Machine learning-driven insights into lipid metabolism and inflammatory pathways in knee osteoarthritis

Abstract

Knee osteoarthritis (KOA) is a multifactorial degenerative joint disease influenced by lipid metabolism, systemic inflammation, and dietary factors. This study integrates clinical data, biochemical markers, and machine learning models to identify key predictors of KOA severity and develop personalized dietary strategies for disease management. A cohort of 600 KOA patients was analyzed, revealing significant correlations between dyslipidemia (low HDL, high LDL) and inflammatory biomarkers (CRP, IL-6). Machine learning models identified BMI, CRP, and IL-6 as critical predictors of pain severity (AUC = 0.93). Based on these findings, we propose targeted dietary recommendations, including increased omega-3 fatty acid intake and reduced saturated fat consumption, to modulate inflammation and improve clinical outcomes. This study highlights the potential of precision nutrition approaches in addressing the metabolic and inflammatory underpinnings of KOA.