Evidence map›Paper›PMID 40370722›Full record

ReviewFrontiers in medicine2025

Application of biomarkers in the diagnosis of kidney disease.

Zuohua Lu, Weifeng Ni, Yuding Wu, Bin Zhai, Qiuyun Zhao, Tian Zheng, Qianqian Liu, Dapeng Ding

Abstract readReview
In one paragraph

Review in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

8 authors.

Zuohua Lu *Department of Clinical Laboratory, Gongli Hospital of Shanghai Pudong New Area, Shanghai, China.
Weifeng Ni *Department of Endocrinology, Rheumatology and Immunology, The Second Affiliated Hospital of Shantou University Medical College, Shantou, China.
Yuding Wu *Goldsite Diagnostics Inc., Shenzhen, China.
Bin ZhaiDepartment of Clinical Laboratory, Baotou Central Hospital, Baotou, China.
Qiuyun ZhaoDepartment of Clinical Laboratory, Guilin Hospital of Integrated Traditional Chinese and Western Medicine, Guilin, China.
Tian ZhengDepartment of Clinical Laboratory, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Qianqian LiuDepartment of Clinical Laboratory, Gongli Hospital of Shanghai Pudong New Area, Shanghai, China.
Dapeng DingDepartment of Clinical Laboratory, First Affiliated Hospital of Dalian Medical University, Dalian, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Worldwide, kidney disease has grown to be an important global public health agenda that reduces longevity. Medical institutions around the globe should enhance screening efforts for kidney disease, to facilitate early kidney disease detection, diagnosis, and intervention. Common screening methods for nephropathy encompass renal tissue biopsy, urine dry chemistry tests, urine formed element analysis, and urine-specific protein assays, among others. These methodologies evaluate renal health by scrutinizing a spectrum of biomarkers. Precise classification and quantitative analysis of these biomarkers can assist in determining the site and extent of kidney injury, as well as in assessing treatment efficacy and prognosis. In this paper, we reviewed the methods and biomarkers for kidney disease and also the integration of multiple biomarkers. With the aim of reasonable applying these markers to the early detection, accurate diagnosis, and scientific management of kidney disease, thereby mitigating the threat posed by kidney disease to human health.

Indexed as

acute kidney diseaseAKDbiomarkerbiomarker combinationchronic kidney diseaseCKDkidney diseasekidney injury

Identifiers

PMID40370722
PMCPMC12075424

What OpenQuestion holds

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Registered trials

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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.