Cargando…
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,...
Autores principales: | , , , , , , , , , , , , , , , , , |
---|---|
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 |
_version_ | 1783577338245545984 |
---|---|
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. |
format | Online Article Text |
id | pubmed-7464323 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT fukuihirokazu usefulnessofmachinelearningbasedgutmicrobiomeanalysisforidentifyingpatientswithirritablebowelssyndrome AT nishidaakifumi usefulnessofmachinelearningbasedgutmicrobiomeanalysisforidentifyingpatientswithirritablebowelssyndrome AT matsudasatoshi usefulnessofmachinelearningbasedgutmicrobiomeanalysisforidentifyingpatientswithirritablebowelssyndrome AT kirafumitaka usefulnessofmachinelearningbasedgutmicrobiomeanalysisforidentifyingpatientswithirritablebowelssyndrome AT watanabesatoshi usefulnessofmachinelearningbasedgutmicrobiomeanalysisforidentifyingpatientswithirritablebowelssyndrome AT kuriyamaminoru usefulnessofmachinelearningbasedgutmicrobiomeanalysisforidentifyingpatientswithirritablebowelssyndrome AT kawakamikazuhiko usefulnessofmachinelearningbasedgutmicrobiomeanalysisforidentifyingpatientswithirritablebowelssyndrome AT aikawayoshiko usefulnessofmachinelearningbasedgutmicrobiomeanalysisforidentifyingpatientswithirritablebowelssyndrome AT odanoritaka usefulnessofmachinelearningbasedgutmicrobiomeanalysisforidentifyingpatientswithirritablebowelssyndrome AT araikenichiro usefulnessofmachinelearningbasedgutmicrobiomeanalysisforidentifyingpatientswithirritablebowelssyndrome AT matsunagaatsushi usefulnessofmachinelearningbasedgutmicrobiomeanalysisforidentifyingpatientswithirritablebowelssyndrome AT nonakamasahiko usefulnessofmachinelearningbasedgutmicrobiomeanalysisforidentifyingpatientswithirritablebowelssyndrome AT nakaikatsuhiko usefulnessofmachinelearningbasedgutmicrobiomeanalysisforidentifyingpatientswithirritablebowelssyndrome AT shinmurawahei usefulnessofmachinelearningbasedgutmicrobiomeanalysisforidentifyingpatientswithirritablebowelssyndrome AT matsumotomasao usefulnessofmachinelearningbasedgutmicrobiomeanalysisforidentifyingpatientswithirritablebowelssyndrome AT morishitashinji usefulnessofmachinelearningbasedgutmicrobiomeanalysisforidentifyingpatientswithirritablebowelssyndrome AT takedaayak usefulnessofmachinelearningbasedgutmicrobiomeanalysisforidentifyingpatientswithirritablebowelssyndrome AT miwahiroto usefulnessofmachinelearningbasedgutmicrobiomeanalysisforidentifyingpatientswithirritablebowelssyndrome |