407-P: Efficacy of Finerenone in Patients with Abnormal Markers of Liver Steatosis and Fibrosis—A FIDELITY Subgroup Analysis
Bibliographic record
Abstract
Introduction: Finerenone, a selective, nonsteroidal mineralocorticoid receptor antagonist, showed cardiorenal benefits vs placebo in patients with chronic kidney disease and type 2 diabetes. Here, we analyzed patients with liver function abnormalities in FIDELITY, a prespecified pooled dataset from the FIDELIO-DKD and FIGARO-DKD trials. Methods: Patients with urine albumin-to-creatinine ratio ≥30-≤5000 mg/g and estimated glomerular filtration rate (eGFR) ≥25 mL/min/1.73 m2 and type 2 diabetes were analyzed according to elevated transaminases, high risk of steatosis (hepatic steatosis index [HSI] >36), and risk of intermediate to advanced fibrosis (Fibrosis-4 Index [FIB-4] scores >1.30, >2.67, and >3.25). Changes in liver function and in kidney (kidney failure, sustained 57% eGFR decline, or renal death) and cardiovascular (CV; CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure) composite outcomes were assessed. Results: Across 13,026 patients, finerenone reduced the risk of kidney and CV outcomes compared with placebo in patients with elevated transaminases (HR=0.75; 95% CI 0.50-1.13 and HR=0.81; 95% CI 0.62-1.07, respectively), at high risk of steatosis (HR=0.75; 95% CI 0.64-0.88 and HR=0.85; 95% CI 0.77-0.95, respectively), and at high risk of intermediate fibrosis (HR=0.75; 95% CI 0.60-0.93 and HR=0.76; 95% CI 0.67-0.87, respectively), with stronger reductions in the risk of the CV composite outcome at higher FIB-4 scores (HR=0.61; 95% CI 0.41-0.92; p-value for interaction=0.13 and HR=0.48; 95% CI 0.25-0.90; p-value for interaction=0.03 for FIB-4 >2.67 and >3.25, respectively). Liver transaminase levels remained consistent between treatment groups throughout the study. Conclusions: Finerenone demonstrated robust and consistent cardiorenal benefits in patients with abnormal liver function and even more profound CV benefits in patients with higher FIB-4 scores. Disclosure N.Perakakis: Advisory Panel; Bayer Inc., Other Relationship; Novo Nordisk, Novo Nordisk. P.Kolkhof: Employee; BAYER AG. R.Lawatscheck: Employee; Bayer Inc. C.Scott: None. G.Bakris: Advisory Panel; Janssen Pharmaceuticals, Inc., Consultant; AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Other Relationship; Bayer Inc. S.R.Bornstein: None. A.L.Birkenfeld: None. A.Linkermann: None. S.Anker: Advisory Panel; AstraZeneca, Novartis AG, Consultant; Bayer Inc., Boehringer Ingelheim Pharma GmbH&Co.KG, Vifor Pharma Management Ltd., Research Support; Abbott, Vifor Pharma Management Ltd. G.Filippatos: Other Relationship; Boehringer-Ingelheim, Bayer Inc., Amgen Inc., Medtronic, Vifor Pharma Management Ltd., Novartis, Servier Laboratories. B.Pitt: Consultant; Bayer Inc., AstraZeneca, Boehringer Ingelheim International GmbH, Vifor Pharma Management Ltd., Lexicon Pharmaceuticals, Inc., PhaseBio Pharmaceuticals, Inc., KBP Bioscience, Merck & Co., Inc. P.Rossing: Other Relationship; Abbott Diagnostics, AstraZeneca, Bayer Inc., Boehringer Ingelheim Inc., Novo Nordisk, Merck KGaA, Gilead Sciences, Inc., Sanofi. L.M.Ruilope: Advisory Panel; Bayer Inc.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.017 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".