Abstract 443: Long-Term Testosterone Deficiency Modifies Frailty and Cardiac Structure and Function in Aging Male Mice
Bibliographic record
Abstract
Objective: Low circulating testosterone levels are correlated with heart diseases in older adults. The belief that low testosterone contributes to poor health and promotes frailty has dramatically increased testosterone prescriptions in recent years. Still links between low testosterone, frailty and the heart are unclear in part because preclinical studies typically use young, healthy animals. Here we investigated effects of chronic testosterone deficiency on frailty, body composition and cardiac structure/function in aging mice. Methods: Male C57BL/6 mice (18-21 mos) underwent a gonadectomy (GDX) or sham surgery (4-wks of age) and then were aged naturally. Sham (n=10-13) and GDX mice (n=11) were tested for frailty (frailty index (FI) tool), echocardiography, electrocardiography (ECG), blood pressure, and serum testosterone (ELISA, facial vein). Body composition was measured with dual-energy X-ray absorptiometry. Mice were anaesthetized (isoflurane) during all procedures except FI scoring. Results: Serum testosterone levels were lower in GDX mice than in sham controls (0.89±0.13 vs. 0.29±0.06 ng/mL; p<0.05). Systolic and diastolic blood pressures were unchanged between groups. Interestingly, GDX mice had lower lean mass than sham mice (23.5±1.2 vs. 27.4±0.7 g; p<0.05) but they were actually less frail (FI scores=0.17±0.05 vs. 0.23±0.01; p<0.05). GDX also reduced heart mass (186±11 vs. 148±15 mg; p<0.05) and prolonged the QRS complex (8.5±0.3 vs. 10.0±0.4 ms; p<0.05) without altering heart rate. While end systolic volume was reduced by GDX (p<0.05), ejection fraction, fractional shortening, and cardiac output remained unchanged. Conclusions: Chronic exposure to low circulating testosterone prolonged ventricular conduction time, reduced heart mass and lowered lean mass in aging male mice. This suggests that testosterone deficiency may promote electrical and contractile dysfunction in the setting of aging. However, despite adverse effects on the heart and lean muscle mass, frailty scores were actually lower in mice with chronic testosterone deficiency. This suggests that the impact of low testosterone on overall health is complex and raises questions about the benefits of testosterone supplementation in frail older adults.
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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) | 0.000 | 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) | 0.006 | 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".