Anthropometric Markers as Cardiovascular Predictors: A Comparison Between Conventional Cut-Off Points and Population Percentiles
Bibliographic record
Abstract
Background: Cardiovascular diseases (CVD) are the leading cause of mortality worldwide. An ongoing debate exists regarding the most appropriate methods for assessing adiposity-associated cardiovascular risk in working populations. The aim of the study was to evaluate the association between different anthropometric markers and the development of cardiovascular events in Peruvian workers. Methods: This is a retrospective cohort study of 10,300 workers (2014 - 2021). Body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), and the WC-BMI index were evaluated as predictors of CVD (myocardial infarction and/or stroke). Population percentiles (75th and 95th) and conventional cut-off points were used. Additionally, conventional cut-off points were employed, such as 0.5 for WHtR, and according to the Adult Treatment Panel III (ATP-III) (≥ 102 cm in men and ≥ 88 cm in women) or International Diabetes Federation (IDF) (≥ 80 cm in men and ≥ 90 cm in women) for abdominal obesity. Results: Obesity, as measured by BMI, showed a significant association with myocardial infarction (adjusted hazard ratio (aHR): 4.07; 95% confidence interval (CI): 1.08 - 15.4). Very high WC and WHtR (95th percentile) presented a greater risk of total cardiovascular events (aHR: 2.40; 95% CI: 1.12 - 5.12 and aHR: 2.57; 95% CI: 1.17-5.64, respectively), being particularly predictive for stroke (aHR: 4.53; 95% CI: 1.13 - 18.1 and aHR: 4.05; 95% CI: 1.01 - 16.3, respectively). No significant associations were found using conventional cut-off points for WHtR and abdominal obesity. Conclusions: Central adiposity markers, especially WC and WHtR, were evaluated through population percentiles and were better predictors of cardiovascular events than BMI or conventional cut-off points in the Peruvian working population. These findings support reorienting obesity definitions toward cardiovascular risk assessment using population-specific percentiles rather than relying exclusively on universal adiposity thresholds.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".