Abstract P258: Traditional Risk Factors and a Genetic Risk Score Are Associated with Age of First Acute Coronary Syndrome
Bibliographic record
Abstract
Background: Early onset myocardial infarction (MI) is frequently attributed to genetic factors that may accelerate the atherosclerotic process. However, early MI may also occur due to a high burden of traditional risk factors. We sought to examine the association between traditional risk factors as well as a genetic risk score on the age of a first acute coronary syndrome (ACS). Methods and Results: We included 460 participants (mean age 59 +/- 12 years, 22.4% female) with a first ACS enrolled in the Recurrence and Inflammation in the Acute Coronary Syndromes (RISCA) cohort. Participants were genotyped for 30 single nucleotide polymorphisms identified from prior myocardial infarction genome-wide association studies to construct a multilocus genetic risk score (GRS). Linear regression models were fit to estimate the association between traditional risk factors (TRFs) and the GRS with age of first ACS. Several TRFs were significantly associated with earlier age of first ACS (all β coefficients in years; p<0.05 for all) : male sex [β=-6.9 (95%CI -9.7,-4.1)], current cigarette smoking [β=-8.1 (95% confidence interval [CI] -10.0, -6.1)], overweight (BMI>25) [β=-2.6 (95%CI -4.8, -0.3)] and obesity (BMI>30) [β=-5.24 (95%CI -7.9, -2.6)]. Use of hormone replacement therapy [β=-4.3 (95%CI -8.4, -0.3) ] and aspirin use were also associated with age of first ACS [β=3.7 (95%CI 0.3, 7.0)]. After multivariable adjustment for TRFs, a one standard deviation increment in the GRS was associated with a 1.0 (95%CI 0.1-2.0) year earlier age of first ACS. Conclusion: Among individuals with a first ACS, a GRS composed of 30 SNPs is associated with a younger age of presentation. Although common genetic predisposition modestly contributes to earlier ACS, a heavy burden of traditional risk factors is strongly associated with markedly earlier ACS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".