Detecting Amyloid Positivity in Elderly With Increased Risk of Cognitive Decline

Timo Pekkala, Anette Hall (Corresponding Author), Tiia Ngandu, Mark van Gils, Seppo Helisalmi, Tuomo Hänninen, Nina Kemppainen, Yawu Liu, Jyrki Lötjönen, Teemu Paajanen, Juha O. Rinne, Hilkka Soininen, Miia Kivipelto, Alina Solomon

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    The importance of early interventions in Alzheimer’s disease (AD) emphasizes the need to accurately and efficiently identify at-risk individuals. Although many dementia prediction models have been developed, there are fewer studies focusing on detection of brain pathology. We developed a model for identification of amyloid-PET positivity using data on demographics, vascular factors, cognition, APOE genotype, and structural MRI, including regional brain volumes, cortical thickness and a visual medial temporal lobe atrophy (MTA) rating. We also analyzed the relative importance of different factors when added to the overall model. The model used baseline data from the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) exploratory PET sub-study. Participants were at risk for dementia, but without dementia or cognitive impairment. Their mean age was 71 years. Participants underwent a brain 3T MRI and PiB-PET imaging. PiB images were visually determined as positive or negative. Cognition was measured using a modified version of the Neuropsychological Test Battery. Body mass index (BMI) and hypertension were used as cardiovascular risk factors in the model. Demographic factors included age, gender and years of education. The model was built using the Disease State Index (DSI) machine learning algorithm. Of the 48 participants, 20 (42%) were rated as Aβ positive. Compared with the Aβ negative group, the Aβ positive group had a higher proportion of APOE ε4 carriers (53 vs. 14%), lower executive functioning, lower brain volumes, and higher visual MTA rating. AUC [95% CI] for the complete model was 0.78 [0.65–0.91]. MRI was the most effective factor, especially brain volumes and visual MTA rating but not cortical thickness. APOE was nearly as effective as MRI in improving detection of amyloid positivity. The model with the best performance (AUC 0.82 [0.71–0.93]) was achieved by combining APOE and MRI. Our findings suggest that combining demographic data, vascular risk factors, cognitive performance, APOE genotype, and brain MRI measures can help identify Aβ positivity. Detecting amyloid positivity could reduce invasive and costly assessments during the screening process in clinical trials.

    Original languageEnglish
    Article number228
    Pages (from-to)228
    JournalFrontiers in Aging Neuroscience
    Publication statusPublished - Jul 2020
    MoE publication typeA1 Journal article-refereed


    The study was funded by European Research Council (Grant 804371), Academy of Finland; Finnish Social Insurance Institution, Alzheimer’s Research & Prevention Foundation, Juho Vainio Foundation, Swedish Research Council, Alzheimerfonden, Region Stockholm ALF and NSV, Center for Innovative Medicine (CIMED) at Karolinska Institutet, Knut and Alice Wallenberg Foundation, Stiftelsen Stockholms sjukhem, Konung Gustaf V:s och Drottning Victorias Frimurarstiftelse (Sweden); Joint Program of Neurodegenerative Disorders – prevention (MIND-AD), and VTR grants of Turku University Hospital. JR was funded by the Sigrid Juselius Foundation, Finnish State Research Funding, Academy of Finland (Grant 310962).


    • Alzheimer’s disease
    • amyloid beta
    • apolipoprotein E
    • cognition
    • machine learning
    • magnetic resonance imaging
    • positron emission tomography


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