Psychological profiles in adults with knee OA-related pain: a replication study

• Published in: Therapeutic advances in musculoskeletal disease Vol. 13; p. 1759720X211059614
• Main Authors: Johnson, Alisa J., Laffitte Nodarse, Chavier, Peraza, Julio A., Valdes-Hernandez, Pedro A., Montesino-Goicolea, Soamy, Huo, Zhiguang, Fillingim, Roger B., Cruz-Almeida, Yenisel
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-12-2021; SAGE PUBLICATIONS, INC; SAGE Publishing
• Subjects: Arthritis; Genotype & phenotype; Health psychology; Knee; Original Research; Osteoarthritis; Pain; pain; brain structure; knee osteoarthritis; psychological factors; adults
• ISSN: 1759-720X; 1759-7218
• DOI: 10.1177/1759720X211059614

Introduction:
Psychological factors have been associated with knee osteoarthritis pain severity and treatment outcomes, yet their combined contribution to phenotypic heterogeneity is poorly understood. In particular, empirically derived psychological profiles must be replicated before they can be targeted or considered for treatment studies. The objectives of this study were to (1) confirm previously identified psychological profiles using unsupervised clustering methods in persons with knee osteoarthritis pain, (2) determine the replicability of profiles using supervised machine learning in a different sample, and (3) examine associations with clinical pain, brain structure, and experimental pain.
Methods:
Participants included two cohorts of individuals with knee osteoarthritis pain recruited as part of the multisite UPLOAD1 (n = 270, mean age = 56.8 ± 7.6, male = 37%) and UPLOAD2 (n = 164, mean age = 57.73 ± 7.8, male = 36%) studies. Similar psychological constructs (e.g. optimism, coping, somatization, affect, depression, and anxiety), sociodemographic and clinical characteristics, and somatosensory function were assessed across samples. UPLOAD2 participants also completed brain magnetic resonance imaging. Unsupervised hierarchical clustering analysis was first conducted in UPLOAD1 data to derive clusters, followed by supervised linear discriminative analysis to predict group membership in UPLOAD2 data. Associations among cluster membership and clinical variables were assessed, controlling for age, sex, education, ethnicity/race, study site, and number of pain sites.
Results:
Four distinct profiles emerged in UPLOAD1 and were replicated in UPLOAD2. Identified psychological profiles were associated with psychological variables (ps < 0.001), and clinical outcomes (ps = 0.001–0.03), indicating good internal and external validation of the cluster solution. Significant associations between psychological profiles and somatosensory function and brain structure were also found.
Conclusions:
This study highlights the importance of considering the biopsychosocial model in knee osteoarthritis pain assessment and treatment.