Genetic Predictors of Cardiovascular Mortality During Intensive Glycemic Control in Type 2 Diabetes: Findings From the ACCORD Clinical Trial
Bibliographic record
Abstract
OBJECTIVE: To identify genetic determinants of increased cardiovascular mortality among subjects with type 2 diabetes who underwent intensive glycemic therapy in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. RESEARCH DESIGN AND METHODS: A total of 6.8 million common variants were analyzed for genome-wide association with cardiovascular mortality among 2,667 self-reported white subjects in the ACCORD intensive treatment arm. Significant loci were examined in the entire ACCORD white genetic dataset (n = 5,360) for their modulation of cardiovascular responses to glycemic treatment assignment and in a Joslin Clinic cohort (n = 422) for their interaction with long-term glycemic control on cardiovascular mortality. RESULTS: , respectively). A genetic risk score (GRS) defined by the two variants was a significant modulator of cardiovascular mortality response to treatment assignment in the entire ACCORD white genetic dataset. Participants with GRS = 0 experienced a fourfold reduction in cardiovascular mortality in response to intensive treatment (hazard ratio [HR] 0.24 [95% CI 0.07-0.86]), those with GRS = 1 experienced no difference (HR 0.92 [95% CI 0.54-1.56]), and those with GRS ≥2 experienced a threefold increase (HR 3.08 [95% CI 1.82-5.21]). The modulatory effect of the GRS on the association between glycemic control and cardiovascular mortality was confirmed in the Joslin cohort (P = 0.029). CONCLUSIONS: Two genetic variants predict the cardiovascular effects of intensive glycemic control in ACCORD. Further studies are warranted to determine whether these findings can be translated into new strategies to prevent cardiovascular complications of diabetes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".