Evidence map›Paper›PMID 30984260›Full record

ArticleInternational journal of endocrinology2019

Gut Microbiota Composition and Structure of the Ob/Ob and Db/Db Mice.

Mingsheng Yang, Yixin Liu, Hengchang Xie, Zhengzheng Wen, Yunxia Zhang, Changjing Wu, Li Huang, Jie Wu, Chensheng Xie, Tao Wang and 4 more

Open access · goldAbstract read
In one paragraph

Article in International journal of endocrinology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
1.0field-weighted citation impact, top 24% 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

16 citing papers in PubMed, 19 citations in OpenAlex.

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  11. Reciprocal interactions between gut microbiota and autophagy.World journal of gastroenterology · 2021
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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

14 authors at 2 institutions in 1 country.

Mingsheng YangInstitute of Neuroscience and Translational Medicine, College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, Henan 466001, China.
Yixin LiuCollege of Science, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China.
Hengchang XieInstitute of Neuroscience and Translational Medicine, College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, Henan 466001, China.
Zhengzheng WenInstitute of Neuroscience and Translational Medicine, College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, Henan 466001, China.
Yunxia ZhangInstitute of Neuroscience and Translational Medicine, College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, Henan 466001, China.
Changjing WuInstitute of Neuroscience and Translational Medicine, College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, Henan 466001, China.
Li HuangInstitute of Neuroscience and Translational Medicine, College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, Henan 466001, China.
Jie WuInstitute of Neuroscience and Translational Medicine, College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, Henan 466001, China.
Chensheng XieInstitute of Neuroscience and Translational Medicine, College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, Henan 466001, China.
Tao WangInstitute of Neuroscience and Translational Medicine, College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, Henan 466001, China.
Weifeng PengInstitute of Neuroscience and Translational Medicine, College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, Henan 466001, China.
Shangqi LiuInstitute of Neuroscience and Translational Medicine, College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, Henan 466001, China.
Long ChenInstitute of Neuroscience and Translational Medicine, College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, Henan 466001, China.
Xiaomeng LiuInstitute of Neuroscience and Translational Medicine, College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, Henan 466001, China.ORCID 0000-0001-7420-715X
Zhoukou Normal University · CNHangzhou Normal University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionGut microbiota is involved in the progression of metabolic diseases such as obesity and type 2 diabetes. The ob/ob and db/db mice are extensively used as models in studies on the pathogenesis of these diseases. The goal of this study is to characterize the composition and structure of gut microbiota in these model mice at different ages. MATERIALS AND

methodsHigh-throughput sequencing was used to obtain the sequences of the highly variable 16S rRNA V3-V4 region from fecal samples. The taxa with high abundance in both model mice were identified by bioinformatics analysis. Moreover, the taxa with divergent abundance in one model mice at different ages or in both model mice at the same age were also recognized. DISCUSSION AND

conclusionThe high abundance of Bacteroidetes and Firmicutes in microbiota composition and their imbalanced ratio in both model mice reflect the state of metabolic disorders of these mice. Differences in microbiota composition between the two model mice of the same age or in one model mice with different ages were assumed to be closely linked to the fluctuation of their blood glucose levels with age. The data on gut microbiota in ob/ob and db/db mice investigated herein has broad implications for the pathogenesis study and drug discovery on obesity and related complications.

Identifiers

PMID30984260
PMCPMC6432735
OpenAlexW2921613971

What OpenQuestion holds

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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.