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Dietary Efficacy Evaluation by Applying a Prediction Model Using Clinical Fecal Microbiome Data of Colorectal Disease to a Controlled Animal Model from an Obesity Perspective

Obesity associated with a Western diet such as a high-fat diet (HFD) is a known risk factor for inflammatory bowel disease (IBD) and colorectal cancer (CRC). In this study, we aimed to develop fecal microbiome data-based deep learning algorithms for the risk assessment of colorectal diseases. The ef...

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Autores principales: Seo, Hochan, Kwon, Cheol-O, Park, Joo-Hyun, Kang, Chil-Sung, Shin, Tae-Seop, Yang, Eun-Young, Jung, Jin Woo, Moon, Byoung-Seok, Kim, Yoon-Keun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9505706/
https://www.ncbi.nlm.nih.gov/pubmed/36144434
http://dx.doi.org/10.3390/microorganisms10091833
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author Seo, Hochan
Kwon, Cheol-O
Park, Joo-Hyun
Kang, Chil-Sung
Shin, Tae-Seop
Yang, Eun-Young
Jung, Jin Woo
Moon, Byoung-Seok
Kim, Yoon-Keun
author_facet Seo, Hochan
Kwon, Cheol-O
Park, Joo-Hyun
Kang, Chil-Sung
Shin, Tae-Seop
Yang, Eun-Young
Jung, Jin Woo
Moon, Byoung-Seok
Kim, Yoon-Keun
author_sort Seo, Hochan
collection PubMed
description Obesity associated with a Western diet such as a high-fat diet (HFD) is a known risk factor for inflammatory bowel disease (IBD) and colorectal cancer (CRC). In this study, we aimed to develop fecal microbiome data-based deep learning algorithms for the risk assessment of colorectal diseases. The effects of a HFD and a candidate food (Nypa fruticans, NF) on IBD and CRC risk reduction were also evaluated. Fecal microbiome data were obtained from 109 IBD patients, 111 CRC patients, and 395 healthy control (HC) subjects by 16S rDNA amplicon sequencing. IBD and CRC risk assessment prediction models were then constructed by deep learning algorithms. Dietary effects were evaluated based on fecal microbiome data from rats fed on a regular chow diet (RCD), HFD, and HFD plus ethanol extracts or water extracts of NF. There were significant differences in taxa when IBD and CRC were compared with HC. The diagnostic performance (area under curve, AUC) of the deep learning algorithm was 0.84 for IBD and 0.80 for CRC prediction. Based on the rat fecal microbiome data, IBD and CRC risks were increased in HFD-fed rats versus RCD-fed rats. Interestingly, in the HFD-induced obesity model, the IBD and CRC risk scores were significantly lowered by the administration of ethanol extracts of NF, but not by the administration of water extracts of NF. In conclusion, changes in the fecal microbiome of obesity by Western diet could be important risk factors for the development of IBD and CRC. The risk prediction model developed in this study could be used to evaluate dietary efficacy.
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spelling pubmed-95057062022-09-24 Dietary Efficacy Evaluation by Applying a Prediction Model Using Clinical Fecal Microbiome Data of Colorectal Disease to a Controlled Animal Model from an Obesity Perspective Seo, Hochan Kwon, Cheol-O Park, Joo-Hyun Kang, Chil-Sung Shin, Tae-Seop Yang, Eun-Young Jung, Jin Woo Moon, Byoung-Seok Kim, Yoon-Keun Microorganisms Review Obesity associated with a Western diet such as a high-fat diet (HFD) is a known risk factor for inflammatory bowel disease (IBD) and colorectal cancer (CRC). In this study, we aimed to develop fecal microbiome data-based deep learning algorithms for the risk assessment of colorectal diseases. The effects of a HFD and a candidate food (Nypa fruticans, NF) on IBD and CRC risk reduction were also evaluated. Fecal microbiome data were obtained from 109 IBD patients, 111 CRC patients, and 395 healthy control (HC) subjects by 16S rDNA amplicon sequencing. IBD and CRC risk assessment prediction models were then constructed by deep learning algorithms. Dietary effects were evaluated based on fecal microbiome data from rats fed on a regular chow diet (RCD), HFD, and HFD plus ethanol extracts or water extracts of NF. There were significant differences in taxa when IBD and CRC were compared with HC. The diagnostic performance (area under curve, AUC) of the deep learning algorithm was 0.84 for IBD and 0.80 for CRC prediction. Based on the rat fecal microbiome data, IBD and CRC risks were increased in HFD-fed rats versus RCD-fed rats. Interestingly, in the HFD-induced obesity model, the IBD and CRC risk scores were significantly lowered by the administration of ethanol extracts of NF, but not by the administration of water extracts of NF. In conclusion, changes in the fecal microbiome of obesity by Western diet could be important risk factors for the development of IBD and CRC. The risk prediction model developed in this study could be used to evaluate dietary efficacy. MDPI 2022-09-14 /pmc/articles/PMC9505706/ /pubmed/36144434 http://dx.doi.org/10.3390/microorganisms10091833 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Seo, Hochan
Kwon, Cheol-O
Park, Joo-Hyun
Kang, Chil-Sung
Shin, Tae-Seop
Yang, Eun-Young
Jung, Jin Woo
Moon, Byoung-Seok
Kim, Yoon-Keun
Dietary Efficacy Evaluation by Applying a Prediction Model Using Clinical Fecal Microbiome Data of Colorectal Disease to a Controlled Animal Model from an Obesity Perspective
title Dietary Efficacy Evaluation by Applying a Prediction Model Using Clinical Fecal Microbiome Data of Colorectal Disease to a Controlled Animal Model from an Obesity Perspective
title_full Dietary Efficacy Evaluation by Applying a Prediction Model Using Clinical Fecal Microbiome Data of Colorectal Disease to a Controlled Animal Model from an Obesity Perspective
title_fullStr Dietary Efficacy Evaluation by Applying a Prediction Model Using Clinical Fecal Microbiome Data of Colorectal Disease to a Controlled Animal Model from an Obesity Perspective
title_full_unstemmed Dietary Efficacy Evaluation by Applying a Prediction Model Using Clinical Fecal Microbiome Data of Colorectal Disease to a Controlled Animal Model from an Obesity Perspective
title_short Dietary Efficacy Evaluation by Applying a Prediction Model Using Clinical Fecal Microbiome Data of Colorectal Disease to a Controlled Animal Model from an Obesity Perspective
title_sort dietary efficacy evaluation by applying a prediction model using clinical fecal microbiome data of colorectal disease to a controlled animal model from an obesity perspective
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9505706/
https://www.ncbi.nlm.nih.gov/pubmed/36144434
http://dx.doi.org/10.3390/microorganisms10091833
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