DIETARY NITRATE SUPPLEMENTS AND AEROBIC EXERCISE MODIFY FRAILTY IN AN AGE-DEPENDENT MANNER IN FEMALE C57BL/6 MICE
Bibliographic record
Abstract
Abstract We previously demonstrated that aerobic exercise attenuates the development of frailty in older female mice. Here we combine the common dietary supplement, sodium nitrate, with aerobic exercise to determine if this combination will work to attenuate frailty across lifespan. Adult (7-9 months) and aged (24-25 months) female mice were given free access to a running wheel and/or sodium nitrate (1mM, drinking water), or neither for 3 months. We measured blood pressure (tail cuff), running volume, frailty (frailty index), and body composition (DEXA). Blood pressure was unaffected by nitrates or exercise at both ages. While young mice ran more than older mice (3.5±0.4 vs 1.2±0.2 km/day), running was unaffected by nitrates. Older mice were frailer at baseline than younger mice (0.13±0.04 vs 0.03±0.004; p< 0.001). In older sedentary controls, frailty increased over time (0.15±0.02 to 0.19±0.03: p=0.03), but this was prevented by nitrates (0.13± 0.02 to 0.12 ± 0.01), exercise (0.12±0.03 to 0.18±0.03), and both (0.12±0.02 to 0.12±0.01). Older sedentary controls saw age-related weight loss (32.5±3.0g to 29.7±2.3g: p=0.02) which was prevented by exercise or nitrates. In older mice, mortality was highest in sedentary controls (46%) and lowest in exercised mice fed nitrates (0%). In contrast, while exercise attenuated frailty in young mice, nitrates did not. Young mice also had few changes in body composition; none died. While nitrates with or without exercise are beneficial for older mice, they had little effect in younger mice. These results suggest that nitrates alone or with exercise, may help prevent frailty in older females.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".