Metabolic and inflammatory profiles define phenotypes with clinical relevance in female knee osteoarthritis patients with joint effusion

Abstract

Objectives: Osteoarthritis has been the subject of abundant research in the last years with limited translation to the clinical practice, probably due to the disease's high heterogeneity. In this study, we aimed to identify different phenotypes in knee osteoarthritis (KOA) patients with joint effusion based on their metabolic and inflammatory profiles.

Methods: A non-supervised strategy based on statistical and machine learning methods was applied to 45 parameters measured on 168 female KOA patients with persistent joint effusion, consecutively recruited at our hospital after a monographic OA outpatient visit. Data comprised anthropometric and metabolic factors and a panel of systemic and local inflammatory markers. The resulting clusters were compared regarding their clinical, radiographic and ultrasound severity at baseline and their radiographic progression at two years.

Results: Our analyses identified four KOA inflammatory phenotypes (KOIP): a group characterized by metabolic syndrome, probably driven by body fat and obesity, and by high local and systemic inflammation (KOIP-1); a metabolically healthy phenotype with mild overall inflammation (KOIP-2); a non-metabolic phenotype with high inflammation levels (KOIP-3); and a metabolic phenotype with low inflammation and cardiovascular risk factors not associated with obesity (KOIP-4). Of interest, these groups exhibited differences regarding pain, functional disability and radiographic progression, pointing to a clinical relevance of the uncovered phenotypes.

Conclusion: Our results support the existence of different KOA phenotypes with clinical relevance and differing pathways regarding their pathophysiology and disease evolution, which entails implications in patients' stratification, treatment tailoring and the search of novel and personalized therapies.

Keywords: clinical severity; inflammatory; knee osteoarthritis; machine learning; metabolism; phenotype.