Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions
Abstract Background A growing body of evidence suggests that the gut microbiota is strongly linked to general human health. Microbiome-directed interventions, such as diet and exercise, are acknowledged as a viable and achievable strategy for preventing disorders and improving human health. However,...
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Format: | Article |
Language: | English |
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BMC
2023-08-01
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Series: | Microbiome |
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Online Access: | https://doi.org/10.1186/s40168-023-01604-z |
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author | Jiarui Chen Sara Leal Siliceo Yueqiong Ni Henrik B. Nielsen Aimin Xu Gianni Panagiotou |
author_facet | Jiarui Chen Sara Leal Siliceo Yueqiong Ni Henrik B. Nielsen Aimin Xu Gianni Panagiotou |
author_sort | Jiarui Chen |
collection | DOAJ |
description | Abstract Background A growing body of evidence suggests that the gut microbiota is strongly linked to general human health. Microbiome-directed interventions, such as diet and exercise, are acknowledged as a viable and achievable strategy for preventing disorders and improving human health. However, due to the significant inter-individual diversity of the gut microbiota between subjects, lifestyle recommendations are expected to have distinct and highly variable impacts to the microbiome structure. Results Here, through a large-scale meta-analysis including 1448 shotgun metagenomics samples obtained longitudinally from 396 individuals during lifestyle studies, we revealed Bacteroides stercoris, Prevotella copri, and Bacteroides vulgatus as biomarkers of microbiota’s resistance to structural changes, and aromatic and non-aromatic amino acid biosynthesis as important regulator of microbiome dynamics. We established criteria for distinguishing between significant compositional changes from normal microbiota fluctuation and classified individuals based on their level of response. We further developed a machine learning model for predicting “responders” and “non-responders” independently of the type of intervention with an area under the curve of up to 0.86 in external validation cohorts of different ethnicities. Conclusions We propose here that microbiome-based stratification is possible for identifying individuals with highly plastic or highly resistant microbial structures. Identifying subjects that will not respond to generalized lifestyle therapeutic interventions targeting the restructuring of gut microbiota is important to ensure that primary end-points of clinical studies are reached. Video Abstract |
first_indexed | 2024-03-09T15:03:09Z |
format | Article |
id | doaj.art-97ed8e066dd147c3bb953263bd0eb73f |
institution | Directory Open Access Journal |
issn | 2049-2618 |
language | English |
last_indexed | 2024-03-09T15:03:09Z |
publishDate | 2023-08-01 |
publisher | BMC |
record_format | Article |
series | Microbiome |
spelling | doaj.art-97ed8e066dd147c3bb953263bd0eb73f2023-11-26T13:47:40ZengBMCMicrobiome2049-26182023-08-0111111610.1186/s40168-023-01604-zIdentification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventionsJiarui Chen0Sara Leal Siliceo1Yueqiong Ni2Henrik B. Nielsen3Aimin Xu4Gianni Panagiotou5Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute -Microbiome DynamicsLeibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute -Microbiome DynamicsLeibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute -Microbiome DynamicsClinical MicrobiomicsState Key Laboratory of Pharmaceutical Biotechnology, The University of Hong KongLeibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute -Microbiome DynamicsAbstract Background A growing body of evidence suggests that the gut microbiota is strongly linked to general human health. Microbiome-directed interventions, such as diet and exercise, are acknowledged as a viable and achievable strategy for preventing disorders and improving human health. However, due to the significant inter-individual diversity of the gut microbiota between subjects, lifestyle recommendations are expected to have distinct and highly variable impacts to the microbiome structure. Results Here, through a large-scale meta-analysis including 1448 shotgun metagenomics samples obtained longitudinally from 396 individuals during lifestyle studies, we revealed Bacteroides stercoris, Prevotella copri, and Bacteroides vulgatus as biomarkers of microbiota’s resistance to structural changes, and aromatic and non-aromatic amino acid biosynthesis as important regulator of microbiome dynamics. We established criteria for distinguishing between significant compositional changes from normal microbiota fluctuation and classified individuals based on their level of response. We further developed a machine learning model for predicting “responders” and “non-responders” independently of the type of intervention with an area under the curve of up to 0.86 in external validation cohorts of different ethnicities. Conclusions We propose here that microbiome-based stratification is possible for identifying individuals with highly plastic or highly resistant microbial structures. Identifying subjects that will not respond to generalized lifestyle therapeutic interventions targeting the restructuring of gut microbiota is important to ensure that primary end-points of clinical studies are reached. Video Abstracthttps://doi.org/10.1186/s40168-023-01604-zGut microbiomeMicrobiome dynamicsResistanceLifestyle interventionMachine learning |
spellingShingle | Jiarui Chen Sara Leal Siliceo Yueqiong Ni Henrik B. Nielsen Aimin Xu Gianni Panagiotou Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions Microbiome Gut microbiome Microbiome dynamics Resistance Lifestyle intervention Machine learning |
title | Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions |
title_full | Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions |
title_fullStr | Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions |
title_full_unstemmed | Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions |
title_short | Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions |
title_sort | identification of robust and generalizable biomarkers for microbiome based stratification in lifestyle interventions |
topic | Gut microbiome Microbiome dynamics Resistance Lifestyle intervention Machine learning |
url | https://doi.org/10.1186/s40168-023-01604-z |
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