Evidence map›Paper›PMID 42851802›Full record

ReviewFrontiers in pharmacology2026

SGLT2 inhibitors, semaglutide, and finerenone in diabetic kidney disease: a Bayesian network meta-analysis.

Zongze He, Xu Wang, Li Zhang, Xiangmei Chen

Abstract readReview
In one paragraph

Review in Frontiers in pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Zongze HeSenior Department of Nephrology, Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Medical Devices and Integrated Traditional Chinese and Western Drug Development for Severe Kidney Diseases, Beijing Key Laboratory of Digital Intelligent TCM for the Prevention and Treatment of Pan-vascular Diseases, Beijing, China.
Xu WangSenior Department of Nephrology, Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Medical Devices and Integrated Traditional Chinese and Western Drug Development for Severe Kidney Diseases, Beijing Key Laboratory of Digital Intelligent TCM for the Prevention and Treatment of Pan-vascular Diseases, Beijing, China.
Li ZhangSenior Department of Nephrology, Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Medical Devices and Integrated Traditional Chinese and Western Drug Development for Severe Kidney Diseases, Beijing Key Laboratory of Digital Intelligent TCM for the Prevention and Treatment of Pan-vascular Diseases, Beijing, China.
Xiangmei ChenSenior Department of Nephrology, Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Medical Devices and Integrated Traditional Chinese and Western Drug Development for Severe Kidney Diseases, Beijing Key Laboratory of Digital Intelligent TCM for the Prevention and Treatment of Pan-vascular Diseases, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Despite optimal renin-angiotensin system (RAS) blockade, patients with diabetic kidney disease (DKD) face substantial residual cardiorenal risk. The 2024 FLOW trial established semaglutide as the first GLP-1 receptor agonist with a dedicated primary renal outcome benefit. We conducted a Bayesian network meta-analysis comparing SGLT2 inhibitors, semaglutide, and finerenone-the three therapies with contemporary phase 3 cardiorenal evidence-to define their comparative efficacy and safety hierarchy in type 2 diabetes with chronic kidney disease. Methods: We systematically searched PubMed, Embase, and the Cochrane Library through 31 July 2025 for phase 3 randomized trials reporting kidney, cardiovascular, or safety outcomes. We treated FIDELIO-DKD and FIGARO-DKD as separate evidence nodes to preserve between-trial heterogeneity. For DAPA-CKD and EMPA-KIDNEY, only the pre-specified type 2 diabetes subgroup data were used. Efficacy was synthesized using hazard ratios (HRs) with 95% credible intervals (CrIs); safety was modeled using a binomial likelihood for treatment discontinuation. Treatment hierarchy was quantified by the surface under the cumulative ranking curve (SUCRA). Four pre-specified sensitivity analyses evaluated robustness (±SCORED trial; fixed-vs. random-effects models). Results: Six trial-level datasets (CREDENCE, DAPA-CKD T2D subgroup, EMPA-KIDNEY T2D subgroup, FIDELIO-DKD, FIGARO-DKD, FLOW; N = 20,521) were included in the main analysis. All trials were at low risk of bias. Kidney outcomes: SGLT2 inhibitors demonstrated the greatest risk reduction (HR 0.70, 95% CrI 0.64-0.76; SUCRA 94.5%), followed by semaglutide (HR 0.76, 95% CrI 0.66-0.88; SUCRA 68.0%) and finerenone (HR 0.84, 95% CrI 0.77-0.92; SUCRA 37.5%). The indirect comparison between SGLT2 inhibitors and finerenone reached statistical significance (HR 0.83, 95% CrI 0.73-0.95; posterior probability of SGLT2i superiority >99.9% [fixed effects]; 96% [random effects]). In contrast to prior NMAs that pooled finerenone trials via the FIDELITY estimate (HR 0.77), our approach of separating FIDELIO-DKD and FIGARO-DKD yielded a pooled finerenone HR of 0.84, allowing this indirect detection. This finding is sensitive to the analytic decision to separate the two finerenone trials and should be interpreted as hypothesis-generating. Semaglutide was numerically but not statistically superior to finerenone (HR 0.90, 95% CrI 0.76-1.07; posterior probability = 88%). Cardiovascular outcomes: All three active agents significantly reduced cardiovascular risk versus placebo, with no statistically significant differences between active agents. Robustness: All key findings were stable across four sensitivity analyses; the posterior probability of SGLT2 inhibitor superiority to finerenone for kidney protection remained ≥96% in all specifications. Conclusion: SGLT2 inhibitors provide the greatest kidney protection in this network; when the two finerenone trials are analyzed separately, an indirect statistically significant SGLT2i advantage emerges (HR 0.83, 95% CrI 0.73-0.95 [fixed effects]). This finding is analytic-choice-dependent and hypothesis-generating. Semaglutide provides substantial renal protection that is numerically superior to finerenone. These findings support a four-pillar framework-RAS blockade and SGLT2 inhibition as the foundational combination, with semaglutide and finerenone as complementary phenotype-matched additions-and provide a probabilistic evidence base for individualized prescribing.

Indexed as

Bayesian network meta-analysiscardiorenal protectiondiabetic kidney diseasefinerenoneFLOW trialGLP-1 receptor agonistssemaglutideSGLT2 inhibitors

Identifiers

PMID42851802
PMCPMC13646529

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.