16-LB: Effects of Finerenone in Patients with CKD and T2D Are Independent of HbA1c at Baseline, HbA1c Variability, and Duration of Diabetes
Bibliographic record
Abstract
Introduction: Finerenone reduced the risk of cardiovascular (CV) and kidney outcomes, without affecting HbA1c, in CKD and T2D patients in the FIDELITY prespecified pooled analysis of the FIDELIO-DKD and FIGARO-DKD studies. Here, we evaluate the effect of finerenone by baseline HbA1c, HbA1c variability, and diabetes duration. Methods: Patients with T2D and CKD (UACR ≥30-≤5000 mg/g and eGFR ≥25 mL/min/1.73 m2) were randomized to finerenone or placebo. Effects of finerenone vs. placebo on CV (CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure) and kidney (kidney failure, sustained ≥57% eGFR decline from baseline, or renal death) composite outcomes were analyzed by baseline HbA1c quartiles, HbA1c variability (first year of treatment) , and diabetes duration quartiles. Results: In 13,026 patients included in the analysis, mean baseline HbA1c was 7.7% and diabetes duration was 15.4 years. Higher baseline HbA1c quartiles had longer diabetes duration and more diabetes-related complications. Risk reductions in the CV and kidney composite outcomes with finerenone vs. placebo were consistent across HbA1c (p-interaction 0.52 and 0.09, respectively) and diabetes duration (p-interaction 0.12 and 0.75) quartiles. HbA1c variability in the first year of treatment was associated with higher cardiorenal risks; each 1 unit increase in mean absolute residual of HbA1c was associated with a 20% increased risk of a CV event (HR 1.20; 95% CI 1.07-1.35; p=0.0016) and a 36% increased risk of a kidney event (HR 1.36; 95% CI 1.21-1.52; p<0.001) . The CV and kidney benefits of finerenone were not modified by HbA1c variability (p-interaction 0.48 and 0.09, respectively) . Conclusion: Greater variability in HbA1c was associated with increased risks of cardiorenal outcomes. Risk reductions in the CV and kidney outcomes with finerenone in patients with CKD and T2D were not modified by baseline HbA1c, HbA1c variability, or duration of diabetes. Disclosure J. B. Mcgill: Advisory Panel; Gilead Sciences, Inc., Lilly Diabetes, MannKind Corporation, Novo Nordisk A/S, Provention Bio, Inc., Salix Pharmaceuticals, Consultant; Bayer AG, Boehringer Ingelheim International GmbH, Research Support; Dexcom, Inc., Novo Nordisk. M. Brinker: Employee; Bayer AG. A. Joseph: Employee; Bayer AG. A. Z. Lage: None. R. Lawatscheck: None. C. Scott: Employee; Bayer AG. P. Rossing: Consultant; Astellas Pharma Inc., AstraZeneca, Bayer AG, Gilead Sciences, Inc., Merck Sharp & Dohme Corp., Novo Nordisk A/S, Sanofi, Speaker’s Bureau; Eli Lilly and Company. R. Agarwal: Advisory Panel; Akebia Therapeutics, Inc., Bayer AG, Board Member; Chinook Therapeutics Inc., DiaMedica Therapeutics, Inc., Vertex Pharmaceuticals Incorporated, Consultant; Boehringer Ingelheim International GmbH, Reata Pharmaceuticals, Inc., Vifor Pharma Management Ltd. S. Anker: Consultant; Bayer AG, Boehringer Ingelheim International GmbH, Novartis AG, Novo Nordisk, Vifor Pharma Management Ltd., Research Support; Abbott. G. Bakris: Consultant; Alnylam Pharmaceuticals, Inc., AstraZeneca, DiaMedica Therapeutics, Inc., Horizon Therapeutics plc, Ionis Pharmaceuticals, Merck & Co., Inc., Other Relationship; Novo Nordisk. G. Filippatos: Other Relationship; Amgen Inc., Amgen Inc., Bayer AG, Boehringer Ingelheim International GmbH, Medtronic, Novartis AG, Servier Laboratories, Vifor Pharma Management Ltd. B. Pitt: Advisory Panel; Merck & Co., Inc., Consultant; AstraZeneca, Bayer AG, Boehringer Ingelheim International GmbH, Lexicon Pharmaceuticals, Inc., Vifor Pharma Management Ltd. L. M. Ruilope: Consultant; Bayer AG. A. L. Birkenfeld: None. L. Caramori: Advisory Panel; Bayer AG, Consultant; AstraZeneca, Boehringer Ingelheim International GmbH, Novo Nordisk, Research Support; Bayer AG, Novartis AG.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".