Evidence map›Paper›PMID 40470717›Full record

ArticleJournal of diabetes investigation2025

Tracing the molecular landscape of diabetic nephropathy: Insights from machine learning and experiment verification.

Shahad W Kattan, Ahmed M Basri, Mohammad H Alhashmi, Amany I Almars, Reem Hasaballah Alhasani, Ifat Alsharif, Ikhlas A Sindi, Ahmad H Mufti, Iman S Abumansour, Nasser A Elhawary and 2 more

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. Review
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.

Shahad W KattanMedical Laboratory Department, College of Applied Medical Sciences, Taibah University, Yanbu, Saudi Arabia.
Ahmed M BasriDepartment of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia.
Mohammad H AlhashmiDepartment of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia.
Amany I AlmarsDepartment of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia.
Reem Hasaballah AlhasaniDepartment of Biology, Faculty of Science, Umm Al-Qura University, Makkah, Saudi Arabia.
Ifat AlsharifDepartment of Biology, Faculty of Science, Umm Al-Qura University, Makkah, Saudi Arabia.
Ikhlas A SindiDepartment of Biotechnology, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia.
Ahmad H MuftiDepartment of Medical Genetics, College of Medicine, Umm Al-Qura University, Mecca, Saudi Arabia.
Iman S AbumansourDepartment of Medical Genetics, College of Medicine, Umm Al-Qura University, Mecca, Saudi Arabia.
Nasser A ElhawaryDepartment of Medical Genetics, College of Medicine, Umm Al-Qura University, Mecca, Saudi Arabia.
Aishah Abdullah QahtaniDepartment of Biology, College of Science, King Khalid University, Abha, Saudi Arabia.
Hailah M AlmohaimeedDepartment of Basic Science, College of Medicine, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.ORCID https://orcid.org/0009-0004-0986-8831

Funding

Deanship of Scientific Research, Princess Nourah Bint Abdulrahman University PNURSP2025R213
6 · The paper itself

Abstract

objectiveDiabetes is a chronic disease resulting from insufficient insulin secretion or impaired insulin function. Diabetic nephropathy (DN) is one of the most common complications of diabetes and a leading cause of end-stage renal disease. Early diagnosis of DN is crucial for timely intervention and effective disease management.

methodsGene expression profiles GSE142025 and GSE220226 were retrieved from the GEO database and combined into a metadata cohort, while GSE189007 was obtained as an independent validation dataset. Differentially expressed genes (DEGs) were identified in 46 glomerular samples from DN patients and 31 control samples. Gene Ontology (GO) and Disease Ontology (DO) enrichment analyses, gene set enrichment analysis (GSEA), least absolute shrinkage and selection operator (LASSO) regression, support vector machine-recursive feature elimination (SVM-RFE) analysis, and area under the curve (AUC) calculations were performed.

resultsA total of 109 DEGs were identified. Among them, DUSP1, EGR1, FPR1, G6PC, GDF15, LOX, LPL, PRKAR2B, PTGDS, and TPPP3 were selected as potential diagnostic biomarkers for DN. These biomarkers exhibited a positive correlation with immune cell infiltration. Experimental validation identified LOX as the most promising novel diagnostic biomarker for DN. This study provides new insights into the early diagnosis, pathogenesis, and molecular mechanisms of DN.

Indexed as

BiomarkersDiabetic NephropathiesMachine LearningTranscriptomeCase-Control StudiesGene Expression ProfilingGene OntologyHumansBiomarkersDiabetic nephropathyDiagnostic BiomarkersImmune Cell Infiltration

Identifiers

PMID40470717
PMCPMC12315251

What Socratic holds

Textmetadata
LicenceCC BY-NC
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.