Cognitive, social, and physical activities, collectively linked to cognitive reserve, are associated with better late-life cognitive outcomes. To better understand the building of cognitive reserve, we investigated which of these activities, during which stages of life, had the strongest associations with late-life cognitive performance. From the Sydney Memory and Aging Study, 546 older Australians, who were community-dwelling and without a dementia diagnosis at recruitment (M 80.13 years, 52.2% female), were asked about their engagement in social, physical, and cognitive activities throughout young adulthood (YA), midlife (ML), and late-life (LL). Comprehensive neuropsychological testing administered biennially over 6 years measured baseline global cognition and cognitive decline. In our study, YA, but not ML nor LL, cognitive activity was significantly associated with late-life global cognition ( 0.315, < .001). A follow-up analysis pointed to the formal education component of the YA cognitive activity measure, rather than YA cognitive leisure activities, as a significant predictor of better late-life global cognition ( 0.146, = .003). YA social activity and LL cognitive activity were significantly associated with less cognitive decline ( 0.023, < .001, and 0.016, = .022, respectively). Physical activity was not found to be associated with global cognition or cognitive decline. Overall, YA cognitive activity was associated with better late-life cognition, and YA social and LL cognitive activities were associated with less cognitive decline. Formal education emerges as the key contributor in the association between YA cognitive activity and late-life global cognition.
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http://dx.doi.org/10.1080/13825585.2023.2181941 | DOI Listing |
Alzheimers Dement
December 2024
Boston University Alzheimer's Disease Research Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Background: Alzheimer's disease (AD) has both genetic and environmental risk factors. Gene-environment interaction may help explain some missing heritability. There is strong evidence for cigarette smoking as a risk factor for AD.
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December 2024
Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
Background: The Apolipoprotein E ε4 (APOE-ε4) allele is common in the population, but acts as the strongest genetic risk factor for late-onset Alzheimer's disease (AD). Despite the strength of the association, there is notable heterogeneity in the population including a strong modifying effect of genetic ancestry, with the APOE-ε4 allele showing a stronger association among individuals of European ancestry (EUR) compared to individuals of African ancestry (AFR). Given this heterogeneity, we sought to identify genetic modifiers of APOE-ε4 related to cognitive decline leveraging APOE-ε4 stratified and interaction genome-wide association analyses (GWAS).
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December 2024
University of Pittsburgh School of Public Health, Pittsburgh, PA, USA.
Background: Many complex traits and diseases show sex-specific biases in clinical presentation and prevalence. For instance, two-thirds of AD cases are female. Studies suggest that women might have higher cognitive reserve but steeper cognitive decline in older age.
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December 2024
Institute of Transformative Molecular Medicine, Case western Reserve University, Cleveland, OH, USA.
Background: Alzheimer's disease (AD) is a severe neurodegenerative condition that affects millions of people worldwide. The TgF344 AD rat model, which exhibits early depression-like behavior followed by later cognitive impairment, is widely used to evaluate putative biomarkers and potential treatments for AD. The P7C3 neuroprotective compounds have shown protective efficacy for both brain pathology and neuropsychiatric impairment in this model.
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December 2024
Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA.
Background: Although high-throughput DNA/RNA sequencing technologies have generated massive genetic and genomic data in human disease, translation of these findings into new patient treatment has not materialized by lack of effective approaches, such as Artificial Intelligence (AL) and Machine Learning (ML) tools.
Method: To address this problem, we have used AI/ML approaches, Mendelian randomization (MR), and large patient's genetic and functional genomic data to evaluate druggable targets using Alzheimer's disease (AD) as a prototypical example. We utilized the genomic instruments from 9 expression quantitative trait loci (eQTL) and 3 protein quantitative trait loci (pQTL) datasets across five human brain regions from three biobanks.
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