Randomised study of evolocumab in patients with type 2 diabetes and dyslipidaemia on background statin: Primary results of the BERSON clinical trial
Bibliographic record
Abstract
AIM: To evaluate the lipid-lowering efficacy and safety of evolocumab combined with background atorvastatin in patients with type 2 diabetes mellitus (T2DM) and hyperlipidaemia or mixed dyslipidaemia. MATERIALS AND METHODS: BERSON was a double-blind, 12-week, phase 3 study (NCT02662569) conducted in 10 countries. Patients ≥18 to ≤80 years with type T2DM received atorvastatin 20 mg/d and were randomised 2:2:1:1 to evolocumab 140 mg every 2 weeks (Q2W) or 420 mg monthly (QM) or placebo Q2W or QM. Co-primary endpoints were the percentage change in low-density lipoprotein cholesterol (LDL-C) from baseline to week 12 and from baseline to the mean of weeks 10 and 12. Additional endpoints included atherogenic lipids, glycaemic measures, and adverse events (AEs). RESULTS: Overall, 981 patients were randomised and received ≥1 dose of study drug. Evolocumab significantly reduced LDL-C versus placebo at week 12 (Q2W, -71.8%; QM, -74.9%) and at the mean of weeks 10 and 12 (Q2W, -70.3%; QM, -70.0%; adjusted P < 0.0001 for all) when administered with atorvastatin. Non-high-density lipoprotein cholesterol, apolipoprotein B100, total cholesterol, lipoprotein (a), triglycerides, high-density lipoprotein cholesterol, and very low-density lipoprotein cholesterol improved significantly with evolocumab versus placebo. The overall incidence of AEs was similar between evolocumab and placebo-treated patients, and there were no clinically meaningful differences in changes over time in glycaemic variables (fasting serum glucose and HbA1c) between the two groups. CONCLUSIONS: In patients with T2DM and hyperlipidaemia or mixed dyslipidaemia on statin, evolocumab significantly reduced LDL-C and other atherogenic lipids, was well tolerated, and had no notable impact on glycaemic measures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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) | 0.007 | 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".