AGE, SEX, AND FRAILTY INFLUENCE AGE-DEPENDENT CHANGES IN VENTRICULAR STRUCTURE AND FUNCTION IN C57BL/6 MICE
Bibliographic record
Abstract
Abstract The heart undergoes maladaptive changes during aging that set the stage for cardiovascular diseases, but frail older individuals are most likely to develop such diseases. We investigated the impact of frailty on left ventricular (LV) remodeling in male and female mice (aged 9-23 mos). Ventricular function/structure and frailty were assessed with echocardiography (Vevo 2100) and a frailty index (FI) tool. Fractional shortening (systolic function) increased with age (9 vs 23 mos) in males (27.7±2.6 vs 38.4±1.6%; p<0.05) and females (26.9±1.4 vs 32.5±1.8%; p<0.05); similar results were seen with ejection fraction. Conversely, E/A ratios (diastolic function) declined with age in males (1.9±0.1 vs 1.3±0.1; p<0.05) and females (2.1±0.3 vs 1.6±0.1; p<0.05). LV mass and LV internal diameter (diastole) increased with age in females but not in males, while intraventricular septum (diastole) increased in males only. As age-dependent changes were heterogeneous, we stratified the data by FI scores. Interestingly, fractional shortening (r=0.52; p=0.006) and ejection fraction (r=0.52; p<0.0001) were graded by FI score, but only in males. By contrast, LV mass was graded by frailty, but only in females (r=0.55; p<0.0001). Thus, diastolic function declines with age in both sexes while systolic function actually increases. Aging females display increased LV mass and LV dilation whereas older males exhibit septal thickening. These maladaptive changes are graded by frailty, suggesting that cardiac aging is prominent in those with poor overall health. Age, sex and frailty influence cardiac aging, which may predispose frail older men and women to develop different cardiovascular diseases as they age.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".