The relationship between ultra processed food consumption and premature coronary artery disease: Iran premature coronary artery disease study (IPAD)
Bibliographic record
Abstract
Background: Ultra-processed foods (UPF) consumption may affect the risk of PCAD through affecting cardio metabolic risk factors. This study aimed to evaluate the association between UPFs consumption and premature coronary artery disease (PCAD). Methods: A case-control study was conducted on 2,354 Iranian adults (≥ 19 years). Dietary intake was assessed using a validated 110-item food frequency questionnaire (FFQ) and foods were classified based on the NOVA system, which groups all foods according to the nature, extent and purposes of the industrial processes they undergo. PCAD was defined as having an stenosis of at least single coronary artery equal and above 75% or left main coronary of equal or more than 50% in women less than 70 and men less than 60 years, determined by angiography. The odds of PCAD across the tertiles of UPFs consumption were assessed by binary logistic regression. Results: After adjustment for potential confounders, participants in the top tertile of UPFs were twice as likely to have PCAD compared with those in the bottom tertile (OR: 2.52; 95% CI: 1.97-3.23). Moreover, those in the highest tertile of the UPFs consumption had more than two times higher risk for having severe PCAD than those in the first tertile (OR: 2.64; 95% CI: 2.16-3.22). In addition, there was a significant upward trend in PCAD risk and PCAD severity as tertiles increased (P-trend < 0.001 for all models). Conclusion: Higher consumption of UPFs was related to increased risk of PCAD and higher chance of having severe PCAD in Iranian adults. Although, future cohort studies are needed to confirm the results of this study, these findings indicated the necessity of reducing UPFs intake.
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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 | 0.001 |
| 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".