Educational attainment and hippocampal atrophy in the Alzheimer's disease neuroimaging initiative cohort

AbstractObjectivesTo examine whether educational attainment and intelligence have causal effects on risk of Alzheimer’s disease (AD), independently of each other.DesignTwo-sample univariable and multivariable Mendelian Randomization (MR) to estimate the causal effects of education on intelligence and vice versa, and the total and independent causal effects of both education and intelligence on risk of AD.Participants17,008 AD cases and 37,154 controls from the International Genomics of Alzheimer’s Project (IGAP) consortiumMain outcome measureOdds ratio of AD per standardised deviation increase in years of schooling and intelligenceResultsThere was strong evidence of a causal, bidirectional relationship between intelligence and educational attainment, with the magnitude of effect being similar in both directions. Similar overall effects were observed for both educational attainment and intelligence on AD risk in the univariable MR analysis; with each SD increase in years of schooling and intelligence, odds of AD were, on average, 37% (95% CI: 23% to 49%) and 35% (95% CI: 25% to 43%) lower, respectively. There was little evidence from the multivariable MR analysis that educational attainment affected AD risk once intelligence was taken into account, but intelligence affected AD risk independently of educational attainment to a similar magnitude observed in the univariate analysis.ConclusionsThere is robust evidence for an independent, causal effect of intelligence in lowering AD risk, potentially supporting a role for cognitive training interventions to improve aspects of intelligence. However, given the observed causal effect of educational attainment on intelligence, there may also be support for policies aimed at increasing length of schooling to lower incidence of AD.

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Hippocampal atrophy based Alzheimer’s disease diagnosis via machine learning methods

Journal of Neuroscience Methods ◽

10.1016/j.jneumeth.2020.108669 ◽

2020 ◽

Vol 337 ◽

pp. 108669 ◽

Cited By ~ 2

Author(s):

Gokce Uysal ◽

Mahmut Ozturk

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Disease Diagnosis ◽

Hippocampal Atrophy ◽

Learning Methods ◽

Machine Learning Methods ◽

Alzheimer’S Disease Diagnosis

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Verbal memory and hippocampal volume predict subsequent fornix microstructure in those at risk for Alzheimer’s disease

Brain Imaging and Behavior ◽

10.1007/s11682-019-00183-8 ◽

2019 ◽

Vol 14 (6) ◽

pp. 2311-2322 ◽

Cited By ~ 1

Author(s):

Junhong Yu ◽

◽

Tatia M. C. Lee

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Verbal Memory ◽

Structural Equation ◽

Structural Equation Models ◽

Diffusion Tensor ◽

Mean Diffusivity ◽

Hippocampal Atrophy ◽

Cross Sectional ◽

Mci Due To Ad

Abstract While strong cross-sectional evidence supported the use of fornix microstructure as a marker for detecting Alzheimer’s disease (AD), longitudinal data remains inconclusive on the sequential nature of fornix microstructure abnormalities and AD progression. An unequivocal longitudinal relationship between fornix microstructure and markers of AD progression –memory impairment and hippocampal atrophy, must be established to validate fornix microstructure as a marker of AD progression. We included 115 participants from the Alzheimer’s Disease Neuroimaging Initiative across the non-demented AD spectrum— defined as those who had at least one AD risk marker at baseline (e.g., mild cognitive impairment (MCI) due to AD diagnosis, amyloid or ApoE4 positivity) and/or ‘cognitively normal individuals who converted to MCI due to AD or AD, with structural and diffusion tensor imaging scans at baseline and two years follow-up. Hippocampal volumes (HV), fractional anisotropy (FA) and mean diffusivity (MD) in the fornix were extracted. Memory was indexed via composite scores of verbal memory tests. Structural equation models tested the bidirectional cross-lagged effects of fornix microstructure, memory, and HV. Impaired memory and smaller HV at baseline significantly predicted worse fornix microstructure (decreased FA and increased MD) two years later. Baseline fornix microstructure was not associated with subsequent changes in memory and HV. Fornix microstructure is compromised likely at a later stage, where significant decline in memory and hippocampal atrophy have occurred. This limits the utility of fornix microstructure in the early detection of AD. Our findings inform the possible pathophysiology and refined the use of AD neural markers.

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