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Multi-omics signature of brain amyloid deposition in asymptomatic individuals at-risk for Alzheimer's disease: The INSIGHT-preAD study
BACKGROUND: One of the biggest challenge in Alzheimer's disease (AD) is to identify pathways and markers of disease prediction easily accessible, for prevention and treatment. Here we analysed blood samples from the INveStIGation of AlzHeimer's predicTors (INSIGHT-preAD) cohort of elderly...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6796577/ https://www.ncbi.nlm.nih.gov/pubmed/31492558 http://dx.doi.org/10.1016/j.ebiom.2019.08.051 |
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author | Xicota, Laura Ichou, Farid Lejeune, François-Xavier Colsch, Benoit Tenenhaus, Arthur Leroy, Inka Fontaine, Gaëlle Lhomme, Marie Bertin, Hugo Habert, Marie-Odile Epelbaum, Stéphane Dubois, Bruno Mochel, Fanny Potier, Marie-Claude |
author_facet | Xicota, Laura Ichou, Farid Lejeune, François-Xavier Colsch, Benoit Tenenhaus, Arthur Leroy, Inka Fontaine, Gaëlle Lhomme, Marie Bertin, Hugo Habert, Marie-Odile Epelbaum, Stéphane Dubois, Bruno Mochel, Fanny Potier, Marie-Claude |
author_sort | Xicota, Laura |
collection | PubMed |
description | BACKGROUND: One of the biggest challenge in Alzheimer's disease (AD) is to identify pathways and markers of disease prediction easily accessible, for prevention and treatment. Here we analysed blood samples from the INveStIGation of AlzHeimer's predicTors (INSIGHT-preAD) cohort of elderly asymptomatic individuals with and without brain amyloid load. METHODS: We performed blood RNAseq, and plasma metabolomics and lipidomics using liquid chromatography-mass spectrometry on 48 individuals amyloid positive and 48 amyloid negative (SUVr cut-off of 0·7918). The three data sets were analysed separately using differential gene expression based on negative binomial distribution, non-parametric (Wilcoxon) and parametric (correlation-adjusted Student't) tests. Data integration was conducted using sparse partial least squares-discriminant and principal component analyses. Bootstrap-selected top-ten features from the three data sets were tested for their discriminant power using Receiver Operating Characteristic curve. Longitudinal metabolomic analysis was carried out on a subset of 22 subjects. FINDINGS: Univariate analyses identified three medium chain fatty acids, 4-nitrophenol and a set of 64 transcripts enriched for inflammation and fatty acid metabolism differentially quantified in amyloid positive and negative subjects. Importantly, the amounts of the three medium chain fatty acids were correlated over time in a subset of 22 subjects (p < 0·05). Multi-omics integrative analyses showed that metabolites efficiently discriminated between subjects according to their amyloid status while lipids did not and transcripts showed trends. Finally, the ten top metabolites and transcripts represented the most discriminant omics features with 99·4% chance prediction for amyloid positivity. INTERPRETATION: This study suggests a potential blood omics signature for prediction of amyloid positivity in asymptomatic at-risk subjects, allowing for a less invasive, more accessible, and less expensive risk assessment of AD as compared to PET studies or lumbar puncture. FUND: Institut Hospitalo-Universitaire and Institut du Cerveau et de la Moelle Epiniere (IHU-A-ICM), French Ministry of Research, Fondation Alzheimer, Pfizer, and Avid. |
format | Online Article Text |
id | pubmed-6796577 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-67965772019-10-22 Multi-omics signature of brain amyloid deposition in asymptomatic individuals at-risk for Alzheimer's disease: The INSIGHT-preAD study Xicota, Laura Ichou, Farid Lejeune, François-Xavier Colsch, Benoit Tenenhaus, Arthur Leroy, Inka Fontaine, Gaëlle Lhomme, Marie Bertin, Hugo Habert, Marie-Odile Epelbaum, Stéphane Dubois, Bruno Mochel, Fanny Potier, Marie-Claude EBioMedicine Research paper BACKGROUND: One of the biggest challenge in Alzheimer's disease (AD) is to identify pathways and markers of disease prediction easily accessible, for prevention and treatment. Here we analysed blood samples from the INveStIGation of AlzHeimer's predicTors (INSIGHT-preAD) cohort of elderly asymptomatic individuals with and without brain amyloid load. METHODS: We performed blood RNAseq, and plasma metabolomics and lipidomics using liquid chromatography-mass spectrometry on 48 individuals amyloid positive and 48 amyloid negative (SUVr cut-off of 0·7918). The three data sets were analysed separately using differential gene expression based on negative binomial distribution, non-parametric (Wilcoxon) and parametric (correlation-adjusted Student't) tests. Data integration was conducted using sparse partial least squares-discriminant and principal component analyses. Bootstrap-selected top-ten features from the three data sets were tested for their discriminant power using Receiver Operating Characteristic curve. Longitudinal metabolomic analysis was carried out on a subset of 22 subjects. FINDINGS: Univariate analyses identified three medium chain fatty acids, 4-nitrophenol and a set of 64 transcripts enriched for inflammation and fatty acid metabolism differentially quantified in amyloid positive and negative subjects. Importantly, the amounts of the three medium chain fatty acids were correlated over time in a subset of 22 subjects (p < 0·05). Multi-omics integrative analyses showed that metabolites efficiently discriminated between subjects according to their amyloid status while lipids did not and transcripts showed trends. Finally, the ten top metabolites and transcripts represented the most discriminant omics features with 99·4% chance prediction for amyloid positivity. INTERPRETATION: This study suggests a potential blood omics signature for prediction of amyloid positivity in asymptomatic at-risk subjects, allowing for a less invasive, more accessible, and less expensive risk assessment of AD as compared to PET studies or lumbar puncture. FUND: Institut Hospitalo-Universitaire and Institut du Cerveau et de la Moelle Epiniere (IHU-A-ICM), French Ministry of Research, Fondation Alzheimer, Pfizer, and Avid. Elsevier 2019-09-03 /pmc/articles/PMC6796577/ /pubmed/31492558 http://dx.doi.org/10.1016/j.ebiom.2019.08.051 Text en © 2019 Published by Elsevier B.V. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research paper Xicota, Laura Ichou, Farid Lejeune, François-Xavier Colsch, Benoit Tenenhaus, Arthur Leroy, Inka Fontaine, Gaëlle Lhomme, Marie Bertin, Hugo Habert, Marie-Odile Epelbaum, Stéphane Dubois, Bruno Mochel, Fanny Potier, Marie-Claude Multi-omics signature of brain amyloid deposition in asymptomatic individuals at-risk for Alzheimer's disease: The INSIGHT-preAD study |
title | Multi-omics signature of brain amyloid deposition in asymptomatic individuals at-risk for Alzheimer's disease: The INSIGHT-preAD study |
title_full | Multi-omics signature of brain amyloid deposition in asymptomatic individuals at-risk for Alzheimer's disease: The INSIGHT-preAD study |
title_fullStr | Multi-omics signature of brain amyloid deposition in asymptomatic individuals at-risk for Alzheimer's disease: The INSIGHT-preAD study |
title_full_unstemmed | Multi-omics signature of brain amyloid deposition in asymptomatic individuals at-risk for Alzheimer's disease: The INSIGHT-preAD study |
title_short | Multi-omics signature of brain amyloid deposition in asymptomatic individuals at-risk for Alzheimer's disease: The INSIGHT-preAD study |
title_sort | multi-omics signature of brain amyloid deposition in asymptomatic individuals at-risk for alzheimer's disease: the insight-pread study |
topic | Research paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6796577/ https://www.ncbi.nlm.nih.gov/pubmed/31492558 http://dx.doi.org/10.1016/j.ebiom.2019.08.051 |
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