Evidence map›Paper›PMID 42707964›Full record

ReviewClinical kidney journal2026

Finerenone in Asian patients with diabetic kidney disease: evaluating the evidence for "better efficacy".

Siqian Gong, Linong Ji

Abstract readReview
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Siqian GongDepartment of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China.
Linong JiDepartment of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China.ORCID https://orcid.org/0000-0002-3262-2168

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetic kidney disease (DKD) has emerged as the primary driver of kidney failure across the Asia-Pacific region. Thus, therapeutic interventions capable of effectively slowing the progression of chronic kidney disease (CKD) and reducing concomitant cardiovascular risk are of critical strategic importance. Finerenone, a novel non-steroidal mineralocorticoid receptor antagonist (nsMRA) approved for the treatment of DKD, exerts cardiorenal protective effects by selectively inhibiting the overactivation of the mineralocorticoid receptor (MR)-a key mediator of pathological inflammatory and fibrotic processes underlying renal and cardiac injury. A consistent finding across major clinical trials, including FIDELIO-DKD, FIGARO-DKD, and the pooled FIDELITY analysis, is the "potentially enhanced" (based on post-hoc subgroup analyses) therapeutic efficacy of finerenone in Asian, predominantly Chinese, patient populations. This review synthesizes emerging evidence indicating a distinct, potentially superior therapeutic response (based on available subgroup analyses) in Asian cohorts compared to global populations, alongside potential genetic and environmental factors contributing to this phenomenon. Additionally, we address the associated safety profile of hyperkalemia in Asian patients. We contend that this risk is manageable through structured monitoring and should not preclude clinicians from utilizing this high-impact therapy. Ultimately, this review positions finerenone as a crucial and effective intervention for Asian patients with high cardiorenal risk, provided that potassium homeostasis is proactively managed.

Indexed as

Asian populationdiabetic kidney diseasefinerenonemineralocorticoid receptor antagonisttype 2 diabetes

Identifiers

PMID42707964
PMCPMC13548850

What OpenQuestion holds

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Registered trials

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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.