Evidence map›Paper›PMID 42512115›Full record

ReviewBiomedicines2026

Pathology-Anchored Biomarker Research Progress for the Early Diagnosis of Diabetic Kidney Disease: From Pathological Association to Early Validation.

Qiu Li, Mei Yang, Yingyu Luo, Nannan Zhang

Abstract readReview
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

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.

Qiu LiDepartment of Nephrology, The First People's Hospital of Shuangliu District, Chengdu 610299, China.
Mei YangDepartment of Nephrology, The First People's Hospital of Shuangliu District, Chengdu 610299, China.
Yingyu LuoNational Center for Birth Defect Monitoring, West China Second University Hospital, Sichuan University, Chengdu 610041, China.
Nannan ZhangNational Center for Birth Defect Monitoring, West China Second University Hospital, Sichuan University, Chengdu 610041, China.

Funding

Chengdu Science and Technology Program 2024-YF09-00013-SNNational Natural Science Foundation of China 81970738the Young Teachers' Science and Technology Innovation Capability Enhancement Project of Sichuan University 2024SCUQJTX037
6 · The paper itself

Abstract

Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease (ESRD). Early diagnosis is therefore critical for improving patient outcomes.However, traditional clinical biomarkers are constrained by diagnostic latency and limited sensitivity, particularly in cases of non-albuminuric DKD. To address these limitations, this review systematically explores the technical framework of the pathology-anchored strategy and proposes a two-phase translational approach, consisting of Pathology Anchoring Discovery and Prospective Early Validation. This strategy employs renal biopsy as the pathological gold standard in conjunction with multi-omics technologies to correlate circulating or urinary molecules with specific renal histological lesions, ultimately identifying non-invasive biomarkers with definitive pathological relevance. While numerous biomarkers demonstrate early warning potential in high-risk populations with normal conventional indicators, the pathology-anchored framework serves as a critical bridge linking these clinical biomarkers and distinct pathological changes. This review presents potential insights for early identification, risk stratification, and prognostic assessment of DKD.

Indexed as

biomarkersdiabetic kidney diseaseearly diagnosismulti-omicspathology-anchored

Identifiers

PMID42512115
PMCPMC13406762

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.