Abstract 5090: Impact of Adding a Single Allele in the 9p21 Locus to Traditional Risk Factors on Risk Classification for Coronary Heart Disease and Implications for Lipid-Modifying Therapy in the White Population of the Atherosclerosis Risk in Communities (ARIC) Study
Bibliographic record
Abstract
Aim: A single nucleotide polymorphism on chromosome 9p21, rs10757274 (9p21 allele), has been shown to be a predictor of coronary heart disease (CHD) in whites. We evaluated if the addition of the 9p21 allele to traditional risk factors (TRF) improved CHD risk prediction in the white population of the Atherosclerosis Risk in Communities (ARIC) study, and whether changes in risk prediction will modify lipid therapy recommendation. Methods: Whites (n=10,004) in the ARIC study for whom the 9p21 genotype and TRF (age, gender, systolic blood pressure, total cholesterol, smoking, diabetes, HDL-C, and anti-hypertensive medication use) information was available were included. Using Cox proportional hazards models, the ARIC Cardiovascular Risk Score (ACRS) which is based on TRF was determined. The impact of adding the 9p21 allele to TRF with respect to the area under the curve (AUC) of a receiver operating characteristic (ROC) curve and then risk strata reclassification was determined. Results: The addition of 9p21 allele to TRF was associated with a hazard ratio (HR) of 1.25 (p<0.0001) and an increase in the AUC for incident CHD from 0.776 to 0.780 (Δ= 0.004, 95% CI=0.001, 0.008). The 9p21 allele’s greatest influence to the ACRS (Table ) was observed in the intermediate (5–10% 10-year CHD risk) and intermediate-high (10 –20% 10-year CHD risk) categories with 19.3% and 16.9% reclassified, respectively, which would impact therapy, as approximately 90% of these individuals had LDL-C >100 mg/dL. Table: Reclassification in the different ACRS categories after the addition of the 9p21 allele to the traditional risk factors
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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 | 0.001 | 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 | 0.000 |
| 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".