Optimal cut-off points for waist circumference in the definition of metabolic syndrome: a cross-sectional study in rural Bangladesh
Bibliographic record
Abstract
OBJECTIVE: To determine optimal waist circumference (WC) cut-off points for identifying metabolic syndrome (MetS) in Bangladeshi adults, with the aim of enhancing diagnostic accuracy specific to this population. DESIGN: Cross-sectional analysis. SETTING: Rural community in Chandra, Bangladesh. PARTICIPANTS: A total of 2293 adults aged 20 years and older. PRIMARY AND SECONDARY OUTCOME MEASURES: MetS was defined using the modified National Cholesterol Education Program Adult Treatment Panel III criteria. Receiver operating characteristic (ROC) curves and Youden's Index were used to identify WC cut-off points that maximised sensitivity and specificity for diagnosing MetS. Restricted cubic spline regression was employed to explore the non-linear relationship between WC and MetS risk. RESULTS: The optimal WC cut-off points for predicting MetS were 90 cm for men (sensitivity 55.2%, specificity 94.3%, OR 12.5, 95% CI 8.6 to 18.0) and 80 cm for women (sensitivity 86.7%, specificity 71.9%, OR 15.6, 95% CI 11.4 to 21.3). The area under the ROC curve was 0.819 for men and 0.827 for women. Non-linear analysis indicated a significant increase in MetS risk beyond these thresholds, with a steeper risk gradient observed in men. CONCLUSIONS: This study establishes WC cut-off points of 90 cm for men and 80 cm for women as optimal for diagnosing MetS in Bangladeshi adults, underscoring the necessity of population-specific diagnostic criteria to improve early detection and management.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".