Brief communicationLifestyle activities in mid-life contribute to cognitive reserve in late-life, independent of education, occupation, and late-life activities
Introduction
The concept of cognitive reserve (CR) is used to explain why some individuals maintain cognitive ability despite impaired brain health as a consequence of aging and diseases such as Alzheimer's disease (Nilsson and Lövdén, 2018, Stern, 2012). Crucially, the CR concept encompasses the notion that late-life cognitive activity is influenced by factors occurring earlier in life. Determination of contributors to CR is therefore important for “successful” aging and prevention of dementia. While epidemiological evidence suggests that education and occupation contribute to CR (Richards and Deary, 2005), there is an increasing interest in the additional contribution of other activities undertaken in mid-life, given their potential modifiability. This interest is amplified by evidence that mid-life activities (MAs) of a social or intellectual nature are associated with higher late-life cognitive ability, after adjusting for childhood cognitive ability (Gow et al., 2017), and by a recent review concluding that low levels of physical and social activity in adulthood represent key risk factors for dementia (Livingston et al., 2017).
Rigorous definitions of CR not only predict that lifestyle factors will relate to late-life cognitive ability, but also that such factors will moderate the relationship between cognitive ability and brain structure. Specifically, the cognitive ability of individuals with high CR should be less dependent on brain structure than those with low CR, possibly as a result of compensatory functional network reorganization (Stern, 2017, see also; Nilsson and Lövdén, 2018). The present study therefore asked whether MAs contribute to CR by testing 2 hypotheses: (1) MAs contribute to late-life cognitive ability independent of early-life education, mid-life occupation, and late-life activities; and (2) MAs moderate the relationship between cognitive ability and brain structure, such that the relationship is weaker in people with who had engaged in more MAs.
Two hundred five individuals (93 female) aged 66–88 years were selected from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN, www.cam-can.com, Shafto et al., 2014) cohort (see Supplementary Materials for further description of this sample). Cognitive ability was measured using the Cattell Culture Fair test of fluid intelligence (Cattell, 1971) and lifestyle activities by the Lifetime of Experiences Questionnaire (LEQ, Valenzuela and Sachdev, 2007), modified for UK participants.
The LEQ measures a broad range of cognitively stimulating experiences and activities during 3 life phases: youth, 13–29 years; mid-life, 30–64 years; and late-life, 65 years onward. Within each phase, activities are subdivided into specific or nonspecific. Specific activities are those that are considered to be undertaken primarily within 1 particular life phase, such as education or working occupation. By contrast, nonspecific activities such as socializing and playing sports are those can be undertaken at any age, and so are applicable to any life phase. The youth specific score (YS, or education) was derived from the UK's National Career Service categories, multiplied by number of years at each category. The mid-life specific score (MS, or occupation) was based on the standard occupational classification codes from the UK Office of National Statistics, summed across 7 mid-life periods. The late-life specific score (LS, or postretirement activities) reflected social and intellectual activities such as travel or participation in volunteer organizations. The current LEQ does not cater for some “specific” activities being undertaken at other life stages (e.g., education in mid- or late-life) and so their potential contribution to CR could not be evaluated in the present study.
Scores for nonspecific activities in youth, mid-life (MA), and late-life were summed across 7 questions about social, intellectual, and physical activities. These addressed participation in (1) travel, (2) social outings, (3) playing a musical instrument, (4) artistic pastimes, (5) physical activity (mild, moderate, vigorous), (6) reading, and (7) speaking a second language. Each of the 6 resulting scores (2 types of activity, specific and nonspecific, across the 3 life phases) was scaled to a score from 0 to 10.
T1- and T2-weighted 1 mm isotropic magnetic resonance imaging scans were available for 196 participants. Brain structure was measured in terms of total gray matter volume (TGM, see Taylor et al., 2017 and Supplementary Materials). Two participants with outlying adjusted TGM values were removed. The analysis used linear regression via the “lm” function in R 3.5.0 (R Core Team, 2016) to relate Cattell scores to the 6 LEQ scores above, plus age and sex. Data and analysis scripts are available on the Open Science Framework here: https://osf.io/32gme/, which includes individual scores for the 13 LEQ questions that comprise the MA sum score.
Section snippets
Results
The covariance and correlation of Cattell, LEQ scores and age are shown in a Supplementary Table (Supplementary Materials). All LEQ scores were significantly positively correlated with each other, and with Cattell.
Multivariable regression of late-life cognitive ability on the LEQ scores, with age and sex as covariates, showed a strong overall association (adjusted R2 = 0.355, F (8196) = 15.0, p < 0.001). In addition to the expected negative effect of age, the coefficients in Table 1 revealed a
Discussion
This study tested the hypothesis that lifestyle activities in midlife contribute to the CR that supports cognitive ability in late-life. Consistent with this, we found that general MAs make an independent contribution to late-life cognition over and above age, sex, education, occupation and current (late-life) activities, replicating the findings of Gow et al. (2017). However, a rigorous determination of CR requires further evidence that it moderates the association between a brain state and
Disclosure statement
The authors have no actual or potential conflicts of interest.
Acknowledgements
DC is funded by the Cambridge National Institute for Health Research (NIHR) Biomedical Research Centre. FM is part-funded by Medical Research Council (MC U105292687). MV is funded by a National Health and Medical Research Council of Australia Career Development Fellowship. The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) research was supported by the Biotechnology and Biological Sciences Research Council (grant number BB/H008217/1). Additional support to RH and RK was from the Medical
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