Quantitative detection and staging of presymptomatic cognitive decline in familial Alzheimer's disease: a retrospective cohort analysis.


Journal

Alzheimer's research & therapy
ISSN: 1758-9193
Titre abrégé: Alzheimers Res Ther
Pays: England
ID NLM: 101511643

Informations de publication

Date de publication:
06 10 2020
Historique:
received: 09 07 2020
accepted: 17 09 2020
entrez: 7 10 2020
pubmed: 8 10 2020
medline: 25 6 2021
Statut: epublish

Résumé

Understanding the earliest manifestations of Alzheimer's disease (AD) is key to realising disease-modifying treatments. Advances in neuroimaging and fluid biomarkers have improved our ability to identify AD pathology in vivo. The critical next step is improved detection and staging of early cognitive change. We studied an asymptomatic familial Alzheimer's disease (FAD) cohort to characterise preclinical cognitive change. Data included 35 asymptomatic participants at 50% risk of carrying a pathogenic FAD mutation. Participants completed a multi-domain neuropsychology battery. After accounting for sex, age and education, we used event-based modelling to estimate the sequence of cognitive decline in presymptomatic FAD, and uncertainty in the sequence. We assigned individuals to their most likely model stage of cumulative cognitive decline, given their data. Linear regression of estimated years to symptom onset against model stage was used to estimate the timing of preclinical cognitive decline. Cognitive change in mutation carriers was first detected in measures of accelerated long-term forgetting, up to 10 years before estimated symptom onset. Measures of subjective cognitive decline also revealed early abnormalities. Our data-driven model demonstrated subtle cognitive impairment across multiple cognitive domains in clinically normal individuals on the AD continuum. Data-driven modelling of neuropsychological test scores has potential to differentiate cognitive decline from cognitive stability and to estimate a fine-grained sequence of decline across cognitive domains and functions, in the preclinical phase of Alzheimer's disease. This can improve the design of future presymptomatic trials by informing enrichment strategies and guiding the selection of outcome measures.

Sections du résumé

BACKGROUND
Understanding the earliest manifestations of Alzheimer's disease (AD) is key to realising disease-modifying treatments. Advances in neuroimaging and fluid biomarkers have improved our ability to identify AD pathology in vivo. The critical next step is improved detection and staging of early cognitive change. We studied an asymptomatic familial Alzheimer's disease (FAD) cohort to characterise preclinical cognitive change.
METHODS
Data included 35 asymptomatic participants at 50% risk of carrying a pathogenic FAD mutation. Participants completed a multi-domain neuropsychology battery. After accounting for sex, age and education, we used event-based modelling to estimate the sequence of cognitive decline in presymptomatic FAD, and uncertainty in the sequence. We assigned individuals to their most likely model stage of cumulative cognitive decline, given their data. Linear regression of estimated years to symptom onset against model stage was used to estimate the timing of preclinical cognitive decline.
RESULTS
Cognitive change in mutation carriers was first detected in measures of accelerated long-term forgetting, up to 10 years before estimated symptom onset. Measures of subjective cognitive decline also revealed early abnormalities. Our data-driven model demonstrated subtle cognitive impairment across multiple cognitive domains in clinically normal individuals on the AD continuum.
CONCLUSIONS
Data-driven modelling of neuropsychological test scores has potential to differentiate cognitive decline from cognitive stability and to estimate a fine-grained sequence of decline across cognitive domains and functions, in the preclinical phase of Alzheimer's disease. This can improve the design of future presymptomatic trials by informing enrichment strategies and guiding the selection of outcome measures.

Identifiants

pubmed: 33023653
doi: 10.1186/s13195-020-00695-2
pii: 10.1186/s13195-020-00695-2
pmc: PMC7539456
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

126

Subventions

Organisme : Medical Research Council
ID : G0900421
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/M003108/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/S03546X/1
Pays : United Kingdom

Références

J Psychiatr Res. 1975 Nov;12(3):189-98
pubmed: 1202204
Sci Transl Med. 2014 Mar 19;6(228):228fs13
pubmed: 24648338
Neuropathology. 2015 Aug;35(4):390-400
pubmed: 25964057
Neurology. 2014 Jul 15;83(3):253-60
pubmed: 24928124
J Neurol Neurosurg Psychiatry. 1996 Jan;60(1):91-3
pubmed: 8558161
Brain. 1998 Sep;121 ( Pt 9):1631-9
pubmed: 9762953
Brain. 1996 Dec;119 ( Pt 6):2001-7
pubmed: 9010004
Lancet Neurol. 2010 Jan;9(1):119-28
pubmed: 20083042
Cortex. 1986 Dec;22(4):611-20
pubmed: 3816245
Brain. 2018 May 1;141(5):1529-1544
pubmed: 29579160
Neurology. 1993 Nov;43(11):2412-4
pubmed: 8232972
Nature. 1995 Jun 29;375(6534):754-60
pubmed: 7596406
JAMA Neurol. 2017 Dec 1;74(12):1455-1463
pubmed: 28973551
Alzheimers Dement. 2020 Jul;16(7):965-973
pubmed: 32489019
Lancet Neurol. 2018 May;17(5):394-395
pubmed: 29573908
Nature. 1991 Feb 21;349(6311):704-6
pubmed: 1671712
Lancet Neurol. 2019 Jan;18(1):88-106
pubmed: 30497964
Lancet Neurol. 2018 Feb;17(2):123-132
pubmed: 29413314
Alzheimers Dement. 2018 Jan;14(1):43-53
pubmed: 28738187
Neuroimage. 2012 Apr 15;60(3):1880-9
pubmed: 22281676
Lancet Neurol. 2013 Apr;12(4):357-67
pubmed: 23477989
Alzheimers Dement. 2016 Jul;12(7):796-804
pubmed: 26852195
J Neurol Neurosurg Psychiatry. 2016 Mar;87(3):235-43
pubmed: 25783437
JAMA Neurol. 2014 Aug;71(8):961-70
pubmed: 24886908
Alzheimers Res Ther. 2011 Jan 06;3(1):1
pubmed: 21211070
Alzheimers Res Ther. 2013 Oct 17;5(5):48
pubmed: 24131566
Alzheimers Dement (Amst). 2016 Dec 18;5:23-34
pubmed: 28054025
Ann Neurol. 2007 Jun;61(6):587-98
pubmed: 17444534
J Alzheimers Dis. 2014;41(2):453-66
pubmed: 24625794
Biomed Res Int. 2015;2015:828120
pubmed: 25922840
J Neurol Neurosurg Psychiatry. 2014 Apr;85(4):363-70
pubmed: 23840054
J Int Neuropsychol Soc. 2018 Aug;24(7):693-702
pubmed: 29706146
Ann Clin Transl Neurol. 2020 May;7(5):776-785
pubmed: 32315118
JAMA. 2017 Jun 13;317(22):2305-2316
pubmed: 28609533
JAMA Neurol. 2016 Apr;73(4):431-8
pubmed: 26902171
Neurology. 2017 Oct 3;89(14):1464-1470
pubmed: 28878053
Alzheimers Dement (Amst). 2016 Oct 18;6:108-121
pubmed: 28239636
Neuropsychology. 2014 Jan;28(1):19-29
pubmed: 24219606
Science. 1995 Aug 18;269(5226):973-7
pubmed: 7638622
N Engl J Med. 2012 Aug 30;367(9):795-804
pubmed: 22784036
Alzheimers Dement. 2017 Sep;13(9):1004-1012
pubmed: 28253478
Brain. 2014 Sep;137(Pt 9):2564-77
pubmed: 25012224
Nat Commun. 2018 Oct 15;9(1):4273
pubmed: 30323170

Auteurs

Antoinette O'Connor (A)

Dementia Research Centre, UCL Queen Square Institute Of Neurology, 8-11 Queen Square, London, WC1N 3AR, UK. antoinette.o'connor@ucl.ac.uk.
UK Dementia Research Institute at UCL, UCL, London, UK. antoinette.o'connor@ucl.ac.uk.

Philip S J Weston (PSJ)

Dementia Research Centre, UCL Queen Square Institute Of Neurology, 8-11 Queen Square, London, WC1N 3AR, UK.

Ivanna M Pavisic (IM)

Dementia Research Centre, UCL Queen Square Institute Of Neurology, 8-11 Queen Square, London, WC1N 3AR, UK.
UK Dementia Research Institute at UCL, UCL, London, UK.

Natalie S Ryan (NS)

Dementia Research Centre, UCL Queen Square Institute Of Neurology, 8-11 Queen Square, London, WC1N 3AR, UK.
UK Dementia Research Institute at UCL, UCL, London, UK.

Jessica D Collins (JD)

Dementia Research Centre, UCL Queen Square Institute Of Neurology, 8-11 Queen Square, London, WC1N 3AR, UK.

Kirsty Lu (K)

Dementia Research Centre, UCL Queen Square Institute Of Neurology, 8-11 Queen Square, London, WC1N 3AR, UK.

Sebastian J Crutch (SJ)

Dementia Research Centre, UCL Queen Square Institute Of Neurology, 8-11 Queen Square, London, WC1N 3AR, UK.

Daniel C Alexander (DC)

Department of Computer Science, UCL Centre for Medical Image Computing, 1st Floor, 90 High Holborn, London, WC1V 6LJ, UK.

Nick C Fox (NC)

Dementia Research Centre, UCL Queen Square Institute Of Neurology, 8-11 Queen Square, London, WC1N 3AR, UK.
UK Dementia Research Institute at UCL, UCL, London, UK.

Neil P Oxtoby (NP)

Department of Computer Science, UCL Centre for Medical Image Computing, 1st Floor, 90 High Holborn, London, WC1V 6LJ, UK. n.oxtoby@ucl.ac.uk.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female
Humans United States Aged Cross-Sectional Studies Medicare Part C
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH