Evidence map›Paper›PMID 35615098›Full record

ArticleFrontiers in medicine2022

Serum Metabolomics Benefits Discrimination Kidney Disease Development in Type 2 Diabetes Patients.

Xiaofeng Peng, Xiaoyi Wang, Xue Shao, Yucheng Wang, Shi Feng, Cuili Wang, Cunqi Ye, Jianghua Chen, Hong Jiang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
3.9field-weighted citation impact, top 6% of its field
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

13 citing papers in PubMed, 24 citations in OpenAlex.

  1. Review
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  11. Gut microbiota microbial metabolites in diabetic nephropathy patients: far to go.Frontiers in cellular and infection microbiology · 2024
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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

9 authors at 3 institutions in 1 country.

Xiaofeng PengKidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Xiaoyi WangKidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Xue ShaoKidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Yucheng WangKidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Shi FengKidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Cuili WangKidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Cunqi YeKidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Jianghua ChenKidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Hong JiangKidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Hangzhou Center for Disease Control and Prevention · CNShanghai Clinical Research Center · CNZhejiang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease, raising a considerable burden worldwide. Recognizing novel biomarkers by metabolomics can shed light on new biochemical insight to benefit DKD diagnostics and therapeutics. We hypothesized that serum metabolites can serve as biomarkers in the progression of DKD. Methods: A cross-sectional study of 1,043 plasma metabolites by untargeted LC/MS among 89 participants identified associations between proteinuria severity and metabolites difference. Pathway analysis from differently expressed metabolites was used to determine perturbed metabolism pathways. The results were replicated in an independent, cross-sectional cohort of 83 individuals. Correlation and prediction values were used to examine the association between plasma metabolites level and proteinuria amount. Results: Diabetes, and diabetic kidney disease with different ranges of proteinuria have shown different metabolites patterns. Cysteine and methionine metabolism pathway, and Taurine and hypotaurine metabolism pathway were distinguishable in the existence of DKD in DC (diabetes controls without kidney disease), and DKD with different ranges of proteinuria. Two interesting tetrapeptides (Asn-Met-Cys-Ser and Asn-Cys-Pro-Pro) circulating levels were elevated with the DKD proteinuria progression. Conclusions: These findings underscore that serum metabolomics provide us biochemical perspectives to identify some clinically relevant physiopathologic biomarkers of DKD progression.

Indexed as

biomarker discoverydiabetic kidney diseasemetabolomicsprogressionproteinuria

Identifiers

PMID35615098
PMCPMC9126316
OpenAlexW4229374124

What OpenQuestion holds

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LicenceCC BY
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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.