Metabolomics has been used as a tool in disease diagnosis and phenotype prediction. A urinary metabolomic study based on GC–MS in combination with multivariate statistics was used here to classify between knee osteoarthritis (OA) and healthy controls. OPLS-DA of the spectral data showed distinct metabolic profile variations between OA patients and healthy controls and between two OA phenotypes. Differential metabolites reveal up-regulated TCA cycle associated with OA and histamine metabolism disorders accompanied with knee effusion symptoms. This metabolomic method is potentially applicable as a novel strategy for OA diagnosis and patient stratification.
