Interval(CI) 1.28–1.61) and intervertebral disc disease (HR = 1.31, 95%CI 1.19–1.45) compared to the non-obese population. No significant differences were observed among subtypes regarding the risk of osteoporosis or rheumatoid arthritis. Metabolomics-based classification of central obesity identifies heterogeneous subgroups with substantially divergent risks of musculoskeletal disorders. The hyperinflammatory-hypercatabolic metabolic phenotype represents an independent high-risk group for sarcopenia and intervertebral disc disease, providing a novel metabolic classification framework for precision prevention and management.