The interactive effect of intra-beat and inter-beat blood pressure variability on neurodegeneration in older adults

The interactive effect of intra-beat and inter-beat blood pressure variability on neurodegeneration in older adults | 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 The interactive effect of intra-beat and inter-beat blood pressure variability on neurodegeneration in older adults View ORCID Profile Trevor Lohman , View ORCID Profile Fatemah Shenasa , View ORCID Profile Isabel Sible , Arunima Kapoor , View ORCID Profile Allison C Engstrom , Shubir Dutt , Elizabeth Head , Lorena Sordo , John Paul M. Alitin , Aimee Gaubert , Amy Nguyen , View ORCID Profile Daniel A Nation doi: https://doi.org/10.1101/2024.05.01.24306724 Trevor Lohman 1 University of Southern California, Leonard Davis School of Gerontology , Los Angeles, CA, USA ; Ph.D Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Trevor Lohman For correspondence: tlohman{at}usc.edu Fatemah Shenasa 2 Department of Psychological Science, University of California , Irvine, Irvine, CA, USA ; B.S Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Fatemah Shenasa Isabel Sible 3 Department of Psychology, University of Southern California , Los Angeles, CA, USA ; M.A Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Isabel Sible Arunima Kapoor 2 Department of Psychological Science, University of California , Irvine, Irvine, CA, USA ; M.Sc Find this author on Google Scholar Find this author on PubMed Search for this author on this site Allison C Engstrom 2 Department of Psychological Science, University of California , Irvine, Irvine, CA, USA ; B.S Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Allison C Engstrom Shubir Dutt 4 Memory and Aging Center, Weill Institute for Neurosciences, Department of Neurology, University of California , San Francisco, San Francisco, CA, USA ; Ph.D Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elizabeth Head 5 Department of Pathology and Laboratory Medicine, University of California , Irvine, Irvine, CA, USA ; Ph.D Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lorena Sordo 5 Department of Pathology and Laboratory Medicine, University of California , Irvine, Irvine, CA, USA ; Ph.D Find this author on Google Scholar Find this author on PubMed Search for this author on this site John Paul M. Alitin 1 University of Southern California, Leonard Davis School of Gerontology , Los Angeles, CA, USA ; B.A Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aimee Gaubert 1 University of Southern California, Leonard Davis School of Gerontology , Los Angeles, CA, USA ; B.A Find this author on Google Scholar Find this author on PubMed Search for this author on this site Amy Nguyen 1 University of Southern California, Leonard Davis School of Gerontology , Los Angeles, CA, USA ; B.A Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daniel A Nation 1 University of Southern California, Leonard Davis School of Gerontology , Los Angeles, CA, USA ; 6 University of Southern California, Keck School of Medicine , Los Angeles, CA, USA Ph.D Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Daniel A Nation For correspondence: danation{at}usc.edu Abstract Full Text Info/History Metrics Preview PDF Abstract Blood pressure variability (BPV) and arterial stiffness are age-related hemodynamic risk factors for neurodegenerative disease, but it remains unclear whether they exert independent or interactive effects on brain health. When combined with high inter-beat BPV, increased intra-beat BPV indicative of arterial stiffness could convey greater pressure wave fluctuations deeper into the cerebrovasculature, exacerbating neurodegeneration. This interactive effect was studied in older adults using multiple markers of neurodegeneration, including medial temporal lobe (MTL) volume, plasma neurofilament light (NfL) and glial fibrillary acidic protein (GFAP). Older adults (N=105) without major neurological or systemic disease were recruited and underwent brain MRI and continuous BP monitoring to quantify inter-beat BPV through systolic average real variability (ARV) and intra-beat variability through arterial stiffness index (ASI). Plasma NfL and GFAP were assessed. The interactive effect of ARV and ASI on MTL atrophy, plasma NfL, and GFAP was studied using hierarchical linear regression. Voxel-based morphometry (VBM) was used to confirm region-of-interest analysis findings. The interaction between higher ARV and higher ASI was significantly associated with left-sided MTL atrophy in both the region-of-interest and false discovery rate-corrected VBM analysis. The interactive effect was also significantly associated with increased plasma NfL, but not GFAP. The interaction between higher ARV and higher ASI is independently associated with increased neurodegenerative markers, including MTL atrophy and plasma NfL, in independently living older adults. Findings could suggest the increased risk for neurodegeneration associated with higher inter-beat BPV may be compounded by increased intra-beat variability due to arterial stiffness. Introduction Increased blood pressure variability (BPV) has emerged as a risk factor for stroke 1 , cerebral small vessel disease (CSVD) 2 , 3 , neurodegenerative disease 4 and dementia 5 , independent of average blood pressure. Various markers of BPV have been investigated in the literature including both visit-to-visit BPV and beat-to-beat BPV, with beat-to-beat BPV emerging as a valid 6 , 7 , high temporal resolution hemodynamic marker associated with cardiovascular function 8 , autonomic function 9 , and vascular aging 10 . Average real variability (ARV) derived from beat-to-beat blood pressure data is a reliable measure of inter-beat blood pressure variability that is less susceptible to outliers than other BPV metrics 11 – 13 . Prior studies have variously quantified ARV and arterial stiffness and identified them separately as risk factors for age-related cognitive decline 14 , 15 , dementia risk 16 , 17 , and cerebral small vessel disease 18 – 20 . The mechanisms underlying the adverse independent effects of increased inter-beat BPV on cerebrovascular and neurodegenerative disease remain unclear, but may be related to the ability of the cerebrovasculature to adapt to chronic fluctuations in pressure and flow while sustaining cerebral perfusion 21 , blood-brain barrier integrity 22 and clearance of brain waste products 23 . Arterial stiffening has also been implicated in cerebrovascular 24 , 25 and neurodegenerative disease 17 , 26 . Arterial stiffness entails age-related loss of compliance and elasticity within the large conducting arteries 27 (e.g., aorta and carotids), resulting in the loss of the buffering of pressure wave amplitude (i.e., the Windkessel effect 28 ). The loss of pressure wave buffering by conducting arteries leads to transmission of higher amplitude pressure waves into muscular and resistance arteries, contributing to cerebrovascular disease and disrupting cerebral microvascular function 24 , 29 . Arterial stiffness also leads to uncoupling of systolic and diastolic blood pressure 30 , resulting in increased intra-beat BPV (i.e., systolic vs. diastolic pressure). Intra-beat BPV can be quantified during continuous blood pressure monitoring by the correlation between systolic and diastolic pressure within each beat. This arterial stiffness index (ASI) has been studied extensively over 24-hour time periods (ambulatory arterial stiffness index) 31 , 32 , but can also be quantified over shorter time periods. ASI is correlated with measures of arterial stiffness including pulse wave velocity 33 , pulse pressure 34 , as well as atherosclerosis 35 , left ventricular mass index/relative wall thickness 34 , and can differentiate between young and old vasculature 36 . Markers of increased intra-beat BPV, such as ASI and pulse pressure, have been studied as stiffness-related hemodynamic markers of interest that, like inter-beat BPV, are correlated with cerebrovascular disease and neurodegenerative disease 26 , 37 . While inter-beat and intra-beat variability are related hemodynamic markers, they are physiologically distinct aspects of blood pressure fluctuation as illustrated conceptually in Figure 1a - 1b that exist to varying degrees among individuals. Arterial stiffening, indexed by higher intra-beat BPV, promotes transmission of high amplitude pressure waves deeper into the cerebral microvasculature. Thus, higher inter-beat BPV in the presence of arterial stiffness may promote transmission of even greater blood pressure fluctuations, both within (intra-beat) and between (inter-beat) cardiac cycles. The transmission of these fluctuating pressure waves deep into the brain could promote brain injury and neurodegeneration by interfering with cerebral perfusion 21 , blood brain barrier integrity 22 , and waste clearance 23 , 38 mechanisms ( Figure 2 ). Download figure Open in new tab Figure 1: Inter-beat and Intra-beat blood pressure variability. A: conceptual continuous blood pressure waveform illustrating high inter-beat blood pressure variability and minimal intra-beat blood pressure variability. The correlation between each cardiac cycle’s systolic blood pressure (SBP) peak and diastolic blood pressure (DBP) trough is illustrated, indicating a positive correlation, indicative of a low arterial stiffness index (ASI= 1 - Pearson’s r). B: conceptual continuous blood pressure waveform illustrating high inter-beat blood pressure variability combined with high intra-beat blood pressure variability. The correlation between each cardiac cycle’s SBP peak and DBP trough is illustrated showing an inverse correlation, which is indicative of a high arterial stiffness index (ASI= 1 - Pearson’s r). Download figure Open in new tab Figure 2: Fluctuating pulse wave transmission hypothesis. An illustration of two arterial pulse waveforms as they transit through systemic circulation. With increased BPV and arterial stiffening there is both greater fluctuation in the oscillation of the pulse wave and greater transmission of the pulse wave oscillations into the cerebral microvascular compartment. Arterial pressure is indicated on the y-axis and the x-axis indicates sequential portions of the arterial tree. To our knowledge, no studies to date have addressed the potential interaction between inter-beat BPV (ARV) and intra-beat BPV (ASI) relating to neurodegeneration markers on MRI (e.g., MTL atrophy) and in plasma [e.g., neurofilament light (NfL) 39 and glial fibrillary acidic protein (GFAP)] 40 . The MTL has been previously identified as susceptible to both age-related hemodynamic insults and neurodegeneration 41 , thus the authors hypothesize that the MTL will be differentially affected by this synergistic interaction, manifesting in MTL volume loss and increased plasma markers of neurodegeneration. To test the hypothesis, the present study will investigate the interactive effect of ARV and ASI on MTL atrophy, plasma NfL, and plasma GFAP. MTL atrophy will be quantified using brain MRI. Methods Participants Participants were recruited from Orange County communities through outreach events, mailing lists, word-of-mouth, online portals, and other modes of community outreach facilitated by the University of California Irvine (UCI), Alzheimer’s Disease Research Center. All procedures were conducted as part of the Vascular Senescence and Cognition (VaSC) Study at UCI. All participants were independently living at the time of recruitment and were aged 55 to 89 years. Study exclusions were a prior diagnosis of dementia, history of clinical stroke, family history of dominantly inherited neurodegenerative disorders, current neurological or major psychiatric disorders that may impact cognitive function, history of moderate-to-severe traumatic brain injury, current use of medications impairing the central nervous system, current organ failure or other uncontrolled systemic illness, and contraindications for brain MRI. Eligibility for the study was verified via clinical interview and review of current medication with both the participant, and an informant study partner when available. The study was approved by the UCI Institutional Review Board (HS-2019-5324), and all participants gave informed consent. Continuous BP Data Acquisition Participants were asked to take medications as normally prescribed and abstain from caffeine the morning of data collection. Beat-to-beat BP measurements were obtained continuously during supine rest during brain MRI, using an MRI compatible non-invasive continuous BP finger cuff device (Biopac®). First, the participant rests for 3 minutes in the supine position prior to the calibration period. During calibration, two sequential static pressures are taken during continuous BP waveform acquisition using a calibrated, MRI compatible, automatic BP device with an inflatable brachial artery cuff (TeslaDUO). These static pressures are used to calibrate the continuous BP monitor using the Caretaker® system (Biopac®). After calibration, continuous BP was monitored during MRI for 7 minutes and further data processing was performed as previously described 42 . Systolic Blood Pressure Average Real Variability ARV calculates the average of absolute changes between consecutive blood pressure readings and is calculated as: Where n represents the number of blood pressure readings obtained during continuous blood pressure monitoring and k represents the beat index of the readings as previously described 11 , 42 . Arterial Stiffness Index ASI 43 is an indirect measure of arterial stiffness that is correlated with pulse wave velocity 33 , and atherosclerosis 35 . ASI was calculated as 1 minus the regression slope of diastolic blood pressure on SBP obtained during 7-minutes of continuous BP monitoring. Higher correlation between systolic and diastolic BP reflects increased arterial compliance, thus a higher ASI (1-Pearson’s r) reflects increased arterial stiffness. Plasma Markers of Neurodegeneration Participant blood samples were obtained by venipuncture and plasma was separated by centrifugation. All plasma samples were kept frozen at -80°C until assay. Plasma levels of GFAP and NfL were determined using single molecule array (Simoa®) Neurology 2-Plex B (N2PB) Kit (Quanterix), following the manufacturer’s protocol on the HD-X machine. Accepted ranges were as follows: NfL = 0 – ∼2000 pg/mL and GFAP = 0 – ∼40000 pg/mL. All biomarker assays were conducted in the same lab at UCI (EH). Brain Volume All participants underwent brain MRI scans conducted on a 3T Siemens Prisma scanner with 20-channel head coil. High-resolution 3D T1-weighted anatomical (Scan parameters: TR = 2300 ms; TE = 2.98 ms; TI = 900 ms; flip angle = 9 deg; FOV = 256 mm; resolution = 1.0 × 1.0 × 1.2 mm 3 ; Scan time = 9 min) images were acquired, using 3-dimensional magnetization-prepared rapid gradient-echo (MPRAGE) sequences. For region-of-interest analysis, post-processing of MPRAGE scans included FreeSurfer 5.3 semi-automated segmentation and parcellation algorithm for quantification of bilateral hippocampal volume (FreeSurfer 6.0) and entorhinal cortical thickness. After automated segmentation, each individual subject was checked for any inaccuracies or misclassifications; manual corrections were made as needed with FreeSurfer’s built-in editing tools, cases were then re-processed, and resulting volumes were used for analyses. For voxel-level indexing of brain regional volume by voxel-based morphometry (VBM) analysis, post-processing was conducted using SPM12 44 and CAT12 45 . T1-weighted images were segmented into gray and white matter tissue classes using SPM12’s unified segmentation procedure, spatially normalized, and smoothed with an 8 mm full-width at half-maximum (FWHM) isotropic Gaussian kernel; the resulting gray matter images were checked for sample homogeneity to identify the presence of any potential outliers prior to VBM analysis. Vascular Risk Factors Vascular risk factor (VRF) burden was determined through clinical interviews with the participant and informant (when available), and review of current medications and medical history. The assessed VRFs included history of cardiovascular disease (e.g., heart failure, angina, stent placement, coronary artery bypass graft, intermittent claudication), hypertension, hyperlipidemia, type 2 diabetes, atrial fibrillation, left ventricular hypertrophy and transient ischemic attack. Total VRFs were summed for each participant and elevated VRF burden was defined as ≥2 VRFs (vs. 0-1) as described previously 46 , 47 . Data Analysis Data from 105 community-dwelling older adults characterized by demographics, brain MRI, and beat-to-beat ARV were screened for outliers (±3 SD). One participant with total intracranial volume (TIV) normalized left hippocampal volume 4.35 SD above the mean and right hippocampal volume 4.31 SD above the mean was removed from the ROI analysis. No outliers were identified during the VBM sample homogeneity screen. A subset of these participants (n=56) also had plasma NfL and GFAP levels for statistical analysis. Hierarchical linear regression models assessed the statistical significance of the relationship between the ARV*ASI interaction term and both MTL atrophy and plasma markers of neurodegeneration adjusted for age, sex, ARV, and ASI. MTL atrophy analyses also included TIV as a covariate. For all models, step 0 included age, sex, and SBP, step 1 added ARV and ASI, and step 2 added the ARV*ASI interaction term. A multicollinearity screen was performed for all predictor variables, with a variance inflation factor (VIF) above 4 indicating significant multicollinearity. VIF was <2 for all predictors used in the present analyses. All analyses were performed in R 48 , with conditional effects figures generated using the Hayes PROCESS macro 49 model 1 (simple moderation) in R. For VBM analyses, voxel-wise multiple regression was conducted with the ARV*ASI product term as the independent variable, gray matter map as the dependent variable, and total intracranial volume as a covariate. As this was a confirmatory analysis based on ROI analysis results, we corrected for false discovery rate and family-wise error but also conducted analyses with the uncorrected significance threshold set at p < .001. Results Participant characteristics and their demographics are displayed in Table 1 . Higher ARV was associated with increased age ( B= 3.43, P = .00002) and with higher ASI ( B= .08, P= .0000004). Higher ASI trended towards a significant relationship with increased age ( B = 9.90, P = .05). No sex differences were observed for either variable. View this table: View inline View popup Download powerpoint Table 1: Participant Characteristics and Demographics (n=105) The ARV*ASI interaction term was significantly associated with decreased left and right hippocampal volumes, independent of age, sex, average SBP and TIV ( Table 2a - 2b ), The adverse effect of higher ARV on hippocampal volume was greatest in participants with higher ASI (left B= -252.79, P= .0002; right B= -193.56, P= .001) as shown in Figure 3a - 3b . ARV*ASI was associated with decreased left entorhinal cortical thickness after accounting for age, sex, average SBP, and TIV ( Table 2c ), and the effect was greatest in participants with high ASI ( B= - .13, P= .007) as shown in Figure 3c . No similar significant relationship between the ARV*ASI interaction term and right entorhinal cortex thickness was observed ( Table 2d ). View this table: View inline View popup Table 2: Hierarchical linear regression model parameters assessing the interactive effects of ARV and ASI on A: left hippocampal volume, B: right hippocampal volume, C: left entorhinal cortex thickness, D: right entorhinal cortex thickness, E: plasma NfL, F: plasma GFAP (pg/ml). Download figure Open in new tab Figure 3: The interaction between ARV and ASI in relation to hippocampal volume, entorhinal cortex thickness, and plasma neurofilament light (NfL). A. ARV in mmHg displayed on x-axis and left hippocampal volume (mm 3 ) displayed on y-axis. Three groups are displayed (-1 SD ASI, mean ASI, and +1 SD ASI) with unstandardized beta coefficients and p-values for the within group relationship between the BPV – left hippocampal volume relationship. ARV*ASI interaction term unstandardized regression coefficient and p-values displayed above the x-axis. B. ARV in mmHg displayed on x-axis and right hippocampal volume (mm 3 ) displayed on y-axis. Three groups are displayed (-1 SD ASI, mean ASI, and +1 SD ASI) with unstandardized beta coefficients and p-values for the within group relationship between the BPV – right hippocampal volume relationship. ARV*ASI interaction term unstandardized regression coefficient and p-values displayed above the x-axis. C. ARV in mmHg displayed on x-axis and left entorhinal cortex thickness (mm) displayed on y-axis. Three groups are displayed (-1 SD ASI, mean ASI, and +1 SD ASI) with unstandardized beta coefficients and p-values for the within group relationship between the ARV – left entorhinal thickness relationship. ARV*ASI interaction term unstandardized regression coefficient and p-values displayed above the x-axis. D. BPV in mmHg displayed on x-axis and plasma NfL in pg/ml displayed on y-axis. Three groups are displayed (-1 SD ASI, mean ASI, and +1 SD ASI) with unstandardized beta coefficients and p-values for the within group relationship between the ARV – plasma NfL relationship. ARV*ASI interaction term unstandardized regression coefficient and p-values displayed above the x-axis. The ARV*ASI interaction term was also significantly associated with increased plasma NfL ( Table 2e ) but not plasma GFAP ( Table 2f ) adjusting for age, sex, and average SBP. The adverse effect of ARV on plasma NfL was greatest in participants with high ASI ( B= 3.88, P= .01) as shown in Figure 3d . Voxel-Based Morphometry Analysis In confirmatory VBM analysis the ARV*ASI interaction term was significantly (uncorrected and FDR corrected) associated with decreased gray matter volume in a cluster centered within the left entorhinal region, left parahippocampal gyrus, left fusiform gyrus, left temporal pole, left inferior temporal gyrus, left amygdala, and left hippocampus ( p uncorrected = .007, q FDR-corrected = .03, p FWE-corrected = .10). A second cluster centered around the right fusiform gyrus, right hippocampus, right parahippocampal gyrus, right amygdala, right entorhinal area, and the right inferior temporal gyrus was significant in the peak level analysis ( p uncorrected <.001) but failed to achieve significance in the cluster level analysis ( p uncorrected =.55) . VBM results are shown in Figure 4 . Download figure Open in new tab Figure 4: Voxel-based Morphometry. Visualization of gray matter reductions associated with the blood pressure variability*arterial stiffness interaction term. One statistically significant cluster was identified centered around the left entorhinal area, left parahippocampal gyrus, left fusiform gyrus, left temporal pole, left inferior temporal gyrus, left amygdala, and left hippocampus. Cluster level statistics with uncorrected p-value, FDR corrected q-value, and family-wise error p-value displayed alongside a t-statistic color bar with higher t-statistic (t-stat) indicating greater gray matter reduction. MNI coordinates along the y-axis indicated for each coronal slice. Discussion The interaction between increased inter-beat BPV, indexed by ARV, and intra-beat BPV, indexed by ASI, was significantly associated with medial temporal atrophy and increased plasma NfL independent of age, sex, and average SBP. Specifically, adverse effects of increased inter-beat BPV were most apparent in individuals with elevated intra-beat BPV. The ROI volumetric analysis was supported by a VBM analysis which confirmed that the interaction between elevated BPV and ASI was specifically related to medial temporal atrophy. Although causality cannot be inferred from this observational study, these statistical findings are consistent with our a priori hypothesis that the combined effect of two different dimensions of BPV, including both inter-beat and intra-beat variation, would have a synergistic effect on neurodegeneration. This hypothesis is based on the theory proposed herein that the combination of age-related arterial stiffness and increased blood pressure fluctuation leads to transmission of fluctuating pulse waves into the cerebral microvasculature causing neurodegeneration. The findings are also consistent with prior studies separately relating visit-to-visit BPV and arterial stiffness to medial temporal perfusion decline and neurodegeneration 21 , 50 – 52 . Both the ROI and VBM analysis revealed increased left-sided involvement, which is consistent with known left-sided neurodegenerative susceptibility 53 in a variety of diseases including Alzheimer’s disease (AD), Parkinson’s disease, frontotemporal dementia, and Huntington’s disease. Interestingly, atherosclerotic plaque development and intima-media common carotid thickening also show a left-sided predilection 54 , potentially due to increased left-sided hemodynamic stress 54 . Left-sided cerebrovascular events may also be more common than right-sided events 55 , 56 . The finding that the interaction between two hemodynamic markers was similarly associated with left-sided MTL atrophy and neurodegenerative plasma markers in the present study could suggest a shared vascular mechanism for neurodegeneration. Plasma NfL, but not GFAP, was associated with the ARV-ASI interaction, such that greater ARV in the presence of high ASI correlated with higher plasma NfL levels. Plasma NfL is more sensitive than GFAP to the presence of CSVD 57 , and both BPV and arterial stiffness are associated with CSVD, potentially accounting for the findings of the present study 2 , 58 . Also, plasma NfL reflects the current severity of neurodegeneration based on MTL atrophy, hypometabolism, and white matter integrity loss 39 , while plasma GFAP is only indirectly associated with neuronal injury, being a marker of the reactive astrogliosis that can occur during neurodegeneration 59 . Given the present study findings relating the ARV-ASI interaction to be both MTL volume levels and plasma NfL levels, the combined effect of BPV and arterial stiffness may be related to both vascular brain injury and neurodegeneration. Although MTL atrophy indicative of neurodegeneration is a key feature of the AD “ATN” (N=neurodegeneration) criteria 60 , neither MTL atrophy nor NfL are disease-specific biomarkers 61 . Both are more generally associated with a variety of age-related neurodegenerative and neurovascular diseases, including CSVD and vascular dementia 61 , 62 , Lewy bodies dementia 63 and Parkinson’s disease 63 . Additionally, both NfL and left-sided MTL volume loss are associated with future cognitive decline 64 , 65 , AD 66 and progression to dementia 67 . Similarly, plasma NfL has been identified as a susceptibility marker given its ability to predict future abnormal alterations in brain structure and function 39 . This could indicate that two hemodynamic markers of vascular aging, BPV and ASI, may interact to confer a susceptibility to future neurological and neurovascular disease in older adults. Strengths of the present study include the test of a novel hypothesis of the interaction between two hemodynamic risk markers to brain injury and neurodegeneration markers in older adults, the combination of imaging and biofluid markers and the confirmation of ROI-volumetric findings with VBM analysis. Study limitations include the cross-sectional study design which limits causal inference. Further research examining the interaction between inter-beat and intra-beat BPV is needed to fully elucidate the relationship between blood pressure fluctuation and risk of neurodegeneration. The novel findings presented here may have significant clinical implications and offer new insights into the pathogenesis of age-related neurodegeneration. Perspectives The present study proposes and tests the fluctuating pulse wave transmission hypothesis, in which blood pressure fluctuations are transmitted further into the delicate cerebral microvasculature. To accomplish this, the effect of the interaction between an index of arterial stiffness (intra-beat BPV) and beat-to-beat BPV was assessed using plasma and neuroimaging biomarkers of medial temporal neurodegeneration. Participants with higher intra-beat and inter-beat BPV displayed greater MTL atrophy and increased plasma NfL. Future studies should assess the interaction of BPV and pulse wave velocity to determine if the observed effects are specific to the interaction between BPV and arterial stiffening, or if BPV is interacting with another physiological phenomenon captured by intra-beat BPV such as ventricular contractility or pulse pressure variability 34 , 68 . Novelty and Relevance What Is New? The discovery of an interactive effect of inter-beat BPV and intra-beat BPV on neurodegeneration markers representing the combined influence of blood pressure fluctuation and arterial stiffness. What is relevant? Higher intra-beat and inter-beat BPV is associated with greater MTL atrophy and increased plasma NfL independent of mean blood pressure level. Clinical/Pathophysiological Implications? These initial results indicate that further study of the synergistic effects of BPV and arterial stiffness are needed to better understand potentially modifiable hemodynamic risk factors for neurodegeneration that are independent of currently treated mean blood pressure levels. Statements and Declarations Funding This research was supported by National Institutes of Health grants (DAN: R01AG064228, R01AG060049, R01AG082073, P01AG052350, P30AG066530), (EH: UCI ADRC P30AG066519), and the Canadian Institutes of Health Research (AK: DFD-170763). Competing Interests The authors have no competing interests to declare. Data availability statement The anonymous data that support the findings of this study are available upon reasonable request from the corresponding author, DN, through appropriate data sharing protocols. Footnotes Conflict of Interest/Disclosures: None Non-standard Abbreviations and Acronyms ARV average real variability ASI Arterial stiffness index BPV blood pressure variability GFAP glial fibrillary acidic protein MTL medial temporal lobe NfL neurofilament light SBP systolic blood pressure VBM voxel-based morphometry References 1. ↵ Heshmatollah A , Ma Y , Fani L , Koudstaal PJ , Ikram MA , Ikram MK. Visit-to-visit blood pressure variability and the risk of stroke in the Netherlands: A population-based cohort study . PLoS Med . 2022 ; 19 : e1003942 . doi: 10.1371/journal.pmed.1003942 OpenUrl CrossRef 2. ↵ Ma Y , Song A , Viswanathan A , Blacker D , Vernooij MW , Hofman A , Papatheodorou S. Blood Pressure Variability and Cerebral Small Vessel Disease . Stroke . 2020 ; 51 : 82 – 89 . doi: 10.1161/STROKEAHA.119.026739 OpenUrl CrossRef 3. ↵ Sible IJ , Bangen KJ , Blanken AE , Ho JK , Nation DA. Antemortem Visit-To-Visit Blood Pressure Variability Predicts Cerebrovascular Lesion Burden in Autopsy-Confirmed Alzheimer’s Disease . J Alzheimers Dis . 2021 ; 83 : 65 – 75 . doi: 10.3233/JAD-210435 OpenUrl Abstract / FREE Full Text 4. ↵ Sible IJ , Nation DA , Alzheimer’s Disease Neuroimaging I. Visit-to-Visit Blood Pressure Variability and CSF Alzheimer Disease Biomarkers in Cognitively Unimpaired and Mildly Impaired Older Adults . Neurology . 2022 ; 98 : e2446 – e2453 . doi: 10.1212/WNL.0000000000200302 OpenUrl CrossRef 5. ↵ Yoo JE , Shin DW , Han K , Kim D , Lee S-P , Jeong S-M , Lee J , Kim S. Blood Pressure Variability and the Risk of Dementia . Hypertension . 2020 ; 75 : 982 – 990 . doi: 10.1161/HYPERTENSIONAHA.119.14033 OpenUrl CrossRef 6. ↵ Gratz I , Deal E , Spitz F , Baruch M , Allen IE , Seaman JE , Pukenas E , Jean S. Continuous Non-invasive finger cuff CareTaker® comparable to invasive intra-arterial pressure in patients undergoing major intra-abdominal surgery . BMC Anesthesiol . 2017 ; 17 : 48 . doi: 10.1186/s12871-017-0337-z OpenUrl CrossRef 7. ↵ Kwon Y , Stafford PL , Baruch MC , Kim SH , Cho Y , Mazimba S , Gimple LW. Tracking of the beat-to-beat blood pressure changes by the Caretaker physiological monitor against invasive central aortic measurement . Blood Press Monit . 2022 ; 27 : 70 – 76 . doi: 10.1097/mbp.0000000000000568 OpenUrl CrossRef 8. ↵ Bakkar NZ , El-Yazbi AF , Zouein FA , Fares SA. Beat-to-beat blood pressure variability: an early predictor of disease and cardiovascular risk . J Hypertens . 2021 ; 39 : 830 – 845 . doi: 10.1097/hjh.0000000000002733 OpenUrl CrossRef PubMed 9. ↵ Tian G , Xiong L , Leung H , Soo Y , Leung T , Wong LK. Beat-to-beat blood pressure variability and heart rate variability in relation to autonomic dysregulation in patients with acute mild-moderate ischemic stroke . J Clin Neurosci . 2019 ; 64 : 187 – 193 . doi: 10.1016/j.jocn.2019.03.003 OpenUrl CrossRef 10. ↵ Tomiyama H. Vascular function: a key player in hypertension . Hypertens Res . 2023 ; 46 : 2145 – 2158 . doi: 10.1038/s41440-023-01354-3 OpenUrl CrossRef 11. ↵ Mena LJ , Felix VG , Melgarejo JD , Maestre GE. 24-Hour Blood Pressure Variability Assessed by Average Real Variability: A Systematic Review and Meta-Analysis . Journal of the American Heart Association . 6 : e006895 . doi: 10.1161/JAHA.117.006895 OpenUrl Abstract / FREE Full Text 12. Mena LJ , Maestre GE , Hansen TW , Thijs L , Liu Y , Boggia J , Li Y , Kikuya M , Björklund-Bodegård K , Ohkubo T , et al. How many measurements are needed to estimate blood pressure variability without loss of prognostic information? Am J Hypertens . 2014 ; 27 : 46 – 55 . doi: 10.1093/ajh/hpt142 OpenUrl CrossRef PubMed 13. ↵ Mena L , Pintos S , Queipo NV , Aizpúrua JA , Maestre G , Sulbarán T. A reliable index for the prognostic significance of blood pressure variability . J Hypertens . 2005 ; 23 : 505 – 511 . doi: 10.1097/01.hjh.0000160205.81652.5a OpenUrl CrossRef PubMed Web of Science 14. ↵ Sun F. The impact of blood pressure variability on cognition: current limitations and new advances . J Hypertens . 2023 ; 41 : 888 – 905 . doi: 10.1097/HJH.0000000000003422 OpenUrl CrossRef 15. ↵ Alvarez-Bueno C , Cunha PG , Martinez-Vizcaino V , Pozuelo-Carrascosa DP , Visier-Alfonso ME , Jimenez-Lopez E , Cavero-Redondo I. Arterial Stiffness and Cognition Among Adults: A Systematic Review and Meta-Analysis of Observational and Longitudinal Studies . Journal of the American Heart Association . 2020 ; 9 : e014621 . doi: 10.1161/JAHA.119.014621 OpenUrl CrossRef PubMed 16. ↵ Ebinger J , Driver M , Huang T , Magraner J , Botting P , Wang M , Chen P-S , Bello N , Ouyang D , Theurer J , et al. Blood pressure variability supersedes heart rate variability as a real-world measure of dementia risk . Scientific Reports . 2024 ; 14 . doi: 10.1038/s41598-024-52406-8 OpenUrl CrossRef 17. ↵ Hughes TM , Kuller LH , Barinas-Mitchell EJ , McDade EM , Klunk WE , Cohen AD , Mathis CA , Dekosky ST , Price JC , Lopez OL. Arterial stiffness and β-amyloid progression in nondemented elderly adults . JAMA Neurol . 2014 ; 71 : 562 – 568 . doi: 10.1001/jamaneurol.2014.186 OpenUrl CrossRef PubMed 18. ↵ Yamaguchi Y , Wada M , Sato H , Nagasawa H , Koyama S , Takahashi Y , Kawanami T , Kato T. Impact of ambulatory blood pressure variability on cerebral small vessel disease progression and cognitive decline in community-based elderly Japanese . Am J Hypertens . 2014 ; 27 : 1257 – 1267 . doi: 10.1093/ajh/hpu045 OpenUrl CrossRef PubMed 19. Filomena J , Riba-Llena I , Vinyoles E , Tovar JL , Mundet X , Castañé X , Vilar A , López-Rueda A , Jiménez-Baladó J , Cartanyà A , et al. Short-Term Blood Pressure Variability Relates to the Presence of Subclinical Brain Small Vessel Disease in Primary Hypertension . Hypertension . 2015 ; 66 : 634 – 640 ; discussion 445. doi: 10.1161/hypertensionaha.115.05440 OpenUrl CrossRef PubMed 20. ↵ Poels MM , Zaccai K , Verwoert GC , Vernooij MW , Hofman A , van der Lugt A , Witteman JC , Breteler MM , Mattace-Raso FU , Ikram MA. Arterial stiffness and cerebral small vessel disease: the Rotterdam Scan Study . Stroke . 2012 ; 43 : 2637 – 2642 . doi: 10.1161/STROKEAHA.111.642264 OpenUrl Abstract / FREE Full Text 21. ↵ Sible IJ , Nation DA. Blood Pressure Variability and Cerebral Perfusion Decline: A Post Hoc Analysis of the SPRINT MIND Trial . Journal of the American Heart Association . 2023 ; 12 : e029797 . doi: 10.1161/JAHA.123.029797 OpenUrl CrossRef 22. ↵ Setiadi A , Korim WS , Elsaafien K , Yao ST. The role of the blood–brain barrier in hypertension . Experimental Physiology . 2018 ; 103 : 337 – 342 . doi: 10.1113/EP086434 OpenUrl CrossRef 23. ↵ Kikuta J , Kamagata K , Takabayashi K , Taoka T , Yokota H , Andica C , Wada A , Someya Y , Tamura Y , Kawamori R , et al. An Investigation of Water Diffusivity Changes along the Perivascular Space in Elderly Subjects with Hypertension . AJNR Am J Neuroradiol . 2022 ; 43 : 48 – 55 . doi: 10.3174/ajnr.A7334 OpenUrl Abstract / FREE Full Text 24. ↵ Jefferson AL , Cambronero FE , Liu D , Moore EE , Neal JE , Terry JG , Nair S , Pechman KR , Rane S , Davis LT , et al. Higher Aortic Stiffness Is Related to Lower Cerebral Blood Flow and Preserved Cerebrovascular Reactivity in Older Adults . Circulation . 2018 ; 138 : 1951 – 1962 . doi: 10.1161/CIRCULATIONAHA.118.032410 OpenUrl Abstract / FREE Full Text 25. ↵ Bown CW , Khan OA , Liu D , Remedios SW , Pechman KR , Terry JG , Nair S , Davis LT , Landman BA , Gifford KA , et al. Enlarged perivascular space burden associations with arterial stiffness and cognition . Neurobiology of Aging . 2023 ; 124 : 85 – 97 . doi: 10.1016/j.neurobiolaging.2022.10.014 OpenUrl CrossRef 26. ↵ Nation DA , Edmonds EC , Bangen KJ , Delano-Wood L , Scanlon BK , Han SD , Edland SD , Salmon DP , Galasko DR , Bondi MW , et al. Pulse Pressure in Relation to Tau-Mediated Neurodegeneration, Cerebral Amyloidosis, and Progression to Dementia in Very Old Adults . JAMA Neurology . 2015 ; 72 : 546 – 553 . doi: 10.1001/jamaneurol.2014.4477 OpenUrl CrossRef PubMed 27. ↵ Lee HY , Oh BH. Aging and arterial stiffness . Circ J . 2010 ; 74 : 2257 - 2262 . doi: 10.1253/circj.cj-10-0910 OpenUrl CrossRef PubMed Web of Science 28. ↵ Westerhof N , Lankhaar J-W , Westerhof BE. The arterial Windkessel . Medical & Biological Engineering & Computing . 2009 ; 47 : 131 - 141 . doi: 10.1007/s11517-008-0359-2 OpenUrl CrossRef PubMed Web of Science 29. ↵ Badji A , Sabra D , Bherer L , Cohen-Adad J , Girouard H , Gauthier CJ. Arterial stiffness and brain integrity: A review of MRI findings . Ageing Research Reviews . 2019 ; 53 : 100907 . doi: 10.1016/j.arr.2019.05.001 OpenUrl CrossRef 30. ↵ Zieman SJ , Melenovsky V , Kass DA. Mechanisms, Pathophysiology, and Therapy of Arterial Stiffness. Arteriosclerosis , Thrombosis, and Vascular Biology . 2005 ; 25 : 932 – 943 . doi: 10.1161/01.ATV.0000160548.78317.29 OpenUrl Abstract / FREE Full Text 31. ↵ Dolan E , Thijs L , Li Y , Atkins N , McCormack P , McClory S , O’Brien E , Staessen JA , Stanton AV. Ambulatory Arterial Stiffness Index as a Predictor of Cardiovascular Mortality in the Dublin Outcome Study . Hypertension . 2006 ; 47 : 365 – 370 . doi: 10.1161/01.HYP.0000200699.74641.c5 OpenUrl CrossRef 32. ↵ Boos CJ , Hein A , Khattab A. Ambulatory arterial stiffness index, mortality, and adverse cardiovascular outcomes; Systematic review and meta-analysis . The Journal of Clinical Hypertension . 2024 ; 26 : 89 – 101 . doi: 10.1111/jch.14755 OpenUrl CrossRef 33. ↵ Li Y , Wang J-G , Dolan E , Gao P-J , Guo H-F , Nawrot T , Stanton AV , Zhu D-L , O’Brien E , Staessen JA. Ambulatory Arterial Stiffness Index Derived From 24-Hour Ambulatory Blood Pressure Monitoring . Hypertension . 2006 ; 47 : 359 – 364 . doi: 10.1161/01.HYP.0000200695.34024.4c OpenUrl Abstract / FREE Full Text 34. ↵ Lee HT , Lim YH , Kim BK , Lee KW , Lee JU , Kim KS , Kim SG , Kim JH , Lim HK , Shin J , et al. The relationship between ambulatory arterial stiffness index and blood pressure variability in hypertensive patients . Korean Circ J . 2011 ; 41 : 235 – 240 . doi: 10.4070/kcj.2011.41.5.235 OpenUrl CrossRef PubMed Web of Science 35. ↵ Zhang S , Mao HJ , Sun B , Wang NN , Zhang B , Zeng M , Xu L , Yu XB , Liu J , Xing CY. The relationship between AASI and arterial atherosclerosis in ESRD patients . Ren Fail . 2015 ; 37 : 22 – 28 . doi: 10.3109/0886022x.2014.963787 OpenUrl CrossRef 36. ↵ Steppan J , Sikka G , Hori D , Nyhan D , Berkowitz DE , Gottschalk A , Barodka V. Seeking a blood pressure-independent measure of vascular properties . Hypertens Res . 2016 ; 39 : 27 – 38 . doi: 10.1038/hr.2015.109 OpenUrl CrossRef 37. ↵ Cooper LL , O’Donnell A , Beiser AS , Thibault EG , Sanchez JS , Benjamin EJ , Hamburg NM , Vasan RS , Larson MG , Johnson KA , et al. Association of Aortic Stiffness and Pressure Pulsatility With Global Amyloid-β and Regional Tau Burden Among Framingham Heart Study Participants Without Dementia . JAMA Neurology . 2022 ; 79 : 710 – 719 . doi: 10.1001/jamaneurol.2022.1261 OpenUrl CrossRef 38. ↵ Mortensen KN , Sanggaard S , Mestre H , Lee H , Kostrikov S , Xavier ALR , Gjedde A , Benveniste H , Nedergaard M. Impaired Glymphatic Transport in Spontaneously Hypertensive Rats . J Neurosci . 2019 ; 39 : 6365 – 6377 . doi: 10.1523/jneurosci.1974-18.2019 OpenUrl Abstract / FREE Full Text 39. ↵ Jung Y , Damoiseaux JS. The potential of blood neurofilament light as a marker of neurodegeneration for Alzheimer’s disease . Brain . 2024 ; 147 : 12 – 25 . doi: 10.1093/brain/awad267 OpenUrl CrossRef 40. ↵ Abdelhak A , Foschi M , Abu-Rumeileh S , Yue JK , D’Anna L , Huss A , Oeckl P , Ludolph AC , Kuhle J , Petzold A , et al. Blood GFAP as an emerging biomarker in brain and spinal cord disorders . Nature Reviews Neurology . 2022 ; 18 : 158 – 172 . doi: 10.1038/s41582-021-00616-3 OpenUrl CrossRef PubMed 41. ↵ de Jong LW , Forsberg LE , Vidal JS , Sigurdsson S , Zijdenbos AP , Garcia M , Eiriksdottir G , Gudnason V , van Buchem MA , Launer LJ. Different susceptibility of medial temporal lobe and basal ganglia atrophy rates to vascular risk factors . Neurobiol Aging . 2014 ; 35 : 72 – 78 . doi: 10.1016/j.neurobiolaging.2013.07.009 OpenUrl CrossRef PubMed Web of Science 42. ↵ Lohman T , Sible IJ , Shenasa F , Engstrom AC , Kapoor A , Alitin JPM , Gaubert A , Thayer JF , Ferrer F , Nation DA. Reliability of beat-to-beat blood pressure variability in older adults . 2024 . doi: 10.21203/rs.3.rs-4190135/v1 OpenUrl CrossRef 43. ↵ Said MA , Eppinga RN , Lipsic E , Verweij N , van der Harst P. Relationship of Arterial Stiffness Index and Pulse Pressure With Cardiovascular Disease and Mortality . Journal of the American Heart Association . 7 : e007621 . doi: 10.1161/JAHA.117.007621 OpenUrl Abstract / FREE Full Text 44. ↵ Penny W , Friston K , Ashburner J , Kiebel S , Nichols T. Statistical Parametric Mapping: The Analysis of Functional Brain Images . 2007 . 45. ↵ Gaser C , Dahnke R , Thompson P , Kurth F , Luders E , Alzheimer’s Disease Neuroimaging I. CAT – A Computational Anatomy Toolbox for the Analysis of Structural MRI Data . In: bioRxiv ; 2022 . 46. ↵ Ferrari-Souza JP , Brum WS , Hauschild LA , Da Ros LU , Ferreira PCL , Bellaver B , Leffa DT , Bieger A , Tissot C , Lussier FZ , et al. Vascular risk burden is a key player in the early progression of Alzheimer’s disease . Neurobiology of Aging . 2024 ; 136 : 88 – 98 . doi: 10.1016/j.neurobiolaging.2023.12.008 OpenUrl CrossRef 47. ↵ Nation DA , Sweeney MD , Montagne A , Sagare AP , D’Orazio LM , Pachicano M , Sepehrband F , Nelson AR , Buennagel DP , Harrington MG , et al. Blood–brain barrier breakdown is an early biomarker of human cognitive dysfunction . Nature Medicine . 2019 ; 25 : 270 – 276 . doi: 10.1038/s41591-018-0297-y OpenUrl CrossRef PubMed 48. ↵ Team RC. R: A language and environment for statistical computing. In: Foundation for Statistical Computing , Vienna, Austria .; 2022 . 49. ↵ Hayes AF. Introduction to mediation, moderation, and conditional process analysis: A regression-based approach . New York, NY, US : Guilford Press ; 2013 . 50. ↵ Sible IJ , Nation DA. Blood pressure variability and medial temporal atrophy in apolipoprotein ɛ4 carriers . Brain Imaging Behav . 2022 ; 16 : 792 – 801 . doi: 10.1007/s11682-021-00553-1 OpenUrl CrossRef 51. Sible IJ , Nation DA , Weiner M , Aisen P , Petersen R , Jack CR , Jagust W , Trojanowki JQ , Toga AW , Beckett L , et al. Visit-to-Visit Blood Pressure Variability and Longitudinal Tau Accumulation in Older Adults . Hypertension . 2022 ; 79 : 629 – 637 . doi : doi: 10.1161/HYPERTENSIONAHA.121.18479 OpenUrl CrossRef 52. ↵ Sible IJ , Yew B , Dutt S , Bangen KJ , Li Y , Nation DA. Visit-to-visit blood pressure variability and regional cerebral perfusion decline in older adults . Neurobiol Aging . 2021 ; 105 : 57 – 63 . doi: 10.1016/j.neurobiolaging.2021.04.009 OpenUrl CrossRef 53. ↵ Claassen DO , McDonell KE , Donahue M , Rawal S , Wylie SA , Neimat JS , Kang H , Hedera P , Zald D , Landman B , et al. Cortical asymmetry in Parkinson’s disease: early susceptibility of the left hemisphere . Brain Behav . 2016 ; 6 : e00573 . doi: 10.1002/brb3.573 OpenUrl CrossRef 54. ↵ Hernández SAR , Kroon AA , van Boxtel MPJ , Mess WH , Lodder J , Jolles J , de Leeuw PW. Is There a Side Predilection for Cerebrovascular Disease? Hypertension . 2003 ; 42 : 56 – 60 . doi: 10.1161/01.HYP.0000077983.66161.6F OpenUrl Abstract / FREE Full Text 55. ↵ Portegies MLP , Selwaness M , Hofman A , Koudstaal PJ , Vernooij MW , Ikram MA. Left-Sided Strokes Are More Often Recognized Than Right-Sided Strokes . Stroke . 2015 ; 46 : 252 – 254 . doi: 10.1161/STROKEAHA.114.007385 OpenUrl Abstract / FREE Full Text 56. ↵ Hedna VS , Bodhit AN , Ansari S , Falchook AD , Stead L , Heilman KM , Waters MF. Hemispheric differences in ischemic stroke: is left-hemisphere stroke more common? J Clin Neurol . 2013 ; 9 : 97 – 102 . doi: 10.3988/jcn.2013.9.2.97 OpenUrl CrossRef PubMed 57. ↵ van Gennip ACE , Satizabal CL , Tracy RP , Sigurdsson S , Gudnason V , Launer LJ , van Sloten TT. Associations of plasma NfL, GFAP, and t-tau with cerebral small vessel disease and incident dementia: longitudinal data of the AGES-Reykjavik Study . GeroScience . 2024 ; 46 : 505 – 516 . doi: 10.1007/s11357-023-00888-1 OpenUrl CrossRef 58. ↵ Lohman T , Sible I , Kapoor A , Engstrom AC , Alitin JP , Gaubert A , Rodgers KE , Bradford D , Mather M , Han SD , et al. Blood pressure variability, central autonomic network dysfunction and cerebral small vessel disease in APOE4 carriers . medRxiv . 2023 . doi: 10.1101/2023.12.13.23299556 OpenUrl Abstract / FREE Full Text 59. ↵ Chatterjee P , Pedrini S , Stoops E , Goozee K , Villemagne VL , Asih PR , Verberk IMW , Dave P , Taddei K , Sohrabi HR , et al. Plasma glial fibrillary acidic protein is elevated in cognitively normal older adults at risk of Alzheimer’s disease . Translational Psychiatry . 2021 ; 11 : 27 . doi: 10.1038/s41398-020-01137-1 OpenUrl CrossRef 60. ↵ Braak H , Braak E. Neuropathological stageing of Alzheimer-related changes . Acta Neuropathol . 1991 ; 82 : 239 – 259 . OpenUrl CrossRef PubMed Web of Science 61. ↵ Molinder A , Ziegelitz D , Maier SE , Eckerström C. Validity and reliability of the medial temporal lobe atrophy scale in a memory clinic population . BMC Neurology . 2021 ; 21 : 289 . doi: 10.1186/s12883-021-02325-2 OpenUrl CrossRef PubMed 62. ↵ Laakso MP , Partanen K , Riekkinen P , Lehtovirta M , Helkala EL , Hallikainen M , Hanninen T , Vainio P , Soininen H. Hippocampal volumes in Alzheimer’s disease, Parkinson’s disease with and without dementia, and in vascular dementia . Neurology . 1996 ; 46 : 678 – 681 . doi: 10.1212/WNL.46.3.678 OpenUrl FREE Full Text 63. ↵ Tam CW , Burton EJ , McKeith IG , Burn DJ , O’Brien JT. Temporal lobe atrophy on MRI in Parkinson disease with dementia: a comparison with Alzheimer disease and dementia with Lewy bodies . Neurology . 2005 ; 64 : 861 – 865 . doi: 10.1212/01.wnl.0000153070.82309.d4 OpenUrl Abstract / FREE Full Text 64. ↵ Eckerström C , Olsson E , Borga M , Ekholm S , Ribbelin S , Rolstad S , Starck G , Edman Å , Wallin A , Malmgren H . Journal of the Neurological Sciences . 2008 ; 272 : 48 – 59 . doi: 10.1016/j.jns.2008.04.024 OpenUrl CrossRef PubMed 65. ↵ Visser PJ , Scheltens P , Verhey FRJ , Schmand B , Launer LJ , Jolles J , Jonker C. Medial temporal lobe atrophy and memory dysfunction as predictors for dementia in subjects with mild cognitive impairment . Journal of Neurology . 1999 ; 246 : 477 – 485 . doi: 10.1007/s004150050387 OpenUrl CrossRef PubMed Web of Science 66. ↵ Devanand DP , Pradhaban G , Liu X , Khandji A , De Santi S , Segal S , Rusinek H , Pelton GH , Honig LS , Mayeux R , et al. Hippocampal and entorhinal atrophy in mild cognitive impairment . Neurology . 2007 ; 68 : 828 – 836 . doi: 10.1212/01.wnl.0000256697.20968.d7 OpenUrl Abstract / FREE Full Text 67. ↵ Korf ESC , Wahlund L-O , Visser PJ , Scheltens P. Medial temporal lobe atrophy on MRI predicts dementia in patients with mild cognitive impairment . Neurology . 2004 ; 63 : 94 – 100 . doi: 10.1212/01.WNL.0000133114.92694.93 OpenUrl Abstract / FREE Full Text 68. ↵ Schillaci G , Pucci G. Is ambulatory arterial stiffness index a marker of large-artery stiffness? Evidence from intervention studies . Hypertension Research . 2015 ; 38 : 799 – 801 . doi: 10.1038/hr.2015.101 OpenUrl CrossRef View the discussion thread. Back to top Previous Next Posted May 03, 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. 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