Nutrition risk, physical activity and fibre intake are associated with body composition in OA: analysis of the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Objective: Sarcopenic obesity is a key feature in osteoarthritis (OA). While ideal OA treatment involves physical activity and diet, how diet influences OA pathophysiology is unclear. We explored the associations between diet, nutrition risk and physical activity with body composition in older adults with OA. Methods: Baseline data from the Canadian Longitudinal Study on Aging data set were analysed. Participants with hip, knee, hand or multiple forms of OA were included in this cross-sectional analysis. Body composition measures (lean, fat and total masses (kg) and body fat percentage) were separate dependent variables. Regression analyses were conducted to explore associations between body composition with dietary intake (high calorie snack, fibre), nutrition risk (SCREEN II) and physical activity (Physical Activity Scale for the Elderly). Results: . Higher fibre cereal intake was associated with higher lean mass (unstandardised beta coefficient 0.5 (0.1, 0.9), p=0.02) and lower body fat percentage (-0.3 (-0.6, 0.0), p=0.046). Lower nutrition risk was associated with higher lean mass (0.1 (0.0, 0.1), p=0.03), lower fat mass (-0.05 (-0.1, 0.0), p=0.009) and lower body fat percentage (-0.1 (-0.1, 0.0), p<0.001). Higher physical activity was associated with higher lean mass (0.01 (0.01, 0.02), p<0.001), lower fat mass (-0.01 (0.0, 0.0), p=0.005) and lower body fat percentage (-0.01 (0.0, 0.0), p<0.001). Conclusion: Greater physical activity and lower nutrition risk were associated with better body composition. While fibre intake was also associated body composition, the CIs were wide suggesting weak associations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".