Association of low-carbohydrate diet score with overweight, obesity and cardiovascular disease risk factors: a cross-sectional study in Iranian women
Bibliographic record
Abstract
Introduction: This study aimed to determine the association of low-carbohydrate-diet score with overweight, obesity and cardiovascular risk factors among Iranian women. Methods: In healthy Iranian women 20-50 years, demographics, anthropometrics, physical activity, blood pressure, fasting blood glucose, blood lipids, and dietary intake (using a validated food frequency questionnaire) were assessed. Participants were divided into deciles of macronutrient intakes. Women in the lowest decile of carbohydrate intake received a score of 9 and women in the highest decile received a score of 0. For protein and fat intakes, women in the lowest decile received a score of 0 for that macronutrient and those in the highest decile received the score of 9. Macronutrient scores were summed to create the low-carbohydrate diet score and women were grouped into tertiles based on these scores. Continuous and qualitative variables were compared among the low-carbohydrate-diet score by one-way ANOVA and chi-square test, respectively. Logistic regression was used to determine the association of low-carbohydrate-diet score and cardiovascular risk factors. Results: A total of 209 women were included in the study. Socioeconomic status significantly increased from tertile 1 to 3 of the low-carbohydrate diet score (P = 0.02). Total dietary glycemic index (GI) significantly differed among tertiles (tertile 1 GI: 63.1 ±0.50, tertile 2 GI: 61.9 ± 0.5, tertile 3 GI: 59.5 ± 0.5; P < 0.001). The odds ratios for overweight, obesity and cardiovascular risk factors were not significantly different among the tertiles of low-carbohydrate diet score. Conclusion: In Iranian women, diets lower in carbohydrate and higher in protein and fat were not associated with overweight, obesity and cardiovascular risk factors.
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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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".