Evidence map›Paper›PMID 42619464›Full record

ArticleOsteoarthritis imaging2026

Association of baseline MRI-defined structural features with knee symptom trajectories over nine years: Data from the osteoarthritis initiative (OAI).

Shen Liu, Xiaoxiao Sun, Yong Ge, Thang Nhat Duong, C Kent Kwoh

Abstract read
In one paragraph

Article in Osteoarthritis imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
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1citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Shen LiuDepartment of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.
Xiaoxiao SunDepartment of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.
Yong GeDepartment of Management Information Systems, Eller College of Management, University of Arizona, Tucson, AZ, USA.
Thang Nhat DuongDepartment of Computer Science, College of Science, University of Arizona, Tucson, AZ, USA.
C Kent KwohThe University of Arizona Arthritis Center, University of Arizona, Tucson, AZ, USA; The University of Arizona College of Medicine, Tucson, AZ, USA. Electronic address: Kwoh@arthritis.arizona.edu.

Funding

Identifying determinants of rapid structural and/or clinical progression in knee osteoarthritis by quantitative assessment of structural features on radiographsR01AR080742 · NIAMS · UNIVERSITY OF ARIZONA · PI JEFFREY W DURYEA, C KENT KWOH · 2022 to 2026
$3.7M
Computational Tools for Predicting and Understanding Knee Pain Spatial PatternsR21AR083495 · NIAMS · UNIVERSITY OF ARIZONA · PI GE, YONG, KWOH, C KENT · 2024 to 2024
$349k
NIAMS NIH HHS R01 AR080742NIAMS NIH HHS R21 AR083495
6 · The paper itself

Abstract

objectiveThe development of knee symptoms in OA over time can present with multiple phenotypes. This study aims to identify MRI-defined predictors of trajectories of knee symptoms.

designAll knees in the Osteoarthritis Initiative (OAI) with at least one baseline score of 0 from one of four knee symptom measurements were included. These measurements included pain severity, knee pain frequency, and the Western Ontario and McMaster Universities Osteoarthritis Index knee pain and knee function scores. The latent class mixed model (LCMM) was employed to categorize knees into different trajectories and to assess their association with various MRI-defined structural features while controlling for important covariates.

resultsA total of 1221 knees were grouped into two classes: 596 with a minimal knee symptom trajectory and 625 with a rapidly progressive trajectory. The class-membership model revealed that the following variables were associated with being in the rapidly progressive phenotype: bone marrow lesions (BMLs) score (Odds Ratio (OR) of 1.12 [95 % Confidence Interval (CI): 1.03, 1.22]) for each unit increase in BML total size scores; females, widespread pain, CES-D, overweight, and obesity. The age group 65 to 79 vs. that of 45 to 55 and highest year of education completed were less likely to be associated with the rapidly progressive knee pain group. Other covariates were not significant.

conclusionTwo distinct phenotypes/trajectories were consistently identified across all four knee symptom measurements in a multi-trajectory model. Modifiable factors such BMLs, BMI, widespread pain and depression may serve as indicators of higher risk of rapid knee symptom progression.

Indexed as

ImagingKnee functionKnee painKnee symptom phenotypesLatent class analysisMRI-defined structural featuresOsteoarthritis

Identifiers

PMID42619464
PMCPMC13639625

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.