Anthropometric indices in predicting 10-year cardiovascular risk among males and females aged 40–74 years in south and southeast Asia: analysis of 12 WHO STEPS survey data
Bibliographic record
Abstract
Background: The relevance of anthropometric indices in predicting cardiovascular disease (CVD) or CVD risk factors is established across different countries, particularly in the high-income countries. However, past studies severely lacked representation from the south and southeast Asian countries. The main aim of this study was to determine the performance of conventional and new anthropometric indices to best predict 10-year cardiovascular disease (CVD) risk in south Asian and southeast Asian populations. Methods: The present study examined data from 14,532 participants in three south Asian and 13,846 participants (all aged between 40 and 74 years) in six southeast Asian countries, drawn from twelve cross-sectional studies (WHO STEPwise approaches to NCD risk factor surveillance [STEPS] survey data from 2008 to 2019). A Predictive performance of ten anthropometric indices were examined for predicting 10-year CVD risk ≥ 10% (CVD-R ≥ 10%). The 10-year CVD-R ≥ 10% was calculated by utilising the WHO CVD risk non-laboratory-based charts. Receiver operating characteristic (ROC) curve analysis was used to identify the optimal anthropometric index. Findings: Among the ten anthropometric indices, a body shape index (ABSI), body adiposity index (BAI), body roundness index (BRI), hip index (HI), and waist-height ratio (WHtR) performed best in predicting 10-year CVD risk among south Asian males and females. Improved performances were found for ABSI, BRI, conicity index (CI), WHtR, and waist-hip ratio (WHR) for 10-year CVD-R ≥ 10% predictions among southeast Asian males. Contrastingly, among southeast Asian females, ABSI and CI demonstrated optimal performance in predicting 10-year CVD-R ≥ 10%. Interpretation: The performance of anthropometric indices in predicting CVD risk varies across countries. ABSI, BAI, BRI, HI, and WHtR showed better predictions in south Asians, whereas ABSI, BRI, CI, WHtR, and WHR displayed enhanced predictions in southeast Asians. Funding: None.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".