ReviewNature reviews. Nephrology2026
Spatial metabolomics and multiomics integration for breakthroughs in precision medicine for kidney disease.
Review in Nature reviews. Nephrology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Natural product therapy in diabetic kidney disease: emerging multiomics-mediated signalling pathway and molecular target.Chinese medicine · 2026Review
- Review
- Spatial ecotype in tumor immune exclusion: from spatial architecture to therapeutic strategies.Molecular cancer · 2026Review
- Gut microbiota and renal fibrosis: novel mechanistic insights and therapeutic potential.Acta pharmacologica Sinica · 2026Review
- Insights from integrative spatial transcriptomics in sepsis-associated acute kidney injury.Nature reviews. Nephrology · 2026Review
- Review
- Renal Tubular Epithelial Cells as Central Hubs of Kidney Disease.Diagnostics (Basel, Switzerland) · 2026Review
- Spatial metabolomics: A new tool for unravelling the metabolic disorders and heterogeneity in diabetic kidney disease (Review).International journal of molecular medicine · 2026Review
- Opportunities and Risks of Technology Convergence in Precision Health.Blockchain in healthcare today · 2026Article
- Gut microbial-derived metabolites: key players in kidney disease and renal fibrosis.International journal of biological sciences · 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
7 authors.
Funding
Abstract
Precision medicine is now a feasible prospect for nephrologists as numerous therapeutic options are available for various forms of kidney disease. However, implementation of this strategy will require high-dimensional diagnostic approaches to identify patients who will respond to an intervention and monitor mechanisms of action relevant to the underlying disease process. With the advent of spatial omics, comprehensive and thorough molecular analysis of biological samples is now possible. In particular, spatial metabolomics analysis of kidney biopsy samples could have an important role in facilitating precision medicine for kidney diseases. Spatial metabolomics can be used to monitor changes in the functional outcomes of genes and proteins in specific anatomical compartments such as the glomeruli, tubules, blood vessels and interstitial spaces. Spatial metabolomics studies have identified adenine in regions of interstitial fibrosis and arteriosclerosis in diabetic kidney disease, provided new insights into the regulation of N-glycans in glomeruli from patients with diabetes, and enabled a new metabolomic classification of kidney cancer subtypes. Use of computational informatic platforms to integrate genomics, transcriptomics, proteomics and epigenomics with metabolomics will further enhance the value of spatial metabolomics for clinical applications.
Indexed as
Identifiers
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.