The link between depression, analgesia usage and function in osteoarthritis: a propensity score-matched analysis from the osteoarthritis initiative cohort
File(s)
Published version
Author(s)
Singh Gill, Saran
Jones, Gareth G
Cobb, Justin
Alagha, M Abdulhadi
Type
Journal Article
Abstract
Knee osteoarthritis (OA) affects around 37% of U.S. adults over 60, with over 25% expe-rience depressive symptoms (DSs), linked to worse pain and outcomes. Yet their impact on analgesic use and recovery remains unclear. This study aimed to assess if DSs influ-ence analgesic use and functional outcomes in knee OA. Using data from the Osteoar-thritis Initiative (n = 3680), we used a Machine Learning (ML)- based Gradient Boosting Machine (GBM) model to perform propensity score matching, matching pa-tients with knee OA and DSs (n = 487) to those without DSs (n = 487). Outcomes at baseline, 1 and 2 years included analgesic use, function (WOMAC), quality of life (KOOS-QoL), and physical health (SF-12 PCS). Regression and timepoint models com-pared follow-up with baseline. DSs alone were not associated with greater opioid use up to Year 2 (OR = 0.89, 95% CI: 0.45–1.73; p = 0.73). Among patients with DSs, SF-12 PCS improvement was less likely at Year 1, while decline was more likely up to Year 2. DSs in OA were linked to poorer physical health, but often greater functional gains than those in OA without DSs, and with no difference in opioid use. These findings highlight the need for multidisciplinary strategies, addressing both pain and psychosocial well-being.
Date Issued
2026-01-06
Date Acceptance
2025-12-17
Citation
Bioengineering, 2026, 13 (1)
ISSN
2306-5354
Publisher
MDPI AG
Journal / Book Title
Bioengineering
Volume
13
Issue
1
Copyright Statement
Copyright This paper is embargoed until publication. Once published the Version of Record (VoR) will be available on immediate open access.
License URL
Identifier
10.3390/bioengineering13010063
Subjects
depression
knee osteoarthritis
biopsychosocial model
Publication Status
Published
Article Number
63
Date Publish Online
2026-01-06