Dataset Information

Comprehensive Metabolomic Profiling of Osteoarthritis Unraveling Sexually Dimorphic Patterns

ABSTRACT: Osteoarthritis (OA) is a chronic disease characterized by high morbidity, affecting multiple body systems and associated with systemic metabolic disorders. The clinical features of OA exhibit significant sexual dimorphism. However, the specific metabolic characteristics underlying this difference remain incompletely understood, with a notable lack of molecular risk factors related to OA. In this study, we established a cohort of 60 OA cases comprising different genders. Employing liquid chromatography-tandem mass spectrometry (LC-MS/MS), we performed untargeted metabolomic analyses on synovial membrane, synovial fluid, urine, and serum samples, obtained simultaneously from each patient. Following rigorous quality control, we successfully constructed and annotated a metabolic profile of OA encompassing multiple sample types. Moreover, we identified metabolites associated with clinical risk factors and delineated a sex-specific subset of metabolites based on sample type. This dataset provides a foundation for further investigation into the impact of sex on OA at metabolic level. Additionally, these findings are expected to enhance the study of potential sex-related biomarkers in OA and offer novel insights into clinical gender-specific management strategies.

INSTRUMENT(S): LC-MS

ORGANISM(S): Human

SUBMITTER: Wei Zhang

PROVIDER: MSV000096636 | MassIVE | Wed Dec 11 05:09:00 GMT 2024

REPOSITORIES: MassIVE

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Sexually Dimorphic Profiling of Osteoarthritis: A Comprehensive Metabolomic Dataset.

Zhang Yuhan Y Jiang Yonghui Y Jia Qixian Q Feng Gengchen G Yang Ziyi Z Zhang Wei W

Scientific data 20250701 1

Osteoarthritis (OA) is a chronic disease characterized by high morbidity, affecting multiple body systems and associated with systemic metabolic disorders. The clinical features of OA exhibit significant sexual dimorphism. However, the specific metabolic characteristics underlying this difference remain incompletely understood, with a notable lack of molecular risk factors related to OA. In this study, we established a cohort of 60 OA cases comprising different genders. Employing liquid chromato ...[more]

PMID: 40593932

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