Correlation of Dietary Protein Intake with Body Composition and Physical Status in Patients with Knee Osteoarthritis
Bibliographic record
Abstract
Background & Objective: Little is known about the association between dietary protein intake and clinical manifestations in osteoarthritis (OA) patients.We aimed to determine the correlation between dietary protein intake and pain severity, functional status, and body composition indices in patients with knee OA. Materials & Methods:This cross-sectional study was performed on 220 OA patients, staged I to Ⅲon Kellgren and Lawrence scale.Patients were selected randomly via cluster sampling method from the health centers of Tabriz between October 2017 and October 2018.We estimated the participants' protein intakes using a semi-quantitative food frequency questionnaire.Western Ontario and McMaster Index (WOMAC) was used to measure the functional status.We used the Visual Analogue Scale to measure pain severity.A bioelectric impedance device measured the patients' body composition.Results: Total dietary protein intake was 55.36±24.14grams per day.Higher dietary total and animal-based protein intakes were associated with lower pain severity.There were reverse correlations between dietary protein intakes (total and animal-based) with the physical disability according to WOMAC total, WOMAC pain, and WOMAC stiffness scores in the subset of patients who didn't meet the 75 percent of recommended dietary allowance.In these patients, higher total, plant-based, and animal-based protein intakes correlated with WOMAC functional scores.Higher total and animal-based protein intakes were associated with higher soft lean and lean body mass in women.Conclusion: Dietary protein intake needs to improve in knee OA patients, and dietary protein intake might be an intermediation objective in these patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".