Associations of Dietary Macroelements with Knee Joint Structures, Symptoms, Quality of Life, and Comorbid Conditions in People with Symptomatic Knee Osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis (OA), the most common joint disease in the elderly, has no cure. Macroelements are vital in human health and their relationships with OA are not clear. Clarifying the relationships between macroelements and OA may assist knee OA management. METHODS: This study was a post-hoc analysis using data from a two-year randomized controlled trial among 392 participants with knee OA. Dietary macroelements, including calcium, magnesium, potassium, and phosphorus were computed-based on a semi-quantitative food frequency questionnaire at baseline. Knee joint structures (including cartilage volume, cartilage defect, bone marrow lesions, and effusion-synovitis volume), OA symptoms, quality of life, and OA comorbid conditions (including lower limb muscle strength and depressive symptoms) were assessed at baseline and month 24. Western Ontario and McMaster Universities (WOMAC) Index and depressive symptoms were assessed at baseline and months 3, 6, 12, and 24. Quality of life and lower limb muscle strength were assessed at baseline and months 6, 12, and 24. All analyses were conducted using mixed-effects models. RESULTS: Higher dietary magnesium and potassium were associated with fewer OA symptoms, higher quality of life, greater lower limb muscle strength, and fewer depressive symptoms, but not with knee joint structures. Higher dietary calcium and phosphorus was not associated with any of the OA-related outcomes, except that dietary phosphorus was associated with greater lower limb muscle strength. CONCLUSIONS: In the longitudinal analyses, higher dietary magnesium and potassium intake are associated with fewer OA symptoms, higher quality of life, and milder comorbid conditions in patients with knee OA, suggesting dietary magnesium and potassium may have beneficial effects on OA and could be used for knee OA management.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".