Evidence map›Paper›PMID 42949643›Full record

ReviewClinical and translational medicine2026

Toward a digital urinary biopsy for early risk stratification in diabetic kidney disease: Anchoring shed cells, vesicles, and cell-free DNA to spatial kidney cell states.

Juan Wu, Ping Sheng, Ying Lu, Ke Chen

Abstract readReview
In one paragraph

Review in Clinical and translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Juan WuDepartment of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Ping ShengDepartment of General Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Ying LuDepartment of General Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Ke ChenDepartment of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China.ORCID https://orcid.org/0009-0007-1557-7282

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiabetic kidney disease (DKD) is the leading cause of end-stage kidney disease worldwide. However, albuminuria and estimated glomerular filtration rate (eGFR), which define and stage DKD, report established injury and do not resolve the cellular heterogeneity underlying variable progression. Urine, the only biofluid to traverse every nephron segment, carries shed cells, extracellular vesicles (EVs) and cell-free DNA (cfDNA), each retaining information about its cell of origin. MAIN BODY: This review develops the concept of a digital urinary biopsy, in which the three compartments are read together against spatial single-cell kidney atlases to infer non-invasively which cell states are active and whether niche-associated programs are engaged, an approach we term atlas-anchored molecular profiling. For each compartment, we tabulate study population, sample size, processing, assay, performance and validation status. Because shed-cell RNA, selectively sorted vesicle cargo and cfDNA methylation differ in origin, measurement scale and bias, each requires its own reference matrix, sampling function and background terms. All three carry renal information in feasibility studies, yet none is a validated clinical assay in DKD. Moreover, a cfDNA methylation reference at the required resolution is lacking, and no named computational method has been tested on urine.

conclusionWe therefore present the framework as a structured research hypothesis and define the required validation, from analytical and clinical validity to prospective demonstration of utility, with non-albuminuric DKD, which albuminuria-based screening misses, as the index use case. It offers a testable route to cell-resolved, non-invasive staging of DKD rather than a demonstrated capability.

Indexed as

Cell-Free Nucleic AcidsDiabetic NephropathiesExtracellular VesiclesKidneyBiopsyHumansRisk AssessmentCell-Free Nucleic Acidscell‐free DNA methylationdiabetic kidney diseaseextracellular vesiclessingle‐cell transcriptomicsurinary liquid biopsy

Identifiers

PMID42949643
PMCPMC13715840

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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.