Article
Reference
Social resources as compensatory cognitive reserve? Interactions of social resources with education in predicting late-life cognition
WINDSOR, Tim D, GHISLETTA, Paolo, GERSTORF, Denis
Abstract
Objective: Access to social relationships has been linked with better cognitive performance.
We examined whether social resources interact with education to predict cognitive outcomes, which could indicate that social resources fulfill a compensatory role in promoting cognitive reserve. Method: We applied multilevel growth models to 6-wave, 13-year longitudinal data from the Berlin Aging Study (aged 70–103 years at first occasion; M = 84.9 years, 50%
women) and have taken into account key individual difference factors, including sociodemographic variables, medically diagnosed comorbidities, and depressive symptoms.
To account for possible reverse causality, analyses were conducted on a subset of the BASE participants without dementia (n = 368), and in follow-up analyses with the full sample (n = 516) using wave-specific longitudinal assessments of probable dementia status as a covariate. Results: Larger networks were associated with better performance on tests of perceptual speed and verbal fluency, but did not interact with education, providing little support for a compensatory reserve hypothesis. An interaction of [...]
WINDSOR, Tim D, GHISLETTA, Paolo, GERSTORF, Denis. Social resources as compensatory cognitive reserve? Interactions of social resources with education in predicting late-life cognition.
Journals of Gerontology. B, Psychological Sciences and Social Sciences, 2020, vol. 75, no. 7, p. 1451-1461
DOI : 10.1093/geronb/gby143
Available at:
http://archive-ouverte.unige.ch/unige:146516
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Social Resources as Compensatory Cognitive Reserve?
Interactions of Social Resources with Education in Predicting Late-Life Cognition
Tim D. Windsor1 PhD, Paolo Ghisletta2 PhD, & Denis Gerstorf3 PhD
1Flinders University, Adelaide, Australia
2University of Geneva, Swiss Distance Learning University, Switzerland, and Swiss National Center of Competence in Research LIVES—Overcoming vulnerability: Life course perspectives, Universities of
Lausanne and of Geneva, Switzerland
3Humboldt University, Berlin, Germany
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Correspondence regarding this manuscript to: Tim D. Windsor, College of Education, Psychology and Social Work, Flinders University, GPO Box 2100 Adelaide SA 5001 Australia, Phone 61 8 82017588, Fax 61 8 82013877, tim.windsor@flinders.edu.au.
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Abstract Objective
Access to social relationships has been linked with better cognitive performance. We examined whether social resources interact with education to predict cognitive outcomes, which could indicate that social resources fulfil a compensatory role in promoting cognitive reserve.
Method
We applied multilevel growth models to six-wave, 13-year longitudinal data from the Berlin Aging Study (aged 70–103 years at first occasion; M = 84.9 years, 50% women) and have taken into account key individual difference factors, including socio-demographic variables, medically diagnosed
comorbidities, and depressive symptoms. To account for possible reverse causality, analyses were conducted on a subset of the BASE participants without dementia (n = 368), and in follow-up analyses with the full sample (n = 516) using wave-specific longitudinal assessments of probable dementia status as a covariate.
Results
Larger networks were associated with better performance on tests of perceptual speed and verbal fluency, but did not interact with education, providing little support for a compensatory reserve hypothesis. An interaction of education with emotional loneliness emerged in the prediction of perceptual speed, suggesting that the educational divide in speed was minimal among people who reported lower levels of loneliness.
Discussion
We discuss our results in the context of differential implications of social resources for cognition, and consider possible mechanisms underlying our findings.
Abstract Words: 212
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Keywords: social networks, cognition, cognitive aging, cognitive reserve, Berlin Aging Study (BASE)
Introduction
Older adults who are more socially engaged and have better quality relationships typically perform better on cognitive tests than those with more limited social resources (Boss, Kang, & Branson, 2015; Kuiper et al., 2016). However, little is known about whether social relationship characteristics interact with other key resources such as education that are known to predict cognitive health. Moreover, studies have typically included samples predominantly comprising older adults in their 60s and 70s (Kuiper et al., 2016), whereas little is known about people in their 80s and beyond who are increasingly susceptible to cognitive and social losses (Baltes & Smith, 2003). This study reports on analysis of 13- year longitudinal data from the Berlin Aging Study (BASE; Baltes & Mayer, 1999; Lindenberger &
Baltes, 1997) undertaken to examine possible interactions between social resources and education in predicting levels and age-related changes in cognitive abilities in late life. The primary aim was to establish whether associations of education with cognitive test performance (processing speed and category fluency) were weaker among those more socially integrated, which could indicate that social resources fulfill a compensatory role in contributing to cognitive reserve – the capacity to use brain networks more flexibly and efficiently as a means of compensating for neuropathology (Stern, 2002).
The Interplay of Social Resources and Cognitive Functioning across Adulthood and Old Age Much of the empirical evidence for links between social resources and cognition has emerged from population-based, longitudinal studies of aging. For example, one earlier cohort study reported that social disengagement predicted subsequent increased risk of incident cognitive decline over 12 years (Bassuk, Glass, & Berkman, 1999). Another study found that restricted network structure as well as low social participation and support were associated with poorer performance on various cognitive outcomes
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(Seeman, Lusignolo, Albert, & Berkman, 2001; Zunzunegui, Alvarado, del Ser, & Otero, 2003).
Research on social participation has also identified positive associations between levels of participation in social activities and cognitive performance. For example, more social participation has been
associated with better performance (Barnes, Mendes de Leon, Wilson, Bienias, & Evans, 2004), and slower rates of decline (James, Wilson, Barnes, & Bennett, 2011) on global composite measures.
Several plausible mechanisms through which social resources might contribute to cognitive health have been advanced in the literature (for overview, see Kuiper et al., 2016). One possibility is that social networks encourage behaviors such as physical exercise that in turn contribute to brain health (e.g., Plassman, Williams, Burke, Holsinger, & Benjamin, 2010). Another is that social support helps to preserve cognition by reducing the frequency and intensity of physiological stress responses that can have deleterious effects on neurological functioning (e.g., Fratiglioni & Qiu, 2011).
Cognitive Reserve and the Role of Education
Another mechanism may be that social networks act as conduits to mentally stimulating
activities that help preserve cognition over the lifespan (e.g., Hertzog, Kramer, Wilson, & Lindenberger, 2008). This notion is in keeping with theories of cognitive reserve (Stern, 2002) developed to account for the lack of a direct correspondence between levels of Alzheimer’s Disease-related neuropathology and clinically significant cognitive losses. Cognitive reserve refers to individual differences in the capacity to use brain networks underlying cognition efficiently and to readily recruit alternative networks in the service of cognitive tasks as compensatory responses to disruptions caused by brain pathology. Life experiences including educational and occupational attainment are believed to contribute to cognitive reserve and are often used as proxy measures (Stern, 2002). Recognizing the potential for neural plasticity across the lifespan, the cognitive reserve perspective posits that cognitively stimulating
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aspects of lifestyle – including social interactions – could contribute to higher levels of cognitive functioning that persist into later life.
In the present study, we examined direct associations of social resources with levels and rates of change in cognitive test performance. Moving one step further, we also focused on interactions of social resources with education as a key marker of cognitive reserve that has been consistently associated with better cognitive performance in studies of older adults (e.g., Gerstorf, Herlitz, & Smith, 2006). We were interested in the possibility that having better access to social resources could allow for a more socially engaged lifestyle that provides compensatory means of contributing to cognitive reserve capacity (Scarmeas & Stern, 2003) for those with less education.
We are aware of only one previous empirical study that has examined interactions between education and social network resources in predicting cognitive performance among older adults.
Specifically, Shankar, Hamer, McMunn, and Steptoe (2013) examined associations of social resources with memory (immediate and delayed recall) and verbal fluency assessed at 4-year follow-up in the English Longitudinal Study of Aging. Results indicated that participants with more education showed consistently better delayed recall performance irrespective of levels of social isolation or loneliness. In contrast, participants with less education but high levels of social integration exhibited less pronounced reductions in delayed recall, thereby providing preliminary support for a protective compensatory role of social resources in the context of low education.
The Present Study
Our aim was to examine the combined effects of education and social resources in predicting cognitive performance and change in late life. With its oldest-old sample (two thirds of participants were aged 80 or older at baseline), six waves of cognitive data assessed over 13 years, and a comprehensive array of measures capturing individual differences in social integration, the BASE provides an ideal
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source of information for examining our research question. The social resource variables included an assessment of social network size, with larger networks being thought to facilitate a more diverse range of support resources and opportunities for engagement (Thoits, 2011). Second, we examined the
potential effect of supporting others in light of recent research highlighting links between prosocial behavior, health, longevity, and cognition. For example, Okun and colleagues (e.g., Brown & Okun, 2014) have proposed a caregiving behavioral system, which comprises cognitions, emotions, and neurophysiological factors that underlie efforts towards actively helping others. Helping behaviors are believed to play a role in deactivating stress responses, which has positive downstream effects for immune functioning and long-term health outcomes, including cognition. This is consistent with emerging empirical research showing links between supporting others and longevity (Hilbrand, Coall, Gerstorf, & Hertwig, 2017; Hilbrand, Coall, Meyer, Gerstorf, & Hertwig, 2017). Third, we made use of loneliness measures that capture perceived dissatisfaction with levels of contact (social loneliness) and the availability of close companionship (emotional loneliness; Russell, Cutrona, Rose, & Yurko, 1984).
We examined the two aspects of loneliness separately because conceptually, social loneliness is more closely aligned with a lack of opportunity for participation, whereas emotional loneliness is relatively more indicative of a lack of emotional support which could increase vulnerability to physiological stress responses (Kuiper et al., 2016).
We selected two measures of cognitive performance that were available at all assessments of the BASE and have been examined in previous studies of social participation and cognition (Ghisletta, Bickel, & Lövdén, 2006; Lövdén, Ghisletta, & Lindenberger, 2005). First, we selected perceptual speed, which is recognized as having superior psychometric properties, as being a strong indicator of age- related cognitive decline (Tucker-Drob et al., 2014). Previous work has shown perceptual speed to be the most age sensitive of the cognitive variables included in the larger BASE cognitive assessment
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battery (Lindenberger & Ghisletta 2009). Second, we selected verbal fluency, which is commonly used as an indicator of executive functioning (Henry & Crawford, 2004) – a cognitive ability that appears subject to short-term boosts in performance resulting from social interaction (Ybarra & Winkielman, 2012).
An earlier study using BASE data (Lövdén et al., 2005) had examined dynamic temporal links between social participation (assessed through participation across a range of activities) and perceptual speed, with the results pointing to social participation being a stronger predictor of subsequent changes in processing speed than the reverse. In the present study, we extend on the capacity of BASE to address questions about late-life social relations and cognition by (i) examining a range of BASE network variables that have not previously been examined in relation to cognition; (ii) examining the combined effects of network characteristics and education in predicting cognitive outcomes, operationally defined by testing their interactions; and (iii) making use of longitudinal information from the most recent waves of BASE (up to six waves over 13 years) that could not be incorporated in earlier analyses (up to three waves over six years). To control for the possibility that more limited social resources were a
consequence of pathological cognitive decline rather than a precursor to cognitive changes (Stoykova, Matharan, Dartigues, & Amieva, 2011; Windsor, Gerstorf, Pearson, Ryan, & Anstey, 2014), we conducted our main analyses with those classified with probable dementia at any assessment excluded.
We also conducted follow-up analyses on the full sample for which probable dementia status was
assessed longitudinally at each wave of measurement and was thus treated as a time-varying covariate.
Based on previous findings (Kuiper et al., 2016), we predicted that larger network size, more participation in prosocial activities, and less loneliness would be associated with higher levels of
cognitive test performance and less pronounced decline in cognitive performance with aging. In keeping with the notion that social resources could fulfil a compensatory role in contributing to cognitive
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reserve, we predicted that the cognitive divide between high and low education strata would be less pronounced for participants with more social network resources.
Method Participants and Procedure
Our analysis included 13-year BASE data collected across six measurement occasions: Time 1 (T1; 1990-1993, N = 516), Time 2 (T2; 1993-1994, N =361), Time 3 (T3; 1995-1996, N = 244), Time 4 (T4; 1997-1998, N = 164), Time 5 (T5; 2000, N = 88), and Time 6 (T6; 2004-2005, N = 48). At Time 1, the BASE sample consisted of 258 men (M age = 84.73, SD = 8.44; M years education = 11.31, SD = 2.50) and 258 women (M age = 85.11, SD = 8.89; M education = 10.19, SD = 2.02). The average time elapsed from T1 to subsequent assessments was 1.95 years to T2 (SD = 0.71); 3.76 years (SD = 0.76) to T3; 5.48 years (SD = 0.80) to T4; 8.94 years (SD = 0.87) to T5; and 12.99 years (SD = 0.90) to T6, respectively. Comprehensive information about the BASE samples and cognitive measures are published in Lindenberger and Ghisletta (2009), and Lindenberger, Mayr, and Kliegl (1993). All
measures reported here were based on the responses of BASE participants (i.e., not informants). Sample descriptive statistics and zero-order correlations among the study variables at T1 are reported in
Supplementary Table 1.
Complete data were available on all social resource variables, covariates and the Digit Letter test at T1. One participant had missing data for Categories at T1, and among those who provided data at assessments subsequent to T1, there were 2 missing values on Categories at T2, and 1 missing value at T6. Aside from attrition, complete data were available for Digit Letter Test. Using a method outlined by Lindenberger, Singer, and Baltes (2002), longitudinal selectivity was quantified by comparing
participants who provided relatively more data points (three or more waves, n = 244) with those who provided data for one or two waves only (n = 272). Comparisons revealed differences of 0.82 SD for the
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Digit Letter test, 0.67 SD for the Categories test, 0.44 SD for network size, 1.18 SD for supporting others, –0.10 SD for social loneliness, –0.39 SD for emotional loneliness, –0.40 SD for comorbidities, and –0.25 SD for depressive symptoms. These results suggest that participants who remained in the study for three or more assessments on average showed better cognitive performance and physical health, had larger networks, and provided more help to others at Time 1 relative to those who dropped out after one or two assessments.
Measures
Cognition. Perceptual speed was assessed using the Digit Letter test (Lindenberger & Baltes, 1997; Lindenberger et al., 1993). This test is similar to the Digit Symbol Substitution task of the Wechsler Adult Intelligence Scale, with the exception that digits are paired with letters rather than symbols. Participants completed up to 21 answer sheets (each containing six digits) while viewing a template with the relevant digit-letter pairings. The measure used here is the number of correct responses after 3 minutes.
Category Fluency was assessed by asking participants to name as many different animals as possible within 90 seconds. Two independent raters scored responses (e.g., excluding incorrect
responses and unnoticed repetitions) to produce a total score based on correct responses (Lindenberger, Mayr, & Kliegl, 1993).
Social resources. Network Size was assessed at T1 using a network-mapping procedure (Antonucci, 1986; Maier & Smith, 1999). Participants were asked to identify network members who they regarded as (i) very close, (ii) less close, and (iii) still important but relatively more distant, using a heuristic of three concentric circles. Network size was calculated as the total number of people
mentioned (Fiori, Smith, & Antonucci, 2007).
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Prosocial activity assessed at T1was captured using a measure of supporting others based on the sum of six items asking about provision of support to network members in the three months prior to T1.
Types of support included instrumental (e.g., help with housework, fixing things) and emotional (e.g., being a confidant). Responses to the items (0 = No, 1 = Yes) were summed to form an index with higher scores representing more provision of support to others (Hilbrand, Coall, Gerstorf, et al., 2017).
Social and Emotional Loneliness were assessed with eight items selected from the revised
UCLA-Loneliness Scale (Russell et al., 1984). Four items each measured social (e.g., “there are people I feel close to”) and emotional (e.g., “I lack companionship”) loneliness. We used the raw mean of the items for each subscale based on the T1 assessments. Loneliness measures based on these items have shown reliable associations with other indicators of psychosocial functioning (e.g., personality, social support) in previous analysis of the BASE (e.g., Smith & Baltes, 1997).
Education. Education was measured as the number of years the individual had spent in formal schooling.
Covariates. We included several covariates in the analysis to account for individual differences in factors known to be associated with social relationships and/or cognitive ability (e.g., Bielak,
Gerstorf, Kiely, Anstey, & Luszcz, 2011). In addition to chronological age and gender, physical health was assessed using a count of comorbidities based on the total number of physician observed diagnoses of moderate to severe chronic conditions, as determined in clinical examinations, and supported by results of additional blood and saliva assessments (Steinhagen-Thiessen & Borchelt, 1999). Depressive symptoms were assessed using a German version of the Center for Epidemiological Studies Depression Scale (CES-D, Radloff, 1977; Rapp, Gerstorf, Helmchen, & Smith, 2008). Both were assessed at baseline (T1). Probable dementia status across occasions was determined by identifying those who fell below a cohort-specific cut-off on the Short Mini-Mental State Examination at any of the six assessment
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waves (SMMSE; Klein et al., 1985; 70–84 years: < 12 points; 85+ years: < 11 points). This method of classification has shown adequate sensitivity and specificity when compared with independent clinical diagnoses of dementia in BASE participants made at T1 and T3 (Gerstorf et al., 2006). At T1, 148 participants were classified as having probable dementia.
Results
Education and social resources as predictors of cognitive test performance. The data were
analyzed using multilevel growth models – details of the analytic approach are provided in the
supplementary materials. Results of the multilevel growth models used to assess trajectories of change in cognition and the role that the social resource variables, education, and the covariates play are reported in Tables 1 and 2 for perceptual speed (Digit Letter) and category fluency (Categories) respectively. For consistency with previous studies that have systematically taken possible biases resulting from reverse causality into account (e.g., Stoykova et al., 2011), our main analysis was based on an n of 368 after exclusion of participants classified with probable dementia at T1. Data points were also excluded at follow-up occasions when participants scored below the cut-off for probable dementia (258 of 905 assessments in total from T2-T6). For each cognitive outcome, Model 1 includes age at T1 and gender only as covariates, with comorbidities and depressive symptoms included in Model 2. To contextualize our findings and assess their robustness, we also ran the analyses on the full BASE sample with inclusion of time-varying probable dementia status as a covariate (reported in supplementary materials, see follow-up analyses).
Consistent with earlier work, we found that the typical trajectories of late-life cognitive change on both tests are characterized by linear decline with some acceleration at later waves. Specifically, the linear component of decline amounted to close to a standard deviation per 10 years for the Digit letter test (γ10DL = – 0.88) and about one third of a standard deviation per 10 years for the Categories test (γ10CA
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= – 0.31), with some concave downward curvature (γ20DL = – 0.05, γ20CA = – 0.10) indicating steeper declines at the later assessments.
Most important for our research questions was the extent to which education, social resources, and their interactions predicted Digit Letter and Categories performance. Focusing first on the main effects, education was reliably and positively associated with the intercept for Digit Letter, with and without adjustment for covariates. For Category fluency, associations of education with the intercept were positive and borderline significant (Model 1, p = .05; Model 2, p = .06). Taken together, the results are consistent with the expectation that those who have received more education perform better on the cognitive tests. However, education was not reliably associated with the linear slope (i.e., rates of change over time) in Digit Letter or Categories performance.
Of the social resource main effects, only network size was consistently associated with cognitive test performance. Consistent with predictions, participants who reported larger social networks also performed better on the Digit Letter and Categories tests relative to those with smaller networks.
Supporting others was associated with better performance on Categories only, whereas neither emotional nor social loneliness were reliably and consistently related to cognition. Contrary to our predictions, having access to more and higher quality social resources was not reliably associated with reduced rates of decline in DigitLetter or Categories scores.
Of key interest in the present study were tests of interactions between education and the social resource variables because these provided a means of assessing the possible compensatory role of social resources for preserving cognitive test performance in the context of lower levels of educational
attainment. For Digit Letter, one statistically reliable interaction emerged, with education X emotional loneliness in the prediction of the intercept. The nature of the interaction is displayed in Figure 1, which shows model-implied trajectories of Digit Letter for hypothetical individuals with different
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combinations of high vs. low (+/– 1 SD) education and emotional loneliness. It can be observed that the combination of low education and high emotional loneliness (solid black line) was associated with particularly low Digit Letter performance consistently across the 13-year study interval. In contrast, people who had either higher or lower levels of education but who shared low levels of emotional loneliness (dashed lines) showed only minor differences in performance on the two cognitive tests. For Categories performance, trends towards a similar interaction of education X emotional loneliness in prediction of the intercept were evident, but these fell short of statistical significance in the two models (Model 1, p = .08; Model 2, p = .09). Similar to the Digit Letter, none of the other Education X social resource interactions emerged as significant in the prediction of Categories test performance.
Follow-up analyses. We conducted three sets of follow-up analyses to help put our findings in
context and examine their robustness. First, we re-ran the main analyses with the inclusion of time- varying probable dementia status and its interactions with the social resource variables as covariates.
Results are presented in Supplementary Tables 2 (Digit Letter) and 3 (Categories). For Digit Letter performance, the pattern of findings was largely consistent with those of the analyses excluding probable dementia cases reported above. For Categories, the alternative model specification revealed additional insights. The main effects for network size and supporting others that were evident in the main analysis remained so and in addition, a reliable interaction of education X social loneliness in predicting trajectories of change in Categories performance emerged. As can be observed in
Supplementary Figure 1, the combination of low education with high social loneliness (solid black line) was associated with marginally steeper decline in Categories over the latter part of the study interval relative to those with higher education combined with lower levels of social loneliness.
As a second set of follow-up analyses, we re-ran the main analyses examining associations between network size and cognition described in the preceding section using different variants of the
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network size variable. This was undertaken in order to determine whether the positive associations of total network size with the intercepts of the cognitive variables reported above were moderated by the reported closeness of network members. To this end, we constructed new variables representing numbers of (1) very close (inner circle) network members only, and (2) very close (inner circle) + less close (middle circle) network members. Considering number of close network members only resulted in non-significant main effects for Digit Letter (B = 0.02, SE = 0.13, ns) and Categories (B = 0.20, SE = 0.15, ns). Addition of middle circle network members to the variable was not sufficient to produce reliable associations between network size and Digit Letter (B = 0.09, SE = 0.08, ns), although it did produce a statistically reliable positive association for Categories (B = 0.21, SE = 0.09, p < .05). One way to interpret these findings is that larger networks of peripheral members are important for associations of total network size with cognition.
As a final set of follow-up analyses, we repeated the main analysis (Tables 1 and 2), with inclusion of an additional variable representing a count of the main categories of activities that participants reported engaging in (e.g., sports, day trips, cultural activities, hobbies, cf. Lövdén et al., 2005), to see whether cognitive performance was associated with a more general assessment of activity engagement. Results revealed a borderline significant main effect for activities in predicting Digit Letter (B = 0.24, SE = 0.12, p = .05), and activities was significantly associated with the slope for digit letter, indicating that those reporting greater activity engagement showed shallower rates of decline in
perceptual speed (B = – 0.05, SE = 0.02, p < .05). Activity engagement was not reliably associated with the intercept or slope for Categories, and education X activities interactions did not emerge as significant in predicting either outcome.
Discussion
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The major goal of the present study was to examine how four different measures of social resources are associated with levels of and change in cognitive functioning among old and very old adults, and how such social measures interact with education in predicting cognitive test performance. To do so, we have made use of six-wave, 13-year longitudinal data from participants of the Berlin Aging Study and have taken into account key individual difference factors, including socio-demographic variables, medically diagnosed comorbidities, and depressive symptoms. Importantly, we also controlled for possible reverse causality by excluding participants with probable dementia in the main analysis and controlling for time-varying probable dementia status in follow-up analyses.
Our results revealed that, among the social resource variables, larger networks were independently associated with overall better performance on tests of perceptual speed and verbal fluency, while participating in supportive behaviors was associated with better verbal fluency performance. Neither of the loneliness variables was consistently associated with better overall cognitive performance. The differential pattern of findings is broadly consistent with models of social support and health (Cohen, 2004), which posit that social connectedness (reflected here by network size and supporting others) directly contributes to positive health outcomes through mechanisms including positive health behaviors and exposure to positive emotions, whereas social support (proxied by our measure of emotional
loneliness) contributes indirectly to health by acting as a stress buffer.
Indeed, the notion of social support primarily acting as a stress buffer is consistent with the
interaction between education and emotional loneliness in predicting perceptual speed that was evident in our findings. The nature of the interaction indicated that the combination of low education and high levels of emotional loneliness was associated with particularly low performance on perceptual speed, whereas the educational divide in cognition was minimal among people who reported low levels of emotional loneliness. A complex interaction of education and social loneliness with time was evident in
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the prediction of category fluency, with those with higher social loneliness and lower education showing relatively lower levels, as well as steeper declines, in fluency. However, this interaction emerged only in the follow-up analyses and was not consistently observed across the different model specifications, raising some questions about its robustness. Taken together, the results pointing to consistent associations of network size with cognition provide some support for broader social engagement
contributing to cognitive reserve (Scarmeas & Stern, 2003). However, the fact that the network resource variables most indicative of the kind of broader social engagement that would be expected to contribute to reserve (network size and supporting others) did not interact with education to predict cognitive outcomes, means that our findings do not provide direct support for social engagement providing a compensatory reserve function in the context of low educational attainment.
Social Resources, Education, and Trajectories of Cognitive Functioning in Old Age
Our findings showing associations of larger social networks with better performance on both cognitive variables, and more engagement in supporting with higher verbal fluency, are broadly
consistent with models that propose a role for social engagement in promoting cognitive health (Hertzog et al., 2008). Moreover, follow-up analyses using different methods of scoring network size indicated that it is not larger numbers of very close social partners, but rather more diverse networks comprising relatively more distant connections, that appear to drive the associations with better cognitive
performance. We had also predicted that social resources would be associated with less pronounced decline in cognitive performance with aging. Although Kuiper et al. (2016) reported associations of social resources with less overall cognitive decline in their meta-analysis, the pooled effect sizes were small, and several of the individual studies reviewed did not find reliable associations of social relations with rates of change in cognitive performance. The absence of consistent associations with cognitive change in our study could be a result of insufficient statistical power. Generally the power to detect
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individual differences in rates of change is substantially lower than the power to detect individual differences in levels in latent growth modelling frameworks (Hertzog, von Oertzen, Ghisletta, &
Lindenberger, 2009); power is also highly influenced by error variance (Brandmaier, von Oertzen, Ghisletta, Lindenberger, & Hertzog, 2018), which is typically of appreciable magnitude in social resources indicators.
Alternatively, the lack of consistent associations between our key predictors and rates of change could also reflect connections between education, social behavior, and cognition being established prior to old age and remaining relatively constant thereafter. For example, investment theory (von Stumm &
Ackerman, 2013) posits that individual differences in tendencies to seek out cognitively stimulating activity lead to individual differences in intelligence that are established early in life and remain evident across the lifespan. Our findings support the possibility that being socially integrated provides a context for (or is a by-product of) intellectual investment, but do not provide direct evidence to indicate that social resources, education or their interaction protect against declines in processing speed and category fluency in very late life. This is also in keeping with longitudinal evidence suggesting that cognitive reserve reflects the maintenance of early differences in cognitive abilities as opposed to individual differences in rates of age-associated decline (Tucker-Drob, Johnson, & Jones, 2009).
In addition to examining social participation, we used measures of loneliness to assess whether perceived quality and availability of social support was associated with cognition. Low scores on emotional loneliness capture feelings of aloneness and isolation, indicating a lack of emotional needs being met by one’s social partners (Russell et al., 1984). Although we can only speculate based on the available data, our findings are consistent with stress-buffering perspectives that recognize the
importance of emotionally supportive relationships in protecting against negative effects of stress on the brain via the neuroendocrine system (Kuiper et al., 2016). Low education is a marker of socioeconomic
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disadvantage, which has been associated with increased stress exposure (Matthews & Gallo, 2011).
Thus, social disadvantage- when coupled with an absence of emotionally supportive relationships- could over time increase the risk of poor cognitive outcomes in later life. In contrast, our results indicated that those with high levels of education tended to perform well on the cognitive tests irrespective of current levels of emotional loneliness, suggesting that supportive relationships may play a less critical role for cognition for those with higher socioeconomic status.
Finally, it was notable that the associations of network size with better performance were consistent across measures of processing speed and verbal fluency. Although previous analysis has shown the speed measure (Digit Letter) to be the most sensitive of the BASE cognitive battery to age-related decline (Lindenberger & Ghisletta, 2009), it is also the case that the two measures are relatively highly correlated (T1 r = .59). Thus our findings may be more indicative of a general association, than the possibility that social resources are differentially associated with discrete domains of intellectual ability.
Ybarra and Winkielman (2012) have suggested that social cognitive functions such as mind-reading and perspective taking that are involved in social interaction could produce short term boosts’ to underlying processes of executive functioning, that might translate into longer-term cognitive gains. Our findings pointing to associations of network size with processing speed could indicate that social connectedness has broader cognitive benefits. It is also possible that larger networks act as a conduit to other forms of intellectually enriching activity apart from social participation, that in turn contribute to cognitive reserve.
Strengths, Limitations, and Outlook
This study contributes to existing knowledge regarding social resources and late-life cognition in several important ways. First, our findings provide some evidence for a protective role of network size, which was associated with better cognitive test performance overall. Second, the results suggested that
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emotionally supportive relationships (as reflected by low emotional loneliness scores) might become important for at least some aspects of cognitive health in the context of low educational attainment.
Although our findings did not provide consistent evidence in support of a compensatory role of social resources in protecting against social disadvantage, the education X social resource interactions that did emerge suggest that further examination of the combined effects of socio-economic status and social integration on late life cognition appears warranted.
We acknowledge several limitations of our study. First, although our statistical procedures handled nonrandom attrition under MAR assumptions (see supplementary materials), it is also the case that participants providing the most longitudinal data points represent a positively selected subset of the sample, which places limits on generalizability. As a second limitation, longitudinal information on several of the social resource variables was not available across all longitudinal assessments, which prompted our decision to base our analysis on social resource information available at T1. Thus our analyses do not shed light on the possible dynamic links between social participation and cognitive changes that may have taken place across the 13-year study interval. Similarly, although it would have been informative to examine links between social resources and additional cognitive domains such as reasoning, longitudinal data for reasoning measures were not available. Finally, there are several possible competing explanations for our results that cannot be ruled out. For example, it may not be larger networks per se that lead to better maintenance of cognitive ability into later life, but a more general predisposition toward intellectually engaging activity that among other things leads to more diverse networks as part of an engaged lifestyle (Curtis, Windsor, & Soubelet, 2015; von Stumm &
Ackerman, 2013). However, more mechanisms-oriented research is necessary to identify how risk and protective factors for cognitive aging operate and to better elucidate how psychosocial attributes contribute to cognitive reserve. One promising route could be to simultaneously examine multiple
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theoretically relevant resources, including education and leisure activities (Scarmeas & Stern, 2003), lifetime occupation (Lane, Windsor, Andel, & Luszcz, 2017), and social resources.
To sum up, our findings indicate that among several social resource variables, network size was the most consistent predictor of levels of perceptual speed and category fluency in very old adulthood.
Our results also suggest a possible higher risk of poor cognitive outcomes in late life among those with a combination of low educational attainment and a lack of emotionally supportive relationships. However, the lack of interaction effects involving social resources indicative of broader engagement (e.g., network size) meant that the findings did not provide direct evidence in support of a compensatory role for social engagement in promoting cognitive reserve.
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Author Notes
Tim D. Windsor, School of Psychology at Flinders University; Paolo Ghisletta, University of Geneva, Swiss Distance Learning University, Switzerland, and Swiss National Center of
Competence in Research LIVES—Overcoming vulnerability: Life course perspectives, Universities of Lausanne and of Geneva, Switzerland; Denis Gerstorf, Department of Psychology at Humboldt University, Berlin, Germany.
This article reports data from the Berlin Aging Study (BASE; www.base-berlin.mpg.de).
The BASE was initiated by the late Paul B. Baltes, in collaboration with Hanfried Helmchen, psychiatry; Elisabeth Steinhagen-Thiessen, internal medicine and geriatrics; and Karl Ulrich Mayer, sociology. Financial support came from the Max Planck Society; the Free University of Berlin; the German Federal Ministry for Research and Technology (1989 –1991, 13 TA 011 + 13 TA 011/A);
the German Federal Ministry for Family, Senior Citizens, Women, and Youth (1992–1998, 314- 1722-102/9 + 314-1722-102/9a); and the Berlin-Brandenburg Academy of Sciences’ Research Group on Aging and Societal Development (1994 –1999). Denis Gerstorf gratefully acknowledges the support provided by the German Research Foundation (DFG, GE 1896/3-1. GE 1896/6-1, and GE 1896/7-1). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.
e-mail: tim.windsor@flinders.edu.au; paolo.ghisletta@unige.ch; denis.gerstorf@hu-berlin.de.
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