key: cord-0961951-rn82p2q7 authors: Bindoff, Aidan D.; Summers, Mathew J.; Hill, Edward; Alty, Jane; Vickers, James C. title: Studying at university in later life slows cognitive decline: A long‐term prospective study date: 2021-09-08 journal: Alzheimers Dement (N Y) DOI: 10.1002/trc2.12207 sha: 654666a785d0b8d0d82781cf4fe4ae132bd0107b doc_id: 961951 cord_uid: rn82p2q7 INTRODUCTION: Declining cognition in later life is associated with loss of independence and quality of life. This decline in cognition may potentially be reduced or reversed through engaging in cognitively stimulating activities. This study examined the potential for university attendance in later life to enhance cognitive function in older adults. METHODS: Cognitively unimpaired adults (n = 485, 69% female, median age 60 years) were given the opportunity to undertake free university study. Repeated neurocognitive assessment was performed over 7 years. RESULTS: Participants in the university education group (n = 383) improved z = .02 SD (.01, .03) per year of the study compared to controls (P = .001; averaged across a battery of cognitive tests). The largest improvements were observed on tests of language and verbal learning, memory, and episodic memory. DISCUSSION: Later‐life university study was associated with improved cognitive trajectories. Later‐life education may preserve cognitive function, specifically for functions associated with communication, social interaction, and maintaining independence. The direct relationship between early life education and later-life cognitive function may be confounded by intelligence quotient (IQ; people who perform well on cognitive tests tend to stay in school longer), and barriers to access including socio-economic disadvantage and socio-cultural factors. 8 The same advantages that are associated with higher education are also associated with other health outcomes. 9 Early life education may advance life-long cognitively stimulating work and leisure activity, thereby contributing to the preservation of cognitive function, reserve against the insults of pathology, and brain maintenance through neuroplasticity. 10 Single-domain cognitive training interventions to date have had mixed results, but have been of shorter-duration (5 to 6 weeks), 11, 12 and initial post-intervention gains have not been sustained at followup. 13 Multidomain trials have been of longer duration (up to 2 years) 14 but have focused largely on older cohorts. 15 It is difficult to attribute a benefit specifically to cognitive training or education in multidomain trial designs because the cognitive training occurs concurrently with other interventions. The Tasmanian Healthy Brain Project (THBP) is an ongoing longitudinal intervention study of cognitively healthy individuals over 50 with a self-selected intervention group who participated in university education. The aim of the present study was to evaluate differences in cognitive trajectories between participants in intervention and comparison groups on cognitive test scores across the domains of language processing, executive function, episodic memory, and working memory. This study extends earlier published work from the THBP, 12,16-18 covering a longer duration (7 years compared to 3 years). Because cognitive benefits of education may be age-and dose-dependent, we investigated whether the putative benefits of later-life education diminished for older participants, and whether benefits increase in proportion to academic load. The Tasmanian Healthy Brain Project (THBP) is a non-randomized prospective, longitudinal cohort study investigating the effect of • Cognitively unimpaired adults 50 and older were given the opportunity to undertake fee-waived university-level study. Participants in later-life education and comparison groups were followed with repeated neurocognitive assessment over 7 years. • Participants in the later-life education group had improved cognitive trajectories relative to controls on tests of language and verbal learning, verbal memory, and episodic memory. • Older participants benefited from later-life education at least as much as their younger peers. • Later-life education may slow age-related cognitive decline, particularly for functions associated with communication, social interaction, and maintaining independence. university-level education on age-related cognitive decline and dementia risk in adults ages 50 to 79 at baseline entry into the study. The design and methods of the THBP have been described in detail previously. 19, 20 Participants were unpaid volunteers recruited through print, television advertising, radio, and community information presentations. Participants were screened to exclude conditions independently associated with cognitive impairment. Baseline assessments beginning in 2011 were completed on 566 participants. To date, 156 participants (27.6%) have withdrawn from the THBP. The majority of withdrawing participants report factors unrelated to the study: 22% relocated, 13% unable to recontact, 9% too busy, 10% medical diagnosis, 6% deceased, 3% work commitments, 2% family issues, 28% provided no reason, and 7% found the assessments too stressful. Medical diagnoses were predominantly cancer and neurological disorders. No participants stated that dementia diagnosis was their reason for withdrawal. Of the 566 enrollments, 438 participants chose to join the intervention group and undertake university study in a course of their choosing. For the purposes of this study, which is to assess the association between later-life education and cognitive trajectories rather than an assessment of fee-waived education as an intervention, we included any participant in the intervention group who completed at least one unit of study. We excluded participants in post-graduate courses because (1) it was not possible to determine an equivalent academic load and (2) there was a high likelihood of recent (but not necessarily post-baseline or over 50) university-level study. Furthermore, following scrutiny of university records, some participants in the comparison group had undertaken university study after the study began and were excluded from this analysis. These criteria resulted in exclusion of n = 55 participants in the intervention group and n = 26 participants in the comparison group (Table 1 ). Assessments were completed annually on a battery of cognitive tests (outlined in Table 2 ) for the first 36 months, and then every 2 years up to 7 years from baseline (six assessments in total have been included in this study). Tests were conducted using pen and paper or computer, A subset of cognitive tests from the full THBP battery was chosen for this study based on sensitivity to early cognitive decline across a range of functional cognitive domains. These are outlined in Table 2 and Stroop tests were first log 10 -transformed and reversed, and paired associates learning (PAL te6) scores were transformed using the formula log e ((score max + 1)∕(score i + 1)) to improve the normality of residuals and correct sign inconsistencies across instruments. The benefit of later-life education is potentially confounded by early life education and other cognitively stimulating activities, so a sociobehavioral proxy of prior (baseline) CR was included as a covariate. The Wechsler Test of Adult Reading (WTAR-FSIQ) 21 All data-handling and statistical analysis were conducted in the R (v3.6) statistical computing environment. 25 Five participants had incomplete surveys at baseline such that prior CR scores could not be computed. CR for these participants was estimated using single imputation with years of education and WTAR-FSIQ as linear predictors. Conditional likelihoods were estimated using the lme4 26 and glmmTMB 27 packages for linear mixed-effects models. Time was modeled as a continuous variable ("Time") in years since baseline. Intervention group membership was encoded as a dummy variable ("group"). Data were in long-form, with a single column "Score" storing the zscores for each test, and another column "test" denoting the cognitive test instrument. For models estimating trajectories conditional on cognitive test instrument ("test"), by-participant random coefficients were fitted for each instrument. Random slopes were not included in these models due to convergence issues. The formula for the adjusted model presented in Table S1 and Figure 1 is, The formula for the (adjusted) by-participant random intercept and slope models presented in Table 4 and Figure 2 is, lme4 :: lmer(Score ∼ scale(age) + PriorCR * Time + Time * group This model estimated by-participant slopes (which capture individual differences in practice effects) and population-level differences in these slopes between university study and comparison groups, adjusted for age and CR at baseline. Note that the short-hand notation for an interaction of the form A * B * C expands to all main effects and lower-order interactions by convention in R, and the random effects structure is given in parentheses. The assumptions of linearity, homogeneity of variance, and normality of residuals were assessed using standard graphical methods, and were judged to be acceptable. Reproducible R code is provided at 28 and all estimated parameters are reported in Table 4 and the Supplementary Tables. To assess group differences in cognitive performance at entry into the study, Bayesian regression analysis was used to compare group means of cognitive tests scores, adjusted for age and CR (these covariates were also included in the primary analysis). Weakly informative student-t priors were specified, and Bayes factors were computed with bridge-sampling using the brms 29 package in R. Unlike p-values, Bayes factors allow a conclusion in favor of the null hypothesis ("no difference in cognitive test scores between groups at baseline") to be drawn if supported by evidence. A concern is whether participants with lower test scores were more likely to drop out depending on whether they were in the university study group or comparison group. To test this we fitted a logistic regression model to estimate the expected probability of remaining in the study at year 5 conditional on the interaction between test scores (we used RAVLT scores) and intervention group. Over 7 years (including up to six assessments per participant) of the project to 2019, this study included 2084 assessments and 37291 test scores (Table 3 ). At the time of data analysis, only 149 assessments had been completed for the 7th year assessments, which are ongoing, and delayed due to coronavirus disease 2019 (COVID-19) restrictions. To the best of our knowledge, these restrictions did not discriminate against any part of our cohort, since they applied to all participants. The most commonly cited reason for withdrawing from the study was moving from Tasmania. Comparison group participants were significantly more likely to withdraw, with 112 leaving from the intervention group and 44 from the comparison group (χ 2 = 6.5, P = .01); however, the proportion at each assessment varied little ( Aggregated over all cognitive test instruments, there was a significant time x group interaction after adjusting for age, prior CR, and a prior CR TA B L E 4 Unadjusted and adjusted (for age and prior CR) linear mixed-effects models assessing differences in trajectories of intervention group relative to the comparison group There was a significant time × group × test interaction (P = .016), suggesting that relative to the comparison group, intervention group trajectories differed across tests. Analysis of deviance statistics for the adjusted and unadjusted models, and a detailed table of coefficients and their CIs are reported in Table S1 . Post hoc analysis using estimated marginal mean trends showed that the significantly different group trajectories were for Boston Naming Test Broadly, the greatest differences were observed on those tests that displayed the weakest practice effects over repeated tests (illustrated in Figure 1 , and Table S2 , which shows the estimated marginal mean timetrends and post hoc contrasts). Participant age had a significant effect on cognitive trajectories (P < .001), with a 1 SD (≈7 year) increase in age-reducing standard- Table S3 ). Aggregated over all test instruments, the dose-dependent effect of academic load was estimated to be z = .004 SD [95% CI −.0004, .0094] per year, but this was not a statistically significant interaction (P = .07). The current study shows that later-life university study improved cognitive trajectories when compared with participants who did not undertake university study. The benefits of university study were F I G U R E 1 Estimated cognitive trajectories (with 95% confidence intervals [CIs]) over years since baseline for intervention group participants (those who undertook university study) and the comparison group (those who did not), holding age at entry into study and cognitive reserve (CR) at their respective means. The greatest group differences appear in tests where comparison group participants did not appear to benefit from re-test practice effects: tests of verbal memory, vocabulary, and comprehension greatest in tests on the domains of verbal memory, verbal episodic memory, and language processing. These tests also had the weakest practice effects (evidenced by estimated marginal time-trends for the comparison group). Older participants benefited the least from practice effects and the most from the intervention, particularly on verbal learning, memory, naming, and comprehension tests. Notably, tests that relate to executive function and visuo-structural memory showed no benefit relative to university study. This was a purposeful longitudinal study by design, focused on a real-world intervention and formal tertiary-level education, and follows a substantial number of studies indicating low levels of educational attainment early in life as a known risk-factor for dementia. 30 With comprehensive neuropsychological data at the mid-point of the study, the current results indicate that engagement in education by older adults provides a protective benefit for specific areas of cognitive function. Previous assessments of cognitive trajectories in the THBP have used factor-derived composite scores of cognitive function. 16, 17 For this analysis, we chose to report standardized scores on test instru-ments used in the THBP assessment battery. Shared and un-shared test variance was partitioned through the model structure to estimate conditional likelihoods for each group and the characteristic trajectories of each test, giving a detailed and directly comparable picture of expected cognitive trajectories on often-used cognitive tests. Substantial variation in trajectories on cognitive test instruments was demonstrated, along with variation in the estimated difference between intervention and comparison groups on these trajectories. There is established evidence that age-related cognitive change is not universal across all cognitive domains. 1 The domains on which the intervention group performed most strongly-verbal fluency and episodic memory-are domains that have been found previously to be most vulnerable to agerelated decline. 15 For tests on other domains, there was little difference in trajectories between intervention and comparison groups, with both groups appearing to benefit from familiarity with the tests. These re-test practice effects did not appear to be moderated by prior CR, which appeared to benefit only the level of age-adjusted cognitive test performance, rather than the slope (similar to other findings for early life education as a proxy of CR; see 23 and 22 for reviews). A plausible F I G U R E 2 Estimated mean cognitive trajectories (with 95% confidence intervals [CIs]) over the years since baseline for intervention group participants (those who undertook university study) and the comparison group (those who did not), holding prior cognitive reserve (CR) as the mean value. Panels show expected trajectories for participants at 55, 65, and 75 years of age at entry into the study. Re-test practice effects are most evident in the panel showing expected trajectories for someone entering the study at age 55. Older participants benefited from re-test practice effects the least and appeared to gain the most from later-life university education. The model decomposes age and re-test time (in years since first assessment) as separate effects, so estimated trajectories are quadratic interpretation is that later-life education has a preservation or compensatory effect, rather than augmenting that which was not lost. Further work could explore potential differences between early and laterlife education in building CR, and how that relates to biological processes of development and aging. The cognitive tests in the battery were not designed to be administered repeatedly over time, although, to minimize practice effects, some of these instruments included alternative forms. Although we anticipated that the relatively long inter-trial intervals (12 and 24 months) would minimize practice effects, it is apparent that some tests (eg., logical memory, Rey complex figure) had substantial practice effects. These were also the tests where the least apparent benefit of later-life education was observed. For statistical adjustment, we assumed that these practice trajectories were linear, but power-law or non-linear location-scale models 31 There has been no qualitative analysis of participant experiences in the THBP; however, anecdotal reports from participants suggest that many have enjoyed being able to study without career expectations or the pressures that normally accompany higher education. The freedom to choose an area of study may be an important factor, both for subjective well-being, and to encourage ongoing compliance and motivation to engage with the intervention over a duration of years. In addition, social aspects of the on-campus study experience may contribute to subjective well-being and cognitive health, albeit we have previously shown no differences in social networks between intervention and comparison groups. 16 Collectively, the current study supports the value of a complex, real-world intervention in the form of engagement in university study to attenuate decline in specific cognitive domains. The long-term goal of the THBP over 15 years is to collect extended longitudinal data from a single cohort to examine whether additional education later in life is associated also with reduced risk of dementia. The long-term nature of the THBP, as well as the relatively high retention rate of subjects, will determine if there is a subsequent mitigation of risk for significant cognitive decline and dementia. 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