Abstract 15052: Cardiovascular Event Reduction Versus New-Onset Diabetes During Atorvastatin Therapy: Effect of Baseline Risk Factors for Diabetes
Bibliographic record
Abstract
Background: Statins reduce cardiovascular (CV) events but increase the incidence of new-onset diabetes (NOD). We previously reported that in each of 3 large randomized trials using atorvastatin 80 mg/day, 4 risk factors were independent predictors of NOD: baseline fasting glucose >100 mg/dl, fasting triglycerides >150 mg/dl, body-mass index >30 kg/m 2 , and a history of hypertension. We sought to compare CV event reduction and NOD in 15,056 non-diabetic patients with coronary disease in the TNT (n=7,595) and IDEAL (n=7,461) trials. Methods and Results: Patients were randomized to atorvastatin 80 vs 10 mg/day (TNT) or atorvastatin 80 vs simvastatin 20-40 mg/day (IDEAL) and followed for a median of 5 years in both trials. CV events included coronary heart disease death, myocardial infarction, stroke and resuscitated cardiac arrest. High-dose atorvastatin was associated with an increased odds of NOD among participants with 2-4 risk factors (OR 1.24, 95% CI 1.08-1.42, P=0.003), but not in those with 0-1 risk factors for NOD (OR 0.97, 95% CI 0.77-1.22, P=0.77), when compared with low-dose atorvastatin or simvastatin ( Figure ). In contrast, high-dose atorvastatin was associated with a decreased odds of CV events regardless of the number of NOD risk factors present ( Figure ). There was a suggestive interaction between treatment and risk factor group in the prediction of NOD (P=0.07). Conclusion: Compared to low-dose statin therapy, treatment with atorvastatin 80 mg/day was not associated with an increased risk of NOD in patients with 0-1 NOD risk factors. Among patients with 2-4 NOD risk factors, atorvastatin 80 mg was associated with a 24% increase in NOD. CV events were significantly reduced with atorvastatin 80 mg regardless of NOD risk factors.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".