Progression along data-driven disease timelines is predictive of Alzheimer's disease in a population-based cohort

M.A. (Arfan) Ikram, for the Alzheimer's Disease Neuroimaging Initiative

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Abstract

Data-driven disease progression models have provided important insight into the timeline of brain changes in
AD phenotypes. However, their utility in predicting the progression of pre-symptomatic AD in a populationbased setting has not yet been investigated. In this study, we investigated if the disease timelines constructed
in a case-controlled setting, with subjects stratified according to APOE status, are generalizable to a populationbased cohort, and if progression along these disease timelines is predictive of AD. Seven volumetric biomarkers
derived from structural MRI were considered. We estimated APOE-specific disease timelines of changes in these
biomarkers using a recently proposed method called co-initialized discriminative event-based modeling (co-init
DEBM). This method can also estimate a disease stage for new subjects by calculating their position along the
disease timelines. The model was trained and cross-validated on the Alzheimer’s Disease Neuroimaging Initiative
(ADNI) dataset, and tested on the population-based Rotterdam Study (RS) cohort. We compared the diagnostic
and prognostic value of the disease stage in the two cohorts. Furthermore, we investigated if the rate of change of
disease stage in RS participants with longitudinal MRI data was predictive of AD. In ADNI, the estimated disease
timeslines for 𝜖4 non-carriers and carriers were found to be significantly different from one another (𝑝 < 0.001).
The estimate disease stage along the respective timelines distinguished AD subjects from controls with an AUC
of 0.83 in both APOE 𝜖4 non-carriers and carriers. In the RS cohort, we obtained an AUC of 0.83 and 0.85 in 𝜖4
non-carriers and carriers, respectively. Progression along the disease timelines as estimated by the rate of change
of disease stage showed a significant difference (𝑝 < 0.005) for subjects with pre-symptomatic AD as compared to
the general aging population in RS. It distinguished pre-symptomatic AD subjects with an AUC of 0.81 in APOE
𝜖4 non-carriers and 0.88 in carriers, which was better than any individual volumetric biomarker, or its rate of
change, could achieve. Our results suggest that co-init DEBM trained on case-controlled data is generalizable to a
population-based cohort setting and that progression along the disease timelines is predictive of the development
of AD in the general population. We expect that this approach can help to identify at-risk individuals from the
general population for targeted clinical trials as well as to provide biomarker based objective assessment in such
trials.
Original languageEnglish
Article number118233
Number of pages11
JournalNeuroImage
Volume238
Early online date4 Jun 2021
DOIs
Publication statusPublished - Sep 2021

Bibliographical note

Acknowledgement
This study is part of the EuroPOND initiative, which is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 666992. This study is also a part of TKI-LSH Health Holland Alzheimer Nederland project (No. LSHM18049). This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (project: ORACLE, grant agreement No. 678543). The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. E.E. Bron acknowledges support from Dutch Heart Foundation (PPP Allowance, 2018B011). E.E. Bron and W.J. Niessen are supported by Medical Delta Diagnostics 3.0: Dementia and Stroke.

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

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