Evidence map›Paper›PMID 40230182›Full record

ArticleDiabetes, obesity & metabolism2025

Chronic kidney disease risk assessment: Findings from backward-looking study using annual health check-up data in Japan.

Chihaya Fukai, Shumpei Chiba, Takaaki Itoga, Gen Kobayashi, Kohei Kaku

Abstract read
In one paragraph

Article in Diabetes, obesity & metabolism, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

5 authors.

Chihaya FukaiEY Strategy and Consulting Co., Ltd, Tokyo, Japan.ORCID 0009-0007-3730-6619
Shumpei ChibaEY Strategy and Consulting Co., Ltd, Tokyo, Japan.ORCID 0009-0006-5335-4576
Takaaki ItogaEY Strategy and Consulting Co., Ltd, Tokyo, Japan.ORCID 0000-0001-7937-925X
Gen KobayashiEY Strategy and Consulting Co., Ltd, Tokyo, Japan.ORCID 0000-0001-7511-9987
Kohei KakuKawasaki Medical School, Okayama, Japan.ORCID 0000-0003-1574-0565

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

AIMS/

introductionWhile studies on kidney disease (KD) in patients with severe metabolic syndrome (MetS) have been reported, research on undiagnosed MetS individuals is limited. This study aimed to investigate KD mechanisms in early MetS stages among Japanese individuals to establish accurate KD prediction models applicable to specific health guidance using annual health check-up (HC) data. MATERIALS AND

methodsCox regression analysis was conducted using the Kokuho Database including HC and claims data over the past 10 years. Survival time was defined as the period from the initial HC during the observation period until estimated glomerular filtration rate (eGFR) fell below the following cut-offs: 60 mL/min/1.73 m

resultsSignificant increases in hazard ratios (HRs) for BMI, HbA1c, TG and SBP were observed for primary and additional cut-offs. BMI, HbA1c and TG showed progressively stronger HR increases with advancing stages. The model for all scenarios demonstrated goodness of fit with the high C-statistics.

conclusionsThis study highlights the necessity of a comprehensive evaluation of MetS factors in CKD risk assessment and shows the model using annual HC data can identify CKD progression effectively and accurately. A risk assessment approach considering multiple CKD stages will be crucial for early intervention and disease prevention strategies.

Indexed as

Metabolic SyndromeRenal Insufficiency, ChronicAdultAgedBlood PressureBody Mass IndexFemaleGlomerular Filtration RateGlycated HemoglobinHumansJapanMaleMiddle AgedProportional Hazards ModelsRisk AssessmentRisk FactorsGlycated Hemoglobinhealth check‐upkidney diseasesKokuho Databasemetabolic syndrome

Identifiers

PMID40230182
PMCPMC12146476

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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.