Genetic Biomarkers and Circulating White Blood Cells in Osteoarthritis: A Bioinformatics and Mendelian Randomization Analysis.

: Osteoarthritis (OA) is a prevalent degenerative joint disease that causes disability and diminishes quality of life. The pathogenesis of OA remains poorly understood, creating an urgent need for biomarkers to aid research, diagnosis, and treatment. : This study integrated transcriptome data from the GEO database with bioinformatics analyses to identify biomarkers associated with OA. The bioinformatics methods utilized include the Limma package, WGCNA, PPI network analysis, and machine learning algorithms. Genetic variants were used as instrumental variables to evaluate the potential causal impact of circulating white blood cell (WBC) counts on OA. Data sources encompassed the largest genome-wide analysis for OA and a comprehensive GWAS summary for circulating WBC counts. Four mendelian randomization (MR) methods were employed to investigate the genetic association, with a primary focus on findings from the inverse variance-weighted (IVW) method. : Total of 233 OA-related genes were identified, showing significant enrichment in pathways associated with WBC function. Key biomarkers, including , , and , were upregulated in OA samples and exhibited strong diagnostic potential. MR analysis findings provided evidence of a genetic association between elevated neutrophil counts and a reduced risk of OA across sites (IVW: OR = 0.97, 95% CI 0.93-1.00, = 0.047). Additionally, higher circulating WBC counts, particularly neutrophil counts, were associated with a suggestive decrease in hip OA (WBC IVW: OR = 0.94, 95% CI 0.89-0.99, = 0.015; neutrophil IVW: OR = 0.93, 95% CI 0.88-0.99, = 0.017). Conversely, reverse MR analysis found no evidence to support a genetic effect of OA on circulating WBC counts. : Our findings suggest that elevated neutrophil counts may offer protective effects against OA, underscoring the interplay between the immune functions and OA pathogenesis. , , and emerge as promising OA biomarkers, meriting further validation in prospective studies.