Evidence map›Paper›PMID 40849329›Full record

ArticleScientific reports2025

Intestinal bacteria translocation promotes β-cell dysfunction in DIO mice.

Xiang-Fang Yu, Chetali Gurung, Zhongjia Yu, Wei Xiao, Goher Kerem, Bin Teng, Jian V Zhang, Zhe Su, Yongjin Zhou, Pei-Gen Ren

Abstract read
In one paragraph

Article in Scientific reports, 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. 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

10 authors.

Xiang-Fang Yu *Department of Endocrinology, Shenzhen Children's Hospital, Shenzhen, China.
Chetali Gurung *Center for Energy Metabolism and Reproduction, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Zhongjia Yu *Center for Energy Metabolism and Reproduction, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Wei Xiao *School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
Goher KeremCenter for Energy Metabolism and Reproduction, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Bin TengCenter for Energy Metabolism and Reproduction, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Jian V ZhangCenter for Energy Metabolism and Reproduction, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Zhe SuDepartment of Endocrinology, Shenzhen Children's Hospital, Shenzhen, China. su_zhe@126.com.
Yongjin ZhouSchool of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China. yjzhou@szu.edu.cn.
Pei-Gen RenShenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Beijing, 100049, China. pg.ren@siat.ac.cn.

Funding

Guangdong Basic and Applied Basic Research Foundation 2024A1515013017Guangdong High-level Hospital Construction Fund Clinical Research Project of Shenzhen Children's Hospital CYJ2022071High-level Key Clinical Specialty project of Guangdong Provincial Health Commission (Supporting construction funds of Shenzhen) SZGSP012National Key Research and Development Program of China 2021YFA0719303National Natural Science Foundation of China 32100572National Natural Science Foundation of China 32271166Shenzhen Science and Technology Program CYJ20200109115441918Shenzhen Science and Technology Program JCYJ20210324102013035Shenzhen Science and Technology Program KCXFZ20201221173400002
6 · The paper itself

Abstract

Awareness of the intestinal microflora involved in insulin resistance and type 2 diabetes (T2DM) has now become more evident. However, direct mechanical insight is required to illustrate the contribution of intestinal microflora in the disease progression. In this study, we aimed to precisely assess the changes in intestinal bacteria translocation (IBT) from gut to the pancreas using combinations of FISH, 16S rRNA amplicon sequencing, and deep learning-assisted methods to track IBT in diet-induced obese (DIO) and antibiotic-induced microbiota disruption (AIMD)-DIO mouse models. Our analysis showed deep learning-assisted quantification enhanced the accuracy and objectivity of bacterial tracking. The DIO mice exhibited increased IBT, likely due to excessive intestinal lipid accumulation and compromised intestinal barrier integrity. Elevated bacterial loads in the pancreas were associated with worsened pancreatic function, indicated by higher fasting blood glucose, impaired glucose tolerance, and dysfunctional insulin secretion. In contrast, the AIMD-DIO group lowered the IBT, maintained the islet structure and improved glucose homeostasis. Comparative study between DIO and AIMD-DIO models revealed a strong correlation between number of translocated bacteria and T2DM severity. These findings provide objective evidence of bacterial migration from the intestine to the pancreas and establishes its pathological relationship with pancreatic impairment and dysfunction and underscores to utilize AI techniques more successfully in the future for evaluating IBT.

Indexed as

Bacterial TranslocationDiabetes Mellitus, Type 2Gastrointestinal MicrobiomeInsulin-Secreting CellsIntestinesObesityAnimalsBlood GlucoseDiet, High-FatDisease Models, AnimalInsulin ResistanceMaleMiceMice, Inbred C57BLMice, ObeseRNA, Ribosomal, 16SBlood GlucoseRNA, Ribosomal, 16S

Identifiers

PMID40849329
PMCPMC12374998

What OpenQuestion 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 OpenQuestion graph.