Anthropometric measures and adverse outcomes in heart failure with reduced ejection fraction: revisiting the obesity paradox
Bibliographic record
Abstract
AIMS: Although body mass index (BMI) is the most commonly used anthropometric measure, newer indices such as the waist-to-height ratio, better reflect the location and amount of ectopic fat, as well as the weight of the skeleton, and may be more useful. METHODS AND RESULTS: The prognostic value of several newer anthropometric indices was compared with that of BMI in patients with heart failure (HF) and reduced ejection fraction (HFrEF) enrolled in prospective comparison of ARNI with ACEI to determine impact on global mortality and morbidity in heart failure. The primary outcome was HF hospitalization or cardiovascular death. The association between anthropometric indices and outcomes were comprehensively adjusted for other prognostic variables, including natriuretic peptides. An 'obesity-survival paradox' related to lower mortality risk in those with BMI ≥25 kg/m2 (compared with normal weight) was identified but this was eliminated by adjustment for other prognostic variables. This paradox was less evident for waist-to-height ratio (as an exemplar of indices not incorporating weight) and eliminated by adjustment: the adjusted hazard ratio (aHR) for all-cause mortality, for quintile 5 vs. quintile 1, was 1.10 [95% confidence interval (CI) 0.87-1.39]. However, both BMI and waist-to-height ratio showed that greater adiposity was associated with a higher risk of the primary outcome and HF hospitalization; this was more evident for waist-to-height ratio and persisted after adjustment e.g. the aHR for HF hospitalization for quintile 5 vs. quintile 1 of waist-to-height ratio was 1.39 (95% CI 1.06-1.81). CONCLUSION: In patients with HFrEF, alternative anthropometric measurements showed no evidence for an 'obesity-survival paradox'. Newer indices that do not incorporate weight showed that greater adiposity was clearly associated with a higher risk of HF hospitalization.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".