<sup>1</sup>H-NMR analysis of the human urinary metabolome in response to an 18-month multi-component exercise program and calcium–vitamin-D<sub>3</sub>supplementation in older men
Bibliographic record
Abstract
The musculoskeletal benefits of calcium and vitamin-D3 supplementation and exercise have been extensively studied, but the effect on metabolism remains contentious. Urine samples were analyzed by (1)H-NMR spectroscopy from participants recruited for an 18-month, randomized controlled trial of a multi-component exercise program and calcium and vitamin-D3 fortified milk consumption. It was shown previously that no increase in musculoskeletal composition was observed for participants assigned to the calcium and vitamin-D3 intervention, but exercise resulted in increased bone mineral density, total lean body mass, and muscle strength. Retrospective metabolomics analysis of urine samples from patients involved in this study revealed no distinct changes in the urinary metabolome in response to the calcium and vitamin-D3 intervention, but significant changes followed the exercise intervention, notably a reduction in creatinine and an increase in choline, guanidinoacetate, and hypoxanthine (p < 0.001, fold change > 1.5). These metabolites are intrinsically involved in anaerobic ATP synthesis, intracellular buffering, and methyl-balance regulation. The exercise intervention had a marked effect on the urine metabolome and markers of muscle turnover but none of these metabolites were obvious markers of bone turnover. Measurement of specific urinary exercise biomarkers may provide a basis for monitoring performance and metabolic response to exercise regimes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".