Osteoarthritis (OA) effects 7% of the global population, equating to more than 500 million people worldwide, and is the leading cause of disability. Its multifaceted etiology includes risk factors ranging from genetics, to aging, obesity, sex, race, and joint injury. OA manifests differently across the patient population where symptom severity, rate of progression, response to treatment, pain perception, as well as others vary person to person posing significant challenges for effective management and prevention. At the cellular level, imbalanced matrix catabolism and anabolism contribute to the breakdown of cartilage, underlying bone, and other tissues affected by OA. Leveraging mass spectrometry-based techniques, particularly metabolomics, offers a promising avenue to dissect OA metabolism across musculoskeletal tissues, while considering individual patient-specific risk factors. Therefore, the goals of this research were to:​ (1) comprehensively characterize OA phenotypes and endotypes and (2) explore OA pathogenesis within the context of disease-associated risk factors.

The first area of research focuses on profiling OA phenotypes and endotypes across disease development. These results provide clear evidence of OA-induced metabolic perturbations in OA cartilage and bone and elucidate mechanisms that shift as disease progresses. Several metabolites and pathways associated with lipid, amino acid, matrix, and vitamin metabolism were differentially regulated between healthy and OA tissues and within OA endotypes.

The second area of research focuses on the impact of OA risk factors – sex, injury, obesity, loading – on the metabolism of circulatory fluids (i.e., serum, synovial fluid) and chondrocytes. Identification of metabolic indicators of disease, such as cervonyl carnitine, and metabolic pathways associated with these risk factors holds potential for improving screening, monitoring disease progression, and guiding preventative strategies.

Overall, this work contributes to our current understanding of OA, its diverse metabolic landscape, risk factors and their interactions. Moreover, it lays the groundwork for personalized medicine by providing detailed insights into individualized phenotypic profiles, thereby advancing the prospect of tailored treatment strategies for OA individuals.