ReviewInternational journal of molecular sciences2026
Metabolomics for Preclinical Detection of Diabetic Kidney Disease: A Comprehensive Review.
Review in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Di(2-Ethylhexyl) Phthalate Exposure and Risk of Diabetic Kidney Disease: Epidemiological Association and Mechanistic Insights.Diabetes/metabolism research and reviews · 2026Article
- Biomarkers in Diabetic Kidney Disease: Early Detection, Prognostic Assessment, and Integration with Multi-Omics Signatures.Life (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic kidney disease (DKD) affects up to 40% of individuals with diabetes and remains the leading cause of end-stage renal disease worldwide. Current biomarkers, such as albuminuria and estimated glomerular filtration rate, detect disease only after substantial kidney injury, limiting early intervention. Metabolomics offers unique potential to identify early biochemical changes preceding the clinical onset of DKD. This review synthesizes evidence from animal and human studies in diabetes without overt kidney disease, highlighting early perturbations in energy metabolism (TCA cycle, beta-oxidation, glycolysis) as well as alterations in amino acid, nucleotide and urea cycle pathways associated with future DKD risk. We discuss methodological considerations, translational relevance, and current research gaps and outline strategies for integrating metabolomics into predictive diagnostics. Early, non-invasive metabolic biomarkers may enable more precise risk stratification and timely intervention to improve patient outcomes.
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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.