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Multi-method investigation of factors influencing amyloid onset and impairment in three cohorts

Alzheimer’s disease biomarkers are becoming increasingly important for characterizing the longitudinal course of disease, predicting the timing of clinical and cognitive symptoms, and for recruitment and treatment monitoring in clinical trials. In this work, we develop and evaluate three methods for...

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Autores principales: Betthauser, Tobey J, Bilgel, Murat, Koscik, Rebecca L, Jedynak, Bruno M, An, Yang, Kellett, Kristina A, Moghekar, Abhay, Jonaitis, Erin M, Stone, Charles K, Engelman, Corinne D, Asthana, Sanjay, Christian, Bradley T, Wong, Dean F, Albert, Marilyn, Resnick, Susan M, Johnson, Sterling C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9679170/
https://www.ncbi.nlm.nih.gov/pubmed/35856240
http://dx.doi.org/10.1093/brain/awac213
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author Betthauser, Tobey J
Bilgel, Murat
Koscik, Rebecca L
Jedynak, Bruno M
An, Yang
Kellett, Kristina A
Moghekar, Abhay
Jonaitis, Erin M
Stone, Charles K
Engelman, Corinne D
Asthana, Sanjay
Christian, Bradley T
Wong, Dean F
Albert, Marilyn
Resnick, Susan M
Johnson, Sterling C
author_facet Betthauser, Tobey J
Bilgel, Murat
Koscik, Rebecca L
Jedynak, Bruno M
An, Yang
Kellett, Kristina A
Moghekar, Abhay
Jonaitis, Erin M
Stone, Charles K
Engelman, Corinne D
Asthana, Sanjay
Christian, Bradley T
Wong, Dean F
Albert, Marilyn
Resnick, Susan M
Johnson, Sterling C
author_sort Betthauser, Tobey J
collection PubMed
description Alzheimer’s disease biomarkers are becoming increasingly important for characterizing the longitudinal course of disease, predicting the timing of clinical and cognitive symptoms, and for recruitment and treatment monitoring in clinical trials. In this work, we develop and evaluate three methods for modelling the longitudinal course of amyloid accumulation in three cohorts using amyloid PET imaging. We then use these novel approaches to investigate factors that influence the timing of amyloid onset and the timing from amyloid onset to impairment onset in the Alzheimer's disease continuum. Data were acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Baltimore Longitudinal Study of Aging (BLSA) and the Wisconsin Registry for Alzheimer's Prevention (WRAP). Amyloid PET was used to assess global amyloid burden. Three methods were evaluated for modelling amyloid accumulation using 10-fold cross-validation and holdout validation where applicable. Estimated amyloid onset age was compared across all three modelling methods and cohorts. Cox regression and accelerated failure time models were used to investigate whether sex, apolipoprotein E genotype and e4 carriage were associated with amyloid onset age in all cohorts. Cox regression was used to investigate whether apolipoprotein E (e4 carriage and e3e3, e3e4, e4e4 genotypes), sex or age of amyloid onset were associated with the time from amyloid onset to impairment onset (global clinical dementia rating ≥1) in a subset of 595 ADNI participants that were not impaired before amyloid onset. Model prediction and estimated amyloid onset age were similar across all three amyloid modelling methods. Sex and apolipoprotein E e4 carriage were not associated with PET-measured amyloid accumulation rates. Apolipoprotein E genotype and e4 carriage, but not sex, were associated with amyloid onset age such that e4 carriers became amyloid positive at an earlier age compared to non-carriers, and greater e4 dosage was associated with an earlier amyloid onset age. In the ADNI, e4 carriage, being female and a later amyloid onset age were all associated with a shorter time from amyloid onset to impairment onset. The risk of impairment onset due to age of amyloid onset was non-linear and accelerated for amyloid onset age >65. These findings demonstrate the feasibility of modelling longitudinal amyloid accumulation to enable individualized estimates of amyloid onset age from amyloid PET imaging. These estimates provide a more direct way to investigate the role of amyloid and other factors that influence the timing of clinical impairment in Alzheimer's disease.
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spelling pubmed-96791702022-11-22 Multi-method investigation of factors influencing amyloid onset and impairment in three cohorts Betthauser, Tobey J Bilgel, Murat Koscik, Rebecca L Jedynak, Bruno M An, Yang Kellett, Kristina A Moghekar, Abhay Jonaitis, Erin M Stone, Charles K Engelman, Corinne D Asthana, Sanjay Christian, Bradley T Wong, Dean F Albert, Marilyn Resnick, Susan M Johnson, Sterling C Brain Original Article Alzheimer’s disease biomarkers are becoming increasingly important for characterizing the longitudinal course of disease, predicting the timing of clinical and cognitive symptoms, and for recruitment and treatment monitoring in clinical trials. In this work, we develop and evaluate three methods for modelling the longitudinal course of amyloid accumulation in three cohorts using amyloid PET imaging. We then use these novel approaches to investigate factors that influence the timing of amyloid onset and the timing from amyloid onset to impairment onset in the Alzheimer's disease continuum. Data were acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Baltimore Longitudinal Study of Aging (BLSA) and the Wisconsin Registry for Alzheimer's Prevention (WRAP). Amyloid PET was used to assess global amyloid burden. Three methods were evaluated for modelling amyloid accumulation using 10-fold cross-validation and holdout validation where applicable. Estimated amyloid onset age was compared across all three modelling methods and cohorts. Cox regression and accelerated failure time models were used to investigate whether sex, apolipoprotein E genotype and e4 carriage were associated with amyloid onset age in all cohorts. Cox regression was used to investigate whether apolipoprotein E (e4 carriage and e3e3, e3e4, e4e4 genotypes), sex or age of amyloid onset were associated with the time from amyloid onset to impairment onset (global clinical dementia rating ≥1) in a subset of 595 ADNI participants that were not impaired before amyloid onset. Model prediction and estimated amyloid onset age were similar across all three amyloid modelling methods. Sex and apolipoprotein E e4 carriage were not associated with PET-measured amyloid accumulation rates. Apolipoprotein E genotype and e4 carriage, but not sex, were associated with amyloid onset age such that e4 carriers became amyloid positive at an earlier age compared to non-carriers, and greater e4 dosage was associated with an earlier amyloid onset age. In the ADNI, e4 carriage, being female and a later amyloid onset age were all associated with a shorter time from amyloid onset to impairment onset. The risk of impairment onset due to age of amyloid onset was non-linear and accelerated for amyloid onset age >65. These findings demonstrate the feasibility of modelling longitudinal amyloid accumulation to enable individualized estimates of amyloid onset age from amyloid PET imaging. These estimates provide a more direct way to investigate the role of amyloid and other factors that influence the timing of clinical impairment in Alzheimer's disease. Oxford University Press 2022-07-20 /pmc/articles/PMC9679170/ /pubmed/35856240 http://dx.doi.org/10.1093/brain/awac213 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Article
Betthauser, Tobey J
Bilgel, Murat
Koscik, Rebecca L
Jedynak, Bruno M
An, Yang
Kellett, Kristina A
Moghekar, Abhay
Jonaitis, Erin M
Stone, Charles K
Engelman, Corinne D
Asthana, Sanjay
Christian, Bradley T
Wong, Dean F
Albert, Marilyn
Resnick, Susan M
Johnson, Sterling C
Multi-method investigation of factors influencing amyloid onset and impairment in three cohorts
title Multi-method investigation of factors influencing amyloid onset and impairment in three cohorts
title_full Multi-method investigation of factors influencing amyloid onset and impairment in three cohorts
title_fullStr Multi-method investigation of factors influencing amyloid onset and impairment in three cohorts
title_full_unstemmed Multi-method investigation of factors influencing amyloid onset and impairment in three cohorts
title_short Multi-method investigation of factors influencing amyloid onset and impairment in three cohorts
title_sort multi-method investigation of factors influencing amyloid onset and impairment in three cohorts
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9679170/
https://www.ncbi.nlm.nih.gov/pubmed/35856240
http://dx.doi.org/10.1093/brain/awac213
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