Evidence map›Paper›PMID 40716954›Full record

ArticleBMJ open diabetes research & care2025

Influence of metabolism-related comorbidities and insulin resistance on new onset of chronic kidney disease in a health check-up population: a two-stage retrospective cohort study.

Guang Yang, Bokai Cheng, Xin Shen, Ying Ding, Yang Zhang, Qingli Cheng, Yansong Zheng, Jiahui Zhao

Abstract read
In one paragraph

Article in BMJ open diabetes research & care, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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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

4 citing papers in PubMed.

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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

8 authors.

Guang Yang *Department of Geriatric Nephrology, General Hospital of the People's Liberation Army, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Beijing, Beijing, China.ORCID http://orcid.org/0000-0001-5075-401X
Bokai Cheng *Department of Geriatric Nephrology, General Hospital of the People's Liberation Army, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Beijing, Beijing, China.ORCID http://orcid.org/0000-0003-3510-5163
Xin ShenDepartment of Geriatric Nephrology, General Hospital of the People's Liberation Army, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Beijing, Beijing, China.
Ying DingDepartment of Geriatric Nephrology, General Hospital of the People's Liberation Army, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Beijing, Beijing, China.
Yang ZhangDepartment of Geriatric Nephrology, General Hospital of the People's Liberation Army, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Beijing, Beijing, China.
Qingli ChengDepartment of Geriatric Nephrology, General Hospital of the People's Liberation Army, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Beijing, Beijing, China.
Yansong ZhengHealth Management Institute, General Hospital of the People's Liberation Army, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Beijing, Beijing, China zhaojiahui@301hospital.com.cn zhengyansong@301hospital.com.cn.
Jiahui ZhaoDepartment of Geriatric Nephrology, General Hospital of the People's Liberation Army, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Beijing, Beijing, China zhaojiahui@301hospital.com.cn zhengyansong@301hospital.com.cn.ORCID http://orcid.org/0000-0001-6711-5759

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionLimited research has focused on the prospective influence of insulin resistance (IR) on new-onset chronic kidney disease (CKD) in healthy screening populations. Therefore, we aimed to investigate how IR, assessed via the estimated glucose disposal rate (eGDR), and metabolism-related comorbidities influence new-onset CKD. RESEARCH DESIGN AND

methodsThis two-stage retrospective cohort study (cross-sectional and longitudinal analyses) used data from health check-up participants at the Chinese People's Liberation Army General Hospital (2009-2021). The cross-sectional analysis included 83 346 participants with or without CKD; the longitudinal analyses included 13 738 participants without prior CKD who visited the hospital at least two times. The cross-sectional phase of this study analyzed the relationship between IR and CKD; the longitudinal phase analyzed the relationship between IR and new-onset CKD. The mediating role of metabolism-related comorbidities was also explored.

resultsIn the cross-sectional analysis, 6.77% (n=5643) of patients had prior CKD. The eGDR was significantly higher in the non-CKD group than in the CKD group (9.16±2.11 vs 7.19±2.32, p<0.001). Higher eGDR was associated with lower CKD prevalence (OR: 0.91, 95% CI: 0.89 to 0.93, P for trend<0.001). In the cohort analysis, the average time to trigger endpoint events was 2.95±2.02 years, with 403 (2.93%) new-onset CKD cases reported. A linear correlation was observed between eGDR and new-onset CKD (p<0.001), with higher eGDR linked to reduced CKD risk (HR: 0.88, 95% CI: 0.82 to 0.96, P for trend=0.002). Mediation analysis revealed significant indirect effects of diabetes mellitus (17.1%), systolic blood pressure (22.0%), glycated hemoglobin (11.1%), and brachial-ankle pulse wave velocity (9.7%) (all p<0.05).

conclusionsIR is independently linked to new-onset CKD, with blood glucose, blood pressure, and arterial stiffness mediating this relationship. These findings underscore the importance of managing IR and metabolic comorbidities to prevent CKD onset in at-risk populations.

Indexed as

BiomarkersInsulin ResistanceRenal Insufficiency, ChronicAdultAgedBlood GlucoseChinaComorbidityCross-Sectional StudiesFemaleFollow-Up StudiesHumansLongitudinal StudiesMaleMiddle AgedPrognosisBiomarkersBlood GlucoseComorbidityInsulin ResistanceKidney DiseasesRisk Assessment

Identifiers

PMID40716954
PMCPMC12306241

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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.