Journal article
Latent transition analysis of pain phenotypes in people at risk of knee osteoarthritis: The MOST cohort study
Osteoarthritis and cartilage, Vol.33(5), pp.616-624
05/2025
DOI: 10.1016/j.joca.2025.01.007
PMCID: PMC12034473
PMID: 40057018
Abstract
Pain phenotypes (PP) have been identified across different stages of knee osteoarthritis (KOA) and understanding the stability of PPs prior to the development of symptomatic KOA can help to inform preventative strategies. We aimed to identify PPs and their transitions in people without radiographic KOA and profile participant characteristics.
Data from 5 - (T1), 7- (T2) and 12-year (T3) visits from the Multicenter Osteoarthritis Study (MOST) were used. Individuals with Kellgren-Lawrence grade 0 and knee pain ≤30/100 at T1 were sampled. PP variables included pressure pain thresholds, temporal summation (with method changed at T3), depressive symptoms, pain catastrophizing, sleep quality, and widespread pain. Latent Transition Analysis using Bayesian Information Criteria informed class numbers and transitions. Unconstrained, constrained, and modified constrained models (conditional response probabilities fixed for indicator variables except for TS) were compared for fit. Participant characteristics were used to profile class membership.
348 individuals (59% females), mean age (SD): 59.3 (6.7) were included. The optimal model fit for data across T1-T3 was a “modified” constrained model with 3 classes (class 1: low pain burden, class 2: high pain sensitization, class 3: high psychological burden). Classes were similar over time except for the increased probability of TS at T3. Most (86%) participants remained in the same class; only 14% transitioned overtime.
Distinct PPs were identified in those at risk of KOA that remained stable over time, suggesting these trait-like features may require consideration for comprehensive management of the symptom experience.
Details
- Title: Subtitle
- Latent transition analysis of pain phenotypes in people at risk of knee osteoarthritis: The MOST cohort study
- Creators
- YV Raghava Neelapala - McMaster UniversityTuhina Neogi - Boston University School of MedicineSteven Hanna - McMaster UniversityLaura A. Frey-Law - Department of Physical therapy and Rehabilitation Science, University of Iowa, Iowa City, IA, United StatesLuciana G. Macedo - McMaster UniversityDylan Kobsar - McMaster UniversityCora E. Lewis - University of Alabama at BirminghamMichael Nevitt - Leavitt Partners (United States)Lisa Carlesso - McMaster University
- Resource Type
- Journal article
- Publication Details
- Osteoarthritis and cartilage, Vol.33(5), pp.616-624
- DOI
- 10.1016/j.joca.2025.01.007
- PMID
- 40057018
- PMCID
- PMC12034473
- NLM abbreviation
- Osteoarthritis Cartilage
- ISSN
- 1063-4584
- eISSN
- 1522-9653
- Publisher
- Elsevier Ltd
- Grant note
- Arthritis Society PhD Salary Award: 22-0000000142 Arthritis SocietyMcMaster Institute of Pain Research and CarePfizer/LillyNIH grants from the National Institute on Aging: U01-AG18947, U01-AG-18832, U01-AG-19069, U01-AG-18820
YVRN is supported by a scholarship from The Arthritis Society PhD Salary Award (22-0000000142) . LCC is funded by The Arthritis Society and McMaster Institute of Pain Research and Care. TN is supported by Pfizer/Lilly and has received consulting fees from Pfizer/Lilly, Novartis, and Regeneron. The MOST Study is supported by NIH grants from the National Institute on Aging to Dr. Lewis (U01-AG18947) , Torner (U01-AG-18832) , Nevitt (U01-AG-19069) , and Felson (U01-AG-18820) . This study was also supported by K24 AR070892 (Neogi) , and P30 AR072571 (Felson) .
- Language
- English
- Electronic publication date
- 03/06/2025
- Date published
- 05/2025
- Academic Unit
- Nursing; Physical Therapy and Rehabilitation Science
- Record Identifier
- 9984800191902771
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