AD risk score for the early phases of disease based on unsupervised machine learning.
Alzheimer's disease
cognitive testing
latent variable
machine learning
multidomain biomarkers
progression
risk score
unsupervised learning
Journal
Alzheimer's & dementia : the journal of the Alzheimer's Association
ISSN: 1552-5279
Titre abrégé: Alzheimers Dement
Pays: United States
ID NLM: 101231978
Informations de publication
Date de publication:
11 2020
11 2020
Historique:
received:
02
12
2019
revised:
28
05
2020
accepted:
08
06
2020
pubmed:
31
7
2020
medline:
28
9
2021
entrez:
31
7
2020
Statut:
ppublish
Résumé
Identifying cognitively normal individuals at high risk for progression to symptomatic Alzheimer's disease (AD) is critical for early intervention. An AD risk score was derived using unsupervised machine learning. The score was developed using data from 226 cognitively normal individuals and included cerebrospinal fluid, magnetic resonance imaging, and cognitive measures, and validated in an independent cohort. Higher baseline AD progression risk scores (hazard ratio = 2.70, P < 0.001) were associated with greater risks of progression to clinical symptoms of mild cognitive impairment (MCI). Baseline scores had an area under the curve of 0.83 (95% confidence interval: 0.75 to 0.91) for identifying subjects who progressed to MCI/dementia within 5 years. The validation procedure, using data from the Alzheimer's Disease Neuroimaging Initiative, demonstrated accuracy of prediction across the AD spectrum. The derived risk score provides high predictive accuracy for identifying which individuals with normal cognition are likely to show clinical decline due to AD within 5 years.
Identifiants
pubmed: 32729964
doi: 10.1002/alz.12140
pmc: PMC7666001
mid: NIHMS1617938
doi:
Types de publication
Journal Article
Observational Study
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1524-1533Subventions
Organisme : NIA NIH HHS
ID : P30 AG066507
Pays : United States
Organisme : NIA NIH HHS
ID : P50 AG005146
Pays : United States
Organisme : NIA NIH HHS
ID : K01 AG050699
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG024904
Pays : United States
Organisme : NIA NIH HHS
ID : U19 AG033655
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG068002
Pays : United States
Informations de copyright
© 2020 the Alzheimer's Association.
Références
Alzheimers Dement. 2016 Jan;12(1):60-4
pubmed: 26710325
J Gerontol. 1982 May;37(3):323-9
pubmed: 7069156
Alzheimers Dement. 2011 May;7(3):270-9
pubmed: 21514249
J Prev Alzheimers Dis. 2016;3(4):229-235
pubmed: 29034223
Ann Neurol. 2012 Jun;71(6):765-75
pubmed: 22488240
Front Aging Neurosci. 2019 Sep 06;11:229
pubmed: 31555121
Neuroimage. 2006 Jul 1;31(3):968-80
pubmed: 16530430
Alzheimers Dement (Amst). 2016 Oct 14;4:159-168
pubmed: 27830173
Hum Brain Mapp. 2015 Jul;36(7):2826-41
pubmed: 25879865
Brain. 2018 Mar 1;141(3):877-887
pubmed: 29365053
Alzheimers Dement. 2011 May;7(3):280-92
pubmed: 21514248
J Neurosci. 2003 Apr 15;23(8):3295-301
pubmed: 12716936
Neuroimage Clin. 2013 Sep 16;3:352-60
pubmed: 24363990
Neuroimage. 2012 Jul 16;61(4):1402-18
pubmed: 22430496
Ann Neurol. 2009 Apr;65(4):403-13
pubmed: 19296504
Lancet Neurol. 2013 Feb;12(2):207-16
pubmed: 23332364
Neurology. 2013 Nov 12;81(20):1753-8
pubmed: 24132375
Curr Alzheimer Res. 2014;11(8):773-84
pubmed: 25212916
Neuroimage. 1999 Feb;9(2):179-94
pubmed: 9931268
Proc Natl Acad Sci U S A. 2000 Sep 26;97(20):11050-5
pubmed: 10984517
Stat Med. 2014 Sep 28;33(22):3946-59
pubmed: 24825728
J Cogn Neurosci. 1993 Spring;5(2):162-76
pubmed: 23972151
Comput Stat Data Anal. 2017 Sep;113:125-135
pubmed: 28966420
JAMA Neurol. 2019 Jun 10;:
pubmed: 31180460
Acta Neuropathol. 2014 Dec;128(6):755-66
pubmed: 25348064
Alzheimers Dement. 2011 May;7(3):263-9
pubmed: 21514250
Biometrics. 1988 Sep;44(3):837-45
pubmed: 3203132
Arch Neurol. 2010 Aug;67(8):949-56
pubmed: 20697045