Dietary Inflammatory Index and Magnetic Resonance Imaging–Detected Knee Structural Change and Pain: A 10.7‐Year Follow‐up Study
Bibliographic record
Abstract
OBJECTIVE: To determine whether the dietary inflammatory index (DII) scores were associated with knee structural changes and pain over a 10.7-year follow-up. METHODS: This study used data from a prospective population-based cohort study (mean age 63 years, 51% female) in which 1,099, 875, 768, and 566 participants completed assessments at baseline, 2.6, 5.1, and 10.7 years, respectively. T1-weighted and T2-weighted magnetic resonance imaging was performed to measure cartilage volume (CV) and bone marrow lesions (BMLs) at baseline and 10.7 years. The Western Ontario and McMaster Universities Osteoarthritis Index pain questionnaire was used to measure knee pain at each visit. Pain trajectories ("minimal pain," "mild pain," and "moderate pain") were previously identified. Baseline energy-adjusted DII (E-DII) scores were calculated. Linear, log-binomial regression, linear mixed-effects modeling, and multi-nominal logistic regression were used for analyses. RESULTS: The mean ± SD E-DII score at baseline was -0.48 ± 1.39. In multivariable analyses, higher E-DII scores were not associated with tibial CV loss or BML size increase except for medial tibial BML size increase. Higher E-DII scores were associated with a higher pain score (β = 0.21; 95% confidence interval [CI] 0.004-0.43) and an increased risk of belonging to the "moderate pain" compared to the "minimal pain" trajectory group (relative risk ratio 1.19; 95% CI 1.02-1.39). CONCLUSION: A proinflammatory diet, as indicated by a higher DII score, may be associated with a greater pain score and higher risk of more severe pain trajectory over 10 years. However, inconsistent findings related to structural changes suggest a discordance between the potential impact of diet on structural damage and pain in knee OA.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".