Obesity, aldosterone, and natriuretic peptides in patients with heart failure and reduced ejection fraction
Bibliographic record
Abstract
AIMS: Experimental evidence suggests that adipose tissue may secrete aldosterone, and mineralocorticoid receptor antagonists (MRAs) appear to be more effective in patients with obesity. Therefore, we examined aldosterone levels according to measures of adiposity in patients with heart failure and reduced ejection fraction (HFrEF) participating in two large trials. METHODS AND RESULTS: Aldosterone, N-terminal pro B-type natriuretic peptide (NT-proBNP), and B-type natriuretic peptide (BNP) levels were compared according to body mass index (BMI) categories: normal weight (<25.0 kg/m2), overweight (25.0-29.9 kg/m2), obesity class I (30.0-34.9 kg/m2), and obesity class II/III (≥35.0 kg/m2). Of the 2201 patients not treated with an MRA, in whom aldosterone levels were measured at baseline in ATMOSPHERE and PARADIGM-HF, the mean age was 67.8 years, and 440 (20.0%) were female. Patients with higher BMI had a higher left ventricular ejection fraction but worse New York Heart Association functional class than those with normal weight. Higher BMI was associated with higher aldosterone levels but lower NT-proBNP and BNP levels (P for trend < 0.001), compared to those with normal weight. This natriuretic peptide trend was also seen for other anthropometric measures. CONCLUSION: Greater adiposity was associated with higher concentrations of aldosterone but lower levels of B-type natriuretic peptides in patients with HFrEF. Adipose tissue may influence the neurohumoral milieu in HFrEF, including the secretion of aldosterone.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".