Evidence map›Paper›PMID 36683149›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2023

The Gut Microbiome Dynamically Associates with Host Glucose Metabolism throughout Pregnancy: Longitudinal Findings from a Matched Case-Control Study of Gestational Diabetes Mellitus.

Zhonghan Sun, Xiong-Fei Pan, Xiao Li, Limiao Jiang, Ping Hu, Yi Wang, Yi Ye, Ping Wu, Bin Zhao, Jianguo Xu and 12 more

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed, 1 pooled it
–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

49 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

22 authors.

Zhonghan SunState Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, China.
Xiong-Fei PanSection of Epidemiology and Population Health, Ministry of Education Key Laboratory of Birth Defects and Related Diseases of Women and Children, West China Second University Hospital & West China Biomedical Big Data Center, West China Hospital, Sichuan University; Shuangliu Institute of Women's and Children's Health, Shuangliu Maternal and Child Health Hospital, Chengdu, Sichuan, China.
Xiao LiState Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, China.
Limiao JiangDepartment of Epidemiology & Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Ping HuKey Laboratory of Environment & Health (Huazhong University of Science and Technology), Ministry of Education, Wuhan, Hubei, China.
Yi WangDepartment of Epidemiology & Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Yi YeDepartment of Epidemiology & Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Ping WuDepartment of Epidemiology & Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Bin ZhaoAntenatal Care Clinics, Shuangliu Maternal and Child Health Hospital, Chengdu, China.
Jianguo XuDepartment of Clinical Laboratories, Shuangliu Maternal and Child Health Hospital, Chengdu, China.
Mengmeng KongState Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, China.
Yanni PuState Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, China.
Manying ZhaoState Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, China.
Jianying HuState Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, China.
Jinfeng WangBeijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, China.
Guo-Chong ChenDepartment of Nutrition and Food Hygiene, School of Public Health, Soochow University, Suzhou, China.
Changzheng YuanSchool of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Yongfu YuSchool of Public Health, Fudan University, Shanghai, China.
Xiang GaoSchool of Public Health, Fudan University, Shanghai, China.
Fangqing ZhaoBeijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, China.
An PanDepartment of Epidemiology & Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Yan ZhengState Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, China.ORCID 0000-0003-1129-3147

Funding

Fudan UniversityHuazhong University of Science and TechnologyNational Key R&D Program of China 2020YFC2005000National Key R&D Program of China 2021YFA1301000National Natural Science Foundation of China 81773517National Natural Science Foundation of China 81930124National Natural Science Foundation of China 81973032
6 · The paper itself

Abstract

Though gut microbiome disturbance may be involved in the etiology of gestational diabetes mellitus (GDM), data on the gut microbiome's dynamic change during pregnancy and associations with gestational glucose metabolism are still inadequate. In this prospective study comprising 120 pairs of GDM patients and matched pregnant controls, a decrease in the diversity of gut microbial species and changes in the microbial community composition with advancing gestation are found in controls, while no such trends are observed in GDM patients. Multivariable analysis identifies 10 GDM-related species (e.g., Alistipes putredinis), and the integrated associations of these species with glycemic traits are modified by habitual intake of fiber-rich plant foods. In addition, the microbial metabolic potentials related to fiber fermentation (e.g., mannan degradation pathways) and their key enzymes consistently emerge as associated with both GDM status and glycemic traits. Microbial features especially those involved in fiber fermentation, provide an incremental predictive value in a prediction model with established risk factors of GDM. These data suggest that the gut microbiome remodeling with advancing gestation is different in GDM patients compared with controls, and dietary fiber fermentation contributes to the influence of gut microbiome on gestational glycemic regulation.

Indexed as

Diabetes, GestationalGastrointestinal MicrobiomeCase-Control StudiesFemaleGlucoseHumansPregnancyProspective StudiesGlucosegestational diabetes mellitusglucose metabolismgut microbiomematched case-control study

Identifiers

PMID36683149
PMCPMC10074094

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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.