Evidence map›Paper›PMID 35832425›Full record

ArticleFrontiers in endocrinology2022

Integrating Choline and Specific Intestinal Microbiota to Classify Type 2 Diabetes in Adults: A Machine Learning Based Metagenomics Study.

Qiang Zeng, Mingming Zhao, Fei Wang, Yanping Li, Huimin Li, Jianqiong Zheng, Xianyang Chen, Xiaolan Zhao, Liang Ji, Xiangyang Gao and 10 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.8field-weighted citation impact, top 14% 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

13 citing papers in PubMed, 22 citations in OpenAlex.

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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

20 authors at 11 institutions in 2 countries.

Qiang ZengHealth Management Institute, the Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China.
Mingming ZhaoChina National Clinical Research Center for Neurological Diseases, Tiantan Hospital, Advanced Innovation Center for Human Brain Protection, The Capital Medical University, Beijing, China.
Fei WangHealth Management Institute, the Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China.
Yanping LiDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, China.
Huimin LiNational Human Genetic Resources Center, Beijing, China, National Research Institute for Family Planning, Beijing, China.
Jianqiong ZhengDepartment of Obstetrics and Gynecology, The Third Clinical Institute Affiliated to Wenzhou Medical University, The Third Affiliated Hospital of Shanghai University, Wenzhou People's Hospital, Wenzhou Maternal and Child Health Care Hospital, Wenzhou, China.
Xianyang ChenBao Feng Key Laboratory of Genetics and Metabolism, Zhongyuan Biotechnology Holdings Group, Beijing, China.
Xiaolan ZhaoSouthwest Hospital, Third Military Medical University, Chongqing, China.
Liang JiThe Institute of Cardiovascular Sciences and Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking University Health Science Center, Key Laboratory of Molecular Cardiovascular Science of Ministry of Education, Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides of Ministry of Health, Beijing Key Laboratory of Cardiovascular Receptors Research, Beijing, China.
Xiangyang GaoHealth Management Institute, the Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China.
Changjie LiuThe Institute of Cardiovascular Sciences and Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking University Health Science Center, Key Laboratory of Molecular Cardiovascular Science of Ministry of Education, Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides of Ministry of Health, Beijing Key Laboratory of Cardiovascular Receptors Research, Beijing, China.
Yu WangHealth Management Center, The 910th Hospital of People's Liberation Army, Quanzhou, China.
Si ChengChina National Clinical Research Center for Neurological Diseases, Tiantan Hospital, Advanced Innovation Center for Human Brain Protection, The Capital Medical University, Beijing, China.
Jie XuChina National Clinical Research Center for Neurological Diseases, Tiantan Hospital, Advanced Innovation Center for Human Brain Protection, The Capital Medical University, Beijing, China.
Bing PanThe Institute of Cardiovascular Sciences and Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking University Health Science Center, Key Laboratory of Molecular Cardiovascular Science of Ministry of Education, Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides of Ministry of Health, Beijing Key Laboratory of Cardiovascular Receptors Research, Beijing, China.
Jing SunHealth Management Center, The China-Japan Union Hospital of Jilin University, Changchun, China.
Yongli LiDepartment of Health Management, Henan Provincial People's Hospital, Zhengzhou, China.
Dongfang LiDepartment of Microbial Research, WeHealthGene Institute, Shenzhen, Guangdong, China.
Yuan HeNational Human Genetic Resources Center, Beijing, China, National Research Institute for Family Planning, Beijing, China.
Lemin ZhengChina National Clinical Research Center for Neurological Diseases, Tiantan Hospital, Advanced Innovation Center for Human Brain Protection, The Capital Medical University, Beijing, China.
Capital Medical University · CNChinese People's Liberation Army · CNPeking University · CNChinese Academy of Medical Sciences & Peking Union Medical College · CNHarvard University · USHenan Provincial People's Hospital · CNJilin University · CNNankai University · CNSouthwest Hospital · CNThe 180th Hospital of PLA · CNWenzhou Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Emerging evidence is examining the precise role of intestinal microbiota in the pathogenesis of type 2 diabetes. The aim of this study was to investigate the association of intestinal microbiota and microbiota-generated metabolites with glucose metabolism systematically in a large cross-sectional study in China. 1160 subjects were divided into three groups based on their glucose level: normal glucose group (n=504), prediabetes group (n=394), and diabetes group (n=262). Plasma concentrations of TMAO, choline, betaine, and carnitine were measured. Intestinal microbiota was measured in a subgroup of 161 controls, 144 prediabetes and 56 diabetes by using metagenomics sequencing. We identified that plasma choline [Per SD of log-transformed change: odds ratio 1.36 (95 confidence interval 1.16, 1.58)] was positively, while betaine [0.77 (0.66, 0.89)] was negatively associated with diabetes, independently of TMAO. Individuals with diabetes could be accurately distinguished from controls by integrating data on choline, and certain microbiota species, as well as traditional risk factors (AUC=0.971). KOs associated with the carbohydrate metabolism pathway were enhanced in individuals with high choline level. The functional shift in the carbohydrate metabolism pathway in high choline group was driven by species

Indexed as

Diabetes Mellitus, Type 2Gastrointestinal MicrobiomePrediabetic StateAdultBetaineCholineCross-Sectional StudiesGlucoseHumansMachine LearningMetagenomicsMethylaminesBetaineCholineGlucoseMethylaminescholineintestinal microbiotamachine learningTMAOtype 2 diabetes

Identifiers

PMID35832425
PMCPMC9271784
OpenAlexW4283580802

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.