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Experimental Pain Phenotypes in Individuals With Symptomatic Knee Osteoarthritis: A Neural Network-Based Clustering
Chiyoung Lee1, Yeri Kim2, Seoyoung Kim2
1The University of Arizona College of Nursing, Tucson, Arizona, USA.
Objective:
Research has emphasized the "phenotyping" of knee osteoarthritis (OA) pain as a priority to effectively target therapies to individual patients. This study identified pain phenotypes based on experimental pain responses in patients with symptomatic knee OA using machine learning approaches and examined their associations with participant characteristics.
Methods:
In this cross-sectional study, participants (N = 208; mean age = 67.64 ± 7.51 years) completed demographic, clinical, and psychological questionnaires, followed by a multimodal quantitative sensory testing (QST) battery. For phenotyping, we implemented a two-layer neural network-based k-means algorithm. Participant characteristics were compared across phenotypes using analysis of variance (ANOVA).
Results:
Five phenotypes were identified, with marked differences across QST measures: (1) "high pressure pain sensitivity and impaired descending inhibition" (n = 86), characterized by average responses on most QST measures, particularly low pressure pain threshold (PPTh) and impaired conditioned pain modulation (CPM), (2) "high heat pain sensitivity" (n = 40), marked by the lowest heat pain thresholds and tolerances, (3) "low pain sensitivity" (n = 46), exhibiting low sensitivity across most QST measures, (4) "high pain sensitivity and impaired pain modulation" (n = 26), with the lowest PPTh, highest punctate mechanical pain, greatest temporal summation of mechanical pain, lowest CPM, and highest cold pain sensitivity, and (5) "pain-resilient" (n = 10), characterized by low sensitivity across most QST measures, along with the highest PPTh and highest CPM. Phenotypes differed significantly by sex, Kellgren-Lawrence score (index knee), pain severity (Numeric Rating Scale), and OA-related symptoms (Western Ontario and McMaster Universities Osteoarthritis Index), particularly physical function (p < 0.05).
Conclusions:
This study identified distinct experimental pain phenotypes, each associated with unique demographic and clinical characteristics. These findings may inform the development of interventions tailored to specific pain profiles to improve pain management and functional outcomes in patients with symptomatic knee OA.
Trial Registration:
ClinicalTrials.gov identifier: NCT04375072.