Evidence map›Paper›PMID 42362828›Full record

ReviewRheumatology international2026

Gouty kidney disease: a tubulointerstitial paradigm reimagined-early detection, precision stratification, and targeted intervention.

Li-Min Liu, Li Qin, Feng Shao

Abstract readReview
PubMed Publisher
In one paragraph

Review in Rheumatology international, 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

3 authors.

Li-Min LiuDepartment of Rheumatology and Immunology, Huzhou Third Municipal Hospital, 2088 East Tiaoxi Road, Wuxing District, Huzhou, 313000, Zhejiang, China. 380693081@qq.com.ORCID http://orcid.org/0009-0009-8936-9114
Li QinDepartment of Rheumatology and Immunology, Huzhou Third Municipal Hospital, 2088 East Tiaoxi Road, Wuxing District, Huzhou, 313000, Zhejiang, China.ORCID http://orcid.org/0009-0004-3622-3916
Feng ShaoDepartment of Rheumatology and Immunology, Huzhou Third Municipal Hospital, 2088 East Tiaoxi Road, Wuxing District, Huzhou, 313000, Zhejiang, China.ORCID http://orcid.org/0009-0001-7700-841X

Funding

Huzhou Municipal Science and Technology Bureau 2025GY099
6 · The paper itself

Abstract

Gouty kidney disease is a subtype of chronic kidney disease (CKD) featuring tubulointerstitial injury caused by urate crystal deposition. Conventional assessments using eGFR and albuminuria cannot detect early tubular damage. Risk stratification tools specific to this condition are lacking. We performed a comprehensive literature search from January 2000 to June 2026 across PubMed, Scopus, Web of Science, DOAJ, CNKI, and Wanfang Data. Keywords included gouty kidney disease, tubular biomarkers, SGLT2 inhibitors, and risk stratification. Renal resistive index (RI), urinary α1-microglobulin, and eGFR slope show potential for identifying early tubular injury. SGLT2 inhibitors provide renoprotective and mild urate-lowering effects relevant to gout-related CKD. The Tubular Risk Stratification (TRS) system is proposed as an exploratory, unvalidated conceptual model. A tubule-centred strategy may improve early detection and management of gout-related renal injury. If validated, the TRS system could support risk-stratified clinical care.

Indexed as

GoutRenal Insufficiency, ChronicBiomarkersEarly DiagnosisGlomerular Filtration RateHumansRisk AssessmentSodium-Glucose Transporter 2 InhibitorsBiomarkersSodium-Glucose Transporter 2 InhibitorsMeSH: gouty kidney diseaseResistive indexRisk stratificationSGLT2 inhibitorsTubular biomarkersTubulointerstitial fibrosis

Identifiers

PMID42362828

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.