Associations Among Sleep, Body Mass Index, Waist Circumference, and Risk of Type 2 Diabetes Among U.S. Childbearing-Age Women: National Health and Nutrition Examination Survey
Bibliographic record
Abstract
BACKGROUND: Women of childbearing age (18-44 years) present an important group for understanding sleep, but few studies have focused on this population. No study has investigated the associations among sleep, overweight/obesity, and risk of type 2 diabetes among childbearing-age women in the National Health and Nutrition Examination Survey (NHANES). METHODS: Data were from NHANES, 2005-2008. The study population consisted of 18-44 year old women. Pregnant women and those diagnosed with sleep disorders were excluded. Sleep duration and quality were self-reported. Body mass index (BMI), waist circumference (WC) measurements, and a 2-hour 75 g oral glucose tolerance test (OGTT) were performed by trained NHANES staff. An unadjusted linear regression analysis; a second adjusted for demographics only (partially adjusted model); and a third adjusted for demographics and variables associated with overweight/obesity and diabetes (fully adjusted model) were computed to assess associations among sleep duration/quality and BMI, WC, and 2-hour OGTT. RESULTS: Total sample consisted of 2388 childbearing-age women. Only sleep duration was significantly associated with BMI and WC in the unadjusted and partially adjusted models, but was no longer significant in the fully adjusted model. Neither sleep duration nor quality was significantly associated with 2-hour OGTT in any of the models. CONCLUSIONS: Targeting sleep duration and sleep quality alone would not likely contribute to significantly lower BMI, WC, or risk of type 2 diabetes in US childbearing-age women. Additional studies, especially longitudinal ones using objective measures of sleep, are needed to confirm these findings.
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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 | 0.001 |
| 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 | 0.000 | 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".