Identification of Pre-Clinical Alzheimer’s Disease in a Population of Elderly Cognitively Normal Participants

Article type: Research Article

Authors: van Havre, Zoe^{a; g} | Maruff, Paul^{b; c} | Villemagne, Victor L.^{d; e} | Mengersen, Kerrie^a | Rousseau, Judith^g | White, Nicole^a | Doecke, James D.^{f; *}

Affiliations: [a] ACEMS, Queensland University of Technology, Queensland, Australia | [b] Mental Health Research Institute, The University of Melbourne, Parkville, Victoria, Australia | [c] CogState Ltd., Victoria, Australia | [d] Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Victoria, Australia | [e] Department of Nuclear Medicine and Centre for PET, Austin Health, Heidelberg, Victoria, Australia | [f] CSIRO Health and Biosecurity/Australian e-Health Research Centre, Herston, Queensland, Australia | [g] CEREMADE, Universite Paris Dauphine, Paris, France

Correspondence: [*] Correspondence to: Dr. James Doecke, Australian eHealth Research Centre, CSIRO Health & Biosecurity, Lev 5, 901/16 Royal Brisbane & Women’s Hospital, Herston QLD 4029, Australia. Tel.: +61 07 3253 3697; E-mail: james.doecke@csiro.au.

Abstract: Alzheimer’s disease (AD) has a long pathological process, with an approximate lead-time of 20 years. During the early stages of the disease process, little evidence of the building pathology is identifiable without cerebrospinal fluid and/or imaging analyses. Clinical manifestations of AD do not present until irreversible pathological changes have occurred. Given an opportunity to provide treatment prior to irreversible pathological change, this study aims to identify a subgroup of cognitively normal (CN) participants from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL), where subtle changes in cognition are indicative of early AD-related pathology. Using a Bayesian method for unsupervised clustering via mixture models, we define an aggregate measure of posterior probabilities (AMPP score) establishing the likelihood of pre-clinical AD. From Baseline through to 54 months, visuo-spatial function had the greatest contribution to the AMPP score, followed by attention and processing speed and visual memory. Participants with the highest AMPP scores had both increasing neo-cortical amyloid burden and decreasing hippocampus volume over 54 months, compared to those in the lowest category with stable amyloid burden and hippocampus volume. The identification of a possible pre-clinical stage in CN participants via this method, without the aid of disease specific biomarkers, represents an important step in utilizing the strength of cognitive composite scores for the early detection of AD pathology.

Keywords: Alzheimer’s disease, Bayesian, mixture models, model averaging, neuropsychological composite score, overfitting, posterior probability, unsupervised clustering

DOI: 10.3233/JAD-191095

Journal: Journal of Alzheimer's Disease, vol. 73, no. 2, pp. 683-693, 2020