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Signature of Alzheimer’s Disease in Intestinal Microbiome: Results From the AlzBiom Study

BACKGROUND: Changes in intestinal microbiome composition have been described in animal models of Alzheimer’s disease (AD) and AD patients. Here we investigated how well taxonomic and functional intestinal microbiome data and their combination with clinical data can be used to discriminate between am...

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Autores principales: Laske, Christoph, Müller, Stephan, Preische, Oliver, Ruschil, Victoria, Munk, Matthias H. J., Honold, Iris, Peter, Silke, Schoppmeier, Ulrich, Willmann, Matthias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9063165/
https://www.ncbi.nlm.nih.gov/pubmed/35516807
http://dx.doi.org/10.3389/fnins.2022.792996
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author Laske, Christoph
Müller, Stephan
Preische, Oliver
Ruschil, Victoria
Munk, Matthias H. J.
Honold, Iris
Peter, Silke
Schoppmeier, Ulrich
Willmann, Matthias
author_facet Laske, Christoph
Müller, Stephan
Preische, Oliver
Ruschil, Victoria
Munk, Matthias H. J.
Honold, Iris
Peter, Silke
Schoppmeier, Ulrich
Willmann, Matthias
author_sort Laske, Christoph
collection PubMed
description BACKGROUND: Changes in intestinal microbiome composition have been described in animal models of Alzheimer’s disease (AD) and AD patients. Here we investigated how well taxonomic and functional intestinal microbiome data and their combination with clinical data can be used to discriminate between amyloid-positive AD patients and cognitively healthy elderly controls. METHODS: In the present study we investigated intestinal microbiome in 75 amyloid-positive AD patients and 100 cognitively healthy controls participating in the AlzBiom study. We randomly split the data into a training and a validation set. Intestinal microbiome was measured using shotgun metagenomics. Receiver operating characteristic (ROC) curve analysis was performed to examine the discriminatory ability of intestinal microbiome among diagnostic groups. RESULTS: The best model for discrimination of amyloid-positive AD patients from healthy controls with taxonomic data was obtained analyzing 18 genera features, and yielded an area under the receiver operating characteristic curve (AUROC) of 0.76 in the training set and 0.61 in the validation set. The best models with functional data were obtained analyzing 17 GO (Gene Ontology) features with an AUROC of 0.81 in the training set and 0.75 in the validation set and 26 KO [Kyoto Encyclopedia of Genes and Genomes (KEGG) ortholog] features with an AUROC of 0.83 and 0.77, respectively. Using ensemble learning for these three models including a clinical model with the 4 parameters age, gender, BMI and ApoE yielded an AUROC of 0.92 in the training set and 0.80 in the validation set. DISCUSSION: In conclusion, we identified a specific Alzheimer signature in intestinal microbiome that can be used to discriminate amyloid-positive AD patients from healthy controls. The diagnostic accuracy increases from taxonomic to functional data and is even better when combining taxonomic, functional and clinical models. Intestinal microbiome represents an innovative diagnostic supplement and a promising area for developing novel interventions against AD.
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spelling pubmed-90631652022-05-04 Signature of Alzheimer’s Disease in Intestinal Microbiome: Results From the AlzBiom Study Laske, Christoph Müller, Stephan Preische, Oliver Ruschil, Victoria Munk, Matthias H. J. Honold, Iris Peter, Silke Schoppmeier, Ulrich Willmann, Matthias Front Neurosci Neuroscience BACKGROUND: Changes in intestinal microbiome composition have been described in animal models of Alzheimer’s disease (AD) and AD patients. Here we investigated how well taxonomic and functional intestinal microbiome data and their combination with clinical data can be used to discriminate between amyloid-positive AD patients and cognitively healthy elderly controls. METHODS: In the present study we investigated intestinal microbiome in 75 amyloid-positive AD patients and 100 cognitively healthy controls participating in the AlzBiom study. We randomly split the data into a training and a validation set. Intestinal microbiome was measured using shotgun metagenomics. Receiver operating characteristic (ROC) curve analysis was performed to examine the discriminatory ability of intestinal microbiome among diagnostic groups. RESULTS: The best model for discrimination of amyloid-positive AD patients from healthy controls with taxonomic data was obtained analyzing 18 genera features, and yielded an area under the receiver operating characteristic curve (AUROC) of 0.76 in the training set and 0.61 in the validation set. The best models with functional data were obtained analyzing 17 GO (Gene Ontology) features with an AUROC of 0.81 in the training set and 0.75 in the validation set and 26 KO [Kyoto Encyclopedia of Genes and Genomes (KEGG) ortholog] features with an AUROC of 0.83 and 0.77, respectively. Using ensemble learning for these three models including a clinical model with the 4 parameters age, gender, BMI and ApoE yielded an AUROC of 0.92 in the training set and 0.80 in the validation set. DISCUSSION: In conclusion, we identified a specific Alzheimer signature in intestinal microbiome that can be used to discriminate amyloid-positive AD patients from healthy controls. The diagnostic accuracy increases from taxonomic to functional data and is even better when combining taxonomic, functional and clinical models. Intestinal microbiome represents an innovative diagnostic supplement and a promising area for developing novel interventions against AD. Frontiers Media S.A. 2022-04-19 /pmc/articles/PMC9063165/ /pubmed/35516807 http://dx.doi.org/10.3389/fnins.2022.792996 Text en Copyright © 2022 Laske, Müller, Preische, Ruschil, Munk, Honold, Peter, Schoppmeier and Willmann. 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 Neuroscience
Laske, Christoph
Müller, Stephan
Preische, Oliver
Ruschil, Victoria
Munk, Matthias H. J.
Honold, Iris
Peter, Silke
Schoppmeier, Ulrich
Willmann, Matthias
Signature of Alzheimer’s Disease in Intestinal Microbiome: Results From the AlzBiom Study
title Signature of Alzheimer’s Disease in Intestinal Microbiome: Results From the AlzBiom Study
title_full Signature of Alzheimer’s Disease in Intestinal Microbiome: Results From the AlzBiom Study
title_fullStr Signature of Alzheimer’s Disease in Intestinal Microbiome: Results From the AlzBiom Study
title_full_unstemmed Signature of Alzheimer’s Disease in Intestinal Microbiome: Results From the AlzBiom Study
title_short Signature of Alzheimer’s Disease in Intestinal Microbiome: Results From the AlzBiom Study
title_sort signature of alzheimer’s disease in intestinal microbiome: results from the alzbiom study
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9063165/
https://www.ncbi.nlm.nih.gov/pubmed/35516807
http://dx.doi.org/10.3389/fnins.2022.792996
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