Women at Risk: A Comparative Study on Socioeconomic Status, Lifestyle, Brain, and Cognition Among Older Females in Japan and Sweden

Women at Risk: A Comparative Study on Socioeconomic Status, Lifestyle, Brain, and Cognition Among Older Females in Japan and Sweden | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Women at Risk: A Comparative Study on Socioeconomic Status, Lifestyle, Brain, and Cognition Among Older Females in Japan and Sweden Yingxu Liu, Yasuko Tatewaki, Carl-johan Boraxbekk, Benjamin Thyreau, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3833392/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Determine and compare lifestyle risks addressing the effects of socioeconomic status (SES) on brain and cognitive variations among females in two community-dwelling cohorts across Japan and Sweden. Method We included 576 (73.7 ± 6.0 years) and 195 (63.9 ± 13.4 years) cognitively healthy females from the Arao (AC, Japan) and Betula (BC, Sweden) cohorts, respectively. SES was defined by educational and occupational categories. Lifestyle-related diseases included obesity, diabetes, hypertension, and depressive disorder; habits including exercise, social activity, sleep, alcohol habits, and smoking status. Brain structural outcomes were derived from T1 weighted magnetic resonance imaging scans. A priori regions of interest included volumes of the hippocampus, amygdala, thalamus, and caudate; thickness of the superior frontal gyrus, inferior temporal gyrus, and middle temporal gyrus. General cognitive performance was evaluated by the Mini-Mental State Examination score. The relationships between SES-lifestyle with the brain and cognition were assessed by structural equation models. Results Positive associations were found between SES and volumetric brain measures and cognition (MMSE) in both cohorts, but not between SES and cortical thickness. Lifestyle-related diseases (including obesity, diabetes, hypertension, and depressive disorder), but not habits such as exercise or sleep, partially explained the positive association between SES and brain volumes (up to 18.6% in the AC). A similar, but non-significant trend, was seen in the SES-cognition association that could be explained by lifestyle-related diseases. Discussion Although statements of causality cannot be made from the current work, our findings suggest management of the lifestyle-related disease is particularly important for females for compensating the maladaptive effects of SES on brain atrophy. Education occupational complexity gender inequality cognitive brain health modifiable lifestyle risks Figures Figure 1 INTRODUCTION Women are particularly susceptible to dementia risk. The prevalence of Alzheimer’s dementia among middle-aged individuals is almost twice as high in females than in males [ 1 ]. A recent meta-analysis indicated that, despite their longer lifespan, lower access to education and occupational level — in other words, socioeconomic status (SES) — may largely explain the observed gender gap in dementia incidence [ 2 ]. Although the downstream effect of low SES on dementia (with up to a 60% incident rate of dementia over 12 years of follow-up [ 3 ]) has been clarified, there are limited resources on how personalized interventions might be constructed to compensate for such socio-background and cognitive disadvantages faced by women. It has been proposed that the elevated risk of dementia among the low SES population may be partly explained by exposure to such unfavorable lifestyle risks [ 4 , 5 ]. Detailly [ 6 ] including obesity, diabetes, hypertension, depression, smoking, high alcohol consumption, and physical and social inactivity. Moreover, these lifestyle factors have been related with faster rates of brain aging, including volume reductions and cortical thinning [ 7 , 8 ]. For instance, depression is related to hippocampal atrophy [ 9 ] and thinning of the cingulate cortex [ 10 ]. Hypertension [ 11 ] and diabetes [ 12 ] cause vascular damage, neuroinflammatory, and neurodegeneration. On the other hand, the amount and intensity of physical activity modulate global and motor region gray matter volume [ 13 , 14 ], and higher social activity is associated with larger limbic lobe volumes [ 15 ]. Still, more knowledge is needed of risk factors associated with unbeneficial brain aging. Differences in lifestyle risks are found across individuals, but also across sociocultural backgrounds [ 16 ]. For example, despite sharing the similarity in economic development and the component of the aging population [ 17 ], Japan and Sweden vary tremendously in the prevalence of lifestyle-related disease and habitual risk factors for dementia. The prevalence of obesity among adults is less than 5% in Japan and over 20% in Sweden by 2019; the depression prevalence among the Swedish population is two times of the Japanese population [ 18 ]. Japanese adults are less physically active than the Swedish as 35% of the population in Japan do not meet the WHO recommended level of physical activity (e.g. at least 150 min of moderate intensity per week ), as compared to 23% in Sweden [ 19 ]. A most recent report by Demnitz et al. 2023 suggests despite a general association between lifestyle factors and brain status, the pattern of observed associations differed substantially between cohorts of different countries, which indicates that study-specific associations is to be expected [ 20 ]. Thus, given the heterogeneity in risk allocation across countries, it is informative to investigate general versus specific elements concerning the link between SES, brain structure, and cognitive performance [ 19 ]. To this end, the present work employed two older cohorts from Sweden and Japan to test the replicability of findings. We focused on a priori -selected regions of interest that have been identified as sensitive to SES. These include the hippocampus, amygdala, thalamus, caudate, and thickness of frontal and temporal cortices, (ref. systematic review by [ 21 ] ). We hypothesized that among older females, higher SES levels would be positively associated with gray volume, cortical thickness, and cognitive performance. Additionally, we hypothesized that the effects of SES on the brain and cognition are partially attributed to lifestyle-related risk factors. METHODS Study sample Data from the Arao Cohort (AC) in Kumamoto, which belongs to one local site of a national-wide cohort study in Japan (JPSC-AD study, refer to[ 22 ] ), and the Betula Cohort (BC) in Umeå, Sweden[ 23 ] were analyzed in the present work. We included 593 female participants (mean age:73.66 ± 5.96 years) from AC and 195 females (mean age: 63.91 ± 13.41) from BC that were tested during 2016–2017 and 2008–2010, respectively. All participants underwent structural magnetic resonance imaging (MRI) and had not been diagnosed with mild cognitive impairment or dementia. The diagnosis of dementia was based on the Diagnostic and Statistical Manual of Mental Disorders (DSM, third edition) in AC [ 24 ], and according to the DSM, 4th edition in BC. Participants’ global cognition was evaluated using the Japanese and Swedish versions of the Mini-Mental State Examination (MMSE) [ 25 , 26 ]. The recruitment of the study participants was approved by the ethical committee of Kumamoto University (GENOME-333) and Umeå University (2013/92–31) respectively. All participants provided written informed consent before any testing was initiated. Additionally, ethical approval for integrating women’s lifestyle and imaging data from the two cohorts was obtained from the ethical committee of Tohoku University (2022-1-600). Note that, although gender differences are prevalently seen in socioeconomic distributions (undeniably important), comparison by gender was not the primary goal of current research. Socioeconomic Status: Educational level and Occupational Complexity. Educational level was categorized on a scale of 1–3 as follows: lower than a high school degree, high school degree only, and college degree or more. The occupational categories in the AC were coded as 1–3 based on the socioeconomic status evaluation in Japan and included housework, holding a part-time job, and a full-time job or self-employed [ 27 ]. Similarly, the occupational categories in the BC were coded as 1–3, corresponding to the manual employee, non-manual employee, and professional or high-level non-manual employee. The latter separation was based on cognitive complexity [ 28 ], as originally defined by the Swedish occupational classification system. The occupation was significantly correlated with education in the BC [r(196) = 0.42, p < 0.001]; and marginally in the AC [r(593) = 0.08, p = 0.054]. Given the multi-faceted nature of the SES construct in mid-life [ 29 ], we combined educational level and occupational category to estimate the accumulation effect of these two features. Lifestyle-related diseases A composite disease indicator score was calculated from obesity, diabetes, hypertension, and depressive disorders. For each indicator, yes was given a score of 1, and no 0. Hence, the total score ranged between 0 to 4. For both cohorts, obesity was defined as a body mass index (BMI) ≥ 30, and hypertension was defined as a blood pressure of ≥ 140/90 mm Hg and/or the use of antihypertensive agents. Diabetes in the AC was defined as fasting blood glucose of ≥ 126 mg/dL, casual blood glucose of ≥ 200 mg/dL, hemoglobin A1c of ≥ 6.5%, and/or the use of glucose-lowering agents. In the BC, diabetes was defined as the use of glucose-lowering agents. Finally, we included depressive disorder, due to the established causal relationship between unhealthy lifestyles and its incidence [ 30 ]. Depressive disorder in AC and BC was defined as a score of ≥ 6 on the Geriatric Depression Scale short version [ 31 ] or a score of ≥ 16 on the Center for Epidemiologic Studies Depression Scale [ 32 ], respectively. Lifestyle habits To distinguish the effects from disease versus habits, we further calculated a score that refers to the presence of regular exercise (yes = 1 point, no = 0), active social activity (yes = 1 point, no = 0), sleep disturbances (no = 1 point, yes = 0), current smoking (no = 1 point, yes = 0), and current alcohol consumption (no = 1 point, yes = 0). That is, the habit score ranges from 0 to 5, with higher scores indicating more favorable habits. Notably, the definition of some habits varies across studies. In detail, regular exercise was defined as any physical activity performed for at least 30 minutes twice per week during the last year in the AC, and as moderate (without sweating) exercise at least 2 hours a week and/or high-intensity exercise (with sweating) for at least 30 minutes more than once per week in BC. Social activity was assessed by the Social Role Index of Competence in the AC [ 33 ]; and Social Participation Questionnaire in the BC [ 34 ]. An active social life was defined by ≥ median score in each social activity evaluation. Sleep difficulty in the AC was defined as scores ≥ 6 on The Pittsburgh Sleep Quality Index [ 35 ]. In the BC, sleep difficulty was self-reported sleeplessness (yes or no). Lifestyle-related diseases and habits were not correlated in either cohort: r(584) = -0.05, P = .19 in the AC, and r(187) = -0.06, P = .38 in the BC. MRI Data Acquisition In the AC, all three-dimensional T1-weighted magnetization-prepared rapid acquisition gradient echo structural images were acquired using a 3-Tesla Philips Achieva dStream with a 32-channel head coil. A T1-weighted 3D sagittal magnetization-prepared rapid gradient echo (MPRAGE) sequence was performed with the following parameters: repetition time (TR) = 8.6ms; echo time (ET) = 3.01 ms; field of view (FOV) = 27 × 27 cm; slice thickness (ST) = 1.2mm; number of slices = 150; flip angle (FA) = 12°; T1-weighted images acquisition matrix = 256 × 256. In the BC, all images were acquired with a 3-Tesla MRI system (General Electric) with a 32-channel radiofrequency head coil. Three-dimensional fast spoiled MPRAGE T1-weighted images in the coronal plane were acquired for volume measurements. Image acquisition parameters were as follows: TE = 3.2 ms; TR = 8.2 ms; FOV = 25 × 25 cm; ST = 1 mm; number of slices = 180; FA = 12°; T1-weighted images acquisition matrix = 256 × 256. Structural MRI Preprocessing T1-weighted structural MRI images were processed using the same automated procedures by Computational Anatomy Toolbox 12 (CAT12; Jena University Hospital, Germany;. Gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) were segmented using Tissue Probability Maps where each voxel is assigned to its most likely tissue type and subsequent segmentation. Then GM and WM images were spatially normalized to the Montreal Neurological Institute (MNI) space by correcting the differences in the subjects’ head positions or orientation during scanning for the global brain shape [ 36 ]. The image intensity of each voxel was modulated by Jacobian determinants and smoothed by convolving them with an 8-mm full width at half-maximum isotropic Gaussian kernel. The thickness map was obtained via reconstruction of the central surface [ 37 ], during which partial-volume information, sulcal blurring, and topological defects, e.g., sulcal asymmetries, were adjusted using spherical harmonics [ 38 ]. Following, the Freesurfer ‘FsAverage’ template was registered and smoothed by convolving them with a 15-mm full width at a half-maximum isotropic Gaussian kernel. The value of thickness was calculated by estimating the distance between the inner surfaces (the boundary between GM and WM) and the outer surfaces (the boundary between GM and CSF) [ 38 ]. Finally, we extracted values of GM volumes (cm 3 ) and cortical thickness (mm) of regions of interest ( [ 21 ]): hippocampus, amygdala, thalamus, and caudate (both the left and right hemispheres included; Neuromorphometrics Atlas), and thickness for the superior frontal gyrus, inferior temporal gyrus, and middle temporal gyrus (Desikan-Killiany Atlas, [ 39 ]). Statistical Analyses All statistical analyses were performed using Stata 16 (StataCorp LP, College Station, TX, USA). The alpha level was set to p < 0.05 for all tests. Comparisons of demographic characteristics of the AC and the BC were evaluated using chi-square tests for categorical variables or two-sided t-tests for continuous variables. We first conducted a series of univariate linear regression models to verify the effect of SES on brain volumes, thickness, and cognition (MMSE) across two cohorts. Given the previous review for selecting brain regions was based on early-life developing studies[ 40 ], this step is also a confirmational test to understand whether the derived regions are sensitive proxies for the older female population. Secondly, the multiple relationships between SES, lifestyle-related disease/habits, brain volumes or thickness, and cognition, as identified in previous regression models, were evaluated by structural equation models (SEM) based on the maximum likelihood method. As prementioned, we aimed to understand the accumulative effects of SES, thus the composite scores of SES, disease, and habits were inputted in the SEM instead of constructing the corresponding latent variables. the Age and intracranial volume were adjusted in each pathway via two hierarchical models. Effect sizes of standardized coefficients (β) and 95% bias-corrected bootstrap confidence intervals (CIs) were generated by bootstrapping 1,000 resamples. Additionally, the mediation effect through lifestyle or lifestyle-related diseases was estimated. RESULT Sample characteristics are presented in Table 1. The BC was generally younger, although the age range was wider. Furthermore, the BC had a larger total GM volume, higher SES levels, and higher MMSE scores. The AC was characterized by healthier lifestyle habits, but more lifestyle-related diseases. Table 1. Sample characteristics. Arao Cohort (N = 593) Betula Cohort (N = 195) Age, years 73.7 (6.0) *** 63.9 (13.4) In marriage, No. (%) 397 (67.0) 126 (64.6) Socioeconomic status a 3.2 (1.0) 4.4 (1.3) *** The occupation category, No. (%) Housework (JP)/manual employee (SWE) 458 (77.2) *** 45 (23.1) Part-time job (JP)/non-manual employee (SWE) 76 (12.8) 110 (56.4) *** Full-time job or self-employed (JP)/professional or high-level non-manual (SWE) 59 (10.0) 40 (20.5) *** Education, No. (%) No high school degrees 177 (29.9) * 42 (21.5) High school degree only 329 (55.5) *** 34 (17.4) College degree or more 87 (14.7) 118 (60.1) *** Mini-Mental State Examination 27.5 (2.3) 28.1 (1.6) * Lifestyle habits b 3.8 (0.9) 2.5 (1.1) *** Regular exercise, No. (%) 321 (54.8) 162 (83.1) *** Active social life, No. (%) 482 (81.6) 74 (37.9) *** Current smoke, No. (%) 11 (1.9) 88 (45.1) *** Current drink, No. (%) 128 (21.6) 178 (91.3) *** Sleep difficulty c , No. (%) 177 (29.9) 63 (32.3) Lifestyle-related diseases d 0.92 (0.7) * 0.78 (0.8) Obesity, No. (%) 25 (4.2) 42 (21.5) *** Diabetes, No. (%) 73 (12.3) ** 11 (5.6) Hypertension, No. (%) 418 (70.5) *** 71 (36.4) Depressive disorder g , No. (%) 29 (4.9) 28 (14.4) *** Total brain gray matter volume (cm 3 ) 548.65 (41.9) 591.4 (57.0) *** a Socioeconomic status was defined by the combination of occupational and educational categories. b Lifestyle index is the combined score of with/without having regular exercise, active social life, current smoking, current drinking, and sleep difficulty. c Sleep difficulty in the AC was defined as scores ≥ 6 on The Pittsburgh Sleep Quality Index, while in the AC, sleep difficulty was self-reported troubles in sleeping. d Disease index is the combined score of with/without obesity, diabetes, hypertension, and depressive disorder. e Depressive disorder in the AC was defined as scores ≥ 6 on the Geriatric Depression Scale short version, while in the AC, ≥16 in The Center for Epidemiologic Studies Depression Scale was considered depressive. Data are presented as mean (SD) unless otherwise indicated. Bold font suggests a significance level at <0.05. * P < 0.05, ** P < 0.01, *** P < 0.001. Obtained by t-tests for continuous variables and chi-squared tests for variables of proportion. Associations between socioeconomic status, brain structure, and cognition Results from the linear regression models for SES with GM volume, cortical thickness, and cognition are listed in Table 2 . For both cohorts, a positive association was found between SES and volumes for the hippocampus, amygdala, thalamus, and also, in relation to MMSE performance. These data suggest that higher SES is found for individuals with larger brain volumes and higher cognitive performance. Findings in relation to cortical thickness were, however, inconsistent across the two cohorts. In the AC, the left (β = 0.009, P = .049) and right superior frontal gyrus (β = 0.083, P = .044) were found to have a positive association with SES, while in the BC the right inferior temporal gyrus (β = 0.152, P = .036), and bilateral middle temporal gyrus (β = 0.195, P = .007) were associated with SES. Table 2 Regression models of structural brain measures and cognition as the outcome of socioeconomic status. Arao Cohort (N = 593) Betula Cohort (N = 195) Outcome a β (SE) P value β (SE) P value Gray matter volume (cm3) Left Hippocampus 0.231 (0.016) < .001 0.322 (0.019) < .001 Right Hippocampus 0.205 (0.018) < .001 0.282 (0.021) < .001 Left Amygdala 0.186 (0.008) < .001 0.341 (0.005) < .001 Right Amygdala 0.154 (0.08) < .001 0.309 (0.028) < .001 Left Thalamus 0.163 (0.026) < .001 0.243 (0.142) < .001 Right Thalamus 0.162 (0.026) < .001 0.247 (0.035) < .001 Left Caudate -0.017 (0.022) .666 0.168 (0.026) .021 Right Caudate -0.002 (0.024) .97 0.135 (0.025) .062 Cortical thickness (cm) Left Superior frontal gyrus 0.009 (0.017) .049 0.106 (0.009) .144 Right Superior frontal gyrus 0.083 (0.042) .044 0.119 (0.009) .102 Left Inferior temporal gyrus -0.078 (0.018) .056 0.136 (0.010) .06 Right Inferior temporal gyrus -0.060 (0.018) .144 0.152 (0.010) .036 Left Middle temporal gyrus -0.052 (0.020) .206 0.234 (0.010) < .001 Right Middle temporal gyrus -0.068 (0.020) .099 0.195 (0.009) .007 Mini-Mental State Examination 0.223 (0.040) < .001 0.219 (0.107) .043 Data are presented as the beta estimate in standard deviation units and standard error (SE). Bold suggests a significance level at P < 0.05. Mediators of the association between SES and neurocognitive measures Table 3 describes the direct and indirect effects of SES, lifestyle-related diseases, brain measures (i.e., those commonly identified volumes in two cohorts: hippocampus, amygdala, and thalamus, see Table 2 ), and MMSE in structural equation models. Age alone or age and total intracranial volume (TIV) were adjusted for in Model 1 and Model 2, respectively. SEM model fits were acceptable with a root mean square error of approximation (RMSEA) lower than 0.001 in two models across both cohorts. Smaller Akaike's information criterion (AIC) and Bayesian information criterion (BIC) indicate a slightly better fit of Model 1 in the AC and Model 2 in the BC. A caution here is that a larger sample size would increase the likelihood of detecting small deviations from the SEM model, thus the AIC and BIC indicators did not refer to a better model fit in the BC compared to the AC. SES was significantly associated with lifestyle-related disease, MMSE, and GM volume. Relationships persisted in Model 2 adjusting for age and TIV. A larger disease index predicted smaller GM volume and lower cognition in the AC. While similar trends were found in the BC, they did not reach statistical significance. Lifestyle-related diseases were found to mediate the association between SES and GM volume (18.5% of variance) in AC, but not in BC which responds to the insignificant association of disease and GM volume in BC). An overview of the mediating effects of lifestyle-related disease is shown in Fig. 1 . Table 3 Standardized estimates of the direct and indirect effects of SES and lifestyle-related diseases on brain measures and cognition using structural equation models.a Arao Cohort (N = 593) Betula Cohort (N = 195) From To Model 1 β (95% CI) Model 2 β (95% CI) Model 1 β (95% CI) Model 2 β (95% CI) Direct effect SES Disease b -0.102 (-0.188, -0.017) * -0.101 (-0.190, -0.013)* -0.283 (-0.420, -0.146)** -0.175 (-0.257, -0.092)** SES MMSE 0.074 (0.149, 0.294)** 0.096 (0.016, 0.177)* 0.219 ( 0.080, 0.358)* 0.209 (0.015 0.403)* SES Volume c 0.064 (-0.141, 0.142) 0.053 (0.005, 0.112)* 0.265 ( 0.238, 0.774)** 0.186 (0.069, 0.256)** Disease Volume -0.081 (-0.153, 0.008)* -0.059 (-0.114, -0004)* -0.115 (-0.731, 0.073) -0.170 (-0.445, 0.105) Disease MMSE -0.094 (-0.186, -0.003)* -0.074 (-0.152, 0.003)* -0.070 (-0.335, 0.195) 0.011 (-0.241, 0.272) Indirect effect SES MMSE via disease 0.018 (-0.007, 0.043) 0.02 (-0.006, 0.043) 0.005 (-0.039, 0.050) -0.002 (-0.017, 0.069) SES Volume via disease 0.014 (0.006, 0.027)* 0.012 (0.003 0.028)* 0.032 (-0.019, 0.141) 0.029 (-0.038, 0.098) Correlation Volume MMSE 0.068 (0.002, 0.134)* 0.072 (0.17, 0.126)* 0.308 (-0.026, 0.642) 0.225 (0.055, 0.506)* Goodness-of-fit RMSEA 0.000 0.000 0.000 0.000 AIC 8112.259 9547.028 1850.843 1814.088 BIC 8173.651 9625.962 1889.870 1862.873 SES socioeconomic status, MMSE Mini-Mental State Examination, RMSEA root mean square error of approximation, AIC Akaike's information criterion, BIC Bayesian information criterion. β estimated in standard deviation units, CI bootstrapped confidence interval for 1000 samples. a Model 1 adjusted for age, Model 2 adjusted for age and total intracranial volume. b Disease index is the combined score of with/without having obesity, diabetes, hypertension, and depressive disorder. c Volume is the combination value of the hippocampus, amygdala and thalamus. Bold font suggests a significance level at P <.05.* P < 0.05, ** P < 0.01 Table 4 provides the SEM results on lifestyle habits, SES, GM volume, and cognition. Despite better lifestyle habits predicting better cognition in both cohorts, lifestyle habits were not directly related to SES or GM volumes. Thus, no mediating effects of lifestyle habits for explaining SES-GM volume and SES-cognition associations were found across the two cohorts. Table 4 Standardized estimates of the direct and indirect effects of SES and lifestyle habits on brain measures and cognition using structural equation models.a Arao Cohort (N = 593) Betula Cohort (N = 195) From To Model 1 β (95% CI) Model 2 β (95% CI) Model 1 β (95% CI) Model 2 β (95% CI) Direct effect SES Habit b 0.014 (-0.068, 0.095) 0.014 (-0.067, 0.094) -0.008 (-0.139, 0.122) -0.011 (-0.133, 0.122) SES MMSE 0.112 (0.031, 0.192)** 0.104 (0.024, 0.185)* 0.211 ( 0.012, 0.358)* 0.193 (0.015 0.390)* SES Volume c 0.067 (0.004, 0.137)* 0.051 (0.005, 0.114)* 0.265 ( 0.238, 0.774)** 0.186 (0.069,0.256)** Habit Volume -0.003 (-0.743, 0.067) -0.002 (-0.061, 0.057) 0.047 ( -0.146, 0.241) 0.034 (-0.214, 0.238) Habit MMSE 0.083 (0.007, 0.159)* 0.083 (0.008, 0.159)* 0.265 (0.038, 0.479)* 0.262 (0.038 0.464)* Indirect effect SES MMSE via habit 0.001 (-0.006, 0.009) 0.001 (-0.006, 0.009) -0.002 (-0.037, 0.032) -0.003 (-0.037, 0.031) SES Volume via habit − .00004 (-0.001, 0.001) − .00003 (-0.0009, 0.0008) 0.001 (-0.007 0.006) 0.000 (-0.005 0.005) Correlations Volume MMSE 0.076 (0.009, 0.143)* 0.079 (0.024, 0.134)* 0.309 (-0.037, 0.645) 0.214 (0.069, 0.497)* Goodness-of-fit RMSEA 0.000 0.000 0.000 0.000 AIC 8000.246 9410.400 1897.790 1865.330 BIC 8061.425 9484.689 1936.434 1913.636 SES socioeconomic status, MMSE Mini-Mental State Examination, RMSEA root mean square error of approximation, AIC Akaike's information criterion, BIC Bayesian information criterion. a Model 1 adjusted for age, Model 2 adjusted for age and total intracranial volume. β estimated in standard deviation units, CI bootstrapped confidence interval for 1000 samples. b Habit index is the combined score of with/without having regular exercise, active social life, current smoking, current drinking, and sleep difficulty. c Volume is the combination value of the hippocampus, amygdala and thalamus. Bold font suggests a significance level at p < 0.05. * p < 0.05, ** p < 0.01 DISCUSSION The influence of SES and lifestyle on neurocognitive health in older age has typically been studied separately, without different sociocultural backgrounds taken into account. Focusing on the female population and using different cohorts from two countries, we observed that despite variant demographic backgrounds, lifestyle-related diseases were more strongly associated with brain volume and general cognition compared to habit (e.g., smoking and alcohol consumption), showing the value of candidate risk management that address brain and cognitive vulnerability due to SES. In contrast to one of our hypotheses, no statistically significant relationships were found between SES and direct lifestyle habits. One potential explanation for this could be the limitations in our SES measurements, notably the absence of income data. Income is a critical component of SES and its exclusion might have restricted to full assessment of its impact on lifestyle habits. Additionally, when examining our participant group, known as the AC, we observed a notably higher completion rate of secondary education (70.2%) compared to the national average (65.5% [ 22 ] ). This discrepancy suggests the possibility of selection bias in our study, as our participant pool appears to be more educated than the general population. Such a bias is important to consider, as a higher education level may have more access to health information and resources which could potentially mitigate the influence of SES on lifestyle choices. The common insignificant association of lifestyle habits on regional brain GM volume in two cohorts could be interpreted as that, awareness of the prevalence of certain diseases encourages older females to engage in a healthier lifestyle. For example, 22% in BC and only 4% of AC were obese, but at the same time, 83% in BC and 55% of AC engaged more frequently in regular exercise. Therefore, it is possible that older Swedish females intentionally maintain daily physical activities because they are aware of the hazards of obesity. Moreover, in recent decades, Japan and Sweden have seen a declining prevalence of behavioral risks, including smoking, drinking, and physical inactivity [ 41 , 42 ]; which reflects increased public awareness resulting from education and prevention campaigns. In contrast, the prevalence of hypertension and diabetes, along with their coexistence, continues to rise significantly; such trends suggest there is still insufficient public awareness and limited access to healthcare professionals [ 43 , 44 ]. Thus, prioritizing lifestyle disease could greatly benefit socioeconomically disadvantaged populations in Japan and Sweden. Another finding worth noting is that cortical thickness was less sensitive to SES, and some parts of the limbic lobe (hippocampus, amygdala, and thalamus) were particularly susceptible to SES among older females, despite the variation in SES definition. In line with our findings, a recent imaging study showed that GM volume is more closely linked to heterogeneity in the social environment, as quantified by education and occupational variables, than thickness [ 45 ]. The stress hypothesis is a candidate to explain the potential mechanism, which denotes that low SES exposes people to a continuing high level of stress [ 46 ]. While the adaptive stress response is important for survival, repeated hormone adaption to stress activation of the hypothalamus—pituitary—adrenal (HPA) axis results in lasting structural and functional disturbances of the limbic lobe [ 47 , 48 , 49 ]. Further, a recent study with longitudinal MRI data showed that aging is characterized more by hippocampal atrophy than by cortical thickness [ 50 ]. These findings together with our results, suggest the burden of SES disadvantage may be more likely to exaggerate towards the limbic lobe with age. Several limitations need to be acknowledged. First, the cross-sectional design precludes causal inferences to explain brain-cognitive relationships due to SES and health conditions. However, findings that are consistent across two different cultural backgrounds suggest the robustness of findings and may hold value for societal policymaking. Second, as prementioned, the possible selection bias due to high education level trends, and heterogeneous definitions of SES. Future studies should investigate these findings with consistency across studies in such definitions. Similarly, the overall high level of cognition (ave. MMSE 27.5 in the AC, 28.1 in the BC) in the two cohorts may limit understanding of the magnitude and progression of cognitive degeneration due to SES disadvantages. In conclusion, although no single risk or protective factor could adequately address the adverse effect of SES on neurocognitive status, four lifestyle-related disease management including diabetes, hypertension, obesity, and depressive disorder can be expected to deliver cost-effective gains to blunt the inequality gap in cognitive health among older women in Japan and Sweden. Declarations Ethics approval and consent to participate Integrated dataset analysis was approved by the ethics committee of Tohoku University (2022-1-600). All participants provided written informed consent for baseline surveys and MRI examinations. Availability of data and materials The data that support the findings of this study are available from Kumamoto University and Umea University but restrictions apply to the availability of these data, which were used under permission for the current study, and so are not publicly available. Data are however available upon reasonable request to corresponding author and with permission of Kumamoto University and Umea University. Founding The present study was supported by the Japan Society for the Promotion of Science, Research Fellowship for Young Scientists(22J14503); and MIRAI 2.0. seed funding. The funders had no role in the design of the study, the collection, analysis, and interpretation of data, or the writing of the manuscript. Author contribution Y.L., C.B., and N.K., applied for funding and negotiated the project details. Y.L., N.K., Y.T., C.B., and B.T., planned the study, performed statistical analyses, and drafted the paper. Y.C., Y.Z., and Y.H., supervised the data analysis and contributed to revising the paper. Y.C., Y.Z., Y.H., N.K., S.Y., M.T., T.N., and Y.T. helped to plan the study, including the instrumentation, and to revise the manuscript. Conflicts of interest The authors declare no conflict of interest. References Cao Q, Tan CC, Xu W, et al. The Prevalence of Dementia: A Systematic Review and Meta-Analysis. J Alzheimers Dis. 2020;73:1157–66. Huque H, Eramudugolla R, Chidiac B et al. Could Country-Level Factors Explain Sex Differences in Dementia Incidence and Prevalence? A Systematic Review and Meta-Analysis. Journal of Alzheimer’s Disease. 2022; Preprint: 1–11. Marden JR, Tchetgen Tchetgen EJ, Kawachi I, Glymour MM. Contribution of Socioeconomic Status at 3 Life-Course Periods to Late-Life Memory Function and Decline: Early and Late Predictors of Dementia Risk. Am J Epidemiol. 2017;186:805–14. Letellier N, Ilango SD, Mortamais M, et al. Socioeconomic inequalities in dementia risk among a French population-based cohort: quantifying the role of cardiovascular health and vascular events. Eur J Epidemiol 2021. 2021;1:1–9. Yang JJ, Keohane LM, Pan X, et al. Association of Healthy Lifestyles with Risk of Alzheimer Disease and Related Dementias in Low-Income Black and White Americans. Neurology; 2022. Livingston G, Huntley J, Sommerlad A, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. The Lancet. 2020;396:413–46. Leong RLF, Lo JC, Sim SKY, et al. Longitudinal brain structure and cognitive changes over 8 years in an East Asian cohort. NeuroImage. 2017;147:852–60. Fjell AM, Walhovd KB. Structural brain changes in aging: courses, causes and cognitive consequences. Rev Neurosci. 2010;21:187–221. O’Shea DM, Dotson VM, Woods AJ et al. Depressive symptom dimensions and their association with hippocampal and entorhinal cortex volumes in community dwelling older adults. Front Aging Neurosci 2018; 10. Binnewies J, Nawijn L, van Tol MJ, van der Wee NJA, Veltman DJ, Penninx BWJH. Associations between depression, lifestyle and brain structure: A longitudinal MRI study. NeuroImage. 2021;231:117834. Kelly DM, Rothwell PM. Blood pressure and the brain: the neurology of hypertension. Pract Neurol. 2020;20:100–11. Antal B, McMahon LP, Sultan SF et al. Type 2 diabetes mellitus accelerates brain aging and cognitive decline: Complementary findings from UK Biobank and meta-analyses. Elife 2022; 11. Erickson KI, Raji CA, Lopez OL, et al. Physical activity predicts gray matter volume in late adulthood: the Cardiovascular Health Study. Neurology. 2010;75:1415–22. Fox FAU, Diers K, Lee H, et al. Association Between Accelerometer-Derived Physical Activity Measurements and Brain Structure: A Population-Based Cohort Study. Neurology. 2022;99:e1202. Liu Y, Zhang Y, Thyreau B, et al. Altruistic Social Activity, Depressive Symptoms, and Brain Regional Gray Matter Volume: Voxel-Based Morphometry Analysis From 8,695 Old Adults. The Journals of Gerontology: Series A. 2022;77:1789–97. Cook WK, Li X, Sundquist K, Kendler KS, Sundquist J, Karriker-Jaffe KJ. Drinking cultures and socioeconomic risk factors for alcohol and drug use disorders among first- and second-generation immigrants: A longitudinal analysis of Swedish population data. Drug Alcohol Depend. 2021;226:108804. Tapia Granados JA. Health at advanced age: Social inequality and other factors potentially impacting longevity in nine high-income countries. Maturitas. 2013;74:137–47. Blüher M. Obesity: global epidemiology and pathogenesis. Nat Reviews Endocrinol 2019. 2019;15:5. Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants. Lancet Glob Health. 2018;6:e1077–86. Demnitz N, Hulme OJ, Siebner HR, et al. Characterising the covariance pattern between lifestyle factors and structural brain measures: a multivariable replication study of two independent ageing cohorts. Neurobiol Aging. 2023;131:115–23. Rakesh D, Whittle S. Socioeconomic status and the developing brain – A systematic review of neuroimaging findings in youth. Neurosci Biobehav Rev. 2021;130:379–407. Ninomiya T, Nakaji S, Maeda T, et al. Study design and baseline characteristics of a population-based prospective cohort study of dementia in Japan: the Japan Prospective Studies Collaboration for Aging and Dementia (JPSC-AD). Environ Health Prev Med. 2020;25:64. Nyberg L, Boraxbekk CJ, Sörman DE, et al. Biological and environmental predictors of heterogeneity in neurocognitive ageing: Evidence from Betula and other longitudinal studies. Ageing Res Rev. 2020;64:101184. Diagnostic and Statistical Manual of Mental Disorders, 3rd ed., revised (DSM-III-R). American Journal of Psychiatry 2006; 145: 1301–1302. Shiya Hisano. The relationship between Revised Hasegawa Dementia Scale (HDS-R), Mini-Mental State Examination (MMSE) and Bed-fast Scale, Dementia Scale. Japanese J Geriatric Psychiatry. 2009;20:883–91. Grut M, Fratiglioni L, Viitanen M, Winblad B. Accuracy of the Mini-Mental Status Examination as a screening test for dementia in a Swedish elderly population. Acta Neurol Scand. 1993;87:312–7. Yasuno F, Minami H, Hattori H. Interaction effect of Alzheimer’s disease pathology and education, occupation, and socioeconomic status as a proxy for cognitive reserve on cognitive performance: in vivo positron emission tomography study. Psychogeriatrics. 2020;20:585–93. Sörman DE, Stenling A, Sundström A, et al. Occupational cognitive complexity and episodic memory in old age. Intelligence. 2021;89:101598. Wang AY, Hu HY, Ou YN, et al. Socioeconomic Status and Risks of Cognitive Impairment and Dementia: A Systematic Review and Meta-Analysis of 39 Prospective Studies. J Prev Alzheimer’s Disease. 2023;10:83–94. Lopresti AL, Hood SD, Drummond PD. A review of lifestyle factors that contribute to important pathways associated with major depression: Diet, sleep and exercise. J Affect Disord. 2013;148:12–27. Morihiro S, Takashi A. Reliability and validity of the Japanese version of Geriatric Depression Scale-Short Version (GDS-S-J). Japanese Soc Cogn Neurosci. 2009;11:87–90. Radloff LS, The CES-D, Scale. A Self-Report Depression Scale for Research in the General Population. http://dx.doi.org/101177/014662167700100306 2016; 1: 385–401. Koyano W, Shibata H, Nakazato K, Haga H, Suyama Y. Measurement of competence: reliability and validity of the TMIG Index of Competence. Arch Gerontol Geriatr. 1991;13:103–16. Eriksson Sörman D, Sundström A, Rönnlund M, Adolfsson R, Nilsson LG. Leisure activity in old age and risk of dementia: a 15-year prospective study. Journals of Gerontology Series B: Psychological Sciences and Social Sciences. 2014;69:493–501. Doi Y, Minowa M, Uchiyama M, et al. Psychometric assessment of subjective sleep quality using the Japanese version of the Pittsburgh Sleep Quality Index (PSQI-J) in psychiatric disordered and control subjects. Psychiatry Res. 2000;97:165–72. Ashburner J, Friston KJ. Voxel-Based Morphometry—The Methods Neuroimage. 2000;11:805–21. Dahnke R, Yotter RA, Gaser C. Cortical thickness and central surface estimation. NeuroImage. 2013;65:336–48. Yotter RA, Dahnke R, Thompson PM, Gaser C. Topological correction of brain surface meshes using spherical harmonics. Hum Brain Mapp. 2011;32:1109–24. Desikan RS, Ségonne F, Fischl B, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage. 2006;31:968–80. Hackman DA, Farah MJ. Socioeconomic status and the developing brain. Trends Cogn Sci. 2009;13:65–73. (12 October 2020, date last accessed). Östergren O, Martikainen P, Tarkiainen L, Elstad JI, Brønnum-Hansen H. Contribution of smoking and alcohol consumption to income differences in life expectancy: evidence using Danish, Finnish, Norwegian and Swedish register data. J Epidemiol Community Health. 2019;73:334–9. Kondo T, Nakano Y, Adachi S, Murohara T. Effects of Tobacco Smoking on Cardiovascular Disease. Circ J. 2019;83:1980–5. Tatsumi Y, Ohkubo T. Hypertension with diabetes mellitus: significance from an epidemiological perspective for Japanese. Hypertens Res 2017. 2017;40:9. Andersson T, Ahlbom A, Carlsson S. Diabetes Prevalence in Sweden at Present and Projections for Year 2050. PLoS ONE. 2015;10:e0143084. Walhovd KB, Fjell AM, Wang Y, et al. Education and Income Show Heterogeneous Relationships to Lifespan Brain and Cognitive Differences Across European and US Cohorts. Cereb Cortex. 2021;00:1–16. Baum A, Garofalo JP, Yali AM. Socioeconomic Status and Chronic Stress: Does Stress Account for SES Effects on Health? Ann N Y Acad Sci. 2006;896:131–44. Gianaros PJ, Jennings JR, Sheu LK, Greer PJ, Kuller LH, Matthews KA. Prospective reports of chronic life stress predict decreased grey matter volume in the hippocampus. NeuroImage. 2007;35:795–803. Wingenfeld K, Wolf OT. Stress, Memory, and the Hippocampus. The Hippocampus in Clinical Neuroscience. 2014;34:109–20. Bang JY, Zhao J, Rahman M, St-Cyr S, McGowan PO, Kim JC. Hippocampus-Anterior Hypothalamic Circuit Modulates Stress-Induced Endocrine and Behavioral Response. Front Neural Circuits 2022; 16. Nyberg L, Andersson M, Lundquist A, et al. Individual differences in brain aging: heterogeneity in cortico-hippocampal but not caudate atrophy rates. Cereb Cortex. 2022;9:10. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3833392","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265666805,"identity":"a48356d0-ddff-4a07-82b2-bc5e24bf27f5","order_by":0,"name":"Yingxu Liu","email":"data:image/png;base64,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","orcid":"","institution":"Tohoku University","correspondingAuthor":true,"prefix":"","firstName":"Yingxu","middleName":"","lastName":"Liu","suffix":""},{"id":265666806,"identity":"a464ec4d-cd44-46f0-b503-a71a7ce6cc3b","order_by":1,"name":"Yasuko Tatewaki","email":"","orcid":"","institution":"Tohoku 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University","correspondingAuthor":false,"prefix":"","firstName":"Toshiharu","middleName":"","lastName":"Ninomiya","suffix":""},{"id":265666816,"identity":"defef05b-cbae-491c-a4f9-0d2e105b6bfd","order_by":11,"name":"Yasuyuki Taki","email":"","orcid":"","institution":"Tohoku University","correspondingAuthor":false,"prefix":"","firstName":"Yasuyuki","middleName":"","lastName":"Taki","suffix":""},{"id":265666817,"identity":"df712d73-df9e-4291-8669-f0926865a5c7","order_by":12,"name":"Nina Karalija","email":"","orcid":"","institution":"Umeå University","correspondingAuthor":false,"prefix":"","firstName":"Nina","middleName":"","lastName":"Karalija","suffix":""}],"badges":[],"createdAt":"2024-01-04 02:59:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3833392/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3833392/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49325964,"identity":"1746f411-9cb6-4ec5-9f06-9153e4489a50","added_by":"auto","created_at":"2024-01-08 17:29:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45537,"visible":true,"origin":"","legend":"\u003cp\u003eStandardized coefficients of SES, lifestyle-related diseases on regional gray matter volumes, and cognition evaluated by structural equation models.\u003c/p\u003e\n\u003cp\u003eSES socioeconomic status, GM gray matter includes the hippocampus, amygdala, and thalamus. Solid arrows represent associations, bidirectional arrows represent correlations. Bolded arrows represent significant associations. Estimates presented in the graph are standardized. * \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, adjusted for age and total intracranial volume.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3833392/v1/60c73913cb0231bfba92cf59.png"},{"id":68724239,"identity":"a45126f3-4e48-4197-939c-48b8b60ea884","added_by":"auto","created_at":"2024-11-11 11:09:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":978135,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3833392/v1/53216438-9188-4a07-a5ea-54d485e17d34.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Women at Risk: A Comparative Study on Socioeconomic Status, Lifestyle, Brain, and Cognition Among Older Females in Japan and Sweden","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eWomen are particularly susceptible to dementia risk. The prevalence of Alzheimer\u0026rsquo;s dementia among middle-aged individuals is almost twice as high in females than in males [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A recent meta-analysis indicated that, despite their longer lifespan, lower access to education and occupational level \u0026mdash; in other words, socioeconomic status (SES) \u0026mdash; may largely explain the observed gender gap in dementia incidence [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although the downstream effect of low SES on dementia (with up to a 60% incident rate of dementia over 12 years of follow-up [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]) has been clarified, there are limited resources on how personalized interventions might be constructed to compensate for such socio-background and cognitive disadvantages faced by women.\u003c/p\u003e \u003cp\u003eIt has been proposed that the elevated risk of dementia among the low SES population may be partly explained by exposure to such unfavorable lifestyle risks [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Detailly [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] including obesity, diabetes, hypertension, depression, smoking, high alcohol consumption, and physical and social inactivity. Moreover, these lifestyle factors have been related with faster rates of brain aging, including volume reductions and cortical thinning [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. For instance, depression is related to hippocampal atrophy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and thinning of the cingulate cortex [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Hypertension [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and diabetes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] cause vascular damage, neuroinflammatory, and neurodegeneration. On the other hand, the amount and intensity of physical activity modulate global and motor region gray matter volume [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and higher social activity is associated with larger limbic lobe volumes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Still, more knowledge is needed of risk factors associated with unbeneficial brain aging.\u003c/p\u003e \u003cp\u003eDifferences in lifestyle risks are found across individuals, but also across sociocultural backgrounds [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. For example, despite sharing the similarity in economic development and the component of the aging population [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], Japan and Sweden vary tremendously in the prevalence of lifestyle-related disease and habitual risk factors for dementia. The prevalence of obesity among adults is less than 5% in Japan and over 20% in Sweden by 2019; the depression prevalence among the Swedish population is two times of the Japanese population [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Japanese adults are less physically active than the Swedish as 35% of the population in Japan do not meet the WHO recommended level of physical activity (e.g. at least 150 min of moderate intensity per week ), as compared to 23% in Sweden [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A most recent report by Demnitz et al. 2023 suggests despite a general association between lifestyle factors and brain status, the pattern of observed associations differed substantially between cohorts of different countries, which indicates that study-specific associations is to be expected [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Thus, given the heterogeneity in risk allocation across countries, it is informative to investigate general versus specific elements concerning the link between SES, brain structure, and cognitive performance [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. To this end, the present work employed two older cohorts from Sweden and Japan to test the replicability of findings. We focused on a \u003cem\u003epriori\u003c/em\u003e-selected regions of interest that have been identified as sensitive to SES. These include the hippocampus, amygdala, thalamus, caudate, and thickness of frontal and temporal cortices, (ref. systematic review by [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] ). We hypothesized that among older females, higher SES levels would be positively associated with gray volume, cortical thickness, and cognitive performance. Additionally, we hypothesized that the effects of SES on the brain and cognition are partially attributed to lifestyle-related risk factors.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy sample\u003c/h2\u003e \u003cp\u003eData from the Arao Cohort (AC) in Kumamoto, which belongs to one local site of a national-wide cohort study in Japan (JPSC-AD study, refer to[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] ), and the Betula Cohort (BC) in Ume\u0026aring;, Sweden[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] were analyzed in the present work. We included 593 female participants (mean age:73.66\u0026thinsp;\u0026plusmn;\u0026thinsp;5.96 years) from AC and 195 females (mean age: 63.91\u0026thinsp;\u0026plusmn;\u0026thinsp;13.41) from BC that were tested during 2016\u0026ndash;2017 and 2008\u0026ndash;2010, respectively. All participants underwent structural magnetic resonance imaging (MRI) and had not been diagnosed with mild cognitive impairment or dementia. The diagnosis of dementia was based on the Diagnostic and Statistical Manual of Mental Disorders (DSM, third edition) in AC [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and according to the DSM, 4th edition in BC. Participants\u0026rsquo; global cognition was evaluated using the Japanese and Swedish versions of the Mini-Mental State Examination (MMSE) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e The recruitment of the study participants was approved by the ethical committee of Kumamoto University (GENOME-333) and Ume\u0026aring; University (2013/92\u0026ndash;31) respectively. All participants provided written informed consent before any testing was initiated. Additionally, ethical approval for integrating women\u0026rsquo;s lifestyle and imaging data from the two cohorts was obtained from the ethical committee of Tohoku University (2022-1-600). Note that, although gender differences are prevalently seen in socioeconomic distributions (undeniably important), comparison by gender was not the primary goal of current research.\u003c/p\u003e \u003cp\u003e \u003cem\u003eSocioeconomic Status: Educational level and Occupational Complexity.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eEducational level was categorized on a scale of 1\u0026ndash;3 as follows: lower than a high school degree, high school degree only, and college degree or more. The occupational categories in the AC were coded as 1\u0026ndash;3 based on the socioeconomic status evaluation in Japan and included housework, holding a part-time job, and a full-time job or self-employed [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Similarly, the occupational categories in the BC were coded as 1\u0026ndash;3, corresponding to the manual employee, non-manual employee, and professional or high-level non-manual employee. The latter separation was based on cognitive complexity [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], as originally defined by the Swedish occupational classification system. The occupation was significantly correlated with education in the BC [r(196)\u0026thinsp;=\u0026thinsp;0.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001]; and marginally in the AC [r(593)\u0026thinsp;=\u0026thinsp;0.08, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.054]. Given the multi-faceted nature of the SES construct in mid-life [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], we combined educational level and occupational category to estimate the accumulation effect of these two features.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eLifestyle-related diseases\u003c/h2\u003e \u003cp\u003eA composite disease indicator score was calculated from obesity, diabetes, hypertension, and depressive disorders. For each indicator, yes was given a score of 1, and no 0. Hence, the total score ranged between 0 to 4. For both cohorts, obesity was defined as a body mass index (BMI)\u0026thinsp;\u0026ge;\u0026thinsp;30, and hypertension was defined as a blood pressure of \u0026ge;\u0026thinsp;140/90 mm Hg and/or the use of antihypertensive agents. Diabetes in the AC was defined as fasting blood glucose of \u0026ge;\u0026thinsp;126 mg/dL, casual blood glucose of \u0026ge;\u0026thinsp;200 mg/dL, hemoglobin A1c of \u0026ge;\u0026thinsp;6.5%, and/or the use of glucose-lowering agents. In the BC, diabetes was defined as the use of glucose-lowering agents. Finally, we included depressive disorder, due to the established causal relationship between unhealthy lifestyles and its incidence [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Depressive disorder in AC and BC was defined as a score of \u0026ge;\u0026thinsp;6 on the Geriatric Depression Scale short version [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] or a score of \u0026ge;\u0026thinsp;16 on the Center for Epidemiologic Studies Depression Scale [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eLifestyle habits\u003c/h2\u003e \u003cp\u003eTo distinguish the effects from disease versus habits, we further calculated a score that refers to the presence of regular exercise (yes\u0026thinsp;=\u0026thinsp;1 point, no\u0026thinsp;=\u0026thinsp;0), active social activity (yes\u0026thinsp;=\u0026thinsp;1 point, no\u0026thinsp;=\u0026thinsp;0), sleep disturbances (no\u0026thinsp;=\u0026thinsp;1 point, yes\u0026thinsp;=\u0026thinsp;0), current smoking (no\u0026thinsp;=\u0026thinsp;1 point, yes\u0026thinsp;=\u0026thinsp;0), and current alcohol consumption (no\u0026thinsp;=\u0026thinsp;1 point, yes\u0026thinsp;=\u0026thinsp;0). That is, the habit score ranges from 0 to 5, with higher scores indicating more favorable habits. Notably, the definition of some habits varies across studies. In detail, regular exercise was defined as any physical activity performed for at least 30 minutes twice per week during the last year in the AC, and as moderate (without sweating) exercise at least 2 hours a week and/or high-intensity exercise (with sweating) for at least 30 minutes more than once per week in BC. Social activity was assessed by the Social Role Index of Competence in the AC [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]; and Social Participation Questionnaire in the BC [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. An active social life was defined by \u0026ge;\u0026thinsp;median score in each social activity evaluation. Sleep difficulty in the AC was defined as scores\u0026thinsp;\u0026ge;\u0026thinsp;6 on The Pittsburgh Sleep Quality Index [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In the BC, sleep difficulty was self-reported sleeplessness (yes or no).\u003c/p\u003e \u003cp\u003eLifestyle-related diseases and habits were not correlated in either cohort: r(584) = -0.05, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.19 in the AC, and r(187) = -0.06, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.38 in the BC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMRI Data Acquisition\u003c/h2\u003e \u003cp\u003eIn the AC, all three-dimensional T1-weighted magnetization-prepared rapid acquisition gradient echo structural images were acquired using a 3-Tesla Philips Achieva dStream with a 32-channel head coil. A T1-weighted 3D sagittal magnetization-prepared rapid gradient echo (MPRAGE) sequence was performed with the following parameters: repetition time (TR)\u0026thinsp;=\u0026thinsp;8.6ms; echo time (ET)\u0026thinsp;=\u0026thinsp;3.01 ms; field of view (FOV)\u0026thinsp;=\u0026thinsp;27 \u0026times; 27 cm; slice thickness (ST)\u0026thinsp;=\u0026thinsp;1.2mm; number of slices\u0026thinsp;=\u0026thinsp;150; flip angle (FA)\u0026thinsp;=\u0026thinsp;12\u0026deg;; T1-weighted images acquisition matrix\u0026thinsp;=\u0026thinsp;256 \u0026times; 256.\u003c/p\u003e \u003cp\u003eIn the BC, all images were acquired with a 3-Tesla MRI system (General Electric) with a 32-channel radiofrequency head coil. Three-dimensional fast spoiled MPRAGE T1-weighted images in the coronal plane were acquired for volume measurements. Image acquisition parameters were as follows: TE\u0026thinsp;=\u0026thinsp;3.2 ms; TR\u0026thinsp;=\u0026thinsp;8.2 ms; FOV\u0026thinsp;=\u0026thinsp;25 \u0026times; 25 cm; ST\u0026thinsp;=\u0026thinsp;1 mm; number of slices\u0026thinsp;=\u0026thinsp;180; FA\u0026thinsp;=\u0026thinsp;12\u0026deg;; T1-weighted images acquisition matrix\u0026thinsp;=\u0026thinsp;256 \u0026times; 256.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStructural MRI Preprocessing\u003c/h2\u003e \u003cp\u003eT1-weighted structural MRI images were processed using the same automated procedures by Computational Anatomy Toolbox 12 (CAT12; Jena University Hospital, Germany;. Gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) were segmented using Tissue Probability Maps where each voxel is assigned to its most likely tissue type and subsequent segmentation. Then GM and WM images were spatially normalized to the Montreal Neurological Institute (MNI) space by correcting the differences in the subjects\u0026rsquo; head positions or orientation during scanning for the global brain shape [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The image intensity of each voxel was modulated by Jacobian determinants and smoothed by convolving them with an 8-mm full width at half-maximum isotropic Gaussian kernel. The thickness map was obtained via reconstruction of the central surface [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], during which partial-volume information, sulcal blurring, and topological defects, e.g., sulcal asymmetries, were adjusted using spherical harmonics [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Following, the Freesurfer \u0026lsquo;FsAverage\u0026rsquo; template was registered and smoothed by convolving them with a 15-mm full width at a half-maximum isotropic Gaussian kernel. The value of thickness was calculated by estimating the distance between the inner surfaces (the boundary between GM and WM) and the outer surfaces (the boundary between GM and CSF) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Finally, we extracted values of GM volumes (cm\u003csup\u003e3\u003c/sup\u003e) and cortical thickness (mm) of regions of interest ( [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]): hippocampus, amygdala, thalamus, and caudate (both the left and right hemispheres included; Neuromorphometrics Atlas), and thickness for the superior frontal gyrus, inferior temporal gyrus, and middle temporal gyrus (Desikan-Killiany Atlas, [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analyses\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using Stata 16 (StataCorp LP, College Station, TX, USA). The alpha level was set to \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all tests. Comparisons of demographic characteristics of the AC and the BC were evaluated using chi-square tests for categorical variables or two-sided t-tests for continuous variables. We first conducted a series of univariate linear regression models to verify the effect of SES on brain volumes, thickness, and cognition (MMSE) across two cohorts. Given the previous review for selecting brain regions was based on early-life developing studies[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], this step is also a confirmational test to understand whether the derived regions are sensitive proxies for the older female population. Secondly, the multiple relationships between SES, lifestyle-related disease/habits, brain volumes or thickness, and cognition, as identified in previous regression models, were evaluated by structural equation models (SEM) based on the maximum likelihood method. As prementioned, we aimed to understand the accumulative effects of SES, thus the composite scores of SES, disease, and habits were inputted in the SEM instead of constructing the corresponding latent variables. the Age and intracranial volume were adjusted in each pathway via two hierarchical models. Effect sizes of standardized coefficients (β) and 95% bias-corrected bootstrap confidence intervals (CIs) were generated by bootstrapping 1,000 resamples. Additionally, the mediation effect through lifestyle or lifestyle-related diseases was estimated.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULT","content":"\u003cp\u003eSample characteristics are presented in Table 1. The BC was generally younger, although the age range was wider. Furthermore, the BC had a larger total GM volume, higher SES levels, and higher MMSE scores. The AC was characterized by healthier lifestyle habits, but more lifestyle-related diseases.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;Table 1. Sample characteristics.\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eArao Cohort (N\u0026thinsp;=\u0026thinsp;593)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eBetula Cohort (N\u0026thinsp;=\u0026thinsp;195)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e73.7 (6.0) ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e63.9 (13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eIn marriage, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e397 (67.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e126 (64.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 37px;\"\u003e\n \u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n \u003cp\u003eSocioeconomic status \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n \u003cp\u003e3.2 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.4 (1.3) ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eThe occupation category, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eHousework (JP)/manual employee (SWE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e458 (77.2) ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e45 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003ePart-time job (JP)/non-manual employee (SWE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e76 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e110 (56.4) ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eFull-time job or self-employed (JP)/professional or high-level non-manual (SWE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e59 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e40 (20.5) ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eEducation, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eNo high school degrees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e177 (29.9) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e42 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eHigh school degree only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e329 (55.5) ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e34 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eCollege degree or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e87 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e118 (60.1) ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eMini-Mental State Examination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e27.5 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e28.1 (1.6) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 37px;\"\u003e\n \u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n \u003cp\u003eLifestyle habits \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n \u003cp\u003e3.8 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.5 (1.1) ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eRegular exercise, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e321 (54.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e162 (83.1) ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eActive social life, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e482 (81.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e74 (37.9) ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eCurrent smoke, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e11 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e88 (45.1) ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eCurrent drink, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e128 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e178 (91.3) ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 37px;\"\u003e\n \u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n \u003cp\u003eSleep difficulty \u003csup\u003ec\u003c/sup\u003e, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n \u003cp\u003e177 (29.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n \u003cp\u003e63 (32.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 37px;\"\u003e\n \u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n \u003cp\u003eLifestyle-related diseases \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.92 (0.7) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n \u003cp\u003e0.78 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eObesity, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e25 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e42 (21.5) ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eDiabetes, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e73 (12.3) **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e11 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eHypertension, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e418 (70.5) ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e71 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 37px;\"\u003e\n \u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n \u003cp\u003eDepressive disorder \u003csup\u003eg\u003c/sup\u003e, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n \u003cp\u003e29 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e28 (14.4) ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 37px;\"\u003e\n \u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n \u003cp\u003eTotal brain gray matter volume (cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n \u003cp\u003e548.65 (41.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e591.4 (57.0) ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 13.8681px;\"\u003e\n \u003ctd style=\"height: 13.8681px;\" colspan=\"3\" align=\"left\"\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eSocioeconomic status was defined by the combination of occupational and educational categories. \u003csup\u003eb\u0026nbsp;\u003c/sup\u003eLifestyle index is the combined score of with/without having regular exercise, active social life, current smoking, current drinking, and sleep difficulty. \u003csup\u003ec\u0026nbsp;\u003c/sup\u003eSleep difficulty in the AC was defined as scores \u0026ge; 6 on The Pittsburgh Sleep Quality Index, while in the AC, sleep difficulty was self-reported troubles in sleeping. \u003csup\u003ed\u0026nbsp;\u003c/sup\u003eDisease index is the combined score of with/without obesity, diabetes, hypertension, and depressive disorder.\u003csup\u003e\u0026nbsp;e\u0026nbsp;\u003c/sup\u003eDepressive disorder in the AC was defined as scores \u0026ge; 6 on the Geriatric Depression Scale short version, while in the AC, \u0026ge;16 in The Center for Epidemiologic Studies Depression Scale was considered depressive.\u003cp\u003eData are presented as mean (SD) unless otherwise indicated.\u003c/p\u003e\n \u003cp\u003eBold font suggests a significance level at \u0026nbsp;\u0026lt;0.05. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. Obtained by t-tests for continuous variables and chi-squared tests for variables of proportion.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eAssociations between socioeconomic status, brain structure, and cognition\u003c/h2\u003e\n \u003cp\u003eResults from the linear regression models for SES with GM volume, cortical thickness, and cognition are listed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. For both cohorts, a positive association was found between SES and volumes for the hippocampus, amygdala, thalamus, and also, in relation to MMSE performance. These data suggest that higher SES is found for individuals with larger brain volumes and higher cognitive performance. Findings in relation to cortical thickness were, however, inconsistent across the two cohorts. In the AC, the left (\u0026beta;\u0026thinsp;=\u0026thinsp;0.009, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.049) and right superior frontal gyrus (\u0026beta;\u0026thinsp;=\u0026thinsp;0.083, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.044) were found to have a positive association with SES, while in the BC the right inferior temporal gyrus (\u0026beta;\u0026thinsp;=\u0026thinsp;0.152, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.036), and bilateral middle temporal gyrus (\u0026beta;\u0026thinsp;=\u0026thinsp;0.195, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.007) were associated with SES.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRegression models of structural brain measures and cognition as the outcome of socioeconomic status.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eArao Cohort (N\u0026thinsp;=\u0026thinsp;593)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBetula Cohort (N\u0026thinsp;=\u0026thinsp;195)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutcome \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026beta; (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026beta; (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGray matter volume (cm3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft Hippocampus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.231 (0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.322 (0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRight Hippocampus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.205 (0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.282 (0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft Amygdala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.186 (0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.341 (0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRight Amygdala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.154 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.309 (0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft Thalamus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.163 (0.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.243 (0.142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRight Thalamus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.162 (0.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.247 (0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft Caudate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.017 (0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.168 (0.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRight Caudate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002 (0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.135 (0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCortical thickness (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft Superior frontal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009 (0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.049\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.106 (0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRight Superior frontal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.083 (0.042)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.044\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.119 (0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft Inferior temporal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.078 (0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.136 (0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRight Inferior temporal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.060 (0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.152 (0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.036\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft Middle temporal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.052 (0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.234 (0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRight Middle temporal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.068 (0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.195 (0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMini-Mental State Examination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.223 (0.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.219 (0.107)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.043\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003eData are presented as the beta estimate in standard deviation units and standard error (SE). Bold suggests a significance level at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eMediators of the association between SES and neurocognitive measures\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e describes the direct and indirect effects of SES, lifestyle-related diseases, brain measures (i.e., those commonly identified volumes in two cohorts: hippocampus, amygdala, and thalamus, see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), and MMSE in structural equation models. Age alone or age and total intracranial volume (TIV) were adjusted for in Model 1 and Model 2, respectively.\u003c/p\u003e\n \u003cp\u003eSEM model fits were acceptable with a root mean square error of approximation (RMSEA) lower than 0.001 in two models across both cohorts. Smaller Akaike\u0026apos;s information criterion (AIC) and Bayesian information criterion (BIC) indicate a slightly better fit of Model 1 in the AC and Model 2 in the BC. A caution here is that a larger sample size would increase the likelihood of detecting small deviations from the SEM model, thus the AIC and BIC indicators did not refer to a better model fit in the BC compared to the AC.\u003c/p\u003e\n \u003cp\u003eSES was significantly associated with lifestyle-related disease, MMSE, and GM volume. Relationships persisted in Model 2 adjusting for age and TIV. A larger disease index predicted smaller GM volume and lower cognition in the AC. While similar trends were found in the BC, they did not reach statistical significance. Lifestyle-related diseases were found to mediate the association between SES and GM volume (18.5% of variance) in AC, but not in BC which responds to the insignificant association of disease and GM volume in BC). An overview of the mediating effects of lifestyle-related disease is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStandardized estimates of the direct and indirect effects of SES and lifestyle-related diseases on brain measures and cognition using structural equation models.a\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eArao Cohort (N\u0026thinsp;=\u0026thinsp;593)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBetula Cohort (N\u0026thinsp;=\u0026thinsp;195)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 1 \u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2 \u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 1 \u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2 \u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.102 (-0.188, -0.017)\u003c/strong\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.101 (-0.190, -0.013)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.283 (-0.420, -0.146)**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.175 (-0.257, -0.092)**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.074 (0.149, 0.294)**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.096 (0.016, 0.177)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.219 ( 0.080, 0.358)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.209 (0.015 0.403)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVolume \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.064 (-0.141, 0.142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.053 (0.005, 0.112)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.265 ( 0.238, 0.774)**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.186 (0.069, 0.256)**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVolume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.081 (-0.153, 0.008)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.059 (-0.114, -0004)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.115 (-0.731, 0.073)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.170 (-0.445, 0.105)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.094 (-0.186, -0.003)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.074 (-0.152, 0.003)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.070 (-0.335, 0.195)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011 (-0.241, 0.272)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMSE via disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018 (-0.007, 0.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02 (-0.006, 0.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005 (-0.039, 0.050)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002 (-0.017, 0.069)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVolume via disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014 (0.006, 0.027)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012 (0.003 0.028)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032 (-0.019, 0.141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029 (-0.038, 0.098)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCorrelation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVolume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.068 (0.002, 0.134)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.072 (0.17, 0.126)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.308 (-0.026, 0.642)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.225 (0.055, 0.506)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGoodness-of-fit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8112.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9547.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1850.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1814.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8173.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9625.962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1889.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1862.873\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eSES socioeconomic status, MMSE Mini-Mental State Examination, RMSEA root mean square error of approximation, AIC Akaike\u0026apos;s information criterion, BIC Bayesian information criterion. \u0026beta; estimated in standard deviation units, CI bootstrapped confidence interval for 1000 samples.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Model 1 adjusted for age, Model 2 adjusted for age and total intracranial volume. \u003csup\u003eb\u003c/sup\u003e Disease index is the combined score of with/without having obesity, diabetes, hypertension, and depressive disorder. \u003csup\u003ec\u003c/sup\u003eVolume is the combination value of the hippocampus, amygdala and thalamus.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eBold font suggests a significance level at \u003cem\u003eP\u003c/em\u003e \u0026lt;.05.* \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTable 4 provides the SEM results on lifestyle habits, SES, GM volume, and cognition. \u0026nbsp;Despite better lifestyle habits predicting better cognition in both cohorts, lifestyle habits were not directly related to SES or GM volumes. \u0026nbsp;Thus, no mediating effects of lifestyle habits for explaining SES-GM volume and SES-cognition associations were found across the two cohorts.\u0026nbsp;\u003c/p\u003e\n \u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStandardized estimates of the direct and indirect effects of SES and lifestyle habits on brain measures and cognition using structural equation models.a\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eArao Cohort (N\u0026thinsp;=\u0026thinsp;593)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBetula Cohort (N\u0026thinsp;=\u0026thinsp;195)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eTo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 1 \u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2 \u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 1 \u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2 \u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eHabit\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014 (-0.068, 0.095)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014 (-0.067, 0.094)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-0.008 (-0.139, 0.122)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.011 (-0.133, 0.122)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.112 (0.031, 0.192)**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.104 (0.024, 0.185)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.211 ( 0.012, 0.358)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.193 (0.015 0.390)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eVolume\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.067 (0.004, 0.137)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.051 (0.005, 0.114)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.265 ( 0.238, 0.774)**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.186 (0.069,0.256)**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHabit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eVolume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003 (-0.743, 0.067)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002 (-0.061, 0.057)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.047 ( -0.146, 0.241)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034 (-0.214, 0.238)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHabit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.083 (0.007, 0.159)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.083 (0.008, 0.159)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.265 (0.038, 0.479)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.262 (0.038 0.464)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMMSE via habit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001 (-0.006, 0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001 (-0.006, 0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-0.002 (-0.037, 0.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003 (-0.037, 0.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eVolume via habit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.00004 (-0.001, 0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.00003 (-0.0009, 0.0008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.001 (-0.007 0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000 (-0.005 0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCorrelations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVolume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.076 (0.009, 0.143)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.079 (0.024, 0.134)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.309 (-0.037, 0.645)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.214 (0.069, 0.497)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGoodness-of-fit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8000.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9410.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e1897.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1865.330\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8061.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9484.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e1936.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1913.636\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" align=\"left\"\u003e\n \u003cp\u003eSES socioeconomic status, MMSE Mini-Mental State Examination, RMSEA root mean square error of approximation, AIC Akaike\u0026apos;s information criterion, BIC Bayesian information criterion.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Model 1 adjusted for age, Model 2 adjusted for age and total intracranial volume. \u0026beta; estimated in standard deviation units, CI bootstrapped confidence interval for 1000 samples. \u003csup\u003eb\u003c/sup\u003e Habit index is the combined score of with/without having regular exercise, active social life, current smoking, current drinking, and sleep difficulty. \u003csup\u003ec\u003c/sup\u003e Volume is the combination value of the hippocampus, amygdala and thalamus.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" align=\"left\"\u003e\n \u003cp\u003eBold font suggests a significance level at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe influence of SES and lifestyle on neurocognitive health in older age has typically been studied separately, without different sociocultural backgrounds taken into account. Focusing on the female population and using different cohorts from two countries, we observed that despite variant demographic backgrounds, lifestyle-related diseases were more strongly associated with brain volume and general cognition compared to habit (e.g., smoking and alcohol consumption), showing the value of candidate risk management that address brain and cognitive vulnerability due to SES.\u003c/p\u003e \u003cp\u003eIn contrast to one of our hypotheses, no statistically significant relationships were found between SES and direct lifestyle habits. One potential explanation for this could be the limitations in our SES measurements, notably the absence of income data. Income is a critical component of SES and its exclusion might have restricted to full assessment of its impact on lifestyle habits. Additionally, when examining our participant group, known as the AC, we observed a notably higher completion rate of secondary education (70.2%) compared to the national average (65.5% [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] ). This discrepancy suggests the possibility of selection bias in our study, as our participant pool appears to be more educated than the general population. Such a bias is important to consider, as a higher education level may have more access to health information and resources which could potentially mitigate the influence of SES on lifestyle choices.\u003c/p\u003e \u003cp\u003eThe common insignificant association of lifestyle habits on regional brain GM volume in two cohorts could be interpreted as that, awareness of the prevalence of certain diseases encourages older females to engage in a healthier lifestyle. For example, 22% in BC and only 4% of AC were obese, but at the same time, 83% in BC and 55% of AC engaged more frequently in regular exercise. Therefore, it is possible that older Swedish females intentionally maintain daily physical activities because they are aware of the hazards of obesity. Moreover, in recent decades, Japan and Sweden have seen a declining prevalence of behavioral risks, including smoking, drinking, and physical inactivity [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]; which reflects increased public awareness resulting from education and prevention campaigns. In contrast, the prevalence of hypertension and diabetes, along with their coexistence, continues to rise significantly; such trends suggest there is still insufficient public awareness and limited access to healthcare professionals [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Thus, prioritizing lifestyle disease could greatly benefit socioeconomically disadvantaged populations in Japan and Sweden.\u003c/p\u003e \u003cp\u003eAnother finding worth noting is that cortical thickness was less sensitive to SES, and some parts of the limbic lobe (hippocampus, amygdala, and thalamus) were particularly susceptible to SES among older females, despite the variation in SES definition. In line with our findings, a recent imaging study showed that GM volume is more closely linked to heterogeneity in the social environment, as quantified by education and occupational variables, than thickness [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The \u003cem\u003estress\u003c/em\u003e hypothesis is a candidate to explain the potential mechanism, which denotes that low SES exposes people to a continuing high level of stress [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. While the adaptive stress response is important for survival, repeated hormone adaption to stress activation of the hypothalamus\u0026mdash;pituitary\u0026mdash;adrenal (HPA) axis results in lasting structural and functional disturbances of the limbic lobe [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Further, a recent study with longitudinal MRI data showed that aging is characterized more by hippocampal atrophy than by cortical thickness [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. These findings together with our results, suggest the burden of SES disadvantage may be more likely to exaggerate towards the limbic lobe with age.\u003c/p\u003e \u003cp\u003eSeveral limitations need to be acknowledged. First, the cross-sectional design precludes causal inferences to explain brain-cognitive relationships due to SES and health conditions. However, findings that are consistent across two different cultural backgrounds suggest the robustness of findings and may hold value for societal policymaking. Second, as prementioned, the possible selection bias due to high education level trends, and heterogeneous definitions of SES. Future studies should investigate these findings with consistency across studies in such definitions. Similarly, the overall high level of cognition (ave. MMSE 27.5 in the AC, 28.1 in the BC) in the two cohorts may limit understanding of the magnitude and progression of cognitive degeneration due to SES disadvantages.\u003c/p\u003e \u003cp\u003eIn conclusion, although no single risk or protective factor could adequately address the adverse effect of SES on neurocognitive status, four lifestyle-related disease management including diabetes, hypertension, obesity, and depressive disorder can be expected to deliver cost-effective gains to blunt the inequality gap in cognitive health among older women in Japan and Sweden.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIntegrated dataset analysis was approved by the ethics committee of Tohoku University (2022-1-600). All participants provided written informed consent for baseline surveys and MRI examinations.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from Kumamoto University and Umea University but restrictions apply to the availability of these data, which were used under permission for the current study, and so are not publicly available. Data are however available upon reasonable request to corresponding author and with permission of Kumamoto University and Umea University.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFounding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was supported by the Japan Society for the Promotion of Science, Research Fellowship for Young Scientists(22J14503); and MIRAI 2.0. seed funding. The funders had no role in the design of the study, the collection, analysis, and interpretation of data, or the writing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor contribution\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eY.L., C.B., and N.K., applied for funding and negotiated the project details. Y.L., N.K., \u0026nbsp;Y.T., C.B., and B.T., planned the study, performed statistical analyses, and drafted the paper. Y.C., Y.Z., and Y.H., supervised the data analysis and contributed to revising the paper. Y.C., Y.Z., Y.H., N.K., S.Y., M.T., T.N., and Y.T. helped to plan the study, including the instrumentation, and to revise the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConflicts of interest\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCao Q, Tan CC, Xu W, et al. The Prevalence of Dementia: A Systematic Review and Meta-Analysis. J Alzheimers Dis. 2020;73:1157\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuque H, Eramudugolla R, Chidiac B et al. Could Country-Level Factors Explain Sex Differences in Dementia Incidence and Prevalence? A Systematic Review and Meta-Analysis. Journal of Alzheimer\u0026rsquo;s Disease. 2022; Preprint: 1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarden JR, Tchetgen Tchetgen EJ, Kawachi I, Glymour MM. Contribution of Socioeconomic Status at 3 Life-Course Periods to Late-Life Memory Function and Decline: Early and Late Predictors of Dementia Risk. Am J Epidemiol. 2017;186:805\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLetellier N, Ilango SD, Mortamais M, et al. Socioeconomic inequalities in dementia risk among a French population-based cohort: quantifying the role of cardiovascular health and vascular events. Eur J Epidemiol 2021. 2021;1:1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang JJ, Keohane LM, Pan X, et al. Association of Healthy Lifestyles with Risk of Alzheimer Disease and Related Dementias in Low-Income Black and White Americans. Neurology; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLivingston G, Huntley J, Sommerlad A, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. The Lancet. 2020;396:413\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeong RLF, Lo JC, Sim SKY, et al. Longitudinal brain structure and cognitive changes over 8 years in an East Asian cohort. NeuroImage. 2017;147:852\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFjell AM, Walhovd KB. Structural brain changes in aging: courses, causes and cognitive consequences. Rev Neurosci. 2010;21:187\u0026ndash;221.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Shea DM, Dotson VM, Woods AJ et al. Depressive symptom dimensions and their association with hippocampal and entorhinal cortex volumes in community dwelling older adults. Front Aging Neurosci 2018; 10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBinnewies J, Nawijn L, van Tol MJ, van der Wee NJA, Veltman DJ, Penninx BWJH. Associations between depression, lifestyle and brain structure: A longitudinal MRI study. NeuroImage. 2021;231:117834.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelly DM, Rothwell PM. Blood pressure and the brain: the neurology of hypertension. Pract Neurol. 2020;20:100\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAntal B, McMahon LP, Sultan SF et al. Type 2 diabetes mellitus accelerates brain aging and cognitive decline: Complementary findings from UK Biobank and meta-analyses. Elife 2022; 11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErickson KI, Raji CA, Lopez OL, et al. Physical activity predicts gray matter volume in late adulthood: the Cardiovascular Health Study. Neurology. 2010;75:1415\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFox FAU, Diers K, Lee H, et al. Association Between Accelerometer-Derived Physical Activity Measurements and Brain Structure: A Population-Based Cohort Study. Neurology. 2022;99:e1202.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Zhang Y, Thyreau B, et al. Altruistic Social Activity, Depressive Symptoms, and Brain Regional Gray Matter Volume: Voxel-Based Morphometry Analysis From 8,695 Old Adults. The Journals of Gerontology: Series A. 2022;77:1789\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCook WK, Li X, Sundquist K, Kendler KS, Sundquist J, Karriker-Jaffe KJ. Drinking cultures and socioeconomic risk factors for alcohol and drug use disorders among first- and second-generation immigrants: A longitudinal analysis of Swedish population data. Drug Alcohol Depend. 2021;226:108804.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTapia Granados JA. Health at advanced age: Social inequality and other factors potentially impacting longevity in nine high-income countries. Maturitas. 2013;74:137\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBl\u0026uuml;her M. Obesity: global epidemiology and pathogenesis. Nat Reviews Endocrinol 2019. 2019;15:5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1\u0026middot;9 million participants. Lancet Glob Health. 2018;6:e1077\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemnitz N, Hulme OJ, Siebner HR, et al. Characterising the covariance pattern between lifestyle factors and structural brain measures: a multivariable replication study of two independent ageing cohorts. Neurobiol Aging. 2023;131:115\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRakesh D, Whittle S. Socioeconomic status and the developing brain \u0026ndash; A systematic review of neuroimaging findings in youth. Neurosci Biobehav Rev. 2021;130:379\u0026ndash;407.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNinomiya T, Nakaji S, Maeda T, et al. Study design and baseline characteristics of a population-based prospective cohort study of dementia in Japan: the Japan Prospective Studies Collaboration for Aging and Dementia (JPSC-AD). Environ Health Prev Med. 2020;25:64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNyberg L, Boraxbekk CJ, S\u0026ouml;rman DE, et al. Biological and environmental predictors of heterogeneity in neurocognitive ageing: Evidence from Betula and other longitudinal studies. Ageing Res Rev. 2020;64:101184.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiagnostic and Statistical Manual of Mental Disorders, 3rd ed., revised (DSM-III-R). American Journal of Psychiatry 2006; 145: 1301\u0026ndash;1302.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShiya Hisano. The relationship between Revised Hasegawa Dementia Scale (HDS-R), Mini-Mental State Examination (MMSE) and Bed-fast Scale, Dementia Scale. Japanese J Geriatric Psychiatry. 2009;20:883\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrut M, Fratiglioni L, Viitanen M, Winblad B. Accuracy of the Mini-Mental Status Examination as a screening test for dementia in a Swedish elderly population. Acta Neurol Scand. 1993;87:312\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYasuno F, Minami H, Hattori H. Interaction effect of Alzheimer\u0026rsquo;s disease pathology and education, occupation, and socioeconomic status as a proxy for cognitive reserve on cognitive performance: in vivo positron emission tomography study. Psychogeriatrics. 2020;20:585\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026ouml;rman DE, Stenling A, Sundstr\u0026ouml;m A, et al. Occupational cognitive complexity and episodic memory in old age. Intelligence. 2021;89:101598.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang AY, Hu HY, Ou YN, et al. Socioeconomic Status and Risks of Cognitive Impairment and Dementia: A Systematic Review and Meta-Analysis of 39 Prospective Studies. J Prev Alzheimer\u0026rsquo;s Disease. 2023;10:83\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLopresti AL, Hood SD, Drummond PD. A review of lifestyle factors that contribute to important pathways associated with major depression: Diet, sleep and exercise. J Affect Disord. 2013;148:12\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorihiro S, Takashi A. Reliability and validity of the Japanese version of Geriatric Depression Scale-Short Version (GDS-S-J). Japanese Soc Cogn Neurosci. 2009;11:87\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRadloff LS, The CES-D, Scale. A Self-Report Depression Scale for Research in the General Population. http://dx.doi.org/101177/014662167700100306 2016; 1: 385\u0026ndash;401.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoyano W, Shibata H, Nakazato K, Haga H, Suyama Y. Measurement of competence: reliability and validity of the TMIG Index of Competence. Arch Gerontol Geriatr. 1991;13:103\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEriksson S\u0026ouml;rman D, Sundstr\u0026ouml;m A, R\u0026ouml;nnlund M, Adolfsson R, Nilsson LG. Leisure activity in old age and risk of dementia: a 15-year prospective study. Journals of Gerontology Series B: Psychological Sciences and Social Sciences. 2014;69:493\u0026ndash;501.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoi Y, Minowa M, Uchiyama M, et al. Psychometric assessment of subjective sleep quality using the Japanese version of the Pittsburgh Sleep Quality Index (PSQI-J) in psychiatric disordered and control subjects. Psychiatry Res. 2000;97:165\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshburner J, Friston KJ. Voxel-Based Morphometry\u0026mdash;The Methods Neuroimage. 2000;11:805\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDahnke R, Yotter RA, Gaser C. Cortical thickness and central surface estimation. NeuroImage. 2013;65:336\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYotter RA, Dahnke R, Thompson PM, Gaser C. Topological correction of brain surface meshes using spherical harmonics. Hum Brain Mapp. 2011;32:1109\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDesikan RS, S\u0026eacute;gonne F, Fischl B, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage. 2006;31:968\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHackman DA, Farah MJ. Socioeconomic status and the developing brain. Trends Cogn Sci. 2009;13:65\u0026ndash;73. (12 October 2020, date last accessed).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Ouml;stergren O, Martikainen P, Tarkiainen L, Elstad JI, Br\u0026oslash;nnum-Hansen H. Contribution of smoking and alcohol consumption to income differences in life expectancy: evidence using Danish, Finnish, Norwegian and Swedish register data. J Epidemiol Community Health. 2019;73:334\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKondo T, Nakano Y, Adachi S, Murohara T. Effects of Tobacco Smoking on Cardiovascular Disease. Circ J. 2019;83:1980\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTatsumi Y, Ohkubo T. Hypertension with diabetes mellitus: significance from an epidemiological perspective for Japanese. Hypertens Res 2017. 2017;40:9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersson T, Ahlbom A, Carlsson S. Diabetes Prevalence in Sweden at Present and Projections for Year 2050. PLoS ONE. 2015;10:e0143084.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalhovd KB, Fjell AM, Wang Y, et al. Education and Income Show Heterogeneous Relationships to Lifespan Brain and Cognitive Differences Across European and US Cohorts. Cereb Cortex. 2021;00:1\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaum A, Garofalo JP, Yali AM. Socioeconomic Status and Chronic Stress: Does Stress Account for SES Effects on Health? Ann N Y Acad Sci. 2006;896:131\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGianaros PJ, Jennings JR, Sheu LK, Greer PJ, Kuller LH, Matthews KA. Prospective reports of chronic life stress predict decreased grey matter volume in the hippocampus. NeuroImage. 2007;35:795\u0026ndash;803.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWingenfeld K, Wolf OT. Stress, Memory, and the Hippocampus. The Hippocampus in Clinical Neuroscience. 2014;34:109\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBang JY, Zhao J, Rahman M, St-Cyr S, McGowan PO, Kim JC. Hippocampus-Anterior Hypothalamic Circuit Modulates Stress-Induced Endocrine and Behavioral Response. Front Neural Circuits 2022; 16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNyberg L, Andersson M, Lundquist A, et al. Individual differences in brain aging: heterogeneity in cortico-hippocampal but not caudate atrophy rates. Cereb Cortex. 2022;9:10.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Education, occupational complexity, gender inequality, cognitive brain health, modifiable lifestyle risks","lastPublishedDoi":"10.21203/rs.3.rs-3833392/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3833392/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eDetermine and compare lifestyle risks addressing the effects of socioeconomic status (SES) on brain and cognitive variations among females in two community-dwelling cohorts across Japan and Sweden.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eWe included 576 (73.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0 years) and 195 (63.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4 years) cognitively healthy females from the Arao (AC, Japan) and Betula (BC, Sweden) cohorts, respectively. SES was defined by educational and occupational categories. Lifestyle-related diseases included obesity, diabetes, hypertension, and depressive disorder; habits including exercise, social activity, sleep, alcohol habits, and smoking status. Brain structural outcomes were derived from T1 weighted magnetic resonance imaging scans. A priori regions of interest included volumes of the hippocampus, amygdala, thalamus, and caudate; thickness of the superior frontal gyrus, inferior temporal gyrus, and middle temporal gyrus. General cognitive performance was evaluated by the Mini-Mental State Examination score. The relationships between SES-lifestyle with the brain and cognition were assessed by structural equation models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePositive associations were found between SES and volumetric brain measures and cognition (MMSE) in both cohorts, but not between SES and cortical thickness. Lifestyle-related diseases (including obesity, diabetes, hypertension, and depressive disorder), but not habits such as exercise or sleep, partially explained the positive association between SES and brain volumes (up to 18.6% in the AC). A similar, but non-significant trend, was seen in the SES-cognition association that could be explained by lifestyle-related diseases.\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e \u003cp\u003eAlthough statements of causality cannot be made from the current work, our findings suggest management of the lifestyle-related disease is particularly important for females for compensating the maladaptive effects of SES on brain atrophy.\u003c/p\u003e","manuscriptTitle":"Women at Risk: A Comparative Study on Socioeconomic Status, Lifestyle, Brain, and Cognition Among Older Females in Japan and Sweden","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-08 17:29:25","doi":"10.21203/rs.3.rs-3833392/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"585236d6-4597-4254-9926-daa6475af03e","owner":[],"postedDate":"January 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-11T11:08:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-08 17:29:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3833392","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3833392","identity":"rs-3833392","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}