Greater lifestyle engagement is associated with better cognitive resilience

Abstract

Previous evidence suggests that modifiable lifestyle factors, such as engagement in leisure activities, might slow the age-related decline of cognitive functions. Less is known, however, about which aspects of lifestyle might be particularly beneficial to healthy cognitive ageing, and whether they are associated with distinct cognitive domains (e.g. fluid and crystallized abilities) differentially. We investigated these questions in the cross-sectional Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data (N=708, age 18-88), using data-driven exploratory structural equation modelling, confirmatory factor analyses, and age-residualized measures of cognitive differences across the lifespan. Specifically, we assessed the relative associations of the following five lifestyle factors on age-related differences of fluid and crystallized resilience: education/SES, physical health, mental health, social engagement, and intellectual engagement. We found that higher education, better physical and mental health, more social engagement and a greater degree of intellectual engagement were each individually correlated with better fluid and crystallized cognitive resilience. A joint path model of all lifestyle factors on crystallized and fluid resilience, which allowed a simultaneous assessment of the lifestyle domains, showed that physical health, social and intellectual engagement and education/SES explained unique, complementary variance, but mental health did not make significant contributions above and beyond the other four lifestyle factors and age. The total variance explained for fluid resilience was 14% and 16% for crystallized resilience. Our results are compatible with the hypothesis that intellectually and physically challenging as well as socially engaging activities are associated with better crystallized and fluid performance across the lifespan. Research Highlights 1.Higher education/SES, better physical and mental health, more social engagement and a greater degree of intellectual engagement are all individually associated with better cognitive health across the lifespan. 2.When all five lifestyle factors were assessed simultaneously, we found that physical health and social as well as intellectual engagement are independently associated with individual differences in cognitive health, above and beyond the effect of education/SES. Mental health makes no additional independent contributions. 3.Education/SES, physical health, intellectual engagement and social engagement show similar patterns with crystallized and fluid cognitive resilience, suggesting that the associations between lifestyle engagement and cognition are global rather than cognitive-domain specific. 4.Age-corrected residuals as a proxy of cognitive health across the lifespan may be a useful tool for future cross-sectional studies comparing different age groups.

Neurophysiological and brain structural markers of cognitive frailty differs from Alzheimer’s disease

“…This is in line with the cognitive reserve hypothesis that an individual's prior education level and cognitive abilities modify the resilience of their brain structure to disease and injury (Stern, 2002). Longer education in early life and continuing diverse cognitive leisure activities in midlife and old age contribute to an individual's cognitive reserve, is related to better cognitive functioning in old age (Borgeest et al, 2018;Brigola et al, 2019;Lavrencic et al, 2018;Shafto et al, 2019;Singh-Manoux et al, 2011) and having fewer symptoms of cognitive decline and neuropathology (Chapko et al, 2018;Mortimer et al, 2003;Yoo et al, 2015).…”

Cognitive Diversity in a Healthy Aging Cohort: Cross-Domain Cognition in the Cam-CAN Project

“…Although education predicted cognitive performance robustly, both social engagement and enrichment activities also demonstrated independent relationships to cognition, and again these would be missed with a focus only on the Typically Declining factor. Social engagement and enrichment activities have both been suggested as potential targets of interventions to support cognitive abilities in later life (Bielak et al, 2014;Bourassa et al, 2017;Clare et al, 2017;James et al, 2011;Marioni et al, 2014), with very few studies of aging including a younger group (but see Borgeest, Henson, Shafto, Samu, & Kievit, 2019;Seeman et al, 2011). The current results suggest that although social engagement relates to Crystallized Abilities across the lifespan, younger adults' cognition was most strongly related to social engagement for the Fluid Abilities and Naming factors.…”

“…A final limitation of the current study is that, although the Cam-CAN data set uses an unusually diverse range of cognitive experiments, the factors that emerge from a PCA or related approach will depend on the variables included, and no single data set can be all-inclusive. Moreover, the current study used PCA as part of an exploratory approach, to provide a summary of the data, so we do not provide a more focused test of underlying factor structure (e.g., see Borgeest et al, 2019). Despite the limitations of any one data set, a picture of diverse cognitive aging can be developed if future cohort studies include more (a) domain-specific measures that are likely to reflect normal rather than pathological individual differences and (b) measures that have differential relationships to age rather than focusing on declines.…”

Transient resting-state network dynamics in cognitive ageing

“…Normal ageing generally results in cognitive decline 2 , though not all cognitive functions follow the same trajectory. In particular, whereas age produces a marked impairment in fluid intelligence (the ability to solve new problems), crystallised intelligence (the ability to rely on acquired knowledge) shows more modest age-related changes [3][4][5][6][7] .…”

“…The tasks are summarized in Table 2, and are fully detailed in Shafto et al 40 . Task data were obtained from Borgeest et al 3 , in which missing data (<12% in all tasks) were interpolated using the Full Information Maximum Likelihood (FIML) method 44 in the Lavaan R package 45 to allow unbiased estimates, applied to the full Stage 2 sample (n = 708).…”