ReviewNature reviews. Nephrology2020
Integrated multi-omics approaches to improve classification of chronic kidney disease.
Review in Nature reviews. Nephrology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers, 2 of them syntheses that pooled 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.
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
111 citing papers in PubMed, 2 syntheses or guidelines pooled it, 176 citations in OpenAlex.
- Machine learning-based risk predictive models for diabetic kidney disease in type 2 diabetes mellitus patients: a systematic review and meta-analysis.Frontiers in endocrinology · 2025Pooled it
- The current use of proteomics and metabolomics in glomerulonephritis: a systematic literature review.Journal of nephrology · 2024Pooled it
- Comparing Kidney Health Outcomes in Children, Adolescents, and Adults With Focal Segmental Glomerulosclerosis.JAMA network open · 2022Trial
- Integrative Multi-Omics Reveals Serum Markers of Tuberculosis in Advanced HIV.Frontiers in immunology · 2021Trial
- Beyond social media: the era of generative AI and intelligent digital platforms in nephrology education.Renal failure · 2026Article
- AI-assisted metabolomic profiling identifies candidate metabolites associated with diabetic kidney disease staging in a Korean cohort.iScience · 2026Article
- Next-generation kidney tissue analysis - spatial omics and digital pathology.Nature reviews. Nephrology · 2026Review
- Bibliometrics and visualization analysis: Big data research on diabetic kidney disease from 2000 to 2025.Medicine · 2026Article
- Automatic Coregistration of High-Resolution MALDI-MSI and Raman Imaging Applied to Cardiac Tissue of Fabry Disease Mouse Models.Analytical chemistry · 2026Article
- Advances in Nephrology.Diagnostics (Basel, Switzerland) · 2026Article
- Multi-Modal, Machine Learning-Driven Framework Integrating Multi-Omics for Personalized Chronic Kidney Disease Management.Journal of clinical medicine · 2026Review
- π-SeqOmics: A Sequential Workflow for Genomic, Transcriptomic, Proteomic, and Phosphoproteomic Profiling From Biopsy-Scale Samples.Molecular & cellular proteomics : MCP · 2026Article
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- The Need for Omics Studies in Chronic Kidney Disease of Unknown Etiology (CKDu): A Narrative Review and Perspective.International journal of molecular sciences · 2026Review
- Treatment of Focal Segmental Glomerulosclerosis in Low- and Middle-Income Countries: Emerging Extracorporeal Therapies.Blood purification · 2026Review
- From GWAS Signals to Causal Genes in Chronic Kidney Disease.Current issues in molecular biology · 2026Review
- Advances in the Identification of Novel Urinary Biomarkers for Diabetic Kidney Disease.Journal of diabetes research · 2026Review
- Research trends and potential molecular intersections between diabetic kidney disease and sarcopenia: a 21-year bibliometric and bioinformatics analysis.Frontiers in endocrinology · 2026Review
- Elucidating the therapeutic efficacy and mechanisms of arctigenin in ameliorating renal fibrosis: a combined transcriptomic and proteomic study.Frontiers in pharmacology · 2026Article
- The lipid-podocyte axis: emerging clues in membranous nephropathy pathogenesis.Frontiers in medicine · 2026Review
51 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Chronic kidney diseases (CKDs) are currently classified according to their clinical features, associated comorbidities and pattern of injury on biopsy. Even within a given classification, considerable variation exists in disease presentation, progression and response to therapy, highlighting heterogeneity in the underlying biological mechanisms. As a result, patients and clinicians experience uncertainty when considering optimal treatment approaches and risk projection. Technological advances now enable large-scale datasets, including DNA and RNA sequence data, proteomics and metabolomics data, to be captured from individuals and groups of patients along the genotype-phenotype continuum of CKD. The ability to combine these high-dimensional datasets, in which the number of variables exceeds the number of clinical outcome observations, using computational approaches such as machine learning, provides an opportunity to re-classify patients into molecularly defined subgroups that better reflect underlying disease mechanisms. Patients with CKD are uniquely poised to benefit from these integrative, multi-omics approaches since the kidney biopsy, blood and urine samples used to generate these different types of molecular data are frequently obtained during routine clinical care. The ultimate goal of developing an integrated molecular classification is to improve diagnostic classification, risk stratification and assignment of molecular, disease-specific therapies to improve the care of patients with CKD.
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What OpenQuestion 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 OpenQuestion graph.