TY - JOUR
T1 - Data-driven models of dominantly-inherited Alzheimer's disease progression
AU - Oxtoby, Neil P.
AU - Young, Alexandra L.
AU - Cash, David M.
AU - Benzinger, Tammie L.S.
AU - Fagan, Anne M.
AU - Morris, John C.
AU - Bateman, Randall J.
AU - Fox, Nick C.
AU - Schott, Jonathan M.
AU - Alexander, Daniel C.
N1 - Funding Information:
This work is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 666992. N.P.O. and D.C.A. acknowledge grants from UK Engineering and Physical Sciences Research Council (EPSRC) through grant numbers EP/M020533/1, EP/ M006093/1, and EP/J020990/01, the European Commission (Horizon 2020), the Alzheimer’s Association, The Michael J Fox Foundation for Parkinson’s Research, Alzheimer’s Research UK, and the Weston Brain Institute, during the conduct of the study. A.L.Y., J.M.S., and D.C.A. acknowledge funding from EPSRC during the conduct of the study. D.M.C. is supported by grants from the Alzheimer Society (AS-PG-15-025), Alzheimer’s Research UK (ARUK-PG2014-1946) and Medical Research Council UK (MR/M023664/1). T.L.S.B. reports grants from NIH, during the conduct of the study; grants from NIH, and Avid Radiopharmaceuticals; other support from Eli Lilly, Roche, Biogen, and National MS Society, outside the submitted work. A.M.F. reports grants and personal fees from Roche; personal fees from IBL International, AbbVie, and DiamiR; grants from Biogen, and Fujirebio; and personal fees from LabCorp, outside the submitted work. J.M.S. reports grants from Medical Research Council, EPSRC, Wolfson Foundation, Alzheimer’s Research UK, Brain Research Trust, the European Commission (Horizon 2020), Alzheimer’s Society, and AVID Radiopharmaceuticals; personal fees from Roche Pharmaceuticals, Eli Lilly, and Axon Neuroscience; non-financial support from AVID Radiopharmaceuticals; all outside the submitted work. R.J.B. reports he, the Chair of Neurology, and Washington University in St. Louis have equity ownership interest in C2N Diagnostics and may receive royalty income based on technology licensed by Washington University to C2N Diagnostics. In addition, R.J.B. has received grants from Alzheimer’s Association, an Anonymous Foundation, BrightFocus Foundation, Cure Alzheimer’s Fund, the Association for Frontotemporal Dementia, the Gerald and Henrietta Rauenhorst (GHR) Foundation, NIH/NIA, other from NIH/State Government Sources, personal fees and other from Washington University, personal fees and non-financial support from Roche, Sanofi, Pfizer, personal fees from Merck, grants from Pharma Consortium (Biogen Idec, Eli Lilly and Co., Hoffman La-Roche Inc., Genentech Inc., Janssen Alzheimer Immunotherapy, Abbvie, Amgen, Astra Zeneca, Novartis Pharma AG, Pfizer Biotherapeutics R and D, Sanofi-Aventi, Eisai), non-financial support from Avid Radiopharmaceuticals outside the submitted work. This work was in part supported by the UK Dementia Research Institute. N.C.F. reports fees (all paid to University College London) for consultancy from Janssen, Roche, Eli Lilly, Novartis, Sanofi, and GlaxoSmithKline; for contracted image analyses from Janssen Alzheimers Immunotherapy; and for serving on a data monitoring committee for Aducanumab/Biogen. J.C.M. reports grants from NIH (P50AG005681, P01AG003991, P01AG026276, UF01AG032438) during the conduct of the study.
Funding Information:
Data collection and sharing for this project was supported by The Dominantly Inherited Alzheimer Network (DIAN; UF1 AG032438; to R.J.B. and J.C.M.), funded by the National Institute on Aging, the German Center for Neurodegenerative Diseases, the Medical Research Council (MRC; to N.C.F.) Dementias Platform UK (MR/ L023784/2 and MR/009076/2), and supported by researchers at the National Institute for Health Research University College London Hospitals Biomedical Research Centre. J.C.M. and R.J.B. receive research support from National Institute of Health. R.J.B. receives research support from the Alzheimer’s Association, BrightFocus Foundation, Cure Alzheimer’s Fund.
Publisher Copyright:
© The Author(s) (2018). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
PY - 2018/5/1
Y1 - 2018/5/1
N2 - See Li and Donohue (doi:10.1093/brain/awy089) for a scientific commentary on this article. Dominantly-inherited Alzheimer's disease is widely hoped to hold the key to developing interventions for sporadic late onset Alzheimer's disease. We use emerging techniques in generative data-driven disease progression modelling to characterize dominantly-inherited Alzheimer's disease progression with unprecedented resolution, and without relying upon familial estimates of years until symptom onset. We retrospectively analysed biomarker data from the sixth data freeze of the Dominantly Inherited Alzheimer Network observational study, including measures of amyloid proteins and neurofibrillary tangles in the brain, regional brain volumes and cortical thicknesses, brain glucose hypometabolism, and cognitive performance from the Mini-Mental State Examination (all adjusted for age, years of education, sex, and head size, as appropriate). Data included 338 participants with known mutation status (211 mutation carriers in three subtypes: 163 PSEN1, 17 PSEN2, and 31 APP) and a baseline visit (age 19-66; up to four visits each, 1.1 ± 1.9 years in duration; spanning 30 years before, to 21 years after, parental age of symptom onset). We used an event-based model to estimate sequences of biomarker changes from baseline data across disease subtypes (mutation groups), and a differential equation model to estimate biomarker trajectories from longitudinal data (up to 66 mutation carriers, all subtypes combined). The two models concur that biomarker abnormality proceeds as follows: amyloid deposition in cortical then subcortical regions (∼ 24 ± 11 years before onset); phosphorylated tau (17 ± 8 years), tau and amyloid-β changes in cerebrospinal fluid; neurodegeneration first in the putamen and nucleus accumbens (up to 6 ± 2 years); then cognitive decline (7 ± 6 years), cerebral hypometabolism (4 ± 4 years), and further regional neurodegeneration. Our models predicted symptom onset more accurately than predictions that used familial estimates: root mean squared error of 1.35 years versus 5.54 years. The models reveal hidden detail on dominantly-inherited Alzheimer's disease progression, as well as providing data-driven systems for fine-grained patient staging and prediction of symptom onset with great potential utility in clinical trials.
AB - See Li and Donohue (doi:10.1093/brain/awy089) for a scientific commentary on this article. Dominantly-inherited Alzheimer's disease is widely hoped to hold the key to developing interventions for sporadic late onset Alzheimer's disease. We use emerging techniques in generative data-driven disease progression modelling to characterize dominantly-inherited Alzheimer's disease progression with unprecedented resolution, and without relying upon familial estimates of years until symptom onset. We retrospectively analysed biomarker data from the sixth data freeze of the Dominantly Inherited Alzheimer Network observational study, including measures of amyloid proteins and neurofibrillary tangles in the brain, regional brain volumes and cortical thicknesses, brain glucose hypometabolism, and cognitive performance from the Mini-Mental State Examination (all adjusted for age, years of education, sex, and head size, as appropriate). Data included 338 participants with known mutation status (211 mutation carriers in three subtypes: 163 PSEN1, 17 PSEN2, and 31 APP) and a baseline visit (age 19-66; up to four visits each, 1.1 ± 1.9 years in duration; spanning 30 years before, to 21 years after, parental age of symptom onset). We used an event-based model to estimate sequences of biomarker changes from baseline data across disease subtypes (mutation groups), and a differential equation model to estimate biomarker trajectories from longitudinal data (up to 66 mutation carriers, all subtypes combined). The two models concur that biomarker abnormality proceeds as follows: amyloid deposition in cortical then subcortical regions (∼ 24 ± 11 years before onset); phosphorylated tau (17 ± 8 years), tau and amyloid-β changes in cerebrospinal fluid; neurodegeneration first in the putamen and nucleus accumbens (up to 6 ± 2 years); then cognitive decline (7 ± 6 years), cerebral hypometabolism (4 ± 4 years), and further regional neurodegeneration. Our models predicted symptom onset more accurately than predictions that used familial estimates: root mean squared error of 1.35 years versus 5.54 years. The models reveal hidden detail on dominantly-inherited Alzheimer's disease progression, as well as providing data-driven systems for fine-grained patient staging and prediction of symptom onset with great potential utility in clinical trials.
KW - biomarker dynamics
KW - differential-equation model
KW - disease progression
KW - dominantly-inherited Alzheimer's disease
KW - event-based model
UR - http://www.scopus.com/inward/record.url?scp=85047093618&partnerID=8YFLogxK
U2 - 10.1093/brain/awy050
DO - 10.1093/brain/awy050
M3 - Article
C2 - 29579160
AN - SCOPUS:85047093618
VL - 141
SP - 1529
EP - 1544
JO - Brain
JF - Brain
SN - 0006-8950
IS - 5
ER -