Determination of the Optimal Waist Circumference Cut-Off Points in Benin Adults
Bibliographic record
Abstract
Obesity is an independent risk factor for chronic diseases such as diabetes, cardiovascular diseases and certain cancers. Early detection of abdominal obesity followed by appropriate interventions will prevent associated diseases. The objective was to determine optimal waist circumference (WC) cut-off points predictive of at least one other component of metabolic syndrome in Benin adults. This longitudinal and observational study included at the baseline 541 apparently healthy subjects aged 25 - 60 years randomly selected in the largest city of Cotonou (n = 200), the smaller-size city of Ouidah (n = 171) and rural surroundings of Ouidah (n = 170) in Benin. Components of the metabolic syndrome considered along with abdominal obesity were high blood pressure, high fasting glycaemia, low HDL-cholesterol and high triglycerides as defined by the Joint Interim Statement. After the baseline survey, the subjects were followed-up for four years. Complete data at baseline and four years later were available in 416 subjects. The receiver operating characteristic (ROC) curve for waist circumference to predict at least one other component of the metabolic syndrome was plotted and the Youden index was used to define the optimal cut-offs of WC. At the baseline of the study, the optimal WC cut-off point to predict the presence of at least one other component of the metabolic syndrome was 80 cm for men (sensitivity = 0.62, specificity = 0.65); the area under the ROC curve was 0.67 (IC 95%: 0.61-0.74). In women, the optimal WC cut-off point was 90 cm (sensitivity = 0.54, specificity = 0.80), and the area under the ROC curve was 0.68 (IC 95%: 0.62-0.75). These optimal cut-offs were similar four years later. Optimum WC cut-off points for both men and women in Benin differ from those currently recommended for sub-Saharan Africans. Therefore, waist circumference cut-off points for abdominal obesity need to be reconsidered for these populations.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".