Waist circumference cut-off points to identify major cardiovascular events and incident diabetes in Latin America: findings from the prospective Urban rural epidemiology study Colombia
Bibliographic record
Abstract
Background: Abdominal obesity (AO) indirectly represents visceral adiposity and can be assessed by waist circumference (WC) measurement. In Latin America, cut-off points for the diagnosis of AO are based on Asian population data. We aim to establish the WC cut-off points to predict major cardiovascular events (MACE) and incident diabetes. Methods: We analyzed data from the cohort PURE study in Colombia. WC cut-off points were defined according to the maximum Youden index. Multivariate logistic regression was used to obtain associations between WC and MACE, diabetes, and cumulative incidence of outcomes visualized using Kaplan-Meier curves. Results: After a mean follow-up of 12 years, 6,580 individuals with a mean age of 50.7 ± 9.7 years were included; 64.2% were women, and 53.5% were from rural areas. The mean WC was 85.2 ± 11.6 cm and 88.3 ± 11.1 cm in women and men, respectively. There were 635 cases of the MACE composite plus incident diabetes (5.25 events per 1,000 person-years). Using a cut-off value of 88.85 cm in men (sensitivity = 0.565) and 85.65 cm in women (sensitivity = 0.558) resulted in the highest value for the prediction of the main outcome. These values were associated with a 1.76 and 1.41-fold increased risk of presenting the composite outcome in men and women, respectively. Conclusions: We defined WC cut-off points of 89 cm in men and 86 cm in women to identify the elevated risk of MACE and incident diabetes. Therefore, we suggest using these values in cardiovascular risk assessment in Latin America.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".