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The EMIF-AD PreclinAD study: study design and baseline cohort overview
Elles Konijnenberg1, Stephen F Carter2, Mara Ten Kate3
1Alzheimer Center, Department of Neurology, VU University Medical Center, Neuroscience Amsterdam, PO Box 7057, 1007 MB, Amsterdam, The Netherlands. e.konijnenberg@vumc.nl.
This study established a dataset of cognitively normal elderly individuals to identify risk factors and biomarkers for Alzheimer's disease (AD) amyloid pathology and cognitive decline. Understanding these predictors is crucial for developing effective AD prevention strategies.
Area of Science:
- Neuroscience
- Gerontology
- Biomarker Research
Background:
- Alzheimer's disease (AD) is characterized by amyloid pathology, which precedes dementia by decades.
- The exact mechanisms linking amyloid pathology to cognitive decline remain unclear.
- Identifying predictors of cognitive decline in at-risk individuals is essential for AD prevention trials.
Purpose of the Study:
- To establish a comprehensive dataset of cognitively normal elderly individuals.
- To identify risk factors and biomarkers associated with amyloid pathology.
- To find predictors of future cognitive decline in individuals at high risk for AD.
Main Methods:
- Enrolled 285 cognitively normal participants aged 60+ from twin registries and research cohorts.
- Collected extensive baseline data including neuropsychological tests, questionnaires, and advanced imaging (MRI, amyloid PET).
- Utilized twin study design to explore genetic and environmental influences on AD biomarkers and cognition.
Main Results:
- The study included 285 participants (average age 74.8 years, 64% female).
- A significant proportion, 22% (58 participants), showed abnormal amyloid PET scans.
- A rich baseline dataset was successfully established.
Conclusions:
- A valuable dataset of cognitively normal elderly individuals was created.
- This dataset can be used to estimate risk factors and biomarkers for amyloid pathology.
- The findings support the development of AD prevention strategies by identifying potential predictors of cognitive decline.
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