Plasma Metabolomic and Lipidomic Signatures for Early Diagnosis of Osteoporosis: A Discovery and Validation Study

This study addresses osteoporosis (OP), a prevalent metabolic bone disease and major global public health concern characterized by reduced bone mass, deteriorated bone microstructure, and elevated fracture risk, with an urgent need for early diagnosis and preventive intervention. Using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS)-based non-targeted metabolomics and lipidomics, the authors analyzed plasma samples from a discovery cohort of 75 OP patients and 140 healthy individuals. Multivariate statistical analyses (including PCA and OPLS-DA) and random forest (RF) modeling were employed to identify differential metabolites and establish a diagnostic model for OP. The model’s classification performance and clinical utility were further validated in an independent cohort consisting of 20 OP patients and 42 healthy controls.