Cardiovascular Outcomes and Efficacy of the PCSK9 Inhibitor Evolocumab in Individuals With Type 1 Diabetes: Insights From the FOURIER Trial
Bibliographic record
Abstract
OBJECTIVE: To evaluate the clinical efficacy of intensive LDL cholesterol (LDL-C) lowering in type 1 diabetes mellitus (T1DM). RESEARCH DESIGN AND METHODS: Further Cardiovascular Outcomes Research With PCSK9 Inhibition in Subjects With Elevated Risk (FOURIER) randomized participants with atherosclerotic cardiovascular disease (ASCVD) on statins to evolocumab or placebo (median follow-up 2.2 years). The primary end point (PEP) was cardiovascular death, myocardial infarction, stroke, hospitalization for unstable angina, or coronary revascularization. RESULTS: Of 27,564 participants, 10,834 (39.3%) had type 2 diabetes mellitus (T2DM), and 197 (0.7%) had T1DM. In the placebo arm, there was a stepwise increase in the 2.5-year PEP Kaplan-Meier rate from 11.0% to 15.2% to 20.4% in participants with no diabetes, T2DM, and T1DM, respectively (P < 0.0001). Hazard ratios for PEP with evolocumab were 0.87 (95% CI 0.79-0.96), 0.84 (0.75-0.93), and 0.66 (0.32-1.38) in the no diabetes, T2DM, and T1DM groups, and absolute risk reduction was 1.3%, 2.5%, and 7.3%, respectively. CONCLUSIONS: Intensive LDL-C lowering may provide substantial clinical benefit in individuals with T1DM and ASCVD. Additional randomized controlled cardiovascular outcomes trials are needed in this population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".