Evidence map›Paper›PMID 40122887›Full record

ReviewTrials2025

The gut microbiota and diabetic nephropathy: an observational study review and bidirectional Mendelian randomization study.

Yi Zhen Han, Yang Zhi Yuan Wang, Xing Yu Zhu, Bo Xuan Du, Yao Xian Wang, Xue Qin Zhang, Jia Meng Jia, Wei Jing Liu, Hui Juan Zheng

Abstract readReview
In one paragraph

Review in Trials, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
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.

Yi Zhen HanDongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
Yang Zhi Yuan WangDongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
Xing Yu ZhuDongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
Bo Xuan DuDongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
Yao Xian WangDongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
Xue Qin ZhangHebei University of Chinese Medicine, Hebei, China.
Jia Meng JiaSchool of Management, Beijing University of Chinese Medicine, Beijing, China.
Wei Jing LiuDongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China. liuweijing-1977@hotmail.com.ORCID http://orcid.org/0000-0002-8394-0844
Hui Juan ZhengDongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China. tcmzhenghuijuan@163.com.

Funding

National Outstanding Youth Science Fund Project of National Natural Science Foundation of China No. 82004196
6 · The paper itself

Abstract

backgroundEarlier studies have implicated a crucial link between diabetic nephropathy (DN) and the gut microbiota (GM) by considering the gut-kidney axis; however, the specific cause-and-effect connections between these processes remain unclear.

methodsTo compare changes in the GM between DN patients and control subjects, a review of observational studies was performed. The examination focused on the phylum, family, genus, and species/genus categories. To delve deeper into the cause-effect relationship, instrumental variables for 211 GM taxa (9 phyla, 16 classes, 20 orders, 35 families, and 131 genera), which were eligible for the mbQTL (microbial quantitative trait locus) mapping analysis, were collected from the Genome Wide Association Study (GWAS). A Mendelian randomization investigation was then conducted to gauge their impact on DN susceptibility using data from the European Bioinformatics Institute (EBI) and the FinnGen consortium. The European Bioinformatics Institute data included 1032 DN patients and 451,248 controls, while the FinnGen consortium data consisted of 3283 DN patients and 210,463 controls. Two-sample Mendelian randomization (TSMR) was utilized to determine the link between the GM and DN. The primary method for analysis was the inverse variance weighted (IVW) approach. Moreover, a reverse Mendelian randomization analysis was carried out, and the findings were validated through sensitivity assessments.

resultsThis review examined 11 observational studies that satisfied the inclusion and exclusion criteria. There was a significant difference in the abundance of 144 GM taxa between DN patients and controls. By employing the MR technique, 13 bacteria were pinpointed as having a causal link to DN (including 3 unknown GM taxa). Even after Bonferroni correction, the protective impact of the phylum Proteobacteria and genus Dialister (Sequeira et al. Nat Microbiol. 5:304-313, 2020; Liu et al. EBioMedicine. 90:104527, 2023) and the harmful impact of the genus Akkermansia, family Verrucomicrobiaceae, order Verrucomicrobia and class Verrucomicrobiae on DN remained significant. No noticeable heterogeneity or horizontal pleiotropy was detected in the instrumental variables (IVs). However, reverse MR investigations have failed to reveal any substantial causal relationship between DN and the GM.

conclusionDifferences in the GM among DN patients and healthy controls are explored in observational studies. We verified the possible connection between certain genetically modified genera and DN, thereby emphasizing the connection between the "gut-kidney" axis and new insights into the GM's role in DN pathogenesis underlying DN. Investigations into this association are necessary, and novel biomarkers for the development of targeted preventive strategies against DN are needed.

Indexed as

Diabetic NephropathiesGastrointestinal MicrobiomeGenome-Wide Association StudyHumansMendelian Randomization AnalysisObservational Studies as TopicDiabetic nephropathyGut–kidney axisGut microbiotaMendelian randomizationObservational studies

Identifiers

PMID40122887
PMCPMC11931829

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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.