Koeppe, Robert A.

Abstract Background Alzheimer Disease (AD) pathology evolves over decades, and understanding this progression is critical to the understanding of the disease and timing therapeutic interventions. Since individuals with Autosomal Dominant AD (ADAD) develop symptoms around the same age as their parent, it is possible to predict symptom onset and stage individuals by their estimated years to symptom onset (EYO). This approach does not generalize to other forms of AD, thus there is a pressing need for the timecourse of ADAD to be defined in broadly relevant terms. The objective of this project is to validate the Sampled Iterative Local Approximation (SILA) algorithm in a cohort with a known disease timecourse. SILA generates an estimate of time from amyloid positivity (A time ) based on longitudinal PET data. Method We evaluated A time in a longitudinal ADAD sample ( N = 316) with PET PiB data in three ways. First, we compared predicted age at amyloid positive (A+) to observed age at A+ for individuals who became A+ during enrollment. Next, using linear regression, we compared estimated age at A+ to estimated age at symptom onset (EYO=0). Finally, we used generalized additive models to compare the amount of variance in concurrent cognitive performance explained both A time and EYO. Result We observed a mean average error of 1.15 years between actual age at A+ ( N = 26) and the SILA‐predicted A time . Across all participants, SILA‐estimated age at A+ explained 39% of the variance in estimated age at symptom onset ( β = 0.918, p < 0.0001). Finally, we observed a nonlinear association between cognition and both A time and EYO. A time explained 19% of the variance in the general cognitive composite while EYO explained 43% of the variance. Conclusion SILA produces a valid estimate of time‐from‐amyloid positivity in ADAD. This work allows for disease stage in ADAD to be compared to staging for broad forms of AD, which was not previously possible using EYO. However, this work also illustrates that there is a high degree of heterogeneity in preclinical disease duration that is not explained by amyloid alone.

Abstract Background Extensive evidence links cognition, tau, and neurodegeneration in Alzheimer's disease (AD). However, in U.S. POINTER imaging (a community‐recruited clinical trial cohort selected for high cardiovascular risk, ethnoracial diversity, and increased risk of cognitive decline and dementia) tau PET is more closely linked to cognition than in ADNI. Here, we further explore how ADNI and POINTER differ with respect to relationships between cross‐sectional AD biomarkers, cortical thickness, and different cognitive domains. Method U.S. POINTER imaging (Baseline; N = 775; 68.9±5.2 years, 62.8% female) and ADNI ( N = 405; 69.9±5.4 years, 58% female) were matched by age, sex, clinical status, and APOE4 genotype (Table 1). We examined associations between baseline AD biomarkers (Amyloid‐beta (Aβ) PET in centiloids, harmonized tau PET measures of MK6240 in POINTER and FTP in ADNI), regional cortical thickness, and verbal memory (Delayed Story Recall), executive function (Trail‐Making B), and global cognition (Preclinical Alzheimer's Cognitive Composite scores) across each cohort, controlling for age, sex, and education. Result Aβ burden in ADNI was associated with worse executive function ( p < .001) and global cognition ( p < .001) but not memory, whereas in POINTER Aβ burden was not associated with any cognitive domain (Figure 1A). Among Aβ+ individuals, tau burden was associated with poorer performance across domains in ADNI, but only with poorer verbal memory in POINTER (Figures 1B&1C). Additionally, there were stronger positive associations between cortical thickness and executive function and global cognition compared to verbal memory, particularly in ADNI Aβ+ individuals even after adjusting for entorhinal tau ( p < .01; Figure 2). Conclusion Cognitive performance was linked to all biomarkers (Aβ, tau, cortical thickness) but these associations were consistently stronger in ADNI compared to POINTER, particularly in Aβ+ individuals. The stronger thickness‐cognition relationships in ADNI were not driven by tau, indicating that there is a tau‐independent role of thickness in explaining cognition. In ADNI, there were subtle differences between cognitive domains such that executive function was more closely linked than verbal memory to Aβ and cortical thickness, whereas verbal memory was linked to tau in both cohorts. Our study highlights that frequently‐reported AD biomarker associations may be attenuated in heterogeneous, community‐recruited cohorts.