Intraocular pressure, systemic blood pressure, and brain volumes: observational and Mendelian Randomization analyses

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Methods The study included 8634 participants for IOP and 36069 participants for BP in observational analyses and 37410 participants for both IOP and BP in Mendelian Randomisation (MR) analyses from UK Biobank. IOP and BP were measured between 2006–2010. Brain volumes were measured using MRI between 2014–2019. Results Higher IOP was associated with smaller volumes of total brain (β (95% CI) for each 5-mmHg increment: -3.24 (-5.05, -1.44) ml) and grey matter (-1.10 (-2.17, -0.03) ml) independent of BP. Diastolic BP (β (95% CI) for each 10-mmHg increment: 0.13 (0.05, 0.21)) was associated with higher white matter hyperintensity (WMH) independent of antihypertensive medications. Associations between IOP and total brain and WMH volumes were stronger in younger individuals or those without hypertension. Associations between DBP/SBP and brain volumes were stronger in younger individuals, women, and lowly educated individuals. All MR analytic methods demonstrated a significant relationship between DBP and WMH (β (95% CI) for each 10-mmHg increment of DBP for inverse-variance weighting method: 0.019 (0.013, 0.026)). The β (95% CI) for grey matter volume (ml) associated with each 5-mmHg increment of IOP for inverse-variance weighting method was − 3.42 (-5.39, -1.45). Conclusions Higher IOP is casually linked to larger grey matter volume reduction while increased DBP casually linked to higher WMH load. Younger or lowly educated individuals deserve more scrutiny for the prevention of brain volume reduction potentially via IOP/DBP lowering. Intraocular pressure blood pressure brain volume Mendelian randomization moderation analysis Figures Figure 1 Figure 2 Figure 3 Introduction Neurological diseases are the leading cause of disability and the second leading cause of death worldwide.[ 1 ] Brain atrophy is a key presentation of many progressive neurological diseases including Alzheimer’s disease,[ 2 , 3 ] and multiple sclerosis.[ 4 ] Importantly, brain volume can be clinically used to measure the degree of neurodegeneration, thereby determining the prognosis and appropriate treatment.[ 2 , 5 , 6 ] However, evidence has shown that conventional factors appeared to explain a small amount of additional variance beyond covariates for brain atrophy.[ 7 ] Therefore, it is important to identify new determinants for brain atrophy. Systemic hypertension has been linked to greater brain atrophy in many previous observational studies.[ 8 – 11 ] Although there is a high correlation between systemic blood pressure (BP) especially systolic BP (SBP), and intraocular pressure(IOP),[ 12 – 14 ] the association between IOP and brain atrophy has been investigated in only a few studies with inconsistent results.[ 15 , 16 ] Traditional observational studies can estimate possible biomarkers in the progression of disease; however, they are prone to bias and cannot be used to refer to causal relationships.[ 17 ] Mendelian randomization (MR) analysis overcomes these problems by using genetic variants as instrumental variables (IVs) to avoid confounding bias and measuring their effects on outcome,[ 17 ] which can be used to infer causal relationships. However, no previous MR analysis has examined potential causal relationships between BP, IOP, and brain volume. Using the UK biobank, we aimed to investigate causal relationships between systemic BP, IOP, and brain volume. Importantly, both MR and traditional observational analyses were performed, providing genetic and phenotypic evidence for any associations. Methods Study population Our analysis was based on the UK Biobank cohort of more than 500,000 participants aged 40–70 years at baseline between 2006 and 2010.[ 18 ] The design of the study has been detailed elsewhere.[ 18 ] The UK Biobank Study’s ethical approval had been granted by the National Information Governance Board for Health and Social Care and the NHS North West Multicenter Research Ethics Committee. All participants provided informed consent through electronic signature at baseline assessment. Brain magnetic resonance imaging Magnetic resonance imaging (MRI) assessment was conducted between August 2014 and October 2019. A standard Siemens Skyra 3T scanner with a standard 32-channel radio-frequency receiver head coil was used to collect MRI data.[ 19 ] T1- and T2-weighted scans were analysed with the Functional MRI of the Brain Software Library. Volumes of total brain, grey matter, white matter, white matter hyperintensity (WMH), and hippocampus were assessed. Total brain volume was calculated by summing the grey matter and white matter volumes (excludes cerebrospinal fluid). Brain volumes were normalized for head size based on the external surface of the skull, using the ratio-corrected method.[ 19 ] Given the positively skewed distribution, WMH was logarithm-transformed in our analysis. Assessment of intraocular pressure and systemic blood pressure IOP was measured once for each eye using an Ocular Response Analyzer noncontact tonometer (Reichert Corp) at baseline (2009). Data on both Goldman-correlated and corneal-compensated IOP were collected. We used Goldman-correlated IOP in the analysis as it is considered the standard for measurement of IOP. The average of the right and left eye IOP measurements were used in the analysis. We used one eye’s IOP value as the participant’s IOP if data were available for only one eye. BP was measured at three surveys (2006–2010, 2012–2013, and 2014–2019). Within each survey, BP was measured twice using a digital sphygmomanometer (Omron 705 IT; OMRON Healthcare Europe B.V., Hoofddorp, Netherlands) by trained nurses. We used the average of the two measurements in the analysis. BP measured concurrently with IOP was used in the analysis. Hypertension was defined by SBP ≥ 140 mmHg or diastolic BP (DBP) ≥ 90 mmHg, or as those who reported a diagnosis of hypertension. Genetic data BiLEVE Axiom array, or the UK Biobank Axiom array was used for genotyping by Affymetrix and ~ 450,000 of the ~ 500,000 UK Biobank participants were genotyped. Prior to data release, genotype imputation using the Haplotype Reference Consortium reference panel was conducted by the UK Biobank researchers and then followed by extensive quality control. Genetic variants associated with IOP were selected from a Genome-wide associated study (GWAS) of 139,555 European participants, from which 133 single nucleotide polymorphisms (SNPs) associated with IOP were used as IVs in MR analyses.[ 20 ] The relevant generic risk score (GRS) has been shown to have great performance in the prediction of glaucoma.[ 20 ] Genetic variants (865 SNPs) associated with BP were selected based on a recent GWAS conducted among over one million people of European ancestry by Evangelou et al.[ 21 ] The SNP-exposure association was derived from the summary statistics of GWAS for IOP/BP mentioned above.[ 20 , 21 ] While the SNP-outcome association were derived for different regions of brain volumes adjusted for age, gender, and 10 principal components. Apolipoprotein E ε4 (APOE4) + dominant model of ε3/ε4 and ε4/ε4 was used to define the presence of APOE4. Covariates Weight was measured using a Tanita BC-418MA body composition analyser (Tanita Corporation, Arlington Heights, IL) and height was measured in a barefoot standing position using the Saca 202 device. BMI was computed as weight (kilograms) divided by height squared (meters). A touchscreen computer was used to collect information including age, gender, ethnicity, education, household income, smoking, alcohol consumption, and sleep duration. Questions about physical activity, which were similar to those used in the short form of the International Physical Activity Questionnaire, were used to estimate excess metabolic equivalent (MET)-hours/week of physical activity during work and leisure time. Glaucoma, depression, and medication use for glaucoma/hypertension at baseline were defined based on self-reported data. Lipids including total cholesterol, HDL-C, LDL-C, and triglycerides were tested by direct enzymatic methods (Konelab, Thermo Fisher Scientific, Waltham, Massachusetts). Glycated haemoglobin (HbA1c) was measured using high-performance liquid chromatography on a Bio-Rad Variant II Turbo. Statistical analysis Data were expressed as frequency (percentage) and means ± standard deviations (SDs) by quintiles of IOP/BP. ANOVA for continuous variables and Chi-square test for categorical variables were used to test the difference across quintiles of IOP/BP. For observational analysis, the association between IOP/BP and brain volumes was examined using general linear regression models. We tested four models: 1) unadjusted; 2) adjusted for age and gender, 3) adjusted for Model 2 plus APOE4, education, income, depression, diabetes, alcohol consumption, physical activity, smoking, sleep duration, BMI, HDL-C, LDL-C, and triglycerides; 4) adjusted for Model 3 plus glaucoma, hypertension, and medication use for hypertension/glaucoma (IOP and BP were mutually adjusted for). Whether associations between IOP/BP and brain volumes were modified by age, gender, education, APOE4, and systemic hypertension was examined using general linear regression models. Two-sample MR analyses were conducted to obtain robust causal estimates for brain volumes affected by IOP or BP. Inverse-variance weighting (IVW), MR-Egger regression (MR-Egger), and weighted median[ 22 ] were performed using MR package in R (version 4.0.3). Sensitivity analyses were performed using MR-Pleiotropy Residual Sum and Outlier method (MR-PRESSO), which detects and excludes SNPs with potential pleiotropic effects.[ 23 ] The association between GRS for IOP/BP and brain volumes was also estimated using general linear regression models. Genetic variants associated with brain volumes were selected from a GWAS study based on the UK Biobank.[ 24 ] Given a large proportion of individuals without MRI data were not included in the analysis, we repeated the observational analysis for the association between IOP/BP and brain volumes using the inverse probability weighting method.[ 25 ] Individuals with complete data are weighted by the inverse of their probability of being a complete case in the analysis. There might be bidirectional associations between IOP/BP and brain volumes. Therefore, whether IOP or BP was affected by brain volumes was tested using two-sample MR analyses. Missing values for categorical variables were assigned as a single category. Missing values for continuous covariates were assigned as the mean. Data analyses for the non-MR study were conducted using SAS 9.4 for Windows (SAS Institute Inc.) and all P values were two-sided with statistical significance set at < 0.05. Results Baseline characteristics Of 502,505 participants with baseline data, individuals who did not have MRI data ( n = 462,809), or those who were non-European ancestry ( n = 1294) were excluded from the analysis (Fig. 1). After excluding those without IOP data ( n = 29,768), 8634 adults (52.5% females) aged 40–70 (mean ± SD: 55.8 ± 7.4) years at baseline were included in the analysis for the association between IOP and brain volumes. They were aged 45–80 (63.3 ± 7.5) years when MRI data were collected 7.7 (interquartile range: 6.3–8.9) years later. After excluding those without BP data (n = 2,333), 36,069 adults (53.0% females) aged 40–70 (mean ± SD: 55.0 ± 7.4) years at baseline were included in the analysis for the association between BP and brain volumes. They were aged 45–81 (63.7 ± 7.5) years when MRI data were collected 8.9 (interquartile range: 7.4–10.0) years later. Individuals with higher IOP were more likely to have lower income, and to be older. Higher IOP was associated with higher prevalence of systemic hypertension, and glaucoma, and higher levels of BMI, LDL-C, and HbA1c (Table 1). Individuals with higher DBP were more likely to be older, males, physically inactive, non-current smokers, and glaucoma patients (Supplementary Table 1). Individuals with higher SBP were more likely to be older, males, physically active, non-current smokers, and glaucoma patients (Supplementary Table 2). Phenotypic association between intraocular pressure and brain volumes Increased IOP was associated with smaller volumes of total brain ( β (95% CI) for quintile 5 versus quintile 1: -19.54 (-24.36, -14.72) ml), grey matter (-12.02 (-15.19, -8.85) ml), white matter (-7.52 (-10.23, -4.81) ml), and hippocampus (-29.58 (-58.69, -0.48) µl). These associations for total brain (-4.82 (-9.03, -0.61) ml) and white matter (-3.20 (-5.92, -0.48) ml) remained significant after adjustment for geographic factors, lifestyle factors, BMI, lipids, BP, self-reported hypertension, glaucoma, and medication use for hypertension/glaucoma. In the multivariable-adjusted analysis, increased IOP was associated smaller volumes of total brain ( β (95% CI) for each 5-mmHg increment: -3.24 (-5.05, -1.44) ml), grey matter (-1.10 (-2.17, -0.03) ml), and white matter (-2.14 (-3.32, -0.97) ml) (Table 2). Phenotypic association between systemic blood pressure and brain volumes Increased SBP was associated with smaller volumes of total brain, grey matter, and white matter, and higher WMH load. The associations for total brain and grey matter volumes were attenuated to be non-significant after adjustment for age and gender. In Model 3, βs (95% CIs) for hippocampal volume and WMH load associated with SBP (quintile 5 versus quintile 1) were − 22.24 (-37.13, -7.35) and 0.29 (0.25, 0.33), respectively. These associations were attenuated to be non-significant after adjustment for mediation use for hypertension (Table 3). Increased DBP was associated with higher WMH load ( β (95% CI) for each 10-mmHg increment: 0.13 (0.05, 0.21)) but not volumes of other regions after adjustment for medication use of hypertension and other covariates (Table 4). Moderation analysis Significant interactions were observed between age and IOP for volumes of total brain and WMH. The association between IOP and total brain volume was stronger in younger ( β (95% CI): -7.51 (-10.08, -4.94) ml) than in older individuals (-2.51 (-5.23, 0.22) ml). The association between IOP and WMH load was significant in younger (0.17 (0.04, 0.30)) but not in older individuals (-0.07 (-0.22, 0.08)). The association between IOP and total brain volume was significant in individuals without systemic hypertension ( β (95% CI): -10.11 (-12.47, -7.74) ml), but not in those with (-2.95 (-7.64, 1.75) ml). Similarly, the association between IOP and grey matter volume was stronger in those without systemic hypertension (-6.28 (-7.81, -4.74) ml) than in those with (-1.05 (-4.21, 2.12)) (Fig. 2). The association between DBP and volumes of total brain, grey matter, and WMH was stronger in older individuals, women, and lowly educated individuals. β (95% CI) for WMH volume associated with DBP was 0.19 (0.17, 0.21), 0.18 (0.16, 0.20), and 0.10 (0.05, 0.15) for individuals with low, moderate, and high education (Supplementary Fig. 1). Similar results were seen for SBP (Supplementary Fig. 2). Mendelian analysis All MR analytic methods demonstrated a significant relationship between DBP and WMH. The β (95% CI) for WMH associated with each 10-mmHg increment of DBP for IVW, MR-Egger, weighted median, and MR-PRESSO was 0.019 (0.013, 0.026), 0.022 (0.01, 0.033), 0.027 (0.018, 0.036), and 0.018 (0.012, 0.024), respectively. The IVW ( β (95% CI): -1.37 (-2.50, -0.24)) and MR-PRESSO (-1.22 (-2.30, -0.14)) but not other MR methods showed a significant association between IOP and total brain volume. The β (95% CI) for grey matter volume associated with each 5-mmHg increment of IOP for IVW, MR-Egger, weighted median, and MR-PRESSO was − 3.42 (-5.39, -1.45) (P-value = 0.0010), -3.21 (-6.54, 0.12) (P-value = 0.0590), -2.55 (-5.12, 0.02) (P-value = 0.0510), and − 2.98 (-4.88, -1.09) (P-value = 0.00271), respectively. No other significant relationships were observed (Fig. 3). Genetic risk score and brain volumes Higher GRS for IOP was associated with smaller total brain and grey matter volumes. In Model 4, βs (95% CIs) for total brain and grey matter volumes (ml) associated with GRS for IOP (quintile 5 versus quintile 1) were − 3.09 (-5.02, -1.17) and − 2.13 (-3.27, -0.98), respectively (Supplementary Table 3). Higher GRS for DBP was associated with higher WMH load ( β (95% CI) for quintile 5 versus quintile 1: 0.08 (0.05, 0.12), Supplementary Table 4). Sensitivity analysis The inverse probability weighting analysis showed that increased IOP was independently associated with smaller volumes of total brain ( β (95% CI) for each 5-mmHg increment of IOP: -2.69 (-4.49, -0.89) ml) and grey matter (-1.24 (-2.31, -0.17) ml, Supplementary Table 5). Multivariable adjusted β (95% CI) for total brain (ml), grey mater (ml), and WMH volumes associated with each 10-mmHg increment of DBP were − 0.97 (-1.67, -0.27), -1.07 (-1.49, -0.65), and 0.11 (0.10, 0.13), respectively (Supplementary Table 6). Increased SBP was associated with smaller hippocampal volume (β (95% CI) for each 10-mmHg increment of SBP: -2.82 (-5.51, -0.14) ml, and higher WMH load (0.05 (0.04, 0.06), Supplementary Table 7). MR analyses showed that the effect of hippocampus on BP was minimal with each 100 µl increase in hippocampus associated with 0.2–0.6 mmHg increase in SBP and 0.1–0.4 mmHg increase in DBP. No other significant associations were observed Supplementary Fig. 3). Discussion In this large cohort study of community-dwelling adults, we found higher IOP was associated with reduced volumes of total brain and grey matter in the phenotypic analysis in dependent of BP and medication use for glaucoma. This association was confirmed by the genetic analysis. Increased SBP was associated with smaller brain volumes whereas these associations were attenuated to be non-significant or even reversed after adjustment for the use of antihypertensive medication. Increased DBP was associated with higher WMH load in the phenotypic analysis independent of antihypertensive medication use and IOP and this association was confirmed in the genetic analysis. The association between IOP and volumes of total brain and WMH was stronger in younger than in older individuals and increased IOP was associated with smaller volumes of total brain and grey matter in those free of systemic hypertension only. The association between DBP/SBP and volumes of total brain, grey matter, and WMH was stronger in younger individuals, women, and lowly educated individuals. In contrast, the effect sizes of brain volumes on BP were minimal and no significant associations between brain volumes and IOP were found in the genetic analyses. A continuous supply of oxygen and glucose from blood to brain is fundamental for brain health, but increased arterial stiffness caused by hypertension[ 26 ] may decrease cerebral blood flow[ 27 – 29 ] thus resulting in brain damage. Numerous observational studies have examined the association between hypertension and brain volume[ 7 , 9 , 30 ]. Strong evidence has suggested a positive association between BP and WMH,[ 9 , 30 , 31 ] but findings regarding the association between BP, total brain volume, and hippocampal volume are inconsistent between previous studies.[ 31 ] We found increased SBP was associated with smaller hippocampal volume and increased DBP was associated with smaller volumes of total brain and grey matter and higher WMH load. Only the association between DBP and WMH load remained significant after adjustment for medication use for hypertension. However, only the association between DBP and WMH load was confirmed by MR analyses. This is consistent with a recent meta-analysis of seven clinical trials showing that antihypertensive treatment is beneficial for WMH changes but not for total brain atrophy.[ 32 ] Whilst WMH than other regions of the brain is more predictive of cognitive impairment and dementia.[ 33 ] Therefore, our findings highlight the importance of BP lowering in the prevention of dementia. Increasing evidence suggests that vision impairment especially that caused by glaucoma is associated with changes in the brain,[ 34 , 35 ] however, less is known regarding the association between IOP and brain volume. Previous studies have demonstrated a high correlation between IOP and intracranial pressure[ 36 , 37 ] suggesting that IOP might be predictive of brain volumes. In another way, increased IOP may damage the optic nerve that sends images to the brain, which results in brain volume loss. It remains debatable regarding the causal relationships of IOP with brain volumes.[ 15 ] We found increased IOP was associated with smaller volumes of total brain and grey matter measured over 7 years later. MR analyses showed that increased IOP is causally linked to grey matter volume loss. This is consistent with a growing amount of literature suggesting that glaucoma affects the brain trans-synaptically.[ 15 , 16 ] Recent research has demonstrated that grey matter volume may be a reliable marker to track disease progression in dementia,[ 38 ] thus IOP may help identify individuals at higher risk of brain atrophy. We found the association between IOP and volumes of total brain and WMH was more prominent in younger than in older individuals. This is in line with previous studies demonstrating that hypertension diagnosed at a younger age was associated with a larger reduction in brain volume.[ 8 , 39 ] The association between IOP and brain volume was stronger in those free of systemic hypertension. The underlying mechanisms are unclear, but a previous study suggests the association between main eye diseases and dementia was stronger among those without systemic diseases.[ 40 ] The lower rate of diagnosis and treatment of hypertension among younger individuals may also explain why the association was stronger among those without hypertension. We also found the association between BP and total brain volume was stronger among individuals with lower education. This may be explained by the fact that individuals who were highly educated were more likely to seek health care and thus less likely to result in brain volume loss with ageing. BP appears to be more predictive of brain volumes in women than in men, which needs to be confirmed in future research. Our findings suggest the effect sizes of BP and IOP might be relatively small, the use of anti-glaucoma or antihypertensive medications, or other treatments might minimize the risk of neurological or cognitive disorders because of these small effects on brain volume maintenance in the aging population. To our knowledge, this is the first study to examine the association of IOP and BP with brain volumes in both observational and MR analyses. Our study has several potential limitations. Firstly, causal relationships cannot be established based on our analysis of the phenotypic association between IOP/BP and brain volumes given the cross-sectional design. However, those observational findings are confirmed by MR analyses. Secondly, MRI and IOP data were collected in a small subgroup of the UK Biobank cohort, which may limit the generalizability of our findings to the whole population. However, the inverse probability weighting analysis showed similar results to the main findings. Thirdly, the analysis was conducted among individuals of European ancestry such that the findings may not be applied to other ethnic groups. In conclusion, our findings suggest higher IOP is casually linked to a larger reduction in grey matter volume while increased DBP is casually linked to higher WMH load. Younger individuals and those without hypertension are more in need of care for the prevention of brain volume reduction potentially via IOP lowering. The association between BP and brain volume reduction is stronger among younger individuals, women, and lowly educated individuals. Abbreviations APOE4 = Apolipoprotein E ε4; DBP = diastolic blood pressure; IOP = intraocular pressure; IV = instrumental variable; IVW = inverse-variance weighting; MR = Mendelian Randomisation; PRESSO = Pleiotropy Residual Sum and Outlier method; SBP = systolic blood pressure; SNP = single nucleotide polymorphism; WMH = white matter hyperintensity Declarations Ethics approval and consent to participate The UK Biobank Study’s ethical approval had been granted by the National Information Governance Board for Health and Social Care and the NHS North West Multicenter Research Ethics Committee. All participants provided informed consent through electronic signature at baseline assessment. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding XS receives support from Postdoctoral Research Funds of Guangdong Provincial People’s Hospital (BY012021047). ZZ receives support from the National Natural Science Foundation of China (82101173), the Research Foundation of Medical Science and Technology of Guangdong Province (B2021237). HY receives support from the National Natural Science Foundation of China (81870663, 82171075), the Outstanding Young Talent Trainee Program of Guangdong Provincial People’s Hospital (KJ012019087), Guangdong Provincial People’s Hospital Scientific Research Funds for Leading Medical Talents and Distinguished Young Scholars in Guangdong Province (KJ012019457), Talent Introduction Fund of Guangdong Provincial People’s Hospital (Y012018145). MH receives support from the High-level Talent Flexible Introduction Fund of Guangdong Provincial People’s Hospital (No. KJ012019530). MH also receives support from the University of Melbourne at Research Accelerator Program and the CERA Foundation. The Centre for Eye Research Australia receives Operational Infrastructure Support from the Victorian State Government. The sponsor or funding organization had no role in the design or conduct of this research. The sponsor or funding organization had no role in the design, conduct, analysis, or reporting of this study. The funding sources did not participate in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Author Contributions Conception and design of the study: XS, YH, HY, XY and MH. Acquisition and analysis of data: XS, YH, SZ, ZZ, XLZ, XYZ and JHL.Writing - original draft: XS and YH. Writing - review & editing: XS, YH, ZZ, XLZ, WW, XYZ, JL, JHL, ST, ZG, YJH, HY, XY and MH. Figure drafting: XS, HY and JL. Acknowledgement This research was conducted using the UK Biobank resource. We thank the participants of the UK Biobank. 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Tables Table 1 Baseline characteristics of participants by quintiles of intraocular pressure Variables Intraocular pressure P-value* Quintile 1 (n = 1731) Quintile 2 (n = 1720) Quintile 3 (n = 1726) Quintile 4 (n = 1730) Quintile 5 (n = 1727) Range (mmHg) Age (years) 18.2 56.9 ± 7.2 < 0.0001 Gender 0.21 Female 898 (51.9) 922 (53.6) 931 (53.9) 929 (53.7) 854 (49.4) Male 833 (48.1) 798 (46.4) 795 (46.1) 801 (46.3) 873 (50.6) APOE4 0.94 No 1301 (75.2) 1268 (73.7) 1283 (74.3) 1306 (75.5) 1278 (74.0) Yes 387 (22.4) 396 (23.0) 407 (23.6) 390 (22.5) 405 (23.5) Missing 43 (2.5) 56 (3.3) 36 (2.1) 34 (2.0) 44 (2.5) Education 0.90 0–5 years 84 (4.9) 99 (5.8) 84 (4.9) 98 (5.7) 108 (6.3) 6–12 years 835 (48.2) 800 (46.5) 834 (48.3) 832 (48.1) 782 (45.3) ≥13 years 808 (46.7) 817 (47.5) 802 (46.5) 792 (45.8) 828 (47.9) Missing 4 (0.2) 4 (0.2) 6 (0.3) 8 (0.5) 9 (0.5) Household income (pounds) < 0.0001 <18,000 174 (10.1) 168 (9.8) 169 (9.8) 182 (10.5) 188 (10.9) 18,000–30,999 328 (18.9) 332 (19.3) 337 (19.5) 348 (20.1) 392 (22.7) 31,000–51,999 473 (27.3) 482 (28.0) 458 (26.5) 488 (28.2) 475 (27.5) 52,000-100,000 485 (28.0) 464 (27.0) 491 (28.4) 433 (25.0) 424 (24.6) >100,000 143 (8.3) 141 (8.2) 135 (7.8) 123 (7.1) 110 (6.4) Unknown 27 (1.6) 27 (1.6) 38 (2.2) 33 (1.9) 37 (2.1) Not answered 101 (5.8) 106 (6.2) 98 (5.7) 123 (7.1) 101 (5.8) Physical activity (MET-minutes/week) 2596 ± 2476 2593 ± 2307 2468 ± 2168 2572 ± 2256 2489 ± 2182 0.18 Alcohol consumption 0.20 Never 35 (2.0) 38 (2.2) 30 (1.7) 33 (1.9) 38 (2.2) Previous 49 (2.8) 33 (1.9) 34 (2.0) 36 (2.1) 30 (1.7) Current 1647 (95.1) 1649 (95.9) 1661 (96.2) 1661 (96.0) 1659 (96.1) Missing 1 (0.1) Smoking 0.93 Never 1067 (61.6) 1053 (61.2) 1049 (60.8) 1044 (60.3) 1053 (61.0) Former 558 (32.2) 565 (32.8) 570 (33.0) 585 (33.8) 579 (33.5) Current 105 (6.1) 99 (5.8) 103 (6.0) 97 (5.6) 89 (5.2) Missing 1 (0.1) 3 (0.2) 4 (0.2) 4 (0.2) 6 (0.3) Sleep duration (hours) 7.11 ± 0.92 7.15 ± 0.92 7.18 ± 0.96 7.19 ± 0.95 7.23 ± 0.99 0.0001 BMI (kg/m 2 ) 26.36 ± 4.06 26.46 ± 4.11 26.74 ± 4.33 26.80 ± 3.92 27.07 ± 4.37 < 0.0001 Total cholesterol (mmol/L) 5.69 ± 1.03 5.76 ± 1.04 5.73 ± 1.08 5.80 ± 1.08 5.79 ± 1.04 0.0032 HDL-C (mmol/L) 1.48 ± 0.35 1.49 ± 0.36 1.49 ± 0.36 1.50 ± 0.36 1.49 ± 0.36 0.13 LDL-C (mmol/L) 3.54 ± 0.79 3.59 ± 0.78 3.57 ± 0.81 3.61 ± 0.81 3.61 ± 0.81 0.0083 Triglycerides (mmol/L) 1.62 ± 0.89 1.67 ± 0.97 1.64 ± 0.90 1.69 ± 0.92 1.67 ± 0.92 0.0733 HbA1c (mmol/mol) 34.66 ± 4.33 35.01 ± 4.54 35.00 ± 4.41 35.47 ± 5.48 35.66 ± 5.98 < 0.0001 Hypertension 279 (16.1) 308 (17.9) 367 (21.3) 371 (21.4) 393 (22.8) < 0.0001 Heart disease 34 (2.0) 27 (1.6) 43 (2.5) 43 (2.5) 42 (2.4) 0.0972 Depression 103 (6.0) 95 (5.5) 85 (4.9) 76 (4.4) 75 (4.3) 0.0091 Glaucoma 8 (0.5) 8 (0.5) 17 (1.0) 25 (1.4) 49 (2.8) < 0.0001 Medication for glaucoma 4 (0.2) 4 (0.2) 5 (0.3) 9 (0.5) 0.0277 Data are mean ± standard deviations, or N (%). BMI, body mass index; HbA1c, glycated haemoglobin; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; MET, metabolic equivalent. *ANOVA was used to test the difference of continuous variables across quintiles of IOP and Chi-square for categorical variables. Table 2 Association between intraocular pressure and brain volumes Intraocular pressure (mmHg) P-value Each 5-mmHg Brain volume Quintile 1, 18.2 (n = 1727) for trend increment* Total brain (ml) β (95% CI), Model 1 † Reference -4.77 (-9.60, 0.06) -8.80 (-13.62, -3.98) -14.84 (-19.66, -10.02) -19.54 (-24.36, -14.72) < 0.0001 -9.50 (-11.63, -7.36) β (95% CI), Model 2 Reference -1.26 (-5.27, 2.76) -2.02 (-6.03, 2.00) -5.68 (-9.70, -1.66) -6.98 (-11.01, -2.95) 0.0001 -3.57 (-5.35, -1.79) β (95% CI), Model 3 Reference -0.58 (-4.74, 3.59) -1.68 (-5.83, 2.47) -4.09 (-8.27, 0.09) -5.19 (-9.39, -0.98) 0.0039 -3.11 (-4.89, -1.33) β (95% CI), Model 4 Reference -0.66 (-4.83, 3.52) -1.81 (-5.97, 2.36) -4.46 (-8.67, -0.24) -5.50 (-9.76, -1.25) 0.0024 -3.24 (-5.05, -1.44) Grey matter (ml) β (95% CI), Model 1 Reference -2.45 (-5.62, 0.72) -5.33 (-8.51, -2.16) -8.13 (-11.30, -4.97) -12.02 (-15.19, -8.85) < 0.0001 -5.93 (-7.33, -4.53) β (95% CI), Model 2 Reference -0.24 (-2.64, 2.16) -0.89 (-3.29, 1.51) -1.99 (-4.39, 0.41) -2.87 (-5.28, -0.47) 0.0064 -1.55 (-2.62, -0.49) β (95% CI), Model 3 Reference 0.13 (-2.34, 2.60) -0.51 (-2.98, 1.95) -0.93 (-3.41, 1.55) -1.83 (-4.33, 0.66) 0.0981 -1.21 (-2.27, -0.16) β (95% CI), Model 4 Reference 0.25 (-2.23, 2.73) -0.22 (-2.69, 2.26) -0.82 (-3.32, 1.69) -1.54 (-4.07, 0.99) 0.15 -1.10 (-2.17, -0.03) White matter (ml) β (95% CI), Model 1 Reference -2.32 (-5.03, 0.39) -3.46 (-6.17, -0.76) -6.71 (-9.41, -4.00) -7.52 (-10.23, -4.81) < 0.0001 -3.56 (-4.76, -2.37) β (95% CI), Model 2 Reference -1.01 (-3.60, 1.58) -1.13 (-3.72, 1.46) -3.69 (-6.28, -1.10) -4.11 (-6.70, -1.51) 0.0002 -2.02 (-3.17, -0.87) β (95% CI), Model 3 Reference -0.70 (-3.40, 1.99) -1.16 (-3.85, 1.52) -3.17 (-5.87, -0.46) -3.35 (-6.07, -0.63) 0.0032 -1.90 (-3.05, -0.74) β (95% CI), Model 4 Reference -0.90 (-3.60, 1.79) -1.59 (-4.28, 1.10) -3.64 (-6.36, -0.91) -3.96 (-6.71, -1.21) 0.0007 -2.14 (-3.32, -0.97) Hippocampus (µl) β (95% CI), Model 1 Reference -5.91 (-35.04, 23.23) -21.26 (-50.37, 7.85) -35.97 (-65.06, -6.88) -29.58 (-58.69, -0.48) < 0.0001 -15.74 (-28.61, -2.88) β (95% CI), Model 2 Reference 10.50 (-16.57, 37.56) 6.43 (-20.64, 33.51) -1.00 (-28.09, 26.10) 3.84 (-23.32, 30.99) 0.90 -1.26 (-13.26, 10.75) β (95% CI), Model 3 Reference 8.34 (-19.74, 36.43) 3.53 (-24.43, 31.49) 2.89 (-25.28, 31.06) 7.79 (-20.54, 36.13) 0.75 0.39 (-11.61, 12.39) β (95% CI), Model 4 Reference 8.16 (-20.01, 36.33) 4.84 (-23.29, 32.96) 2.79 (-25.66, 31.24) 9.72 (-19.01, 38.45) 0.67 1.74 (-10.46, 13.94) White matter hyperintensity (ml) β (95% CI), Model 1 Reference -0.23 (-0.49, 0.02) -0.05 (-0.32, 0.23) -0.09 (-0.34, 0.17) 0.16 (-0.10, 0.41) 0.11 0.08 (-0.03, 0.18) β (95% CI), Model 2 Reference -0.20 (-0.43, 0.02) -0.11 (-0.36, 0.13) -0.16 (-0.39, 0.06) 0.02 (-0.20, 0.24) 0.73 0.02 (-0.07, 0.16) β (95% CI), Model 3 Reference -0.20 (-0.44, 0.04) -0.05 (-0.31, 0.21) -0.16 (-0.41, 0.08) 0.01 (-0.22, 0.25) 0.74 0.03 (-0.06, 0.12) β (95% CI), Model 4 Reference -0.21 (-0.45, 0.03) -0.06 (-0.32, 0.20) -0.21 (-0.46, 0.03) -0.02 (-0.26, 0.22) 0.97 1.74 (-10.46, 13.94) *General linear regression models were used to estimate the association between intraocular pressure and brain volumes. † Model 1 was unadjusted model; Model 2 was adjusted for age and gender; Model 3 was adjusted for Model 2 plus APOE4, education, income, depression, diabetes, alcohol consumption, physical activity, smoking, sleep duration, BMI, HDL-C, LDL-C, and triglycerides; Model 4 was adjusted for Model 3 plus blood pressure, hypertension, glaucoma, and medication use for hypertension/glaucoma. Table 3 Association between systolic blood pressure and brain volumes Brain volume Systolic blood pressure (mmHg) P-value Each 10-mmHg Quintile 1, 148.0 for trend increment* (n = 6717) (n = 7331) (n = 7170) (n = 7407) Total brain (ml) β (95% CI), Model 1 † Reference -13.90 (-16.26, -11.54) -21.69 (-24.00, -19.38) -28.40 (-30.73, -26.08) -40.75 (-43.05, -38.45) < 0.0001 -7.88 (-8.30, -7.46) β (95% CI), Model 2 Reference -1.35 (-3.36, 0.67) -1.01 (-3.00, 0.99) 0.67 (-1.37, 2.70) -0.39 (-2.45, 1.68) 0.62 0.06 (-0.32, 0.43) β (95% CI), Model 3 Reference -1.53 (-3.61, 0.56) -0.27 (-2.36, 1.81) 1.18 (-0.96, 3.33) 0.37 (-1.81, 2.56) 0.17 0.24 (-0.14, 0.63) β (95% CI), Model 4 Reference 3.16 (-1.33, 7.65) 0.50 (-4.26, 5.26) 6.15 (0.82, 11.47) 8.70 (2.77, 14.62) 0.0032 1.00 (0.19, 1.81) Grey matter (ml) β (95% CI), Model 1 Reference -12.60 (-14.13, -11.06) -20.16 (-21.65, -18.66) -26.67 (-28.18, -25.17) -35.60 (-37.10, -34.11) < 0.0001 -6.92 (-7.19, -6.65) β (95% CI), Model 2 Reference -1.30 (-2.51, -0.09) -1.96 (-3.15, -0.76) -2.06 (-3.28, -0.84) -3.11 (-4.34, -1.87) < 0.0001 -0.57 (-0.79, -0.34) β (95% CI), Model 3 Reference -1.11 (-2.35, 0.13) -1.13 (-2.37, 0.11) -1.08 (-2.36, 0.19) -1.88 (-3.18, -0.58) 0.0133 -0.28 (-0.51, -0.05) β (95% CI), Model 4 Reference 1.65 (-1.01, 4.32) -0.80 (-3.63, 2.03) 2.05 (-1.12, 5.22) 2.66 (-0.86, 6.19) 0.15 0.11 (-0.37, 0.59) White matter (ml) β (95% CI), Model 1 Reference -1.30 (-2.65, 0.04) -1.54 (-2.85, -0.22) -1.73 (-3.05, -0.41) -5.15 (-6.46, -3.84) < 0.0001 -0.96 (-1.20, -0.72) β (95% CI), Model 2 Reference -0.05 (-1.34, 1.25) 0.95 (-0.34, 2.24) 2.73 (1.41, 4.04) 2.72 (1.38, 4.05) < 0.0001 0.62 (0.38, 0.87) β (95% CI), Model 3 Reference -0.42 (-1.77, 0.93) 0.86 (-0.49, 2.21) 2.26 (0.88, 3.65) 2.25 (0.84, 3.67) < 0.0001 0.52 (0.27, 0.77) β (95% CI), Model 4 Reference 1.51 (-1.40, 4.41) 1.30 (-1.78, 4.38) 4.10 (0.65, 7.54) 6.03 (2.20, 9.86) 0.0012 0.89 (0.36, 1.42) Hippocampus (µl) β (95% CI), Model 1 Reference 9.01 (-5.57, 23.59) 8.60 (-5.65, 22.86) -7.48 (-21.81, 6.86) -59.69 (-73.91, -45.47) < 0.0001 -12.34 (-14.92, -9.77) β (95% CI), Model 2 Reference -2.65 (-16.35, 11.06) -2.08 (-15.67, 11.52) -3.26 (-17.13, 10.62) -19.87 (-33.95, -5.80) 0.0122 -3.83 (-6.40, -1.26) β (95% CI), Model 3 Reference -6.53 (-20.73, 7.66) -1.48 (-15.69, 12.74) -2.53 (-17.15, 12.08) -22.24 (-37.13, -7.35) 0.0161 -4.00 (-6.63, -1.37) β (95% CI), Model 4 Reference -3.77 (-34.06, 26.53) 1.03 (-31.11, 33.16) -3.25 (-39.22, 32.71) -30.95 (-70.96, 9.05) 0.17 -1.63 (-7.12, 3.85) WMH (ml) β (95% CI), Model 1 Reference 0.23 (0.19, 0.27) 0.35 (0.31, 0.39) 0.53 (0.49, 0.57) 0.76 (0.72, 0.80) < 0.0001 0.15 (0.14, 0.16) β (95% CI), Model 2 Reference 0.11 (0.07, 0.14) 0.15 (0.12, 0.19) 0.24 (0.21, 0.28) 0.33 (0.29, 0.37) < 0.0001 0.07 (0.06, 0.08) β (95% CI), Model 3 Reference 0.10 (0.06, 0.13) 0.13 (0.09, 0.17) 0.21 (0.17, 0.25) 0.29 (0.25, 0.33) < 0.0001 0.06 (0.05, 0.07) β (95% CI), Model 4 Reference 0.18 (-0.09, 0.44) 0.01 (-0.26, 0.28) 0.05 (-0.25, 0.36) 0.10 (-0.27, 0.46) 0.90 0.04 (-0.01, 0.09) *General linear regression models were used to estimate the association between systolic blood pressure and brain volumes. † Model 1 was unadjusted model; Model 2 was adjusted for age and gender; Model 3 was adjusted for Model 2 plus APOE4, education, income, depression, diabetes, alcohol consumption, physical activity, smoking, sleep duration, BMI, HDL-C, LDL-C, and triglycerides; Model 4 was adjusted for Model 3 plus IOP, glaucoma, and medication use for hypertension/glaucoma. Table 4 Association between diastolic blood pressure and brain volumes Brain volume Diastolic blood pressure (mmHg) P-value Each 10-mmHg Quintile 1, 89.0 for trend increment* (n = 7601) (n = 6525) (n = 7254) (n = 7243) (n = 7446) Total brain (ml) β (95% CI), Model 1 † Reference -6.60 (-9.01, -4.20) -10.08 (-12.41, -7.74) -16.08 (-18.41, -13.74) -18.55 (-20.87, -16.22) < 0.0001 -6.48 (-7.23, -5.72) β (95% CI), Model 2 Reference -0.39 (-2.39, 1.61) -1.79 (-3.74, 0.17) -3.53 (-5.50, -1.56) -4.34 (-6.31, -2.37) < 0.0001 -1.64 (-2.29, -1.00) β (95% CI), Model 3 Reference 0.65 (-1.42, 2.73) -0.67 (-2.71, 1.37) -2.09 (-4.18, -0.00) -2.63 (-4.76, -0.50) 0.0014 -1.20 (-1.88, -0.52) β (95% CI), Model 4 Reference -2.35 (-6.73, 2.04) -4.26 (-8.77, 0.24) -5.72 (-10.70, -0.74) -4.94 (-10.68, 0.80) 0.0477 -0.38 (-1.80, 1.03) Grey matter (ml) β (95% CI), Model 1 Reference -8.02 (-9.59, -6.45) -10.87 (-12.40, -9.34) -16.25 (-17.78, -14.72) -20.07 (-21.59, -18.55) < 0.0001 -6.90 (-7.39, -6.40) β (95% CI), Model 2 Reference -1.97 (-3.17, -0.77) -2.68 (-3.85, -1.51) -4.08 (-5.26, -2.90) -5.87 (-7.05, -4.69) < 0.0001 -2.01 (-2.40, -1.63) β (95% CI), Model 3 Reference -1.16 (-2.39, 0.08) -1.52 (-2.74, -0.31) -2.42 (-3.66, -1.18) -3.80 (-5.06, -2.53) < 0.0001 -1.31 (-1.71, -0.90) β (95% CI), Model 4 Reference -2.21 (-4.82, 0.39) -3.43 (-6.11, -0.75) -3.81 (-6.77, -0.85) -3.25 (-6.66, 0.16) 0.0395 -0.82 (-1.66, 0.02) White matter (ml) β (95% CI), Model 1 Reference 1.42 (0.06, 2.77) 0.79 (-0.52, 2.10) 0.18 (-1.14, 1.49) 1.53 (0.22, 2.83) 0.19 0.42 (-0.00, 0.85) β (95% CI), Model 2 Reference 1.58 (0.29, 2.87) 0.89 (-0.37, 2.15) 0.55 (-0.72, 1.82) 1.53 (0.26, 2.80) 0.14 0.37 (-0.05, 0.78) β (95% CI), Model 3 Reference 1.81 (0.46, 3.16) 0.85 (-0.47, 2.17) 0.33 (-1.03, 1.68) 1.16 (-0.22, 2.54) 0.55 0.11 (-0.33, 0.55) β (95% CI), Model 4 Reference -0.13 (-2.96, 2.70) -0.83 (-3.74, 2.08) -1.92 (-5.14, 1.30) -1.69 (-5.40, 2.02) 0.24 0.43 (-0.49, 1.35) Hippocampus (µl) β (95% CI), Model 1 Reference 19.44 (4.81, 34.08) 21.77 (7.54, 36.01) 19.40 (5.16, 33.64) 35.21 (21.07, 49.36) < 0.0001 12.27 (7.66, 16.87) β (95% CI), Model 2 Reference 5.78 (-7.88, 19.43) 1.14 (-12.19, 14.47) -7.47 (-20.90, 5.97) -3.69 (-17.13, 9.74) 0.20 -2.05 (-6.44, 2.34) β (95% CI), Model 3 Reference 4.67 (-9.49, 18.83) -4.43 (-18.34, 9.49) -9.87 (-24.12, 4.38) -8.77 (-23.28, 5.74) 0.0623 -4.66 (-9.28, -0.03) β (95% CI), Model 4 Reference -13.78 (-43.36, 15.81) 13.01 (-17.37, 43.40) 3.54 (-30.08, 37.15) 19.38 (-19.38, 58.13) 0.25 2.18 (-7.40, 11.75) WMH (ml) β (95% CI), Model 1 Reference 0.13 (0.09, 0.17) 0.23 (0.19, 0.27) 0.35 (0.30, 0.39) 0.50 (0.46, 0.54) < 0.0001 0.18 (0.17, 0.19) β (95% CI), Model 2 Reference 0.07 (0.04, 0.11) 0.15 (0.11, 0.19) 0.23 (0.19, 0.26) 0.37 (0.34, 0.41) < 0.0001 0.14 (0.13, 0.15) β (95% CI), Model 3 Reference 0.07 (0.03, 0.10) 0.13 (0.09, 0.17) 0.19 (0.15, 0.23) 0.33 (0.29, 0.37) < 0.0001 0.13 (0.11, 0.14) β (95% CI), Model 4 Reference 0.11 (-0.14, 0.36) 0.12 (-0.15, 0.39) 0.21 (-0.09, 0.51) 0.39 (0.04, 0.73) 0.0386 0.13 (0.05, 0.21) *General linear regression models were used to estimate the association between diastolic blood pressure and brain volumes. † Model 1 was unadjusted model; Model 2 was adjusted for age and gender; Model 3 was adjusted for Model 2 plus APOE4, education, income, depression, diabetes, alcohol consumption, physical activity, smoking, sleep duration, BMI, HDL-C, LDL-C, and triglycerides; Model 4 was adjusted for Model 3 plus IOP, glaucoma, and medication use for hypertension/glaucoma. 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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-2798166","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":194077148,"identity":"7a9d1808-75fc-4a64-aa79-c80230ddffdf","order_by":0,"name":"Xianwen Shang","email":"","orcid":"","institution":"Guangdong Academy of Medical Sciences: Guangdong Provincial People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xianwen","middleName":"","lastName":"Shang","suffix":""},{"id":194077149,"identity":"5311587c-a120-49ca-8132-cf6c00ed8b45","order_by":1,"name":"Yu 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Medical Sciences: Guangdong Provincial People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaohong","middleName":"","lastName":"Yang","suffix":""},{"id":194077162,"identity":"d8c4314e-ba64-4efb-9a39-a90ea50fa45e","order_by":14,"name":"Mingguang He","email":"","orcid":"","institution":"Guangdong Academy of Medical Sciences: Guangdong Provincial People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mingguang","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2023-04-10 11:18:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2798166/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2798166/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":36292375,"identity":"6d27549d-81ff-4348-b64b-8a5aca722958","added_by":"auto","created_at":"2023-04-25 19:20:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":103492,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart for population selection from the UK Biobank\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIOP = intraocular pressure; MRI = magnetic resonance imaging\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-2798166/v1/b603432d53ddd39ba4bc4835.png"},{"id":36292074,"identity":"ce9ea272-4538-412d-b357-25a3aa8dab13","added_by":"auto","created_at":"2023-04-25 19:12:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":60135,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModeration analysis for the association between intraocular pressure and brain volume\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAPOE4 = apolipoprotein E ε4; CI = confidence interval; WMH = white matter hyperintensity.\u003c/p\u003e\n\u003cp\u003eGeneral linear regression models were used to examine whether associations between intraocular pressure and brain volumes were moderated by age, gender, APOE4, education, or systemic hypertension. Analysis was adjusted for age, gender, education, income, smoking, physical activity, alcohol consumption, and sleep duration. Horizontal lines indicate the ranges of the 95% CIs and the vertical dash lines indicate the mean of 0.0.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-2798166/v1/10fdb330b11ed03b888aa1f0.png"},{"id":36292072,"identity":"a7e4f30a-0370-4901-9fa6-605a13cdbae8","added_by":"auto","created_at":"2023-04-25 19:12:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61615,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMendelian randomization analyses for the effect of intraocular pressure and blood pressure on brain volumes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCI = confidence interval; DBP = diastolic blood pressure; IOP = intraocular pressure; IVW = inverse-variance weighting; MR-PRESSO = Mendelian Randomization Pleiotropy Residual Sum and Outlier; MR-Egger = Mendelian randomisation Egger; SBP = systolic blood pressure; WMH = white matter hyperintensity. Horizontal lines indicate the ranges of the 95% CIs and the vertical dash lines indicate the mean of 0.0\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-2798166/v1/ce426da46468378c0daf817d.png"},{"id":37293162,"identity":"b2e2e5e8-18af-45ce-97b5-492134adec66","added_by":"auto","created_at":"2023-05-21 17:29:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1214222,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2798166/v1/e8701317-95ed-4823-ac3e-af092b16fd6e.pdf"},{"id":36292076,"identity":"04df990f-3cf4-4903-80c0-18674363889f","added_by":"auto","created_at":"2023-04-25 19:12:19","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1672776,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2798166/v1/a8ac8ca7861b9234e575b89d.jpg"},{"id":36292077,"identity":"8901d060-eca1-49ac-964c-aaf37b5f92b2","added_by":"auto","created_at":"2023-04-25 19:12:19","extension":"doc","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":97792,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEreportingguidelinechecklist.doc","url":"https://assets-eu.researchsquare.com/files/rs-2798166/v1/74ce7828df825a4a688f090f.doc"},{"id":36292376,"identity":"b7ce126d-2b31-41d1-9bbe-074b350fc7be","added_by":"auto","created_at":"2023-04-25 19:20:19","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":128856,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-2798166/v1/0b5a64ac0618bed5b5d2a4fe.docx"}],"financialInterests":"","formattedTitle":"Intraocular pressure, systemic blood pressure, and brain volumes: observational and Mendelian Randomization analyses","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNeurological diseases are the leading cause of disability and the second leading cause of death worldwide.[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] Brain atrophy is a key presentation of many progressive neurological diseases including Alzheimer\u0026rsquo;s disease,[\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e] and multiple sclerosis.[\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e] Importantly, brain volume can be clinically used to measure the degree of neurodegeneration, thereby determining the prognosis and appropriate treatment.[\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e] However, evidence has shown that conventional factors appeared to explain a small amount of additional variance beyond covariates for brain atrophy.[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e] Therefore, it is important to identify new determinants for brain atrophy.\u003c/p\u003e\n\u003cp\u003eSystemic hypertension has been linked to greater brain atrophy in many previous observational studies.[\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e] Although there is a high correlation between systemic blood pressure (BP) especially systolic BP (SBP), and intraocular pressure(IOP),[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e] the association between IOP and brain atrophy has been investigated in only a few studies with inconsistent results.[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e] Traditional observational studies can estimate possible biomarkers in the progression of disease; however, they are prone to bias and cannot be used to refer to causal relationships.[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e] Mendelian randomization (MR) analysis overcomes these problems by using genetic variants as instrumental variables (IVs) to avoid confounding bias and measuring their effects on outcome,[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e] which can be used to infer causal relationships. However, no previous MR analysis has examined potential causal relationships between BP, IOP, and brain volume.\u003c/p\u003e\n\u003cp\u003eUsing the UK biobank, we aimed to investigate causal relationships between systemic BP, IOP, and brain volume. Importantly, both MR and traditional observational analyses were performed, providing genetic and phenotypic evidence for any associations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur analysis was based on the UK Biobank cohort of more than 500,000 participants aged 40\u0026ndash;70 years at baseline between 2006 and 2010.[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e] The design of the study has been detailed elsewhere.[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e] The UK Biobank Study\u0026rsquo;s ethical approval had been granted by the National Information Governance Board for Health and Social Care and the NHS North West Multicenter Research Ethics Committee. All participants provided informed consent through electronic signature at baseline assessment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBrain magnetic resonance imaging\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMagnetic resonance imaging (MRI) assessment was conducted between August 2014 and October 2019. A standard Siemens Skyra 3T scanner with a standard 32-channel radio-frequency receiver head coil was used to collect MRI data.[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e] T1- and T2-weighted scans were analysed with the Functional MRI of the Brain Software Library. Volumes of total brain, grey matter, white matter, white matter hyperintensity (WMH), and hippocampus were assessed. Total brain volume was calculated by summing the grey matter and white matter volumes (excludes cerebrospinal fluid). Brain volumes were normalized for head size based on the external surface of the skull, using the ratio-corrected method.[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e] Given the positively skewed distribution, WMH was logarithm-transformed in our analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of intraocular pressure and systemic blood pressure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIOP was measured once for each eye using an Ocular Response Analyzer noncontact tonometer (Reichert Corp) at baseline (2009). Data on both Goldman-correlated and corneal-compensated IOP were collected. We used Goldman-correlated IOP in the analysis as it is considered the standard for measurement of IOP. The average of the right and left eye IOP measurements were used in the analysis. We used one eye\u0026rsquo;s IOP value as the participant\u0026rsquo;s IOP if data were available for only one eye.\u003c/p\u003e\n\u003cp\u003eBP was measured at three surveys (2006\u0026ndash;2010, 2012\u0026ndash;2013, and 2014\u0026ndash;2019). Within each survey, BP was measured twice using a digital sphygmomanometer (Omron 705 IT; OMRON Healthcare Europe B.V., Hoofddorp, Netherlands) by trained nurses. We used the average of the two measurements in the analysis. BP measured concurrently with IOP was used in the analysis. Hypertension was defined by SBP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg or diastolic BP (DBP)\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, or as those who reported a diagnosis of hypertension.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBiLEVE Axiom array, or the UK Biobank Axiom array was used for genotyping by Affymetrix and ~\u0026thinsp;450,000 of the ~\u0026thinsp;500,000 UK Biobank participants were genotyped. Prior to data release, genotype imputation using the Haplotype Reference Consortium reference panel was conducted by the UK Biobank researchers and then followed by extensive quality control.\u003c/p\u003e\n\u003cp\u003eGenetic variants associated with IOP were selected from a Genome-wide associated study (GWAS) of 139,555 European participants, from which 133 single nucleotide polymorphisms (SNPs) associated with IOP were used as IVs in MR analyses.[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e] The relevant generic risk score (GRS) has been shown to have great performance in the prediction of glaucoma.[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e] Genetic variants (865 SNPs) associated with BP were selected based on a recent GWAS conducted among over one million people of European ancestry by Evangelou et al.[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e] The SNP-exposure association was derived from the summary statistics of GWAS for IOP/BP mentioned above.[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e] While the SNP-outcome association were derived for different regions of brain volumes adjusted for age, gender, and 10 principal components.\u003c/p\u003e\n\u003cp\u003eApolipoprotein E \u0026epsilon;4 (APOE4)\u0026thinsp;+\u0026thinsp;dominant model of \u0026epsilon;3/\u0026epsilon;4 and \u0026epsilon;4/\u0026epsilon;4 was used to define the presence of APOE4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWeight was measured using a Tanita BC-418MA body composition analyser (Tanita Corporation, Arlington Heights, IL) and height was measured in a barefoot standing position using the Saca 202 device. BMI was computed as weight (kilograms) divided by height squared (meters). A touchscreen computer was used to collect information including age, gender, ethnicity, education, household income, smoking, alcohol consumption, and sleep duration. Questions about physical activity, which were similar to those used in the short form of the International Physical Activity Questionnaire, were used to estimate excess metabolic equivalent (MET)-hours/week of physical activity during work and leisure time.\u003c/p\u003e\n\u003cp\u003eGlaucoma, depression, and medication use for glaucoma/hypertension at baseline were defined based on self-reported data. Lipids including total cholesterol, HDL-C, LDL-C, and triglycerides were tested by direct enzymatic methods (Konelab, Thermo Fisher Scientific, Waltham, Massachusetts). Glycated haemoglobin (HbA1c) was measured using high-performance liquid chromatography on a Bio-Rad Variant II Turbo.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were expressed as frequency (percentage) and means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SDs) by quintiles of IOP/BP. ANOVA for continuous variables and Chi-square test for categorical variables were used to test the difference across quintiles of IOP/BP.\u003c/p\u003e\n\u003cp\u003eFor observational analysis, the association between IOP/BP and brain volumes was examined using general linear regression models. We tested four models: 1) unadjusted; 2) adjusted for age and gender, 3) adjusted for Model 2 plus APOE4, education, income, depression, diabetes, alcohol consumption, physical activity, smoking, sleep duration, BMI, HDL-C, LDL-C, and triglycerides; 4) adjusted for Model 3 plus glaucoma, hypertension, and medication use for hypertension/glaucoma (IOP and BP were mutually adjusted for). Whether associations between IOP/BP and brain volumes were modified by age, gender, education, APOE4, and systemic hypertension was examined using general linear regression models.\u003c/p\u003e\n\u003cp\u003eTwo-sample MR analyses were conducted to obtain robust causal estimates for brain volumes affected by IOP or BP. Inverse-variance weighting (IVW), MR-Egger regression (MR-Egger), and weighted median[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e] were performed using MR package in R (version 4.0.3). Sensitivity analyses were performed using MR-Pleiotropy Residual Sum and Outlier method (MR-PRESSO), which detects and excludes SNPs with potential pleiotropic effects.[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] The association between GRS for IOP/BP and brain volumes was also estimated using general linear regression models. Genetic variants associated with brain volumes were selected from a GWAS study based on the UK Biobank.[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e\n\u003cp\u003eGiven a large proportion of individuals without MRI data were not included in the analysis, we repeated the observational analysis for the association between IOP/BP and brain volumes using the inverse probability weighting method.[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] Individuals with complete data are weighted by the inverse of their probability of being a complete case in the analysis. There might be bidirectional associations between IOP/BP and brain volumes. Therefore, whether IOP or BP was affected by brain volumes was tested using two-sample MR analyses.\u003c/p\u003e\n\u003cp\u003eMissing values for categorical variables were assigned as a single category. Missing values for continuous covariates were assigned as the mean.\u003c/p\u003e\n\u003cp\u003eData analyses for the non-MR study were conducted using SAS 9.4 for Windows (SAS Institute Inc.) and all P values were two-sided with statistical significance set at \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf 502,505 participants with baseline data, individuals who did not have MRI data (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;462,809), or those who were non-European ancestry (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1294) were excluded from the analysis (Fig.\u0026nbsp;1). After excluding those without IOP data (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;29,768), 8634 adults (52.5% females) aged 40\u0026ndash;70 (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD: 55.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4) years at baseline were included in the analysis for the association between IOP and brain volumes. They were aged 45\u0026ndash;80 (63.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5) years when MRI data were collected 7.7 (interquartile range: 6.3\u0026ndash;8.9) years later. After excluding those without BP data (n\u0026thinsp;=\u0026thinsp;2,333), 36,069 adults (53.0% females) aged 40\u0026ndash;70 (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD: 55.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4) years at baseline were included in the analysis for the association between BP and brain volumes. They were aged 45\u0026ndash;81 (63.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5) years when MRI data were collected 8.9 (interquartile range: 7.4\u0026ndash;10.0) years later.\u003c/p\u003e\n\u003cp\u003eIndividuals with higher IOP were more likely to have lower income, and to be older. Higher IOP was associated with higher prevalence of systemic hypertension, and glaucoma, and higher levels of BMI, LDL-C, and HbA1c (Table\u0026nbsp;1). Individuals with higher DBP were more likely to be older, males, physically inactive, non-current smokers, and glaucoma patients (Supplementary Table\u0026nbsp;1). Individuals with higher SBP were more likely to be older, males, physically active, non-current smokers, and glaucoma patients (Supplementary Table\u0026nbsp;2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhenotypic association between intraocular pressure and brain volumes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIncreased IOP was associated with smaller volumes of total brain (\u003cem\u003e\u0026beta;\u003c/em\u003e (95% CI) for quintile 5 versus quintile 1: -19.54 (-24.36, -14.72) ml), grey matter (-12.02 (-15.19, -8.85) ml), white matter (-7.52 (-10.23, -4.81) ml), and hippocampus (-29.58 (-58.69, -0.48) \u0026micro;l). These associations for total brain (-4.82 (-9.03, -0.61) ml) and white matter (-3.20 (-5.92, -0.48) ml) remained significant after adjustment for geographic factors, lifestyle factors, BMI, lipids, BP, self-reported hypertension, glaucoma, and medication use for hypertension/glaucoma. In the multivariable-adjusted analysis, increased IOP was associated smaller volumes of total brain (\u003cem\u003e\u0026beta;\u003c/em\u003e (95% CI) for each 5-mmHg increment: -3.24 (-5.05, -1.44) ml), grey matter (-1.10 (-2.17, -0.03) ml), and white matter (-2.14 (-3.32, -0.97) ml) (Table\u0026nbsp;2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhenotypic association between systemic blood pressure and brain volumes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIncreased SBP was associated with smaller volumes of total brain, grey matter, and white matter, and higher WMH load. The associations for total brain and grey matter volumes were attenuated to be non-significant after adjustment for age and gender. In Model 3, \u003cem\u003e\u0026beta;s\u003c/em\u003e (95% CIs) for hippocampal volume and WMH load associated with SBP (quintile 5 versus quintile 1) were \u0026minus;\u0026thinsp;22.24 (-37.13, -7.35) and 0.29 (0.25, 0.33), respectively. These associations were attenuated to be non-significant after adjustment for mediation use for hypertension (Table\u0026nbsp;3). Increased DBP was associated with higher WMH load (\u003cem\u003e\u0026beta;\u003c/em\u003e (95% CI) for each 10-mmHg increment: 0.13 (0.05, 0.21)) but not volumes of other regions after adjustment for medication use of hypertension and other covariates (Table\u0026nbsp;4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModeration analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSignificant interactions were observed between age and IOP for volumes of total brain and WMH. The association between IOP and total brain volume was stronger in younger (\u003cem\u003e\u0026beta;\u003c/em\u003e (95% CI): -7.51 (-10.08, -4.94) ml) than in older individuals (-2.51 (-5.23, 0.22) ml). The association between IOP and WMH load was significant in younger (0.17 (0.04, 0.30)) but not in older individuals (-0.07 (-0.22, 0.08)). The association between IOP and total brain volume was significant in individuals without systemic hypertension (\u003cem\u003e\u0026beta;\u003c/em\u003e (95% CI): -10.11 (-12.47, -7.74) ml), but not in those with (-2.95 (-7.64, 1.75) ml). Similarly, the association between IOP and grey matter volume was stronger in those without systemic hypertension (-6.28 (-7.81, -4.74) ml) than in those with (-1.05 (-4.21, 2.12)) (Fig.\u0026nbsp;2).\u003c/p\u003e\n\u003cp\u003eThe association between DBP and volumes of total brain, grey matter, and WMH was stronger in older individuals, women, and lowly educated individuals. \u003cem\u003e\u0026beta;\u003c/em\u003e (95% CI) for WMH volume associated with DBP was 0.19 (0.17, 0.21), 0.18 (0.16, 0.20), and 0.10 (0.05, 0.15) for individuals with low, moderate, and high education (Supplementary Fig.\u0026nbsp;1). Similar results were seen for SBP (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMendelian analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll MR analytic methods demonstrated a significant relationship between DBP and WMH. The \u003cem\u003e\u0026beta;\u003c/em\u003e (95% CI) for WMH associated with each 10-mmHg increment of DBP for IVW, MR-Egger, weighted median, and MR-PRESSO was 0.019 (0.013, 0.026), 0.022 (0.01, 0.033), 0.027 (0.018, 0.036), and 0.018 (0.012, 0.024), respectively. The IVW (\u003cem\u003e\u0026beta;\u003c/em\u003e (95% CI): -1.37 (-2.50, -0.24)) and MR-PRESSO (-1.22 (-2.30, -0.14)) but not other MR methods showed a significant association between IOP and total brain volume. The \u003cem\u003e\u0026beta;\u003c/em\u003e (95% CI) for grey matter volume associated with each 5-mmHg increment of IOP for IVW, MR-Egger, weighted median, and MR-PRESSO was \u0026minus;\u0026thinsp;3.42 (-5.39, -1.45) (P-value\u0026thinsp;=\u0026thinsp;0.0010), -3.21 (-6.54, 0.12) (P-value\u0026thinsp;=\u0026thinsp;0.0590), -2.55 (-5.12, 0.02) (P-value\u0026thinsp;=\u0026thinsp;0.0510), and \u0026minus;\u0026thinsp;2.98 (-4.88, -1.09) (P-value\u0026thinsp;=\u0026thinsp;0.00271), respectively. No other significant relationships were observed (Fig.\u0026nbsp;3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic risk score and brain volumes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigher GRS for IOP was associated with smaller total brain and grey matter volumes. In Model 4, \u003cem\u003e\u0026beta;s\u003c/em\u003e (95% CIs) for total brain and grey matter volumes (ml) associated with GRS for IOP (quintile 5 versus quintile 1) were \u0026minus;\u0026thinsp;3.09 (-5.02, -1.17) and \u0026minus;\u0026thinsp;2.13 (-3.27, -0.98), respectively (Supplementary Table\u0026nbsp;3). Higher GRS for DBP was associated with higher WMH load (\u003cem\u003e\u0026beta;\u003c/em\u003e (95% CI) for quintile 5 versus quintile 1: 0.08 (0.05, 0.12), Supplementary Table\u0026nbsp;4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe inverse probability weighting analysis showed that increased IOP was independently associated with smaller volumes of total brain (\u003cem\u003e\u0026beta;\u003c/em\u003e (95% CI) for each 5-mmHg increment of IOP: -2.69 (-4.49, -0.89) ml) and grey matter (-1.24 (-2.31, -0.17) ml, Supplementary Table\u0026nbsp;5). Multivariable adjusted \u003cem\u003e\u0026beta;\u003c/em\u003e (95% CI) for total brain (ml), grey mater (ml), and WMH volumes associated with each 10-mmHg increment of DBP were \u0026minus;\u0026thinsp;0.97 (-1.67, -0.27), -1.07 (-1.49, -0.65), and 0.11 (0.10, 0.13), respectively (Supplementary Table\u0026nbsp;6). Increased SBP was associated with smaller hippocampal volume (\u0026beta; (95% CI) for each 10-mmHg increment of SBP: -2.82 (-5.51, -0.14) ml, and higher WMH load (0.05 (0.04, 0.06), Supplementary Table\u0026nbsp;7). MR analyses showed that the effect of hippocampus on BP was minimal with each 100 \u0026micro;l increase in hippocampus associated with 0.2\u0026ndash;0.6 mmHg increase in SBP and 0.1\u0026ndash;0.4 mmHg increase in DBP. No other significant associations were observed Supplementary Fig.\u0026nbsp;3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large cohort study of community-dwelling adults, we found higher IOP was associated with reduced volumes of total brain and grey matter in the phenotypic analysis in dependent of BP and medication use for glaucoma. This association was confirmed by the genetic analysis. Increased SBP was associated with smaller brain volumes whereas these associations were attenuated to be non-significant or even reversed after adjustment for the use of antihypertensive medication. Increased DBP was associated with higher WMH load in the phenotypic analysis independent of antihypertensive medication use and IOP and this association was confirmed in the genetic analysis. The association between IOP and volumes of total brain and WMH was stronger in younger than in older individuals and increased IOP was associated with smaller volumes of total brain and grey matter in those free of systemic hypertension only. The association between DBP/SBP and volumes of total brain, grey matter, and WMH was stronger in younger individuals, women, and lowly educated individuals. In contrast, the effect sizes of brain volumes on BP were minimal and no significant associations between brain volumes and IOP were found in the genetic analyses.\u003c/p\u003e\n\u003cp\u003eA continuous supply of oxygen and glucose from blood to brain is fundamental for brain health, but increased arterial stiffness caused by hypertension[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e] may decrease cerebral blood flow[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e] thus resulting in brain damage. Numerous observational studies have examined the association between hypertension and brain volume[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. Strong evidence has suggested a positive association between BP and WMH,[\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e] but findings regarding the association between BP, total brain volume, and hippocampal volume are inconsistent between previous studies.[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e] We found increased SBP was associated with smaller hippocampal volume and increased DBP was associated with smaller volumes of total brain and grey matter and higher WMH load. Only the association between DBP and WMH load remained significant after adjustment for medication use for hypertension. However, only the association between DBP and WMH load was confirmed by MR analyses. This is consistent with a recent meta-analysis of seven clinical trials showing that antihypertensive treatment is beneficial for WMH changes but not for total brain atrophy.[\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e] Whilst WMH than other regions of the brain is more predictive of cognitive impairment and dementia.[\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e] Therefore, our findings highlight the importance of BP lowering in the prevention of dementia.\u003c/p\u003e\n\u003cp\u003eIncreasing evidence suggests that vision impairment especially that caused by glaucoma is associated with changes in the brain,[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e] however, less is known regarding the association between IOP and brain volume. Previous studies have demonstrated a high correlation between IOP and intracranial pressure[\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e] suggesting that IOP might be predictive of brain volumes. In another way, increased IOP may damage the optic nerve that sends images to the brain, which results in brain volume loss. It remains debatable regarding the causal relationships of IOP with brain volumes.[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e] We found increased IOP was associated with smaller volumes of total brain and grey matter measured over 7 years later. MR analyses showed that increased IOP is causally linked to grey matter volume loss. This is consistent with a growing amount of literature suggesting that glaucoma affects the brain trans-synaptically.[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e] Recent research has demonstrated that grey matter volume may be a reliable marker to track disease progression in dementia,[\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e] thus IOP may help identify individuals at higher risk of brain atrophy.\u003c/p\u003e\n\u003cp\u003eWe found the association between IOP and volumes of total brain and WMH was more prominent in younger than in older individuals. This is in line with previous studies demonstrating that hypertension diagnosed at a younger age was associated with a larger reduction in brain volume.[\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e] The association between IOP and brain volume was stronger in those free of systemic hypertension. The underlying mechanisms are unclear, but a previous study suggests the association between main eye diseases and dementia was stronger among those without systemic diseases.[\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e] The lower rate of diagnosis and treatment of hypertension among younger individuals may also explain why the association was stronger among those without hypertension. We also found the association between BP and total brain volume was stronger among individuals with lower education. This may be explained by the fact that individuals who were highly educated were more likely to seek health care and thus less likely to result in brain volume loss with ageing. BP appears to be more predictive of brain volumes in women than in men, which needs to be confirmed in future research.\u003c/p\u003e\n\u003cp\u003eOur findings suggest the effect sizes of BP and IOP might be relatively small, the use of anti-glaucoma or antihypertensive medications, or other treatments might minimize the risk of neurological or cognitive disorders because of these small effects on brain volume maintenance in the aging population. To our knowledge, this is the first study to examine the association of IOP and BP with brain volumes in both observational and MR analyses. Our study has several potential limitations. Firstly, causal relationships cannot be established based on our analysis of the phenotypic association between IOP/BP and brain volumes given the cross-sectional design. However, those observational findings are confirmed by MR analyses. Secondly, MRI and IOP data were collected in a small subgroup of the UK Biobank cohort, which may limit the generalizability of our findings to the whole population. However, the inverse probability weighting analysis showed similar results to the main findings. Thirdly, the analysis was conducted among individuals of European ancestry such that the findings may not be applied to other ethnic groups.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our findings suggest higher IOP is casually linked to a larger reduction in grey matter volume while increased DBP is casually linked to higher WMH load. Younger individuals and those without hypertension are more in need of care for the prevention of brain volume reduction potentially via IOP lowering. The association between BP and brain volume reduction is stronger among younger individuals, women, and lowly educated individuals.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAPOE4 = Apolipoprotein E \u0026epsilon;4; DBP = diastolic blood pressure; IOP = intraocular pressure; IV = instrumental variable; IVW = inverse-variance weighting; MR = Mendelian Randomisation; PRESSO = Pleiotropy Residual Sum and Outlier method; SBP = systolic blood pressure; SNP = single nucleotide polymorphism; WMH = white matter hyperintensity\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe UK Biobank Study\u0026rsquo;s ethical approval had been granted by the National Information Governance Board for Health and Social Care and the NHS North West Multicenter Research Ethics Committee. All participants provided informed consent through electronic signature at baseline assessment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXS receives support from Postdoctoral Research Funds of Guangdong Provincial People\u0026rsquo;s Hospital (BY012021047). ZZ receives support from the National Natural Science Foundation of China (82101173), the Research Foundation of Medical Science and Technology of Guangdong Province (B2021237). HY receives support from the National Natural Science Foundation of China (81870663, 82171075), the Outstanding Young Talent Trainee Program of Guangdong Provincial People\u0026rsquo;s Hospital (KJ012019087), Guangdong Provincial People\u0026rsquo;s Hospital Scientific Research Funds for Leading Medical Talents and Distinguished Young Scholars in Guangdong Province (KJ012019457), Talent Introduction Fund of Guangdong Provincial People\u0026rsquo;s Hospital (Y012018145). MH receives support from the High-level Talent Flexible Introduction Fund of Guangdong Provincial People\u0026rsquo;s Hospital (No. KJ012019530). MH also receives support from the University of Melbourne at Research Accelerator Program and the CERA Foundation. The Centre for Eye Research Australia receives Operational Infrastructure Support from the Victorian State Government. The sponsor or funding organization had no role in the design or conduct of this research. The sponsor or funding organization had no role in the design, conduct, analysis, or reporting of this study. The funding sources did not participate in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design of the study: XS, YH, HY, XY and MH. Acquisition and analysis of data: XS, YH, SZ, ZZ, XLZ, XYZ and JHL.Writing - original draft: XS and YH. Writing - review \u0026amp; editing: XS, YH, ZZ, XLZ, WW, XYZ, JL, JHL, ST, ZG, YJH, HY, XY and MH. Figure drafting: XS, HY and JL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was conducted using the UK Biobank resource. We thank the participants of the UK Biobank.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available in a public, open access repository (https://www.ukbiobank.ac.uk/)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFeigin V.L., Nichols E., Alam T., Bannick M.S., Beghi E., Blake N., Culpepper W.J., Dorsey R.A., Elbaz A., Ellenbogen R.G.: Global, regional, and national burden of neurological disorders, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. \u003cem\u003eLancet Neurol \u003c/em\u003e2019, 18(5):459-480.\u003c/li\u003e\n\u003cli\u003eSweeney MD, Kisler K, Montagne A, Toga AW, Zlokovic BV: The role of brain vasculature in neurodegenerative disorders. \u003cem\u003eNat Neurosci \u003c/em\u003e2018, 21(10):1318-1331.\u003c/li\u003e\n\u003cli\u003eWang M, Roussos P, McKenzie A, Zhou X, Kajiwara Y, Brennand KJ, De Luca GC, Crary JF, 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der Flier WM, Barkhof F, Scheltens P, Tijms BM: Grey matter network trajectories across the Alzheimer\u0026apos;s disease continuum and relation to cognition. \u003cem\u003eBrain communications \u003c/em\u003e2020, 2(2):fcaa177.\u003c/li\u003e\n\u003cli\u003ePase MP, Davis-Plourde K, Himali JJ, Satizabal CL, Aparicio H, Seshadri S, Beiser AS, DeCarli C: Vascular risk at younger ages most strongly associates with current and future brain volume. \u003cem\u003eNeurology \u003c/em\u003e2018, 91(16):e1479-e1486.\u003c/li\u003e\n\u003cli\u003eShang X, Zhu Z, Huang Y, Zhang X, Wang W, Shi D, Jiang Y, Yang X, He M: Associations of ophthalmic and systemic conditions with incident dementia in the UK Biobank. \u003cem\u003eBr J Ophthalmol \u003c/em\u003e2021.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":" Tables","content":"\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eBaseline characteristics of participants by quintiles of intraocular pressure\u003c/div\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eVariables\u003c/div\u003e\n\u003c/th\u003e\n\u003cth colspan=\"5\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eIntraocular pressure\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eP-value*\u003c/div\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eQuintile 1\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;1731)\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eQuintile 2\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;1720)\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eQuintile 3\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;1726)\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eQuintile 4\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;1730)\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eQuintile 5\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;1727)\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eRange (mmHg)\u003c/span\u003e\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eAge (years)\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;12.5\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e54.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e12.5\u0026ndash;14.3\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e55.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e14.4\u0026ndash;16.0\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e55.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e16.1\u0026ndash;18.2\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e56.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026gt;\u0026thinsp;18.2\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e56.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eGender\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.21\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eFemale\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e898 (51.9)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e922 (53.6)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e931 (53.9)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e929 (53.7)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e854 (49.4)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eMale\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e833 (48.1)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e798 (46.4)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e795 (46.1)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e801 (46.3)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e873 (50.6)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eAPOE4\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.94\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1301 (75.2)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1268 (73.7)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1283 (74.3)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1306 (75.5)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1278 (74.0)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eYes\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e387 (22.4)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e396 (23.0)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e407 (23.6)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e390 (22.5)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e405 (23.5)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eMissing\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e43 (2.5)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e56 (3.3)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e36 (2.1)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e34 (2.0)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e44 (2.5)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eEducation\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.90\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0\u0026ndash;5 years\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e84 (4.9)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e99 (5.8)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e84 (4.9)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e98 (5.7)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e108 (6.3)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e6\u0026ndash;12 years\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e835 (48.2)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e800 (46.5)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e834 (48.3)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e832 (48.1)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e782 (45.3)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026ge;13 years\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e808 (46.7)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e817 (47.5)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e802 (46.5)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e792 (45.8)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e828 (47.9)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eMissing\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e4 (0.2)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e4 (0.2)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e6 (0.3)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e8 (0.5)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e9 (0.5)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eHousehold income (pounds)\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;18,000\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e174 (10.1)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e168 (9.8)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e169 (9.8)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e182 (10.5)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e188 (10.9)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e18,000\u0026ndash;30,999\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e328 (18.9)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e332 (19.3)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e337 (19.5)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e348 (20.1)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e392 (22.7)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e31,000\u0026ndash;51,999\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e473 (27.3)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e482 (28.0)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e458 (26.5)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e488 (28.2)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e475 (27.5)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e52,000-100,000\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e485 (28.0)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e464 (27.0)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e491 (28.4)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e433 (25.0)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e424 (24.6)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026gt;100,000\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e143 (8.3)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e141 (8.2)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e135 (7.8)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e123 (7.1)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e110 (6.4)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eUnknown\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e27 (1.6)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e27 (1.6)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e38 (2.2)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e33 (1.9)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e37 (2.1)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eNot answered\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e101 (5.8)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e106 (6.2)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e98 (5.7)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e123 (7.1)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e101 (5.8)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003ePhysical activity (MET-minutes/week)\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2596\u0026thinsp;\u0026plusmn;\u0026thinsp;2476\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2593\u0026thinsp;\u0026plusmn;\u0026thinsp;2307\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2468\u0026thinsp;\u0026plusmn;\u0026thinsp;2168\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2572\u0026thinsp;\u0026plusmn;\u0026thinsp;2256\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2489\u0026thinsp;\u0026plusmn;\u0026thinsp;2182\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.18\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eAlcohol consumption\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.20\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eNever\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e35 (2.0)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e38 (2.2)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e30 (1.7)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e33 (1.9)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e38 (2.2)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003ePrevious\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e49 (2.8)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e33 (1.9)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e34 (2.0)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e36 (2.1)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e30 (1.7)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCurrent\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1647 (95.1)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1649 (95.9)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1661 (96.2)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1661 (96.0)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1659 (96.1)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eMissing\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1 (0.1)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eSmoking\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.93\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eNever\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1067 (61.6)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1053 (61.2)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1049 (60.8)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1044 (60.3)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1053 (61.0)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eFormer\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e558 (32.2)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e565 (32.8)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e570 (33.0)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e585 (33.8)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e579 (33.5)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCurrent\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e105 (6.1)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e99 (5.8)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e103 (6.0)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e97 (5.6)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e89 (5.2)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eMissing\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1 (0.1)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3 (0.2)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e4 (0.2)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e4 (0.2)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e6 (0.3)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eSleep duration (hours)\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e7.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e7.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e7.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e7.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e7.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eBMI (kg/m\u003c/span\u003e\u003csup\u003e\u003cspan class=\"Bold\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003cspan class=\"Bold\"\u003e)\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e26.36\u0026thinsp;\u0026plusmn;\u0026thinsp;4.06\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e26.46\u0026thinsp;\u0026plusmn;\u0026thinsp;4.11\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e26.74\u0026thinsp;\u0026plusmn;\u0026thinsp;4.33\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e26.80\u0026thinsp;\u0026plusmn;\u0026thinsp;3.92\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e27.07\u0026thinsp;\u0026plusmn;\u0026thinsp;4.37\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eTotal cholesterol (mmol/L)\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e5.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e5.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e5.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e5.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e5.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0032\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eHDL-C (mmol/L)\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.13\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eLDL-C (mmol/L)\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0083\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eTriglycerides (mmol/L)\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0733\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eHbA1c (mmol/mol)\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e34.66\u0026thinsp;\u0026plusmn;\u0026thinsp;4.33\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e35.01\u0026thinsp;\u0026plusmn;\u0026thinsp;4.54\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e35.00\u0026thinsp;\u0026plusmn;\u0026thinsp;4.41\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e35.47\u0026thinsp;\u0026plusmn;\u0026thinsp;5.48\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e35.66\u0026thinsp;\u0026plusmn;\u0026thinsp;5.98\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eHypertension\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e279 (16.1)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e308 (17.9)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e367 (21.3)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e371 (21.4)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e393 (22.8)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eHeart disease\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e34 (2.0)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e27 (1.6)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e43 (2.5)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e43 (2.5)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e42 (2.4)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0972\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eDepression\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e103 (6.0)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e95 (5.5)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e85 (4.9)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e76 (4.4)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e75 (4.3)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0091\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eGlaucoma\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e8 (0.5)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e8 (0.5)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e17 (1.0)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e25 (1.4)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e49 (2.8)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eMedication for glaucoma\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e4 (0.2)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e4 (0.2)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e5 (0.3)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e9 (0.5)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0277\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003eData are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations, or N (%). BMI, body mass index; HbA1c, glycated haemoglobin; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; MET, metabolic equivalent.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003e*ANOVA was used to test the difference of continuous variables across quintiles of IOP and Chi-square for categorical variables.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAssociation between intraocular pressure and brain volumes\u003c/div\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"5\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eIntraocular pressure (mmHg)\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eP-value\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eEach 5-mmHg\u003c/div\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eBrain volume\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eQuintile 1,\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;12.5\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;1731)\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eQuintile 2,\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e12.5\u0026ndash;14.3\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;1720)\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eQuintile 3,\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e14.4\u0026ndash;16.0\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;1726)\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eQuintile 4,\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e16.1\u0026ndash;18.2\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;1730)\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eQuintile 5,\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026gt;\u0026thinsp;18.2\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;1727)\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003efor trend\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eincrement*\u003c/div\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eTotal brain (ml)\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 1\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-4.77 (-9.60, 0.06)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-8.80 (-13.62, -3.98)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-14.84 (-19.66, -10.02)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-19.54 (-24.36, -14.72)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-9.50 (-11.63, -7.36)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.26 (-5.27, 2.76)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-2.02 (-6.03, 2.00)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-5.68 (-9.70, -1.66)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-6.98 (-11.01, -2.95)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-3.57 (-5.35, -1.79)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.58 (-4.74, 3.59)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.68 (-5.83, 2.47)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-4.09 (-8.27, 0.09)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-5.19 (-9.39, -0.98)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0039\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-3.11 (-4.89, -1.33)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.66 (-4.83, 3.52)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.81 (-5.97, 2.36)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-4.46 (-8.67, -0.24)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-5.50 (-9.76, -1.25)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0024\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-3.24 (-5.05, -1.44)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eGrey matter (ml)\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-2.45 (-5.62, 0.72)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-5.33 (-8.51, -2.16)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-8.13 (-11.30, -4.97)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-12.02 (-15.19, -8.85)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-5.93 (-7.33, -4.53)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.24 (-2.64, 2.16)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.89 (-3.29, 1.51)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.99 (-4.39, 0.41)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-2.87 (-5.28, -0.47)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0064\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.55 (-2.62, -0.49)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.13 (-2.34, 2.60)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.51 (-2.98, 1.95)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.93 (-3.41, 1.55)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.83 (-4.33, 0.66)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0981\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.21 (-2.27, -0.16)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.25 (-2.23, 2.73)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.22 (-2.69, 2.26)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.82 (-3.32, 1.69)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.54 (-4.07, 0.99)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.15\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.10 (-2.17, -0.03)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eWhite matter (ml)\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-2.32 (-5.03, 0.39)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-3.46 (-6.17, -0.76)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-6.71 (-9.41, -4.00)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-7.52 (-10.23, -4.81)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-3.56 (-4.76, -2.37)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.01 (-3.60, 1.58)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.13 (-3.72, 1.46)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-3.69 (-6.28, -1.10)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-4.11 (-6.70, -1.51)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0002\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-2.02 (-3.17, -0.87)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.70 (-3.40, 1.99)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.16 (-3.85, 1.52)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-3.17 (-5.87, -0.46)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-3.35 (-6.07, -0.63)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0032\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.90 (-3.05, -0.74)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.90 (-3.60, 1.79)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.59 (-4.28, 1.10)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-3.64 (-6.36, -0.91)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-3.96 (-6.71, -1.21)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0007\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-2.14 (-3.32, -0.97)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eHippocampus (\u0026micro;l)\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-5.91 (-35.04, 23.23)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-21.26 (-50.37, 7.85)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-35.97 (-65.06, -6.88)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-29.58 (-58.69, -0.48)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-15.74 (-28.61, -2.88)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e10.50 (-16.57, 37.56)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e6.43 (-20.64, 33.51)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.00 (-28.09, 26.10)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3.84 (-23.32, 30.99)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.90\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.26 (-13.26, 10.75)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e8.34 (-19.74, 36.43)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3.53 (-24.43, 31.49)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2.89 (-25.28, 31.06)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e7.79 (-20.54, 36.13)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.75\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.39 (-11.61, 12.39)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e8.16 (-20.01, 36.33)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e4.84 (-23.29, 32.96)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2.79 (-25.66, 31.24)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e9.72 (-19.01, 38.45)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.67\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.74 (-10.46, 13.94)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eWhite matter hyperintensity (ml)\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.23 (-0.49, 0.02)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.05 (-0.32, 0.23)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.09 (-0.34, 0.17)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.16 (-0.10, 0.41)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.11\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.08 (-0.03, 0.18)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.20 (-0.43, 0.02)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.11 (-0.36, 0.13)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.16 (-0.39, 0.06)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.02 (-0.20, 0.24)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.73\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.02 (-0.07, 0.16)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.20 (-0.44, 0.04)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.05 (-0.31, 0.21)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.16 (-0.41, 0.08)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.01 (-0.22, 0.25)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.74\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.03 (-0.06, 0.12)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.21 (-0.45, 0.03)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.06 (-0.32, 0.20)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.21 (-0.46, 0.03)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026minus;\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.02 (-0.26, 0.22)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.97\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.74 (-10.46, 13.94)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003e*General linear regression models were used to estimate the association between intraocular pressure and brain volumes.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eModel 1 was unadjusted model; Model 2 was adjusted for age and gender; Model 3 was adjusted for Model 2 plus APOE4, education, income, depression, diabetes, alcohol consumption, physical activity, smoking, sleep duration, BMI, HDL-C, LDL-C, and triglycerides; Model 4 was adjusted for Model 3 plus blood pressure, hypertension, glaucoma, and medication use for hypertension/glaucoma.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAssociation between systolic blood pressure and brain volumes\u003c/div\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eBrain volume\u003c/div\u003e\n\u003c/th\u003e\n\u003cth colspan=\"5\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSystolic blood pressure (mmHg)\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eP-value\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eEach 10-mmHg\u003c/div\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eQuintile 1,\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;120.0\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;7444)\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eQuintile 2,\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e120.0-128.0\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eQuintile 3,\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e129.0-137.0\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eQuintile 4,\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e138.0-148.0\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eQuintile 5,\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026gt;\u0026thinsp;148.0\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003efor trend\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eincrement*\u003c/div\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;6717)\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;7331)\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;7170)\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;7407)\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eTotal brain (ml)\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 1\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-13.90 (-16.26, -11.54)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-21.69 (-24.00, -19.38)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-28.40 (-30.73, -26.08)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-40.75 (-43.05, -38.45)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-7.88 (-8.30, -7.46)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.35 (-3.36, 0.67)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.01 (-3.00, 0.99)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.67 (-1.37, 2.70)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.39 (-2.45, 1.68)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.62\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.06 (-0.32, 0.43)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.53 (-3.61, 0.56)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.27 (-2.36, 1.81)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.18 (-0.96, 3.33)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.37 (-1.81, 2.56)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.17\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.24 (-0.14, 0.63)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3.16 (-1.33, 7.65)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.50 (-4.26, 5.26)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e6.15 (0.82, 11.47)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e8.70 (2.77, 14.62)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0032\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.00 (0.19, 1.81)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eGrey matter (ml)\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-12.60 (-14.13, -11.06)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-20.16 (-21.65, -18.66)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-26.67 (-28.18, -25.17)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-35.60 (-37.10, -34.11)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-6.92 (-7.19, -6.65)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.30 (-2.51, -0.09)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.96 (-3.15, -0.76)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-2.06 (-3.28, -0.84)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-3.11 (-4.34, -1.87)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.57 (-0.79, -0.34)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.11 (-2.35, 0.13)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.13 (-2.37, 0.11)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.08 (-2.36, 0.19)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.88 (-3.18, -0.58)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0133\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.28 (-0.51, -0.05)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.65 (-1.01, 4.32)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.80 (-3.63, 2.03)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2.05 (-1.12, 5.22)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2.66 (-0.86, 6.19)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.15\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.11 (-0.37, 0.59)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eWhite matter (ml)\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.30 (-2.65, 0.04)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.54 (-2.85, -0.22)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.73 (-3.05, -0.41)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-5.15 (-6.46, -3.84)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.96 (-1.20, -0.72)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.05 (-1.34, 1.25)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.95 (-0.34, 2.24)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2.73 (1.41, 4.04)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2.72 (1.38, 4.05)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.62 (0.38, 0.87)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.42 (-1.77, 0.93)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.86 (-0.49, 2.21)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2.26 (0.88, 3.65)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2.25 (0.84, 3.67)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.52 (0.27, 0.77)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.51 (-1.40, 4.41)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.30 (-1.78, 4.38)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e4.10 (0.65, 7.54)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e6.03 (2.20, 9.86)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0012\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.89 (0.36, 1.42)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eHippocampus (\u0026micro;l)\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e9.01 (-5.57, 23.59)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e8.60 (-5.65, 22.86)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-7.48 (-21.81, 6.86)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-59.69 (-73.91, -45.47)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-12.34 (-14.92, -9.77)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-2.65 (-16.35, 11.06)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-2.08 (-15.67, 11.52)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-3.26 (-17.13, 10.62)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-19.87 (-33.95, -5.80)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0122\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-3.83 (-6.40, -1.26)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-6.53 (-20.73, 7.66)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.48 (-15.69, 12.74)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-2.53 (-17.15, 12.08)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-22.24 (-37.13, -7.35)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0161\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-4.00 (-6.63, -1.37)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-3.77 (-34.06, 26.53)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.03 (-31.11, 33.16)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-3.25 (-39.22, 32.71)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-30.95 (-70.96, 9.05)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.17\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.63 (-7.12, 3.85)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eWMH (ml)\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.23 (0.19, 0.27)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.35 (0.31, 0.39)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.53 (0.49, 0.57)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.76 (0.72, 0.80)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.15 (0.14, 0.16)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.11 (0.07, 0.14)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.15 (0.12, 0.19)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.24 (0.21, 0.28)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.33 (0.29, 0.37)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.07 (0.06, 0.08)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.10 (0.06, 0.13)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.13 (0.09, 0.17)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.21 (0.17, 0.25)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.29 (0.25, 0.33)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.06 (0.05, 0.07)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.18 (-0.09, 0.44)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.01 (-0.26, 0.28)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.05 (-0.25, 0.36)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.10 (-0.27, 0.46)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.90\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.04 (-0.01, 0.09)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003e*General linear regression models were used to estimate the association between systolic blood pressure and brain volumes.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eModel 1 was unadjusted model; Model 2 was adjusted for age and gender; Model 3 was adjusted for Model 2 plus APOE4, education, income, depression, diabetes, alcohol consumption, physical activity, smoking, sleep duration, BMI, HDL-C, LDL-C, and triglycerides; Model 4 was adjusted for Model 3 plus IOP, glaucoma, and medication use for hypertension/glaucoma.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAssociation between diastolic blood pressure and brain volumes\u003c/div\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eBrain volume\u003c/div\u003e\n\u003c/th\u003e\n\u003cth colspan=\"5\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eDiastolic blood pressure (mmHg)\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eP-value\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eEach 10-mmHg\u003c/div\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eQuintile 1,\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;73.0\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eQuintile 2,\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e73.0\u0026ndash;78.0\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eQuintile 3,\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e79.0\u0026ndash;83.0\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eQuintile 4,\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e84.0\u0026ndash;89.0\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eQuintile 5,\u003c/div\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026gt;\u0026thinsp;89.0\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003efor trend\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eincrement*\u003c/div\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;7601)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;6525)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;7254)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;7243)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;7446)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eTotal brain (ml)\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 1\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-6.60 (-9.01, -4.20)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-10.08 (-12.41, -7.74)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-16.08 (-18.41, -13.74)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-18.55 (-20.87, -16.22)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-6.48 (-7.23, -5.72)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.39 (-2.39, 1.61)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.79 (-3.74, 0.17)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-3.53 (-5.50, -1.56)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-4.34 (-6.31, -2.37)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.64 (-2.29, -1.00)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.65 (-1.42, 2.73)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.67 (-2.71, 1.37)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-2.09 (-4.18, -0.00)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-2.63 (-4.76, -0.50)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0014\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.20 (-1.88, -0.52)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-2.35 (-6.73, 2.04)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-4.26 (-8.77, 0.24)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-5.72 (-10.70, -0.74)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-4.94 (-10.68, 0.80)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0477\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.38 (-1.80, 1.03)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eGrey matter (ml)\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-8.02 (-9.59, -6.45)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-10.87 (-12.40, -9.34)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-16.25 (-17.78, -14.72)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-20.07 (-21.59, -18.55)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-6.90 (-7.39, -6.40)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.97 (-3.17, -0.77)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-2.68 (-3.85, -1.51)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-4.08 (-5.26, -2.90)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-5.87 (-7.05, -4.69)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-2.01 (-2.40, -1.63)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.16 (-2.39, 0.08)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.52 (-2.74, -0.31)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-2.42 (-3.66, -1.18)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-3.80 (-5.06, -2.53)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.31 (-1.71, -0.90)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-2.21 (-4.82, 0.39)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-3.43 (-6.11, -0.75)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-3.81 (-6.77, -0.85)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-3.25 (-6.66, 0.16)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0395\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.82 (-1.66, 0.02)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eWhite matter (ml)\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.42 (0.06, 2.77)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.79 (-0.52, 2.10)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.18 (-1.14, 1.49)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.53 (0.22, 2.83)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.19\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.42 (-0.00, 0.85)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.58 (0.29, 2.87)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.89 (-0.37, 2.15)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.55 (-0.72, 1.82)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.53 (0.26, 2.80)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.14\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.37 (-0.05, 0.78)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.81 (0.46, 3.16)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.85 (-0.47, 2.17)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.33 (-1.03, 1.68)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.16 (-0.22, 2.54)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.55\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.11 (-0.33, 0.55)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.13 (-2.96, 2.70)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-0.83 (-3.74, 2.08)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.92 (-5.14, 1.30)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.69 (-5.40, 2.02)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.24\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.43 (-0.49, 1.35)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eHippocampus (\u0026micro;l)\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e19.44 (4.81, 34.08)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e21.77 (7.54, 36.01)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e19.40 (5.16, 33.64)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e35.21 (21.07, 49.36)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e12.27 (7.66, 16.87)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e5.78 (-7.88, 19.43)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.14 (-12.19, 14.47)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-7.47 (-20.90, 5.97)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-3.69 (-17.13, 9.74)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.20\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-2.05 (-6.44, 2.34)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e4.67 (-9.49, 18.83)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-4.43 (-18.34, 9.49)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-9.87 (-24.12, 4.38)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-8.77 (-23.28, 5.74)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0623\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-4.66 (-9.28, -0.03)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-13.78 (-43.36, 15.81)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e13.01 (-17.37, 43.40)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3.54 (-30.08, 37.15)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e19.38 (-19.38, 58.13)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.25\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2.18 (-7.40, 11.75)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eWMH (ml)\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.13 (0.09, 0.17)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.23 (0.19, 0.27)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.35 (0.30, 0.39)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.50 (0.46, 0.54)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.18 (0.17, 0.19)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.07 (0.04, 0.11)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.15 (0.11, 0.19)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.23 (0.19, 0.26)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.37 (0.34, 0.41)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.14 (0.13, 0.15)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.07 (0.03, 0.10)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.13 (0.09, 0.17)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.19 (0.15, 0.23)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.33 (0.29, 0.37)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.0001\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.13 (0.11, 0.14)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026beta; (95% CI), Model 4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eReference\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.11 (-0.14, 0.36)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.12 (-0.15, 0.39)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.21 (-0.09, 0.51)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.39 (0.04, 0.73)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.0386\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.13 (0.05, 0.21)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003e*General linear regression models were used to estimate the association between diastolic blood pressure and brain volumes.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eModel 1 was unadjusted model; Model 2 was adjusted for age and gender; Model 3 was adjusted for Model 2 plus APOE4, education, income, depression, diabetes, alcohol consumption, physical activity, smoking, sleep duration, BMI, HDL-C, LDL-C, and triglycerides; Model 4 was adjusted for Model 3 plus IOP, glaucoma, and medication use for hypertension/glaucoma.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\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":"Intraocular pressure, blood pressure, brain volume, Mendelian randomization, moderation analysis","lastPublishedDoi":"10.21203/rs.3.rs-2798166/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2798166/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIt is unclear whether brain volumes are causally affected by Intraocular pressure (IOP) is highly correlated with blood pressure (BP).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe study included 8634 participants for IOP and 36069 participants for BP in observational analyses and 37410 participants for both IOP and BP in Mendelian Randomisation (MR) analyses from UK Biobank. IOP and BP were measured between 2006\u0026ndash;2010. Brain volumes were measured using MRI between 2014\u0026ndash;2019.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eHigher IOP was associated with smaller volumes of total brain (β (95% CI) for each 5-mmHg increment: -3.24 (-5.05, -1.44) ml) and grey matter (-1.10 (-2.17, -0.03) ml) independent of BP. Diastolic BP (β (95% CI) for each 10-mmHg increment: 0.13 (0.05, 0.21)) was associated with higher white matter hyperintensity (WMH) independent of antihypertensive medications. Associations between IOP and total brain and WMH volumes were stronger in younger individuals or those without hypertension. Associations between DBP/SBP and brain volumes were stronger in younger individuals, women, and lowly educated individuals. All MR analytic methods demonstrated a significant relationship between DBP and WMH (β (95% CI) for each 10-mmHg increment of DBP for inverse-variance weighting method: 0.019 (0.013, 0.026)). The β (95% CI) for grey matter volume (ml) associated with each 5-mmHg increment of IOP for inverse-variance weighting method was \u0026minus;\u0026thinsp;3.42 (-5.39, -1.45).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eHigher IOP is casually linked to larger grey matter volume reduction while increased DBP casually linked to higher WMH load. Younger or lowly educated individuals deserve more scrutiny for the prevention of brain volume reduction potentially via IOP/DBP lowering.\u003c/p\u003e","manuscriptTitle":"Intraocular pressure, systemic blood pressure, and brain volumes: observational and Mendelian Randomization analyses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-04-25 19:12:14","doi":"10.21203/rs.3.rs-2798166/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":"75a22ee6-60f4-4803-8852-72311172ad3f","owner":[],"postedDate":"April 25th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-05-21T17:29:47+00:00","versionOfRecord":[],"versionCreatedAt":"2023-04-25 19:12:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2798166","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2798166","identity":"rs-2798166","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}