Abstract

Background. The understanding of osteoarthritis (OA) pathogenesis is often fragmented, with studies focusing on individual tissues. A holistic view integrating multi-tissue molecular changes with systemic metabolic shifts is urgently needed. Glutamine metabolism, a central bioenergetic and biosynthetic hub, represents a critical but largely unexplored nexus in this disease network. This study leverages a multi-omics, multi-tissue approach to deconstruct the role of glutamine metabolism in OA and identify a robust, blood-based signature for potential diagnostic use.

Methods. We conducted a comprehensive bioinformatic investigation by integrating multiple GEO datasets, encompassing transcriptomic and methylation data from cartilage, synovium, subchondral bone, and peripheral blood. A machine learning pipeline, incorporating WGCNA and LASSO regression, was employed to identify a core glutamine metabolism-related gene (GMRG) signature. The signature's clinical relevance was then validated in an independent cohort of 62 subjects using RT-qPCR on peripheral blood samples and targeted plasma metabolomics. Furthermore, we computationally explored its potential regulatory mechanisms and predicted candidate therapeutic compounds.

Results. Our multi-layered analysis identified a core three-gene signature (F13A1, IRS2, RELA). Functional analysis immediately linked this signature to pathways crucial for OA pathogenesis, including extracellular matrix remodeling, energy metabolism, and inflammatory responses. Clinical validation in our independent cohort not only confirmed the significant downregulation of these genes in the peripheral blood of OA patients ( P < .001) but also revealed a negative correlation with disease severity and a direct link to alterations in circulating glutamine-related metabolites. The diagnostic model constructed from this signature demonstrated robust discriminatory performance across both training and validation cohorts. Finally, exploratory analyses suggested potential regulatory links to DNA methylation and identified several candidate drugs capable of modulating the signature's expression.

Conclusions. Our study bridges the gap between localized joint pathology and systemic metabolic dysfunction in OA by identifying a blood-based, multi-omics-derived gene signature. This signature, reflecting systemic dysregulation of glutamine metabolism, serves as a robust biomarker for non-invasive diagnosis and a potential nexus for therapeutic exploration. These findings provide a new framework for understanding OA and offer tangible avenues for patient stratification and future drug discovery.