Contrasting influences of renal function on blood pressure and HbA1c reductions with empagliflozin: Pooled analysis of phase III trials
Bibliographic record
Abstract
Objective: Glucose lowering with empagliflozin is dependent on renal function. We investigated the influence of chronic kidney disease (CKD) on changes in HbA1c and blood pressure (BP) with empagliflozin. Methods: Using pooled data from five, 24-week randomized Phase III trials in patients with type 2 diabetes (T2DM), we assessed changes from baseline in systolic BP (SBP) and HbA1c with empagliflozin 25 mg versus placebo in subgroups by baseline eGFR (MDRD equation; normal renal function [≥90 mL/min/1.73 m 2 ], stage 2 CKD [≥60 to < 90 mL/min/1.73 m 2 ], stage 3 CKD [≥30 to < 60 mL/min/1.73 m 2 ] and stage 4 CKD [< 30 mL/min/1.73 m 2 ]). Results: In patients with normal renal function, or stage 2 or 3 CKD, empagliflozin significantly reduced HbA1c and SBP versus placebo. As expected, placebo-corrected HbA1c reductions with empagliflozin decreased with decreasing eGFR (normal renal function: -0.84% [95% CI -0.95,-0.72]; stage 2 CKD: -0.60% [95% CI -0.70,-0.51]; stage 3 CKD: -0.38% [95% CI -0.52,-0.24]; stage 4 CKD: -0.04% [95% CI -0.37, 0.29]). In contrast, placebo-corrected SBP reductions with empagliflozin appeared to be maintained with decreasing eGFR (normal renal function: -3.2 mmHg [95% CI -4.9,-1.5]; stage 2 CKD: -4.0 mmHg [95% CI -5.4,-2.6]; stage 3 CKD: -5.5 mmHg [95% CI -7.6,-3.4]; stage 4 CKD: -6.6 mmHg [95% CI -11.4,-1.8]). Conclusion: Unlike HbA1c, SBP reductions with empagliflozin in patients with T2DM appeared to be maintained in patients with lower eGFR. SBP modulation with empagliflozin may involve pathways other than urinary glucose excretion such as natriuresis, weight loss, reduced arterial stiffness or direct vascular effects.
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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 | 0.035 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.031 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 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".