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

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 modeling 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 modeling amyloid accumulation using 10-fold cross-validation and hold-out validation where applicable. Estimated amyloid onset age was compared across all three modeling 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 prior to amyloid onset.Model prediction and estimated amyloid onset age were similar across all three amyloid modeling 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 nonlinear and accelerated for amyloid onset age >65. These findings demonstrate the feasibility of modeling 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.

Plasma pTau-217 in preclinical Alzheimer’s disease

“…32 Estimated age of amyloid onset (EAOA) was obtained from observed global Pib DVR using a combination of group-based trajectory modeling and Bayes' theorem 33 using either the most recent PET scan (for those who were PiB-) or the scan closest to the PiB+ threshold. Amyloid duration was then estimated as age at plasma sample minus EAOA and the corresponding estimated PiB DVR (GBTM-DVR) was calculated via linear transformation as described in Betthauser et al 34 Centiloids were estimated from these DVRs according to the following equation: 𝐶𝐶𝐿𝐿 = 148.33 × 𝐷𝐷𝐷𝐷𝑅𝑅 − 154.96.…”

Prediction of Longitudinal Cognitive Decline in Preclinical Alzheimer Disease Using Plasma Biomarkers

“…Mean cortical PiB (cerebellar gray matter reference region) distribution volume ratios were estimated using the Logan method. A global PiB distribution volume ratio greater than 1.19, which corresponds to a centiloid estimate of 22, 8,28 was used to define Aβpositive status 3,27 in the WRAP study.…”

“…7 However, there is variability in the rates of cognitive decline in preclinical AD. 2,3,[8][9][10][11] Inclusion of individuals with low risk of decline reduces clinical trial power. 4 Successful trials of preclinical AD would benefit from methods to enrich for individuals likely to experience cognitive decline.…”

“…n Alzheimer disease (AD), β-amyloid (Aβ) aggregation starts before the onset of symptoms. [1][2][3] The importance of this presymptomatic, or preclinical, stage has been reported by studies finding that Aβ measured by cerebrospinal fluid (CSF) biomarkers or positron emission tomography (PET) predicted cognitive decline in people who were cognitively unimpaired (CU). 4,5 Recent breakthroughs suggest that disease modification may be possible in clinical stages of AD.…”

Item-Level Story Recall Predictors of Amyloid-Beta in Late Middle-Aged Adults at Increased Risk for Alzheimer’s Disease

“…Another item-level analysis of the MMSE-37 in a Spanish speaking population found that language items were among the best at discriminating between groups with dementia and healthy controls ( Prieto et al, 2012 ). Although we did not examine people with dementia, dementia severity, or progression of AD, it is possible that proper name recall is an ability that is particularly vulnerable to early amyloid pathology; future studies can evaluate item sensitivity to estimated age of onset or projected rate of amyloid accumulation using methods developed by our group ( Koscik et al, 2020 ; Betthauser et al, 2021 ).…”