Evidence map›Paper›PMID 33121301›Full record

ArticleRenal failure2020

The gut microbiome in differential diagnosis of diabetic kidney disease and membranous nephropathy.

Wei Yu, Jin Shang, Ruixue Guo, Fanliang Zhang, Weifeng Zhang, Yiding Zhang, Feng Wu, Hongyan Ren, Chao Liu, Jing Xiao and 1 more

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

Article in Renal failure, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 3 pooled it
2.3field-weighted citation impact, top 11% 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

22 citing papers in PubMed, 3 syntheses or guidelines pooled it, 42 citations in OpenAlex.

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  7. Microbiota and kidney disease: the road ahead.Nature reviews. Nephrology · 2025
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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

11 authors at 1 institution in 1 country.

Wei YuDepartment of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China.
Jin ShangDepartment of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China.
Ruixue GuoDepartment of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China.
Fanliang ZhangDepartment of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China.
Weifeng ZhangDepartment of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China.
Yiding ZhangDepartment of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China.
Feng WuDepartment of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China.
Hongyan RenShanghai Mobio Biomedical Technology Co, Ltd, Shanghai, P.R. China.
Chao LiuShanghai Mobio Biomedical Technology Co, Ltd, Shanghai, P.R. China.
Jing XiaoDepartment of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China.
Zhanzheng ZhaoDepartment of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China.
First Affiliated Hospital of Zhengzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiabetic kidney disease (DKD) and membranous nephropathy (MN) are the two major causes of end-stage renal disease (ESRD). Increasing evidence has shown that intestinal dysbiosis is associated with many diseases. The aim of this study was to explore the composition of the gut microbiome in DKD and MN patients.

methods16S rRNA gene sequencing was performed on 271 fecal samples (DKD = 129 and MN = 142), and taxonomic annotation of microbial composition and function was completed.

resultsWe observed distinct microbial communities between the two groups, with MN samples exhibiting more severe dysbiosis than DKD samples. Relative increases in genera producing short-chain fatty acids (SCFAs) in DKD and a higher proportion of potential pathogens in MN were the main contributors to the microbiome alterations in the two groups. Five-fold cross-validation was performed on a random forest model, and four operational taxonomic unit (OTU)-based microbial markers were selected to distinguish DKD from MN. The results showed 92.42% accuracy in the training set and 94.52% accuracy in the testing set, indicating high potential for these microbiome-based markers in separating MN from DKD. Overexpression of several amino acid metabolic pathways, carbohydrate metabolism and lipid metabolism was found in DKD, while interconversion of pentose/glucoronate and membrane transport in relation to ABC transporters and the phosphotransferase system were increased in MN.

conclusionThe composition of the gut microbiome appears to differ considerably between patients with DKD and those with MN. Thus, microbiome-based markers could be used as an alternative tool to distinguish DKD and MN.

Indexed as

Diabetic NephropathiesDiagnosis, DifferentialDysbiosisFecesGastrointestinal MicrobiomeGlomerulonephritis, MembranousHumansRNA, Ribosomal, 16SROC CurveRNA, Ribosomal, 16S16S rRNAdiabetic kidney diseaseGut microbiomemembranous nephropathy

Identifiers

PMID33121301
PMCPMC7599019
OpenAlexW3096690288

What OpenQuestion 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 OpenQuestion graph.