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Record W4390107208 · doi:10.1002/ejhf.3118

Natriuretic Peptides, Body Mass Index and Heart Failure Risk: Pooled Analyses of SAVOR-TIMI 53, DECLARE-TIMI 58 and CAMELLIA-TIMI 61

2023· article· en· W4390107208 on OpenAlexafffund
Siddharth M. Patel, David A. Morrow, Andrea Bellavia, David D. Berg, Deepak L. Bhatt, Petr Jarolı́m, Lawrence A. Leiter, Darren K. McGuire, Itamar Raz, Philippe Gabríel Steg, John Wilding, Marc S. Sabatine, Stephen D. Wiviott, Eugene Braunwald, Benjamin M. Scirica, Erin A. Bohula

Bibliographic record

VenueEuropean Journal of Heart Failure · 2023
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersNational Heart, Lung, and Blood InstituteEsperion TherapeuticsHLS TherapeuticsIdorsia PharmaceuticalsDaiichi Sankyo EuropeServierAssistance publique-Hôpitaux de ParisRoche DiagnosticsShionogiDuke Clinical Research InstituteEisaiDaiichi-SankyoZora BiosciencesPfizerModernaARCA BiopharmaMedicines CompanyRegado BiosciencesBelvoir Media GroupNovo NordiskMyoKardiaAllerganAstraZenecaAmarin CorporationAmerican Heart AssociationIntarcia TherapeuticsIronwood Pharmaceuticals, IncorporatedBrigham and Women's HospitalBoston VA Research InstituteBoston Scientific CorporationCleveland ClinicBristol-Myers SquibbEli Lilly and CompanyAbbott LaboratoriesCSL BehringAmgenSt. Jude MedicalKowa CompanyAbiomedSanofiNational Institutes of HealthRegeneron Pharmaceuticals
KeywordsMedicineTIMIInterquartile rangeInternal medicineHazard ratioHeart failureCardiologyConfidence intervalBody mass indexNatriuretic peptidePercutaneous coronary interventionMyocardial infarction

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.013
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.017
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.021
GPT teacher head0.283
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2023
Admission routes2
Has abstractyes

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