Association of Dietary Patterns with Metabolic Syndrome among Middle‐Aged Adults in Shiraz, Iran: Shiraz Heart Study (SHS)
Bibliographic record
Abstract
Introduction. Metabolic syndrome (MetS) is a noncommunicable disease with a high burden, including the development of type 2 diabetes mellitus, cardiovascular events, and death. It is characterized by abdominal obesity, elevated blood pressure, increased fasting plasma glucose levels, hypertriglyceridemia, and reduced levels of high‐density lipoprotein (HDL) cholesterol. MetS is preventable by modifying lifestyle and dietary patterns, which are major contributing factors. This research aimed to investigate the dietary patterns of the Shiraz Heart Study (SHS) and their associations with the occurrence of MetS and its components among middle‐aged residents of Shiraz. Methods. Based on data from the Shiraz Heart Study (SHS), a prospective cohort study, the nutritional status of 1,675 participants was assessed using a food frequency questionnaire (FFQ). Three food patterns were extracted from the analysis named as vegan, western, and carbohydrate. Subjects were categorized into three levels for three major dietary patterns: low, moderate, and high, based on their adherence to each pattern. After adjusting the effect of co‐founder variables, the relationship between dietary patterns, and the risk of developing MetS was analyzed. Results. Of the 1,675 participants, 728 (43.5%) of them were male. The prevalence of MetS was 47.2%. Multivariate logistic regression analysis showed that high adherence to the vegan pattern was negatively associated with the occurrence of MetS (P value <0.001), while low adherence to the western pattern was also negatively associated (P value <0.05). Conclusion. Healthier diets, such as vegan diets, are significantly related to lower rates of MetS among the 40–70‐year‐old people in Shiraz, Iran.
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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 | 0.002 |
| 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".