Title: A [C-11]PiB PET Comparison of Beta-Amyloid Accumulation Rates between Down Syndrome and Neurotypical Populations
Legend: Modeling of Down Syndrome (DS) and neurotypical (NT) participant amyloid accumulation measured using [C-11]PiB PET imaging. A: Sampled Iterative Local Approximation (SILA) model used to normalize [C-11]PiB scans in time. Based on the global [C-11]PiB SUVR at different ages the SILA model approximates time from a cutoff point for each participant scan, in this case an SUVR of 1.40. Example images from the same participant at different time points in the AD continuum are also shown. B: Logistic growth model showing the expected trajectory of amyloid over time that was fit to the normalized [C-11]PiB PET data. C: Upper: Global [C-11]PiB trajectories for the NT (left) and DS (right) cohorts showing amyloid burden against age of the participant. Lower: Amyloid trajectories normalized in time using the SILA model and then fit with the logistic growth model for the two cohorts. D: The logistic growth model for DS (red) and NT (blue) participants normalized such that they have a t50 = 0 for visual comparison. The DS cohort has a significantly steeper curve with no overlap between 95% confidence intervals.
Citation: McVea, A. (2024, May 19). A [C-11]PiB PET Comparison of Beta-Amyloid Accumulation Rates between Down Syndrome and Neurotypical Populations [Oral Conference Presentation]. NeuroReceptor Mapping. Montreal, QC, Canada.
Abstract: Introduction – Earlier beta-amyloid (Aβ) plaque accumulation and a higher prevalence of Alzheimer’s Disease is seen in the Down syndrome (DS) population due to the triplication of chromosome 21 containing the amyloid precursor protein (APP) gene. In this study we compare accumulation rates of Aβ measured with [C-11]PiB PET between large longitudinal cohorts of DS and neurotypical (NT) participants at a single site. Methods – Individuals with ≥2 PiB scans with at least 2 years between scans at the University of Wisconsin were included in this study with some participants having 10+ years of PiB data. DS participants were selected from the Alzheimer’s Biomarker Consortium–DS (ABC-DS) study (n = 57) and NT participants from ongoing studies at our site (n = 162). An identical imaging procedure was conducted for all subjects 50-70 minutes after injection with a nominal injected dose of 15mCi PiB. Reconstructed PET images were processed using a standardized pipeline and converted into SUVR images using the cerebellar grey matter reference region. Global SUVR composed of grey matter regions in the anterior cingulate, precuneus, striatum and frontal, parietal and temporal cortices from the AAL atlas was used as a metric for Aβ deposition. Global PiB SUVR data was input into a trajectory model (Betthauser, 2021) to estimate time from Aβ(+) (SUVR ≥ 1.40) for each participant scan (Fig. 1). This output was then used to normalize all PiB data in time, which were then fit using the logistic growth curve () with the trajectories centered to t50 = 0. Nonspecific binding (NS), found by taking the average SUVR of all Aβ(-) subject scans (n=491) at our site, and carrying capacity (K), represented by the highest total binding seen in any participant scanned, were set to 1.12 and 2.40 respectively. Aβ accumulation rate (r) was determined for the two populations by fitting all normalized participant data in each group. Results – The DS rate of Aβ accumulation was 0.25±0.02 and 0.17±0.01/yr for NT, corresponding to 0.065 and 0.038 SUVR/yr respectively when becoming Aβ(+). There was no overlap between the 95% CI of the DS (0.21-0.29) and NT (0.15-0.19) populations. Conclusions – We observed a faster rate of Aβ accumulation in the DS cohort compared to NT. There was a 47% increase in r between groups, however, the high variability in Aβ rates requires further investigation towards understanding how genetic and lifestyle factors contribute to this process.
Slide Author: Andrew McVea, MS
Investigator: Bradley T Christian, PhD
About the Lab: Research in the Christian lab focuses on developing and translating novel PET methods for the study of neurodevelopment and neuropsychiatric illness. This involves using PET methodologies to investigate neurochemical changes in the brain and studying novel radioligands to characterize neurotransmitter-protein interactions and how they are influenced by development, genes, environment and drugs. These imaging methods are being applied to investigate the etiologies and mechanisms in diseases such as Down syndrome, affective disorders, schizophrenia, Alzheimer’s disease and Tourette syndrome.
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