Traditional risk factors and a Genetic Risk Score are associated with age of first acute coronary syndrome
Bibliographic record
Abstract
OBJECTIVE: To examine the association between traditional risk factors (TRF) and a Genetic Risk Score (GRS) with age of first acute coronary syndrome (ACS). Early onset ACS may occur due to a high burden of TRFs or to genetic factors that accelerate atherosclerosis. Whether recently discovered genetic variants for ACS are more prevalent at earlier age of first ACS remains unknown. METHODS: To construct a multilocus GRS, participants were genotyped for 30 single nucleotide polymorphisms (SNP) identified from prior genome-wide association studies. Linear regression models were fit to estimate the association between TRFs and GRS with age of first ACS. RESULTS: We included 460 participants with a first ACS enrolled in the Recurrence and Inflammation in the Acute Coronary Syndromes (RISCA) cohort. Several TRFs were associated (all p<0.05) with earlier age of first ACS: male sex (6.9 years earlier (95% CI 4.1 to 9.7)), current cigarette smoking (8.1 years (95% CI 6.1 to 10.0)), overweight (Body Mass Index, BMI >25) and obesity (BMI>30) (5.2 years (95% CI 2.6 to 7.9)). In women, hormone replacement therapy was also associated with earlier age of first ACS (4.8 years earlier (95% CI 0.3 to 8.4)). After multivariable adjustment for TRFs, a 1 SD increment in the GRS was associated with a 1.0 (95% CI 0.1 to 2.0) year earlier age of first ACS. CONCLUSIONS: Among individuals with a first ACS, a GRS composed of 30 SNPs is associated with younger age of presentation. Although genetic predisposition modestly contributes to earlier ACS, a heavy burden of TRF is associated with markedly earlier ACS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".