Using amyloid PET as a biomarker to detect progression of early Alzheimer's disease. (31st December 2021)

• Record Type: Journal Article
• Authors: Ali, Muhammad; Farias, Fabiana H.G.; Sung, Yun Ju; Wang, Fengxian; Cruchaga, Carlos
• Is Part Of: Alzheimer's & dementia. Volume 17(2021)Supplement 5
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-31
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.053006
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Alzheimer's disease (AD), the most common form of dementia, is a complex polygenic disease with genetic, cellular, pathologic, and clinical heterogeneity. Recently, significant attempts have been made for identifying AD biomarkers for reliably tracking disease progression in its early asymptomatic stages. To this end, amyloid imaging technique has provided a breakthrough in deciphering the pathophysiology and accumulation of amyloid beta (Aβ) and hyperphosphorylated tau deposits in the brain. As such, our lab and multiple other research groups have used biomarker levels such as CSF Aβ tau, TREM2, and amyloid positivity for genetic association studies and identified novel genes and loci associated to AD. In a recent study, Raghavan et al., analyzed 4, 314 participants and identified a novel locus for amyloidosis within RBFOX1 gene, however, this finding has not been replicated yet. Method: In order to investigate the underlying genetic basis for brain amyloidosis in AD, we aim at systematically analyzing the largest collection of amyloid imaging (N=6, 243), data across multiple ethnicities from multicenter cohorts (ADRC, A4, DIAN, ADNI, and ADNIDOD) as a quantitative trait to identify the functional variants and genes driving the association of AD. Result: Results and key findings will be presented at the AAIC meeting. Conclusion: We will leverage these datasets to generate prediction models for amyloid positivity and Mandelian Randomization (MR) analyses. Furthermore, the identification of AD‐specific molecular signatures and pathways will enable the characterization of appropriate therapeutic targets for the prevention and/or treatment of AD.