Abstract 745: Voluntary Late-life Exercise Attenuates Frailty and Improves Maladaptive Changes Associated With Cardiac Aging in Female Mice
Bibliographic record
Abstract
Voluntary aerobic exercise has many positive health benefits and may actually attenuate frailty. Exercise may also improve cardiac function in the setting of aging, although links between exercise, frailty and age-dependent cardiac remodeling are unclear. To investigate age-related effects of exercise, 21-23 month old female C57BL/6 mice were divided into two groups, an exercise group (n=9) and a sedentary group (n=9). Frailty was measured with a clinical frailty index (FI) tool, then mice were randomly assigned to either exercise or sedentary groups matched by their initial FI scores. The exercise group was allowed constant access to a running wheel for 13 weeks, while sedentary animals had no wheel access. Cardiac function was measured with echocardiography at baseline, midpoint (6 weeks) and endpoint (13 weeks) in both groups. FI scores increased with age in sedentary mice (0.18 ± 0.02 to 0.34 ± 0.05; baseline vs endpoint) but not in the exercise group (0.18 ± 0.02 to 0.20 ± 0.01) and this difference was significant (at endpoint; p<0.05). Fractional shortening (FS) decreased with age in sedentary mice (34.81 ± 3.3% to 22.86 ± 5.5%; p<0.05; at endpoint) but exercise protected hearts against this decline (34.64 ± 2.3% to 36.16 ± 3.1%). A similar pattern was seen with ejection fraction (EF) where EF deteriorated with age (63.28 ± 4.2% to 44.70 ± 9.2%; p<0.05 at endpoint) but stayed similar in exercised animals (63.79 ± 3.6% to 65.74 ± 4.1%). Isovolumic relaxation time decreased with age in sedentary mice (20.43 ± 1.1 ms to 16.50 ± 1.7 ms; p<0.05) but not in exercised mice (19.32 ± 1.1 ms to 16.80 ± 1.1 ms). In addition, left ventricular (LV) mass was actually smaller in the exercise group compared to the sedentary group at endpoint (138.4 ± 19 mg vs 211.6 ± 34 mg; p<0.05). Interestingly, age-associated changes in EF, FS and LV mass at the endpoint were correlated with and closely graded by FI score in all mice (e.g. EF: r= 0.67, p<0.05, FS: r=0.68, p<0.05; LV mass: r=0.68, p<0.05). These results show that voluntary exercise attenuates frailty and prevents negative signs of cardiac aging in older female mice. These data suggest that cardiac function is closely linked to overall health, quantified as frailty, and that late-life interventions to reduce frailty can improve heart health.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".