Dataset Information

Detecting Amyloid Positivity in Elderly With Increased Risk of Cognitive Decline.

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 A? positive. Compared with the A? negative group, the A? positive group had a higher proportion of APOE ?4 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 A? positivity. Detecting amyloid positivity could reduce invasive and costly assessments during the screening process in clinical trials.

SUBMITTER: Pekkala T

PROVIDER: S-EPMC7406705 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Detecting Amyloid Positivity in Elderly With Increased Risk of Cognitive Decline.

Pekkala Timo T Hall Anette A Ngandu Tiia T van Gils Mark M Helisalmi Seppo S Hänninen Tuomo T Kemppainen Nina N Liu Yawu Y Lötjönen Jyrki J Paajanen Teemu T Rinne Juha O JO Soininen Hilkka H Kivipelto Miia M Solomon Alina A

Frontiers in aging neuroscience 20200730

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, <i>APOE</i> genotype, and structural MRI, including regional brain volumes, cortical thickness and a vi ...[more]

PMID: 32848707

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Project description:BackgroundStage 1 of the National Institute on Aging-Alzheimer's Association's proposed Alzheimer's disease continuum is defined as amyloid-β (Aβ) positive but cognitively normal. Identifying at-risk individuals before Aβ reaches pathological levels could have great benefits for early intervention. Although Aβ levels become abnormal long before severe cognitive impairments appear, increasing evidence suggests that subtle cognitive changes may begin early, potentially before Aβ surpasses the threshold for abnormality. We examined whether baseline cognitive performance would predict progression from normal to abnormal levels of Aβ.MethodsWe examined the association of baseline cognitive composites (Preclinical Alzheimer Cognitive Composite, Alzheimer's Disease Neuroimaging Initiative (ADNI) memory factor composite) with progression to Aβ positivity in 292 nondemented, Aβ-negative ADNI participants. Additional analyses included continuous cerebrospinal fluid biomarker levels to examine the effects of subthreshold pathology.ResultsForty participants progressed to Aβ positivity during follow-up. Poorer baseline performance on both cognitive measures was significantly associated with increased odds of progression. More abnormal levels of baseline cerebrospinal fluid phosphorylated tau and subthreshold Aβ were associated with increased odds of progression to Aβ positivity. Nevertheless, baseline ADNI memory factor composite performance predicted progression even after controlling for baseline biomarker levels and APOE genotype (Preclinical Alzheimer Cognitive Composite was trend level). Survival analyses were largely consistent: controlling for baseline biomarker levels, baseline Preclinical Alzheimer Cognitive Composite still significantly predicted progression time to Aβ positivity (ADNI memory factor composite was trend level).ConclusionsThe possibility of intervening before Aβ reaches pathological levels is of obvious benefit. Low-cost, noninvasive cognitive measures can be informative for determining who is likely to progress to Aβ positivity, even after accounting for baseline subthreshold biomarker levels.

Project description:Importance:Identifying asymptomatic individuals at high risk of impending cognitive decline because of Alzheimer disease is crucial for successful prevention of dementia. Vascular risk and ?-amyloid (A?) pathology commonly co-occur in older adults and are significant causes of cognitive impairment. Objective:To determine whether vascular risk and A? burden act additively or synergistically to promote cognitive decline in clinically normal older adults; and, secondarily, to evaluate the unique influence of vascular risk on prospective cognitive decline beyond that of commonly used imaging biomarkers, including A? burden, hippocampal volume, fludeoxyglucose F18-labeled (FDG) positron emission tomography (PET), and white matter hyperintensities, a marker of cerebrovascular disease. Design, Setting, and Participants:In this longitudinal observational study, we examined clinically normal older adults from the Harvard Aging Brain Study. Participants were required to have baseline imaging data (FDG-PET, A?-PET, and magnetic resonance imaging), baseline medical data to quantify vascular risk, and at least 1 follow-up neuropsychological visit. Data collection began in 2010 and is ongoing. Data analysis was performed on data collected between 2010 and 2017. Main Outcomes and Measures:Vascular risk was quantified using the Framingham Heart Study general cardiovascular disease (FHS-CVD) risk score. We measured A? burden with Pittsburgh Compound-B PET. Cognition was measured annually with the Preclinical Alzheimer Cognitive Composite. Models were corrected for baseline age, sex, years of education, and apolipoprotein E ?4 status. Results:Of the 223 participants, 130 (58.3%) were women. The mean (SD) age was 73.7 (6.0) years, and the mean (SD) follow-up time was 3.7 (1.2) years. Faster cognitive decline was associated with both a higher FHS-CVD risk score (??=?-0.064; 95% CI, -0.094 to -0.033; P?<?.001) and higher A? burden (??=?-0.058; 95% CI, -0.079 to -0.037; P?<?.001). The interaction of the FHS-CVD risk score and A? burden with time was significant (??=?-0.040, 95% CI, -0.062 to -0.018; P?<?.001), suggesting a synergistic effect. The FHS-CVD risk score remained robustly associated with prospective cognitive decline (??=?-0.055; 95% CI, -0.086 to -0.024; P?<?.001), even after adjustment for A? burden, hippocampal volume, FDG-PET uptake, and white matter hyperintensities. Conclusions and Relevance:In this study, vascular risk was associated with prospective cognitive decline in clinically normal older adults, both alone and synergistically with A? burden. Vascular risk may complement imaging biomarkers in assessing risk of prospective cognitive decline in preclinical Alzheimer disease.

Dataset Information

Detecting Amyloid Positivity in Elderly With Increased Risk of Cognitive Decline.

Publications

Detecting Amyloid Positivity in Elderly With Increased Risk of Cognitive Decline.

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