Does Waist Circumference Predict Diabetes and Cardiovascular Disease Beyond Commonly Evaluated Cardiometabolic Risk Factors?
Bibliographic record
Abstract
OBJECTIVE: While the measurement of waist circumference (WC) is recommended in current clinical guidelines, its clinical utility was questioned in a recent consensus statement. In response, we sought to determine whether WC predicts diabetes and cardiovascular disease (CVD) beyond that explained by BMI and commonly obtained cardiometabolic risk factors including blood pressure, lipoproteins, and glucose. RESEARCH DESIGN AND METHODS: Subjects consisted of 5,882 adults from the 1999-2004 National Health and Nutrition Examination Survey, which is nationally representative and cross-sectional. Subjects were grouped into sex-specific WC and BMI tertiles. Blood pressure, triglycerides, LDL and HDL cholesterol, and glucose were categorized using standard clinical thresholds. Logistic regression analyses were used to calculate the odds for diabetes and CVD according to WC tertiles. RESULTS: After controlling for basic confounders, the medium and high WC tertiles were more likely to have diabetes and CVD compared with the low WC tertile (P < 0.05). After inclusion of BMI and cardiometabolic risk factors in the regression models, the magnitude of the odds ratios were attenuated (i.e., for diabetes the magnitude decreased from 6.54 to 5.03 for the high WC group) but remained significant in the medium and high WC tertiles for the prediction of diabetes, though not for CVD. CONCLUSIONS: WC predicted diabetes, but not CVD, beyond that explained by traditional cardiometabolic risk factors and BMI. The findings lend critical support for the recommendation that WC be a routine measure for identification of the high-risk, abdominally obese patient.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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".