Risk Factors for Premature Myocardial Infarction: A Systematic Review and Meta-analysis of 77 Studies
Bibliographic record
Abstract
Objective To evaluate the magnitude of the association between risk factors and premature myocardial infarction (MI) (men aged 18-55 years; women aged 18-65 years). Patients and Methods We searched MEDLINE and other databases from inception through April 30, 2020, as well as bibliography of articles selected for data extraction. We selected observational studies reporting the magnitude of the association of at least 1 risk factor (demographic characteristics, lifestyle factors, clinical risk factors, or biomarkers) with premature MI and a control group. Pooled risk estimates (random effects) from all studies unadjusted and adjusted for risk factors were reported as summary odds ratios (ORs) with 95% CIs. Results From 35,320 articles, we extracted data on 19 risk factors in 12.7 million participants from 77 studies across 58 countries. Men had a higher risk of premature MI (OR, 2.39; 95% CI, 1.71 to 3.35) than did women. Family history of cardiac disease was associated with a higher risk of premature MI (OR, 2.67; 95% CI, 2.29 to 3.27). Major modifiable risk factors associated with higher risk were current smoking (OR, 4.34; 95% CI, 3.68 to 5.12 vs no/former), diabetes mellitus (OR, 3.54; 95% CI, 2.69 to 4.65), dyslipidemia (OR, 2.94; 95% CI, 1.76 to 4.91), and hypertension (OR, 2.85; 95% CI, 2.48 to 3.27). Higher body mass index carried higher risk (OR, 1.46; 95% CI, 1.24 to 1.71 for ≥25 kg/m 2 vs <25 kg/m 2 ). Biomarkers associated with 2- to 3-fold higher risk were total cholesterol levels greater than 200 mg/dL, triglyceride levels higher than 150 mg/dL, and high-density lipoprotein cholesterol levels less than 60 mg/dL (to convert to mmol/L, multiply by 0.0259). Conclusion Major risk factors for premature MI are mostly amenable to patient, population, and policy level interventions. Mild elevations in body mass index and triglyceride levels were associated with higher risk, which has implications for the growing worldwide epidemic of cardiometabolic diseases.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".