Evidence map›Paper›PMID 39849979›Full record

ArticleDiabetes & metabolism journal2025

Revealing VCAN as a Potential Common Diagnostic Biomarker of Renal Tubules and Glomerulus in Diabetic Kidney Disease Based on Machine Learning, Single-Cell Transcriptome Analysis and Mendelian Randomization.

Li Jiang, Jie Jian, Xulin Sai, Xiai Wu

Abstract read
In one paragraph

Article in Diabetes & metabolism journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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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

3 citing papers in PubMed.

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4 · The record

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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

4 authors.

Li Jiang *Diabetes Department of Integrated Chinese and Western Medicine, China National Center for Integrated Traditional Chinese and Western Medicine, China- Japan Friendship Hospital, Beijing, China.
Jie Jian *Mental Health Center of Dongcheng District, Beijing, China.
Xulin SaiDongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, China.
Xiai WuDiabetes Department of Integrated Chinese and Western Medicine, China National Center for Integrated Traditional Chinese and Western Medicine, China- Japan Friendship Hospital, Beijing, China.

Funding

National High Level Hospital Clinical Research Funding 2022-NHLHCRF-LX-02-0101National Natural Science Foundation of China 82004357
6 · The paper itself

Abstract

backgruoundDiabetic kidney disease (DKD) is recognized as a significant complication of diabetes mellitus and categorized into glomerular DKDs and tubular DKDs, each governed by distinct pathological mechanisms and biomarkers.

methodsThrough the identification of common features observed in glomerular and tubular lesions in DKD, numerous differentially expressed gene were identified by the machine learning, single-cell transcriptome and mendelian randomization.

resultsThe diagnostic markers versican (VCAN) was identified, offering supplementary options for clinical diagnosis. VCAN significantly highly expressed in glomerular parietal epithelial cell and proximal convoluted tubular cell. It was mainly involved in the up-regulation of immune genes and infiltration of immune cells like mast cell. Mendelian randomization analysis confirmed that serum VCAN protein levels were a risky factor for DKD, while there was no reverse association. It exhibited the good diagnostic potential for estimated glomerular filtration rate and proteinuria in DKD.

conclusionVCAN showed the prospects into DKD pathology and clinical indicator.

Indexed as

Diabetic NephropathiesKidney GlomerulusKidney TubulesVersicansBiomarkersFemaleGene Expression ProfilingHumansMachine LearningMaleMendelian Randomization AnalysisMiddle AgedSingle-Cell AnalysisSingle-Cell Gene Expression AnalysisTranscriptomeBiomarkersVersicansDiabetic nephropathiesKidney glomerulusKidney tubulesMachine learningMendelian randomization analysisSingle-cell gene expression analysisVCAN protein, human

Identifiers

PMID39849979
PMCPMC12086553

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