The importance of biological sex in cardiac cachexia
Bibliographic record
Abstract
Cardiac cachexia is a catabolic muscle-wasting syndrome observed in approximately 1 in 10 patients with heart failure. Increased skeletal muscle atrophy leads to frailty and limits mobility, which impacts quality of life, exacerbates clinical care, and is associated with higher rates of mortality. Heart failure is known to exhibit a wide range of prevalence and severity when examined across individuals of different ages and with comorbidities related to diabetes, renal failure, and pulmonary dysfunction. It is also recognized that men and women exhibit striking differences in the pathophysiology of heart failure, as well as skeletal muscle homeostasis. Given that both skeletal muscle and heart failure physiology are in part sex-dependent, the diagnosis and treatment of cachexia in patients with heart failure may depend on a comprehensive examination of how these organs interact. In this review, we explore the potential for sex-specific differences in cardiac cachexia. We summarize advantages and disadvantages of clinical methods used to measure muscle mass and function and provide alternative measurements that should be considered in preclinical studies. In addition, we summarize sex-dependent effects on muscle wasting in preclinical models of heart failure, disuse, and cancer. Lastly, we discuss the endocrine function of the heart and outline unanswered questions that could directly impact patient care.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".