## Abstract

Higher occupational attainment has previously been associated with increased Alzheimer's disease (AD) neuropathology when individuals are matched for cognitive function, indicating occupation could provide cognitive reserve. We examined whether occupational complexity (OCC) associates with decreased hippocampal volume and increased whole-brain atrophy given comparable cognitive function in middle-aged adults at risk for AD. Participants (n = 323) underwent structural MRI, cognitive evaluation, and work history assessment. Three complexity ratings (work with data, people, and things) were obtained, averaged across up to 3 reported jobs, weighted by years per job, and summed to create a composite OCC rating. Greater OCC was associated with decreased hippocampal volume and increased whole-brain atrophy when matched for cognitive function; results remained substantively unchanged after adjusting for several demographic, AD risk, vascular, mental health, and socioeconomic characteristics. These findings suggest that, in people at risk for AD, OCC may confer resilience to the adverse effects of neuropathology on cognition.

## Introduction

The cognitive reserve (CR) hypothesis posits that individuals with higher CR are able to better maintain cognitive function despite Alzheimer's disease (AD) pathology, likely due to compensatory approaches or preexisting cognitive strategies (Stern, 2012). These compensatory approaches and strategies are thought to be a direct result of various life exposures—such as high occupational or educational attainment—which lead to lessened adverse effects of AD pathology on cognition (Garibotto et al., 2008; Lo & Jagust, 2013; Stern et al., 1995).

One approach to testing the CR hypothesis is to investigate the association between a CR proxy (e.g., occupation) and brain pathology while holding cognitive function constant (Stern, Alexander, Prohovnik, & Mayeux, 1992). Holding cognitive function constant effectively matches individuals for variations in cognitive symptoms and allows for statistical comparison of individuals at the “same” level of cognitive functioning. In this paradigm, CR would be in effect if better scores on the CR proxy are associated with increased brain pathology, indicating that those with higher reserve are able to compensate for identified brain pathology and maintain a comparable level of cognitive function to individuals with less pathology (Stern, 2012; Stern et al., 1992). This method for testing CR has been used in prior studies with occupation as the CR proxy (Garibotto et al., 2008, 2012; Stern et al., 1995). In a seminal report, Stern and colleagues (1995) demonstrated an association between higher occupation and decreased parietal blood flow in AD patients while controlling for clinical dementia severity. More recently, Garibotto and colleagues (2008, 2012) showed similar results in both mild cognitive impairment (MCI) and AD patients, where higher occupational attainment was associated with cerebral hypometabolism while adjusting for cognitive function. Findings of a similar inverse nature have also been reported using other proxies for CR, including education (Kemppainen et al., 2008; Perneczky et al., 2006; Stern et al., 1992), socioeconomic status (SES; Fotenos, Mintun, Snyder, Morris, & Buckner, 2008), and cognitively stimulating activities (Scarmeas et al., 2003).

Occupational complexity (OCC) is a unique way to assess “occupation” because—in contrast to mere job title—it captures particular skills that, in turn, may reflect specific cognitive processes used in diverse occupations. Our group (Jonaitis et al., 2013) and others (Andel, Kareholt, Parker, Thorslund, & Gatz, 2007; Smart, Gow, & Deary, 2014) have shown that OCC is associated with better cognitive performance. However, no studies to date have examined the association between OCC and brain health given matched cognitive status in the fashion described earlier. Additionally, this concept has not been tested within a cohort of middle-aged adults who harbor specific risk factors for AD, including parental history of AD and apolipoprotein E4 (APOE4) genotype. This is a critical knowledge gap, seeing as a cohort at risk for AD is a choice population for implementing preventative strategies to protect against AD.

Therefore, the objective of this study was to determine whether higher OCC is associated with (a) comparatively lower hippocampal volume given the same level of memory function and (b) comparatively greater whole-brain atrophy given the same level of global cognitive function in a middle-aged cohort at risk for AD.

## Methods

### Participants

The Wisconsin Registry for Alzheimer's Prevention (WRAP) is an ongoing longitudinal study consisting of ∼1,500 cognitively healthy, middle-aged adults aged 40–65 at study entry (Sager, Hermann, & La Rue, 2005). For the present study, 332 participants were initially selected based on having available T1 MRI data. Two were subsequently excluded due to missing occupational data and seven were excluded due to missing covariate information. Thus, 323 participants were included in this study. As with the overall WRAP cohort, this study's sample was enriched with persons who had parental history of AD (74.0%) and who were APOE4 positive (41.2%). Additional relevant background characteristics are listed in Table 1. The University of Wisconsin Institutional Review Board approved all study procedures and informed consent was obtained from all participants included in the study.

Table 1.

Participant characteristics

Characteristics Valuea
Demographics
Age (years) 60.38 (6.09)
Women (%) 68.1
Education (years) 16.05 (2.33)
Family history positive (%) 74.0
APOE4 status
Noncarrier (%) 58.8
Heterozygote %) 38.7
Homozygote (%) 2.5
Household income $50,000 or greater (%)b 79.9 Occupational complexity Total OCC 9.80 (3.15) Complexity with data 4.17 (1.16) Complexity with people 3.51 (2.18) Complexity with things 2.11 (2.30) Cognitive and mood measures MMSE 29.30 (1.05) RAVLT composite score 75.42 (12.14) Mean cognitive factor score 0.12 (0.64) CES-D 6.00 (6.12) Stress 3.14 (2.83) Vascular risk indices Hypertension (%) 22.0 Diabetes (%) 3.7 Smoker (current) (%) 2.8 Characteristics Valuea Demographics Age (years) 60.38 (6.09) Women (%) 68.1 Education (years) 16.05 (2.33) Family history positive (%) 74.0 APOE4 status Noncarrier (%) 58.8 Heterozygote %) 38.7 Homozygote (%) 2.5 Household income$50,000 or greater (%)b 79.9
Occupational complexity
Total OCC 9.80 (3.15)
Complexity with data 4.17 (1.16)
Complexity with people 3.51 (2.18)
Complexity with things 2.11 (2.30)
Cognitive and mood measures
MMSE 29.30 (1.05)
RAVLT composite score 75.42 (12.14)
Mean cognitive factor score 0.12 (0.64)
CES-D 6.00 (6.12)
Stress 3.14 (2.83)
Vascular risk indices
Hypertension (%) 22.0
Diabetes (%) 3.7
Smoker (current) (%) 2.8

Notes: OCC = occupational complexity; APOE4 = apolipoprotein E4 allele; RAVLT = Rey Auditory Verbal Learning Test; MMSE = Mini-Mental State Exam; CES-D = Center for Epidemiologic Studies—Depression scale.

aValues are mean (SD) unless otherwise indicated.

### OCC and Hippocampal Volume

The results of the linear regression showed that higher OCC was significantly associated with lower hippocampal volume, holding RAVLT composite test score constant, and additionally controlling for age, sex, education, intracranial volume, and MRI scan-cognitive testing time interval (Model 1, see Table 2). This result remained significant when additively adjusting for confounding factors, which successively included AD risk factors (Model 2), vascular risk factors (Model 3), mental health measures (Model 4), and participant/parental SES (Model 5; see Table 2 and Fig. 1). Vascular risk factors and SES seemed to have the greatest attenuating effects on the relationship between OCC and hippocampal volume, although the initial finding remained significant for all subsequent models.

Table 2.

Association between OCC and hippocampal volume

β (SE) t p
Model 1 −14.69 (6.96) −2.11 .036
Model 2 −14.82 (6.98) −2.12 .034
Model 3 −14.16 (6.95) −2.04 .042
Model 4 −14.61 (6.97) −2.10 .037
Model 5 −14.21 (7.14) −1.99 .048
β (SE) t p
Model 1 −14.69 (6.96) −2.11 .036
Model 2 −14.82 (6.98) −2.12 .034
Model 3 −14.16 (6.95) −2.04 .042
Model 4 −14.61 (6.97) −2.10 .037
Model 5 −14.21 (7.14) −1.99 .048

Notes: The regression statistics reported here are for the association between total OCC and hippocampal volume while adjusting for the following covariates: Model 1 = age, sex, years of education, MRI scan-cognitive testing time interval, intracranial volume, and composite RAVLT score; Model 2 = Model 1 plus FH positivity and APOE4 status; Model 3 = Model 2 plus hypertension, diabetes, and smoking; Model 4 = Model 3 plus stress and CES-D; Model 5 = Model 4 plus participant/parental SES. OCC = occupational complexity; RAVLT = Rey Auditory Verbal Learning Test; FH = family history of Alzheimer's disease; APOE4 = apolipoprotein E4 allele; CES-D = Center for Epidemiological Studies—Depression scale; SES = socioeconomic status.

Fig. 1.

Inverse relationship between OCC and hippocampal volume. Higher OCC is associated with lower hippocampal volumes given same memory test scores and adjusted for age, sex, education, ICV, MRI scan-cognitive testing time interval, AD risk factors, vascular risk factors, mental health factors, and SES. For graphing purposes, OCC was dichotomized at the median into the depicted low versus high groups. OCC = occupational complexity; ICV = intracranial volume; MRI = magnetic resonance imaging; AD = Alzheimer's disease; SES = socioeconomic status.

Fig. 1.

Inverse relationship between OCC and hippocampal volume. Higher OCC is associated with lower hippocampal volumes given same memory test scores and adjusted for age, sex, education, ICV, MRI scan-cognitive testing time interval, AD risk factors, vascular risk factors, mental health factors, and SES. For graphing purposes, OCC was dichotomized at the median into the depicted low versus high groups. OCC = occupational complexity; ICV = intracranial volume; MRI = magnetic resonance imaging; AD = Alzheimer's disease; SES = socioeconomic status.

In order to understand if specific occupational complexities (complexity of work with data, people, or things) were contributing differentially to the association between total OCC and hippocampal volume, we fitted fully adjusted models (Model 5) for each of the three specific occupational complexities. There were no associations between either complexity of work with data or complexity of work with things and hippocampal volume (p ≥ .545); however, complexity of work with people was negatively associated with hippocampal volume while adjusting for RAVLT memory test score, demographics, MRI scan-cognitive testing time interval, AD risk factors, vascular risk factors, mental health measures, and SES (β (SE) = −23.95 (10.77); t = −2.22; p = .027).

### OCC and VBR

Regression analyses revealed that higher total OCC was significantly associated with greater brain atrophy while adjusting for mean cognitive factor score in addition to age, sex, education, and MRI scan-cognitive test time interval (Model 1, see Table 3). Incremental adjustment for AD risk factors, vascular risk factors, mental health measures, and parental/participant SES in subsequent models did not change this association (see Table 3 and Fig. 2). Parental/participant SES seemed to have the greatest attenuating effect on the relationship between total OCC and VBR, but again, the original association remained significant even while accounting for these additional potentially confounding factors. When these analyses were repeated with MMSE as our measure of global cognition, the results remained substantively unchanged (data not shown).

Table 3.

Association between OCC and brain atrophy

β (SE) t p
Model 1 0.094 (0.039) 2.43 .016
Model 2 0.095 (0.039) 2.44 .015
Model 3 0.096 (0.039) 2.47 .014
Model 4 0.097 (0.039) 2.48 .014
Model 5 0.088 (0.040) 2.21 .028
β (SE) t p
Model 1 0.094 (0.039) 2.43 .016
Model 2 0.095 (0.039) 2.44 .015
Model 3 0.096 (0.039) 2.47 .014
Model 4 0.097 (0.039) 2.48 .014
Model 5 0.088 (0.040) 2.21 .028

Notes: The regression statistics reported here are for the association between total OCC and VBR while adjusting for the following covariates: Model 1 = age, sex, years of education, MRI scan-cognitive testing time interval, and mean cognitive factor score; Model 2 = Model 1 plus FH positivity and APOE4 status; Model 3 = Model 2 plus hypertension, diabetes, and smoking; Model 4 = Model 3 plus stress and CES-D; Model 5 = Model 4 plus participant/parental SES. OCC = occupational complexity; VBR = ventricle-to-brain ratio; FH = family history of Alzheimer's disease; APOE4 = apolipoprotein E4 allele; CES-D = Center for Epidemiological Studies—Depression scale; SES = socioeconomic status.

Fig. 2.

Higher OCC is associated with increased brain atrophy. Higher OCC is associated with greater brain atrophy given same global cognitive performance and adjusted for age, sex, education, MRI scan-cognitive testing time interval, AD risk factors, vascular risk factors, mental health factors, and SES. For graphing purposes, OCC was dichotomized at the median into the depicted Low versus High groups. OCC = occupational complexity; MRI = magnetic resonance imaging; AD = Alzheimer's disease; SES = socioeconomic status.

Fig. 2.

Higher OCC is associated with increased brain atrophy. Higher OCC is associated with greater brain atrophy given same global cognitive performance and adjusted for age, sex, education, MRI scan-cognitive testing time interval, AD risk factors, vascular risk factors, mental health factors, and SES. For graphing purposes, OCC was dichotomized at the median into the depicted Low versus High groups. OCC = occupational complexity; MRI = magnetic resonance imaging; AD = Alzheimer's disease; SES = socioeconomic status.

Additionally, we tested whether complexity of work with data, people, or things would exhibit differential associations with VBR. Similar to our hippocampal volume results, there were no significant associations between either complexity of work with data or complexity of work with things and VBR (p ≥ .143). However, complexity of work with people was significantly associated with VBR, with higher complexity corresponding to greater VBR values (β (SE) = 0.23 (0.06); t = 3.86; p < .001).

### OCC and Cognition

Linear regressions indicated that total OCC was positively associated with RAVLT composite score (β (SE) = 0.41 (0.20); t = 2.02; p = .044), adjusting for age, sex, education, family history of AD, APOE4 status, hypertension, diabetes, smoking, CES-D, total stress, and participant and parental SES. Total OCC was also associated with mean cognitive factor score (β (SE) = 0.03 (0.01); t = 2.34; p = .020) and trended toward significance with MMSE score (β (SE) = 0.04 (0.02); t = 1.81; p = .071). These results correspond with previous findings from the larger WRAP cohort demonstrating relationships between OCC and cognitive function (Jonaitis et al., 2013).

## Discussion

In this study, we found that even after adjusting for potentially confounding variables that captured SES, AD risk, vascular health, and mental health, OCC (a CR proxy) was associated with decreased hippocampal volume and increased brain atrophy when participants were matched for cognitive function. These results suggest that in middle-aged persons at risk for AD, those with higher OCC are able to better tolerate AD-like pathology and maintain a similar level of cognitive performance compared with those with less pathology. Additionally, we found that of the three OCC components (i.e., data, people, and things), only complexity of work with people was significantly associated with decreased hippocampal volume and increased brain atrophy while controlling for cognitive function and other covariates, indicating that social components of occupation may have the greatest relevance to CR.

Although previous reports have investigated CR in a similar fashion to our analysis (Fotenos et al., 2008; Kemppainen et al., 2008; Perneczky et al., 2006), perhaps the more “traditional” method for investigating CR involves examining a CR proxy, such as education or occupation, as a modifier of the link between brain pathology and cognitive function. For example, Rentz and colleagues (2010) showed that CR, as measured by education, modified the relationship between amyloid deposition and neuropsychological performance such that those with greater CR did not exhibit poorer neuropsychological performance with increased amyloid deposition. A similarly designed study by Roe and colleagues (2008) found comparable results, wherein higher education ameliorated the deleterious effects of amyloid deposition on cognition. Our results, although presented in a different statistical fashion, also support the CR hypothesis because both approaches demonstrate CR buffering the otherwise adverse effect of AD pathology on cognitive function.

Interestingly, our study showed that when OCC was broken down to its individual components, complexity of work with people seemed to have the greatest impact on brain health. Prior research has indicated that social interaction is thought to help curb the progression of AD (Fratiglioni, Wang, Ericsson, Maytan, & Winblad, 2000; Zuelsdorff et al., 2013). Additionally, two studies that examined the effects of occupation and leisure activities on cognition found that both occupation and participating in social activities exerted similar protective effects on cognitive function (Adam, Bonsang, Grotz, & Perelman, 2013; Andel, Silverstein, & Kareholt, 2015). Furthermore, in Andel and colleagues (2015), the influence of complexity of work with people on later life cognition was attenuated when participation in social activity was taken into account. These findings are clinically significant, especially for individuals with jobs involving little social interaction, because they suggest that participating in social activities, such as clubs or organizations, could have as beneficial an impact on cognitive health as engaging in complex interactions with others in an occupational setting.

Some limitations of this study include its cross-sectional nature, which does not allow for understanding the effect OCC may have on neuropathological accumulation over time. Future studies are needed to test this relationship using longitudinal measures of hippocampal and whole-brain atrophy, as opposed to cross-sectional proxies such as VBR. Additionally, many of our measures, including OCC, SES, and vascular risk factors, rely on self-report, which could affect the accuracy of our results. Finally, OCC ratings were determined from the 1970 U.S. Census Dictionary of Occupational Titles, which may not be congruent with current occupational classifications; for example, homemakers were excluded from the Dictionary.

A major strength of this study was our ability to adjust for a variety of factors that had not been controlled for as rigorously in previous studies, including education, vascular risk factors, mental health factors, and SES. These factors are all highly correlated with either occupation or AD (Garibotto et al., 2008; Ngandu et al., 2007; Wilson et al., 2002), and it was necessary to adjust for them in order to discern the unique association between OCC and neuropathology. Even so, it was interesting that SES had the greatest attenuating effect on the relationship between OCC and neuropathology, possibly reflecting the intrinsic similarities between SES and OCC. More research is needed to elucidate which CR proxies have the greatest protective function against cognitive decline. Another strength of this study was the use of a robust composite OCC measure and, more importantly, the ability to explicate which components of OCC (specifically, complexity of work with people) made the greatest individual contribution to CR.

In conclusion, this study found that greater OCC is associated with decreased hippocampal volume and increased brain atrophy in a middle-aged cohort at risk for AD while holding cognition constant. This indicates that OCC is protective against cognitive deterioration in the face of AD pathology, providing support for the CR hypothesis and demonstrating the potential for a modifiable lifestyle factor to slow the clinical phenotype of AD.

## Funding

This work was supported by the National Institute on Aging (K23 AG045957 to OCO, R01 AG021155 to SCJ, R01 AG027161 to SCJ, P50 AG033514 to SA, and P50 AG033514-S1 to OCO), Veterans Administration Merit Review Grant (I01CX000165 to SCJ), and Clinical and Translational Science Award (UL1RR025011) to the University of Wisconsin, Madison. Portions of this research were supported by the Wisconsin Alumni Research Foundation, the Helen Bader Foundation, Northwestern Mutual Foundation, Extendicare Foundation, and from the Veterans Administration including facilities and resources at the Geriatric Research Education and Clinical Center of the William S. Middleton Memorial Veterans Hospital, Madison, WI.

None declared.

## Acknowledgements

We thank Caitlin A. Cleary, Sandra Harding, Jennifer Bond, Nancy Davenport-Sis, Janet Rowley, Amy Hawley, and the WRAP psychometrists for helping with study data collection; researchers and staff at the Waisman Center, University of Wisconsin-Madison, where the brain scans took place; and finally, we thank study participants in the WRAP for their continued dedication.

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