Cardiovascular outcomes and LDL-cholesterol levels in EMPA-REG OUTCOME <sup>®</sup>
Bibliographic record
Abstract
Objective: It is well established that higher low-density lipoprotein (LDL)-cholesterol levels are associated with increased cardiovascular risk. We analyzed whether effects of empagliflozin on cardiovascular outcomes varied by different LDL-cholesterol levels at baseline in EMPA-REG OUTCOME. Methods: Participants with type 2 diabetes and high cardiovascular risk received empagliflozin (10/25 mg) or placebo in addition to standard of care. We investigated the time to first 3P-MACE, cardiovascular death, hospitalization for heart failure (HHF) and all-cause mortality for empagliflozin versus placebo between baseline LDL-cholesterol categories <1.8, 1.8–<2.2, 2.2– <2.6, 2.6–3.0, and > 3.0 mmol/L, by a Cox regression including the interaction of baseline LDL-cholesterol category and treatment. Results: Of the 7020 participants randomized and treated, 81.0% received lipid lowering therapy (77.0% statins). Mean ± SD LDL-cholesterol was 2.2 ± 0.9 mmol/L, and 38%/18%, had LDL-cholesterol <1.8/>3.0 mmol/L. Age, BMI, and HbA1c levels were balanced between the LDL-cholesterol subgroups, but those in the lowest versus highest group, had more coronary artery disease (83.0% vs 59.9%) and statin treatment (88.2% vs 50.9%). Empagliflozin consistently reduced all outcomes across LDL-cholesterol categories (all interaction p-values > 0.05). Conclusion: The beneficial cardiovascular effects of empagliflozin was consistent across higher and lower LDL-cholesterol levels at baseline.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".