Characterization of gut microbiota profiles in coronary artery disease patients using data mining analysis of terminal restriction fragment length polymorphism: gut microbiota could be a diagnostic marker of coronary artery disease

The association between atherosclerosis and gut microbiota has been attracting increased attention. We previously demonstrated a possible link between gut microbiota and coronary artery disease. Our aim of this study was to clarify the gut microbiota profiles in coronary artery disease patients usin...

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Veröffentlicht in:	Heart and vessels Jg. 32; H. 1; S. 39 - 46
Hauptverfasser:	Emoto, Takuo, Yamashita, Tomoya, Kobayashi, Toshio, Sasaki, Naoto, Hirota, Yushi, Hayashi, Tomohiro, So, Anna, Kasahara, Kazuyuki, Yodoi, Keiko, Matsumoto, Takuya, Mizoguchi, Taiji, Ogawa, Wataru, Hirata, Ken-ichi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Tokyo Springer Japan 01.01.2017 Springer Nature B.V
Schlagworte:	Aged Atherosclerosis Bacteria Biomarkers Biomedical Engineering and Bioengineering Cardiac Surgery Cardiology Cardiovascular disease Case-Control Studies Coronary Artery Disease - diagnosis Coronary Artery Disease - microbiology Data analysis Data Mining Decision trees DNA, Bacterial - genetics Feces - microbiology Female Gastrointestinal Microbiome Humans Japan Male Medicine Medicine & Public Health Middle Aged Original Article Polymorphism Polymorphism, Restriction Fragment Length Vascular Surgery Gut microbiota Terminal restriction fragment length polymorphism (T-RFLP) Data mining analysis Coronary artery disease Decision tree
ISSN:	0910-8327, 1615-2573
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Abstract	The association between atherosclerosis and gut microbiota has been attracting increased attention. We previously demonstrated a possible link between gut microbiota and coronary artery disease. Our aim of this study was to clarify the gut microbiota profiles in coronary artery disease patients using data mining analysis of terminal restriction fragment length polymorphism (T-RFLP). This study included 39 coronary artery disease (CAD) patients and 30 age- and sex- matched no-CAD controls (Ctrls) with coronary risk factors. Bacterial DNA was extracted from their fecal samples and analyzed by T-RFLP and data mining analysis using the classification and regression algorithm. Five additional CAD patients were newly recruited to confirm the reliability of this analysis. Data mining analysis could divide the composition of gut microbiota into 2 characteristic nodes. The CAD group was classified into 4 CAD pattern nodes (35/39 = 90 %), while the Ctrl group was classified into 3 Ctrl pattern nodes (28/30 = 93 %). Five additional CAD samples were applied to the same dividing model, which could validate the accuracy to predict the risk of CAD by data mining analysis. We could demonstrate that operational taxonomic unit 853 (OTU853), OTU657, and OTU990 were determined important both by the data mining method and by the usual statistical comparison. We classified the gut microbiota profiles in coronary artery disease patients using data mining analysis of T-RFLP data and demonstrated the possibility that gut microbiota is a diagnostic marker of suffering from CAD.
AbstractList	The association between atherosclerosis and gut microbiota has been attracting increased attention. We previously demonstrated a possible link between gut microbiota and coronary artery disease. Our aim of this study was to clarify the gut microbiota profiles in coronary artery disease patients using data mining analysis of terminal restriction fragment length polymorphism (T-RFLP). This study included 39 coronary artery disease (CAD) patients and 30 age- and sex- matched no-CAD controls (Ctrls) with coronary risk factors. Bacterial DNA was extracted from their fecal samples and analyzed by T-RFLP and data mining analysis using the classification and regression algorithm. Five additional CAD patients were newly recruited to confirm the reliability of this analysis. Data mining analysis could divide the composition of gut microbiota into 2 characteristic nodes. The CAD group was classified into 4 CAD pattern nodes (35/39 = 90 %), while the Ctrl group was classified into 3 Ctrl pattern nodes (28/30 = 93 %). Five additional CAD samples were applied to the same dividing model, which could validate the accuracy to predict the risk of CAD by data mining analysis. We could demonstrate that operational taxonomic unit 853 (OTU853), OTU657, and OTU990 were determined important both by the data mining method and by the usual statistical comparison. We classified the gut microbiota profiles in coronary artery disease patients using data mining analysis of T-RFLP data and demonstrated the possibility that gut microbiota is a diagnostic marker of suffering from CAD. The association between atherosclerosis and gut microbiota has been attracting increased attention. We previously demonstrated a possible link between gut microbiota and coronary artery disease. Our aim of this study was to clarify the gut microbiota profiles in coronary artery disease patients using data mining analysis of terminal restriction fragment length polymorphism (T-RFLP). This study included 39 coronary artery disease (CAD) patients and 30 age- and sex- matched no-CAD controls (Ctrls) with coronary risk factors. Bacterial DNA was extracted from their fecal samples and analyzed by T-RFLP and data mining analysis using the classification and regression algorithm. Five additional CAD patients were newly recruited to confirm the reliability of this analysis. Data mining analysis could divide the composition of gut microbiota into 2 characteristic nodes. The CAD group was classified into 4 CAD pattern nodes (35/39 = 90 %), while the Ctrl group was classified into 3 Ctrl pattern nodes (28/30 = 93 %). Five additional CAD samples were applied to the same dividing model, which could validate the accuracy to predict the risk of CAD by data mining analysis. We could demonstrate that operational taxonomic unit 853 (OTU853), OTU657, and OTU990 were determined important both by the data mining method and by the usual statistical comparison. We classified the gut microbiota profiles in coronary artery disease patients using data mining analysis of T-RFLP data and demonstrated the possibility that gut microbiota is a diagnostic marker of suffering from CAD.
Author	Kobayashi, Toshio Yamashita, Tomoya Kasahara, Kazuyuki Mizoguchi, Taiji Hayashi, Tomohiro Hirata, Ken-ichi Yodoi, Keiko Matsumoto, Takuya Emoto, Takuo So, Anna Hirota, Yushi Ogawa, Wataru Sasaki, Naoto
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Keywords	Gut microbiota Terminal restriction fragment length polymorphism (T-RFLP) Data mining analysis Coronary artery disease Decision tree
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Snippet	The association between atherosclerosis and gut microbiota has been attracting increased attention. We previously demonstrated a possible link between gut...
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SubjectTerms	Aged Atherosclerosis Bacteria Biomarkers Biomedical Engineering and Bioengineering Cardiac Surgery Cardiology Cardiovascular disease Case-Control Studies Coronary Artery Disease - diagnosis Coronary Artery Disease - microbiology Data analysis Data Mining Decision trees DNA, Bacterial - genetics Feces - microbiology Female Gastrointestinal Microbiome Humans Japan Male Medicine Medicine & Public Health Middle Aged Original Article Polymorphism Polymorphism, Restriction Fragment Length Vascular Surgery
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Title	Characterization of gut microbiota profiles in coronary artery disease patients using data mining analysis of terminal restriction fragment length polymorphism: gut microbiota could be a diagnostic marker of coronary artery disease
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