Mid-life association between cardiovascular risk factors and cerebral blood flow in a multi-ethnic population

Mid-life association between cardiovascular risk factors and cerebral blood flow in a multi-ethnic population | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Mid-life association between cardiovascular risk factors and cerebral blood flow in a multi-ethnic population View ORCID Profile Esther M.C. Vriend , View ORCID Profile Mathijs B.J. Dijsselhof , View ORCID Profile Thomas A. Bouwmeester , Oscar H. Franco , View ORCID Profile Henrike Galenkamp , View ORCID Profile Didier Collard , Aart J. Nederveen , View ORCID Profile Bert-Jan H. van den Born , View ORCID Profile Henk J.M.M. Mutsaerts doi: https://doi.org/10.1101/2024.10.04.24314929 Esther M.C. Vriend a Amsterdam UMC, University of Amsterdam, Department of Internal Medicine, Section Vascular Medicine, Amsterdam Cardiovascular Sciences , Amsterdam, The Netherlands b Amsterdam UMC, University of Amsterdam, Department of Public and Occupational Health, Amsterdam Public Health Research institute , Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Esther M.C. Vriend Mathijs B.J. Dijsselhof c Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Vrije Universiteit , Amsterdam, The Netherlands d Amsterdam Neuroscience , Brain Imaging, Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mathijs B.J. Dijsselhof For correspondence: m.b.j.dijsselhof{at}gmail.com Thomas A. Bouwmeester a Amsterdam UMC, University of Amsterdam, Department of Internal Medicine, Section Vascular Medicine, Amsterdam Cardiovascular Sciences , Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Thomas A. Bouwmeester Oscar H. Franco e Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht , Utrecht, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Henrike Galenkamp b Amsterdam UMC, University of Amsterdam, Department of Public and Occupational Health, Amsterdam Public Health Research institute , Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Henrike Galenkamp Didier Collard a Amsterdam UMC, University of Amsterdam, Department of Internal Medicine, Section Vascular Medicine, Amsterdam Cardiovascular Sciences , Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Didier Collard Aart J. Nederveen f Department of Radiology and Nuclear Medicine, Academic Medical Center (AMC), University of Amsterdam , Amsterdam, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bert-Jan H. van den Born a Amsterdam UMC, University of Amsterdam, Department of Internal Medicine, Section Vascular Medicine, Amsterdam Cardiovascular Sciences , Amsterdam, The Netherlands b Amsterdam UMC, University of Amsterdam, Department of Public and Occupational Health, Amsterdam Public Health Research institute , Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Bert-Jan H. van den Born Henk J.M.M. Mutsaerts c Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Vrije Universiteit , Amsterdam, The Netherlands d Amsterdam Neuroscience , Brain Imaging, Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Henk J.M.M. Mutsaerts Abstract Full Text Info/History Metrics Preview PDF Abstract Background Cardiovascular (CV) risk factors are associated with cerebrovascular damage and cognitive decline in late life. However, it is unknown how different ethnic CV risk profiles are related to cerebral haemodynamics in mid-life. We aimed to investigate associations of CV risk factors with cerebral haemodynamics at two timepoints and examine the impact of ethnicity on these measures. Methods From the HELIUS study (53.0 years, 44.8% female), participants of Dutch (n=236), Moroccan (n=122), or South-Asian Surinamese (n=173) descent were included. Cerebral blood flow (CBF) and its spatial coefficient of variation (sCoV, marker of macrovascular efficiency) were obtained in both grey (GM) and white matter (WM). Associations of CV risk factors, WM hyperintensities (WMH), and carotid plaques with cerebral haemodynamics were investigated using linear regressions. Results CBF and sCoV differed per ethnicity. Only at the second visit associations were found, without an interaction with ethnicity; history of CV disease with lower GM CBF and higher WM sCoV, higher total cholesterol and lower WMH volume with lower WM CBF, smoking with higher WM sCoV, and higher SBP with lower GM sCoV. Conclusions These findings emphasise the need to further explore the longitudinal effects of midlife risk factors and cerebrovascular health, and its interaction with ethnicity. Introduction Systemic cardiovascular (CV) risk factors exhibit a robust association with cardiovascular disease (CVD), cerebrovascular pathology, and cognitive impairment ( 1 , 2 ). Unlike white matter hyperintensities (WMH) - a commonly used but relatively late-stage structural indicator of cerebrovascular damage ( 3 ) – cerebral haemodynamics may be able to detect pathological cerebral changes earlier ( 4 , 5 ). Cerebral haemodynamics can be non-invasively assessed using arterial spin labelling (ASL) magnetic resonance imaging (MRI) ( 6 ), measuring cerebral blood flow (CBF) and its spatial coefficient of variation (sCoV) as a proxy of macrovascular efficiency ( 7 ). Previous studies investigating the association between CV risk factors and CBF in late-life (>65 years) have shown conflicting results. For example, some studies found a negative longitudinal association between blood pressure (BP) and CBF ( 8 - 10 ), differential relations of systolic BP (SBP), or longitudinal development of SBP with grey matter (GM) CBF ( 11 ). Other studies found increases in GM CBF after hypertensive treatment ( 12 ), or did not find any associations of BP with GM CBF and sCoV at all ( 13 ). Perhaps, the variability of accumulated cerebrovascular pathology is high in late-life, possibly due to differences in CV risk factor exposure or treatment, and might develop non-linear in time ( 14 ). Furthermore, ethnic differences in CV risk factors and CV disease may also impact the association between CV risk factors and CBF ( 15 ). This encouraged us to further explore the relationship between mid-life CV risk factors and cerebral haemodynamics over time in a multi-ethnic population. Hence, within a longitudinal multi-ethnic cohort study, we investigate 1) demographical and ethnic differences in CBF and sCoV, 2) the associations of CV risk factors, carotid plaque presence, and WMH with CBF and sCoV and 3) the impact of ethnicity on these associations. Methods Study population Data were derived from the HEalthy Life In an Urban Setting (HELIUS) study. The study design and procedures of the HELIUS study have been described in detail elsewhere ( 16 ). Briefly, participants were randomly invited, stratified by ethnic background, using the municipality register of Amsterdam, The Netherlands. Data collection consisted of a questionnaire/interview, physical examination at a research location, and the collection of biological samples. For the present study, conducted in accordance with the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) guidelines, we used the CV risk factor data from the baseline HELIUS data collection (2011-2015), and the second visit (2019-2022) ( Figure 1 ). Data were collected at both time points using the same methods and procedures. Download figure Open in new tab Figure 1: Flowchart of included participants. ASL = arterial spin labelling. HELIUS = Healthy Life in an Urban Setting. IQR = interquartile range. MRI = magnetic resonance imaging. Between 2021 and 2023, a subset of all HELIUS participants underwent carotid ultrasonography, in which we determined the carotid intima-media thickness (cIMT) and the presence of carotid plaques. Details on the ultrasonography measurements are available elsewhere ( 17 ). Inclusion for this substudy was limited to participants aged between 35 and 65 at baseline of Dutch, Moroccan, and South-Asian Surinamese descent with complete baseline and follow-up measurements. A subset of all participants in the carotid ultrasonography substudy received an additional MRI examination (n = 571), from which we used the anatomical and perfusion MRI data. A number of 268 participants were enrolled for MRI examination based on the presence of carotid plaque formation on carotid ultrasound (defined as a carotid plaque of ≥ 2.5 mm on one or both sides or maximum cIMT of >1.0 mm). An additional 303 participants were randomly selected from the carotid ultrasonography sub-study, stratified by ethnic group, as the reference group. Exclusion criteria for MRI comprised unwillingness to participate in the MRI examination or MRI contra-indications. Due to MRI scanning time constraints, ASL scans were available in 556 individuals. After excluding those with insufficient data quality (arterial transit artefacts and motion artefacts), 531 participants were included in the present study. The MRI examinations were performed at a median of 8.4 years [IQR 7.4 – 9.5 years] after the first CV risk factor visit (baseline) and a median of 2.2 years [IQR 1.8 – 2.6 years] after the second CV risk factor visit. The HELIUS study aligns with the Declaration of Helsinki, and was approved by the Amsterdam UMC, location AMC institutional review board. Written informed consent was obtained from all participants. Definitions and measurements Ethnic background was determined based on the participant’s country of birth and that of their parents. Non-Dutch ethnicity was defined as a participant being born outside the Netherlands and having at least one parent born outside the Netherlands, or if a participant was born in the Netherlands but both parents were born outside the Netherlands. For those of Surinamese ethnicity, further categorization was performed based on their self-reported ethnic origin (‘South-Asian’, ‘African’, or ‘other’). Cardiovascular disease history was defined as a self-reported history of stroke, myocardial infarction, or coronary or peripheral revascularization. Self-reported smoking status was categorized as current, former, or never smokers. Fasting plasma samples were used to measure the concentrations of haematocrit, creatinine, total cholesterol, low-density lipoprotein (LDL) and high-density lipoprotein cholesterol (HDL), and glucose levels. The estimated glomerular filtration rate (eGFR) was calculated using the revised CKD-EPI 2021 creatinine equation ( 18 ). Body mass index (BMI) was calculated by dividing measured weight (in kg) by measured height (in m 2 ). BP was measured twice on the left arm of participants while seated, after a minimum of 5 minutes of rest, using a validated semi-automatic oscillometric device (Microlife WatchBP Home; Microlife AG, Switzerland). The average of the two measurements was used to determine SBP and diastolic BP (DBP) levels. Pulse pressure (PP) was defined as the difference between SBP and DBP levels, and mean arterial pressure (MAP) was defined as the DBP plus one-third of the PP. Blood pressure-lowering medication was classified using the Anatomical Therapeutic Chemical classification system. Hypertension was defined as elevated BP levels (≥ 140/90 mmHg) or the use of BP-lowering medication. Diabetes mellitus was defined as elevated fasting glucose levels (≥ 7 mmol/L) and/or the use of glucose-lowering medication. MRI acquisition MRI was performed using a 3T Ingenia scanner (Philips Healthcare, Best, The Netherlands) equipped with a 32-channel head coil. 3D T1-weighted (T1w) magnetization-prepared rapid gradient echo (MP-RAGE) scans were acquired with the following parameters: voxel size = 1x1x1 mm, echo time (TE) = 3.30 ms, repetition time (TR) = 7000 ms, flip angle (FA) = 9 degrees, and inversion time (TI) = 900 ms. 3D fluid-attenuated inversion recovery (FLAIR) scans were obtained with the following parameters: voxel size = 1.10 x 1.10 x 1.12 mm 3 , TE = 356 ms, TR = 4800 ms, FA = 40 degrees and TI = 1650 ms. Pseudo-continuous ASL 2D Echo-Planar Imaging scans were acquired with the following parameters: voxelsize = 3 x 3 x 7 mm 3 , TR = 4445 ms, TE = 17 ms, FA = 90 degrees, post-labelling delay (PLD) range (considering timing differences between 2D slices) = 1800-2574 ms, labelling duration = 1800 ms, 36 averages, background suppression, accompanied by an equilibrium magnetisation image (M0) without labelling or background suppression (TR = 2000 ms). The images were analysed using ExploreASL version 1.11.0 ( 19 ). Briefly, T1-weighted images were used to segment GM, white matter (WM), and cerebrospinal fluid (CSF) using the Computational Anatomy Toolbox 12 ( 20 ), while correcting for white matter hyperintensities (WMH) from FLAIR images using the Lesion Segmentation Toolbox ( 21 ). Next, ASL images were registered to the T1w images using rigid-body registration, and the equilibrium magnetisation was calculated voxelwise in the brain tissue using the M0 scan. The recommended single-compartment model was used to quantify CBF ( 22 ). All images were non-linearly registered to the Montreal Neurological Institute space. CBF and sCoV values were corrected for haematocrit levels obtained at follow-up. CBF values were partial volume corrected. GM and deep WM regions-of-interest (ROIs) were created by combining existing atlases with individual GM and WM segmentations (partial volume > 0.5 in the ASL resolution), after subtraction of WMH partial volume. To avoid GM signal contamination, the WM ROI was eroded by a 4-voxel-sphere to form a deep WM ROI, hereafter referred to as WM. Mean CBF and sCoV were calculated in the total GM and deep WM. Statistical analyses Sample characteristics of the included population were described as mean (SD), median [IQR] or n (%), stratified by ethnicity. All data were tested for normal distribution using the Shapiro-Wilk test and analysed using the appropriate tests (ANOVA, Kruskal Wallis, Chi-squared test) depending on the distribution of the data. We created line diagrams to illustrate the cross-sectional association between age and both CBF and sCoV, stratified by ethnicity. Separate linear regression models were performed to determine the association of the CV risk factors determined at the first and second visit with the outcome measures (GM and WM CBF and sCoV). Initially, risk factor-specific models containing a single CV risk factor were created and adjusted for age, sex, ethnicity, and follow-up time between risk factor measurement and MRI. We investigated the following risk factors: BMI, smoking, diabetes mellitus, SBP, DBP, total cholesterol, eGFR levels, and history of CVD. In participants without prevalent CVD, we also assessed the effect of SCORE2 (the 10-year risk of cardiovascular disease in Europe ( 23 ). Subsequently, multivariate models were built, including BMI, smoking, diabetes mellitus, SBP, DBP, total cholesterol, eGFR levels and history of CVD, similarly adjusted for age, sex, ethnicity, and follow-up time between CV risk factor measurement and MRI. Furthermore, we assessed the associations of carotid plaque presence as determined by carotid ultrasonography and log-transformed WMH as determined by MRI with the outcome variables. We tested for ethnicity interactions in the observed associations by performing subgroup analyses. Inverse probability weighting was applied to address disparities in population characteristics between the reference and case group, with weights based on age, sex, and ethnicity, winsorised at 0.1 and 0.9. Repeating the analyses with a group indicator in the regression analysis (0 = reference group, 1 = case group) or without correction for study design did not affect the results (data not shown). We further tested whether the observed observations were attenuated after correction for motion, which was not the case (data not shown). Moreover, we substituted the SCORE2 with the Framingham risk score (FRS) and BP values with MAP, PP and hypertension and evaluated the effect of changes in risk factors over time, corrected for baseline values. Additionally, we investigated whether the results changed after adjusting BP values for the use of anti-hypertensive medication by increasing SBP levels with 10 mmHg and DBP levels with 5 mmHg ( 24 ). As prior research has indicated a correlation between body height and carotid plaque presence, we also explored whether the outcome measures were affected by body height and if this impacted the observed associations ( 17 ). P < 0.05 was considered statistically significant. As sensitivity analyses, we adjusted the analyses for multiple corrections using the Benjamini-Hochberg Procedure (FDR). Furthermore, models for GM CBF were repeated with and without partial volume correction and with and without correction for haematocrit. Additionally, we repeated the GM CBF analysis with the exclusion of participants with a positive history of CVD. Rstudio (version 4.3.2) was used for all statistical analyses. Results Population characteristics Of the 531 participants, 236 individuals were of Dutch, 173 of South-Asian Surinamese, and 122 were of Moroccan descent ( Table 1 ). The median age of the population at the first CV risk factor visit was 53.0 years [IQR 47.0; 58.0], with 44.8% being female. In both the reference and case groups, the prevalence of a positive history of CVD at the first visit was highest in the South-Asian Surinamese group, while mean BMI levels were highest in the Moroccan group. The percentage of smoking participants was highest in the Dutch population, followed by the South-Asian Surinamese and the Moroccans. At the second CV risk factor visit, similar disparities were found. Mean GM and WM CBF values were 55.8 ± 8.5 mL/100g/min and 13.6 ± 3.2 mL/100g/min respectively ( Figure 3 ). Mean sCoV levels were 42.7 ± 6.0 % and 69.6 ± 22.1% for GM and WM, respectively. No significant differences in characteristics between the participants included in the analyses (n = 531) and all participants (n = 571) were found (Supplementary Table 1). Download figure Open in new tab Figure 2: Cross-sectional association between CBF and age in grey matter (A) and white matter (B), stratified by ethnicity. CBF = cerebral blood flow. GM = grey matter. WM = white matter. Download figure Open in new tab Figure 3: Mean CBF images of participants. Data of n = 531 participants were included. CBF = cerebral blood flow. View this table: View inline View popup Download powerpoint Table 1: Characteristics of the included population, stratified by ethnic descent. Reference group consists of participants randomly chosen from the HELIUS ultrasonography substudy, stratified by ethnic descent (n = 282). Case groups consist of participants with the presence of atherosclerosis on carotid ultrasound (n = 250). CV = cardiovascular. IQR = interquartile range. SD = standard deviation. BMI = body mass index. SBP = systolic blood pressure. DBP = diastolic blood pressure. CVD = cardiovascular disease. GM = grey matter. WM = white matter. WMH = white matter hyperintensities. CBF = cerebral blood flow. sCoV = spatial coefficient of variation. SA Surinamese = South-Asian Surinamese. Associations between demographic variables and cerebral haemodynamics Only GM CBF was negatively associated with age (Pearson correlation: -0.15 mL/100g/min per year, 95% CI -0.24; -0.07, P < 0.001, Figure 2 ). The association between demographics and CBF derived from multivariate regression analyses can be found in Supplementary Table 2A. No associations between sex and GM or WM CBF were observed (P > 0.05). Differences in CBF levels were identified across ethnic groups, with the South-Asian Surinamese having similar GM CBF values compared to their Dutch counterparts, but lower WM CBF values (- 1.57 mL/100g/min, 95% CI -2.26; -0.88, P < 0.001), while individuals of Moroccan descent had lower GM CBF values compared to the Dutch (-1.36 mL/100g/min, 95% CI -2.31; -0.4, P = 0.024). Only GM sCoV was positively associated with age (0.19% per year, 95% CI 0.11; 0.28, P <0.001, Supplementary Figure 2). Estimates for the association between demographic variables and sCoV as derived from multivariable analyses are available in Supplementary Table 2B. We found significantly lower sCoV values for both WM and GM in females compared to males (-8.34, 95% CI -12.26; -4.41, P < 0.001 for WM and -4.04, 95% CI -5.16; -2.92, P < 0.001 for GM in females). We found slightly higher WM sCoV levels in the South-Asian Surinamese compared to the Dutch (6.89%, 95% CI 2.56; 11.22, P = 0.002). Associations between CV risk factors and CBF For the first CV risk factor visit ( Table 2 ), we found a multivariate association between history of CVD and GM CBF levels (-3.34 mL/100g/min, 95% CI -6.46; -0.22, P = 0.036). Conversely, we found an association between diabetes mellitus and GM CBF in the risk-factor specific models (2.88 mL/100g/min, 95% CI 0.37; 5.39, P = 0.025). View this table: View inline View popup Download powerpoint Table 2: Association of first and second CV risk factor visit with grey and white matter CBF, as derived from linear regression analyses. Risk factor-specific models were adjusted for age, sex, ethnicity, and follow-up time between the CV risk factor visits and MRI measurements. Multivariate models were adjusted for age, sex, ethnicity, follow-up time, BMI, smoking, diabetes mellitus, hypertension, total cholesterol levels, eGFR levels, and a history of CVD. Inverse probability weighting was used to correct for study design. * Statistically significant (P < 0.05). ✝ Statistically significant with FDR correction. BMI = body mass index. CBF = cerebral blood flow. CI = confidence interval. CVD = cardiovascular diseases. DBP = diastolic blood pressure. eGFR = estimated glomerular filtration rate. FDR = false discovery rate. SBP = systolic blood pressure. View this table: View inline View popup Download powerpoint Table 3: Association of first and second CV risk factor visit with grey and white matter sCoV, as derived from linear regression analyses. Risk factor-specific models were adjusted for age, sex, ethnicity, and follow-up time between the CV risk factor visits and MRI measurements. Multivariate models were adjusted for age, sex, ethnicity, follow-up time, BMI, smoking, diabetes mellitus, hypertension, total cholesterol levels, eGFR levels, and a positive history of CVD. Inverse probability weighting was used to correct for study design. * Statistically significant (P<0.05). ✝ Statistically significant with FDR correction. BMI = body mass index. CI = confidence interval. CVD = cardiovascular diseases. DBP = diastolic blood pressure. eGFR = estimated glomerular filtration rate. FDR = false discovery rate. SBP = systolic blood pressure. sCoV = spatial coefficient of variation. For the second CV risk factor visit ( Table 2 ), we found an association between history of CVD and GM CBF in the risk factor-specific model (-3.16 mL/100g/min, 95% CI -5.62; - 0.69, P = 0.029) and multivariate model (-4.06, 95% CI -6.62; -1.51, P = 0.002). Furthermore, we found an association between total cholesterol levels and WM CBF in both the risk-factor specific (-0.28 mL/100g/min, 95% CI -0.51; -0.04, P = 0.020) and multivariate models (-0.35 mL/100g/min, 95% CI -0.6; -0.09. P = 0.007). No significant associations were found when analysing changes over time in SBP, DBP, and BMI (data not shown). The observed associations could not be reproduced after stratification for ethnic subgroups (P > 0.05, Supplementary Table 3). Additionally, associations were found of body height with both GM CBF (0.19 mL/100g/min per cm increase in height, 95% CI 0.07; 0.30, p = 0.002), and WM CBF (0.07 mL/100g/min per cm increase in height, 95% CI 0.03; 0.12, p = 0.001). Associations between CV risk factors and sCoV For the first CV risk factor visit, we found an association of SBP levels (-0.07%, 95% CI - 012; -0.02, P = 0.003) and DBP (0.09%, 95% CI 0.0; 0.17, P = 0.040) levels with GM sCoV in the multivariate models only. For the second CV risk factor visit, SBP levels showed an association with GM sCoV in the risk-factor specific (-0.03%, 95% CI -0.06; 0, P = 0.033) and multivariate models (-0.09%, 95 CI -0.14; -0.05, P < 0.001), whereas DBP levels had an association with GM sCoV only in the multivariate models (0.13%, 95% CI 0.05; 0.21, P = 0.002). Smoking was strongly associated with WM sCoV in both risk-factor specific (8.66%, 95% CI 3.89; 13.44, P < 0.001) and multivariate models (8.76%, 95% CI 3.912; 13.6, P < 0.001). We also found an association between history of CVD and WM sCoV in both the risk-factor specific (6.45%, 95% CI 0.5; 12.4, P = 0.034) and multivariate models (6.88%, 95% CI 0.78; 12.98, P = 0.027) and between SBP levels and WM sCoV in the multivariate models (-0.16%, 95% CI -0.32; - 0.01, P = 0.040) Furthermore, only for change in BMI over time an association was found with WM sCoV (-1.19%, 95% CI -2.26; -0.12, P = 0.030). Lastly, we found an association between body height and GM sCoV (0.08%, 95% CI 0.00; 0.16, P = 0.039). When exploring the observed associations in the different ethnic subgroups, we found an association of smoking and history of CVD with WM sCoV in the Dutch group, but not in the Moroccan and South-Asian Surinamese subgroup (Supplementary Table 3). Associations of carotid plaque and WMH with cerebral haemodynamics Carotid plaque presence was only associated with WM sCoV in the risk-factor specific model (4.39 mL/100g/min, 95% CI 0.08; 8.71, P = 0.046). WMH was associated with WM CBF in both the risk-factor specific (0.43 mL/100g/min, 95% CI 0.09; 0.76, P = 0.013) and multivariate models (0.5 mL/100g/min, 9% CI -1.96; -0.02, P = 0.004). Sensitivity analyses Substituting the SCORE2 with the Framingham risk score or SBP and DBP with the presence of hypertension, MAP or PP did not reveal associations with CBF. For sCoV, however, it did reveal an association between PP and GM sCoV (data not shown) and an association between hypertension and GM sCoV (data not shown). When adjusting BP values for the effect of anti-hypertensive medication by adding 10 and 5 mmHg to SBP and DBP values in cases where BP-lowering medication was used, SBP remained associated with GM sCoV (data not shown). When adjusting for multiple comparisons, only the associations of WMH volume with WM CBF (P = 0.026) and WM sCoV with smoking (P = 0.003) remained statistically significant in the risk-factor specific models. We found negligible disparities in the associations between CV risk factors and CBF as well as sCoV between models with and without partial volume correction (Supplementary Table 4) and with and without haematocrit correction (Supplementary Table 5). Additionally, the exclusion of participants with a positive history of CVD elicited similar results (Supplementary Table 6). Discussion Main findings In this multi-ethnic population-based cohort, we identified several differences in CBF and sCoV between participants of Dutch, Moroccan, and South-Asian Surinamese descent. While we did not find any associations at the first visit, at the second visit, we found mild associations between CVD history and both GM CBF and WM sCoV, total cholesterol levels and WM CBF, SBP and GM sCoV, smoking and WM sCoV, and between WMH volume and WM CBF. No associations with carotid plaque presence were found. Overall, no ethnic differences were found in the association between CV risk factors and CBF or sCoV. Apparently, mild associations start to appear only late in mid-life, are not equally distributed across all risk factors, and appear independent of ethnic CV risk profiles. Associations with demographics Our negative association between age and GM CBF is in agreement with other literature ( 25 , 26 ), however, our lack of association between age and WM CBF agrees with one study ( 27 ) but contrasts with two other studies ( 28 , 29 ). Differences between these studies might be explained by different age, sex, or ethnicity distributions or by the use of different ROI definitions such as global white matter instead of deep white matter, partial volume effects, ASL acquisitions, or PLD timings ( 30 ). Additionally, one study reported slight increases of WM CBF until around 60 years of age ( 28 ), suggesting that ageing-related changes in the WM act non-linear in time at older age. This could explain our results, as the median age of our population was 63 years imaging visit. Consistent with other studies, we found the South-Asian Surinamese sample had a higher prevalence of CV risk factors ( 31 , 32 ). In addition, they showed a lower WM CBF and higher WM sCoV, implying reduced macrovascular efficiency as a potential consequence of their relatively high CV burden. The fact that we did not observe this in the other participants, suggest that CV risk factors affect cerebral haemodynamics earlier in South-Asian Surinamese. While this might increase the development of WMH ( 33 ), we did not observe this in our cohort. Perhaps, the previously reported ethnic disparities in WMH ( 34 - 36 ) occur only after the age of our cohort. In participants of Moroccan descent, GM CBF was found to be lower than in participants of Dutch descent. Few studies have explored risk factor differences between individuals of Moroccan and European descent ( 37 , 38 ), some reporting a higher prevalence of CV risk factors in Moroccans (diabetes mellitus) while others indicate lower prevalence (alcohol consumption or smoking). To our knowledge, the present study is the first to assess the relationship between CV risk factors and cerebral haemodynamics in participants of Moroccan descent. Associations with CV risk factors In our study, history of CVD was associated with lower GM CBF in risk factor specific and both lower GM and WM CBF in the multivariate models, which is in line with previous studies correlating low CBF with low cardiac output ( 39 ) and stroke ( 40 ). Furthermore, a history of CVD was positively associated with WM sCoV, suggesting lower macrovascular efficiency that may have translated to the observed lower WM CBF. These results could imply cerebral haemodynamics are directly affected by (cardio)vascular injury, possibly through changes in cardiac output ( 41 ). An alternative indirect explanation to our association is that cardiovascular risk factors equally affect cardiovascular and cerebrovascular circulation. Similarly to one study higher total cholesterol levels were also found to be associated with lower WM CBF ( 42 ), which agrees with previous studies correlating LDL cholesterol with WMH load ( 43 ) and cardiorespiratory fitness with ATT ( 44 ). To what extent such associations are direct or indirect cannot be differentiated with our data. Our positive association between smoking and WM sCoV is consistent with the finding of a lower WM CBF ( 45 ) and decreased localised GM CBF ( 46 , 47 ) in previous studies. In line with two other studies ( 15 , 48 ), we found no associations between CBF or sCoV and other risk factors or their composite scores. However, two other studies did report an association between lower GM CBF and higher cardiovascular risk factor composite scores ( 49 , 50 ). These differences might be attributed to a lower mean age of the population in our study, different ethnic disparities in cardiovascular risk severity present in the aforementioned studies compared to HELIUS, and the use of other ASL MRI parameters. Higher SBP was related to lower GM sCoV but not GM CBF in our study, in contrast to previous late-life studies that found both hypertension and use of anti-hypertensive medication to be associated with higher sCoV ( 5 , 13 ) and MAP to be associated with longer arterial transit time (ATT) ( 40 ). Perhaps, adequate average perfusion is maintained by improving macrovascular efficiency as a compensatory effect for higher SBP in mid-life. Associations with WMH load and carotid plaque Interestingly, WM CBF showed an unexpected positive association with WMH load. While studies have reported negative associations between WMH load and GM CBF, WM(H) CBF, and WM sCoV ( 45 , 51 , 52 ), positive associations or lack of associations have also been reported ( 33 ). The relative low presence of WMH in the mid-life HELIUS cohort compared to other (mostly late-life) studies ( 14 ), might explain why a positive association has been found in this study, possibly suggesting a compensatory effect in normal-appearing white matter. Aside from the low WMH load, indicating an early stage of WMH development, the discrepancies in the findings of our study and others might also be explained by differences in the CBF ROIs. Compared to our deep WM ROI which excluded WMH lesions, other studies may have assessed CBF in total or lobar WM, or within WMH ROIs resulting in different findings ( 14 ). No associations between carotid plaque presence and cerebral haemodynamics were found, contrasting previous studies that show lower CBF ( 53 - 55 ), lower cerebral artery flow ( 56 ), more arterial transit artefacts, and increased ATT in patients with carotid stenosis ( 55 , 57 ). The fact that we could not replicate these associations could be explained by the relatively low prevalence of carotid artery stenosis in mid-life in our relatively healthy population. Interactions with ethnicity We did not find any ethnicity interaction effects on the association between CV risk factors and cerebrovascular haemodynamics, and when stratified for ethnicity, only associations between both smoking and history of CVD with WM sCoV remained for participants of Dutch descent. This fits with the lack of any associations with risk factors at the first CV risk factor visit and the few but relatively weak associations at the second CV risk factor visit. Perhaps, prolonged exposure to CV risk factors affects cerebral haemodynamics only later in life, which seems to be the case for WMH and cognitive decline as well ( 43 , 58 , 59 ). While several (mostly late-life) studies found that associations between CV risk and structural cerebrovascular pathology — in the form of WMH or stroke — differed between ethnicities ( 60 - 63 ), few studies have included cerebral haemodynamics. In one cohort around 62 years of age, ethnicity also did not influence the associations between CV risk factors and perfusion despite ethnic differences in CV risk factor profiles ( 15 ). On the other hand, in another cohort around 71 years of age, ethnicity affected the association of whole-brain CBF with memory and executive functioning at 71 years ( 64 ). Perhaps, our included study population is too young to detect commonly found cerebral haemodynamic changes from CV risk factors in older populations or any ethnic interaction effects on these associations. Alternatively, mid-life haemodynamics changes could be too subtle to be picked up with whole-brain resting-state haemodynamics, as one study showed that cumulative FRS between 47 and 67 years was associated with lower GM CBF only in 40% of the GM ( 50 ). On the other hand, possible mid-life compensatory effects —such as our positive association between WMH load and WM CBF — might precede and affect the associations that are commonly observed in late-life studies ( 14 ). Therefore, future (longitudinal) mid-life studies are suggested to investigate regional cerebral haemodynamics to understand the relationship between CV risk factors, cerebral haemodynamics, and development of WMH, as well as possible compensatory mechanisms. Limitations While we believe our general sample size is sufficient to explore associations between CV risk factors and cerebral haemodynamics, the interaction analysis for detecting ethnic-based disparities in these associations may be underpowered as sample sizes differed per ethnicity. Furthermore, a significant proportion of participants were lost to follow-up. This could result in a non-response bias and overrepresentation of healthier participants in our analyses exploring the effects of CV risk factors on cerebral haemodynamics. Lastly, the use of a deep WM ROI without WMH has the advantage that signal contamination from GM, WMH, and CSF are reduced, associations between WM CBF and WMH volume might behave differently compared to studies that use a less strict WM mask ( 14 ). Conclusion In contrast to previous late-life studies, this mid-life multi-ethnic cohort study found several associations between CV risk factors and cerebral haemodynamics at the second visit only. No associations were affected by ethnicity despite differences in their cerebral haemodynamics. Combined with positive associations of CBF and sCoV with WMH volume, these findings indicate that commonly found associations between CV risk factors, cerebral haemodynamics, and WMH might develop only later in life, possibly preceded by compensatory mechanisms. Future studies could extend our work by exploring the link between mid-to-late-life CV risk factors, regional changes in cerebrovascular haemodynamics, and development of WMH in underrepresented populations. Author contribution statement Esther M.C. Vriend: Data curation; Investigation; Methodology; Formal Analysis; Visualisation; Writing - Original draft; Writing - Review & Editing. Mathijs B.J. Dijsselhof: Data curation; Investigation; Methodology; Formal Analysis; Visualisation; Writing - Original draft; Writing - Review & Editing. Thomas A. Bouwmeester: Data acquisition; Investigation; Methodology; Conceptualization; Writing - Review & Editing. Oscar H. Franco: Methodology; Conceptualization; Writing - Review & Editing; Funding acquisition. Henrike Galenkamp: Methodology; Conceptualization; Writing - Review & Editing. Didier Collard: Methodology; Conceptualization; Writing - Review & Editing. Aart J. Nederveen: Data curation; Methodology; Writing - Review & Editing. Bert-Jan H. van den Born: Supervision; Writing - Original draft; Writing - Review & Editing; Project administration; Funding acquisition. Henk J.M.M. Mutsaerts: Supervision; Writing - Original draft; Writing - Review & Editing; Project administration; Funding acquisition. Disclosures None of the authors have conflicts of interest to report. Sources of funding The HELIUS study is conducted by the Amsterdam UMC, location AMC, and the Public Health Service (GGD) of Amsterdam. Both organizations provided core support for HELIUS. The HELIUS study is also funded by the Dutch Heart Foundation, the Netherlands Organization for Health Research and Development (ZonMw), the European Union (FP-7), and the European Fund for the Integration of non-EU immigrants (EIF). The HELIUS follow-up measurement was additionally supported by the Netherlands Organization for Health Research and Development (ZonMw; 10430022010002), Novo Nordisk (18157/80927), the University of Amsterdam (Research Priority Area 25-08-2020 “Personal Microbiome Health”) and the Dutch Kidney Foundation (Collaboration Grant 19OS004). EV and the HELIUS sub-study are funded by the Swiss National Foundation under grant number 189235 for LYRICA (Lifestyle Prevention of Cardiovascular Ageing) project. MD and HM are supported by the Dutch Heart Foundation (03-004-2020-T049). HM is supported by the Eurostars-2 joint programme with co-funding from the European Union Horizon 2020 research and innovation programme [ASPIRE E!113701], provided by the Netherlands Enterprise Agency (RvO). Supplementary information Supplementary material for this article is available online. Data availability The HELIUS data are owned by the Amsterdam University Medical Centers, location AMC, in Amsterdam, the Netherlands. Any researcher can request the data by submitting a proposal to the HELIUS Executive Board, as outlined at http://www.heliusstudy.nl/en/researchers/collaboration , by email to heliuscoordinator{at}amsterdamumc.nl . The HELIUS Executive Board will check proposals for compatibility with the general objectives, ethical approvals, and informed consent forms of the HELIUS study. There are no other restrictions to obtaining the data and all data requests will be processed in the same manner. The microbial genomic sequences from the HELIUS cohort, which were used for this study, are stored under protected access on the European Genome-Phenome Archive ( https://ega-archive.org/datasets/EGAD00001004106 ). Abbreviations ASL arterial spin labelling ATT arterial transit time BMI body mass index BP blood pressure CBF cerebral blood flow cIMT carotid intima-media thickness CSF cerebrospinal fluid CV cardiovascular CVD cardiovascular disease DBP diastolic blood pressure eGFR estimated glomerular filtration rate FA flip angle FDR Benjamini-Hochberg Procedure FLAIR fluid-attenuated inversion recovery FRS Framingham risk score GM grey matter HDL high-density lipoprotein cholesterol HELIUS study HEalthy Life In an Urban Setting study LDL low-density lipoprotein M0 equilibrium magnetisation image MAP mean arterial pressure MRI magnetic resonance imaging PLD post-labelling delay PP pulse pressure ROIs regions-of-interests SBP systolic blood pressure SCORE2 Systematic Coronary Risk Evaluation 2 sCoV spatial coefficient of variation STROBE STrengthening the Reporting of OBservational studies in Epidemiology T1w T1-weighted TE echo time TI inversion time TR repetition time WM white matter WMH white matter hyperintensities References 1. ↵ O’Donnell MJ , Xavier D , Liu L , Zhang H , Chin SL , Rao-Melacini P , et al. Risk factors for ischaemic and intracerebral haemorrhagic stroke in 22 countries (the INTERSTROKE study): a case-control study . Lancet . 2010 ; 376 ( 9735 ): 112-23 . OpenUrl 2. ↵ Yaffe K , Vittinghoff E , Hoang T , Matthews K , Golden SH , Zeki Al Hazzouri A . Cardiovascular Risk Factors Across the Life Course and Cognitive Decline: A Pooled Cohort Study . Neurology . 2021 ; 96 ( 17 ): e2212 – e9 . OpenUrl 3. ↵ Wardlaw JM , Valdés Hernández MC , Muñoz-Maniega S . What are white matter hyperintensities made of? Relevance to vascular cognitive impairment . J Am Heart Assoc . 2015 ; 4 ( 6 ): 001140 . OpenUrl CrossRef PubMed 4. ↵ Lindner T , Bolar DS , Achten E , Barkhof F , Bastos-Leite AJ , Detre JA , et al. Current state and guidance on arterial spin labeling perfusion MRI in clinical neuroimaging . Magn Reson Med . 2023 ; 89 ( 5 ): 2024 – 47 . OpenUrl 5. ↵ Gyanwali B , Tan CS , Petr J , Escobosa LLT , Vrooman H , Chen C , et al. Arterial Spin-Labeling Parameters and Their Associations with Risk Factors, Cerebral Small-Vessel Disease, and Etiologic Subtypes of Cognitive Impairment and Dementia . AJNR Am J Neuroradiol . 2022 ; 43 ( 10 ): 1418 – 23 . OpenUrl Abstract / FREE Full Text 6. ↵ Haller S , Zaharchuk G , Thomas DL , Lovblad KO , Barkhof F , Golay X . Arterial Spin Labeling Perfusion of the Brain: Emerging Clinical Applications . Radiology . 2016 ; 281 ( 2 ): 337 – 56 . OpenUrl CrossRef PubMed 7. ↵ Mutsaerts HJ , Petr J , Václavů L , van Dalen JW , Robertson AD , Caan MW , et al. The spatial coefficient of variation in arterial spin labeling cerebral blood flow images . J Cereb Blood Flow Metab . 2017 ; 37 ( 9 ): 3184 – 92 . OpenUrl CrossRef 8. ↵ Deverdun J , Akbaraly TN , Charroud C , Abdennour M , Brickman AM , Chemouny S , et al. Mean arterial pressure change associated with cerebral blood flow in healthy older adults . Neurobiol Aging . 2016 ; 46 : 49 – 57 . OpenUrl 9. Christie IN , Windsor R , Mutsaerts HJ , Tillin T , Sudre CH , Hughes AD , et al. Cerebral perfusion in untreated, controlled, and uncontrolled hypertension . J Cereb Blood Flow Metab . 2022 ; 42 ( 12 ): 2188 – 90 . OpenUrl 10. ↵ Alosco ML , Gunstad J , Xu X , Clark US , Labbe DR , Riskin-Jones HH , et al. The impact of hypertension on cerebral perfusion and cortical thickness in older adults . J Am Soc Hypertens . 2014 ; 8 ( 8 ): 561 – 70 . OpenUrl CrossRef PubMed 11. ↵ Glodzik L , Rusinek H , Tsui W , Pirraglia E , Kim HJ , Deshpande A , et al. Different Relationship Between Systolic Blood Pressure and Cerebral Perfusion in Subjects With and Without Hypertension . Hypertension . 2019 ; 73 ( 1 ): 197 – 205 . OpenUrl CrossRef 12. ↵ Dolui S , Detre JA , Gaussoin SA , Herrick JS , Wang DJJ , Tamura MK , et al. Association of Intensive vs Standard Blood Pressure Control With Cerebral Blood Flow: Secondary Analysis of the SPRINT MIND Randomized Clinical Trial . JAMA Neurol . 2022 ; 79 ( 4 ): 380 – 9 . OpenUrl 13. ↵ van Dalen JW , Mutsaerts HJ , Petr J , Caan MW , van Charante EPM , MacIntosh BJ , et al. Longitudinal relation between blood pressure, anti-hypertensive use and cerebral blood flow, using arterial spin labelling MRI . J Cereb Blood Flow Metab . 2021 ; 41 ( 7 ): 1756 – 66 . OpenUrl 14. ↵ Stewart CR , Stringer MS , Shi Y , Thrippleton MJ , Wardlaw JM . Associations Between White Matter Hyperintensity Burden, Cerebral Blood Flow and Transit Time in Small Vessel Disease: An Updated Meta-Analysis . Front Neurol . 2021 ; 12 : 647848 . 15. ↵ Clark LR , Zuelsdorff M , Norton D , Johnson SC , Wyman MF , Hancock LM , et al. Association of Cardiovascular Risk Factors with Cerebral Perfusion in Whites and African Americans . J Alzheimers Dis . 2020 ; 75 ( 2 ): 649 – 60 . OpenUrl 16. ↵ Snijder MB , Galenkamp H , Prins M , Derks EM , Peters RJG , Zwinderman AH , Stronks K . Cohort profile: the Healthy Life in an Urban Setting (HELIUS) study in Amsterdam, The Netherlands . BMJ Open . 2017 ; 7 ( 12 ): e017873 . OpenUrl Abstract / FREE Full Text 17. ↵ Vriend EMC , Bouwmeester TA , Artola Arita VA , Bots ML , Meijer R , Galenkamp H , et al. Ethnic Differences in Carotid Intima Media Thickness and Plaque Presence - The HELIUS study . Cerebrovasc Dis . 2023 . 18. ↵ Inker LA , Eneanya ND , Coresh J , Tighiouart H , Wang D , Sang Y , et al. New Creatinine- and Cystatin C-Based Equations to Estimate GFR without Race . N Engl J Med . 2021 ; 385 ( 19 ): 1737 – 49 . OpenUrl CrossRef PubMed 19. ↵ Mutsaerts H , Petr J , Groot P , Vandemaele P , Ingala S , Robertson AD , et al. ExploreASL: An image processing pipeline for multi-center ASL perfusion MRI studies . Neuroimage . 2020 ; 219 : 117031 . 20. ↵ de Sitter A , Steenwijk MD , Ruet A , Versteeg A , Liu Y , van Schijndel RA , et al. Performance of five research-domain automated WM lesion segmentation methods in a multi-center MS study . Neuroimage . 2017 ; 163 : 106 – 14 . OpenUrl CrossRef 21. ↵ Mietchen D , Gaser C . Computational morphometry for detecting changes in brain structure due to development, aging, learning, disease and evolution . Front Neuroinform . 2009 ; 3 : 25 . 22. ↵ Alsop DC , Detre JA , Golay X , Günther M , Hendrikse J , Hernandez-Garcia L , et al. Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: A consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia . Magn Reson Med . 2015 ; 73 ( 1 ): 102 – 16 . OpenUrl CrossRef PubMed 23. ↵ SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe . Eur Heart J . 2021 ; 42 ( 25 ): 2439-54 . OpenUrl 24. ↵ Johnson AD , Newton-Cheh C , Chasman DI , Ehret GB , Johnson T , Rose L , et al. Association of hypertension drug target genes with blood pressure and hypertension in 86,588 individuals . Hypertension . 2011 ; 57 ( 5 ): 903 – 10 . OpenUrl CrossRef 25. ↵ Clement P , Mutsaerts H-J , Václavů L , Ghariq E , Pizzini FB , Smits M , et al. Variability of physiological brain perfusion in healthy subjects - A systematic review of modifiers. Considerations for multi-center ASL studies . J Cereb Blood Flow Metab . 2018 ; 38 ( 9 ): 1418 – 37 . OpenUrl CrossRef 26. ↵ Staffaroni AM , Cobigo Y , Elahi FM , Casaletto KB , Walters SM , Wolf A , et al. A longitudinal characterization of perfusion in the aging brain and associations with cognition and neural structure . Hum Brain Mapp . 2019 ; 40 ( 12 ): 3522 – 33 . OpenUrl 27. ↵ Han H , Ning Z , Yang D , Yu M , Qiao H , Chen S , et al. Associations between cerebral blood flow and progression of white matter hyperintensity in community-dwelling adults: a longitudinal cohort study . Quant Imaging Med Surg . 2022 ; 12 ( 8 ): 4151 – 65 . OpenUrl 28. ↵ Juttukonda MR , Li B , Almaktoum R , Stephens KA , Yochim KM , Yacoub E , et al. Characterizing cerebral hemodynamics across the adult lifespan with arterial spin labeling MRI data from the Human Connectome Project-Aging . Neuroimage . 2021 ; 230 : 117807 . 29. ↵ Biagi L , Abbruzzese A , Bianchi MC , Alsop DC , Del Guerra A , Tosetti M . Age dependence of cerebral perfusion assessed by magnetic resonance continuous arterial spin labeling . J Magn Reson Imaging . 2007 ; 25 ( 4 ): 696 – 702 . OpenUrl CrossRef PubMed 30. ↵ Dolui S , Fan AP , Zhao MY , Nasrallah IM , Zaharchuk G , Detre JA . Reliability of arterial spin labeling derived cerebral blood flow in periventricular white matter . Neuroimage Rep . 2021 ; 1 ( 4 ). 31. ↵ Cainzos-Achirica M , Fedeli U , Sattar N , Agyemang C , Jenum AK , McEvoy JW , et al. Epidemiology, risk factors, and opportunities for prevention of cardiovascular disease in individuals of South Asian ethnicity living in Europe . Atherosclerosis . 2019 ; 286 : 105 – 13 . OpenUrl PubMed 32. ↵ Saeed S , Kanaya AM , Bennet L , Nilsson PM . Cardiovascular risk assessment in South and Middle-East Asians living in the Western countries . Pak J Med Sci Q . 2020 ; 36 ( 7 ): 1719 – 25 . OpenUrl 33. ↵ Stewart CR , Stringer MS , Shi Y , Thrippleton MJ , Wardlaw JM . Associations Between White Matter Hyperintensity Burden, Cerebral Blood Flow and Transit Time in Small Vessel Disease: An Updated Meta-Analysis . Front Neurol . 2021 ; 12 : 647848 . 34. ↵ Hilal S , Mok V , Youn YC , Wong A , Ikram MK , Chen CL-H . Prevalence, risk factors and consequences of cerebral small vessel diseases: data from three Asian countries . J Neurol Neurosurg Psychiatry . 2017 ; 88 ( 8 ): 669 – 74 . OpenUrl Abstract / FREE Full Text 35. Mok V , Srikanth V , Xiong Y , Phan TG , Moran C , Chu S , et al. Race-ethnicity and cerebral small vessel disease--comparison between Chinese and White populations . Int J Stroke . 2014 ; 9 Suppl A100 : 36 – 42 . OpenUrl CrossRef PubMed 36. ↵ Ho FK , Gray SR , Welsh P , Gill JMR , Sattar N , Pell JP , Celis-Morales C . Ethnic differences in cardiovascular risk: examining differential exposure and susceptibility to risk factors . BMC Med . 2022 ; 20 ( 1 ): 149 . OpenUrl 37. ↵ Elyamani R , Soulaymani A , Hami H . Epidemiology of Cardiovascular Diseases in Morocco: A Systematic Review . Rev Diabet Stud . 2021 ; 17 ( 2 ): 57 – 67 . OpenUrl 38. ↵ Snijder MB , Agyemang C , Peters RJ , Stronks K , Ujcic-Voortman JK , van Valkengoed IGM . Case Finding and Medical Treatment of Type 2 Diabetes among Different Ethnic Minority Groups: The HELIUS Study . J Diabetes Res . 2017 ;2017: 9896849 . 39. ↵ Meng L , Hou W , Chui J , Han R , Gelb AW . Cardiac Output and Cerebral Blood Flow: The Integrated Regulation of Brain Perfusion in Adult Humans . Anesthesiology . 2015 ; 123 ( 5 ): 1198 – 208 . OpenUrl CrossRef PubMed 40. ↵ Robertson AD , Matta G , Basile VS , Black SE , Macgowan CK , Detre JA , MacIntosh BJ . Temporal and Spatial Variances in Arterial Spin-Labeling Are Inversely Related to Large-Artery Blood Velocity . AJNR Am J Neuroradiol . 2017 ; 38 ( 8 ): 1555 – 61 . OpenUrl Abstract / FREE Full Text 41. ↵ Ogoh S , Sugawara J , Shibata S . Does Cardiac Function Affect Cerebral Blood Flow Regulation? J Clin Med . 2022 ; 11 ( 20 ). 42. ↵ Meyer JS , Rogers RL , Mortel KF , Judd BW . Hyperlipidemia is a risk factor for decreased cerebral perfusion and stroke . Arch Neurol . 1987 ; 44 ( 4 ): 418 – 22 . OpenUrl CrossRef PubMed Web of Science 43. ↵ Dickie DA , Ritchie SJ , Cox SR , Sakka E , Royle NA , Aribisala BS , et al. Vascular risk factors and progression of white matter hyperintensities in the Lothian Birth Cohort 1936 . Neurobiol Aging . 2016 ; 42 : 116 – 23 . OpenUrl PubMed 44. ↵ Feron J , Segaert K , Rahman F , Fosstveit S , Joyce K , Gilani A , et al. Determinants of cerebral blood flow and arterial transit time in healthy older adults 2023 . 45. ↵ Gyanwali B , Tan CS , Petr J , Escobosa LLT , Vrooman H , Chen C , et al. Arterial Spin-Labeling Parameters and Their Associations with Risk Factors, Cerebral Small-Vessel Disease, and Etiologic Subtypes of Cognitive Impairment and Dementia . AJNR Am J Neuroradiol . 2022 ; 43 ( 10 ): 1418 – 23 . OpenUrl Abstract / FREE Full Text 46. ↵ Durazzo TC , Meyerhoff DJ , Murray DE . Comparison of Regional Brain Perfusion Levels in Chronically Smoking and Non-Smoking Adults . Int J Environ Res Public Health . 2015 ; 12 ( 7 ): 8198 – 213 . OpenUrl CrossRef 47. ↵ Launer LJ , Lewis CE , Schreiner PJ , Sidney S , Battapady H , Jacobs DR , et al. Vascular factors and multiple measures of early brain health: CARDIA brain MRI study . Plos One . 2015 ; 10 ( 3 ): e0122138 . OpenUrl CrossRef PubMed 48. ↵ Glodzik L , Rusinek H , Brys M , Tsui WH , Switalski R , Mosconi L , et al. Framingham cardiovascular risk profile correlates with impaired hippocampal and cortical vasoreactivity to hypercapnia . J Cereb Blood Flow Metab . 2011 ; 31 ( 2 ): 671 – 9 . OpenUrl CrossRef PubMed 49. ↵ Jennings JR , Heim AF , Kuan DC-H , Gianaros PJ , Muldoon MF , Manuck SB . Use of total cerebral blood flow as an imaging biomarker of known cardiovascular risks . Stroke . 2013 ; 44 ( 9 ): 2480 – 5 . OpenUrl Abstract / FREE Full Text 50. ↵ Suri S , Topiwala A , Chappell MA , Okell TW , Zsoldos E , Singh-Manoux A , et al. Association of Midlife Cardiovascular Risk Profiles With Cerebral Perfusion at Older Ages . JAMA Netw Open . 2019 ; 2 ( 6 ): e195776 . OpenUrl 51. ↵ Huang H , Zhao K , Zhu W , Li H , Zhu W . Abnormal Cerebral Blood Flow and Functional Connectivity Strength in Subjects With White Matter Hyperintensities . Front Neurol . 2021 ; 12 : 752762 . 52. ↵ van Dalen JW , Mutsaerts HJMM , Nederveen AJ , Vrenken H , Steenwijk MD , Caan MWA , et al. White Matter Hyperintensity Volume and Cerebral Perfusion in Older Individuals with Hypertension Using Arterial Spin-Labeling . AJNR Am J Neuroradiol . 2016 ; 37 ( 10 ): 1824 – 30 . OpenUrl Abstract / FREE Full Text 53. ↵ Liu Y , Huo R , Xu H , Zhou G , Wang T , Yuan H , Zhao X . Associations Between Carotid Plaque Characteristics and Perioperative Cerebral Blood Flow Determined by Arterial Spin Labeling Imaging in Patients With Moderate-to-Severe Stenosis Undergoing Carotid Endarterectomy . Front Neurol . 2022 ; 13 : 899957 . 54. Hashimoto N , Hama S , Yamane K , Kurisu K . Carotid arterial intraplaque hemorrhage and calcification influences cerebral hemodynamics . Neurosurg Rev . 2013 ; 36 ( 3 ): 421 – 7 . OpenUrl CrossRef PubMed 55. ↵ Bokkers RPH , van der Worp HB , Mali WPTM , Hendrikse J . Noninvasive MR imaging of cerebral perfusion in patients with a carotid artery stenosis . Neurology . 2009 ; 73 ( 11 ): 869 – 75 . OpenUrl CrossRef PubMed 56. ↵ Shakur SF , Hrbac T , Alaraj A , Du X , Aletich VA , Charbel FT , Amin-Hanjani S . Effects of extracranial carotid stenosis on intracranial blood flow . Stroke . 2014 ; 45 ( 11 ): 3427 – 9 . OpenUrl Abstract / FREE Full Text 57. ↵ Di Napoli A , Cheng SF , Gregson J , Atkinson D , Markus JE , Richards T , et al. Arterial Spin Labeling MRI in Carotid Stenosis: Arterial Transit Artifacts May Predict Symptoms . Radiology . 2020 ; 297 ( 3 ): 652 – 60 . OpenUrl PubMed 58. ↵ Moroni F , Ammirati E , Rocca MA , Filippi M , Magnoni M , Camici PG . Cardiovascular disease and brain health: Focus on white matter hyperintensities . Int J Cardiol Heart Vasc . 2018 ; 19 : 63 – 9 . OpenUrl 59. ↵ Breteler MM , van Swieten JC , Bots ML , Grobbee DE , Claus JJ , van den Hout JH, et al. Cerebral white matter lesions, vascular risk factors, and cognitive function in a population-based study: the Rotterdam Study . Neurology . 1994 ; 44 ( 7 ): 1246 – 52 . OpenUrl CrossRef PubMed 60. ↵ Hajat C , Tilling K , Stewart JA , Lemic-Stojcevic N , Wolfe CD . Ethnic differences in risk factors for ischemic stroke: a European case-control study . Stroke . 2004 ; 35 ( 7 ): 1562 – 7 . OpenUrl Abstract / FREE Full Text 61. Bonnechère B , Liu J , Thompson A , Amin N , van Duijn C . Does ethnicity influence dementia, stroke and mortality risk? Evidence from the UK Biobank . Front Public Health . 2023 ; 11 : 1111321 . 62. Farkhondeh V , DeCarli C . White matter hyperintensities in diverse populations: A systematic review of literature in the United States . Cereb Circ Cogn Behav . 2024 ; 6 : 100204 . 63. ↵ Morrison C , Dadar M , Manera AL , Collins DL . Racial differences in white matter hyperintensity burden in older adults . Neurobiol Aging . 2023 ; 122 : 112 – 9 . OpenUrl 64. ↵ Leeuwis AE , Smith LA , Melbourne A , Hughes AD , Richards M , Prins ND , et al. Cerebral Blood Flow and Cognitive Functioning in a Community-Based, Multi-Ethnic Cohort: The SABRE Study . Front Aging Neurosci . 2018 ; 10 : 279 . View the discussion thread. Back to top Previous Next Posted October 06, 2024. Download PDF Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Mid-life association between cardiovascular risk factors and cerebral blood flow in a multi-ethnic population Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Mid-life association between cardiovascular risk factors and cerebral blood flow in a multi-ethnic population Esther M.C. Vriend , Mathijs B.J. Dijsselhof , Thomas A. Bouwmeester , Oscar H. Franco , Henrike Galenkamp , Didier Collard , Aart J. Nederveen , Bert-Jan H. van den Born , Henk J.M.M. Mutsaerts medRxiv 2024.10.04.24314929; doi: https://doi.org/10.1101/2024.10.04.24314929 Share This Article: Copy Citation Tools Mid-life association between cardiovascular risk factors and cerebral blood flow in a multi-ethnic population Esther M.C. Vriend , Mathijs B.J. Dijsselhof , Thomas A. Bouwmeester , Oscar H. Franco , Henrike Galenkamp , Didier Collard , Aart J. Nederveen , Bert-Jan H. van den Born , Henk J.M.M. Mutsaerts medRxiv 2024.10.04.24314929; doi: https://doi.org/10.1101/2024.10.04.24314929 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Radiology and Imaging Subject Areas All Articles Addiction Medicine (573) Allergy and Immunology (865) Anesthesia (303) Cardiovascular Medicine (4457) Dentistry and Oral Medicine (445) Dermatology (383) Emergency Medicine (610) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1517) Epidemiology (15244) Forensic Medicine (30) Gastroenterology (1132) Genetic and Genomic Medicine (6620) Geriatric Medicine (669) Health Economics (1002) Health Informatics (4557) Health Policy (1372) Health Systems and Quality Improvement (1615) Hematology (543) HIV/AIDS (1272) Infectious Diseases (except HIV/AIDS) (15936) Intensive Care and Critical Care Medicine (1106) Medical Education (624) Medical Ethics (147) Nephrology (670) Neurology (6634) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1148) Occupational and Environmental Health (957) Oncology (3348) Ophthalmology (980) Orthopedics (369) Otolaryngology (421) Pain Medicine (436) Palliative Medicine (130) Pathology (665) Pediatrics (1696) Pharmacology and Therapeutics (693) Primary Care Research (714) Psychiatry and Clinical Psychology (5463) Public and Global Health (9257) Radiology and Imaging (2210) Rehabilitation Medicine and Physical Therapy (1371) Respiratory Medicine (1198) Rheumatology (598) Sexual and Reproductive Health (716) Sports Medicine (532) Surgery (714) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a033f58c3b3e0db4',t:'MTc4MDA0MTU2MQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();