Hypoglycemia, Cardiovascular Outcomes, and Death: The LEADER Experience
Bibliographic record
Abstract
OBJECTIVE In the Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results (LEADER) cardiovascular (CV) outcomes trial (NCT01179048), liraglutide significantly reduced the risk of CV events (by 13%) and hypoglycemia versus placebo. This post hoc analysis examines the associations between hypoglycemia and CV outcomes and death. RESEARCH DESIGN AND METHODS Patients with type 2 diabetes and high risk for CV disease (n = 9,340) were randomized 1:1 to liraglutide or placebo, both in addition to standard treatment, and followed for 3.5–5 years. The primary end point was time to first major adverse cardiovascular event (MACE) (1,302 first events recorded), and secondary end points included incidence of hypoglycemia. We used Cox regression to analyze time to first MACE, CV death, non-CV death, or all-cause death with hypoglycemia as a factor or time-dependent covariate. RESULTS A total of 267 patients experienced severe hypoglycemia (liraglutide n = 114, placebo n = 153; rate ratio 0.69; 95% CI 0.51, 0.93). These patients had longer diabetes duration, higher incidence of heart failure and kidney disease, and used insulin more frequently at baseline than those without severe hypoglycemia. In combined analysis (liraglutide and placebo), patients with severe hypoglycemia were more likely to experience MACE, CV death, and all-cause death, with higher risk shortly after hypoglycemia. The impact of liraglutide on risk of MACE was similar in patients with and without severe hypoglycemia (P-interaction = 0.90). CONCLUSIONS Patients experiencing severe hypoglycemia were at greater risk of CV events and death, particularly shortly after the hypoglycemic episode. While causality remains unclear, reducing hypoglycemia remains an important goal in diabetes management.
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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".