Influence of dietary patterns on the risk of acute myocardial infarction in China population: the INTERHEART China study
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Some dietary patterns are risk factors for acute myocardial infarction (AMI). Chinese traditional food and habits vary from other cultures. The present study determined whether different dietary patterns were associated with AMI in Chinese people. METHODS: We conducted a case-control study. There were 1312 cases of first AMI and 2235 control subjects who did not have previous angina, diabetes mellitus, hypertension or stroke. Controls were matched to cases on age and gender. Diet was measured with a validated, 19 item food frequency questionnaire. We identified three major dietary patterns using factor analysis: vitamin and microelement pattern (high intake of vegetables, fruits and tofu), carbohydrate pattern (high in grain), and fat and protein pattern (high in meat, fish, eggs and fried foods). RESULTS: After adjusting for all risk factors, the vitamin and microelement pattern was inversely associated with AMI risk (global P value, 0.0001). Compared with the first quartile, the adjusted ORs of AMI were 0.81 (95%CI: 0.66 - 1.00) for the second quartile, 0.67 (95%CI: 0.54 - 0.82) for the third, and 0.70 (95%CI: 0.56 - 0.88) for the fourth. Several dietary frequencies (serves per week) including vegetables, fruits and tofu were closely associated with decrease of AMI risk. Carbohydrate pattern showed weak relationship with AMI. We observed a U-shaped association between frequencies of fat and protein pattern and AMI risk. Excessive fat intake increased the AMI risk. The adjusted OR of AMI associated with the higher level of green vegetables was 0.37 (95%CI: 0.24 - 0.57) in women and 0.65 (95%CI: 0.51 - 0.82) in men (P value for heterogeneity, 0.0140). CONCLUSIONS: Unhealthy dietary intake can increase the AMI risk. Improving intake of vegetables, fruits and tofu have the potential to partially prevent the rising epidemic of cardiovascular disease in China.
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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 | 0.001 | 0.001 |
| 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 it