The Vietnam Era Twin Study of Aging (VETSA) study provides a unique opportunity to examine genetic and environmental influences on early cognitive change and associated risk factors. In VETSA 2, we propose our first 5-6 year follow-up of a large, age-homogenous sample that was middle-aged (50s) at baseline. Longitudinal studies of cognitive aging have employed cohort-sequential designs with age-heterogeneous samples that are usually weighted toward older subjects. There is solid evidence of change in midlife to early old age, but because mean change does tend to be modest, increased ability to examine individual differences is key. While no single design can provide all the answers, our novel design does enhance the ability to study differences in within-individual change patterns. By focusing on midlife, our design also has increased potential to identify early predictors of cognitive decline. Moreover, we are including an objective measure of allocation of cognitive effort that will be an important indicator, even in the absence of performance changes. We will follow 720 middle-aged twin pairs (1440 individuals) 5-6 years after collection of baseline neurocognitive, biomedical, and psychosocial data. Mean age at our VETSA 2 follow-up will be 60 (57-66). Applications from 2 Principal Investigators (Kremen, UCSD;Lyons, Boston Univ.) comprise a single integrated project. Our focus is to characterize genetic and environmental influences on early age-related changes in cognitive effort and performance (Aims 1, 2), and to examine major factors that mediate or moderate those changes: APOE [Aim 3];other biomedical risks [Aim 4];lifestyle/psychosocial factors [Aim 5]).
Aims are: 1) Characterize genetic and environmental influences on cognitive change over time (and specific component processes driving change);2) Investigate cognitive efficiency (i.e., ratio of performance to effortful resource allocation [based on task-evoked pupillary responses]) as a key cognitive aging process;3) Examine the relationship of APOE genotype to changes in cognitive function over time;4) Elucidate biomedical risk factors related to cognition and identify specific health risk factors that best predict cognitive aging (with multivariate genetic models not previously used);5) Examine lifestyle and psychosocial factors related to cognitive aging. We will obtain comprehensive assessments in multiple domains, utilize a novel approach that integrates the twin method with experimental/neuroscience approaches of parsing cognitive component processes, and use a cost-effective psychophysiological method (pupillometry) to measure cognitive effort.
VETSA 2 builds upon the unique resource of VETSA 1 and creates an invaluable resource for the study of change from midlife (an under-studied area) to early old age, and for understanding the interplay of biological and environmental factors that are key early predictors of declining or successful aging. The VETSA 2 project builds upon the unique resource of VETSA 1 and creates an invaluable resource for the study of midlife (an under-studied area) and for understanding the interplay of biological and environmental factors that are key determinants of successful aging.
|Vuoksimaa, Eero; Panizzon, Matthew S; Hagler Jr, Donald J et al. (2016) Heritability of white matter microstructure in late middle age: A twin study of tract-based fractional anisotropy and absolute diffusivity indices. Hum Brain Mapp :|
|Reynolds, Chandra A; Gatz, Margaret; Christensen, Kaare et al. (2016) Gene-Environment Interplay in Physical, Psychological, and Cognitive Domains in Mid to Late Adulthood: Is APOE a Variability Gene? Behav Genet 46:4-19|
|Walhovd, Kristine B; Krogsrud, Stine K; Amlien, Inge K et al. (2016) Neurodevelopmental origins of lifespan changes in brain and cognition. Proc Natl Acad Sci U S A 113:9357-62|
|Franz, Carol E; Finkel, Deborah; Panizzon, Matthew S et al. (2016) Facets of Subjective Health From Early Adulthood to Old Age. J Aging Health :|
|Fennema-Notestine, Christine; McEvoy, Linda K; Notestine, Randy et al. (2016) White matter disease in midlife is heritable, related to hypertension, and shares some genetic influence with systolic blood pressure. Neuroimage Clin 12:737-745|
|Vuoksimaa, Eero; Panizzon, Matthew S; Chen, Chi-Hua et al. (2016) Is bigger always better? The importance of cortical configuration with respect to cognitive ability. Neuroimage 129:356-66|
|RomÃ¡n, Francisco J; Lewis, Lindsay B; Chen, Chi-Hua et al. (2016) Gray matter responsiveness to adaptive working memory training: a surface-based morphometry study. Brain Struct Funct 221:4369-4382|
|Jelenkovic, Aline; Hur, Yoon-Mi; Sund, Reijo et al. (2016) Genetic and environmental influences on adult human height across birth cohorts from 1886 to 1994. Elife 5:|
|Kremen, William S; Panizzon, Matthew S; Cannon, Tyrone D (2016) Genetics and neuropsychology: A merger whose time has come. Neuropsychology 30:1-5|
|Petkus, Andrew J; Reynolds, Chandra A; Wetherell, Julie Loebach et al. (2016) Anxiety is associated with increased risk of dementia in older Swedish twins. Alzheimers Dement 12:399-406|
Showing the most recent 10 out of 127 publications