Evidence map›Paper›PMID 41209705›Full record

ArticleMaterials today. Bio2025

Serum and urine metabolic fingerprints enable diagnosis and prognosis for IgA nephropathy.

Beili Wang, Ruimin Wang, Wenqi Shao, Baishen Pan, Xiaoqiang Ding, Juxiang Zhang, Yanxi Yang, Yiqin Shi, Jiao Wu, Wei Guo

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Materials today. Bio, 2025. 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

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

1 citing paper in PubMed.

  1. Article
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

10 authors.

Beili WangDepartment of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Ruimin WangState Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200030, China.
Wenqi ShaoDepartment of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Baishen PanDepartment of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Xiaoqiang DingDepartment of Nephrology, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Juxiang ZhangState Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200030, China.
Yanxi YangState Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200030, China.
Yiqin ShiDepartment of Nephrology, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Jiao WuState Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200030, China.
Wei GuoDepartment of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

IgA nephropathy (IgAN), the most common primary glomerulonephritis worldwide, displays a pronounced geographical variation and progresses to end-stage renal disease within 20 years in 20-40 % of patients. However, current clinical management remains challenged by the invasive nature of kidney biopsy. To meet the urgent need for non-invasive strategies, we developed a dual-fluid metabolic profiling approach for early diagnosis and prognostic evaluation. By leveraging nanoparticle-enhanced laser desorption/ionization mass spectrometry (NPELDI-MS) to obtain metabolic fingerprints from serum and urine, combined with machine learning integration, our model achieved high diagnostic performance (AUC = 0.81-0.95) in distinguishing IgAN from healthy donors. Moreover, we tracked dynamic changes in key metabolites throughout treatment, revealing distinct metabolic pathways among different prognostic subgroups (p < 0.05). This study highlights the promising application of metabolic profiling as a non-invasive tool for precise management of IgAN.

Indexed as

Diagnostic biomarkersIgA nephropathyMachine learningMetabolic profilingNanoparticle-enhanced laser desorption/ionization mass spectrometryNon-invasive diagnosis

Identifiers

PMID41209705
PMCPMC12595359

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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.