ArticleRenal failure2025
Diagnostic immune-related markers for diabetic kidney disease: a bioinformatics and machine learning approach.
Article in Renal failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- A study on the association between polycyclic aromatic hydrocarbon (PAH) exposure and kidney function indicators in adolescents: an integrated research combining statistical mediation analysis and network toxicology.International urology and nephrology · 2026Article
- Leveraging ICT Tools to Improve Kidney Health: A Comprehensive Review of Innovations in Nephrology.Healthcare (Basel, Switzerland) · 2026Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveDiabetic kidney disease (DKD) is a leading cause of chronic kidney disease, with chronic inflammation driving its progression. This study aimed to identify immune-related diagnostic biomarkers for DKD and explore their association with immune cell infiltration.
methodsThree glomerular transcriptomic datasets (53 DKD, 36 controls) were analyzed via batch-corrected differential expression analysis to screen immune-related differentially expressed genes (DEGs). Machine learning algorithms (least absolute shrinkage and selection operator, support vector machine - recursive feature elimination) prioritized biomarkers, validated by RT-PCR in db/db mice. Immune infiltration was assessed via CIBERSORT and EPIC.
resultsThirteen immune DEGs were identified, enriched in cytokine signaling and leukocyte chemotaxis. Three biomarkers (albumin (ALB), AP - 1 transcription factor subunit (FOS), and S100 calcium binding protein A9) showed strong correlations with T cell, natural killer cell, and macrophage infiltration, validated by RT-PCR (
conclusionALB and FOS serve as novel immunogenetic biomarkers for DKD, highlighting chronic inflammation as a key driver. This framework supports precision immunomodulation, though clinical validation in larger cohorts is needed.
Indexed as
Identifiers
What Socratic holds
Registered trials
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.