Fecal Metabolome and Bacterial Composition in Severe Obesity: Impact of Diet and Bariatric Surgery

[EN] The aim of this study was to monitor the impact of a preoperative low-calorie diet and bariatric surgery on the bacterial gut microbiota composition and functionality in severe obesity and to compare sleeve gastrectomy (SG) versus Roux-en-Y gastric bypass (RYGB). The study also aimed to incorpo...

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Detalles Bibliográficos
Autores: Salazar, Nuria, Ponce-Alonso, Manuel, Garriga, María, Sánchez-Carrillo, Sergio, Hernández-Barranco, Ana María, Redruello, Begoña, Fernández García, María, Botella-Carretero, José I., Vega-Piñero, Belén, Galeano, Javier, Zamora, Javier, Ferrer, Manuel, González de los Reyes-Gavilán, Clara, Campo, Rosa del
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/296388
Acceso en línea:http://hdl.handle.net/10261/296388
Access Level:acceso abierto
Palabra clave:Gut microbiota
Bariatric surgery
Metabolomic
SCFAs
Machine learning for loss of weight excess prediction
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oai_identifier_str oai:digital.csic.es:10261/296388
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Fecal Metabolome and Bacterial Composition in Severe Obesity: Impact of Diet and Bariatric Surgery
title Fecal Metabolome and Bacterial Composition in Severe Obesity: Impact of Diet and Bariatric Surgery
spellingShingle Fecal Metabolome and Bacterial Composition in Severe Obesity: Impact of Diet and Bariatric Surgery
Salazar, Nuria
Gut microbiota
Bariatric surgery
Metabolomic
SCFAs
Machine learning for loss of weight excess prediction
title_short Fecal Metabolome and Bacterial Composition in Severe Obesity: Impact of Diet and Bariatric Surgery
title_full Fecal Metabolome and Bacterial Composition in Severe Obesity: Impact of Diet and Bariatric Surgery
title_fullStr Fecal Metabolome and Bacterial Composition in Severe Obesity: Impact of Diet and Bariatric Surgery
title_full_unstemmed Fecal Metabolome and Bacterial Composition in Severe Obesity: Impact of Diet and Bariatric Surgery
title_sort Fecal Metabolome and Bacterial Composition in Severe Obesity: Impact of Diet and Bariatric Surgery
dc.creator.none.fl_str_mv Salazar, Nuria
Ponce-Alonso, Manuel
Garriga, María
Sánchez-Carrillo, Sergio
Hernández-Barranco, Ana María
Redruello, Begoña
Fernández García, María
Botella-Carretero, José I.
Vega-Piñero, Belén
Galeano, Javier
Zamora, Javier
Ferrer, Manuel
González de los Reyes-Gavilán, Clara
Campo, Rosa del
author Salazar, Nuria
author_facet Salazar, Nuria
Ponce-Alonso, Manuel
Garriga, María
Sánchez-Carrillo, Sergio
Hernández-Barranco, Ana María
Redruello, Begoña
Fernández García, María
Botella-Carretero, José I.
Vega-Piñero, Belén
Galeano, Javier
Zamora, Javier
Ferrer, Manuel
González de los Reyes-Gavilán, Clara
Campo, Rosa del
author_role author
author2 Ponce-Alonso, Manuel
Garriga, María
Sánchez-Carrillo, Sergio
Hernández-Barranco, Ana María
Redruello, Begoña
Fernández García, María
Botella-Carretero, José I.
Vega-Piñero, Belén
Galeano, Javier
Zamora, Javier
Ferrer, Manuel
González de los Reyes-Gavilán, Clara
Campo, Rosa del
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia e Innovación (España)
Instituto de Salud Carlos III
Ministerio de Economía y Competitividad (España)
Fundación para la Investigación y la Innovación Biosanitaria del Principado de Asturias
Fundación Científica Asociación Española Contra el Cáncer
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Gut microbiota
Bariatric surgery
Metabolomic
SCFAs
Machine learning for loss of weight excess prediction
topic Gut microbiota
Bariatric surgery
Metabolomic
SCFAs
Machine learning for loss of weight excess prediction
description [EN] The aim of this study was to monitor the impact of a preoperative low-calorie diet and bariatric surgery on the bacterial gut microbiota composition and functionality in severe obesity and to compare sleeve gastrectomy (SG) versus Roux-en-Y gastric bypass (RYGB). The study also aimed to incorporate big data analysis for the omics results and machine learning by a Lasso-based analysis to detect the potential markers for excess weight loss. Forty patients who underwent bariatric surgery were recruited (14 underwent SG, and 26 underwent RYGB). Each participant contributed 4 fecal samples (baseline, post-diet, 1 month after surgery and 3 months after surgery). The bacterial composition was determined by 16S rDNA massive sequencing using MiSeq (Illumina). Metabolic signatures associated to fecal concentrations of short-chain fatty acids, amino acids, biogenic amines, gamma-aminobutyric acid and ammonium were determined by gas and liquid chromato-graphy. Orange 3 software was employed to correlate the variables, and a Lasso analysis was employed to predict the weight loss at the baseline samples. A correlation between Bacillota (formerly Firmicutes) abundance and excess weight was observed only for the highest body mass indexes. The low-calorie diet had little impact on composition and targeted metabolic activity. RYGB had a deeper impact on bacterial composition and putrefactive metabolism than SG, although the excess weight loss was comparable in the two groups. Significantly higher ammonium concentrations were detected in the feces of the RYGB group. We detected individual signatures of composition and functionality, rather than a gut microbiota characteristic of severe obesity, with opposing tendencies for almost all measured variables in the two surgical approaches. The gut microbiota of the baseline samples was not useful for predicting excess weight loss after the bariatric process.
publishDate 2022
dc.date.none.fl_str_mv 2022
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/296388
url http://hdl.handle.net/10261/296388
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/MINECO//IJCI-2014-19885
info:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 (ISCIII)/AC17%2F00022
info:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 (ISCIII)/ICI21%2F00012
The underlying dataset has been published as supplementary material of the article in the publisher platform at 10.1080/19490976.2022.2106102
https://doi.org/10.1080/19490976.2022.2106102
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
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spelling Fecal Metabolome and Bacterial Composition in Severe Obesity: Impact of Diet and Bariatric SurgerySalazar, NuriaPonce-Alonso, ManuelGarriga, MaríaSánchez-Carrillo, SergioHernández-Barranco, Ana MaríaRedruello, BegoñaFernández García, MaríaBotella-Carretero, José I.Vega-Piñero, BelénGaleano, JavierZamora, JavierFerrer, ManuelGonzález de los Reyes-Gavilán, ClaraCampo, Rosa delGut microbiotaBariatric surgeryMetabolomicSCFAsMachine learning for loss of weight excess prediction[EN] The aim of this study was to monitor the impact of a preoperative low-calorie diet and bariatric surgery on the bacterial gut microbiota composition and functionality in severe obesity and to compare sleeve gastrectomy (SG) versus Roux-en-Y gastric bypass (RYGB). The study also aimed to incorporate big data analysis for the omics results and machine learning by a Lasso-based analysis to detect the potential markers for excess weight loss. Forty patients who underwent bariatric surgery were recruited (14 underwent SG, and 26 underwent RYGB). Each participant contributed 4 fecal samples (baseline, post-diet, 1 month after surgery and 3 months after surgery). The bacterial composition was determined by 16S rDNA massive sequencing using MiSeq (Illumina). Metabolic signatures associated to fecal concentrations of short-chain fatty acids, amino acids, biogenic amines, gamma-aminobutyric acid and ammonium were determined by gas and liquid chromato-graphy. Orange 3 software was employed to correlate the variables, and a Lasso analysis was employed to predict the weight loss at the baseline samples. A correlation between Bacillota (formerly Firmicutes) abundance and excess weight was observed only for the highest body mass indexes. The low-calorie diet had little impact on composition and targeted metabolic activity. RYGB had a deeper impact on bacterial composition and putrefactive metabolism than SG, although the excess weight loss was comparable in the two groups. Significantly higher ammonium concentrations were detected in the feces of the RYGB group. We detected individual signatures of composition and functionality, rather than a gut microbiota characteristic of severe obesity, with opposing tendencies for almost all measured variables in the two surgical approaches. The gut microbiota of the baseline samples was not useful for predicting excess weight loss after the bariatric process.This work was supported by the Spanish Ministry of Science and Innovation, Instituto de Salud Carlos III (ICI21/00012), and co-funded by the NextGenerationEU and PRTR. NS was financed by a postdoctoral Juan de la Cierva contract (Ref. IJCI-2014-19885) from Ministerio de Economia y Competitividad, Spain and is the recipient of a postdoctoral contract awarded by the Fundación para la Investigación y la Innovación Biosanitaria del Principado de Asturias (FINBA). MPA was supported by a Rio Hortega contract (CM19/00069) from the Instituto de Salud Carlos III and co-funded by the European Social Fund (ESF, “Investing in your future”). MF acknowledge the financial support by the Instituto de Salud Carlos III, and Fundación Agencia Española contra el Cáncer (projects ERA NET TRANSCAN-2 AC17/00022) and co- financed by the European Regional Development Fund (ERDF)Peer reviewedTaylor & FrancisMinisterio de Ciencia e Innovación (España)Instituto de Salud Carlos IIIMinisterio de Economía y Competitividad (España)Fundación para la Investigación y la Innovación Biosanitaria del Principado de AsturiasFundación Científica Asociación Española Contra el CáncerConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202320232022info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/296388reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/MINECO//IJCI-2014-19885info:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 (ISCIII)/AC17%2F00022info:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 (ISCIII)/ICI21%2F00012The underlying dataset has been published as supplementary material of the article in the publisher platform at 10.1080/19490976.2022.2106102https://doi.org/10.1080/19490976.2022.2106102Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2963882026-05-22T06:33:51Z
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