Testing for associations and interactions between APOE e4 genotype and lifestyle, with brain structural imaging phenotypes in UK Biobank

Testing for associations and interactions between APOE e4 genotype and lifestyle, with brain structural imaging phenotypes in UK Biobank | 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 Article Testing for associations and interactions between APOE e4 genotype and lifestyle, with brain structural imaging phenotypes in UK Biobank Yating You, Frederick Ho, Joey Ward, Donald Lyall, Claire Hastie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6297741/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 Apolipoprotein E gene ( APOE ) e4 allele is the strongest common genetic risk factor for dementia. High genetic risk of dementia could be offset by healthy lifestyle. However, the effects of daily lifestyle and genetic risk on brain structural imaging phenotypes, including white matter (WM) tract integrity, remain unclear. Multiple linear regression and interaction analyses were performed to examine the associations between APOE e4 genotype and lifestyle factors (smoking, alcohol drinking, diet, physical activity, and sedentariness) with 14 brain health metrics among N = 24,912 non-demented participants from UK Biobank, including after adjustment for potential confounders. There were significant associations between APOE e4 presence and smaller hippocampal volume (standardised β range 0.042 to 0.048 versus absence), and multiple metrics of worse WM tract integrity. Compared with participants with favourable lifestyles, moderate and unfavourable lifestyles showed smaller grey matter, total brain, and hippocampal volumes (standardised β range 0.004 to 0.096) per one-unit worse lifestyle significantly. The unhealthiest lifestyle factors were mostly associated with poorer brain health. A 1-point better lifestyle score was associated with better brain health (standardised β range 0.001 to 0.017 per unit). The interactions between lifestyle levels and APOE e4 status on brain metrics were generally non-significant. The presence of APOE e4 and unfavourable lifestyle were mostly associated with worse brain health, and this was largely linear rather than synergistic. The benefit of healthy lifestyle on brain health does not vary by genetic risk for dementia. These findings warrant additional large-scale longitudinal studies to confirm and investigate associations across more brain structural phenotypes. Health sciences/Risk factors Biological sciences/Psychology/Human behaviour Health sciences/Biomarkers/Predictive markers Health sciences/Medical research/Genetics research Figures Figure 1 Figure 2 Introduction Dementia leads to progressive deterioration in cognition function, along with ability loss in daily activities, and is the seventh leading cause of death globally [ 1 ]. Around 50 million people suffered from dementia in 2020, and it is estimated to increase to 152 million by 2050[ 2 ]. It gives individuals and their families economic burdens where over 1.3 trillion US dollars were spent on treatments and care globally annually [ 1 ]. However, no effective treatments are currently available for dementia. Efforts to understand the contributions of modifiable risk factors are priorities to prevent dementia. Apolipoprotein E gene ( APOE ) e4 allele is known as the strongest common genetic risk factor for Alzheimer’s disease (AD) dementia [ 3 ]. Even among healthy people with APOE e4, brain structural differences can be observed and these may be considered markers of pre-symptomatic AD years before disease onset [ 4 ]. The hippocampus is a known brain region associated with memory loss, as one of the key symptoms of AD [ 5 ]. The potential biological rationale is that the APOE locus regulates lipid metabolism, impacting brain-related factors such as WM myelination and neuronal repair[ 6 ]. Although significant associations of APOE e4 were reported on decreased volumes of hippocampus and white matter (WM) structures[ 7 , 8 ], null associations were also reported[ 9 , 10 ]. APOE e4 elevates resting state connectivity and the balance between brain networks and higher hippocampal activation, which could contribute to increased Aβ production[ 6 ]. Increased hippocampal activity was associated with reduced cortical thickness in the medial temporal lobe and brain regions that are vulnerable to AD pathology[ 6 ]. Consequently, individuals carrying the e4 allele may be more susceptible and vulnerable to the adverse effects of suboptimal lifestyle risk factors [ 6 , 11 ]. Hence, further research is needed on the relationship between APOE e4 and lifestyle factors in brain health. People with healthy lifestyle, such as never smoking, light alcohol drinking, healthy diet, adequate physical activity and less sedentary behaviour, may reduce risk of dementia[ 12 – 14 ]. High genetic risk of dementia can be offset by higher adherence to healthy lifestyle[ 13 ]. The relationship between individual lifestyle factors and brain health metrics has been well-demonstrated, for example, smoking was associated with smaller grey matter (GM) [ 15 ], and chronic alcohol use with disruption of WM tracts[ 16 ], as well as unhealthy diet with decreased brain volume and connectivity[ 17 ]. However, these studies did not investigate the complex effects of multiple lifestyle factors on brain structural phenotypes. Only one cross-sectional study including 2,413 participants in PRECISE study and 19,822 participants in UK biobank showed significant associations between a healthier lifestyle and lower degree of five limited brain imaging markers (total brain, GM, WM, hippocampus, and WM hyperintensities volumes) without considering any genetic impact though [ 18 ]. It is important to understand to what extent lifestyle factors contribute to brain structural differences in non-demented older people, between carriers and non-carriers of e4, which might aid the development of corresponding interventions for better brain health. Neuroimaging markers can detect evidence of cognition impairment and dementia [ 19 ]. Associations between risk factors and traditional measures of WM integrity tract with diffusion tensor imaging (DTI) have been examined, including decreased fractional anisotropy (FA: the directional coherence of water molecule diffusion) and increased mean diffusivity (MD: the magnitude of water molecule diffusion) [ 20 – 22 ]. Compared with traditional measures, advances in WM integrity phenotypes - neurite orientation dispersion and density imaging (NODDI) have been shown more sensitive and specific to clinical outcomes and target pathologies than traditional ones. NODDI provides estimates of neurite density, diffusion of water molecules in the free-water component, and neurite orientation dispersion using intracellular volume fraction (ICVF), isotropic volume fraction (ISOVF), and orientation dispersion (OD), respectively[ 23 ]. Better brain health is anticipated to have a negative correlation with both ISOVF and OD, and a positive correlation with ICVF, which can reflect the dendritic microstructural arrangement and the composition of GM [ 24 ]. In general, the associations of APOE e4 genotype and lifestyle with brain structures have been explored in small samples with inconsistent findings, and the role of certain imaging methods e.g. DTI-NODDI remain unexplored. Hence, this study aimed to examine the associations between APOE e4 status and comprehensive lifestyle – and their potential interaction - with multiple brain structural phenotypes among non-demented 24,912 participants from UK Biobank. Materials and methods Study design and participants The UK Biobank is a large community-based cohort with 502,359 participants aged 37–73 years[ 25 ]. All UK Biobank participants provided written informed consent, and the North West Multi-Centre Ethics Committee granted ethical approval. In 2014, brain imaging began, and this is ongoing until the aim of 100,000 participants have been scanned. As of the time of analysis (data last updated September 2023), a total of 71,091 participants aged 44–85 years had attended imaging assessment centres and completed touchscreen questionnaires to provide a series of physical, sociodemographic, and medical information. Two-thirds of them had available derived brain MRI phenotype data. We excluded participants who reported chronic neurological diseases which could directly affect cognitive function (see Table S1 ). This research was conducted using UK Biobank application number 17689. Genetic data UK Biobank genotyping was conducted by Affymetrix using a bespoke BiLEVE Axiom array for about 50,000 participants and the remaining around 450,000 on the Affymetrix UK Biobank Axiom array. All genetic data were quality controlled by UK Biobank [ 26 ]. The APOE e genotype is directly genotyped. Further information on the genotyping process is available ( http://www.ukbiobank.ac.uk/scientists-3/genetic-data ). APOE e4 presence (verses non e4 alleles as absence) was derived based on two directly genotyped single nucleotide polymorphisms (rs7412 and rs429358). Lifestyle factors and score Available data for lifestyle factors in UK Biobank were used to generate an unweighted lifestyle score using smoking status, alcohol drinking, diet, physical activity, and sedentary behaviour. Lifestyle variables comprising the score were derived from questionnaire responses ( Table S2 ). Smoking status was classified as never, former, and current smoker based on self-reporting. Alcohol drinking was reported as types of alcohol per week and grouped based on national guidelines: 0–14 units average weekly alcohol units as low risk of diseases; 15–35 units for females or 15–50 units for males as moderate risk; > 35 units for females and > 50 units for males as high risk[27]. Diet was categorized as healthy, moderate, and unhealthy diet based on a developed diet score [28]. Briefly, this score was constructed by the unweighted sum of adherence to the UK dietary guideline, including: processed meat, red meat individually less than twice per week, total fish more than twice per week, consumption of semi- and skimmed, soya milk, no spread intake, more than five bowls of cereal intake, no salt added to food, more than six glasses of water and more than five servings per day of fruits and vegetables. Sedentary behaviour duration and metabolic equivalents task units (METs) for physical activity were classified as low, medium, and high levels according to tertiles[29]. Tertile was used as there is currently no guideline for sedentary behaviour, and the WHO guideline for physical activity would result in a highly skewed distribution. We assigned two points for membership of each respective healthiest category: never smoking; low risk drinking; healthy diet; low duration sedentariness; high level physical activity. For example, someone who never smoked and had low risk drinking would receive four points. One point was assigned for each of less healthy, intermediate lifestyles, such as former smoking, moderate risk drinking, moderate diet, medium duration sedentariness, and medium level physical activity, and 0 for the least healthy ones. The intercorrelations among five lifestyle factors are shown in Table S3 . The lifestyle score ranged from 0 to 10, indicating a better lifestyle with a higher score. Participants who scored 0, 1, 2 or 3 were classed as unfavourable lifestyle; 4, 5, or 6 as moderate lifestyle; 7, 8, 9, or 10 as favourable lifestyle (see Figure S1 ). Imaging data All brain MRI data were acquired at four sites (Cheadle, Newcastle, Reading and Bristol) on the same 3T Siemens Skyra scanner, according to a freely available protocol (http://www.fmrib.ox.ac.uk/ukbiobank/protocol/V4_23092014.pdf ), documentation (http://biobank.ctsu. ox.ac.uk/crystal/docs/brain_mri.pdf ), and publication [30]. The primary outcomes were differences in eight volumetric variables of total GM, WM, total WM hyperintensity (WMH), total brain, left hippocampus, right hippocampus, (summed to generate) total hippocampus, hippocampus volumetric asymmetry (left minus right) [31],and six general components of WM integrity assessed with DTI by APOE e4 status and lifestyle levels groups. All variables were visually inspected to ascertain whether they were distributed normally. WMH volume was log-transformed before statistical analyses due to the positively skewed distribution. Principal components analysis was used to produce unrotated general components for each of the five WM microstructural measurements (FA, MD, ICVF, ISOVF and OD) based on 27 tracts, and general volumes of frontal lobe GM described in Table S4 . The six general components were gFA (eigenvalue=12.3, 44.9% variance explained) gMD (eigenvalue=13.4, 49.8% variance explained), gICVF (eigenvalue=18.6, 68.9% variance explained), gISOVF (eigenvalue=10.1, 37.4% variance explained), gOD (eigenvalue=7.3, 26.9% variance explained) and gFrontal (eigenvalue=6.9, 43.3% variance explained). All brain MRI metrics were raw and transformed into Z-scores based on the final analysis sample to ease interpretation (i.e. similar to standardised betas on a per-SD scale). Covariates Covariates were collected when participants attended their first MRI assessment. Demographics were age at the time of imaging and sex. Body mass index (BMI) was categorized as underweight (<18.5 kg/m 2 ), normal weight (18.5-24.9 kg/m 2 ), overweight (25.0-29.9 kg/m 2 ), and obesity (≥30 kg/m 2 ) [32]. Sleep duration was assessed with the question “About how many hours of sleep do you get in every 24h?” and then grouped into three groups: short (9h) because of potential non-linear association [33]. Socially isolated status was categorized as “yes” or “no” by assessing one functional and two structural component measures of social connections [34]. Townsend deprivation score was derived from the postcode of residence and categorized as quintiles [35]. Higher scores indicated higher levels of area-based socioeconomic deprivation. Participants were asked whether they were diagnosed with each of diabetes, hypertension or depression. Genetic arrays and ten genetic principal components (PCs) for stratification were also selected. Statistical analysis An APOE e4 dominant model (presence vs. absence) was used based on previous research [21]. Characteristics were summarized across the APOE e4 presence with the Chi-square tests for categorical variables and t-tests for normally distributed continuous variables. Multiple linear regression analyses were performed to determine whether brain metrics were significantly different across the APOE e4 status or lifestyle groups. Standardized betas (i.e. on a per-SD [standard deviation] scale of effect) and 95% confidence intervals (CIs) were reported. First, the associations between APOE e4 presence and each of the brain imaging phenotypes were examined. Second, the associations between lifestyle and brain phenotypes were investigated in four ways: continuous lifestyle score, three-level lifestyle including unfavourable, moderate, favourable ones, and three-level individual lifestyle factor. Third, interactions between APOE e4 status with lifestyle levels, and each lifestyle were also tested. All brain volumetric variables were adjusted for using intracranial volume (ICV) that was generated by summing the volume of WM, GM, and ventricular cerebrospinal fluid [36]. Two adjustment models were performed to explore whether different confounders impacted the associations. The first model was partially adjusted for age, sex, genotypic array, MRI assessment centre, and 10 PCs for genetic stratification; ICV additionally adjusted for volumetric variables including gFrontal. The second model was fully adjusted: additionally, Townsend deprivation score, BMI, sleep duration, isolated status, historic diabetes, hypertension, and self-reported depression. All statistical analyses were performed using Stata 18.0 and R (version 4.2.3). A two-tailed P < 0.05 was considered statistical significance. Because of multiple testing, we also present the False Discovery Rate (FDR) corrected p-values. Results Characteristics of participants A total of 24,912 participants had complete lifestyle factors, genetics, brain imaging biomarkers and covariates at their first MRI assessment (see Figure S2 ). Descriptive statistics are shown in Table 1 . The mean (SD) age was 64.18 (7.62) years. There were 12,368 (49.64%) female participants. One-quarter of participants carried at least one APOE e4 allele. Half of the participants had moderate lifestyle, followed by favourable lifestyle (n = 10,977 /44.06%) and unfavourable lifestyle (n = 1,623/ 6.51%). The mean (SD) lifestyle score was 6.17 (1.71). APOE e4 carriers were more likely to have low sedentariness, more physical activity, better lifestyle scores, and larger uncorrected GM compared with non-carriers. Table 1 Characteristics of participants APOE e4 absent (N = 18426) APOE e4 present (N = 6486) P value n (%) n (%) Age in years (mean [SD]) 64.32(7.63) 63.78(7.56) < 0.001 Sex 0.313 Female 9113(49.46) 3255(50.19) Male 9313(50.54) 3231(49.81) BMI 0.090 Underweight 121(0.66) 62(0.96) Normal weight 7492(40.66) 2665(41.09) Obesity 7683(41.7) 2672(41.2) Overweight 3130(16.99) 1087(16.76) Sleep duration 0.125 Short sleep 4168(22.62) 1543(23.79) Normal sleep 14030(76.14) 4857(74.88) Long sleep 228(1.24) 86(1.33) Social isolated (Yes) 1492(8.1) 500(7.71) 0.321 Deprivation quintile 0.160 Q1 (least deprived) 3664(19.88) 1321(20.37) Q2 3712(20.15) 1274(19.64) Q3 3738(20.29) 1239(19.1) Q4 3668(19.91) 1314(20.26) Q5 (most deprived) 3644(19.78) 1338(20.63) Diabetes (Yes) 1029(5.58) 308(4.75) 0.010 Hypertension (Yes) 6026(32.70) 2057(31.71) 0.143 Depression (Yes) 2277(12.36) 808(12.46) 0.833 Smoking 0.085 Current smoker 540(2.93) 220(3.39) Former smoker 6343(34.42) 2276(35.09) Never 11543(62.65) 3990(61.52) Alcohol drinking 0.556 High risk 966(5.24) 318(4.90) Moderate risk 6497(35.26) 2304(35.52) Low risk 10963(59.50) 3864(59.57) Diet pattern 0.140 Unhealthy diet 3590(19.48) 1219(18.79) Moderate diet 12695(68.9) 4460(68.76) Healthy diet 2141(11.62) 807(12.44) Sedentariness 0.032 High duration 4781(25.95) 1610(24.82) Medium duration 7080(38.42) 2452(37.8) Low duration Physical activity 0.001 Low activity 6256(33.95) 2048(31.58) Medium activity 6128(33.26) 2181(33.63) High activity 6042(32.79) 2257(34.8) Lifestyle categories Unfavourable 1213(6.58) 410(6.32) 0.061 Moderate 9175(49.79) 3137(48.37) Favourable 8038(43.62) 2939(45.31) Lifestyle score (mean [SD]) 6.15(1.70) 6.22(1.72) 0.002 Brain MRI values (Mean [SD]) Grey matter volume (mm 3 ) 616596.40(55023.75) 618382.70(55223.91) 0.025 White matter volume (mm 3 ) 547944.70(61273.84) 548665.80(61560.75) 0.416 Total brain volume (mm 3 ) 1164541.00(110318.00) 1167049.00(110741.50) 0.116 Left hippocampal volume (mm 3 ) 3780.54(486.74) 3771.57(483.06) 0.201 Right hippocampal volume (mm 3 ) 3898.00(500.24) 3886.40(508.45) 0.110 Total hippocampus volume (mm 3 ) 7678.55(895.49) 7657.97(896.41) 0.112 Hippocampus differences (mm 3 ) 117.46(415.21) 114.83(424.51) 0.662 Median (IQR) White matter hyperintensities volume (mm 3 ) 2878.00(4307.00) 2844.00(4330.00) 0.870 Note: Brain MRI volumes are raw values (uncorrected for head size). Bold type P values indicates FDR significant (q < 0.05). APOE, aplipoprotein E; BMI, body mass index; MRI, magnetic resonance imaging; SD, standard deviation; IQR, interquartile range Associations between APOE e4 status and brain structural phenotypes As shown in Table 2 , there were significant associations between the presence of APOE e4 with smaller hippocampus (β ranges 0.042 to 0.048), and higher log WMH volume (β = 0.034) in the fully adjusted models. In terms of DTI, e4 was significantly associated with worse WM tract integrity: higher gISOVF (β = 0.032), and gMD (β = 0.040), lower gFA (β = -0.028) and gOD (β = -0.028) in the fully-adjusted model. A positive beta is worse for WMH volume, gMD, gOD and gISOVF whereas for other values negative betas are worse. The associations between the presence of APOE e4 and lower gOD and gFA did not survive after FDR correction. Table 2 Associations between APOE e4 genotype and brain structural phenotypes Partially adjusted 95% CI Fully adjusted 95% CI Brain structural phenotypes β Lower Upper P values β Lower Upper P values Grey matter 0.000 -0.010 0.009 0.950 -0.001 -0.010 0.008 0.824 White matter 0.000 -0.010 0.009 0.950 -0.002 -0.010 0.006 0.618 Total brain -0.001 -0.005 0.002 0.433 -0.002 -0.005 0.002 0.355 Left hippocampus -0.040 -0.065 -0.015 0.002 -0.042 -0.067 -0.016 0.001 Right hippocampus -0.044 -0.069 -0.019 0.001 -0.045 -0.070 -0.020 < 0.001 Whit matter hyperintensities 0.032 0.008 0.056 0.008 0.034 0.011 0.058 0.004 Total hippocampus -0.047 -0.071 -0.022 < 0.001 -0.048 -0.072 -0.023 < 0.001 Hippocampus differences 0.006 -0.022 0.035 0.663 0.005 -0.023 0.034 0.706 gFrontal -0.009 -0.026 0.007 0.257 -0.009 -0.026 0.007 0.262 gICVF -0.026 -0.054 0.001 0.058 -0.026 -0.053 0.002 0.065 gISOVF 0.033 0.006 0.059 0.016 0.032 0.006 0.059 0.016 gOD -0.028 -0.054 -0.001 0.041 -0.028 -0.054 -0.001 0.042 gFA -0.029 -0.056 -0.001 0.039 -0.028 -0.055 -0.001 0.043 gMD 0.040 0.015 0.066 0.002 0.040 0.014 0.065 0.002 Note: Partially adjusted: age, sex, genotypic array, four MRI assessment centres, first ten genetic principal components for general factors except for gFrontal; ICV additionally adjusted for the volumatric variables. Fully adjuested: additionally, lifestyle levels, deprivation index, BMI, sleep duration, isolated status, history of diabetes, hypertension, depression. Bold type P values indicates FDR significant (q < 0.05). gFA, general factors of fractional anisotropy; gMD, general factors of mean diffusivity; gICVF, general factors of intracellular volume fraction; gISOVF, general factors of isotropic volume fraction; gOD, general factors of orientation dispersion. Associations between risk factors and brain structural phenotypes Given the independent effects of each lifestyle factor, we investigated the associations between genetic, lifestyle factors and each brain structural metric in both individual and simultaneous modelling of lifestyle factors (i.e. each individually then simultaneously) in Fig. 1 . We found that the APOE e4 presence was associated with smaller hippocampus (β range [0.041] to [0.048]). Compared with each favourable level of lifestyle factor, unfavourable levels were mostly associated with poorer brain health. For example, significant associations were generally observed between current smoking, high risk drinking and poorer brain health, across GM and WM, as well as both traditional WM tract and NODDI metrics (standardised β range [0.014] to [0.209] for current smoking, and [0.030] to [0.152] for high-risk drinking). Unhealthy diet was associated with worse NODDI measures: higher gISOVF, and gOD (β range [0.065] to [0.081]). However, high sedentariness was associated with better GM health (β range [0.020] to [0.031]): larger GM, frontal lobe, and smaller left hippocampus (β range [0.014] to [0.037]), but poorer WM tract: higher WMH and gOD (β range [0.037] to [0.063]). Low levels of physical activity were associated with better brain health: lower WMH, gOD, and larger frontal lobe (β range [0.031] to [0.041]). Most significant associations survived from FDR correction (see Table S5 and S6 ). Associations between lifestyle score/levels and brain structural phenotypes As shown in Table 3 , a 1 -point better lifestyle score was associated with better brain health: larger GM (β = 0.006), total brain (β = 0.002), left/right hippocampus and total hippocampus (β range [0.012] to [0.014]) and smaller WMH volume (β = -0.017) in the fully adjusted models. Notably, 1-point higher was also associated with smaller WM in the partially adjusted model but became non-significant after full adjustment. All significant associations survived FDR correction in the full adjustment models. Table 3 Associations between lifestyle score and brain structural phenotypes Partially adjusted 95% CI Fully adjusted 95% CI Brain structural phenotypes β Lower Upper P values β Lower Upper P values Grey matter 0.011 0.009 0.013 < 0.001 0.006 0.003 0.008 < 0.001 White matter -0.005 -0.007 -0.003 < 0.001 -0.001 -0.003 0.001 0.321 Total brain 0.003 0.002 0.004 < 0.001 0.002 0.001 0.003 < 0.001 Left hippocampus 0.016 0.009 0.022 < 0.001 0.013 0.006 0.020 < 0.001 Right hippocampus 0.013 0.006 0.019 < 0.001 0.012 0.005 0.019 0.001 Whit matter hyperintensities -0.035 -0.041 -0.028 < 0.001 -0.017 -0.024 -0.011 < 0.001 Total hippocampus 0.016 0.009 0.022 < 0.001 0.014 0.007 0.021 < 0.001 Hippocampus differences 0.003 -0.005 0.010 0.437 0.001 -0.007 0.009 0.850 gFrontal 0.002 -0.002 0.006 0.403 0.001 -0.003 0.006 0.593 gICVF -0.002 -0.010 0.005 0.535 0.000 -0.007 0.008 0.911 gISOVF -0.002 -0.009 0.005 0.546 -0.001 -0.008 0.006 0.847 gOD -0.006 -0.013 0.001 0.101 -0.003 -0.010 0.004 0.448 gFA 0.003 -0.004 0.011 0.349 0.000 -0.008 0.007 0.898 gMD -0.005 -0.011 0.002 0.187 -0.005 -0.012 0.002 0.124 Note: Partially adjusted: age, sex, genotypic array, four MRI assessment centres, first ten genetic principal components for general factors except for gFrontal; ICV additionally adjusted for the volumatric variables. Fully adjuested: additionally, APOE4 status, deprivation index, BMI, sleep duration, isolated status, history of diabetes, hypertension, depression. Bold type P values indicates FDR significant (q < 0.05). gFA, general factors of fractional anisotropy; gMD, general factors of mean diffusivity; gICVF, general factors of intracellular volume fraction; gISOVF, general factors of isotropic volume fraction; gOD, general factors of orientation dispersion. Associations between lifestyle levels and brain imaging phenotypes are reported in Fig. 2 . Compared with participants with favourable lifestyles, those with unfavourable and moderate lifestyles were independently associated with smaller GM, total brain and hippocampus volumes (β range [0.004] to [0.096]). Of them, those with unfavourable lifestyle was associated with a lower degree of a variety of brain structural markers compared with those with moderate lifestyle. Moreover, unfavourable lifestyle was also associated with poorer WM measures: lower gFA (β = -0.059, 95% CI = -0.110 to -0.007, P = 0.025), higher gMD (β = 0.083, 95% CI = 0.034 to 0.131, P = 0.004) and WMH (β = 0.111, 95% CI = 0.067 to 0.156, P < 0.001). Interaction between lifestyle level and APOE e4 presence The interactions between lifestyle levels (i.e. favourable; moderate; unfavourable) and APOE e4 status on brain MRI metrics were all non-significant (see Table S7 ). There were four significant interactions for each lifestyle component by APOE e4 presence shown in Table S8 . However, no interactions survived correction for FDR (see Table S8 and S9 ). Discussion This is the first study to examine the potentially interactive associations of APOE e4 genotype and lifestyle factors with brain structural phenotypes. We found that the APOE e4 presence was associated with smaller hippocampus, and worse WM tracts assessed several ways. Less favourable lifestyle was associated with worse brain health across GM and hippocampus. A 1-point better (higher) lifestyle score was associated with better brain health. We provide insights into the relative contributions of different lifestyle factors to brain health: current smoking and high-risk drinking were associated with poorer GM and WM measures, and notably, high duration sedentariness was associated with better GM and poorer WM tracts. Previous literature Although some studies reported null associations between APOE e4 and hippocampus volumes (N = 52 to 8,395) [ 21 , 37 ], we found significant associations between the carriers of APOE e4 and smaller hippocampus in a relatively large sample size (N = 24,912) considering the impact of relatively comprehensive lifestyle factors. This finding aligns with previous research showing the influence of the e4 allele on hippocampus atrophy [ 38 ]. APOE is known as a highly pleiotropic genetic locus and may influence brain health through a variety of pathways including neuroinflammation, inhibition of the ‘heart-brain loop’ via atherosclerotic processes, and the promotion/inhibited clearance of amyloid beta (which characterises AD)[ 39 ]. A positive relationship with the APOE e4 and WMH was found, being consistent with our previous research in an earlier sub-sample of UK Biobank (N = 8,395) [ 21 ]. Our findings reinforce the possibility that APOE e4 contributes to cognitive decline partly through a cerebrovascular-type pathway [ 40 ]. In a systematic analysis of APOE e4 genotype versus blood biomarkers in UK Biobank, Ferguson et al. reported a predominantly cardiometabolic influence of APOE e4 genotype on non-demented UK Biobank participants (N = ~ 502k), which aligns with this WMH observation[ 41 ]. NODDI metrics were applied to characterise the relationship of APOE e4 on WM microstructure. We found that APOE e4 was associated with lower WM integrity using measures including FA and MD, ISOVF, indicating less restricted diffusion, representing neuronal loss or inflammation in WM. This is in line with previous research[ 42 ], but that study did not investigate the impact of lifestyle, and prior evidence showed that an unhealthier lifestyle was associated with worse cognition[ 43 ]. We found a cumulative effect such that participants with one point higher/better in lifestyle score were associated with better brain health, being partly consistent with the previous research[ 44 ]. This study showed participants with higher unhealthier lifestyle scores were associated with lower GM, but null associations with total hippocampus. In the current study, we affirmed the associations of better lifestyles with larger GM, and additionally, larger total hippocampus and total brain, and smaller WMH. A cross-sectional study of PRECISE also showed that healthier participants adopting four or five healthy lifestyle factors were associated with better brain health: larger GM, total brain, and smaller WMH[ 18 ]. Our results also suggest that participants with less healthy lifestyle levels were associated with worse brain health, being consistent with prior studies[ 20 ]. For example, a study in UK Biobank showed the relation of aggregate vascular risk factors (three lifestyle- and four health-based factors) with lower GM and higher WMH[ 20 ]. Generally, compared with these studies, the current study emphasised up to 14 brain structural measurements, which provided comprehensive evidence on the associations of five prevalent modifiable lifestyle factors with the consideration of genetic effects. All lifestyle factors showed significant associations with brain structural phenotypes even considering the mutual effects of each other. For example, current smoking, high risk of alcohol drinking, and unhealthy diet were associated with smaller brain volumes/integrity, being consistent with previous research[ 15 – 17 ]. This is because smoking may accelerate brain ageing due to atherosclerotic processes i.e. thickening of arteries[ 45 ]. Heavy alcohol intake is associated with the risk of dementia potentially due to the nutritional deficiency, the direct neurotoxic effects of ethanol, and the indirect negative impacts through increasing the risk of cardiometabolic diseases (including diabetes, hypertension, and stroke)[ 46 ]. Unhealthy diet is associated with cardiovascular disease, sharing the same risk factors with cognitive disease [ 2 , 28 ]. Moreover, high sedentariness and low levels of physical activity were associated with better brain health, conflicting with previous evidence[ 47 , 48 ]. This could be explained by people with healthier brains may naturally engage in more sedentary activities or occupations, such as cognitive engagement having protective effects on the brain, counteracting the lack of physical activity. Hence, their brain health may allow them to be less physically active without suffering adverse effects. Engaging in physical activity improves cardiorespiratory fitness and cardiovascular heath, which might protect from cognitive decline with age and brain atrophy[ 49 ]. Prolonged sedentary behaviour impairs glucose and lipid metabolism, which are regarded as risk factors for cognitive decline and all-cause dementia[ 50 ]. Further, the upper limit of low levels of MET (1428 MET-min/week) in the current study was much higher than the physical activity recommendations (i.e. ≥ 600 MET) [ 51 ], indicating that participants with medium or high levels of MET might be associated with much better brain health in other studies. Few studies have explored the relationship between each or overall lifestyle factors and NODDI with the consideration of APOE e4 presence. Current smokers were related with higher ISOVF and MD, and lower FA, being inconsistent with another study reporting null associations[ 52 ]. High risk drinking was associated with lower ICVF, in line with previous research[ 53 ]. Yet, the sample sizes in those studies were small (N = 34 to 44), and evidence from large sample sizes is warranted. We also found that high duration sedentariness was associated with better GM health but poorer WM microstructure. This could be explained by vascular and metabolic differences: GM areas might receive adequate blood flow and metabolic support through cognitive activities attributed to high level of sedentary behaviours. This highlights the complex and multifaceted relationship between different types of brain tissue and lifestyle factors, which needs further research. There were no statistically significant interactions between lifestyle levels and APOE e4 on brain MRI metrics, generally aligning with our previous study on cognitive abilities[ 43 ], which could be reflected by brain structures[ 54 ]. A few significant interactions of certain level of lifestyle factors existed. For instance, high risk drinking with the APOE e4 on larger left hippocampus rather right hippocampus, being consistent with previous research with a small sample size (n = 16) [ 55 ]. Further research is needed within the larger sample size. Implications Both genetic and environmental factors play an important role in contributing to brain health. For example, the APOE e4 presence and unhealthy modifiable lifestyle factors are generally associated with worse brain health[ 18 , 21 ]. Our findings suggest that lifestyle interventions may benefit brain health regardless of APOE4 status. Public health strategies promoting healthy habits across populations could mitigate neurodegenerative risks, while targeted approaches for publicly acknowledged high-risk subgroups (e.g., APOE4 homozygotes) warrant further investigation, such as smoking cessation, reducing alcohol intake, adopting healthy diets, decreasing sedentary behaviour, and maintaining regular physical activity. By integrating genetic screening with targeted lifestyle interventions, it is possible to delay the onset of cognitive impairment and dementia and other neurodegenerative conditions. Limitations First, the cross-sectional study design may limit the possibility of casual inference between lifestyle factors, genetics and brain structures; a longitudinal within-participants design may be more informative. Second, the UK Biobank imaging sample shows a tendency to live in less deprived areas than other UK Biobank participants, who are already range-restricted compared with the general population, which may limit generalizability. Further research needs to be conducted in more general populations. Third, although we have excluded participants with at least one neurological condition, those with cognitive impairments were not specifically identified in this population-based sample. The results in our study might be overestimated as much evidence shows that cognitive impairment was associated with worse brain imaging biomarkers compared with healthy ones[ 56 ]. Fourth, information on lifestyle factors were self-reported, which might result in overestimated or underestimated associations. Conclusion APOE e4 and unfavourable lifestyles were consistently associated with poorer brain health after adjustment for confounders. Our findings show that lifestyle has a strong, consistent relationship with multiple aspects of brain health assessed with detailed imaging, and these associations manifest regardless of APOE e4 genotypic status. These findings warrant additional large-scale longitudinal studies to confirm and investigate associations across more brain structural phenotypes. Declarations Acknowledgements The authors thank all participants and staff of the UK Biobank study. This work uses data provided by patients and collected by the NHS as part of their care and support. The UK Biobank was established by the Wellcome Trust, Medical Research Council, Department of Health, Scottish Government and Northwest Regional Development Agency. UK Biobank has also had funding from the Welsh Assembly Government and the British Heart Foundation. Author contributions Study concept: YY, FKH, DML, CEH. Obtained data: DML, JW. Design: YY, FKH, DML, CEH. Conducted analyses: YY. Drafted original manuscript: YY. Reviewed for intellectual content: all co-authors. Funding YY acknowledges financial support from China Scholarship Council. Competing interests The authors have no conflicts of interest to disclose. Additional information Supplementary information is available at website. References WHO. Dementia. 2023. https://www.who.int/news-room/fact-sheets/detail/dementia. Accessed May 21 2024. Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. The Lancet. 2020;396(10248):413-46. Serrano-Pozo A, Das S, Hyman BT. APOE and Alzheimer's disease: advances in genetics, pathophysiology, and therapeutic approaches. The Lancet Neurology. 2021;20(1):68-80. Gordon BA, Blazey TM, Su Y, Hari-Raj A, Dincer A, Flores S, et al. Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer's disease: a longitudinal study. 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Metabolic equivalents (METS) in exercise testing, exercise prescription, and evaluation of functional capacity. Clinical cardiology. 1990;13(8):555-65. Alfaro-Almagro F, Jenkinson M, Bangerter NK, Andersson JL, Griffanti L, Douaud G, et al. Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage. 2018;166:400-24. Donix M, Burggren AC, Scharf M, Marschner K, Suthana NA, Siddarth P, et al. APOE associated hemispheric asymmetry of entorhinal cortical thickness in aging and Alzheimer's disease. Psychiatry Research: Neuroimaging. 2013;214(3):212-20. Obesity WHOCo, World Health O. WHO technical report series ; 894 (World Health Organization, Geneva, 2000). Ferrie JE, Shipley MJ, Cappuccio FP, Brunner E, Miller MA, Kumari M, et al. A Prospective Study of Change in Sleep Duration: Associations with Mortality in the Whitehall II Cohort. Sleep. 2007;30(12):1659-66. Foster HME, Gill JMR, Mair FS, Celis-Morales CA, Jani BD, Nicholl BI, et al. Social connection and mortality in UK Biobank: a prospective cohort analysis. BMC Medicine. 2023;21(1):384. Townsend P, Phillimore P, Beattie A. Health and deprivation: inequality and the North . Routledge; 2023. Lyall DM, Royle NA, Harris SE, Bastin ME, Maniega SM, Murray C, et al. Alzheimer’s Disease Susceptibility Genes APOE and TOMM40, and Hippocampal Volumes in the Lothian Birth Cohort 1936. PLoS ONE. 2013;8(11):e80513. Jak AJ, Houston WS, Nagel BJ, Corey-Bloom J, Bondi MW. Differential cross-sectional and longitudinal impact of APOE genotype on hippocampal volumes in nondemented older adults. Dementia and geriatric cognitive disorders. 2007;23(6):382-89. Jack CR, Jr, Vemuri P, Wiste HJ, Weigand SD, Lesnick TG, Lowe V, et al. Shapes of the Trajectories of 5 Major Biomarkers of Alzheimer Disease. Archives of Neurology. 2012;69(7):856-67. Riedel BC, Thompson PM, Brinton RD. Age, APOE and sex: Triad of risk of Alzheimer’s disease. J Steroid Biochem Mol Biol. 2016;160:134-47. Leys D, Englund E, Del Ser T, Inzitari D, Fazekas F, Bornstein N, et al. White matter changes in stroke patients. Relationship with stroke subtype and outcome. Eur Neurol. 1999;42(2):67-75. Ferguson AC, Tank R, Lyall LM, Ward J, Celis-Morales C, Strawbridge R, et al. Alzheimer’s Disease Susceptibility Gene Apolipoprotein E (APOE) and Blood Biomarkers in UK Biobank (N = 395,769). Journal of Alzheimer's Disease. 2020;76(4):1541-51. Nir TM, Nabulsi L, Lawrence KE, Villalon‐Reina JE, Abaryan Z, Gari IB, et al. Effect of APOE4 and APOE2 genotype on white matter microstructure. Alzheimer's & Dementia. 2021;17:e053061. Lyall DM, Celis-Morales C, Lyall LM, Graham C, Graham N, Mackay DF, et al. Assessing for interaction between APOE ε4, sex, and lifestyle on cognitive abilities. Neurology. 2019;92(23):e2691-e98. Binnewies J, Nawijn L, Brandmaier AM, Baaré WFC, Boraxbekk C-J, Demnitz N, et al. Lifestyle-related risk factors and their cumulative associations with hippocampal and total grey matter volume across the adult lifespan: A pooled analysis in the European Lifebrain consortium. Brain Research Bulletin. 2023;200:110692. Linli Z, Feng J, Zhao W, Guo S. Associations between smoking and accelerated brain ageing. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 2022;113:110471. Sabia S, Fayosse A, Dumurgier J, Dugravot A, Akbaraly T, Britton A, et al. Alcohol consumption and risk of dementia: 23 year follow-up of Whitehall II cohort study. BMJ. 2018;362:k2927. Erickson KI, Hillman CH, Kramer AF. Physical activity, brain, and cognition. Current Opinion in Behavioral Sciences. 2015;4:27-32. Maasakkers CM, Weijs RWJ, Dekkers C, Gardiner PA, Ottens R, Olde Rikkert MGM, et al. Sedentary behaviour and brain health in middle-aged and older adults: A systematic review. Neuroscience & Biobehavioral Reviews. 2022;140:104802. Scarapicchia V, MacDonald S, Gawryluk JR. The relationship between cardiovascular risk and lifestyle activities on hippocampal volumes in normative aging. Aging Brain. 2022;2:100033. Falck RS, Davis JC, Liu-Ambrose T. What is the association between sedentary behaviour and cognitive function? A systematic review. British journal of sports medicine. 2017;51(10):800-11. Guo W, Bradbury KE, Reeves GK, Key TJ. Physical activity in relation to body size and composition in women in UK Biobank. Annals of epidemiology. 2015;25(6):406-13. e6. Francis AN, Sebille S, Whitfield-Gabrieli S, Camprodon JA. Multimodal 7T imaging reveals enhanced functional coupling between salience and frontoparietal networks in young adult tobacco cigarette smokers. Brain Imaging and Behavior. 2024. Yoder KK, Chumin EJ, Mustafi SM, Kolleck KA, Halcomb ME, Hile KL, et al. Effects of acute alcohol exposure and chronic alcohol use on neurite orientation dispersion and density imaging (NODDI) parameters. Psychopharmacology. 2023;240(7):1465-72. Oschwald J, Guye S, Liem F, Rast P, Willis S, Röcke C, et al. Brain structure and cognitive ability in healthy aging: a review on longitudinal correlated change. Reviews in the Neurosciences. 2019;31(1):1-57. Beresford TP, Arciniegas DB, Alfers J, Clapp L, Martin B, Du Y, et al. Hippocampus volume loss due to chronic heavy drinking. Alcoholism: Clinical and Experimental Research. 2006;30(11):1866-70. Ries ML, Carlsson CM, Rowley HA, Sager MA, Gleason CE, Asthana S, et al. Magnetic Resonance Imaging Characterization of Brain Structure and Function in Mild Cognitive Impairment: A Review. Journal of the American Geriatrics Society. 2008;56(5):920-34. Additional Declarations There is NO conflict of interest to disclose. 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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-6297741","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":435029069,"identity":"90870279-d729-47fd-9a97-f8fe4502b90b","order_by":0,"name":"Yating You","email":"","orcid":"","institution":"University of Glasgow","correspondingAuthor":false,"prefix":"","firstName":"Yating","middleName":"","lastName":"You","suffix":""},{"id":435029070,"identity":"675345e9-d8a4-423d-b95b-7e642c1af4bf","order_by":1,"name":"Frederick Ho","email":"","orcid":"","institution":"University of 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2","display":"","copyAsset":false,"role":"figure","size":403816,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between lifestyle levels and brain structural phenotypes\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6297741/v1/606e7396c385ff181d18ab57.png"},{"id":81710805,"identity":"79d1520a-50ba-49eb-be55-9b7ef494c54e","added_by":"auto","created_at":"2025-04-30 14:30:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2128874,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6297741/v1/b7b95d5a-91ad-413a-b09a-8a050198f9a7.pdf"},{"id":80755390,"identity":"bd8b8efe-c37d-4314-8259-2668b9ce234e","added_by":"auto","created_at":"2025-04-16 17:40:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":196543,"visible":true,"origin":"","legend":"Supplemental material","description":"","filename":"6Suppleinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6297741/v1/9048bde3e47e34223765357a.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Testing for associations and interactions between \u003ci\u003eAPOE\u003c/i\u003e e4 genotype and lifestyle, with brain structural imaging phenotypes in UK Biobank","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDementia leads to progressive deterioration in cognition function, along with ability loss in daily activities, and is the seventh leading cause of death globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Around 50\u0026nbsp;million people suffered from dementia in 2020, and it is estimated to increase to 152\u0026nbsp;million by 2050[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It gives individuals and their families economic burdens where over 1.3 trillion US dollars were spent on treatments and care globally annually [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, no effective treatments are currently available for dementia. Efforts to understand the contributions of modifiable risk factors are priorities to prevent dementia.\u003c/p\u003e \u003cp\u003e \u003cem\u003eApolipoprotein E\u003c/em\u003e gene (\u003cem\u003eAPOE\u003c/em\u003e) e4 allele is known as the strongest common genetic risk factor for Alzheimer\u0026rsquo;s disease (AD) dementia [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Even among healthy people with \u003cem\u003eAPOE\u003c/em\u003e e4, brain structural differences can be observed and these may be considered markers of pre-symptomatic AD years before disease onset [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The hippocampus is a known brain region associated with memory loss, as one of the key symptoms of AD [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The potential biological rationale is that the \u003cem\u003eAPOE\u003c/em\u003e locus regulates lipid metabolism, impacting brain-related factors such as WM myelination and neuronal repair[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although significant associations of \u003cem\u003eAPOE\u003c/em\u003e e4 were reported on decreased volumes of hippocampus and white matter (WM) structures[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], null associations were also reported[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. \u003cem\u003eAPOE\u003c/em\u003e e4 elevates resting state connectivity and the balance between brain networks and higher hippocampal activation, which could contribute to increased Aβ production[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Increased hippocampal activity was associated with reduced cortical thickness in the medial temporal lobe and brain regions that are vulnerable to AD pathology[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Consequently, individuals carrying the e4 allele may be more susceptible and vulnerable to the adverse effects of suboptimal lifestyle risk factors [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Hence, further research is needed on the relationship between \u003cem\u003eAPOE\u003c/em\u003e e4 and lifestyle factors in brain health.\u003c/p\u003e \u003cp\u003ePeople with healthy lifestyle, such as never smoking, light alcohol drinking, healthy diet, adequate physical activity and less sedentary behaviour, may reduce risk of dementia[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. High genetic risk of dementia can be offset by higher adherence to healthy lifestyle[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The relationship between individual lifestyle factors and brain health metrics has been well-demonstrated, for example, smoking was associated with smaller grey matter (GM) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and chronic alcohol use with disruption of WM tracts[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], as well as unhealthy diet with decreased brain volume and connectivity[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, these studies did not investigate the complex effects of multiple lifestyle factors on brain structural phenotypes. Only one cross-sectional study including 2,413 participants in PRECISE study and 19,822 participants in UK biobank showed significant associations between a healthier lifestyle and lower degree of five limited brain imaging markers (total brain, GM, WM, hippocampus, and WM hyperintensities volumes) without considering any genetic impact though [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. It is important to understand to what extent lifestyle factors contribute to brain structural differences in non-demented older people, between carriers and non-carriers of e4, which might aid the development of corresponding interventions for better brain health.\u003c/p\u003e \u003cp\u003eNeuroimaging markers can detect evidence of cognition impairment and dementia [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Associations between risk factors and traditional measures of WM integrity tract with diffusion tensor imaging (DTI) have been examined, including decreased fractional anisotropy (FA: the directional coherence of water molecule diffusion) and increased mean diffusivity (MD: the magnitude of water molecule diffusion) [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Compared with traditional measures, advances in WM integrity phenotypes - neurite orientation dispersion and density imaging (NODDI) have been shown more sensitive and specific to clinical outcomes and target pathologies than traditional ones. NODDI provides estimates of neurite density, diffusion of water molecules in the free-water component, and neurite orientation dispersion using intracellular volume fraction (ICVF), isotropic volume fraction (ISOVF), and orientation dispersion (OD), respectively[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Better brain health is anticipated to have a negative correlation with both ISOVF and OD, and a positive correlation with ICVF, which can reflect the dendritic microstructural arrangement and the composition of GM [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn general, the associations of \u003cem\u003eAPOE\u003c/em\u003e e4 genotype and lifestyle with brain structures have been explored in small samples with inconsistent findings, and the role of certain imaging methods e.g. DTI-NODDI remain unexplored. Hence, this study aimed to examine the associations between \u003cem\u003eAPOE\u003c/em\u003e e4 status and comprehensive lifestyle \u0026ndash; and their potential interaction - with multiple brain structural phenotypes among non-demented 24,912 participants from UK Biobank.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eStudy design and participants\u003c/p\u003e \u003cp\u003eThe UK Biobank is a large community-based cohort with 502,359 participants aged 37\u0026ndash;73 years[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. All UK Biobank participants provided written informed consent, and the North West Multi-Centre Ethics Committee granted ethical approval. In 2014, brain imaging began, and this is ongoing until the aim of 100,000 participants have been scanned. As of the time of analysis (data last updated September 2023), a total of 71,091 participants aged 44\u0026ndash;85 years had attended imaging assessment centres and completed touchscreen questionnaires to provide a series of physical, sociodemographic, and medical information. Two-thirds of them had available derived brain MRI phenotype data. We excluded participants who reported chronic neurological diseases which could directly affect cognitive function (see \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). This research was conducted using UK Biobank application number 17689.\u003c/p\u003e \u003cp\u003eGenetic data\u003c/p\u003e \u003cp\u003eUK Biobank genotyping was conducted by Affymetrix using a bespoke BiLEVE Axiom array for about 50,000 participants and the remaining around 450,000 on the Affymetrix UK Biobank Axiom array. All genetic data were quality controlled by UK Biobank [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The \u003cem\u003eAPOE\u003c/em\u003e e genotype is directly genotyped. Further information on the genotyping process is available (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ukbiobank.ac.uk/scientists-3/genetic-data\u003c/span\u003e\u003cspan address=\"http://www.ukbiobank.ac.uk/scientists-3/genetic-data\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ). \u003cem\u003eAPOE\u003c/em\u003e e4 presence (verses non e4 alleles as absence) was derived based on two directly genotyped single nucleotide polymorphisms (rs7412 and rs429358).\u003c/p\u003e \u003cp\u003eLifestyle factors and score\u003c/p\u003e\u003cp\u003eAvailable data for lifestyle factors in UK Biobank were used to generate an unweighted lifestyle score using smoking status, alcohol drinking, diet, physical activity, and sedentary behaviour. Lifestyle variables comprising the score were derived from questionnaire responses (\u003cstrong\u003eTable S2\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSmoking status was classified as never, former, and current smoker based on self-reporting. Alcohol drinking was reported as types of alcohol per week and grouped based on national guidelines: 0\u0026ndash;14 units average weekly alcohol units as low risk of diseases; 15\u0026ndash;35 units for females or 15\u0026ndash;50 units for males as moderate risk; \u0026gt; 35 units for females and \u0026gt; 50 units for males as high risk[27]. Diet was categorized as healthy, moderate, and unhealthy diet based on a developed diet score [28]. Briefly, this score was constructed by the unweighted sum of adherence to the UK dietary guideline, including: processed meat, red meat individually less than twice per week, total fish more than twice per week, consumption of semi- and skimmed, soya milk, no spread intake, more than five bowls of cereal intake, no salt added to food, more than six glasses of water and more than five servings per day of fruits and vegetables. Sedentary behaviour duration and metabolic equivalents task units (METs) for physical activity were classified as low, medium, and high levels according to tertiles[29]. Tertile was used as there is currently no guideline for sedentary behaviour, and the WHO guideline for physical activity would result in a highly skewed distribution.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe assigned two points for membership of each respective healthiest category: never smoking; low risk drinking; healthy diet; low duration sedentariness; high level physical activity. For example, someone who never smoked and had low risk drinking would receive four points. One point was assigned for each of less healthy, intermediate lifestyles, such as former smoking, moderate risk drinking, moderate diet, medium duration sedentariness, and medium level physical activity, and 0 for the least healthy ones. The intercorrelations among five lifestyle factors are shown in \u003cstrong\u003eTable S3\u003c/strong\u003e. The lifestyle score ranged from 0 to 10, indicating a better lifestyle with a higher score. Participants who scored 0, 1, 2 or 3 were classed as unfavourable lifestyle; 4, 5, or 6 as moderate lifestyle; 7, 8, 9, or 10 as favourable lifestyle (see \u003cstrong\u003eFigure S1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eImaging data\u003c/p\u003e\n\u003cp\u003eAll brain MRI data were acquired at four sites (Cheadle, Newcastle, Reading and Bristol) on the same 3T Siemens Skyra scanner, according to a freely available protocol (http://www.fmrib.ox.ac.uk/ukbiobank/protocol/V4_23092014.pdf ), documentation (http://biobank.ctsu. ox.ac.uk/crystal/docs/brain_mri.pdf ), and publication [30]. The primary outcomes were differences in eight volumetric variables of total GM, WM, total WM hyperintensity (WMH), total brain, left hippocampus, right hippocampus, (summed to generate) total hippocampus, hippocampus volumetric asymmetry (left minus right) [31],and six general components of WM integrity assessed with DTI by APOE e4 status and lifestyle levels groups. \u0026nbsp;All variables were visually inspected to ascertain whether they were distributed normally. WMH volume was log-transformed before statistical analyses due to the positively skewed distribution. Principal components analysis was used to produce unrotated general components for each of the five WM microstructural measurements (FA, MD, ICVF, ISOVF and OD) based on 27 tracts, and general volumes of frontal lobe GM described in \u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eS4\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The six general components were gFA (eigenvalue=12.3, 44.9% variance explained) gMD (eigenvalue=13.4, 49.8% variance explained), gICVF (eigenvalue=18.6, 68.9% variance explained), gISOVF (eigenvalue=10.1, 37.4% variance explained), gOD (eigenvalue=7.3, 26.9% variance explained) and gFrontal (eigenvalue=6.9, 43.3% variance explained). All brain MRI metrics were raw and transformed into Z-scores based on the final analysis sample to ease interpretation (i.e. similar to standardised betas on a per-SD scale). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCovariates\u003c/p\u003e\n\u003cp\u003eCovariates were collected when participants attended their first MRI assessment. Demographics were age at the time of imaging and sex. Body mass index (BMI) was categorized as underweight (\u0026lt;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e), normal weight (18.5-24.9 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (25.0-29.9 kg/m\u003csup\u003e2\u003c/sup\u003e), and obesity (\u0026ge;30 kg/m\u003csup\u003e2\u003c/sup\u003e) [32]. Sleep duration was assessed with the question \u0026ldquo;About how many hours of sleep do you get in every 24h?\u0026rdquo; and then grouped into three groups: short (\u0026lt;7h), normal (7\u0026minus;9h), and long sleep (\u0026gt;9h) because of potential non-linear association [33]. Socially isolated status was categorized as \u0026ldquo;yes\u0026rdquo; or \u0026ldquo;no\u0026rdquo; by assessing one functional and two structural component measures of social connections [34]. Townsend deprivation score was derived from the postcode of residence and categorized as quintiles [35]. \u0026nbsp;Higher scores indicated higher levels of area-based socioeconomic deprivation. Participants were asked whether they were diagnosed with each of diabetes, hypertension or depression. Genetic arrays and ten genetic principal components (PCs) for stratification were also selected.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eAn \u003cem\u003eAPOE\u003c/em\u003e e4 dominant model (presence vs. absence) was used based on previous research [21]. Characteristics were summarized across the \u003cem\u003eAPOE\u003c/em\u003e e4 presence with the Chi-square tests for categorical variables and t-tests for normally distributed continuous variables.\u003c/p\u003e\n\u003cp\u003eMultiple linear regression analyses were performed to determine whether brain metrics were significantly different across the \u003cem\u003eAPOE\u0026nbsp;\u003c/em\u003ee4 status or lifestyle groups. Standardized betas (i.e. on a per-SD [standard deviation] scale of effect) and 95% confidence intervals (CIs) were reported. First, the associations between \u003cem\u003eAPOE\u003c/em\u003e e4 presence and each of the brain imaging phenotypes were examined. Second, the associations between lifestyle and brain phenotypes were investigated in four ways: continuous lifestyle score, three-level lifestyle including unfavourable, moderate, favourable ones, and three-level individual lifestyle factor. Third, interactions between \u003cem\u003eAPOE\u003c/em\u003e e4 status with lifestyle levels, and each lifestyle were also tested.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll brain volumetric variables were adjusted for using intracranial volume (ICV) that was generated by summing the volume of WM,\u0026nbsp;GM, and ventricular cerebrospinal fluid\u0026nbsp;[36].\u0026nbsp;Two adjustment models were performed to explore whether different confounders impacted the associations. The first model was partially adjusted for age, sex, genotypic array, MRI assessment centre, and 10 PCs for genetic stratification; ICV additionally adjusted for volumetric variables including gFrontal. The second model was fully adjusted: additionally, Townsend deprivation score, BMI, sleep duration, isolated status, historic diabetes, hypertension, and self-reported depression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using Stata 18.0 and R (version 4.2.3). A two-tailed P \u0026lt; 0.05 was considered statistical significance. Because of multiple testing, we also present the False Discovery Rate (FDR) corrected p-values.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eCharacteristics of participants\u003c/p\u003e \u003cp\u003eA total of 24,912 participants had complete lifestyle factors, genetics, brain imaging biomarkers and covariates at their first MRI assessment (see \u003cb\u003eFigure S2\u003c/b\u003e). Descriptive statistics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean (SD) age was 64.18 (7.62) years. There were 12,368 (49.64%) female participants. One-quarter of participants carried at least one \u003cem\u003eAPOE\u003c/em\u003e e4 allele. Half of the participants had moderate lifestyle, followed by favourable lifestyle (n\u0026thinsp;=\u0026thinsp;10,977 /44.06%) and unfavourable lifestyle (n\u0026thinsp;=\u0026thinsp;1,623/ 6.51%). The mean (SD) lifestyle score was 6.17 (1.71). \u003cem\u003eAPOE\u003c/em\u003e e4 carriers were more likely to have low sedentariness, more physical activity, better lifestyle scores, and larger uncorrected GM compared with non-carriers.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e e4 absent (N\u0026thinsp;=\u0026thinsp;18426)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e e4 present (N\u0026thinsp;=\u0026thinsp;6486)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge in years (mean [SD])\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.32(7.63)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.78(7.56)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9113(49.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3255(50.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9313(50.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3231(49.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e121(0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62(0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7492(40.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2665(41.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7683(41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2672(41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3130(16.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1087(16.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep duration\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort sleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4168(22.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1543(23.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal sleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14030(76.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4857(74.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong sleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e228(1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86(1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial isolated (Yes)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1492(8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e500(7.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeprivation quintile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1 (least deprived)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3664(19.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1321(20.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3712(20.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1274(19.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3738(20.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1239(19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3668(19.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1314(20.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ5 (most deprived)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3644(19.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1338(20.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes (Yes)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1029(5.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e308(4.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension (Yes)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6026(32.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2057(31.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDepression (Yes)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2277(12.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e808(12.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e540(2.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e220(3.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6343(34.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2276(35.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11543(62.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3990(61.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol drinking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e966(5.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e318(4.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6497(35.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2304(35.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10963(59.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3864(59.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiet pattern\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnhealthy diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3590(19.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1219(18.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12695(68.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4460(68.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthy diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2141(11.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e807(12.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSedentariness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4781(25.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1610(24.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7080(38.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2452(37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical activity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6256(33.95)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2048(31.58)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6128(33.26)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2181(33.63)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6042(32.79)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2257(34.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLifestyle categories\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnfavourable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1213(6.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e410(6.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9175(49.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3137(48.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFavourable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8038(43.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2939(45.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLifestyle score (mean [SD])\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6.15(1.70)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e6.22(1.72)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBrain MRI values (Mean [SD])\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrey matter volume (mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e616596.40(55023.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e618382.70(55223.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite matter volume (mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e547944.70(61273.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e548665.80(61560.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal brain volume (mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1164541.00(110318.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1167049.00(110741.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft hippocampal volume (mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3780.54(486.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3771.57(483.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight hippocampal volume (mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3898.00(500.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3886.40(508.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal hippocampus volume (mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7678.55(895.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7657.97(896.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHippocampus differences (mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e117.46(415.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114.83(424.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedian (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite matter hyperintensities volume (mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2878.00(4307.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2844.00(4330.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Brain MRI volumes are raw values (uncorrected for head size). Bold type P values indicates FDR significant (q\u0026thinsp;\u0026lt;\u0026thinsp;0.05). APOE, aplipoprotein E; BMI, body mass index; MRI, magnetic resonance imaging; SD, standard deviation; IQR, interquartile range\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAssociations between \u003cem\u003eAPOE\u003c/em\u003e e4 status and brain structural phenotypes\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, there were significant associations between the presence of \u003cem\u003eAPOE\u003c/em\u003e e4 with smaller hippocampus (β ranges 0.042 to 0.048), and higher log WMH volume (β\u0026thinsp;=\u0026thinsp;0.034) in the fully adjusted models. In terms of DTI, e4 was significantly associated with worse WM tract integrity: higher gISOVF (β\u0026thinsp;=\u0026thinsp;0.032), and gMD (β\u0026thinsp;=\u0026thinsp;0.040), lower gFA (β = -0.028) and gOD (β = -0.028) in the fully-adjusted model. A positive beta is worse for WMH volume, gMD, gOD and gISOVF whereas for other values negative betas are worse. The associations between the presence of \u003cem\u003eAPOE\u003c/em\u003e e4 and lower gOD and gFA did not survive after FDR correction.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between APOE e4 genotype and brain structural phenotypes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePartially adjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFully adjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain structural phenotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrey matter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite matter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal brain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft hippocampus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.040\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.065\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-0.067\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight hippocampus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.069\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-0.070\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhit matter hyperintensities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.056\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.058\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal hippocampus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.071\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.048\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-0.072\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHippocampus differences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egFrontal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egICVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egISOVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.059\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.059\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.066\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.065\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: Partially adjusted: age, sex, genotypic array, four MRI assessment centres, first ten genetic principal components for general factors except for gFrontal; ICV additionally adjusted for the volumatric variables. Fully adjuested: additionally, lifestyle levels, deprivation index, BMI, sleep duration, isolated status, history of diabetes, hypertension, depression. Bold type P values indicates FDR significant (q\u0026thinsp;\u0026lt;\u0026thinsp;0.05). gFA, general factors of fractional anisotropy; gMD, general factors of mean diffusivity; gICVF, general factors of intracellular volume fraction; gISOVF, general factors of isotropic volume fraction; gOD, general factors of orientation dispersion.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAssociations between risk factors and brain structural phenotypes\u003c/p\u003e \u003cp\u003eGiven the independent effects of each lifestyle factor, we investigated the associations between genetic, lifestyle factors and each brain structural metric in both individual and simultaneous modelling of lifestyle factors (i.e. each individually then simultaneously) in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We found that the \u003cem\u003eAPOE\u003c/em\u003e e4 presence was associated with smaller hippocampus (β range [0.041] to [0.048]). Compared with each favourable level of lifestyle factor, unfavourable levels were mostly associated with poorer brain health. For example, significant associations were generally observed between current smoking, high risk drinking and poorer brain health, across GM and WM, as well as both traditional WM tract and NODDI metrics (standardised β range [0.014] to [0.209] for current smoking, and [0.030] to [0.152] for high-risk drinking). Unhealthy diet was associated with worse NODDI measures: higher gISOVF, and gOD (β range [0.065] to [0.081]). However, high sedentariness was associated with better GM health (β range [0.020] to [0.031]): larger GM, frontal lobe, and smaller left hippocampus (β range [0.014] to [0.037]), but poorer WM tract: higher WMH and gOD (β range [0.037] to [0.063]). Low levels of physical activity were associated with better brain health: lower WMH, gOD, and larger frontal lobe (β range [0.031] to [0.041]). Most significant associations survived from FDR correction (see \u003cb\u003eTable S5 and S6\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAssociations between lifestyle score/levels and brain structural phenotypes\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, a \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-point better lifestyle score was associated with better brain health: larger GM (β\u0026thinsp;=\u0026thinsp;0.006), total brain (β\u0026thinsp;=\u0026thinsp;0.002), left/right hippocampus and total hippocampus (β range [0.012] to [0.014]) and smaller WMH volume (β = -0.017) in the fully adjusted models. Notably, 1-point higher was also associated with smaller WM in the partially adjusted model but became non-significant after full adjustment. All significant associations survived FDR correction in the full adjustment models.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between lifestyle score and brain structural phenotypes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePartially adjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFully adjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain structural phenotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrey matter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite matter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal brain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft hippocampus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight hippocampus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhit matter hyperintensities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal hippocampus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHippocampus differences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egFrontal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egICVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egISOVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: Partially adjusted: age, sex, genotypic array, four MRI assessment centres, first ten genetic principal components for general factors except for gFrontal; ICV additionally adjusted for the volumatric variables. Fully adjuested: additionally, APOE4 status, deprivation index, BMI, sleep duration, isolated status, history of diabetes, hypertension, depression. Bold type P values indicates FDR significant (q\u0026thinsp;\u0026lt;\u0026thinsp;0.05). gFA, general factors of fractional anisotropy; gMD, general factors of mean diffusivity; gICVF, general factors of intracellular volume fraction; gISOVF, general factors of isotropic volume fraction; gOD, general factors of orientation dispersion.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAssociations between lifestyle levels and brain imaging phenotypes are reported in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Compared with participants with favourable lifestyles, those with unfavourable and moderate lifestyles were independently associated with smaller GM, total brain and hippocampus volumes (β range [0.004] to [0.096]). Of them, those with unfavourable lifestyle was associated with a lower degree of a variety of brain structural markers compared with those with moderate lifestyle. Moreover, unfavourable lifestyle was also associated with poorer WM measures: lower gFA (β = -0.059, 95% CI = -0.110 to -0.007, P\u0026thinsp;=\u0026thinsp;0.025), higher gMD (β\u0026thinsp;=\u0026thinsp;0.083, 95% CI\u0026thinsp;=\u0026thinsp;0.034 to 0.131, P\u0026thinsp;=\u0026thinsp;0.004) and WMH (β\u0026thinsp;=\u0026thinsp;0.111, 95% CI\u0026thinsp;=\u0026thinsp;0.067 to 0.156, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInteraction between lifestyle level and \u003cem\u003eAPOE\u003c/em\u003e e4 presence\u003c/p\u003e \u003cp\u003eThe interactions between lifestyle levels (i.e. favourable; moderate; unfavourable) and \u003cem\u003eAPOE\u003c/em\u003e e4 status on brain MRI metrics were all non-significant (see \u003cb\u003eTable S7\u003c/b\u003e). There were four significant interactions for each lifestyle component by \u003cem\u003eAPOE\u003c/em\u003e e4 presence shown in \u003cb\u003eTable S8\u003c/b\u003e. However, no interactions survived correction for FDR (see \u003cb\u003eTable S8 and S9\u003c/b\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first study to examine the potentially interactive associations of \u003cem\u003eAPOE\u003c/em\u003e e4 genotype and lifestyle factors with brain structural phenotypes. We found that the \u003cem\u003eAPOE\u003c/em\u003e e4 presence was associated with smaller hippocampus, and worse WM tracts assessed several ways. Less favourable lifestyle was associated with worse brain health across GM and hippocampus. A 1-point better (higher) lifestyle score was associated with better brain health. We provide insights into the relative contributions of different lifestyle factors to brain health: current smoking and high-risk drinking were associated with poorer GM and WM measures, and notably, high duration sedentariness was associated with better GM and poorer WM tracts.\u003c/p\u003e \u003cp\u003ePrevious literature\u003c/p\u003e \u003cp\u003eAlthough some studies reported null associations between \u003cem\u003eAPOE\u003c/em\u003e e4 and hippocampus volumes (N\u0026thinsp;=\u0026thinsp;52 to 8,395) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], we found significant associations between the carriers of \u003cem\u003eAPOE\u003c/em\u003e e4 and smaller hippocampus in a relatively large sample size (N\u0026thinsp;=\u0026thinsp;24,912) considering the impact of relatively comprehensive lifestyle factors. This finding aligns with previous research showing the influence of the e4 allele on hippocampus atrophy [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. \u003cem\u003eAPOE\u003c/em\u003e is known as a highly pleiotropic genetic locus and may influence brain health through a variety of pathways including neuroinflammation, inhibition of the \u0026lsquo;heart-brain loop\u0026rsquo; via atherosclerotic processes, and the promotion/inhibited clearance of amyloid beta (which characterises AD)[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. A positive relationship with the \u003cem\u003eAPOE\u003c/em\u003e e4 and WMH was found, being consistent with our previous research in an earlier sub-sample of UK Biobank (N\u0026thinsp;=\u0026thinsp;8,395) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Our findings reinforce the possibility that \u003cem\u003eAPOE\u003c/em\u003e e4 contributes to cognitive decline partly through a cerebrovascular-type pathway [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In a systematic analysis of \u003cem\u003eAPOE\u003c/em\u003e e4 genotype versus blood biomarkers in UK Biobank, Ferguson et al. reported a predominantly cardiometabolic influence of APOE e4 genotype on non-demented UK Biobank participants (N\u0026thinsp;=\u0026thinsp;~\u0026thinsp;502k), which aligns with this WMH observation[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. NODDI metrics were applied to characterise the relationship of \u003cem\u003eAPOE\u003c/em\u003e e4 on WM microstructure. We found that \u003cem\u003eAPOE\u003c/em\u003e e4 was associated with lower WM integrity using measures including FA and MD, ISOVF, indicating less restricted diffusion, representing neuronal loss or inflammation in WM. This is in line with previous research[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], but that study did not investigate the impact of lifestyle, and prior evidence showed that an unhealthier lifestyle was associated with worse cognition[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe found a cumulative effect such that participants with one point higher/better in lifestyle score were associated with better brain health, being partly consistent with the previous research[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This study showed participants with higher unhealthier lifestyle scores were associated with lower GM, but null associations with total hippocampus. In the current study, we affirmed the associations of better lifestyles with larger GM, and additionally, larger total hippocampus and total brain, and smaller WMH. A cross-sectional study of PRECISE also showed that healthier participants adopting four or five healthy lifestyle factors were associated with better brain health: larger GM, total brain, and smaller WMH[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Our results also suggest that participants with less healthy lifestyle levels were associated with worse brain health, being consistent with prior studies[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For example, a study in UK Biobank showed the relation of aggregate vascular risk factors (three lifestyle- and four health-based factors) with lower GM and higher WMH[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Generally, compared with these studies, the current study emphasised up to 14 brain structural measurements, which provided comprehensive evidence on the associations of five prevalent modifiable lifestyle factors with the consideration of genetic effects.\u003c/p\u003e \u003cp\u003eAll lifestyle factors showed significant associations with brain structural phenotypes even considering the mutual effects of each other. For example, current smoking, high risk of alcohol drinking, and unhealthy diet were associated with smaller brain volumes/integrity, being consistent with previous research[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This is because smoking may accelerate brain ageing due to atherosclerotic processes i.e. thickening of arteries[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Heavy alcohol intake is associated with the risk of dementia potentially due to the nutritional deficiency, the direct neurotoxic effects of ethanol, and the indirect negative impacts through increasing the risk of cardiometabolic diseases (including diabetes, hypertension, and stroke)[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Unhealthy diet is associated with cardiovascular disease, sharing the same risk factors with cognitive disease [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Moreover, high sedentariness and low levels of physical activity were associated with better brain health, conflicting with previous evidence[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This could be explained by people with healthier brains may naturally engage in more sedentary activities or occupations, such as cognitive engagement having protective effects on the brain, counteracting the lack of physical activity. Hence, their brain health may allow them to be less physically active without suffering adverse effects. Engaging in physical activity improves cardiorespiratory fitness and cardiovascular heath, which might protect from cognitive decline with age and brain atrophy[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Prolonged sedentary behaviour impairs glucose and lipid metabolism, which are regarded as risk factors for cognitive decline and all-cause dementia[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Further, the upper limit of low levels of MET (1428 MET-min/week) in the current study was much higher than the physical activity recommendations (i.e. \u0026ge; 600 MET) [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], indicating that participants with medium or high levels of MET might be associated with much better brain health in other studies.\u003c/p\u003e \u003cp\u003eFew studies have explored the relationship between each or overall lifestyle factors and NODDI with the consideration of \u003cem\u003eAPOE\u003c/em\u003e e4 presence. Current smokers were related with higher ISOVF and MD, and lower FA, being inconsistent with another study reporting null associations[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. High risk drinking was associated with lower ICVF, in line with previous research[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Yet, the sample sizes in those studies were small (N\u0026thinsp;=\u0026thinsp;34 to 44), and evidence from large sample sizes is warranted. We also found that high duration sedentariness was associated with better GM health but poorer WM microstructure. This could be explained by vascular and metabolic differences: GM areas might receive adequate blood flow and metabolic support through cognitive activities attributed to high level of sedentary behaviours. This highlights the complex and multifaceted relationship between different types of brain tissue and lifestyle factors, which needs further research.\u003c/p\u003e \u003cp\u003eThere were no statistically significant interactions between lifestyle levels and \u003cem\u003eAPOE\u003c/em\u003e e4 on brain MRI metrics, generally aligning with our previous study on cognitive abilities[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], which could be reflected by brain structures[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. A few significant interactions of certain level of lifestyle factors existed. For instance, high risk drinking with the \u003cem\u003eAPOE\u003c/em\u003e e4 on larger left hippocampus rather right hippocampus, being consistent with previous research with a small sample size (n\u0026thinsp;=\u0026thinsp;16) [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Further research is needed within the larger sample size.\u003c/p\u003e \u003cp\u003eImplications\u003c/p\u003e \u003cp\u003eBoth genetic and environmental factors play an important role in contributing to brain health. For example, the \u003cem\u003eAPOE\u003c/em\u003e e4 presence and unhealthy modifiable lifestyle factors are generally associated with worse brain health[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Our findings suggest that lifestyle interventions may benefit brain health regardless of APOE4 status. Public health strategies promoting healthy habits across populations could mitigate neurodegenerative risks, while targeted approaches for publicly acknowledged high-risk subgroups (e.g., APOE4 homozygotes) warrant further investigation, such as smoking cessation, reducing alcohol intake, adopting healthy diets, decreasing sedentary behaviour, and maintaining regular physical activity. By integrating genetic screening with targeted lifestyle interventions, it is possible to delay the onset of cognitive impairment and dementia and other neurodegenerative conditions.\u003c/p\u003e \u003cp\u003eLimitations\u003c/p\u003e \u003cp\u003eFirst, the cross-sectional study design may limit the possibility of casual inference between lifestyle factors, genetics and brain structures; a longitudinal within-participants design may be more informative. Second, the UK Biobank imaging sample shows a tendency to live in less deprived areas than other UK Biobank participants, who are already range-restricted compared with the general population, which may limit generalizability. Further research needs to be conducted in more general populations. Third, although we have excluded participants with at least one neurological condition, those with cognitive impairments were not specifically identified in this population-based sample. The results in our study might be overestimated as much evidence shows that cognitive impairment was associated with worse brain imaging biomarkers compared with healthy ones[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Fourth, information on lifestyle factors were self-reported, which might result in overestimated or underestimated associations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e \u003cem\u003eAPOE\u003c/em\u003e e4 and unfavourable lifestyles were consistently associated with poorer brain health after adjustment for confounders. Our findings show that lifestyle has a strong, consistent relationship with multiple aspects of brain health assessed with detailed imaging, and these associations manifest regardless of \u003cem\u003eAPOE\u003c/em\u003e e4 genotypic status. These findings warrant additional large-scale longitudinal studies to confirm and investigate associations across more brain structural phenotypes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all participants and staff of the UK Biobank study. This work uses data provided by patients and collected by the NHS as part of their care and support. The UK Biobank was established by the Wellcome Trust, Medical Research Council, Department of Health, Scottish Government and Northwest Regional Development Agency. UK Biobank has also had funding from the Welsh Assembly Government and the British Heart Foundation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy concept: YY, FKH, DML, CEH.\u003c/p\u003e\n\u003cp\u003eObtained data: DML, JW.\u003c/p\u003e\n\u003cp\u003eDesign: YY, FKH, DML, CEH.\u003c/p\u003e\n\u003cp\u003eConducted analyses: YY.\u003c/p\u003e\n\u003cp\u003eDrafted original manuscript: YY.\u003c/p\u003e\n\u003cp\u003eReviewed for intellectual content: all co-authors.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYY acknowledges financial support from China Scholarship Council.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary information is available at website.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO. 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Journal of the American Geriatrics Society. 2008;56(5):920-34.\u003c/li\u003e\n\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":"","lastPublishedDoi":"10.21203/rs.3.rs-6297741/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6297741/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eApolipoprotein E\u003c/em\u003e gene (\u003cem\u003eAPOE\u003c/em\u003e) e4 allele is the strongest common genetic risk factor for dementia. High genetic risk of dementia could be offset by healthy lifestyle. However, the effects of daily lifestyle and genetic risk on brain structural imaging phenotypes, including white matter (WM) tract integrity, remain unclear. Multiple linear regression and interaction analyses were performed to examine the associations between \u003cem\u003eAPOE\u003c/em\u003e e4 genotype and lifestyle factors (smoking, alcohol drinking, diet, physical activity, and sedentariness) with 14 brain health metrics among N\u0026thinsp;=\u0026thinsp;24,912 non-demented participants from UK Biobank, including after adjustment for potential confounders. There were significant associations between \u003cem\u003eAPOE\u003c/em\u003e e4 presence and smaller hippocampal volume (standardised β range 0.042 to 0.048 versus absence), and multiple metrics of worse WM tract integrity. Compared with participants with favourable lifestyles, moderate and unfavourable lifestyles showed smaller grey matter, total brain, and hippocampal volumes (standardised β range 0.004 to 0.096) per one-unit worse lifestyle significantly. The unhealthiest lifestyle factors were mostly associated with poorer brain health. A 1-point better lifestyle score was associated with better brain health (standardised β range 0.001 to 0.017 per unit). The interactions between lifestyle levels and \u003cem\u003eAPOE\u003c/em\u003e e4 status on brain metrics were generally non-significant. The presence of \u003cem\u003eAPOE\u003c/em\u003e e4 and unfavourable lifestyle were mostly associated with worse brain health, and this was largely linear rather than synergistic. The benefit of healthy lifestyle on brain health does not vary by genetic risk for dementia. These findings warrant additional large-scale longitudinal studies to confirm and investigate associations across more brain structural phenotypes.\u003c/p\u003e","manuscriptTitle":"Testing for associations and interactions between APOE e4 genotype and lifestyle, with brain structural imaging phenotypes in UK Biobank","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-16 17:32:01","doi":"10.21203/rs.3.rs-6297741/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":"bf2f7203-55a1-4c6c-a109-7c283a33a8b9","owner":[],"postedDate":"April 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46324755,"name":"Health sciences/Risk factors"},{"id":46324756,"name":"Biological sciences/Psychology/Human behaviour"},{"id":46324757,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":46324758,"name":"Health sciences/Medical research/Genetics research"}],"tags":[],"updatedAt":"2025-04-30T14:22:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-16 17:32:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6297741","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6297741","identity":"rs-6297741","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}