Multi-omics signature of brain amyloid deposition in asymptomatic individuals at-risk for Alzheimer's disease: The INSIGHT-preAD study.
Alzheimer
Amyloid PET
Asymptomatic
Biomarkers
Multi-omics
Prediction
Journal
EBioMedicine
ISSN: 2352-3964
Titre abrégé: EBioMedicine
Pays: Netherlands
ID NLM: 101647039
Informations de publication
Date de publication:
Sep 2019
Sep 2019
Historique:
received:
25
06
2019
revised:
23
08
2019
accepted:
23
08
2019
pubmed:
8
9
2019
medline:
6
2
2020
entrez:
8
9
2019
Statut:
ppublish
Résumé
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. 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. 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. 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.
Sections du résumé
BACKGROUND
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
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
RESULTS
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
CONCLUSIONS
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.
Identifiants
pubmed: 31492558
pii: S2352-3964(19)30573-0
doi: 10.1016/j.ebiom.2019.08.051
pmc: PMC6796577
pii:
doi:
Substances chimiques
Amyloid beta-Peptides
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
518-528Informations de copyright
Copyright © 2019. Published by Elsevier B.V.
Références
Brief Bioinform. 2018 Nov 27;19(6):1356-1369
pubmed: 29106465
Nat Protoc. 2011 Jun 30;6(7):1060-83
pubmed: 21720319
J Alzheimers Dis. 2013;34(2):399-405
pubmed: 23234877
Genome Biol. 2014;15(12):550
pubmed: 25516281
BBA Clin. 2015 Jan 16;3:123-5
pubmed: 26675661
Neurobiol Dis. 2019 Apr;124:555-562
pubmed: 30639291
PLoS Comput Biol. 2017 Nov 3;13(11):e1005752
pubmed: 29099853
Eur J Radiol. 2017 Sep;94:16-24
pubmed: 28941755
Anal Chem. 2012 Jun 5;84(11):5035-9
pubmed: 22533540
Nat Med. 2014 Apr;20(4):415-8
pubmed: 24608097
Int J Geriatr Psychiatry. 2019 Jun;34(6):846-854
pubmed: 30714214
J Lipid Res. 2012 Mar;53(3):567-576
pubmed: 22203775
Mol Neurobiol. 2016 Nov;53(9):5902-5911
pubmed: 26510741
Lancet Neurol. 2018 Apr;17(4):335-346
pubmed: 29500152
J Neuropathol Exp Neurol. 2012 May;71(5):362-81
pubmed: 22487856
JAMA Neurol. 2019 Apr 1;76(4):420-429
pubmed: 30640362
Acta Neuropathol. 2018 Dec;136(6):821-853
pubmed: 30488277
Philos Trans R Soc Lond B Biol Sci. 2014 Sep 26;369(1652):
pubmed: 25135963
Biostatistics. 2007 Jan;8(1):118-27
pubmed: 16632515
Front Neurol. 2019 Mar 19;10:218
pubmed: 30941085
BMC Bioinformatics. 2013 Apr 15;14:128
pubmed: 23586463
Alzheimers Dement. 2018 Jun;14(6):734-742
pubmed: 29352855
Toxicol Sci. 2015 Jan;143(1):46-53
pubmed: 25304213
Anal Chem. 2006 Feb 1;78(3):779-87
pubmed: 16448051
Nucleic Acids Res. 2019 Jan 8;47(D1):D590-D595
pubmed: 30321428
J Alzheimers Dis. 2012;32(1):169-81
pubmed: 22785402
Neurobiol Dis. 2019 Apr;124:454-468
pubmed: 30557660
Neurology. 2017 Feb 7;88(6):569-576
pubmed: 28062720
Ann Neurol. 2019 Jan;85(1):155-160
pubmed: 30521086
JAMA Neurol. 2018 Sep 1;75(9):1124-1131
pubmed: 29799986
Sci Rep. 2018 Apr 3;8(1):5530
pubmed: 29615645
J Alzheimers Dis. 2016 Oct 18;54(4):1373-1383
pubmed: 27589530
Neurology. 2012 Feb 7;78(6):387-95
pubmed: 22302550
J Alzheimers Dis. 2013;33(3):737-53
pubmed: 23042217
Biostatistics. 2014 Jul;15(3):569-83
pubmed: 24550197
Alzheimer Dis Assoc Disord. 2014 Jul-Sep;28(3):226-33
pubmed: 24731980
Brain. 2019 Apr 1;142(4):1134-1147
pubmed: 30851100
Bioinformatics. 2015 May 1;31(9):1493-5
pubmed: 25527831
J Alzheimers Dis. 2011;23(1):109-19
pubmed: 20930264
Lipids. 2000 Dec;35(12):1305-12
pubmed: 11201991
Nucleic Acids Res. 2017 Jan 4;45(D1):D353-D361
pubmed: 27899662
PLoS One. 2011;6(7):e21643
pubmed: 21779331
Nucleic Acids Res. 2018 Jan 4;46(D1):D608-D617
pubmed: 29140435
Neurology. 2017 Sep 5;89(10):1028-1034
pubmed: 28794245
Psychometrika. 2017 May 23;:
pubmed: 28536930
J Alzheimers Dis. 2015;43(1):93-108
pubmed: 25079797
Mol Psychiatry. 2011 Sep;16(9):903-7
pubmed: 21556001
Electrophoresis. 2018 Jun;39(11):1375-1381
pubmed: 29500834
Alzheimers Dement. 2017 Sep;13(9):965-984
pubmed: 28341160
Pharmacol Ther. 2019 Jul;199:173-187
pubmed: 30877021
BMC Bioinformatics. 2016 Dec 6;17(1):496
pubmed: 27923348
Bioinformatics. 2009 Oct 15;25(20):2700-7
pubmed: 19648135
JAMA. 2019 Jun 18;321(23):2316-2325
pubmed: 31211344
Psychiatr Genet. 2005 Mar;15(1):1-6
pubmed: 15722950
Ann Nucl Med. 2018 Feb;32(2):75-86
pubmed: 29218458
PLoS One. 2013 May 20;8(5):e63644
pubmed: 23700429
BMC Bioinformatics. 2016 Jan 20;17 Suppl 2:15
pubmed: 26821531
Brain. 2018 Jun 1;141(6):1855-1870
pubmed: 29608645
Neurology. 2019 May 28;92(22):1053-1059
pubmed: 31028129
Metabolomics. 2016;12:91
pubmed: 27110228
Nucleic Acids Res. 2000 Jan 1;28(1):27-30
pubmed: 10592173
Front Genet. 2017 Jun 16;8:84
pubmed: 28670325
J Proteome Res. 2014 May 2;13(5):2649-58
pubmed: 24694177
Science. 2018 Dec 14;362(6420):1250-1251
pubmed: 30545877
Int J Geriatr Psychiatry. 2008 Nov;23(11):1191-202
pubmed: 18500688
Lancet Neurol. 2018 Jan;17(1):84-93
pubmed: 29263011
Transl Psychiatry. 2011 Dec 13;1:e57
pubmed: 22832349
Electron J Stat. 2015;9(1):1583-1607
pubmed: 26279737
Neurobiol Aging. 2017 Oct;58:225-237
pubmed: 28716532
Lancet Neurol. 2018 Mar;17(3):241-250
pubmed: 29397305
J Chromatogr B Analyt Technol Biomed Life Sci. 2014 Sep 1;966:34-47
pubmed: 24815365
Mol Neurobiol. 2016 Aug;53(6):3596-3605
pubmed: 26099307
Bioinformatics. 2008 Nov 1;24(21):2534-6
pubmed: 18606607
Neurobiol Dis. 2011 Sep;43(3):698-705
pubmed: 21669286