Evidence map›Paper›PMID 39835101›Full record

ArticleFrontiers in medicine2024

Proteomics analysis reveals age-related proteins in the urine of chronic kidney disease patients.

Lin Xiong, Changwei Wu, Sipei Chen, Yong Zhang, Li Wang, Yi Li, Guisen Li

Abstract read
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Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. Deciphering the impact ofGenes & diseases · 2026
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

7 authors.

Lin XiongDepartment of Nephrology and Institute of Nephrology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Sichuan Clinical Research Center for Kidney Diseases, Chengdu, China.
Changwei WuDepartment of Nephrology and Institute of Nephrology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Sichuan Clinical Research Center for Kidney Diseases, Chengdu, China.
Sipei ChenDepartment of Nephrology and Institute of Nephrology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Sichuan Clinical Research Center for Kidney Diseases, Chengdu, China.
Yong ZhangInstitutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China.
Li WangDepartment of Nephrology and Institute of Nephrology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Sichuan Clinical Research Center for Kidney Diseases, Chengdu, China.
Yi LiDepartment of Nephrology and Institute of Nephrology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Sichuan Clinical Research Center for Kidney Diseases, Chengdu, China.
Guisen LiDepartment of Nephrology and Institute of Nephrology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Sichuan Clinical Research Center for Kidney Diseases, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic kidney disease (CKD) is closely linked to the aging process, making the identification of protein biomarkers that reflect aging in specific organs and tissues crucial for a deeper understanding of this phenomenon. This study aimed to identify potential aging-related proteins present in the urine of CKD patients. Utilizing liquid chromatography-tandem mass spectrometry (LC-MS/MS) proteomic analysis, we identified a total of 1,712 proteins in the urine samples from both healthy controls and CKD patients in our discovery cohort. Among the 845 proteins that overlapped, we found that 161 proteins were associated with aging. By applying a threshold of

Indexed as

agingbiomarkerchronic kidney diseaseLC–MS/MSurine proteomics

Identifiers

PMID39835101
PMCPMC11743183

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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.