Natriuretic Peptides, Body Mass Index and Heart Failure Risk: Pooled Analyses of SAVOR-TIMI 53, DECLARE-TIMI 58 and CAMELLIA-TIMI 61
Bibliographic record
Abstract
Abstract Aim N-terminal pro-B-type natriuretic peptide (NT-proBNP) concentrations are lower in patients with obesity. The interaction between body mass index (BMI) and NT-proBNP with respect to heart failure risk remains incompletely defined. Methods and results Data were pooled across three randomized clinical trials enrolling predominantly patients who were overweight or obese with established cardiometabolic disease: SAVOR-TIMI 53, DECLARE-TIMI 58 and CAMELLIA-TIMI 61. Hospitalization for heart failure (HHF) was examined across strata of baseline BMI and NT-proBNP. The effect of dapagliflozin versus placebo was assessed for a treatment interaction across BMI categories in patients with or without an elevated baseline NT-proBNP (≥125 pg/ml). Among 24 455 patients, the median NT-proBNP was 96 (interquartile range [IQR]: 43–225) pg/ml and the median BMI was 33 (IQR 29–37) kg/m2, with 68% of patients having a BMI ≥30 kg/m2. There was a significant inverse association between NT-proBNP and BMI which persisted after adjustment for all clinical variables (p < 0.001). Within any range of NT-proBNP, those at higher BMI had higher risk of HHF at 2 years (comparing BMI <30 vs. ≥40 kg/m2 for NT-proBNP ranges of <125, 125–<450 and ≥450 pg/ml: 0.0% vs. 0.6%, 1.3% vs. 4.0%, and 8.1% vs. 13.8%, respectively), which persisted after multivariable adjustment (adjusted hazard ratio [HRadj] 7.47, 95% confidence interval [CI] 3.16–17.66, HRadj 3.22 [95% CI 2.13–4.86], and HRadj 1.87 [95% CI 1.35–2.60], respectively). In DECLARE-TIMI 58, dapagliflozin versus placebo consistently reduced HHF across BMI categories in those with an elevated NT-proBNP (p-trend for HR across BMI = 0.60), with a pattern of greater absolute risk reduction (ARR) at higher BMI (ARR for BMI <30 to ≥40 kg/m2: 2.2% to 4.7%; p-trend = 0.059). Conclusions The risk of HHF varies across BMI categories for any given range of circulating NT-proBNP. These findings showcase the importance of considering BMI when applying NT-proBNP for heart failure risk stratification, particularly for patients with low-level elevations in NT-proBNP (125–<450 pg/ml) where there appears to be a clinically meaningful absolute and relative risk gradient.
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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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.017 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".