Abstract
There is a pressing need to identify markers of cognitive and neural decline in healthy late-midlife participants. We explored the relationship between cross-sectional structural brain-imaging derived phenotypes (IDPs) and cognitive ability, demographic, health and lifestyle factors (non-IDPs). Participants were recruited from the 1953 Danish Male Birth Cohort (N=193). Applying an extreme group design, members were selected in 2 groups based on cognitive change between IQ at age ~20y (IQ-20) and age ~57y (IQ-57). Subjects showing the highest (n=95) and lowest (n=98) change were selected (at age ~57) for assessments on multiple IDPs and non-IDPs. We investigated the relationship between 453 IDPs and 70 non-IDPs through pairwise correlation and multivariate canonical correlation analysis (CCA) models. Significant pairwise associations included positive associations between IQ-20 and gray-matter volume of the temporal pole. CCA identified a richer pattern - a single “positive-negative” mode of population co-variation coupling individual cross-subject variations in IDPs to an extensive range of non-IDP measures (r = 0.75, Pcorrected < 0.01). Specifically, this mode linked higher cognitive performance, positive early-life social factors, and mental health to a larger brain volume of several brain structures, overall volume, and microstructural properties of some white matter tracts. Interestingly, both statistical models identified IQ-20 and gray-matter volume of the temporal pole as important contributors to the inter-individual variation observed. The converging patterns provide novel insight into the importance of early adulthood intelligence as a significant marker of late-midlife neural decline and motivates additional study.
Highlights
In countries with advanced economies, changes in age distributions, largely due to lower birth rates and increased life expectancy, has meant that the world’s population is increasingly older, with the number of persons over 80 projected to triple by 2050 [1]
We visualize results with Manhattan plots that show log10 p-values for imaging derived phenotypes (IDPs)-by-non-imaging derived phenotypes (non-IDP) correlations, arranged by non-IDPs on the x-axis, multiple testing thresholds across all pairwise associations are marked with a horizontal line (here only familywise error rate (FWE) is indicated as False Discovery Rate (FDR) identified no additional significant result)
We identified two significant univariate associations between specific imaging derived phenotypes (IDP) and non-imaging derived phenotypes both non-adjusted and adjusted for the effects of cognitive change, Figure1 and Supplementary Figure 7A-7B
Summary
In countries with advanced economies, changes in age distributions, largely due to lower birth rates and increased life expectancy, has meant that the world’s population is increasingly older, with the number of persons over 80 projected to triple by 2050 [1]. A series of other possible determinants (physical activity, tobacco smoking, hypertension, obesity, reduced cardiac output, nutrition), with smaller, but significant effects on cognitive function and brain aging have been identified that may explain the remaining variability [6,7,8,9,10,11, 17, 18, 22, 32,33,34,35] Many of these measures have been branded as proxy markers of lower early-life intelligence
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