Evidence map›Paper›PMID 41247492›Full record

ArticleDiabetologia2026

Diverse combination of factors associated with the development of diabetic kidney disease among data-driven diabetes subtypes: analysis of the J-DREAMS registry.

Kiriko Watanabe-Shimoji, Hayato Tanabe, Mitsuru Ohsugi, Eiryo Kawakami, Kenichi Tanaka, Junichiro J Kazama, Kohjiro Ueki, Michio Shimabukuro, J-DREAMS Investigators

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diabetologia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

9 authors.

Kiriko Watanabe-ShimojiDepartment of Diabetes, Endocrinology, and Metabolism, Fukushima Medical University, Fukushima, Japan.
Hayato TanabeDepartment of Diabetes, Endocrinology, and Metabolism, Fukushima Medical University, Fukushima, Japan.
Mitsuru OhsugiDepartment of Diabetes, Endocrinology and Metabolism, Japan Institute for Health Security, Tokyo, Japan.
Eiryo KawakamiDepartment of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan.
Kenichi TanakaDepartment of Nephrology and Hypertension, Fukushima Medical University, Fukushima, Japan.
Junichiro J KazamaDepartment of Nephrology and Hypertension, Fukushima Medical University, Fukushima, Japan.
Kohjiro UekiDepartment of Diabetes, Endocrinology and Metabolism, Japan Institute for Health Security, Tokyo, Japan.
Michio ShimabukuroDepartment of Diabetes, Endocrinology, and Metabolism, Fukushima Medical University, Fukushima, Japan. mshimabukuro-ur@umin.ac.jp.
J-DREAMS Investigators

Funding

Japan Agency for Medical Research and Development 965304Japan Society for the Promotion of Science 22K11729Japan Society for the Promotion of Science 23K15397Japan Society for the Promotion of Science JP16K01823
6 · The paper itself

Abstract

aims/hypothesisAvailable methods for predicting the onset and progression of diabetic kidney disease (DKD) and end-stage kidney disease (ESKD) are not yet ready for clinical application. We used a Japanese diabetes cohort study (J-DREAMS) to examine whether the Ahlqvist et al diabetes clustering is useful for stratifying DKD or ESKD outcomes independent of known risk factors in real-world settings.

methodsData-driven cluster analysis using k-means was performed based on GAD antibody levels, age at diagnosis, BMI, HbA

resultsDiabetes clustering classified individuals in the J-DREAMS cohort into five subtypes, the clinical characteristics of which were comparable to those of the previously reported five subtypes. Kaplan-Meier curve analysis showed that events for chronic kidney disease (CKD) stages 3b, 4 and 5 were highest in the severe insulin-resistant diabetes subtype. The Cox proportional hazards model showed that the severe insulin-resistant diabetes subtype had significant HRs after correction for multiple confounding factors. The Cox proportional hazards model showed that each subtype had a diverse combination of factors associated with CKD stage 3b and proteinuria events. CONCLUSIONS/

interpretationData-driven analysis provides diabetes subtyping, which can predict the probability of developing DKD/ESKD; each subtype has diverse combinations of factors predisposing to DKD development and progression. Data-driven diabetes subtyping to predict the likelihood of developing DKD/ESKD and mitigating predisposing factors may help personalise prevention strategies.

Indexed as

Diabetes Mellitus, Type 1Diabetes Mellitus, Type 2Diabetic NephropathiesAdultAgedCluster AnalysisCohort StudiesDisease ProgressionFemaleHumansInsulin ResistanceKaplan-Meier EstimateKidney Failure, ChronicMaleMiddle AgedProportional Hazards ModelsClusteringDiabetes subtypesDiabetic kidney diseaseEnd-stage kidney diseaseMachine learningType 2 diabetes

Identifiers

PMID41247492
PMCPMC12957631

What Socratic holds

Textmetadata
LicenceCC BY
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.