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Usefulness of Machine Learning-Based Gut Microbiome Analysis for Identifying Patients with Irritable Bowels Syndrome

Irritable bowel syndrome (IBS) is diagnosed by subjective clinical symptoms. We aimed to establish an objective IBS prediction model based on gut microbiome analyses employing machine learning. We collected fecal samples and clinical data from 85 adult patients who met the Rome III criteria for IBS,...

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Autores principales: Fukui, Hirokazu, Nishida, Akifumi, Matsuda, Satoshi, Kira, Fumitaka, Watanabe, Satoshi, Kuriyama, Minoru, Kawakami, Kazuhiko, Aikawa, Yoshiko, Oda, Noritaka, Arai, Kenichiro, Matsunaga, Atsushi, Nonaka, Masahiko, Nakai, Katsuhiko, Shinmura, Wahei, Matsumoto, Masao, Morishita, Shinji, Takeda, Aya K., Miwa, Hiroto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7464323/
https://www.ncbi.nlm.nih.gov/pubmed/32727141
http://dx.doi.org/10.3390/jcm9082403
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author Fukui, Hirokazu
Nishida, Akifumi
Matsuda, Satoshi
Kira, Fumitaka
Watanabe, Satoshi
Kuriyama, Minoru
Kawakami, Kazuhiko
Aikawa, Yoshiko
Oda, Noritaka
Arai, Kenichiro
Matsunaga, Atsushi
Nonaka, Masahiko
Nakai, Katsuhiko
Shinmura, Wahei
Matsumoto, Masao
Morishita, Shinji
Takeda, Aya K.
Miwa, Hiroto
author_facet Fukui, Hirokazu
Nishida, Akifumi
Matsuda, Satoshi
Kira, Fumitaka
Watanabe, Satoshi
Kuriyama, Minoru
Kawakami, Kazuhiko
Aikawa, Yoshiko
Oda, Noritaka
Arai, Kenichiro
Matsunaga, Atsushi
Nonaka, Masahiko
Nakai, Katsuhiko
Shinmura, Wahei
Matsumoto, Masao
Morishita, Shinji
Takeda, Aya K.
Miwa, Hiroto
author_sort Fukui, Hirokazu
collection PubMed
description Irritable bowel syndrome (IBS) is diagnosed by subjective clinical symptoms. We aimed to establish an objective IBS prediction model based on gut microbiome analyses employing machine learning. We collected fecal samples and clinical data from 85 adult patients who met the Rome III criteria for IBS, as well as from 26 healthy controls. The fecal gut microbiome profiles were analyzed by 16S ribosomal RNA sequencing, and the determination of short-chain fatty acids was performed by gas chromatography–mass spectrometry. The IBS prediction model based on gut microbiome data after machine learning was validated for its consistency for clinical diagnosis. The fecal microbiome alpha-diversity indices were significantly smaller in the IBS group than in the healthy controls. The amount of propionic acid and the difference between butyric acid and valerate were significantly higher in the IBS group than in the healthy controls (p < 0.05). Using LASSO logistic regression, we extracted a featured group of bacteria to distinguish IBS patients from healthy controls. Using the data for these featured bacteria, we established a prediction model for identifying IBS patients by machine learning (sensitivity >80%; specificity >90%). Gut microbiome analysis using machine learning is useful for identifying patients with IBS.
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spelling pubmed-74643232020-09-04 Usefulness of Machine Learning-Based Gut Microbiome Analysis for Identifying Patients with Irritable Bowels Syndrome Fukui, Hirokazu Nishida, Akifumi Matsuda, Satoshi Kira, Fumitaka Watanabe, Satoshi Kuriyama, Minoru Kawakami, Kazuhiko Aikawa, Yoshiko Oda, Noritaka Arai, Kenichiro Matsunaga, Atsushi Nonaka, Masahiko Nakai, Katsuhiko Shinmura, Wahei Matsumoto, Masao Morishita, Shinji Takeda, Aya K. Miwa, Hiroto J Clin Med Article Irritable bowel syndrome (IBS) is diagnosed by subjective clinical symptoms. We aimed to establish an objective IBS prediction model based on gut microbiome analyses employing machine learning. We collected fecal samples and clinical data from 85 adult patients who met the Rome III criteria for IBS, as well as from 26 healthy controls. The fecal gut microbiome profiles were analyzed by 16S ribosomal RNA sequencing, and the determination of short-chain fatty acids was performed by gas chromatography–mass spectrometry. The IBS prediction model based on gut microbiome data after machine learning was validated for its consistency for clinical diagnosis. The fecal microbiome alpha-diversity indices were significantly smaller in the IBS group than in the healthy controls. The amount of propionic acid and the difference between butyric acid and valerate were significantly higher in the IBS group than in the healthy controls (p < 0.05). Using LASSO logistic regression, we extracted a featured group of bacteria to distinguish IBS patients from healthy controls. Using the data for these featured bacteria, we established a prediction model for identifying IBS patients by machine learning (sensitivity >80%; specificity >90%). Gut microbiome analysis using machine learning is useful for identifying patients with IBS. MDPI 2020-07-27 /pmc/articles/PMC7464323/ /pubmed/32727141 http://dx.doi.org/10.3390/jcm9082403 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Fukui, Hirokazu
Nishida, Akifumi
Matsuda, Satoshi
Kira, Fumitaka
Watanabe, Satoshi
Kuriyama, Minoru
Kawakami, Kazuhiko
Aikawa, Yoshiko
Oda, Noritaka
Arai, Kenichiro
Matsunaga, Atsushi
Nonaka, Masahiko
Nakai, Katsuhiko
Shinmura, Wahei
Matsumoto, Masao
Morishita, Shinji
Takeda, Aya K.
Miwa, Hiroto
Usefulness of Machine Learning-Based Gut Microbiome Analysis for Identifying Patients with Irritable Bowels Syndrome
title Usefulness of Machine Learning-Based Gut Microbiome Analysis for Identifying Patients with Irritable Bowels Syndrome
title_full Usefulness of Machine Learning-Based Gut Microbiome Analysis for Identifying Patients with Irritable Bowels Syndrome
title_fullStr Usefulness of Machine Learning-Based Gut Microbiome Analysis for Identifying Patients with Irritable Bowels Syndrome
title_full_unstemmed Usefulness of Machine Learning-Based Gut Microbiome Analysis for Identifying Patients with Irritable Bowels Syndrome
title_short Usefulness of Machine Learning-Based Gut Microbiome Analysis for Identifying Patients with Irritable Bowels Syndrome
title_sort usefulness of machine learning-based gut microbiome analysis for identifying patients with irritable bowels syndrome
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7464323/
https://www.ncbi.nlm.nih.gov/pubmed/32727141
http://dx.doi.org/10.3390/jcm9082403
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