Genetic Risk Factors for CVD in Type 1 Diabetes: The DCCT/EDIC Study
Bibliographic record
Abstract
OBJECTIVE The role of genetic factors in the risk of cardiovascular disease (CVD) for patients with type 1 diabetes (T1D) remains unknown. We therefore examined whether previously identified genetic factors for coronary artery disease (CAD) are associated with the risk of CVD above and beyond established demographic and clinical factors in the Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) study. RESEARCH DESIGN AND METHODS Polygenic risk scores (PRS) and individual genetic variants identified in previous studies were obtained from genome-wide genotyping performed in 1,371 DCCT/EDIC participants. Two composite CVD outcomes were considered: major adverse cardiovascular events (MACE) (CVD death or nonfatal myocardial infarction [MI] or stroke) and any CVD (MACE plus confirmed angina, silent MI, revascularization, or congestive heart failure). Cox proportional hazards models assessed the association between the genetic factors and the risk of CVD with adjustment for other factors (including age, lipids, blood pressure, and glycemia). RESULTS CAD PRS was strongly associated with the subsequent risk of any CVD (42% and 38% higher risk per 1-SD increase in unadjusted and fully adjusted models, respectively; P < 0.0001) and with the risk of MACE (50% and 40% higher risk per 1-SD increase in unadjusted and fully adjusted models, respectively; P < 0.0001). Several individual single nucleotide polymorphisms were also nominally associated with the risk of any CVD and MACE. CONCLUSIONS Genetic factors are associated with the risk of subsequent CVD in individuals with T1D above and beyond the effect of established risk factors such as age, lipids, blood pressure, and glycemia.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".