Metabolic Syndrome in Normal-Weight Americans
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence rates and likelihood of the metabolic syndrome and its individual components in normal-weight and slightly overweight individuals (BMI 18.5-26.9 kg/m(2)). RESEARCH DESIGN AND METHODS: There were a total of 7,602 adult participants of the Third National Health and Nutrition Examination Survey, a nationally representative cross-sectional survey. Prevalence and odds ratios (ORs) of the metabolic syndrome, defined according to National Cholesterol Education Program Adult Treatment Panel III criteria, were computed according to 2.0- to 2.5-unit increments in BMI. RESULTS: Depending on ethnicity and sex, the prevalence of the metabolic syndrome increased in a graded fashion from 0.9-3.0% at BMI 18.5-20.9 kg/m(2) to 9.6-22.5% at BMI 25.0-26.9 kg/m(2). Compared with men with BMI 18.5-20.9 kg/m(2), the odds for the metabolic syndrome were 4.13 (95% CI 1.57-10.87) for men with BMI 21-22.9 kg/m(2), 5.35 (2.41-11.86) for men with BMI 23-24.9 kg/m(2), and 9.08 (4.23-19.52) for men with BMI 25-26.9 kg/m(2) after controlling for age, ethnicity, education, income, physical activity, smoking status, and alcohol and total fat, saturated fat, carbohydrate, and fiber intakes. The corresponding ORs in women were 4.34 (2.08-9.07), 7.77 (3.95-15.26), and 17.34 (9.29-32.38). CONCLUSIONS: Individuals in the upper normal-weight and slightly overweight BMI range have a relatively high prevalence and are at increased risk of having the metabolic syndrome. Therefore, screening in individuals with normal or slightly elevated BMI is important in the prevention of diabetes and cardiovascular disease.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".