Waist-To-Hip Ratio and Mortality in Heart Failure
Bibliographic record
Abstract
AIMS: A higher body mass index (BMI) is associated with better survival in heart failure (HF) patients, also known as the obesity paradox. However, BMI does not account for body composition. We therefore analysed the association between abdominal fat, measured via waist-to-hip ratio (WHR), BMI and all-cause mortality in patients with HF. METHODS AND RESULTS: For this analysis, 1738 patients from the Scottish BIOlogy Study to TAilored Treatment in Chronic Heart Failure (BIOSTAT-CHF) validation study were included. Patients without waist and hip measurements were excluded. WHR was defined as waist circumference/hip circumference, divided into tertiles and split for sex. A linear regression of principal components from an extensive panel of biomarkers was performed to provide insight in the pathophysiology behind a higher WHR. In total, 1479 patients were included, of which 33% were female and mean age was 75 ±11 years. A higher WHR was independently associated with a higher BMI, a higher prevalence of diabetes and higher New York Heart Association functional class. There was a significant interaction between sex and WHR on its association with mortality (P <0.001). In women, a higher WHR was associated with a higher mortality risk [hazard ratio (HR) 2.23, 95% confidence interval (CI) 1.37-3.63; P =0.001], whereas no significant association was found in men (HR 0.87, 95% CI 0.63-1.20; P = 0.409). We found a strong association between a higher WHR and elevated markers of inflammation and MAPK cascade in women, while these associations were less profound in men. CONCLUSIONS: A higher WHR was associated with a higher risk of death in female but not in male HF patients. These findings challenge the obesity paradox, and suggest that fat deposition is pathophysiologically harmful and may be a target for therapy in female patients with HF.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".