Longitudinal association of dietary habits and the risk of cardiovascular disease among Iranian population between 2001 and 2013: The Isfahan Cohort Study
Bibliographic record
Abstract
Abstract Purpose: There has been a steady rise in the incidence of cardiovascular disease (CVD) in Iranian population. The aim of this study is to investigate the association of Global Dietary Index (GDI) and CVD risk among Iranian adults population.Method: This study was conducted based on Isfahan Cohort Study, a longitudinal study which collected data between 2001 to 2013on 6405 adults. Dietary intakes were assessed by a validated FFQ to calculate GDI. All participants were followed every two years by phone call to ask about death, any hospitalization or cardiovascular events to examine CVD events.Results: During 55017 person-years of follow-up, one unit GDI increase was associated with the higher risk of MI, stroke and CVD by 72% (HR: 1.72; 95% CI: 1.04-2.84), 76% (HR: 1.76; 95% CI: 1.09-2.85) and 48% (HR: 1.48; 95% CI: 1.03-2.14), respectively and higher risk of CHD more than 2 times (HR: 2.32; 95% CI: 1.50-3.60) and CVD mortality and all-cause mortality over than 3 times [(HR: 3.65; 95% CI: 1.90-7.01) and (HR: 3.10; 95% CI: 1.90-5.06), respectively] in total population. Conclusions: Higher GDI had a significant relationship with the increased risk of CVD events and all-cause mortality. Further randomized clinical trial studies are suggested to confirm our 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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".