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Can the Salivary Microbiome Predict Cardiovascular Diseases? Lessons Learned From the Qatari Population

Background: Many studies have linked dysbiosis of the gut microbiome to the development of cardiovascular diseases (CVD). However, studies assessing the association between the salivary microbiome and CVD risk on a large cohort remain sparse. This study aims to identify whether a predictive salivary...

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Autores principales: Murugesan, Selvasankar, Elanbari, Mohammed, Bangarusamy, Dhinoth Kumar, Terranegra, Annalisa, Al Khodor, Souhaila
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8703018/
https://www.ncbi.nlm.nih.gov/pubmed/34956135
http://dx.doi.org/10.3389/fmicb.2021.772736
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author Murugesan, Selvasankar
Elanbari, Mohammed
Bangarusamy, Dhinoth Kumar
Terranegra, Annalisa
Al Khodor, Souhaila
author_facet Murugesan, Selvasankar
Elanbari, Mohammed
Bangarusamy, Dhinoth Kumar
Terranegra, Annalisa
Al Khodor, Souhaila
author_sort Murugesan, Selvasankar
collection PubMed
description Background: Many studies have linked dysbiosis of the gut microbiome to the development of cardiovascular diseases (CVD). However, studies assessing the association between the salivary microbiome and CVD risk on a large cohort remain sparse. This study aims to identify whether a predictive salivary microbiome signature is associated with a high risk of developing CVD in the Qatari population. Methods: Saliva samples from 2,974 Qatar Genome Project (QGP) participants were collected from Qatar Biobank (QBB). Based on the CVD score, subjects were classified into low-risk (LR < 10) (n = 2491), moderate-risk (MR = 10–20) (n = 320) and high-risk (HR > 30) (n = 163). To assess the salivary microbiome (SM) composition, 16S-rDNA libraries were sequenced and analyzed using QIIME-pipeline. Machine Learning (ML) strategies were used to identify SM-based predictors of CVD risk. Results: Firmicutes and Bacteroidetes were the predominant phyla among all the subjects included. Linear Discriminant Analysis Effect Size (LEfSe) analysis revealed that Clostridiaceae and Capnocytophaga were the most significantly abundant genera in the LR group, while Lactobacillus and Rothia were significantly abundant in the HR group. ML based prediction models revealed that Desulfobulbus, Prevotella, and Tissierellaceae were the common predictors of increased risk to CVD. Conclusion: This study identified significant differences in the SM composition in HR and LR CVD subjects. This is the first study to apply ML-based prediction modeling using the SM to predict CVD in an Arab population. More studies are required to better understand the mechanisms of how those microbes contribute to CVD.
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spelling pubmed-87030182021-12-25 Can the Salivary Microbiome Predict Cardiovascular Diseases? Lessons Learned From the Qatari Population Murugesan, Selvasankar Elanbari, Mohammed Bangarusamy, Dhinoth Kumar Terranegra, Annalisa Al Khodor, Souhaila Front Microbiol Microbiology Background: Many studies have linked dysbiosis of the gut microbiome to the development of cardiovascular diseases (CVD). However, studies assessing the association between the salivary microbiome and CVD risk on a large cohort remain sparse. This study aims to identify whether a predictive salivary microbiome signature is associated with a high risk of developing CVD in the Qatari population. Methods: Saliva samples from 2,974 Qatar Genome Project (QGP) participants were collected from Qatar Biobank (QBB). Based on the CVD score, subjects were classified into low-risk (LR < 10) (n = 2491), moderate-risk (MR = 10–20) (n = 320) and high-risk (HR > 30) (n = 163). To assess the salivary microbiome (SM) composition, 16S-rDNA libraries were sequenced and analyzed using QIIME-pipeline. Machine Learning (ML) strategies were used to identify SM-based predictors of CVD risk. Results: Firmicutes and Bacteroidetes were the predominant phyla among all the subjects included. Linear Discriminant Analysis Effect Size (LEfSe) analysis revealed that Clostridiaceae and Capnocytophaga were the most significantly abundant genera in the LR group, while Lactobacillus and Rothia were significantly abundant in the HR group. ML based prediction models revealed that Desulfobulbus, Prevotella, and Tissierellaceae were the common predictors of increased risk to CVD. Conclusion: This study identified significant differences in the SM composition in HR and LR CVD subjects. This is the first study to apply ML-based prediction modeling using the SM to predict CVD in an Arab population. More studies are required to better understand the mechanisms of how those microbes contribute to CVD. Frontiers Media S.A. 2021-12-10 /pmc/articles/PMC8703018/ /pubmed/34956135 http://dx.doi.org/10.3389/fmicb.2021.772736 Text en Copyright © 2021 Murugesan, Elanbari, Bangarusamy, Terranegra and Al Khodor. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Microbiology
Murugesan, Selvasankar
Elanbari, Mohammed
Bangarusamy, Dhinoth Kumar
Terranegra, Annalisa
Al Khodor, Souhaila
Can the Salivary Microbiome Predict Cardiovascular Diseases? Lessons Learned From the Qatari Population
title Can the Salivary Microbiome Predict Cardiovascular Diseases? Lessons Learned From the Qatari Population
title_full Can the Salivary Microbiome Predict Cardiovascular Diseases? Lessons Learned From the Qatari Population
title_fullStr Can the Salivary Microbiome Predict Cardiovascular Diseases? Lessons Learned From the Qatari Population
title_full_unstemmed Can the Salivary Microbiome Predict Cardiovascular Diseases? Lessons Learned From the Qatari Population
title_short Can the Salivary Microbiome Predict Cardiovascular Diseases? Lessons Learned From the Qatari Population
title_sort can the salivary microbiome predict cardiovascular diseases? lessons learned from the qatari population
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8703018/
https://www.ncbi.nlm.nih.gov/pubmed/34956135
http://dx.doi.org/10.3389/fmicb.2021.772736
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