Evidence map›Paper›PMID 39539021›Full record

ArticleJournal of diabetes investigation2025

A machine learning model for predicting worsening renal function using one-year time series data in patients with type 2 diabetes.

Mari Watanabe, Shu Meguro, Kaiken Kimura, Michiaki Furukoshi, Tsuyoshi Masuda, Makoto Enomoto, Hiroshi Itoh

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of diabetes investigation, 2025. 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. A nomograph model for predicting the risk of diabetes nephropathy.International urology and nephrology · 2025
    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

7 authors.

Mari WatanabeDivision of Nephrology, Endocrinology and Metabolism, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan.ORCID https://orcid.org/0009-0002-6869-0389
Shu MeguroDivision of Nephrology, Endocrinology and Metabolism, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan.ORCID https://orcid.org/0000-0003-3528-128X
Kaiken KimuraAsahi Kasei Corporation, Tokyo, Japan.
Michiaki FurukoshiAsahi Kasei Corporation, Tokyo, Japan.
Tsuyoshi MasudaAsahi Kasei Corporation, Tokyo, Japan.
Makoto EnomotoAsahi Kasei Corporation, Tokyo, Japan.
Hiroshi ItohDivision of Nephrology, Endocrinology and Metabolism, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan.

Funding

Asahi Kasei Corporation
6 · The paper itself

Abstract

BACKGROUND AND

aimsTo prevent end-stage renal disease caused by diabetic kidney disease, we created a predictive model for high-risk patients using machine learning. METHODS AND

resultsThe reference point was the time at which each patient's estimated glomerular filtration rate (eGFR) first fell below 60 mL/min/1.73 m

conclusionThe accuracy of prognosis prediction can be improved by considering the variable components of urinary protein and eGFR levels. This model will allow us to identify patients whose renal functions are relatively preserved with eGFR of more than 60 mL/min/1.73 m

Indexed as

Diabetes Mellitus, Type 2Diabetic NephropathiesGlomerular Filtration RateMachine LearningAgedDisease ProgressionFemaleHumansKidneyMaleMiddle AgedPrognosisDiabetic nephropathiesMachine learningPrognosis

Identifiers

PMID39539021
PMCPMC11693562

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.