Cohort-Specific Optimization of Models Predicting Preclinical Alzheimer’s Disease, to Enhance Screening Performance in the Middle of Preclinical Alzheimer’s Disease Clinical Studies - 21/11/24
Alzheimer’s disease Neuroimaging Initiative
Japanese Alzheimer’s disease Neuroimaging Initiative
The A4 Study Team
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Abstract |
Background |
Models that can predict brain amyloid beta (Aβ) status more accurately have been desired to identify participants for clinical trials of preclinical Alzheimer’s disease (AD). However, potential heterogeneity between different cohorts and the limited cohort size have been the reasons preventing the development of reliable models applicable to the Asian population, including Japan.
Objectives |
We aim to propose a novel approach to predict preclinical AD while overcoming these constraints, by building models specifically optimized for ADNI or for J-ADNI, based on the larger samples from A4 study data.
Design & Participants |
This is a retrospective study including cognitive normal participants (CDR-global = 0) from A4 study, Alzheimer Disease Neuroimaging Initiative (ADNI), and Japanese-ADNI (J-ADNI) cohorts.
Measurements |
The model is made up of age, sex, education years, history of AD, Clinical Dementia Rating-Sum of Boxes, Preclinical Alzheimer Cognitive Composite score, and APOE genotype, to predict the degree of amyloid accumulation in amyloid PET as Standardized Uptake Value ratio (SUVr). The model was at first built based on A4 data, and we can choose at which SUVr threshold configuration the A4-based model may achieve the best performance area under the curve (AUC) when applied to the random-split half ADNI or J-ADNI subset. We then evaluated whether the selected model may also achieve better performance in the remaining ADNI or J-ADNI subsets.
Result |
When compared to the results without optimization, this procedure showed efficacy of AUC improvement of up to approximately 0.10 when applied to the models “without APOE;” the degree of AUC improvement was larger in the ADNI cohort than in the J-ADNI cohort.
Conclusions |
The obtained AUC had improved mildly when compared to the AUC in case of literature-based predetermined SUVr threshold configuration. This means our procedure allowed us to predict preclinical AD among ADNI or J-ADNI second-half samples with slightly better predictive performance. Our optimizing method may be practically useful in the middle of the ongoing clinical study of preclinical AD, as a screening to further increase the prior probability of preclinical AD before amyloid testing.
Il testo completo di questo articolo è disponibile in PDF.Key words : Amyloid beta, preclinical Alzheimer’s disease, machine learning, predictive model
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| Description about the ADNI: 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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| Description about the A4 study: The A4 Study is a secondary prevention trial in preclinical Alzheimer’s disease, aiming to slow cognitive decline associated with brain amyloid accumulation in clinically normal older individuals. The A4 Study is funded by a public-private-philanthropic partnership, including funding from the National Institutes of Health-National Institute on Aging, Eli Lilly and Company, Alzheimer’s Association, Accelerating Medicines Partnership, GHR Foundation, an anonymous foundation and additional private donors, with in-kind support from Avid and Cogstate. The companion observational Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) Study is funded by the Alzheimer’s Association and GHR Foundation. The A4 and LEARN Studies are led by Dr. Reisa Sperling at Brigham and Women’s Hospital, Harvard Medical School and Dr. Paul Aisen at the Alzheimer’s Therapeutic Research Institute (ATRI), University of Southern California. The A4 and LEARN Studies are coordinated by ATRI at the University of Southern California, and the data are made available through the Laboratory for Neuro Imaging at the University of Southern California. The participants screening for the A4 Study provided permission to share their de-identified data in order to advance the quest to find a successful treatment for Alzheimer’s disease. We would like to acknowledge the dedication of all the participants, the site personnel, and all of the partnership team members who continue to make the A4 and LEARN Studies possible. The complete A4 Study Team list is available on: a4study.org/a4-study-team. |
Vol 8 - N° 4
P. 503-512 - Aprile 2021 Ritorno al numero
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