Late-life high blood pressure is associated with enlarged perivascular spaces in community-dwelling Japanese older persons: The Japan Prospective Studies Collaboration for Aging and Dementia (JPSC-AD) study

doi:10.21203/rs.3.rs-2132146/v1

Background

Enlarged perivascular spaces (EPVS) of the brain may be involved in dementia, such as Alzheimer’s disease and cerebral small vessel disease (CSVD). Hypertension has been reported to be a risk factor for dementia and CSVD, but the association between blood pressure (BP) and perivascular spaces is still unclear. The aim of this study was to determine the association between BP and EPVS volumes and to examine the interactions of relevant factors.

Methods

A total of 9,296 community-dwelling subjects aged ≥ 65 years participated in a brain magnetic resonance imaging and health status screening examination. Perivascular volume was measured using a software package based on deep learning that was developed in-house. The associations between BP and the EPVS volumes were examined by analysis of covariance and multiple regression analysis.

Results

The mean EPVS volumes increased significantly with rising systolic and diastolic BP levels (p for trend = 0.003, p for trend < 0.001, respectively). In addition, the mean EPVS volumes increased significantly for every 1-mmHg-increment in systolic and diastolic BPs (both p values < 0.001). These significant associations were still observed in the sensitivity analysis after excluding subjects with dementia.

Conclusions

The present data suggest that higher systolic and diastolic BP levels are associated with greater EPVS volumes in cognitively normal older people.

perivascular spaces

blood pressure

dementia

population-based study

systolic blood pressure

Perivascular spaces in the brain are potential spaces containing fluid around the walls of vessels. Enlarged perivascular spaces (EPVS) can be visualized on magnetic resonance imaging (MRI) ¹. Conventionally, the presence of EPVS is known to be an age-related phenomenon ². On the other hand, perivascular spaces are considered to be involved in the clearance of waste products from the brain including amyloid-β (Aβ) ^3–5. Recent epidemiological studies have reported that EPVS are significantly associated with dementia and Alzheimer’s disease (AD) ^6–8. In addition, EPVS are widely recognized as a feature of cerebral small vessel diseases (CSVD) associated with stroke and cognitive decline ⁹. Dementia is a rapidly growing public health problem worldwide. According to the World Alzheimer Report 2019, by 2050, the number of persons with dementia will reach 152 million ¹⁰. There is an urgent need to reduce the risks of dementia. Therefore, the importance of elucidating the pathophysiology of EPVS is increasingly being recognized.

Hypertension has been acknowledged to be an important risk factor for CSVD and dementia ¹¹. Several studies using brain MRI data have investigated the association of hypertension or blood pressure (BP) with EPVS, but the results of epidemiological studies are inconsistent ^2,12−14. Several studies have reported that hypertension is significantly associated with EPVS ^12–14, whereas one study reported a negative association of hypertension with EPVS ². In addition, there have been no large-scale, population-based studies that adjusted for possible confounding factors such as environmental and genetic factors.

The Japan Prospective Studies Collaboration for Aging and Dementia (JPSC-AD) is a multi-site, population-based, prospective cohort study designed to enroll at least 10,000 community-dwelling older residents ¹⁵. JPSC-AD was established to identify environmental and genetic risk factors for dementia, as well as their interactions ^16–18. The aim of the present study was to investigate the associations of BP with EPVS volumes and to investigate the interactions between BP and several variables, including dementia, using cross-sectional data from a general Japanese older population.

Study population

The JPSC-AD study is a multisite, population-based, prospective cohort study of dementia involving 8 sites in Japan ¹⁵. This study was approved by the Medical Ethics Review Board of Ehime University (approval number 1610004) and Kyusyu University’s Institutional Review Board for clinical research (approval number 686-06). Written, informed consent was obtained from all participants. This study was performed in accordance with the Declaration of Helsinki. From 2016 to 2018, a total of 11,957 residents participated in the present study. Of these, after exclusion of 1,867 participants without available brain MRI data with three-dimensional T1-weighted images (T1W1), 17 participants for whom an automated FreeSurfer analysis was not successfully completed, 151 participants with outlier data for estimated total intracranial volume (eTIV) and regional brain volume, 443 participants aged less than 65 years, and 183 participants with missing BP data, the remaining 9,296 participants were eligible for the present study (Fig. 1).

Mri Acquisition

The equipment for brain MRI was set with T1WI parameters according to the protocol of brain MRI for the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study at all research sites ¹⁹. Details of the MRI at each site are described in Supplemental Table 1.

Mri Analysis

Volumetric processing and segmentation were performed using image analysis software at Tohoku University: a systematic, automated FreeSurfer 5.3 run (http://surfer.nmr.mgh.harvard.edu) intended for general-purpose structural brain analysis, and some additional processing for vessel formation. Vessel formation was segmented using in-house software based on machine-learning, which can detect well-demarcated linear or round hypo-intensities around the putaminal region on 3D-T1WI. A visual review of the perivascular segmentations derived from the FreeSurfer software was not satisfactory for the needs of this study, and so deep learning-based software was developed specifically for the present study, using a method similar to a previous process ¹⁸, to identify and segment both the putaminal region and the perivascular space (Fig. 2).

All results were visually confirmed before proceeding further to the volume analysis. The estimated total intracranial volume (eTIV) of each subject was also calculated using the standard FreeSurfer processing pipeline by exploiting the relationship between the intracranial volume and the linear transformation to the atlas template ²⁰. Vessel volume was used as the sum volume of perivascular space and vasculature in this study, which was calculated as the sum of the right and left vessel volumes measured by the in-house method. In the present study, vessel volume was evaluated as an indicator of EPVS.

Measurement Of Bp

Sitting BP was measured with an automated sphygmomanometer 3 times after at least 5 min of rest, and the mean of the 3 measurements was calculated. Pulse pressure (PP) was calculated as systolic blood pressure (SBP) – diastolic blood pressure (DBP) (mmHg). SBP and DBP levels were classified into 4 categories according to the criteria of the eighth report of the Joint National Committee on the Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC-8) ²¹. PP levels were divided into quartiles as follows: Q1 (< 52 mmHg), Q2 (52–60 mmHg), Q3 (61–70 mmHg), and Q4 (≥ 71 mmHg).

Measurements Of Other Risk Factors

Each participant answered a self-administered questionnaire that included educational background, medical history, alcohol consumption, smoking, medical treatments (antihypertensive and antidiabetic medications), and physical activity. Diabetes mellitus was defined as follows: fasting glucose level ≥ 7.0 mmol/L, casual glucose levels ≥ 11.1 mmol/L, or HbA1c ≥ 6.5%, using the 2010 American Diabetes Association (ADA) criteria ²² and/or current use of glucose-lowering agents. Serum total cholesterol levels were measured enzymatically. Hypercholesterolemia was defined as serum total cholesterol ≥ 5.7 mmol/L or current use of lipid-lowering medication. The body mass index (kg/m²) was measured as an indicator of obesity. Obesity was defined as body mass index ≥ 25 kg/m². Chronic kidney disease was defined as an estimated glomerular filtration rate < 60 ml/min/1.73 m², which was calculated using the modified Chronic Kidney Disease Epidemiology Collaboration Study equation multiplied by the Japanese coefficient of 0.813 ^23,24. Electrocardiogram abnormalities were defined as ST depression (Minnesota Code, 4 − 1,2,3), left ventricular hypertrophy (3 − 1), or atrial fibrillation (8 − 3). Regular exercise was defined as any physical activity performed for at least 30 minutes twice per week over the most recent year or longer. Alcohol consumption, smoking, and antihypertensive medications were classified as current or not.

Dementia was diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders, 3rd Revised Edition ²⁵. The diagnosis of dementia was made by expert psychiatrists and neurologists in the study team using a diagnostic system that was standardized across the 8 research sites. If the members of the endpoint adjudication committee were in agreement, the diagnosis was confirmed; if not, a meeting of the endpoint adjudication committee was held, and the diagnosis was confirmed through discussion. The diagnostic procedure has been reported previously ¹⁵. The apolipoprotein E (APOE) ε4 carrier state was defined as the presence of ε2/ε4, ε3/ε4, or ε4/ε4 alleles.

Statistical Analyses

Clinical characteristics were evaluated using logistic regression analysis for binary variables and linear regression analysis for continuous variables. The vessel volumes (hereafter denoted as the EPVS volumes) were log-transformed because of their skewed distribution and multiplied by a factor of 10² prior to the analyses and back-transformed for data presentation to facilitate the comparisons. The age and sex-adjusted and multivariate-adjusted mean values of the log₁₀ (EPVS volumes) in the groups defined by SBP, DBP, and PP values, respectively, were calculated using analysis of covariance (ANCOVA), and their trends across SBP, DBP, and PP groups were tested by multiple regression analyses. Multivariable adjustment was performed for age, sex, low educational level, use of antihypertensive medication, diabetes mellitus, serum total cholesterol, body mass index, electrocardiogram abnormalities, chronic kidney disease, history of stroke, current smoker status, current drinker status, regular exercise, dementia, apolipoprotein E ε4 carrier status, research site, and eTIV. The reason for adding eTIV as a covariate was to adjust for head size. Overall, 7.6% (= 707/9,296) of the participants selected for the analysis were excluded from the multivariable-adjusted analysis because of missing data for any covariate. Multiple regression analysis was used to investigate the relationships between SBP, DBP, and PP taken as continuous variables and log₁₀ (EPVS volumes). In the multivariable-adjusted model, the abovementioned factors were included. Heterogeneity in the associations of SBP and DBP with EPVS volumes between subgroups was tested by adding a multiplicative interaction term in the relevant statistical model. P values lower than 0.05 were considered significant. All analyses were performed using the Statistical Package for the Social Sciences (version 23; SPSS Inc., Chicago, IL, USA).

Baseline Characteristics of the Study Population

The clinical characteristics of the study population are summarized by SBP, DBP, and PP levels (Table 1, Supplemental Table 2). The proportions of participants with low educational attainment, diabetes mellitus, current alcohol use, current antihypertensive medication, electrocardiogram abnormalities, and mild cognitive impairment and the mean serum total cholesterol and body mass index levels increased significantly with higher SBP, whereas the proportions females, those with chronic kidney disease, and those with dementia decreased significantly with higher SBP (Table 1). By DBP levels, similar associations were observed for the proportions of participants of female sex, with current alcohol use, electrocardiogram abnormalities, and dementia and the mean serum total cholesterol and body mass index levels, whereas mean age and the proportions of those having low educational attainment, stroke, diabetes mellitus, chronic kidney disease, current antihypertensive medication, and mild cognitive impairment decreased significantly with higher DBP levels (Table 1). With regard to PP levels, the mean age and body mass index and the proportions of participants having low educational attainment, stroke, diabetes mellitus, chronic kidney disease, current antihypertensive medication, electrocardiogram abnormalities, mild cognitive impairment, and dementia increased significantly, and the proportion of those with current alcohol use decreased significantly with rising PP levels (Supplemental Table 2).

Associations of BP levels with EPVS volumes

First, the associations of BP levels with the EPVS volumes were investigated. As shown in Supplemental Figure 1, the age- and sex-adjusted mean values of log₁₀(EPVS volumes) increased significantly with increased SBP levels, being 2.049, 2.061, 2.070, and 2.092, for individuals with SBP <120, 120-139, 140-159, and ≥160 mmHg, respectively (p for trend <0.001). The values were significantly greater in those with SBP of 140-159 mmHg and SBP ≥160 mmHg than those with SBP <120 mmHg (p =0.008 and p <0.001, respectively). Similarly, higher DBP levels were associated significantly with the age- and sex-adjusted mean values of log₁₀(EPVS volumes), being 2.059, 2.072, 2.079, and 2.105 for individuals with DBP <80, 80-89, 90-99, and ≥100 mmHg, respectively (p for trend <0.001). Individuals with DBP of 80-89 mmHg, DBP 90-99 mmHg, and DBP>100 mmHg had significantly higher adjusted mean values of log₁₀(EPVS volumes) than those with DBP <80 mmHg (p=0.018, 0.010 and <0.001, respectively). With regards to PP, higher levels were also significantly associated with the age- and sex-adjusted mean values of log₁₀(EPVS volumes), being 2.058, 2.060, 2.066, and 2.085 for individuals with PP <52, 52-60, 61-70, and ≥71 mmHg, respectively (p for trend <0.001). Individuals with PP ≥71 mmHg had significantly higher adjusted mean values of log₁₀(EPVS volumes) than those with DBP <52 mmHg (p<0.001). Similar significant associations were observed after adjusting for age, sex, low educational level, use of antihypertensive medication, diabetes mellitus, serum total cholesterol, body mass index, electrocardiogram abnormalities, chronic kidney disease, history of stroke, current smoker status, current drinker status, regular exercise, dementia, apolipoprotein E ε4 carrier status, research site, and eTIV, except for PP levels (Figure 3). The multivariable-adjusted mean values of log₁₀(EPVS volumes) were 1.984, 1.990, 1.993, and 2.013 for individuals with SBP <120, 120-139, 140-159, and ≥160 mmHg (p for trend =0.003) and 1.984, 1.999, 2.003, and 2.026 for individuals with DBP <80, 80-89, 90-99, and ≥100 mmHg, respectively (both p values for trend <0.01). On the other hand, there was no evidence of significant associations between PP levels and the multivariable-adjusted mean values of log₁₀ (EPVS volumes) (p for trend =0.13). These significant associations of SBP and DBP with EPVS volumes were still observed in the sensitivity analysis after excluding 414 individuals with dementia (Supplemental Figure 2).

Next, the associations of SBP, DBP, or PP taken as a continuous variable with the EPVS volumes were investigated, incorporating the abovementioned variables to control for the effects of potential confounders. The multivariable-adjusted mean values of log₁₀(EPVS volumes) increased significantly for every 1-mmHg-increment in SBP and DBP (both p <0.001). (Table 2). With regards to PP, Change in mean values (95% CI) of log₁₀(EPVS volumes) per 1-mmHg-increment was 0.0002, but there were no similar significant relationships (p=0.15).

In addition, subgroup analyses of the associations of SBP or DBP with EPVS volumes for age (<75 or ≥75 years), sex, educational attainment, use of antihypertensive medication, diabetes mellitus, hypercholesterolemia, obesity, electrocardiogram abnormalities, chronic kidney disease, history of stroke, current smoker status, current drinker status, regular exercise, dementia, and APOE ε4 carrier status were performed. There was significant heterogeneity in the associations of SBP or DBP with EPVS volumes by age group, sex, use of antihypertensive medication, diabetes mellitus, and history of stroke (all p values for heterogeneity <0.05) after adjustment for other variables (Table 2).

The present study showed associations of higher SBP and DBP levels with greater EPVS volumes in a general older Japanese population. Even after multivariable adjustment, the EPVS volumes increased with rising SBP and DBP levels. SBP and DBP taken as continuous variables were positively correlated with EPVS volumes. In addition, these associations remained significant even in participants without dementia.

Several studies have examined the association between hypertension and EPVS ^2,12,14. However, only 3 studies have examined the associations of BP levels with EPVS ^12,14. One study reported the association of hypertension with EPVS, but no associations were found between SBP, DBP, or PP and EPVS ¹⁴. Another study reported the association of higher SBP or PP with EPVS, whereas no association of DBP with EPVS was observed ²⁶, at least in terms of statistical significance. Yet another study reported that higher day SBP and DBP levels were associated with EPVS, but PP levels were not examined ¹². The present findings were consistent with this study ¹² and may be more generalizable to older people than previous studies because of the larger sample size and the ability to adjust for confounding factors.

There may be several mechanisms underlying the associations between rising SBP and DBP levels and EPVS. Arterial wall motion is the principal mechanism driving the flow of cerebrospinal fluid through perivascular spaces in mice ²⁷. The study also provides evidence that hypertension causes arterial stiffness, which in turn induces changes in vessel dynamics and decreases flow in perivascular spaces. Therefore, rising SBP and DBP levels may decrease perivascular pumping and secondarily enlarge perivascular spaces. Another possible mechanism is that rising intraluminal pressure might increase extravasation of fluid through the small arteries into perivascular spaces, causing EPVS. This hypothesis is supported by the finding that the permeability of endothelial cells increases in hypertension ²⁸.

In the subgroup analyses, there was no evidence of heterogeneity in the associations of SBP or DBP with EPVS volumes in most of the subgroups, but significant heterogeneity in the association was detected among the subgroups by age, sex, use of antihypertensive medication, diabetes mellitus, and history of stroke (Table 2). Although there was significant heterogeneity in the associations among the subgroups by age and sex, the magnitude of the association was similar among each of the subgroups. On the other hand, the exact reasons for the heterogeneity in the associations among the subgroups by use of antihypertensive medication, diabetes mellitus, and history of stroke are unclear. The strength of the association between DBP and EPVS volumes was greater in the subgroup with diabetes mellitus than in that without diabetes mellitus. This finding may be the result of chance, or it may suggest that some diabetic factor affects the association between BP and EPVS. On the other hand, EPVS volumes are likely to be affected by the use of antihypertensive medication or history of stroke. Certainly, the EPVS volumes were greater in the participants with antihypertensive medication or history of stroke than in those without, even at lower BP levels (Supplemental Fig. 3). Moreover, the use of antihypertensive medication is likely to alter the association of BP with EPVS through its effect on BP. They are likely to mask the association between SBP or DBP and EPVS in antihypertensive medication users. With regard to a history of stroke, the previous study showed the association of SBP and DBP with EPVS in participants with a history of stroke ¹², but the present study found no significant association. The small sample size of participants with a history of stroke may have affected the results. Moreover, participants with a history of stroke are likely to be undergoing therapeutic treatment related to blood pressure or other risk factors. Therefore, further studies are needed to verify the associations between dementia and EPVS, which may lead to a better understanding of dementia.

The strengths of the present study are the large sample size and sufficient power to detect small changes. This gave the opportunity to adjust for possible confounding factors. In addition, the parameter used as an indicator of perivascular spaces is highly accurate. However, several limitations should be noted. First, this was a cross-sectional study. Therefore, the causal association between BP levels and perivascular spaces could not be evaluated. Second, the volume of perivascular spaces was estimated using in-house ROI software, with vessels that do not label a specific region, but rather contain several structures. This may have led to underestimation or overestimation of the volume of perivascular spaces. Third, individuals who did not undergo brain MRI were excluded from the present study. This might have resulted in selection bias. Finally, the generalizability of the present findings to other populations of ethnicities may be limited due to differing lifestyles and genetic backgrounds.

The present data showed that rising SBP and DBP levels are significantly associated with EPVS in cognitively normal older people. Further prospective longitudinal and basic research studies are required to clarify causal associations.

Data availability

All the processed data generated during this study are provided in the main article and Supplementary Information. The raw data is not openly available to protect the confidentiality of participants and to comply with the terms of participant consent. Requests related to the raw data should be addressed to the principal investigator, Toshiharu Ninomiya (Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan) [[email protected]] and the Japan Agency for Medical Research and Development.

Acknowledgments

The authors would like to acknowledge the diligent work and contributions of all researchers and investigators in the JPSC-AD Study Group. They would also like to thank the participants for their time in taking part in the JPSC-AD study.

Participating institutes and principal collaborators for the JPSC-AD Study group

Kyushu University ([Epidemiology and Public Health] Toshiharu Ninomiya, Jun Hata, and Mao Shibata, Takanori Honda, [Neuropsychiatry] Tomoyuki Ohara, [Ocular pathology and Imaging Science] Masato Akiyama); Hirosaki University (Shigeyuki Nakaji, Koichi Murashita, Tatsuya Mikami, Songee Jung, and Mina Misawa); Iwate Medical University (Tetsuya Maeda, Naoki Ishizuka, and Hiroshi Akasaka); Shonan Keiiku Hospital (Yasuo Terayama); Japanese Red Cross Morioka Hospital (Hisashi Yonezawa); Morioka Tsunagi Onsen Hospital (Junko Takahashi); Kanazawa University (Kenjiro Ono, Moeko Noguchi-Shinohara, Junji Komatsu, Shutaro Shibata, and Sohshi Yuki-Nozaki); Keio University School of Medicine (Masaru Mimura, Shogyoku Bun, Hidehito Niimura, Ryo Shikimoto, and Hisashi Kida); Matsue Medical Center (Kenji Nakashima, Yasuyo Fukada, and Hisanori Kowa); Kawasaki Medical School (Kenji Wada); Tottori Red Cross Hospital (Masafumi Kishi); Ehime University (Jun-ichi Iga, Tomoki Ozaki, Ayumi Tachibana, Yuta Yoshino, and Shu-ichi Ueno); Kumamoto University (Minoru Takebayashi, Tomohisa Ishikawa, Seiji Yuki, Asuka Koyama and Naoto Kajitani); Osaka University Medical School (Manabu Ikeda); Kindai University Faculty of Medicine (Mamoru Hashimoto); National Cerebral and Cardiovascular Center (Yoshihiro Kokubo); Nakamura-Gakuen University (Kazuhiro Uchida, and Midori Esaki); Tohoku University (Yasuyuki Taki, Yasuko Tatewaki, and Benjamin Thyreau); University of the Ryukyus (Koji Yonemoto); Osaka Metropolitan University Graduate School of Medicine (Hisako Yoshida); University of Tokyo (Kaori Muto, Yusuke Inoue); RIKEN Center for Integrative Medical Sciences (Yukihide Momozawa and Chikashi Terao); Hisayama Research Institute for Lifestyle Diseases (Michiaki Kubo and Yutaka Kiyohara)

Sources of funding

This study was supported by the Japan Agency for Medical Research and Development (JP22dk0207053) and Suntory Holdings Limited (Osaka, Japan). The funders had no role in the design of the study, the collection, analysis, and interpretation of data, or the writing of the manuscript.

Disclosure statement

Toshiharu Ninomiya received funds from the Japan Agency for Medical Research and Development and Suntory Holdings Limited (Osaka, Japan). The other authors assert that they have no competing interests.

Author contributions

AT, JI, TM, YT and TN designed research; BT, YT, HC, TO, TY, YY, HS, TM, YT, SN, TM, KO, MM, KN, JI, TN, MT contributed to the acquisition of data and the critical revision of the manuscript. YF, MS, TO, JH contributed to the critical revision of the manuscript; AT, JI and SU wrote the paper with inputs from all authors. All authors read and approved the final manuscript.

Kwee, R. M. & Kwee, T. C. Virchow-Robin spaces at MR imaging. Radiographics 27, 1071–1086, doi:10.1148/rg.274065722 (2007).
Heier, L. A. et al. Large Virchow-Robin spaces: MR-clinical correlation. AJNR Am J Neuroradiol 10, 929–936 (1989).
Iliff, J. J. et al. A paravascular pathway facilitates CSF flow through the brain parenchyma and the clearance of interstitial solutes, including amyloid β. Sci Transl Med 4, 147ra111, doi:10.1126/scitranslmed.3003748 (2012).
Gouveia-Freitas, K. & Bastos-Leite, A. J. Perivascular spaces and brain waste clearance systems: relevance for neurodegenerative and cerebrovascular pathology. Neuroradiology 63, 1581–1597, doi:10.1007/s00234-021-02718-7 (2021).
Nedergaard, M. & Goldman, S. A. Glymphatic failure as a final common pathway to dementia. Science 370, 50–56, doi:10.1126/science.abb8739 (2020).
Banerjee, G. et al. MRI-visible perivascular space location is associated with Alzheimer's disease independently of amyloid burden. Brain 140, 1107–1116, doi:10.1093/brain/awx003 (2017).
Gertje, E. C., van Westen, D., Panizo, C., Mattsson-Carlgren, N. & Hansson, O. Association of Enlarged Perivascular Spaces and Measures of Small Vessel and Alzheimer Disease. Neurology 96, e193-e202, doi:10.1212/wnl.0000000000011046 (2021).
Paradise, M. et al. Association of Dilated Perivascular Spaces With Cognitive Decline and Incident Dementia. Neurology 96, e1501-e1511, doi:10.1212/wnl.0000000000011537 (2021).
Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol 12, 822–838, doi:10.1016/S1474-4422(13)70124-8 (2013).
Australia D, Baker S, Banerjee S. Alzheimer’s Disease International. World Alzheimer report 2019: Attitudes to dementia; Alzheimer’s Disease International: London, UK (2019).
de Leeuw, F. E. et al. Hypertension and cerebral white matter lesions in a prospective cohort study. Brain 125, 765–772, doi:10.1093/brain/awf077 (2002).
Klarenbeek, P. et al. Higher ambulatory blood pressure relates to enlarged Virchow-Robin spaces in first-ever lacunar stroke patients. J Neurol 260, 115–121, doi:10.1007/s00415-012-6598-z (2013).
Zhu, Y. C. et al. Severity of dilated Virchow-Robin spaces is associated with age, blood pressure, and MRI markers of small vessel disease: a population-based study. Stroke 41, 2483–2490, doi:10.1161/STROKEAHA.110.591586 (2010).
Gutierrez, J., Rundek, T., Ekind, M. S., Sacco, R. L. & Wright, C. B. Perivascular spaces are associated with atherosclerosis: an insight from the Northern Manhattan Study. AJNR Am J Neuroradiol 34, 1711–1716, doi:10.3174/ajnr.A3498 (2013).
Ninomiya, T. et al. Study design and baseline characteristics of a population-based prospective cohort study of dementia in Japan: the Japan Prospective Studies Collaboration for Aging and Dementia (JPSC-AD). Environ Health Prev Med 25, 64, doi:10.1186/s12199-020-00903-3 (2020).
Noguchi-Shinohara, M. et al. Diabetes Mellitus, Elevated Hemoglobin A1c, and Glycated Albumin Are Associated with the Presence of All-Cause Dementia and Alzheimer's Disease: The JPSC-AD Study. J Alzheimers Dis 85, 235–247, doi:10.3233/JAD-215153 (2022).
Liu, Y. et al. Altruistic Social Activity, Depressive Symptoms, and Brain Regional Gray Matter Volume: Voxel-Based Morphometry Analysis from 8695 Old Adults. J Gerontol A Biol Sci Med Sci, doi:10.1093/gerona/glac093 (2022).
Thyreau, B. et al. Higher-resolution quantification of white matter hypointensities by large-scale transfer learning from 2D images on the JPSC-AD cohort. Hum Brain Mapp, doi:10.1002/hbm.25899 (2022).
Jack, C. R., Jr. et al. The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging 27, 685–691, doi:10.1002/jmri.21049 (2008).
Buckner, R. L. et al. A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume. Neuroimage 23, 724–738, doi:10.1016/j.neuroimage.2004.06.018 (2004).
James, P. A. et al. 2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). Jama 311, 507–520, doi:10.1001/jama.2013.284427 (2014).
Diagnosis and classification of diabetes mellitus. Diabetes Care 33 Suppl 1, S62-69, doi:10.2337/dc10-S062 (2010).
Horio, M., Imai, E., Yasuda, Y., Watanabe, T. & Matsuo, S. Modification of the CKD epidemiology collaboration (CKD-EPI) equation for Japanese: accuracy and use for population estimates. Am J Kidney Dis 56, 32–38, doi:10.1053/j.ajkd.2010.02.344 (2010).
Levey, A. S. et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 150, 604–612, doi:10.7326/0003-4819-150-9-200905050-00006 (2009).
American Psychiatric Association. Diagnostic and statistical manual of mental disorders, 3rd Revised Edition. (1987).
Del Brutto, O. H., Mera, R. M. & Atahualpa Project, I. Enlarged Basal Ganglia Perivascular Spaces are Associated with Pulsatile Components of Blood Pressure. Eur Neurol 79, 86–89, doi:10.1159/000486308 (2018).
Mestre, H. et al. Flow of cerebrospinal fluid is driven by arterial pulsations and is reduced in hypertension. Nat Commun 9, 4878, doi:10.1038/s41467-018-07318-3 (2018).
Martinez-Quinones, P. et al. Hypertension Induced Morphological and Physiological Changes in Cells of the Arterial Wall. Am J Hypertens 31, 1067–1078, doi:10.1093/ajh/hpy083 (2018).

Table 1: Clinical characteristics of the study participants by systolic and diastolic blood pressure levels (n=9,296)
Variable	Systolic blood pressure, mmHg					Diastolic blood pressure, mmHg
	<120	120-139	140-159	≥160	P for trend	<80	80-89	90-99	≥100	p for trend
	n=1,201	n=3,581	n=3,191	n=1,323	P for trend	n=5,193	n=2,629	n=1,144	n=330	p for trend
Age, y	73.3 (6.5)	73.1 (6.3)	73.5 (6.3)	73.5 (6.3)	0.04	74.4 (6.5)	72.5 (6.1)	71.1 (5.4)	71.3 (5.3)	<0.001
Female, %	65.9	60.0	54.2	51.9	<0.001	62.4	53.9	48.3	44.8	<0.001
Education ≤9 y, %	28.7	30.4	33.6	30.7	0.03	33.6	29.6	27.2	23.0	<0.001
Use of antihypertensive medication, %	39.4	49.5	56.1	52.3	<0.001	52.3	50.0	48.5	42.8	<0.001
Diabetes mellitus, %	13.3	15.3	18.5	20.4	<0.001	18.3	15.4	14.5	13.3	<0.001
Serum total cholesterol, mmol/L/	5.3 (1.0)	5.3 (0.9)	5.4 (0.9)	5.5 (1.0)	<0.001	5.3 (0.9)	5.4 (0.9)	5.5 (0.9)	5.6 (1.0)	<0.001
BMI, kg/m²	22.7 (3.3)	23.3 (3.3)	23.7 (3.3)	23.5 (3.3)	<0.001	23.2 (3.3)	23.6 (3.3)	23.8 (3.3)	24.0 (3.3)	<0.001
Chronic kidney disease, %	22.4	18.3	17.3	17.1	0.001	21.4	15.6	11.2	15.6	<0.001
ECG abnormalities, %	11.3	12.0	14.6	16.9	<0.001	12.6	14.4	13.1	21.2	0.001
History of stroke, %	5.4	5.3	6.2	4.2	0.67	6.3	5.1	3.6	3.3	<0.001
Current smoker, %	6.9	8.3	8.4	7.4	0.76	7.4	8.4	9.3	10.6	0.004
Current drinker, %	36.6	40.9	44.3	48.9	<0.001	37.7	47.2	51.1	55.9	<0.001
Regular exercise, %	43.8	41.2	42.8	44.0	0.40	42.4	42.7	41.7	45.0	0.77
MCI, %	16.6	15.3	17.8	17.6	0.05	17.5	16.2	14.7	13.0	0.002
Dementia, %	5.4	4.5	4.5	3.4	0.03	5.6	3.6	1.7	2.4	<0.001
APOE ε4 carrier	17.7	19.7	18.8	19.2	0.74	19.1	18.5	19.0	23.3	0.45
Estimated total intracranial volume, x10³ mm³	1416.3	1421.0	1417.5	1421.7	0.46	1414.8	1427.4	1436.4	1434.2	<0.001
EPVS volume^a), mm³	108.7	112.8	116.7	123.4	<0.01	113.5	116.1	117.1	125.4	0.01
Abbreviations: HT, hypertension; BMI, body mass index; ECG, electrocardiogram; MCI, mild cognitive impairment; APOE, apolipoprotein E; EPVS, enlarged perivascular spaces Values are shown as means (standard deviation) or frequencies. Values are shown as geometric mean values.

Table 2: Change in multivariable-adjusted mean values of log₁₀(EPVS volumes) per every 1-mmHg-increment in systolic blood pressure and diastolic blood pressure in overall study participants and in subgroups of each covariate
Subgroups	Systolic blood pressure			Diastolic blood pressure
Subgroups	Change in mean values (95% CI) of log₁₀(EPVS volumes) per 1-mmHg-increment	p value	p for interaction	Change in mean values (95% CI) of log₁₀(EPVS volumes) per 1-mmHg-increment	p value	p for interaction
*Overall*	0.0005 (0.0002 to 0.0008)	<0.001		0.0010 (0.0006 to 0.0015)	<0.001

*Age, y*
<75	0.0005 (0.0002 to 0.0008)	0.001	<0.001	0.0009 (0.0004 to 0.0015)	0.001	<0.001
≥75	0.0006 (0.0001 to 0.0011)	0.01	<0.001	0.0010 (0.0002 to 0.0018)	0.02	<0.001
*Sex*
Male	0.0006 (0.0001 to 0.0010)	0.01	<0.001	0.0007 (–0.0001 to 0.0014)	0.08	0.001
Female	0.0005 (0.0002 to 0.0008)	0.003	<0.001	0.0014 (0.0008 to 0.0019)	<0.001	0.001
*Education* ≤*9 y*
No	0.0004 (–0.0001 to 0.0009)	0.15	0.88	0.0003 (–0.0006 to 0.0011)	0.58	0.97
Yes	0.0006 (0.0002 to 0.0009)	<0.001	0.88	0.0013 (0.0008 to 0.0018)	<0.001	0.97
*Use of antihypertensive medication*
No	0.0009 (0.0005 to 0.0012)	<0.001	<0.001	0.0014 (0.0008 to 0.0020)	<0.001	<0.001
Yes	0.0001 (–0.0003 to 0.0006)	0.53	<0.001	0.0007 (0.00001 to 0.0014)	0.05	<0.001
*Diabetes mellitus*
No	0.0005 (0.0002 to 0.0008)	<0.001	0.026	0.0008 (0.0003 to 0.0013)	0.001	0.003
Yes	0.0004 (–0.0003 to 0.0011)	0.24	0.026	0.0020 (0.0009 to 0.0032)	<0.001	0.003
*Hypercholesterolemia*
No	0.0008 (0.0004 to 0.0013)	<0.001	0.89	0.0011 (0.0003 to 0.0019)	0.007	0.99
Yes	0.0004 (0.0001 to 0.0007)	0.02	0.89	0.0010 (0.0005 to 0.0015)	<0.001	0.99
*Obesity*
No	0.0007 (0.0004 to 0.0010)	<0.001	0.59	0.0013 (0.0008 to 0.0018)	<0.001	0.50
Yes	–0.0001 (–0.0006 to 0.0005)	0.83	0.59	0.0003 (–0.0006 to 0.0012)	0.47	0.50
*ECG abnormalities*
No	0.0006 (0.0003 to 0.0008)	<0.001	0.089	0.0012 (0.0007 to 0.0017)	<0.001	0.11
Yes	0.0002 (–0.0006 to 0.0010)	0.64	0.089	–0.00002 (–0.0013 to 0.0013)	0.97	0.11
*Chronic kidney disease*
No	0.0006 (0.0003 to 0.0008)	<0.001	0.40	0.0011 (0.0006 to 0.0016)	<0.001	0.35
Yes	0.0003 (–0.0004 to 0.0009)	0.44	0.40	0.0007 (–0.0004 to 0.0018)	0.20	0.35
*History of stroke*
No	0.0006 (0.0003 to 0.0008)	<0.001	<0.001	0.0011 (0.0007 to 0.0016)	<0.001	<0.001
Yes	–0.0007 (–0.0023 to 0.0009)	0.37	<0.001	–0.0007 (–0.0032 to 0.0018)	0.58	<0.001
*Current smoker*
No	0.0005 (0.0002 to 0.0008)	<0.001	0.46	0.0010 (0.0005 to 0.0015)	<0.001	0.43
Yes	0.0007 (–0.0003 to 0.0018)	0.17	0.46	0.0014 (–0.0002 to 0.0031)	0.09	0.43
*Current drinker*
No	0.0004 (0.0001 to 0.0008)	0.01	0.85	0.0012 (0.0006 to 0.0018)	<0.001	0.92
Yes	0.0006 (0.0001 to 0.0010)	0.008	0.85	0.0008 (0.0001 to 0.0015)	0.03	0.92
*Regular exercise*
No	0.0006 (0.0002 to 0.0009)	0.001	0.995	0.0012 (0.0006 to 0.0018)	<0.001	0.99
Yes	0.0004 (0.00001 to 0.0008)	0.05	0.995	0.0008 (0.0001 to 0.0015)	0.02	0.99
*Dementia*
No	0.0006 (0.0003 to 0.0008)	<0.001	0.50	0.0011 (0.0006 to 0.0015)	<0.001	0.43
*Yes*	–0.0010 (–0.0025 to 0.0005)	0.19	0.50	0.00002 (–0.0025 to 0.0025)	0.999	0.43
*APOE ε4 carrier*
No	0.0006 (0.0003 to 0.0008)	<0.001	0.40	0.0010 (0.0005 to 0.0015)	<0.001	0.28
Yes	0.0002 (–0.0004 to 0.0008)	0.43	0.40	0.0009 (–0.0001 to 0.0019)	0.09	0.28
Abbreviations: PVS, perivascular spaces; CI, confidence interval; ECG, electrocardiogram; APOE, Apolipoprotein E Values were adjusted for age, sex, low educational level, use of antihypertensive medication diabetes mellitus, serum total cholesterol, body mass index, ECG abnormalities, chronic kidney disease, history of stroke, current smoker status, current drinker status, regular exercise, dementia, APOE ε4 carrier status, research site, and estimated total intracranial volume. The variable relevant to the subgroup was excluded from each model.

No competing interests reported.

20221008SupplementaryInformation.pdf

Late-life high blood pressure is associated with enlarged perivascular spaces in community-dwelling Japanese older persons: The Japan Prospective Studies Collaboration for Aging and Dementia (JPSC-AD) study

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Methods

Study population

Mri Acquisition

Mri Analysis

Measurement Of Bp

Measurements Of Other Risk Factors

Statistical Analyses

Results

Discussion

Conclusions

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1