Physical activity is associated with changes in knee cartilage microstructure.

OBJECTIVE: The purpose of this study was to determine if there is an association between objectively measured physical activity and longitudinal changes in knee cartilage microstructure. METHODS: We used accelerometry and T2-weighted magnetic resonance imaging (MRI) data from the Osteoarthritis Initiative, restricting the analysis to men aged 45-60 years, with a body mass index (BMI) of 25-27 kg/m2 and no radiographic evidence of knee osteoarthritis. After computing 4-year changes in mean T2 relaxation time for six femoral cartilage regions and mean daily times spent in the sedentary, light, moderate, and vigorous activity ranges, we performed canonical correlation analysis (CCA) to find a linear combination of times spent in different activity intensity ranges (Activity Index) that was maximally correlated with a linear combination of regional changes in cartilage microstructure (Cartilage Microstructure Index). We used leave-one-out pre-validation to test the robustness of the model on new data. RESULTS: Nineteen subjects satisfied the inclusion criteria. CCA identified an Activity Index and a Cartilage Microstructure Index that were significantly correlated (r = .82, P < .0001 on test data). Higher levels of sedentary time and vigorous activity were associated with greater medial-lateral differences in longitudinal T2 changes, whereas light activity was associated with smaller differences. CONCLUSIONS: Physical activity is better associated with an index that contrasts microstructural changes in different cartilage regions than it is with univariate or cumulative changes, likely because this index separates the effect of activity, which is greater in the medial loadbearing region, from that of patient-specific natural aging.

Modeling and predicting osteoarthritis progression: data from the osteoarthritis initiative.

OBJECTIVE: The goal of this study was to model the longitudinal progression of knee osteoarthritis (OA) and build a prognostic tool that uses data collected in 1 year to predict disease progression over 8 years. DESIGN: To model OA progression, we used a mixed-effects mixture model and 8-year data from the Osteoarthritis Initiative (OAI)-specifically, joint space width measurements from X-rays and pain scores from the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) questionnaire. We included 1243 subjects who at enrollment were classified as being at high risk of developing OA based on age, body mass index (BMI), and medical and occupational histories. After clustering subjects based on radiographic and pain progression, we used clinical variables collected within the first year to build least absolute shrinkage and selection (LASSO) regression models for predicting the probabilities of belonging to each cluster. Areas under the receiver operating characteristic curve (AUC) represent predictive performance on held-out data. RESULTS: Based on joint space narrowing, subjects clustered as progressing or non-progressing. Based on pain scores, they clustered as stable, improving, or worsening. Radiographic progression could be predicted with high accuracy (AUC = .86) using data from two visits spanning 1 year, whereas pain progression could be predicted with high accuracy (AUC = .95) using data from a single visit. Joint space narrowing and pain progression were not associated. CONCLUSION: Statistical models for characterizing and predicting OA progression promise to improve clinical trial design and OA prevention efforts in the future.