Body Mass Index, Waist Circumference, and Health Risk
Bibliographic record
Abstract
BACKGROUND: No evidence supports the waist circumference (WC) cutoff points recommended by the National Institutes of Health to identify subjects at increased health risk within the various body mass index (BMI; calculated as weight in kilograms divided by the square of height in meters) categories. OBJECTIVE: To examine whether the prevalence of hypertension, type 2 diabetes mellitus, dyslipidemia, and the metabolic syndrome is greater in individuals with high compared with normal WC values within the same BMI category. METHODS: The subjects consisted of 14 924 adult participants of the Third National Health and Nutrition Examination Survey, which is a nationally representative cross-sectional survey. Subjects were grouped by BMI and WC in accordance with the National Institutes of Health cutoff points. Within the normal-weight (18.5-24.9), overweight (25.0-29.9), and class I obese (30.0-34.9) BMI categories, we computed odds ratios for hypertension, diabetes, dyslipidemia, and the metabolic syndrome and compared subjects in the high-risk (men, >102 cm; women, >88 cm) and normal-risk (men, <or=102 cm; women, <or=88 cm) WC categories. RESULTS: With few exceptions, within the 3 BMI categories, those with high WC values were increasingly likely to have hypertension, diabetes, dyslipidemia, and the metabolic syndrome compared with those with normal WC values. Many of these associations remained significant after adjusting for the confounding variables (age, race, poverty-income ratio, physical activity, smoking, and alcohol intake) in normal-weight, overweight, and class I obese women and overweight men. CONCLUSIONS: The National Institutes of Health cutoff points for WC help to identify those at increased health risk within the normal-weight, overweight, and class I obese BMI categories.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".