Sex-Specific Differences in the Pathophysiology of Hypertension
Bibliographic record
Abstract
Hypertension is one of the most common comorbidities in cardiometabolic diseases, affecting nearly one third of adults. As a result, its pathophysiological mechanisms have been studied extensively and are focused around pressure natriuresis, the renin-angiotensin system (RAS), the sympathetic nervous system, oxidative stress, and endothelial dysfunction. Additionally, hypertension secondary to other underlying etiologies also exists. While clinical evidence has clearly shown differences in hypertension development in males and females, relatively little is known about the pathophysiological mechanisms behind these differences. Sex hormones likely play a key role, as they modulate many factors related to hypertension development. In this review, we postulate the potential role for sexually dimorphic fat metabolism in the physiology of hypertension. In brief, estrogen promotes subcutaneous fat deposition over visceral fat and increases in mass via adaptive hyperplasia rather than pathogenic hypertrophy. This adipose tissue subsequently produces anti-inflammatory effects and inhibits metabolic dysfunction-associated fatty liver disease (MAFLD) and RAS activation, ultimately leading to decreased levels of hypertension in pre-menopausal females. On the other hand, androgens and the lack of estrogens promote visceral and ectopic fat deposition, including in the liver, and lead to increased circulating pro-inflammatory cytokines and potentially subsequent RAS activation and hypertension development in males and post-menopausal females. Understanding the sex-specific differences in fat metabolism may provide deeper insights into the patho-mechanisms associated with hypertension and lead to more comprehensive sex-specific 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".