Li, Yan

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 INTRODUCTION Biomarkers play a crucial role in understanding Alzheimer's disease (AD) pathogenesis and treatment effects. However, comparing biomarker measures without standardization and appreciating their magnitude relative to the disease can be challenging. METHODS To address this issue, we propose the CentiMarker approach, similar to Centiloid, which provides a standardized scale between normal (0) and nearly maximum abnormal AD (100) ranges. We applied this scale to cerebrospinal fluid (CSF) biomarkers in dominantly inherited AD and sporadic AD cohorts. RESULTS CentiMarkers facilitated the interpretation of disease abnormality, demonstrating comparable changes and distributions of AD biomarkers across disease stages. CentiMarkers make the treatment effect more comparable than their original scales across various biomarkers. DISCUSSION The versatility of CentiMarkers makes it a valuable tool for standardized biomarker comparison in AD research, enabling informed cross‐study comparisons and contributing to accelerated therapeutic development. Adoption of the CentiMarker scale could enhance biomarker reporting and advance our understanding of AD. Highlights Comparing fluid biomarkers without appreciating their magnitude relative to the disease can be challenging. We propose a CentiMarker metric to standardize biomarker measures from normal (0) and nearly maximum abnormal AD (100) ranges. CentiMarkers make the treatment effect more comparable across various biomarkers than when using the original scales.