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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.
Afiliación
  • Betthauser TJ; Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
  • Bilgel M; Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA.
  • Koscik RL; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
  • Jedynak BM; Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
  • An Y; Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA.
  • Kellett KA; Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
  • Moghekar A; Department of Mathematics and Statistics, Portland State University, Portland, OR, USA.
  • Jonaitis EM; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
  • Stone CK; Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
  • Engelman CD; Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA.
  • Asthana S; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
  • Christian BT; Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
  • Wong DF; Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA.
  • Albert M; Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
  • Resnick SM; Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA.
  • Johnson SC; Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
Brain ; 145(11): 4065-4079, 2022 11 21.
Article en En | MEDLINE | ID: mdl-35856240
ABSTRACT
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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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Enfermedad de Alzheimer / Disfunción Cognitiva / Amiloidosis Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans / Male Idioma: En Revista: Brain Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Enfermedad de Alzheimer / Disfunción Cognitiva / Amiloidosis Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans / Male Idioma: En Revista: Brain Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos