Abstract
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.
Highlights
The importance of dementia prevention and early interventions in Alzheimer’s disease (AD) (Winblad et al, 2016) has emphasized the increasing need for accurate identification of at-risk individuals who may benefit most from such interventions
Participants had to be eligible for magnetic resonance imaging (MRI) and positron emission tomography (PET) scans, in addition to meeting all inclusion criteria for the FINGER main study: age 60 to 77 years; Cardiovascular Risk Factors, Aging, and Dementia (CAIDE) score at or above six points indicating elevated risk for dementia; and cognitive performance at the mean level or slightly lower than expected for age according to Finnish population norms for the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) test as previously described in detail (Kivipelto et al, 2013)
Compared with the Aβ negative group, the Aβ positive group had a higher proportion of apolipoprotein E (APOE) ε4 carriers (53 vs. 14%), lower executive functioning, lower brain volumes, and higher visual medial temporal lobe atrophy (MTA) rating
Summary
The importance of dementia prevention and early interventions in Alzheimer’s disease (AD) (Winblad et al, 2016) has emphasized the increasing need for accurate identification of at-risk individuals who may benefit most from such interventions. The prevalence of Aβ pathology from ages 50 to 80 years has been estimated to range from 10 to 33% in cognitively normal individuals, and from 27 to 60% in individuals with mild cognitive impairment (MCI) (Jansen et al, 2015). This complicates the screening process in e.g., randomized controlled trials testing interventions that target Aβ, since assessment of Aβ pathology in cerebrospinal fluid (CSF) or on positron emission tomography (PET) scans can become inefficient due to invasiveness, costs, and/or PET availability. More selective use of CSF or PET assessments to confirm the presence of Aβ pathology could reduce costly screening failures and improve screening efficiency
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