Evidence map›Paper›PMID 42761566›Full record

ArticleiScience2026

AI-assisted metabolomic profiling identifies candidate metabolites associated with diabetic kidney disease staging in a Korean cohort.

Inha Jung, Sungjin Park, Moongi Ji, So Young Park, Da Young Lee, Ji Hee Yu, Ji A Seo, Man-Jeong Paik, Hyeong Kyu Park, Soon Hyo Kwon and 2 more

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In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

12 authors.

Inha JungDivision of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea.
Sungjin ParkInstitute of Human Behavior and Genetics, Korea University College of Medicine, Seoul, Republic of Korea.
Moongi JiCollege of Pharmacy, Sunchon National University, Suncheon, Republic of Korea.
So Young ParkDivision of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea.
Da Young LeeDivision of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea.
Ji Hee YuDivision of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea.
Ji A SeoDivision of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea.
Man-Jeong PaikCollege of Pharmacy, Sunchon National University, Suncheon, Republic of Korea.
Hyeong Kyu ParkDivision of Endocrinology and Metabolism, Soonchunhyang University Seoul Hospital, Yongsan-gu, Seoul, Republic of Korea.
Soon Hyo KwonDivision of Nephrology, Hyonam Kidney Laboratory, Soonchunhyang University Seoul Hospital, Yongsan-gu, Seoul, Republic of Korea.
Dae Ho LeeDepartment of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon, Republic of Korea.
Nan Hee KimDivision of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetic kidney disease (DKD) is commonly staged using albumin-to-creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR), yet complementary molecular markers are needed. We profiled urine and serum metabolites from 92 Korean participants (72 with type 2 diabetes and 20 healthy controls) using gas chromatography-tandem mass spectrometry (GC-MS/MS) and liquid chromatography-tandem mass spectrometry (LC-MS/MS). An exploratory restricted Boltzmann machine framework compared five DKD staging criteria and prioritized candidate metabolites; robustness was assessed against LASSO, linear support vector machine (SVM), and random forest using nested cross-validation. ACR-based staging showed the highest metabolomic discrimination. Urinary adenosine and 5'-methylthioadenosine decreased, whereas serum N2,N2-dimethylguanosine and

Indexed as

adenosine-NT5E axisartificial intelligencediabetic kidney diseasemetabolomicsmulti-omics integration

Identifiers

PMID42761566
PMCPMC13586821

What OpenQuestion holds

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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.