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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Vydáno v:Heart and vessels Ročník 32; číslo 1; s. 39 - 46
Hlavní autoři: 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
Médium: Journal Article
Jazyk:angličtina
Vydáno: Tokyo Springer Japan 01.01.2017
Springer Nature B.V
Témata:
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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Shrnutí: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.
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ISSN:0910-8327
1615-2573
DOI:10.1007/s00380-016-0841-y