Longitudinal Associations of Serum 25-hydroxyvitamin D, Physical Activity, and Knee Pain and Dysfunction with Muscle Loss in Community-dwelling Older Adults
Bibliographic record
Abstract
Aim: To describe the associations of between-person and within-person variability in serum 25-hydroxyvitamin D (25(OH)D), physical activity (PA), and knee pain and dysfunction with muscle mass, strength, and muscle quality over 10 years in community-dwelling older adults. Method: Participants (N = 1033; 51% women; mean age 63 ± 7.4 years) were measured at baseline, 2.5, 5, and 10 years. Lower limb lean mass (LLM) was assessed using dual energy X-ray absorptiometry, lower limb muscle strength (LMS) using a dynamometer, and lower limb muscle quality (LMQ) calculated as LMS/LLM. Knee pain and dysfunction were assessed using the Western Ontario and McMaster Universities Osteoarthritis (WOMAC) index. PA was measured using pedometers. Linear-mixed effect regression models, with adjustment for confounders, were used to estimate the association of within-person and between-person variability in PA, 25(OH)D, and WOMAC scores with muscle mass, strength, and muscle quality. Results: Both between-person and within-person increases in PA were associated with LLM, LMS, and LMQ (all P < 0.05). Within-person and between-person increases in knee pain and dysfunction were associated with LLS and LMQ, but not with LLM (all P < 0.05). Between-person effects showed that higher average 25(OH)D was associated with a higher 10-year average LLM, LMS, and LMQ (all P < 0.05), whereas within-person increases in average 25(OH)D were associated with a higher LMS and LMQ, but not with LLM. Conclusions: Variability in 25(OH)D, pain, and dysfunction within an individual over time is related to muscle changes in that individual. Increasing one's own PA level further increases muscle mass, strength, and quality supporting the clinical recommendation of promoting PA to reduce age-related muscle loss.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 | 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".