Detecting Amyloid Positivity in Elderly With Increased Risk of Cognitive Decline
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 i...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-07-01
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| Series: | Frontiers in Aging Neuroscience |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fnagi.2020.00228/full |
| _version_ | 1811223761131667456 |
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| author | Timo Pekkala Anette Hall Tiia Ngandu Tiia Ngandu Mark van Gils Seppo Helisalmi Tuomo Hänninen Nina Kemppainen Nina Kemppainen Yawu Liu Yawu Liu Jyrki Lötjönen Teemu Paajanen Juha O. Rinne Juha O. Rinne Hilkka Soininen Hilkka Soininen Miia Kivipelto Miia Kivipelto Miia Kivipelto Miia Kivipelto Alina Solomon Alina Solomon |
| author_facet | Timo Pekkala Anette Hall Tiia Ngandu Tiia Ngandu Mark van Gils Seppo Helisalmi Tuomo Hänninen Nina Kemppainen Nina Kemppainen Yawu Liu Yawu Liu Jyrki Lötjönen Teemu Paajanen Juha O. Rinne Juha O. Rinne Hilkka Soininen Hilkka Soininen Miia Kivipelto Miia Kivipelto Miia Kivipelto Miia Kivipelto Alina Solomon Alina Solomon |
| author_sort | Timo Pekkala |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T08:37:58Z |
| format | Article |
| id | doaj.art-e917c947721746b8a2e06443fd8219f3 |
| institution | Directory Open Access Journal |
| issn | 1663-4365 |
| language | English |
| last_indexed | 2024-04-12T08:37:58Z |
| publishDate | 2020-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj.art-e917c947721746b8a2e06443fd8219f32022-12-22T03:39:58ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652020-07-011210.3389/fnagi.2020.00228546971Detecting Amyloid Positivity in Elderly With Increased Risk of Cognitive DeclineTimo Pekkala0Anette Hall1Tiia Ngandu2Tiia Ngandu3Mark van Gils4Seppo Helisalmi5Tuomo Hänninen6Nina Kemppainen7Nina Kemppainen8Yawu Liu9Yawu Liu10Jyrki Lötjönen11Teemu Paajanen12Juha O. Rinne13Juha O. Rinne14Hilkka Soininen15Hilkka Soininen16Miia Kivipelto17Miia Kivipelto18Miia Kivipelto19Miia Kivipelto20Alina Solomon21Alina Solomon22Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, FinlandInstitute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, FinlandPublic Health Promotion Unit, Finnish Institute for Health and Welfare, Helsinki, FinlandDivision of Clinical Geriatrics, Center for Alzheimer Research, NVS, Karolinska Institutet, Stockholm, SwedenVTT Technical Research Centre of Finland Ltd., Tampere, FinlandInstitute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, FinlandNeurocenter/Neurology, Kuopio University Hospital, Kuopio, FinlandTurku PET Centre, University of Turku, Turku, FinlandDivision of Clinical Neurosciences, Turku University Hospital, Turku, FinlandInstitute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, FinlandDepartment of Clinical Radiology, Kuopio University Hospital, Kuopio, FinlandCombinostics, Tampere, Finland0Finnish Institute of Occupational Health, Helsinki, FinlandTurku PET Centre, University of Turku, Turku, FinlandDivision of Clinical Neurosciences, Turku University Hospital, Turku, FinlandInstitute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, FinlandNeurocenter/Neurology, Kuopio University Hospital, Kuopio, FinlandInstitute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, FinlandDivision of Clinical Geriatrics, Center for Alzheimer Research, NVS, Karolinska Institutet, Stockholm, Sweden1Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland2Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, United KingdomInstitute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, FinlandDivision of Clinical Geriatrics, Center for Alzheimer Research, NVS, Karolinska Institutet, Stockholm, SwedenThe 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.https://www.frontiersin.org/article/10.3389/fnagi.2020.00228/fullamyloid betapositron emission tomographycognitionmagnetic resonance imagingapolipoprotein Emachine learning |
| spellingShingle | Timo Pekkala Anette Hall Tiia Ngandu Tiia Ngandu Mark van Gils Seppo Helisalmi Tuomo Hänninen Nina Kemppainen Nina Kemppainen Yawu Liu Yawu Liu Jyrki Lötjönen Teemu Paajanen Juha O. Rinne Juha O. Rinne Hilkka Soininen Hilkka Soininen Miia Kivipelto Miia Kivipelto Miia Kivipelto Miia Kivipelto Alina Solomon Alina Solomon Detecting Amyloid Positivity in Elderly With Increased Risk of Cognitive Decline Frontiers in Aging Neuroscience amyloid beta positron emission tomography cognition magnetic resonance imaging apolipoprotein E machine learning |
| title | Detecting Amyloid Positivity in Elderly With Increased Risk of Cognitive Decline |
| title_full | Detecting Amyloid Positivity in Elderly With Increased Risk of Cognitive Decline |
| title_fullStr | Detecting Amyloid Positivity in Elderly With Increased Risk of Cognitive Decline |
| title_full_unstemmed | Detecting Amyloid Positivity in Elderly With Increased Risk of Cognitive Decline |
| title_short | Detecting Amyloid Positivity in Elderly With Increased Risk of Cognitive Decline |
| title_sort | detecting amyloid positivity in elderly with increased risk of cognitive decline |
| topic | amyloid beta positron emission tomography cognition magnetic resonance imaging apolipoprotein E machine learning |
| url | https://www.frontiersin.org/article/10.3389/fnagi.2020.00228/full |
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