Integrating Choline and Specific Intestinal Microbiota to Classify Type 2 Diabetes in Adults: A Machine Learning Based Metagenomics Study
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 st...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-06-01
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| Series: | Frontiers in Endocrinology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2022.906310/full |
| _version_ | 1811238975352864768 |
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| author | Qiang Zeng Mingming Zhao Mingming Zhao Fei Wang Yanping Li Huimin Li Huimin Li Jianqiong Zheng Xianyang Chen Xianyang Chen Xiaolan Zhao Liang Ji Xiangyang Gao Changjie Liu Yu Wang Si Cheng Jie Xu Bing Pan Jing Sun Yongli Li Dongfang Li Dongfang Li Yuan He Yuan He Lemin Zheng Lemin Zheng |
| author_facet | Qiang Zeng Mingming Zhao Mingming Zhao Fei Wang Yanping Li Huimin Li Huimin Li Jianqiong Zheng Xianyang Chen Xianyang Chen Xiaolan Zhao Liang Ji Xiangyang Gao Changjie Liu Yu Wang Si Cheng Jie Xu Bing Pan Jing Sun Yongli Li Dongfang Li Dongfang Li Yuan He Yuan He Lemin Zheng Lemin Zheng |
| author_sort | Qiang Zeng |
| collection | DOAJ |
| description | 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 Ruminococcus lactaris, Coprococcus catus and Prevotella copri. We demonstrated the potential ability for classifying diabetic population by choline and specific species, and provided a novel insight of choline metabolism linking the microbiota to impaired glucose metabolism and diabetes. |
| first_indexed | 2024-04-12T12:51:45Z |
| format | Article |
| id | doaj.art-7fcc31cff32140a9819739862c3674a6 |
| institution | Directory Open Access Journal |
| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-04-12T12:51:45Z |
| publishDate | 2022-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj.art-7fcc31cff32140a9819739862c3674a62022-12-22T03:32:27ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922022-06-011310.3389/fendo.2022.906310906310Integrating Choline and Specific Intestinal Microbiota to Classify Type 2 Diabetes in Adults: A Machine Learning Based Metagenomics StudyQiang Zeng0Mingming Zhao1Mingming Zhao2Fei Wang3Yanping Li4Huimin Li5Huimin Li6Jianqiong Zheng7Xianyang Chen8Xianyang Chen9Xiaolan Zhao10Liang Ji11Xiangyang Gao12Changjie Liu13Yu Wang14Si Cheng15Jie Xu16Bing Pan17Jing Sun18Yongli Li19Dongfang Li20Dongfang Li21Yuan He22Yuan He23Lemin Zheng24Lemin Zheng25Health Management Institute, the Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, ChinaChina National Clinical Research Center for Neurological Diseases, Tiantan Hospital, Advanced Innovation Center for Human Brain Protection, The Capital Medical University, Beijing, ChinaThe 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, ChinaHealth Management Institute, the Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, ChinaDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, ChinaNational Human Genetic Resources Center, Beijing, China, National Research Institute for Family Planning, Beijing, ChinaChinese Academy of Medical Sciences & Peking Union Medical College, Beijing, ChinaDepartment 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, ChinaBao Feng Key Laboratory of Genetics and Metabolism, Zhongyuan Biotechnology Holdings Group, Beijing, ChinaZhong Guan Cun Biological and Medical Big Data Center, Zhong Guan Cun Medical Engineering & Health Industry Base, Beijing, China0Southwest Hospital, Third Military Medical University, Chongqing, ChinaThe 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, ChinaHealth Management Institute, the Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, ChinaThe 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, China1Health Management Center, The 910th Hospital of People's Liberation Army, Quanzhou, ChinaChina National Clinical Research Center for Neurological Diseases, Tiantan Hospital, Advanced Innovation Center for Human Brain Protection, The Capital Medical University, Beijing, ChinaChina National Clinical Research Center for Neurological Diseases, Tiantan Hospital, Advanced Innovation Center for Human Brain Protection, The Capital Medical University, Beijing, ChinaThe 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, China2Health Management Center, The China-Japan Union Hospital of Jilin University, Changchun, China3Department of Health Management, Henan Provincial People’s Hospital, Zhengzhou, China4Department of Microbial Research, WeHealthGene Institute, Shenzhen, Guangdong, China5Institute of Statistics, NanKai University, Tianjin, ChinaNational Human Genetic Resources Center, Beijing, China, National Research Institute for Family Planning, Beijing, ChinaChinese Academy of Medical Sciences & Peking Union Medical College, Beijing, ChinaChina National Clinical Research Center for Neurological Diseases, Tiantan Hospital, Advanced Innovation Center for Human Brain Protection, The Capital Medical University, Beijing, ChinaThe 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, ChinaEmerging 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 Ruminococcus lactaris, Coprococcus catus and Prevotella copri. We demonstrated the potential ability for classifying diabetic population by choline and specific species, and provided a novel insight of choline metabolism linking the microbiota to impaired glucose metabolism and diabetes.https://www.frontiersin.org/articles/10.3389/fendo.2022.906310/fullcholineintestinal microbiotaTMAOtype 2 diabetesmachine learning |
| spellingShingle | Qiang Zeng Mingming Zhao Mingming Zhao Fei Wang Yanping Li Huimin Li Huimin Li Jianqiong Zheng Xianyang Chen Xianyang Chen Xiaolan Zhao Liang Ji Xiangyang Gao Changjie Liu Yu Wang Si Cheng Jie Xu Bing Pan Jing Sun Yongli Li Dongfang Li Dongfang Li Yuan He Yuan He Lemin Zheng Lemin Zheng Integrating Choline and Specific Intestinal Microbiota to Classify Type 2 Diabetes in Adults: A Machine Learning Based Metagenomics Study Frontiers in Endocrinology choline intestinal microbiota TMAO type 2 diabetes machine learning |
| title | Integrating Choline and Specific Intestinal Microbiota to Classify Type 2 Diabetes in Adults: A Machine Learning Based Metagenomics Study |
| title_full | Integrating Choline and Specific Intestinal Microbiota to Classify Type 2 Diabetes in Adults: A Machine Learning Based Metagenomics Study |
| title_fullStr | Integrating Choline and Specific Intestinal Microbiota to Classify Type 2 Diabetes in Adults: A Machine Learning Based Metagenomics Study |
| title_full_unstemmed | Integrating Choline and Specific Intestinal Microbiota to Classify Type 2 Diabetes in Adults: A Machine Learning Based Metagenomics Study |
| title_short | Integrating Choline and Specific Intestinal Microbiota to Classify Type 2 Diabetes in Adults: A Machine Learning Based Metagenomics Study |
| title_sort | integrating choline and specific intestinal microbiota to classify type 2 diabetes in adults a machine learning based metagenomics study |
| topic | choline intestinal microbiota TMAO type 2 diabetes machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2022.906310/full |
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