INTERACTING DISCIPLINES: Cardiac natriuretic peptides and obesity: perspectives from an endocrinologist and a cardiologist
Bibliographic record
Abstract
Since their discovery in 1981, the cardiac natriuretic peptides (cNP) atrial natriuretic peptide (also referred to as atrial natriuretic factor) and brain natriuretic peptide have been well characterised in terms of their renal and cardiovascular actions. In addition, it has been shown that cNP plasma levels are strong predictors of cardiovascular events and mortality in populations with no apparent heart disease as well as in patients with established cardiac pathology. cNP secretion from the heart is increased by humoral and mechanical stimuli. The clinical significance of cNP plasma levels has been shown to differ in obese and non-obese subjects. Recent lines of evidence suggest important metabolic effects of the cNP system, which has been shown to activate lipolysis, enhance lipid oxidation and mitochondrial respiration. Clinically, these properties lead to browning of white adipose tissue and to increased muscular oxidative capacity. In human association studies in patients without heart disease higher cNP concentrations were observed in lean, insulin-sensitive subjects. Highly elevated cNP levels are generally observed in patients with systolic heart failure or high blood pressure, while obese and type-2 diabetics display reduced cNP levels. Together, these observations suggest that the cNP system plays a role in the pathophysiology of metabolic vascular disease. Understanding this role should help define novel principles in the treatment of cardiometabolic disease.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".