Preprint · Jan 2, 2020
An amyloid sensitive composite model to determine subtle cognitive differences in preclinical Alzheimer’s disease
By Hahn AMJ, Kim YJ, Kim HJ et al.
Abstract Background: Recently, the focus of Alzheimer's disease (AD) research has shifted from the clinical and symptomatic stage to the preclinical and asymptomatic stage. However, studies on the distinctive neuropsychological features of preclinical AD with adjusted measurement errors are insufficient. The present study aimed to investigate the distinctive cognitive features of preclinical AD and develop a cognitive composite model that can sensitively distinguish between the amyloid positive (Aβ+) and amyloid negative (Aβ-) status in cognitively normal (CN) elderly participants. Methods: A total of 423 CN elderly participants with amyloid positron emission tomography (PET) images were recruited. Using the multiple-indicator multiple-cause (MIMIC) model, sensitive cognitive domains to the Aβ+ group were found. Then, several cognitive tests were selected to create the cognitive composite model as a result of the multivariate analysis of covariance (MANCOVA). Results: The MIMIC model revealed that the domains of episodic memory (verbal and visual) and executive functions were significantly different between Aβ- and Aβ+ ( p <0.05) participants. According to MANCOVA, the Seoul Verbal Learning Test-Elderly’s version (SVLT-E) and the Rey-Osterrieth Complex Figure Test (RCFT) both distinguished between the Aβ+ group and the Aβ- group on the delayed recall tests in terms of episodic memory ( p <0.1). Furthermore, on tests of executive functions, the Aβ+ group performed worse than the Aβ- group in color reading of the Korean-Color Word Stroop test (K-CWST) and the animal naming of the Controlled Oral Word Association Test (COWAT) ( p <0.1). Consequently, the Preclinical Amyloid Sensitive Composite (PASC) model was comprised of SVLT-E delayed recall, RCFT delayed recall, K-CWST color reading, COWAT-animal, and the Korean Mini-Mental State Examination (K-MMSE) according to the results from our study and previous studies. Conclusions: In the present study, we developed our own composite PASC model with the distinctive cognitive profiles of Aβ+ CN individuals. Hence, this composite model can eventually contribute to not only early detection but also early interventions for AD.