Vermunt, Lisa

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 The quantification of Neurofilament Light chain (NfL) in blood and cerebrospinal fluid (CSF) has proven valuable for diagnosing and prognosing various neurological disorders, including Alzheimer's disease, frontotemporal dementia, amyotrophic lateral sclerosis, parkinsonism, multiple sclerosis, and ischemic damage. However, there is considerable variability in clinical and laboratory practices between centers, primarily due to different contexts of use (COU), analytical methods, and the application of cutoffs or scales. Additionally, for the same biochemical profile, the interpretation and reporting of results may vary from one center to another, raising concerns about the commutability of the tests. To date, no consensus has been reached among laboratories to define the most appropriate use of cutoffs or conclusions/comments based on NfL profiles. Method This international project involves 38 laboratories across 18 countries, all specialized in the biochemical diagnosis of neurological disorders. Using a questionnaire, we obtained descriptions from each center regarding their COU, pre‐analytical and analytical protocols (including the biological fluid and methods used to quantify NfL). Through a consensus approach, we developed a harmonized strategy for the use and reporting of NfL results across different centres according to their respective COU. Results Among the centers, 63% quantified NfL in the CSF, 87% in blood and 53% in both fluids. COU were as follow: frontotemporal dementia (71%), Alzheimer disease (71%), multiple sclerosis (61%), amyotrophic lateral sclerosis (61%), psychiatric syndrome (45%), Creutzfeldt‐Jakob disease (32%), Parkinson disease (39%), peripheral neuropathy (29%), traumatic brain injury (21%), Huntington's disease (13%), amyloid transthyretin related (11%) and cardiac arrest (8%). Most of the centers define pathological cutoffs based on publications (50%) and take age into account for this purpose (42%). Reporting is mostly transmitted through numeric results (95%). Conclusion Harmonizing cutoffs, reporting, and interpretation across various clinical contexts will facilitate the incorporation of this biomarker into routine clinical practice.