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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Autor*in:	Jiarui Chen [verfasserIn] Sara Leal Siliceo [verfasserIn] Yueqiong Ni [verfasserIn] Henrik B. Nielsen [verfasserIn] Aimin Xu [verfasserIn] Gianni Panagiotou [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Gut microbiome Microbiome dynamics Resistance Lifestyle intervention Machine learning

Übergeordnetes Werk:	In: Microbiome - BMC, 2013, 11(2023), 1, Seite 16
Übergeordnetes Werk:	volume:11 ; year:2023 ; number:1 ; pages:16

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s40168-023-01604-z

Katalog-ID:	DOAJ101092016

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520			\|a 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
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allfields	10.1186/s40168-023-01604-z doi (DE-627)DOAJ101092016 (DE-599)DOAJ97ed8e066dd147c3bb953263bd0eb73f DE-627 ger DE-627 rakwb eng QR100-130 Jiarui Chen verfasserin aut Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 Gut microbiome Microbiome dynamics Resistance Lifestyle intervention Machine learning Microbial ecology Sara Leal Siliceo verfasserin aut Yueqiong Ni verfasserin aut Henrik B. Nielsen verfasserin aut Aimin Xu verfasserin aut Gianni Panagiotou verfasserin aut In Microbiome BMC, 2013 11(2023), 1, Seite 16 (DE-627)734146140 (DE-600)2697425-3 20492618 nnns volume:11 year:2023 number:1 pages:16 https://doi.org/10.1186/s40168-023-01604-z kostenfrei https://doaj.org/article/97ed8e066dd147c3bb953263bd0eb73f kostenfrei https://doi.org/10.1186/s40168-023-01604-z kostenfrei https://doaj.org/toc/2049-2618 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 1 16
spelling	10.1186/s40168-023-01604-z doi (DE-627)DOAJ101092016 (DE-599)DOAJ97ed8e066dd147c3bb953263bd0eb73f DE-627 ger DE-627 rakwb eng QR100-130 Jiarui Chen verfasserin aut Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 Gut microbiome Microbiome dynamics Resistance Lifestyle intervention Machine learning Microbial ecology Sara Leal Siliceo verfasserin aut Yueqiong Ni verfasserin aut Henrik B. Nielsen verfasserin aut Aimin Xu verfasserin aut Gianni Panagiotou verfasserin aut In Microbiome BMC, 2013 11(2023), 1, Seite 16 (DE-627)734146140 (DE-600)2697425-3 20492618 nnns volume:11 year:2023 number:1 pages:16 https://doi.org/10.1186/s40168-023-01604-z kostenfrei https://doaj.org/article/97ed8e066dd147c3bb953263bd0eb73f kostenfrei https://doi.org/10.1186/s40168-023-01604-z kostenfrei https://doaj.org/toc/2049-2618 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 1 16
allfields_unstemmed	10.1186/s40168-023-01604-z doi (DE-627)DOAJ101092016 (DE-599)DOAJ97ed8e066dd147c3bb953263bd0eb73f DE-627 ger DE-627 rakwb eng QR100-130 Jiarui Chen verfasserin aut Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 Gut microbiome Microbiome dynamics Resistance Lifestyle intervention Machine learning Microbial ecology Sara Leal Siliceo verfasserin aut Yueqiong Ni verfasserin aut Henrik B. Nielsen verfasserin aut Aimin Xu verfasserin aut Gianni Panagiotou verfasserin aut In Microbiome BMC, 2013 11(2023), 1, Seite 16 (DE-627)734146140 (DE-600)2697425-3 20492618 nnns volume:11 year:2023 number:1 pages:16 https://doi.org/10.1186/s40168-023-01604-z kostenfrei https://doaj.org/article/97ed8e066dd147c3bb953263bd0eb73f kostenfrei https://doi.org/10.1186/s40168-023-01604-z kostenfrei https://doaj.org/toc/2049-2618 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 1 16
allfieldsGer	10.1186/s40168-023-01604-z doi (DE-627)DOAJ101092016 (DE-599)DOAJ97ed8e066dd147c3bb953263bd0eb73f DE-627 ger DE-627 rakwb eng QR100-130 Jiarui Chen verfasserin aut Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 Gut microbiome Microbiome dynamics Resistance Lifestyle intervention Machine learning Microbial ecology Sara Leal Siliceo verfasserin aut Yueqiong Ni verfasserin aut Henrik B. Nielsen verfasserin aut Aimin Xu verfasserin aut Gianni Panagiotou verfasserin aut In Microbiome BMC, 2013 11(2023), 1, Seite 16 (DE-627)734146140 (DE-600)2697425-3 20492618 nnns volume:11 year:2023 number:1 pages:16 https://doi.org/10.1186/s40168-023-01604-z kostenfrei https://doaj.org/article/97ed8e066dd147c3bb953263bd0eb73f kostenfrei https://doi.org/10.1186/s40168-023-01604-z kostenfrei https://doaj.org/toc/2049-2618 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 1 16
allfieldsSound	10.1186/s40168-023-01604-z doi (DE-627)DOAJ101092016 (DE-599)DOAJ97ed8e066dd147c3bb953263bd0eb73f DE-627 ger DE-627 rakwb eng QR100-130 Jiarui Chen verfasserin aut Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 Gut microbiome Microbiome dynamics Resistance Lifestyle intervention Machine learning Microbial ecology Sara Leal Siliceo verfasserin aut Yueqiong Ni verfasserin aut Henrik B. Nielsen verfasserin aut Aimin Xu verfasserin aut Gianni Panagiotou verfasserin aut In Microbiome BMC, 2013 11(2023), 1, Seite 16 (DE-627)734146140 (DE-600)2697425-3 20492618 nnns volume:11 year:2023 number:1 pages:16 https://doi.org/10.1186/s40168-023-01604-z kostenfrei https://doaj.org/article/97ed8e066dd147c3bb953263bd0eb73f kostenfrei https://doi.org/10.1186/s40168-023-01604-z kostenfrei https://doaj.org/toc/2049-2618 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 1 16
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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. 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callnumber-first	Q - Science
author	Jiarui Chen
spellingShingle	Jiarui Chen misc QR100-130 misc Gut microbiome misc Microbiome dynamics misc Resistance misc Lifestyle intervention misc Machine learning misc Microbial ecology Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions
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topic_title	QR100-130 Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions Gut microbiome Microbiome dynamics Resistance Lifestyle intervention Machine learning
topic	misc QR100-130 misc Gut microbiome misc Microbiome dynamics misc Resistance misc Lifestyle intervention misc Machine learning misc Microbial ecology
topic_unstemmed	misc QR100-130 misc Gut microbiome misc Microbiome dynamics misc Resistance misc Lifestyle intervention misc Machine learning misc Microbial ecology
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title	Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions
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title_full	Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions
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author_browse	Jiarui Chen Sara Leal Siliceo Yueqiong Ni Henrik B. Nielsen Aimin Xu Gianni Panagiotou
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title_sort	identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions
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title_auth	Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions
abstract	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
abstractGer	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
abstract_unstemmed	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
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title_short	Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions
url	https://doi.org/10.1186/s40168-023-01604-z https://doaj.org/article/97ed8e066dd147c3bb953263bd0eb73f https://doaj.org/toc/2049-2618
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author2	Sara Leal Siliceo Yueqiong Ni Henrik B. Nielsen Aimin Xu Gianni Panagiotou
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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. 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Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions

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