Short‐Term Changes in Albuminuria and Risk of Cardiovascular and Renal Outcomes in Type 2 Diabetes Mellitus: A Post Hoc Analysis of the EMPA‐REG OUTCOME Trial
Bibliographic record
Abstract
Background Early reduction in albuminuria with an SGLT2 (sodium‐glucose cotransporter 2) inhibitor may be a positive indicator of long‐term cardiovascular and renal benefits. We assessed changes in albuminuria during the first 12 weeks of treatment and subsequent long‐term cardiovascular and renal risks associated with the SGLT2 inhibitor, empagliflozin, in the EMPA‐REG OUTCOME (Empagliflozin Cardiovascular Outcome Event Trial in Type 2 diabetes Mellitus Patients) trial. Methods and Results We calculated the percentage urinary albumin creatinine ratio (UACR) change from baseline to week 12 in 6820 participants who did not experience a cardiovascular outcome (including 3‐point major cardiovascular events and cardiovascular death or hospitalization for heart failure) or renal outcome (defined as 40% decline in estimated glomerular filtration rate from baseline, estimated glomerular filtration rate <15 mL/min per 1.73 m 2 , need for continuous renal‐replacement therapy, or renal death) during the first 12 weeks. Multivariable Cox regression models were used to estimate the hazard ratio (HR) for each 30% reduction in UACR with outcomes. Empagliflozin reduced UACR by 18% (95% CI, 14–22) at week 12 compared with placebo, and increased the likelihood of a >30% reduction in UACR (odds ratio, 1.42; 95% CI, 1.27–1.58; P <0.001). During 3.0 years of follow‐up, 704 major cardiovascular events, 440 cardiovascular deaths/hospitalizations for heart failure, and 168 renal outcomes were observed. Each 30% decrease in UACR during the first 12 weeks was statistically significantly associated with a lower hazard for major cardiovascular events (HR, 0.96; 95% CI, 0.93–0.99; P =0.012), cardiovascular deaths/hospitalizations for heart failure (HR, 0.94; 95% CI, 0.91–0.98; P =0.003), and renal outcomes (HR, 0.83; 95% CI, 0.78–0.89; P <0.001). Conclusions Short‐term reduction in UACR was more common with empagliflozin and was statistically significantly associated with a decreased risk of long‐term cardiovascular and renal outcomes. Registration URL: https://www.clinicaltrials.gov . Unique identifier: NCT01131676.
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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".