Exploring metabolic syndrome and dietary quality in Iranian adults: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Metabolic syndrome (MetS) is a cluster of cardiovascular risk factors affecting a quarter of the global population, with diet playing a significant role in its progression. The aim of this study is to compare the effectiveness of the Dietary Diabetes Risk Reduction Score (DDRRS) and the Macronutrient Quality Index (MQI) scoring systems in assessing the diet-related risk of metabolic syndrome. METHODS: In this cross-sectional study, data from 7431 individuals aged between 30 and 70 years, obtained from the Mashhad Cohort Study, were utilized to evaluate the risk factors of metabolic syndrome. A valid semi-quantitative food frequency questionnaire was used to assess participants' dietary intake. The MQI was calculated based on carbohydrate, fat, and healthy protein components, while the DDRRS was also computed. Anthropometric measurements and blood samples were taken to determine the presence of metabolic syndrome. Logistic regression analyses were conducted to assess the association between MQI and DDRRS with metabolic syndrome and its components. RESULTS: According to the crude model, we observed lower odds of MetS in the highest quartile of DDRRS and MQI compared to the lowest quartile (P-trend < 0.001). This trend persisted in the fully adjusted models, revealing odds ratios of 0.399 (95% CI: 0.319-0.500) and 0.597 (95% CI: 0.476-0.749) for DDRRS and MQI, respectively. After controlling for all potential confounders, we observed lower odds of central obesity in the highest quartile of MQI (OR: 0.818, 95% CI: 0.676-0.989, P-trend = 0.027). Furthermore, we found that the odds of high triglyceride levels were lower in the highest quartile of DDRRS compared to the lowest quartile (OR: 0.633, 95% CI: 0.521, 0.770, P-trend < 0.001). CONCLUSION: In conclusion, our study indicates that greater adherence to both DDRRS and MQI is linked to a decreased risk of metabolic syndrome and its components. These findings hold significant implications for public health and the development of personalized nutrition strategies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".