Does genetic risk help to predict amyloid burden in a non-demented population? A Bayesian approach.

Abstract

INTRODUCTIONIn this study we investigate the association between A{beta} levels in cerebrospinal fluid (CSF) and genetic risk in a non-demented population. This paper presents the first analysis to use a Bayesian methodology in this area.\n\nMETHODSData from the Alzheimers Disease Neuroimaging Initiative (ADNI) and the EDAR* and DESCRIPA** studies was used in a Bayesian logistic regression analysis. We modeled CSF A{beta} burden using age, diagnosis (healthy control or mild cognitive impairment), APOE and a polygenic risk score (PGRS) associated with Alzheimers Disease (AD). We compared models built using informative priors on age, diagnosis and APOE with non-informative priors on all variables.\n\nRESULTSThe use of informative priors did not improve model performance in the majority of cases. Models using only age, diagnosis and APOE genotype showed the best predictive ability.\n\nDISCUSSIONA previous study indicated that a PGRS of AD case/control status was associated with CSF A{beta} burden in healthy controls. The current study suggests that this association does not lead to models that are more predictive of amyloid positivity than already known factors such as age and APOE.\n\n* Beta amyloid oligomers in the early diagnosis of AD and as marker for treatment response\n\n** Development of screening guidelines and criteria for pre-dementia Alzheimers disease