ArticleFrontiers in endocrinology2022
REG1A and RUNX3 Are Potential Biomarkers for Predicting the Risk of Diabetic Kidney Disease.
Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 11 citations in OpenAlex.
- Multimodal predictions of end stage chronic kidney disease from asymptomatic individuals for discovery of genomic biomarkers.BMC nephrology · 2026Article
- Matrine Alleviates Atherosclerosis by Targeting REG1A and Activating the PI3K/AKT/mTOR Pathway to Inhibit Endothelial Cell Ferroptosis.Biochemical genetics · 2026Article
- Olink Proteomic Profiling of Vitreous Humor and Plasma From Proliferative Diabetic Retinopathy Patients Identifies a Novel Inflammatory Molecular Endotype.Investigative ophthalmology & visual science · 2026Article
- Article
- A multi-modal fusion model with enhanced feature representation for chronic kidney disease progression prediction.Briefings in bioinformatics · 2024Article
- Circulating Factors as Potential Biomarkers of Cardiovascular Damage Progression Associated with Type 2 Diabetes.Proteomes · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. Clinical features are traditionally used to predict DKD, yet with low diagnostic efficacy. Most of the recent biomarkers used to predict DKD are based on transcriptomics and metabolomics; however, they also should be used in combination with many other predictive indicators. The purpose of this study was thus to identify a simplified class of blood biomarkers capable of predicting the risk of developing DKD. The Gene Expression Omnibus database was screened for DKD biomarkers, and differentially expressed genes (DEGs) in human blood and kidney were identified
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.