Use of the waist‐to‐height ratio to predict cardiovascular risk in patients with diabetes: <scp>R</scp> esults from the <scp>ADVANCE‐ON</scp> study
Bibliographic record
Abstract
AIMS: Patients with type 2 diabetes have a high risk of cardiovascular disease (CVD). Central obesity has been particularly associated with this risk relationship. We aimed to evaluate waist to height ratio (WHtR) as a predictor of risk in such patients. METHODS: WHtR was evaluated as a predictor of the risk of CVD and mortality amongst 11 125 participants with type 2 diabetes in the ADVANCE and ADVANCE-ON studies, and was compared with body mass index (BMI), waist circumference and waist hip ratio (WHR). Primary outcome was a composite of death from CVD, non-fatal myocardial infarction or non-fatal stroke. Secondary outcomes were myocardial infarction, stroke, cardiovascular death and death from any cause. Cox models were used, with bootstrapping to compare associations between anthropometric measures for the primary outcome. RESULTS: Median follow-up time was 9.0 years. There was a positive association between WHtR and adverse outcomes. The hazard ratio (HR) (confidence interval), per SD higher WHtR, was 1.16 (1.11-1.22) for the primary endpoint, with no heterogeneity by sex or region, but a stronger effect in individuals aged 66 years or older. The other 3 anthropometric measurements showed similar associations, although there was evidence that WHtR marginally outperformed BMI and WHR. Based on commonly used BMI cut-points, the equivalent WHtR cut-points were estimated to be 0.55 and 0.6, with no evidence of a difference across subgroups. CONCLUSIONS: In patients with diabetes, WHtR is a useful indicator of future adverse risk, with similar effects in different population subgroups.
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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 | 0.004 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".