The Impact of Empagliflozin on Obstructive Sleep Apnea and Cardiovascular and Renal Outcomes: An Exploratory Analysis of the EMPA-REG OUTCOME Trial
Bibliographic record
Abstract
OBJECTIVE To explore the effects of empagliflozin on the incidence of obstructive sleep apnea (OSA) and its effects on metabolic, cardiovascular (CV), and renal outcomes among participants with or without OSA in the EMPA-REG OUTCOME trial. RESEARCH DESIGN AND METHODS Participants with diabetes and CV disease were randomized to empagliflozin (10 and 25 mg) or placebo daily in addition to standard of care. OSA was assessed by investigator report using Medical Dictionary for Regulatory Activities version 18.0, and CV outcomes were independently adjudicated. Analyses were performed using multivariable-adjusted Cox regression models. RESULTS OSA was reported in 391 of 7,020 (5.6%) participants at baseline. Those with OSA were more likely to be male (83% vs. 71%) and to have moderate to severe obesity (BMI ≥35 kg/m2; 55% vs. 18%). Over a median of 3.1 years, empagliflozin had similar placebo-adjusted reductions in HbA1c, waist circumference, and systolic blood pressure, regardless of OSA status, but a larger effect on weight (adjusted mean ± SE difference at week 52: OSA vs. no OSA −2.9 ± 0.5 vs. −1.9 ± 0.1 kg). Incidence of 3-point major adverse CV events, CV death, heart failure hospitalization, and incident or worsening nephropathy in the placebo group was 1.2- to 2.0-fold higher for those with baseline OSA compared with those without. Empagliflozin significantly reduced the risk for outcomes regardless of OSA status (P-interaction all >0.05). Fifty patients reported a new diagnosis of OSA through 7 days after medication discontinuation, and this occurred less often with empagliflozin treatment (hazard ratio 0.48 [95% CI 0.27, 0.83]). CONCLUSIONS In EMPA-REG OUTCOME, participants with OSA had greater comorbidity and higher frequency of CV and renal events. Empagliflozin had favorable effects on risk factors and CV and renal outcomes regardless of preexisting OSA and may also reduce the risk for new-onset OSA.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| 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 | 0.002 |
| 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".