Identifying specific profiles in patients with different degrees of painful knee osteoarthritis based on serological biochemical and mechanistic pain biomarkers: a diagnostic approach based on cluster analysis.

Biochemical and pain biomarkers can be applied to patients with painful osteoarthritis profiles and may provide more details compared with conventional clinical tools. The aim of this study was to identify an optimal combination of biochemical and pain biomarkers for classification of patients with different degrees of knee pain and joint damage. Such profiling may provide new diagnostic and therapeutic options. A total of 216 patients with different degrees of knee pain (maximal pain during the last 24 hours rated on a visual analog scale [VAS]) (VAS 0-100) and 64 controls (VAS 0-9) were recruited. Patients were separated into 3 groups: VAS 10 to 39 (N = 81), VAS 40 to 69 (N = 70), and VAS 70 to 100 (N = 65). Pressure pain thresholds, temporal summation to pressure stimuli, and conditioning pain modulation were measured from the peripatellar and extrasegmental sites. Biochemical markers indicative for autoinflammation and immunity (VICM, CRP, and CRPM), synovial inflammation (CIIIM), cartilage loss (CIIM), and bone degradation (CIM) were analyzed. WOMAC, Lequesne, and pain catastrophizing scores were collected. Principal component analysis was applied to select the optimal variable subset, and cluster analysis was applied to this subset to create distinctly different knee pain profiles. Four distinct knee pain profiles were identified: profile A (N = 27), profile B (N = 59), profile C (N = 85), and profile D (N = 41). Each knee pain profile had a unique combination of biochemical markers, pain biomarkers, physical impairments, and psychological factors that may provide the basis for mechanism-based diagnosis, individualized treatment, and selection of patients for clinical trials evaluating analgesic compounds. These results introduce a new profiling for knee OA and should be regarded as preliminary.

RCT Entities:   Population Interventions Outcomes