Oxtoby, N;
Young, AL;
Cash, DM;
Benzinger, TLS;
Fagan, AM;
Morris, JC;
Bateman, RJ;
... Alexander, DC; + view all
(2018)
Data-driven models of dominantly-inherited Alzheimer's disease progression.
Brain
, 141
(5)
pp. 1529-1544.
10.1093/brain/awy050.
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Abstract
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 characterise dominantly-inherited Alzheimer’s disease progression with unprecedented resolution, and without relying upon familial estimates of years until symptom onset (EYO). 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: 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 sub-cortical regions (approximately 24±11 years before onset); CSF p-tau (17±8 years), tau and Aβ42 changes; 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 EYO: 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.
| Type: | Article |
|---|---|
| Title: | Data-driven models of dominantly-inherited Alzheimer's disease progression |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/brain/awy050 |
| Publisher version: | https://doi.org/10.1093/brain/awy050 |
| Language: | English |
| Additional information: | © The Author(s) (2018). Published by Oxford University Press on behalf of the Guarantors of Brain. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creative commons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Event based model, differential equation model, disease progression, neurology, dominantly-inherited Alzheimer's disease, biomarkers |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10041766 |
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