Sex Differences in the Biology and Pathology of the Aging Heart
Bibliographic record
Abstract
The knowledge that advanced age is a major risk factor for cardiovascular disease (CVD) has stimulated interest in cardiac aging. Understanding how the heart remodels with age can help us appreciate why older individuals are more likely to acquire heart disease. Growing evidence in both humans and animals shows that the heart exhibits distinct structural and functional changes as a consequence of age. These changes occur even in the absence of overt cardiovascular disease and are often maladaptive. For example, atrial hypertrophy and fibrosis may increase susceptibility to atrial fibrillation in older adults. Age-dependent increases in left ventricular fibrosis, stiffness, and wall thickness promote diastolic dysfunction, predisposing to heart failure with preserved ejection fraction. The influence of age on the heart is evident at rest but is even more prominent during exercise. There is also evidence for sex-specific variation in age-associated remodelling. For instance, there is some evidence that the number of ventricular myocytes declines with age through apoptosis in men but not in women. This helps explain why older men are more likely than women to experience heart failure with reduced ejection fraction. Emerging evidence from preclinical studies suggests that frailty rather than chronological age promotes adverse cardiac remodelling. Mechanisms implicated in cardiac aging include impaired calcium handling, excessive activation of the ß-adrenergic and renin-angiotensin systems, and mitochondrial dysfunction. Further research into cardiac aging in both sexes is needed, because it may be possible to modify disease treatment if the substrate upon which the disease first develops is better understood.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".