Rapid Progression of Knee Pain and Osteoarthritis Biomarkers Greatest for Patients with Combined Obesity and Depression: Data from the Osteoarthritis Initiative
Bibliographic record
Abstract
Objective To compare the progression of biochemical biomarkers of osteoarthritis (OA), knee pain, and function between nonobese patients (NON), obese patients without depression (OBESE), and obese patients with comorbid depression (O + D). Design Utilizing the FNIH OA Biomarkers Consortium dataset, we categorized knee OA patients into NON, OBESE, and O + D groups based on body mass index and Center for Epidemiological Studies–Depression (CES-D) scores. Subjective symptoms (Knee injury and Osteoarthritis Outcome Score Quality of Life subscale (KOOS QOL), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) Pain and Physical Function scores, and the Short Form–12 (SF-12) Physical Component Score [PCS]) and objective measures of cartilage degradation and bone remodeling (urinary CTXII and CTXIα) were compared among groups at baseline and 2-year follow-up. Results Of the 600 patients, 282 (47%) were NON, 285 (47.5%) OBESE, and 33 (5.5%) O + D. The O + D group had significantly worse pain and function both at baseline and 2-year follow-up ( P < 0.001 for all comparisons) as evidenced by self-reported measures on KOOS QOL, WOMAC Pain, WOMAC Physical Function, and SF-12 PCS. The O + D group also demonstrated significant increases in CTXII ( P = 0.01) and CTXIα ( P = 0.005), whereas the NON and OBESE groups did not. Conclusions The combination of inferior knee pain, physical function, and significantly greater increases in biomarkers of cartilage degradation and bony remodelling suggest a more rapid progression for obese OA patients with comorbid depression. The link between systemic disease, inflammatory burden, and progressive cartilage degradation is in line with increasing concerns about a degenerative synovial environment in early osteoarthritic knees that progress to treatment failure with biologic restoration procedures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".