Associations of neighborhood environment with brain imaging outcomes in the Australian Imaging, Biomarkers and Lifestyle cohort

Alzheimer’s & Dementia - (2016) 1-11

Featured Article

Associations of neighborhood environment with brain imaging outcomes in the AIBL cohort Ester Cerina,b,c,*, Stephanie R. Rainey-Smithd,e, David Amesf,g, Nicola T. Lautenschlagerf, S. Lance Macaulayh, Christopher Fowleri, Joanne S. Robertsoni, Christopher C. Rowej, Paul Maruffi,k, Ralph N. Martinsd,e, Colin L. Mastersi, Kathryn A. Ellisf a

Institute for Health and Ageing, Australian Catholic University, Melbourne, Victoria, Australia b School of Public Health, The University of Hong Kong, Hong Kong, China c School of Exercise and Nutrition Sciences, Deakin University, Burwood, Victoria, Australia d Sir James McCusker Alzheimer’s Disease Research Unit (Hollywood Private Hospital), Perth, Western Australia, Australia e Centre of Excellence for Alzheimer’s Disease Research and Care, School of Medical Sciences, Edith Cowan University, Perth, Western Australia, Australia f Academic Unit for Psychiatry of Old Age, Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia g National Ageing Research Institute, Parkville, Victoria, Australia h CSIRO Food and Nutrition, Parkville, Victoria, Australia i The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia j Austin Health, Heidelberg, Victoria, Australia k Cogstate Ltd., Melbourne, Victoria, Australia

Abstract

Introduction: “Walkable” neighborhoods offer older adults opportunities for activities that may benefit cognition-related biological mechanisms. These have not previously been examined in this context. Methods: We objectively assessed neighborhood walkability for participants (n 5 146) from the Australian Imaging, Biomarkers and Lifestyle study with apolipoprotein E (APOE) genotype and two 18-month-apart brain volumetric and/or amyloid b burden assessments. Linear mixed models estimated associations of neighborhood walkability with levels and changes in brain imaging outcomes, the moderating effect of APOE ε4 status, and the extent to which associations were explained by physical activity. Results: Cross-sectionally, neighborhood walkability was predictive of better neuroimaging outcomes except for left hippocampal volume. These associations were to a small extent explained by physical activity. APOE ε4 carriers showed slower worsening of outcomes if living in walkable neighborhoods. Discussion: These findings indicate associations between neighborhood walkability and brain imaging measures (especially in APOE ε4 carriers) minimally attributable to physical activity. Ó 2016 Published by Elsevier Inc. on behalf of the Alzheimer’s Association.

Keywords:

Place of residence; Walkability; Enriched environment; Brain volumetric measures; Amyloid beta depositions; Apolipoprotein E genotype; Hippocampus; Community dwellers

1. Introduction

The authors have no conflicts of interest declared. *Corresponding author. Tel.: +61-3-9230-8260; Fax: +61-3-9663 5726. E-mail address: [email protected]

Cortical levels of amyloid b (Ab) are posited as prime causes of synaptic dysfunction and neurodegeneration (cerebral atrophy) related to Alzheimer’s disease (AD) and dementia [1]. Recent estimates indicate that Ab deposition

http://dx.doi.org/10.1016/j.jalz.2016.06.2364 1552-5260/Ó 2016 Published by Elsevier Inc. on behalf of the Alzheimer’s Association.

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E. Cerin et al. / Alzheimer’s & Dementia - (2016) 1-11

and cerebral atrophy reach their thresholds of clinical positivity w17 and 4 years before the onset of dementia, respectively [2,3]. Given the current limited ability to slow cognitive decline in clinical AD [4], it is important to identify modifiable factors related to Ab deposition and cerebral atrophy which would assist the development of interventions for delaying cognitive decline and the onset of dementia. Living environments are sources of modifiable factors influencing neurodegeneration [5]. Animal studies using the experimental paradigm of enriched environments (EEs) have shown that aging-related cerebral atrophy is affected by environmental factors [6]. In animal research, EEs are defined as complex living conditions that facilitate enhanced sensory, cognitive, social, and motor stimulation and affect the brain at molecular, cellular, network, and behavioral levels [5]. For example, animal models exposed to EE have shown higher levels of brain plasticity defined as brain volumetric measures [7], whereas the effects of EE on Ab deposition have been mixed, with some studies showing reductions [8,9] and others increases [10] in Ab deposition. These contradictory findings have been in part attributed to differences in activities offered by specific EE, with EE primarily promoting physical activity (PA) deemed to slow down Ab accumulation [10]. Walkable urban habitats can be thought of as real-life EE for humans [11]. Walkable neighborhoods, as they have been defined in the PA and urban planning literature [11,12], are typified by a mix of recreational, residential, commercial, and other land uses providing opportunities for cognitionenhancing and neuroprotective lifestyle behaviors, such as social [13,14], cognitive [14–17], and physical activities [12,18–21]. With their intricate interconnected street networks, high volumes of human traffic, and variety of destinations, walkable urban streetscapes facilitate walking for transport and the usage of local services [19,22–24] and offer high levels of environmental complexity and sensory stimulation. Moreover, the mere act of navigating along complex routes may benefit cognitive health [25]. Although the neighborhood environment may plausibly have an indirect effect on neurogenesis and neurodegeneration in older adults, studies on the neighborhood environmentcognition nexus in humans have mainly focused on the effects of neighborhood-level socioeconomic status on cognitive function as measured by psychometric tests. Only two studies have examined other neighborhood characteristics and noted that greater availability of community resources, proximity to public transit, and access to well-maintained public spaces were associated with better or smaller declines in cognitive functioning [17,26]. It is important to identify neighborhood characteristics associated with neurodegeneration (brain volumetric measures) and related processes (Ab deposition) because neighborhoods can potentially affect large populations for a sustained amount of time. This is especially critical for aging populations who typically spend more time in their neighborhood and are vulnerable to environmental challenges.

Behavior and the environment are not the only determinants of cognitive function. It is now well established that the apolipoprotein E genotype (APOE), specifically the APOE ε4 allele, is associated with cognitive decline and/or its biological mechanisms [27–29]. Evidence suggests that APOE genotype moderates the effects of cognitionpromoting activities [27] and environmental stressors [30], and that environmental factors are particularly important for those with higher genetic risk of dementia [30]. An interactionist approach is needed that establishes the extent to which relationships of neighborhood characteristics with brain imaging outcomes depend on genetic risk of dementia. Consequently, we examined cross-sectional and longitudinal associations of objectively assessed neighborhood walkability with brain imaging volumetric and Ab burden measures, investigated the moderating effects of APOE genotype on these associations, and examined the extent to which associations were mediated (i.e., explained) by participation in physical activities deemed to be influenced by neighborhood walkability (leisure-time and transportrelated PA) [12]. We hypothesized that neighborhood walkability and its components (dwelling density, intersection density, and land use mix) would be associated with better brain imaging volumetric outcomes and possibly lower Ab depositions, and that these associations would be stronger in APOE ε4 carriers and would be in part mediated by participation in PA.

2. Methods 2.1. Study design and sample The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of Ageing is a cohort study established in 2006 designed to examine participants every 18 months on clinical, neuropsychological, magnetic resonance imaging (MRI), and amyloid positron emission tomography (PET) brain imaging performance measures relevant to AD [31]. Methods of recruitment, assessment, inclusion, and exclusion have been previously detailed [31,32]. Briefly, participants with mild cognitive impairment (MCI) or AD were recruited in Melbourne and Perth (Australia) from the community and from tertiary Memory Disorder Clinics. Healthy controls (HCs) were recruited by advertisement in the community or were spouses/partners of the participants with MCI or AD. At each assessment, a clinical review panel considered all available information to classify clinical status for MCI and AD and considered HC participants with neuropsychological scores outside normative ranges. The panel was blinded to amyloid brain imaging results. Written consent was obtained from all participants, and approval for the AIBL study was obtained from the human research ethics committees of Austin Health, St Vincent’s Hospital, Edith Cowan University and Hollywood Private Hospital. The

E. Cerin et al. / Alzheimer’s & Dementia - (2016) 1-11

residential address geocoding component was approved by the human research ethics committee of Deakin University. This study includes participants from the Melbourne site with MRI brain volumetric and/or PET Ab burden measures, for whom APOE genotyping, baseline clinical diagnostic classification (HC, MCI or AD), and baseline street–level (street name and postcode) residential address details were available (n 5 146; 124 with both MRI and PET measures, 19 with only PET, and three with only MRI measures). The characteristics of these participants are listed in Table 1. The sample consisted of older residents, prevalently retired, living with a spouse or partner, in stable living arrangements, highly educated (.56% with .12 years of education), and residing in medium-to-high income neighborhoods (the median weekly household income in Melbourne is AUD 1230). At baseline, w20% of the sample had AD, a similar proportion had MCI, whereas the remaining were classified as HC. Nearly 40% of participants were APOE ε4 carriers. Of the 127 participants with MRI brain imaging, 37 had one assessment and the remaining had an additional 18-month postbaseline assessment. Of the 143 participants with PET brain imaging, 30 had one Ab burden assessment, whereas 113 had an additional 18-month postbaseline assessment. 2.2. Measures 2.2.1. MRI and PET brain imaging (outcomes) MRI scans were undertaken at baseline and 18-month follow-up using a 3D T1 magnetization prepared rapid gradient echo (MPRAGE) for screening and coregistration with the PET images as previously described [3,32]. In this study, primary outcome measures were cortical gray matter, right hippocampal, left hippocampal, and ventricular volumes (cm3) adjusted for intracranial volume using the method described by Raz et al [33]. PET Ab imaging was conducted using 11C-Pittsburgh Compound B (11C-PiB) at baseline and 18-month follow-up. A 30-minute acquisition started 40 minutes after 11C-PiB injection ultimately yielded standardized uptake value (SUV) data which were summed and normalized to the cerebellar cortex SUV, resulting in the SUV ratio (SUVR), which was the primary PET outcome of Ab burden. A detailed description of the PET methodology is available elsewhere [3,32]. 2.2.2. APOE genotype (moderator) APOE genotype was determined by direct sequencing from a blood sample taken from each participant at baseline as previously described [34]. 2.2.3. Participants’ characteristics (covariates) Participants completed a survey including sociodemographic information (e.g., age, gender, and education); family and personal medical history; smoking; medication, alcohol, and illicit drug use; and the International Physical Activity Questionnaire—Long Form (IPAQ-LF) [31,35].

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Changes in sociodemographics and health were recorded at each visit. In this study, we used IPAQ-LF data to determine total weekly MET (metabolic equivalents) minutes of leisure-time plus transport-related PA. 2.2.4. Attributes of participants’ residential areas (exposure variables) Geographic Information System (GIS) data on dwelling density (dwellings/hectare), street intersection density (number of
3 arm intersections/km2; a measure of street connectivity, and road-network complexity), land use mix (a score ranging from 0 to 1 rescaled to 0–10; a measure of destination availability and diversity), and median weekly household income for Australian Bureau of Statistics level 1 statistical areas (SA1) falling within greater Melbourne were used to quantify attributes of the locations surrounding participants’ street-level residential addresses. Land use mix was computed using information on the within-buffer proportions of land (pi) devoted to retail; residential; office; health, welfare, and community; and entertainment, culture, P and recreation uses using the formula 21 pi$ln(pi)/ln(N), where N is the number of land uses and “ln” is the natural logarithm [12]. Data on dwelling density, street intersection density, and land use mix were standardized in the form of z scores. These three measures were then summed to obtain a walkability index. GIS data were obtained from the University of Melbourne. For privacy reasons, only street name and postcode (not house number) of the participants’ residential addresses were provided. Individual residential areas are typically operationalized as buffers surrounding a residential address. The residence represents the center of the buffer, whereas the edges of the buffer are determined by the points along the street network that are 0.5 km or 1 km from the residential address, which are considered to be walkable distances [12]. In the absence of a street-number level address, local residential areas are defined as a sequence of buffers centered along all points of the street and postcode where a participant resided. We created, for each participant, 0.5km and 1-km street-network buffers along the street of residence within a specific postcode (Fig. 1) and computed their size (in km2), dwelling density, street intersection density, land use mix, median weekly household income, and a transport-related walkability index. The last five variables represented weighted averages with weights based on the proportion of buffer area covered by specific SA1. GIS work was conducted using ESRI’s ArcGIS software v10.2. 2.3. Data analyses For MRI volumetric and Ab burden measures, main analyses were based on data from two assessments 18 months apart. Linear mixed (regression) models with random intercepts were used to estimate all direct (not mediated by PA), indirect (PA mediated), and total effects (PA mediated 1 not mediated by PA; the term “effects”

E. Cerin et al. / Alzheimer’s & Dementia - (2016) 1-11

4 Table 1 Sample characteristics Characteristic

Table 1 Sample characteristics (Continued ) MRI brain imaging sample (n 5 127)

Sociodemographics at baseline Age, y 74.8 (7.1) Male sex 54 (42.5%) Years of education ,9 11 (8.7%) 9–12 43 (33.9%) 13–15 29 (22.8%) 151 44 (34.7%) Living with spouse/ 94 (74.0%) partner Retired 119 (93.7%) Recent changes to 1 (0.8%) living arrangements Positive for APOE ε4 49 (38.6%) Health behavior and status at baseline Ever smoked 50 (39.4%) Ever had alcoholic 91 (72.7%) drink History or current 5 (3.9%) stroke History or current 9 (7.1%) diabetes History or current 54 (42.5%) hypertension History or current 10 (7.9%) coronary heart disease History or current 21 (16.5%) depression Cognitive clinical status at baseline Healthy control 79 (62.2%) Mild cognitive 25 (19.7%) impairment Alzheimer’s disease 23 (18.1%) Neuropsychological measures at baseline MMSE 27.1 (4.1) CDR-SOB 1.2 (2.5) Premorbid IQ 107.9 (8.0) Self-reported physical 1747 (2503) activity (MET.min/ week)* Brain imaging measures at baseline Right hippocampal 2.94 (0.37) volume (cm3)y Left hippocampal 3.09 (0.33) volume (cm3)y Gray matter volume 661.60 (28.29) (cm3)y Ventricle volume 37.28 (16.77) (cm3)y Amyloid b burden — (SUVR) 18-month changes in brain imaging measures Right hippocampal 20.04 (0.08) volume (cm3)y Left hippocampal 20.03 (0.08) volume (cm3)y Gray matter volume 24.09 (11.27) (cm3)y Ventricle volume 3.02 (4.70) (cm3)y

PET brain imaging sample (n 5 143) 75.0 (7.3) 63 (44.1%) 14 (9.8%) 49 (34.3%) 33 (23.1%) 47 (32.9%) 110 (76.9%) 134 (93.7%) 1 (0.7%) 57 (39.9%) 59 (41.3%) 99 (69.2%) 6 (4.2%) 11 (7.7%) 61 (42.7%) 15 (10.5%)

29 (20.3%)

85 (59.4%) 32 (22.4%) 26 (18.2%) 27.0 (4.2) 1.3 (2.5) 107.5 (8.2) 1836 (2646)

— — — — 1.71 (0.60)

— — — — (Continued )

Characteristic

MRI brain imaging sample (n 5 127)

PET brain imaging sample (n 5 143)

Amyloid b burden — 0.014 (0.067) (SUVR) Environmental variables (residential address at baseline) Dwelling density— 11.9 (7.6) 11.6 (7.3) 0.5 km buffer (dwellings/hectare) Street intersection 73.1 (28.2) 72.4 (27.1) density—0.5 km buffer (intersections/ km2) Land use mix—0.5 km 2.8 (0.8) 2.8 (0.8) buffer (index from 0 to 10) Walkability index— 20.09 (1.78) 20.11 (1.69) 0.5 km bufferz Household income— 1552 (402) 1530 (388) 0.5 km buffer (AUD/wk) 0.5 km buffer size 1.77 (1.16) 1.91 (1.40) (km2) Dwelling density— 11.8 (7.1) 11.8 (7.2) 1 km buffer (dwellings/hectare) Street intersection 74.5 (31.5) 75.1 (33.1) density—1 km buffer (intersections/ km2) Land use mix—1 km 2.9 (1.1) 3.0 (1.1) buffer (index from 0 to 10) Walkability index— 20.15 (1.71) 20.16 (1.64) 1 km bufferz Household income— 1580 (607) 1599 (665) 0.5 km buffer (AUD/week) 1 km buffer size (km2) 5.07 (2.30) 5.36 (2.78) Abbreviations: MRI, magnetic resonance imaging; PET, positron emission tomography; APOE ε4, apolipoprotein E ε4 allele; MMSE, MiniMental State Examination; CDR-SOB, Clinical Dementia Rating Scale– Sum of Boxes; IQ, intelligence quotient; MET.min, metabolic equivalents minutes; SUVR, standardized uptake value ratio; AUD, Australian dollars. *Includes leisure-time and transport-related physical activity. y Corrected for intracranial volume. z Walkability index calculated as the sum of z scores of dwelling density, street intersection density and land use mix.

does not imply causality) of neighborhood walkability on levels, and changes in, brain imaging outcomes; and the moderating effect of APOE genotyping (allele ε4 carriers vs. non-carriers; see Supplementary Material for details). These models accounted for the fact that multiple participants were recruited from the same postcodes and multiple assessments were taken on the same participants. All models were adjusted for age at baseline, gender, educational attainment, APOE ε4 status, time of assessment, APOE ε4 status by time of assessment interaction, and median weekly household income. Additional medical history and sociodemographic confounders were identified and included in the models (Supplementary Material).

E. Cerin et al. / Alzheimer’s & Dementia - (2016) 1-11

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Fig. 1. Example of 0.5 km buffers along streets of residence within participants’ postcodes.

A first set of models, unadjusted for PA, estimated the total effects of neighborhood walkability on brain imaging outcomes. After current recommendations [36], the PAmediated indirect effects of neighborhood walkability on brain imaging outcomes were estimated using the bootstrap-based product-of-coefficients test [37], whereas the direct effects (not mediated by PA) were derived from the first set of models with additional adjustment for PA (Supplementary Material). All the above models were also rerun adjusting for diagnostic category (AD, MCI, or HC) as participants with MCI or AD, and with worse brain imaging outcomes, may move to neighborhoods with specific characteristics due to their cognitive status. We reported the results of both sets of models because adjustment for diagnostic category would underestimate the strength of relationships if the environment contributes to the risk of onset of MCI/AD and further neurodegeneration. All the above models were first estimated for each of the walkability (composite) indices. Statistically significant (P , .05) total effects of walkability on a brain imaging outcome were then further examined by estimating the total, direct, and PA-mediated indirect effects of each component (dwelling density, land use mix, and intersection density) on the outcome. Supplementary models, unadjusted for diagnostic category and excluding participants with AD, were estimated to examine the extent to which the main findings might be potentially generalizable to cognitively normal older adults. All analyses were conducted in Stata 10.

3. Results The variability of environmental attributes of residential areas where participants resided at baseline were relatively large with dwelling density ranging from 3 to 48 dwellings

per hectare, street intersection density from 25 to 206 intersections/km2, and land use mix score from 1.3 to 5.3 (Table 1). Table 2 summarizes the cross-sectional total, direct (not mediated by PA), and PA-mediated indirect effects of environmental attributes on average brain imaging outcome values across baseline and 18-month assessments in the form of regression coefficients and 95% CIs. Both walkability indices were positively associated with right hippocampal volume even after adjusting for diagnostic category. For example, each unit increase in walkability within 1 km radius residential buffers was associated with 0.038 to 0.043 cm3 greater volume (see Total effects in Table 2). These effects were attenuated but remained statistically significant, after excluding participants with AD (Supplementary Table 1). All walkability components contributed to these associations (Supplementary Table 2). The estimated direct effects of walkability and their components on right hippocampal volume were w4 times larger than the PA-mediated indirect effects (see direct and PA-mediated indirect effects in Table 2 and Supplementary Table 2). PA-mediated effects were small due to PA being weakly associated with imaging outcomes after adjustment for neighborhood walkability rather than walkability being weakly associated with PA (see a and b coefficients in Table 2). No total significant associations were found of neighborhood walkability with left hippocampal volume and amyloid b burden. However, significant PA-mediated indirect effects of walkability were observed in models unadjusted for diagnostic category. These were positive in relation to the left hippocampal volume, whereas they were negative in relation to amyloid b burden (Table 2). The latter were also observed after excluding participants with AD (Supplementary Table 1). Both walkability indices were positively related to gray matter volume and negatively related to ventricle volume (Table 2). After excluding participants with AD, only the

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Table 2 Cross-sectional total, direct [not mediated by physical activity (PA)], and PA-mediated indirect effects of neighborhood walkability on brain imaging outcomes

Walkability index (sum of z scores)

Effects

Left hippocampal volume, cm3

Gray matter volume, cm3

Ventricle volume, cm3

Amyloid b burden, SUVR

b (95% CI) [a:b]

b (95% CI) [a:b]

b (95% CI) [a:b]

b (95% CI) [a:b]

b (95% CI) [a:b]

2.84* (0.35 to 5.33)

22.14* (23.88 to 20.39)

20.047 (20.100 to 0.006)

0.044y (0.010 to 0.077)

0.011 (20.019 to 0.041)

0.036* (0.000 to 0.071) 0.008* (0.000 to 0.021) [391**:2.1$1025*] 0.043* (0.009 to 0.077)

0.004 (20.027 to 0.036) 0.007 (20.001 to 0.018) [383**:1.8$1025] 0.008 (20.023 to 0.038)

2.51 (20.14 to 5.18) 0.33 (20.33 to 1.18) [391**:8.6$1024] 2.97* (0.28 to 5.66)

21.97* (23.77 to 20.17) 20.17 (20.71 to 0.28) [384**:24.5$1024] 22.50y (24.22 to 20.78)

20.034 (20.087 to 0.020) 20.013* (20.025 to 20.001) [345**:23.8$1025*] 20.035 (20.090 to 0.020)

0.035 (20.001 to 0.071) 0.008 (20.001 to 0.020) [363**:2.1$1025]

0.001 (20.031 to 0.033) 0.007* (0.000 to 0.017) [352**:1.9$1025*]

2.50 (20.23 to 5.22) 0.47 (20.16 to 1.28) [363**:1.2$1023]

22.35* (24.17 to 20.53) 20.15 (20.66 to 0.26) [353**:24.4$1024]

20.027 (20.082 to 0.028) 20.008* (20.023 to 20.001) [300**:22.9$1025*]

2.35* (0.03 to 4.70)

21.79* (23.45 to 20.13)

20.045 (20.101 to 0.009)

0.037* (0.005 to 0.069)

0.007 (20.023 to 0.037)

0.029 (20.004 to 0.062) 0.008 (20.001 to 0.019) [390**:2.0$1025] 0.038* (0.005 to 0.071)

0.001 (20.030 to 0.031) 0.006 (20.001 to 0.017) [382**:1.7$1025] 0.005 (20.026 to 0.035)

1.93 (20.59 to 4.46) 0.42 (20.21 to 1.21) [390**:1.1$1023] 2.54* (0.03 to 5.05)

21.63 (23.35 to 0.08) 20.16 (20.67 to 0.26) [382**:24.2$1024] 22.24y (23.93 to 20.56)

20.034 (20.087 to 0.020) 20.011 (20.030 to 0.001) [342**:23.3$1025] 20.031 (20.087 to 0.025)

0.030 (20.003 to 0.064) 0.008 (20.001 to 0.019) [362**:2.0$1025]

20.002 (20.033 to 0.030) 0.007 (20.001 to 0.017) [353**:1.8$1025]

2.07 (20.48 to 4.60) 0.47 (20.10 to 1.21) [361**:1.3$1023]

22.11* (23.84 to 20.39) 20.13 (20.61 to 0.26) [352**:23.8$1024]

20.022 (20.078 to 0.039) 20.009 (20.029 to 0.002) [309*:22.8$1025]

Abbreviations: SUVR, standardized uptake value ratio; b, regression coefficient; 95% CI, 95% confidence intervals. NOTE. a 5 regression coefficient quantifying the confounder-adjusted associations between the walkability indices and PA. The values indicate the difference in PA expressed as MET.min per week associated with a unit increase in the walkability index. b 5 regression coefficient quantifying the confounder-adjusted associations between PA and a specific imaging outcome. The values indicate the difference in the imaging outcome (expressed as cm3 or SUVR) associated with a MET.min per week increase in PA. The estimates of the PA-mediated indirect effects of walkability represent the product of a and b regression coefficients. NOTE. All models adjusted for age at baseline, gender, educational attainment, median weekly household income, APOE ε4 status, time of assessment, and APOE ε4 status by time of assessment interaction. Specific models also adjusted for other relevant medical history and sociodemographic confounders (see Supplementary Material). Models adjusted for diagnostic category had diagnostic category included as two indicator variables (healthy controls was the reference category compared with people with MCI or AD). The term “effects” does not imply causality. *P , .05; **P , .001. y P , .01.

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Models unadjusted for diagnostic category Walkability index—0.5 km Total buffer Direct PA-mediated indirect Walkability index—1.0 km Total buffer Direct PA-mediated indirect Models adjusted for diagnostic category Walkability index—0.5 km Total buffer Direct PA-mediated indirect Walkability index—1.0 km Total buffer Direct PA-mediated indirect

Right hippocampal volume, cm3

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associations with ventricular volume remained statistically significant (Supplementary Table 1). Although all walkability components contributed to the associations with ventricle volume, land use mix was the main contributor to the associations with gray matter volume (Supplementary Table 2). The direct effects of walkability on gray matter volume were 4.4 to 7.6 larger than the PA-mediated indirect effects. The PAmediated indirect effects of walkability on ventricle volume were negligible, with most of the associations being attributable to direct effects (Table 2, Supplementary Table 1). No significant gene-environment interactions on average brain imaging outcomes were found. No significant associations were found between neighborhood walkability and 18-month change in brain imaging outcomes in the whole sample (Table 3). A moderating effect of APOE ε4 status by neighborhood walkability (0.5 km buffers) was found with respect to right hippocampal volume (P ,.01), mainly due to land use mix (P ,.05; Supplementary Table 3). Although walkability and land use mix were not associated with changes in right hippocampal volume in non-APOE ε4 carriers, higher levels of walkability and land use mix were predictive of smaller declines in volume in APOE ε4 carriers (P ,.05; Table 4 and Supplementary Table 3). No such moderating effects were found after excluding participants with AD from the analyses (data not shown). Moderating effects of APOE ε4 status were observed in relation to 18-month changes in Ab burden (Tables 3 and 4), with walkability showing negative associations in APOE ε4 carriers only (Table 3). Significant 18-month increases in Ab burden were observed only in APOE ε4 carriers living in neighborhoods with below average walkability (Table 4). These effects persisted after exclusion of participants with AD (Supplementary Table 4). Follow-up analyses showed that dwelling and intersection densities contributed to these effects (Supplementary Table 3). The PA-mediated indirect effects of walkability on changes in the above outcomes in APOE ε4 carriers were negligible (0%–2% of the total effects). Hence, only total effects were reported. 4. Discussion Neighborhood environments can impact participation in cognitive, social, and physical activities and, hence, potentially influence biological mechanisms implicated in cognitive decline in aging populations. However, no previous studies have examined associations of neighborhood environments with brain imaging parameters related to cognitive function and risk of dementia. This information is important, as strategies for maintaining cognition need to start in the early stages of the disease process, which can be evidenced by the imaging markers that we have investigated. Also, to have a sufficient level of reach and effectiveness, strategies need to be multisectoral and multilevel and target individuals as well as the environment they live in. We found that objectively determined indices of neighborhood walkability were associated, cross-sectionally or

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longitudinally, in the expected direction with brain imaging markers of better cognitive health, that is, lower Ab deposition, smaller ventricular volume, and larger hippocampal and gray matter volume. Although attenuated, several total effects, direct effects, and PA-mediated indirect effects of neighborhood walkability on brain imaging outcomes persisted after the exclusion of participants with AD. Larger studies powered to conduct robust analyses by diagnostic category will need to ascertain if the effect attenuation observed after the exclusion of AD cases may be due to selection bias and other confounding factors or to residing in walkable environments helping delay AD progression. Only small portions of the cross-sectional associations were explained by self-reported leisure-time and transportrelated PA, suggesting that, apart from facilitating the adoption of a physically active lifestyle, walkable neighborhoods may have a positive effect on their residents’ cognitive health through other mechanisms, such as engagement in social, navigational, and cognitive activities. The fact that PA was not identified as a mediator of longitudinal environment-brain imaging outcome associations, and only a modest mediator of cross-sectional associations may be due to the modest measurement properties of the IPAQ-LF [35] and PA being a more volatile factor compared to the neighborhood built environment. PA has been previously linked to better brain volumetric outcomes [5] and lower Ab burden [9]. Yet, given that most leisure time and transportation physical activities are not purely physical but can include social (group activities or walking with friends) and cognitive (navigation and route finding) components, the potential behavioral mechanisms responsible for the observed associations of neighborhood walkability with cerebral atrophy and Ab deposition remain unclear. The multicomponent nature of physical activities may also explain why the associations with hippocampal volume were region specific and significant only for the right hippocampus, which has been implicated in retention of spatial rather than nonspatial information, and memory of locations within an environment [38]. One of the numerous factors contributing to this finding could be long-term daily exposure to spatially and visually complex environments while walking in neighborhoods with high intersection and destination densities, which may assist neurogenesis in this region of the brain due to the type of cognitive processing required to function in such environments. The geographical scale (0.5 km vs. 1 km buffer zones around residential addresses) of the neighborhood attribute measures did not seem to moderate the strength of cross-sectional associations with brain imaging outcomes. However, changes in brain imaging outcomes tended to show stronger relationships with environmental attributes based on the smaller buffers (0.5 km; corresponding to w5– 10-minute walk). More proximal places to home may be more relevant to aging residents, especially when they are likely to experience reductions in their life space (the geographic area a person covers in daily life) due to retirement

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Table 3 Associations (total effects) of neighborhood walkability with 18-month changes in brain imaging outcomes

Sample

Models unadjusted for diagnostic category Walkability index—0.5 km All buffer Non-APOE ε4 carriers APOE ε4 carriers Walkability index—1.0 km All buffer Non-APOE ε4 carriers APOE ε4 carriers Models adjusted for diagnostic category Walkability index—0.5 km All buffer Non-APOE ε4 carriers APOE ε4 carriers Walkability index—1.0 km All buffer Non-APOE ε4 carriers APOE ε4 carriers

Left hippocampal volume, cm3

Gray matter volume, cm3

Ventricle volume, cm3

Amyloid b burden, SUVR

b (95% CI)

b (95% CI)

b (95% CI)

b (95% CI)

b (95% CI)

0.003 (20.007 to 0.012)

0.004 (20.005 to 0.014)

0.91 (20.53 to 2.35)

20.47 (21.06 to 0.11)

20.003 (20.012 to 0.007)

20.001 (0.010 to 0.009) 0.034* (0.005 to 0.062) 20.001 (20.011 to 0.009)

— — 0.000 (20.010 to 0.011)

— — 0.71 (20.80 to 2.23)

— — 20.39 (21.01 to 0.23)

0.001 (20.009 to 0.011) 20.029* (20.054 to 20.003) 0.000 (20.010 to 0.010)

— —

— —

— —

— —

0.004 (20.006 to 0.015) 20.026* (20.052 to 20.003)

0.003 (20.007 to 0.013)

0.005 (20.005 to 0.015)

0.93 (20.57 to 2.43)

20.48 (21.09 to 0.12)

20.002 (20.011 to 0.008)

0.000 (20.010 to 0.010) 0.032* (0.002 to 0.062) 0.000 (0.010 to 20.010)

— — 0.001 (20.009 to 0.012)

— — 0.83 (20.76 to 2.41)

— — 20.42 (21.06 to 0.22)

0.002 (20.008 to 0.011) 20.024* (20.051 to 20.001) 0.001 (20.010 to 0.011)

— —

— —

— –

0.002 (20.008 to 0.011) 20.023* (20.046 to 20.001)

— —

Abbreviations: SUVR, standardized uptake value ratio; b, regression coefficient; 95% CI, 95% confidence intervals. NOTE. All models adjusted for age at baseline, gender, educational attainment, median weekly household income, APOE ε4 status, time of assessment, and APOE ε4 status by time of assessment interaction. Specific models also adjusted for other relevant medical history and sociodemographic confounders (see Supplementary Material). Models adjusted for diagnostic category had diagnostic category included as two indicator variables (healthy controls was the reference category). *P , .05.

E. Cerin et al. / Alzheimer’s & Dementia - (2016) 1-11

Walkability index (sum of z scores)

Right hippocampal volume, cm3

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Table 4 Eighteen-month changes (and 95% confidence intervals) in right hippocampal volume (cm3) amyloid b burden (SUVR) by values of neighborhood walkability in APOE ε4 carriers (only significant APOE ε4 by walkability index interactions are reported) Walkability index (sum of z scores) Walkability index (0.5 km buffer) 1 SD below average Average 1 SD above average

Adjusted for diagnostic category

Change in right hippocampal volume cm3 (95% CI)

Walkability index (sum of z scores)

Adjusted for diagnostic category

Change in amyloid b SUVR (95% CI)

No Yes No Yes No Yes

0.063y (0.020 to 0.107) 0.053* (0.007 to 0.098) 0.014 (20.011 to 0.040) 0.016 (20.011 to 0.044) 20.035 (20.090 to 0.021) 20.020 (20.077 to 0.037)

No Yes No Yes No Yes

0.054y (0.013 to 0.096) 0.044* (0.001 to 0.087) 0.016 (20.010 to 0.040) 0.014 (20.015 to 0.043) 20.025 (20.081 to 0.030) 20.018 (20.082 to 0.047)

Walkability index (0.5 km buffer) No Yes No Yes No Yes

20.133y (20.182 to 20.084) 20.112y (20.166 to 20.059) 20.074y (20.101 to 20.046) 20.058y (20.088 to 20.027) 20.014 (20.078 to 0.050) 20.003 (20.071 to 0.065)

1 SD below average Average 1 SD above average Walkability index (1 km buffer) 1 SD below average Average 1 SD above average

Abbreviations: SUVR, standardized uptake value ratio; 95% CI, 95% confidence intervals; SD, standard deviation. NOTE. All values adjusted for age at baseline, gender, educational attainment, median weekly household income, APOE ε4 status, time of assessment, and APOE ε4 status by time of assessment interaction. Specific models also adjusted for other relevant medical history and sociodemographic confounders (see Supplementary Material). Models adjusted for diagnostic category had diagnostic category included as two indicator variables (healthy controls was the reference category). *P , .05. y P , .01.

and declines in mobility and cognition [39,40]. The observed differences in findings between neighborhood geographical scales would have implications for the planning of the location of aged care facilities, and the creation of agefriendly communities supporting active ageing in one’s own home [41]. Although we found numerous cross-sectional associations of environmental attributes with brain volumetric measures, this was not the case when 18-month changes were examined. In a sample consisting of a large proportion of HC, 18-month changes in brain volumetric measures may not be sufficiently large to determine the environmental correlates of such changes. However, it could be argued that cross-sectional associations may reflect long-term cumulative effects of the environment on cognition-related mechanisms that would, by definition, be stronger than shorter term effects. When considering 18-month changes in brain imaging outcomes in the context of APOE genotype, only APOE ε4 carriers showed several significant associations with environmental measures. Better outcomes (smaller declines in right hippocampal volume and smaller increases in Ab burden) were observed for APOE ε4 carriers living in more walkable neighborhoods typified by higher levels of residential density and street connectivity. The effects on Ab burden were also observed after exclusion of AD cases from the analyses. Environmental stimulation and living in an environment that supports engagement in physical and other activities, and thus potentially beneficial to cognition-related biological mechanisms, may be more critical for individuals with higher genetic risk of dementia. This finding is in line with prior

epidemiologic evidence of stronger associations in APOE ε4 carriers of neighborhood-level environmental stressors [30] and participation in PA with cognition-related outcomes [27]. This study has several limitations. This is an observational study based on a convenience rather than a probability sample. Thus, causality cannot be inferred and potential sampling bias is a threat to internal validity. The adopted sampling strategy did not include stratification by environmental characteristics, which would help maximize the variability in exposures for an accurate estimation of dose-response relations [20]. Nevertheless, substantial environmental variability was observed. The sample was small for an epidemiological study and insufficient to allow examination of associations by diagnostic category (e.g., HC vs MCI and AD), although we also conducted analyses excluding AD cases. Many outcomes were examined, increasing the risk of type I error. The environmental variables were limited by the available GIS and residential data. Data on length of residence, and details about destination types and activity locations were unavailable, reducing the accuracy of the exposure measures. Yet, this concern is somewhat attenuated by reports of Australians aged 701 years having a median length of residence of 151 years [42]. PA was measured via self-reports which have lower validity than their objective counterparts (e.g., accelerometry) [43]. Self-selection into neighborhoods based on lifestyle preferences is another concern which could not be addressed [22]. Despite these limitations, this study has identified interesting findings and raised interesting questions that warrant further examination in future research.

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Acknowledgments Ester Cerin was supported by an Australian Research Council Future Fellowship (FT140100085). Geocoding for this study was supported by a grant from the Centre for Physical Activity and Nutrition Research, Deakin University. Funding for the AIBL study was provided by the CSIRO Flagship Collaboration Fund and the Science and Industry Endowment Fund (SIEF) in partnership with Edith Cowan University (ECU), The Florey Institute of Neuroscience and Mental Health, Alzheimer’s Australia (AA), National Ageing Research Institute (NARI), Austin Health, CogState Ltd., Hollywood Private Hospital, and Sir Charles Gairdner Hospital. The study also receives funding from the National Health and Medical Research Council (NHMRC), the Dementia Collaborative Research Centres program (DCRC2) and the McCusker Alzheimer’s Research Foundation, and Operational Infrastructure Support from the Government of Victoria. Supplementary data Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.jalz.2016.06.2364.

RESEARCH IN CONTEXT

1. Systematic review: The authors reviewed the literature using traditional databases (e.g., PubMed). Although the neighborhood environment has been identified as an important contributor to older adult dwellers’ cognition-enhancing activities (e.g., physical activity), it has seldom been examined as a potential correlate of cognitive function/decline and related biological mechanisms. This is despite a wealth of animal studies showing that enriched living conditions can benefit cognition-related biological mechanisms. The dearth of knowledge on humans motivated this study. 2. Interpretation: The findings suggest that complex, destination-rich neighborhood environments may positively contribute to cognition-related biological mechanisms and slow biological changes preceding cognitive decline especially in those with a higher genetic risk. This raises the possibility of early intervention through environmental stimulation. 3. Future directions: The article discusses methodological limitations and questions that future studies should address to robustly examine the interactive effects of the neighborhood environment and their residents’ genetic make up on cognition-related biological mechanisms.

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