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. Author manuscript; available in PMC: 2023 Dec 1.
Published in final edited form as: Neurobiol Aging. 2022 Aug 27;120:60–67. doi: 10.1016/j.neurobiolaging.2022.08.011

Blood pressure changes impact corticospinal integrity and downstream gait and balance control

Elizabeth A Coon 1, Anna M Castillo 2, Timothy G Lesnick 2, Sheelakumari Raghavan 2, Michelle M Mielke 1,2, Robert I Reid 3, B Gwen Windham 5, Ronald C Petersen 1, Clifford R Jack Jr 4, Jonathan Graff-Radford 1, Prashanthi Vemuri 4
PMCID: PMC9613619  NIHMSID: NIHMS1839633  PMID: 36122540

Abstract

Blood pressure (BP) plays an important role in white matter integrity. We sought to determine the role of intra-individual BP changes on white matter and evaluate the impact on gait speed and imbalance by sex. We identified 990 eligible participants in the population-based Mayo Clinic Study of Aging and analyzed fractional anisotropy (FA) in white matter regions. Using structural equation models (SEM), we assessed the effect of BP slope on corticospinal tract (CST) FA and downstream effects on gait speed and imbalance after age and sex effects. Of 990 participants, 438 (44%) were female with mean age of 76 years. In linear models predicting CST FA, a greater change in BP slope (0.0004; p = 0.026) and female sex (0.017; p<0.001) were significant predictors of lower CST FA. SEMs showed that older age, female sex, and higher BP slope predicted lower CST FA, and lower CST FA predicted worse downstream motor control. Therefore, intra-individual BP slope and variability impact corticospinal tract microstructural properties of white matter with females having increased susceptibility to damage.

Keywords: sex differences, blood pressure, imbalance, DTI, gait, white matter tracts

Introduction

Blood pressure (BP) plays an important role in cerebrovascular health with increased BP contributing to impaired white matter (WM) integrity (Maillard et al. 2012; Raz et al. 2007; Ma et al. 2020). There is greater vulnerability to BP changes in frontal brain regions and WM tracts important for cognitive and motor function (Maillard et al. 2012; Salat et al. 2012). While BP patterns including slope and greater inter-individual variability are associated with cognitive impairment and WM integrity (Salat et al. 2012; Walker et al. 2019), the role of intra-individual BP variability as a contributor to WM dysfunction is less clear, particularly involving motor control regions.

Sex-specific mechanisms influence WM pathology with sexual dimorphism in brain tissue microstructure (Canales-Rodriguez et al. 2021). Females show greater white matter hyperintensities (WMH) compared to males after accounting for WM volume and mid-life risk factors (Fatemi et al. 2018). Elderly females are also more likely to demonstrate motor impairment of slowed gait speed (Izawa et al. 2015) and suffer falls with severe injuries(Ellis and Trent 2001).

Therefore, we hypothesized that WM tracts and regions involved in motor control were susceptible to damage due to intra-individual BP changes, which would influence motor function, and that females would be more vulnerable to damage. In this study, we aimed to investigate the association between BP changes over time, sex, and imaging biomarkers of WM tracts and motor control areas in a population-based sample using two separate methods to understand these complex associations. In the first set of analyses, we evaluated the impact of BP slope and variability on individual WM tracts and regions, accounting for age. In the next set of analyses, we evaluated markers of gait speed and imbalance as markers of BP changes damaging brain structures. We then evaluated the impact of sex on WM tract and motor control areas assessing the impact on gait speed and imbalance.

Materials and methods

Participants

All participants were enrolled in the Mayo Clinic Study of Aging (MCSA), a population-based cohort study of Olmsted County, Minnesota (Roberts et al. 2008; Petersen et al. 2010). The Rochester Epidemiology Project (REP) medical record linkage system was used to abstract vascular risk factors and clinical features consistent with motor impairment including imbalance from Olmsted County patients (Rocca et al. 2012; St Sauver et al. 2012; St Sauver et al. 2011). A total of 990 patients met inclusion criteria (Table 1). Inclusion criteria included cognitively unimpaired participants using consensus criteria (nurse, physician and neuropsychologist evaluation) (Petersen et al. 2010; Roberts et al. 2008), age ≥ 50 years, head MRI imaging sequences including diffusion tensor imaging (DTI) and with at least 2 BP measurements obtained at MCSA study visits before baseline MRI.

Table 1.

Demographic, clinical, and imaging characteristics by sex

All (N= 990) Female (N=438) Male (N=552) p value
Demographics
Age at MRI with DTI (years) 75.9 (9.8) 75.7 (9.7) 76.1 (9.9) 0.48
Education/occupation score 12.6 (2.6) 12.2 (2.6) 12.9 (2.6) < 0.001
APOE-e4 carrier 266 (27.0%) 124 (28.5%) 142 (25.7%) 0.33
Vascular Risk Factors
Hypertension 677 (68.4%) 292 (66.7%) 385 (69.7%) 0.31
Dyslipidemia 799 (81.9%) 346 (80.3%)
N=431		 453 (83.3%)
N=544		 0.23
Diabetes 186 (18.8%) 72 (16.4%) 114 (20.7%) 0.09
Blood Pressure Variables
Systolic BP 140.24 (18.77) 139.22 (18.24) 141.05 (19.17) 0.13
Systolic BP slope 0.51 (5.12) 0.36 (4.86) 0.62 (5.32) 0.41
Systolic BP variability 11.77 (6.98) 11.57 (6.86) 11.93 (7.08) 0.42
Years between first and last systolic BP recording 4.99 (2.64)
2.10–12.45		 4.92 (2.53)
2.16–12.45		 5.06 (2.72)
2.10–12.17		 0.40
Motor Variables
Gait speed (m/s) 1.12 (0.27)
N=952		 1.12 (0.28)
N=418		 1.13 (0.27)
N=534		 0.51
Gait imbalance 96 (9.7%) 39 (8.9%) 57 (10.3%) 0.45
Cognitive Variables
Cognitive impairment 117 (11.8%)
N=989		 45 (10.3%)
N=437		 72 (13.0%) 0.18
MMSE* 28.21 (1.70) 28.37 (1.61)
N=432		 28.08 (1.75)
N= 547		 0.009
Global cognition z-score 0.03 (1.24) 0.18 (1.25)
N=397		 −0.09 (1.21)
N=503		 0.001
Diffusion regions that are influenced by changing BP slope
Corticospinal tract FA 0.54 (0.03) 0.53 (0.03) 0.55 (0.03) < 0.001
Globus pallidus MD 731.50 (81.62) 724.76 (78.83) 736.85 (83.45) 0.021
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Data are displayed as mean (standard deviation). Range is included for BP follow-up time.

*

MMSE is calculated from short test of mental status. MD values are scaled by 10^6 for ease of interpretation.

Abbreviations: BP, blood pressure; MD, mean diffusivity; MMSE, mini mental status examination

Informed consent was obtained from all participants. This study was approved by Mayo Clinic and Olmsted Medical Center institutional review boards. Data from this study are available from the authors upon reasonable request.

BP changes

The BP slope was analyzed via linear regression for systolic and diastolic BP using all measurements. Variability was evaluated adjusted for slope over time (root mean squared error as the standard deviation of the residuals of a regression model of BP over time).

Cardiovascular and metabolic risk factors

The REP medical records linkage system was used by trained nurses to abstract vascular risk factors including diagnosis of hypertension (with or without treatment), dyslipidemia, diabetes, and smoking status. Motor variables extracted from the REP included the presence of imbalance obtained from ICD-9 and ICD-10 codes corresponding to gait imbalance within 2 years before the patient’s MCSA visit. Gait speed was assessed in MCSA study visits as 7.62 m (25 ft) divided by time (seconds).

MRI and DTI

MRI images were acquired on a 3T GE MRI (GE Medical Systems, Milwaukee, WI) with DTI acquisition and computation of fractional anisotropy (FA) and mean diffusivity (MD) as published (Vemuri et al. 2018). The DTI acquisition protocol was performed using a single-shot echo-planar imaging sequence with an isotropic resolution of 2.7 mm, 5 non diffusion-weighted images, and 41 b=1000 s/mm2 diffusion-encoding gradient directions that were spread over the whole sphere. The data were preprocessed as follows: skull stripping (Reid et al. 2018), denoising (Veraart, Fieremans, and Novikov 2016), correcting for head motion and eddy current distortion (Andersson et al. 2016), Gibbs ringing (Kellner et al. 2016), and then debiasing (Koay, Ozarslan, and Basser 2009). A nonlinear least squares fitting algorithm implemented in dipy (Garyfallidis et al. 2014) were used to fit diffusion tensors. FA maps were then generated. ANTs (advanced normalization tools symmetric normalization) (Avants et al. 2011) were used to nonlinearly register each participant’s FA image to the in-house version of John’s Hopkins University (JHU) “Eve” WM atlas (Oishi et al. 2009). Regional FA measures were then computed. Values of FA and MD were characterized using the median of voxel values within each JHU region of interest. To reduce the partial volume contamination from the edge voxels of each region, the median was used. Voxels with MD > 2 × 10−3 or < 7 × 10−5mm2/s were excluded as they were mostly cerebrospinal fluid or air, respectively.

Regions of interest were selected for importance in posture and gait control (Takakusaki 2017). FA evaluation was performed for selected WM tracts: genu of the corpus callosum, precentral WM, inferior cerebellar peduncle, middle cerebellar peduncle, anterior limb of the interior capsule, corticospinal tract (CST), cerebral peduncle, globus pallidus, medulla, and midbrain. MD evaluation was performed for globus pallidus, which is mostly comprised of gray matter and was of interest in linear models. Voxel-weighted median FA and MD were used in analyses.

Statistical methods

Demographic, clinical, and imaging characteristics of the participants were summarized using means and standard deviations for continuous variables and counts and percentages for categorical variables. Male and female groups were compared using t-tests or χ2 tests. Boxplots of CST FA and motor control measures (gait speed and imbalance) were also created for comparisons by sex and age. Linear regressions were used to model associations of sex and systolic BP slope with globus pallidus MD and CST FA and other regions of interest, before focusing on those with significant associations. MD and FA were the outcomes, and age, sex, BP slope, BP variability, age*BP slope, age*BP variability, sex*BP slope, and sex*BP variability were the potential predictors. We formed parsimonious models using backwards selection. MD was multiplied by 106 to scale the regression coefficients and make them more easily interpretable. RStudio Version 3.6.2 and SAS Studio Version 9.4 were used for analyses and data management. Adjusting for multiple comparisons was not used in certain analyses as we were not interested in a universal null hypothesis and did not want to inflate the possibility of a Type II error (Rothman 1990; Perneger 1998).

Structural Equation Models

We performed path analyses (structural equation models [SEM] with only manifest variables) using Mplus version 8.0 structural equations software as previously published (Vemuri et al. 2017). The full models consisted of 4 tiers: age and sex (tier 1) as exogenous predictors, BP slope and variability (tier 2), FA or MD (tier 3), and gait speed or imbalance (tier 4) as outcomes. Variables in each tier could be predicted by any variables in a lower tier. The possible associations are direct effects (arrow directly joins variables), indirect effects (arrows pass through one or more mediators), and total effects (sum of direct and indirect). We report regression coefficients with associated standard errors and p values. The coefficients provide the predicted change in the outcome (higher tier) variable per unit increase in the predictor (lower tier) variable. For imbalance as an outcome, we also report the estimated odds ratio (OR), since the regression coefficient for a binary outcome estimates the log odds ratio. SEM model fitting is sensitive to scaling of the variables, and to improve the process, variables were scaled as follows: MD/10, FA*10, age/10 (decades), slope/100, and RMSE/100. The path analysis was pruned using goodness of fit measures (Bayesian information criterion, χ2 test of model fit, root mean square error of approximation [RMSEA], standardized root mean square residual [SRMR], Tucker-Lewis Index [TLI], and Comparative Fit Index [CFI] and individual p values) until all paths in the final model were significant, and the model fit observed data well.

Results

Of the 990 patients, 438 were female (44%) and the mean age was 75.9 years (Table 1). The time range between first and last BP measurements of individual patients was 2–12 years with a mean of 5 years (standard deviation 2.6 years). There were no sex differences in BP slope, variability, or value at time of imaging. Motor variables of gait speed and presence of gait imbalance were similar between sexes (Table 1). Gait speed decreased with age whereas imbalance occurred with older age at MRI, with no sex differences (Figure 1).

Figure 1. Box plots for corticospinal tract fractional anisotropy and measures of motor control by age and evaluating for sex differences.

Figure 1.

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Changes in motor control measures and corticospinal tract fractional anisotropy (FA) are shown in different age groups in the top two images with presence of gait imbalance by age at MRI shown in the lower image. Sex differences were evaluated in all the groups. The top panel shows box blots of gait speed by age in decades with no significant differences between sexes. The middle panel shows lower fractional anisotropy in females compared to males that is present throughout age groups. The lower panel shows presence or absence of gait imbalance by age at the time of MRI with no differences between sexes.

Abbreviations: CST, corticospinal tract; FA; fractional anisotropy

The DTI regions of interest are shown in Figure 2. Of the regions analyzed in linear models, CST FA and globus pallidus MD were susceptible to BP slope (Supplemental Table). We found that sex and age also had significant associations with CST FA in final models (Table 2). Predicted value of CST FA remained significantly lower in patients with higher BP slope (−0.0004, p = 0.026) and in females compared to males (0.017; p <0.001). Predicted value of CST FA also decreased with age (−0.0002, p = 0.047) (Figure 3). We found a significant interaction between sex and BP slope in predicting globus pallidus MD (2.769, p = 0.005), but results of a sensitivity SEM analysis did not detect a significant association with downstream gait imbalance.

Figure 2. Atlas image with overlay of regions of interest.

Figure 2.

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Regions of interest involved in motor control that were analyzed in this study are shown on the atlas image.

Abbreviations: CST, corticospinal tract

Table 2.

Results for two separate linear models with corticospinal tract FA and globus pallidus MD as the response variables.

Variable Estimate (SE) p value
Model for CST FA
(Intercept) 0.553 (0.008) <0.001
Age −0.0002 (0.0001) 0.047
Sex (male) 0.0172 (0.002) <0.001
SP slope −0.0004 (0.0002) 0.026
SP variability −0.0002 (0.0002) 0.15
Model for Globus Pallidus MD
(Intercept) 530.143 (19.656) <0.001
Age at MRI 2.621 (0.261) <0.001
Sex (male) −11.227 (9.643) 0.24
SP slope −2.094 (0.764) 0.006
SP variability −0.258 (0.550) 0.64
Male sex*SP slope 2.769 (0.979) 0.005
Male*SP variability 1.767 (0.707) 0.013
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Legend: Weighted median FA of the corticospinal tract evaluated by sex. Weighted median MD of the globus pallidus evaluated by sex.

Abbreviations: CST, corticospinal tract; FA, fractional anisotropy; MD, mean diffusivity

Figure 3. Predicted value plot for corticospinal tract fractional anisotropy with actual data points.

Figure 3.

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Predicted values versus age for fractional anisotropy of CST (A) and globus pallidus (B) by sex and represented by quartile of systolic BP slope with actual data points shown in pink and blue for female and male participants, respectively. Model used includes CST FA, age, sex, systolic BP slope and systolic BP variability.

Abbreviations: BP, blood pressure; SBP, systolic blood pressure

Given the susceptibility to BP changes of the CST, with sex differences between groups, and the role of this region in motor control and gait, we analyzed whether this region influences gait speed and imbalance. We fit a SEM to predict gait speed (Figure 4; Table 3a) with scatterplots of the variables used. The pruned model using CST FA predicting gait speed fit the data very well with RMSEA < 0.001 (95% CI 0.000–0.032), SRMR = 0.008, CFI = 1.00, and TLI = 1.00. In this model, age (coefficient [standard error] 0.005 [0.002]; p = 0.001) was a direct predictor of systolic BP slope. The total effect of age on CST FA (−0.028 [0.010]; p = 0.006) was the sum of a direct effect and an indirect effect age→BP slope→CST FA. Male sex had a direct effect on CST FA (0.171 [0.020]; p<0.001) as did systolic BP slope (−0.435 [0.199]; p=0.028). The total effect of age on gait speed (−0.152 [0.008]; p<0.001) was the sum of a direct effect and indirect effects age→BP slope→CST FA→gait speed and age→CST FA→gait speed. The total effect of male sex on gait speed (0.017 [0.004]; p<0.001) was an indirect effect sex (male)→CST FA→gait speed. The total effect of BP slope on gait speed (−0.042 [0.022]; p=0.052) was an indirect effect but not significant. The total effect of CST FA on gait speed (0.097 [0.023]; p<0.001) was a direct effect. Our model predicts that older age, female sex, and lower CST FA would all lead to lower (worse) gait speeds.

Figure 4. Final structural equation model involving CST fractional anisotropy along with significant associations shown by solid arrows.

Figure 4.

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Based on this model, we would predict that lower fractional anisotropy would predict lower gait speed (A) and higher odds of gait imbalance (B). Systolic BP slope was a predictor of CST fractional anisotropy but systolic BP variability was not. The standardized coefficients, standard errors (in brackets), and p values are shown by the arrows.

Table 3a.

Results of SEM for corticospinal tract FA and gait speed.

Direct Effect Estimate (SE) p value
Age → Gait −0.152 (0.008) <0.001
CST FA → Gait 0.097 (0.023) <0.001
Age → CST FA −0.026 (0.010) 0.012
Sex (male) → CST FA 0.171 (0.020) <0.001
Slope → CST FA −0.435 (0.199) 0.028
Age → Slope 0.005 (0.002) 0.001
Total Effect
Age → Gait −0.152 (0.008) <0.001
Sex (male) → Gait 0.017 (0.004) <0.001
Slope → Gait −0.042 (0.022) 0.052
Age → CST FA −0.028 (0.010) 0.006
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We also fit a SEM to predict imbalance (Figure 4; Table 3b). The pruned model using CST FA to predict odds of imbalance also fit the data very well with RMSEA= 0.025 (95% CI 0.000–0.064), SRMR = 0.028, CFI = 0.986, and TLI = 0.958. In this model, age (coefficient 0.005 [0.002]; p = 0.005) was a direct predictor of systolic BP slope. The total effect of age on CST FA (−0.028 [0.010]; p = 0.006) was the sum of a direct effect and an indirect effect age→BP slope→CST FA. Male sex had a direct effect on CST FA (0.168 [0.021]; p<0.001) as did systolic BP slope (−0.411 [0.209]; p=0.049). The total effect of age on imbalance (0.425 [0.061]; odds ratio (OR)=1.53; p<0.001) was the sum of a direct effect and indirect effects age→BP slope→CST FA→imbalance and age→CST FA→imbalance. The total effect of male sex on imbalance (−0.064 [0.027]; OR=0.938; p=0.017) was an indirect effect sex (male)→CST FA→imbalance. The total effect of BP slope on imbalance (0.156 [0.098]; OR=1.169; p=0.11) was an indirect effect but not significant. The total effect of CST FA on imbalance (−0.379 [0.152]; OR=0.685; p=0.013) was a direct effect. Our model predicts that older age, female sex, and lower CST FA all increase the odds of imbalance.

Table 3b.

Results of SEM for corticospinal tract FA and gait imbalance

Direct Effect Estimate (SE) Odds Ratio (95% CI) p value
Age → Imbalance 0.414 (0.061) 1.513 (1.342–1.705) <0.001
CST FA → Imbalance −0.379 (0.152) 0.685 (0.508–0.922) 0.013
Age → CST FA −0.026 (0.010) 0.011
Sex (male) → CST FA 0.168 (0.021) <0.001
Slope → CST FA −0.411 (0.209) 0.049
Age → Slope 0.005 (0.002) 0.005
Total Effect
Age → Imbalance 0.425 (0.061) 1.530 (1.357–1.724) <0.001
Male → Imbalance −0.064 (0.027) 0.938 (0.890–0.989) 0.017
Slope → Imbalance 0.156 (0.098) 1.169 (0.965–1.416) 0.11
Age → CST FA −0.028 (0.010) 0.006
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Legend: Results of SEM for corticospinal tract FA and gait speed (A) and gait imbalance (B). Effects, estimates and p shown in Figure 3 are shown in bold.

Abbreviations: CST, corticospinal tract; FA, fractional anisotropy

Discussion

Using a large, population-based cohort with longitudinal BP measurements, we found that systolic BP slope and variability impact brain regions important for motor control and that the associations differ by sex. Based on SEMs, BP slope affects CST FA, which varies between sexes, with lower FA predicting slower gait speed and higher odds of imbalance. While BP changes influenced globus pallidus MD, which also differed by sex, it did not directly influence motor variables.

The findings of individual BP slope and variability impacting brain regions is of clinical importance. Blood pressure variability is established as a risk factor for cardiovascular events and morbidity (Rothwell et al. 2010; Whittle et al. 2016). High blood pressure variability is associated with cognitive changes and dementia based on studies done in a variety of populations (Bohm et al. 2015; Nagai et al. 2012; Qin et al. 2016; Sabayan et al. 2013). While a causative relationship has not been entirely established, the role in variability in BP causing damage to white matter structures has been posited as a potential cause for cognitive decline. Both short and long-term fluctuations in BP are risk factors for cognitive impairment and dementia (Zhou et al. 2019; Sabayan et al. 2013; Epstein et al. 2013) with evidence of sex differences on imaging findings (Haring et al. 2019). White matter structures involved in control of gait and balance could also be susceptible to BP variations. Indeed, we found that a greater increase in change in BP was associated with lower CST FA which was associated with slower gait speed and higher odds of imbalance. Our findings add to evidence suggesting that BP variability and trends, including slope, have implications on brain health that include motor function (Walker et al. 2019; Sible, Bangen, et al. 2021; Sible, Yew, et al. 2021).

Pathophysiologically, various mechanisms have been proposed to account for cerebral injury associated with BP variability. While the precise pathophysiology remains unknown, there is speculation as to the mechanisms behind the effect of BP variability on brain health. Autoregulation involves maintaining stable cerebral blood flow when systemic BP changes within the upper and lower limits of autoregulation. Exaggerated BP variability may have an effect at either the high or low end of autoregulation. Around the lower limit, BP changes may contribute to hypoperfusion which may lead to dysfunction of structural proteins critical for white matter function (Duncombe et al. 2017). Fluctuations above the upper limit of autoregulation may contribute to hypertensive pathophysiology including vascular structural changes such as increased arterial stiffness (Shimbo et al. 2013) which may explain why systolic BP variability tends to be more influential than diastolic BP (Qin et al. 2016). Other proposed mechanisms for BP variability influencing white matter includes endothelial dysfunction and subclinical inflammation (Haring et al. 2019; Qin et al. 2016; Nagai et al. 2014; Nagai and Kario 2013). Future studies should assess whether normalization of BP variability over time may lead to a slower decline in gait speed and less imbalance.

Our findings of lower FA in females is consistent with studies showing sex differences in WM tracts (Menzler et al. 2011; Chou et al. 2011; Inano et al. 2011) and WMH (Fatemi et al. 2018). Sexual dimorphism of WM microstructure may be contributing to the sex differences seen with BP changes (Chou et al. 2011; Inano et al. 2011; Canales-Rodriguez et al. 2021) although the underlying mechanism is unclear and may be related to sex hormone exposure (van Hemmen et al. 2017), chromosomal or environmental etiologies (McCarthy and Arnold 2011). Sex differences in WM structure and vascular risk factors have been evaluated related to cognition and dementia, yet the effects of sex differences on motor function, including balance, is uncertain. Evaluating motor pathways is important as motor problems are common with more than one third of older adults suffering a fall each year (Hornbrook et al. 1994; Hausdorff, Rios, and Edelberg 2001). Additionally, there are sex differences regarding falls with females tending to have more severe falls and higher injury rates (Stevens and Sogolow 2005).

While this technique is very sensitive in detecting changes in tissue microstructure, it should be used in conjunction with other measures to analyze white matter health and disease (Jones, Knosche, and Turner 2013). A major limitation of our analysis is that we did not evaluate imaging findings with regards to treatment for hypertension. This has implications as has been shown in the Atherosclerosis Risk in Communities prospective population-based cohort study which identified different patterns of BP control and hypertension that led to different cognitive outcomes (Walker et al. 2019). Additionally, BP was not assessed at the same time of day for all subjects at each appointment which could influence BP slope. A major strength of our study is the availability of a large population-based sample with imaging, longitudinal BP measurements, and comprehensive risk factor assessment. This data should also be interpreted within the constraints of DTI-MRI studies.

Further in-depth evaluation of the role of BP changes over time on individual brain regions and association of sex differences is indicated to understand differences in brain aging and optimize brain health.

Supplementary Material

1

Highlights:

  • Blood pressure variability over time impacts corticospinal tract integrity

  • Women have increased susceptibility to damage of the corticospinal tract

  • Corticospinal tract susceptibility to damage may influence gait and balance

Acknowledgments:

We thank Teresa Christianson for help with data collection with the Rochester Epidemiology Project. We thank all the study participants and staff in the Mayo Clinic Study of Aging, Mayo Alzheimer’s Disease Research Center, and Aging and Dementia Imaging Research laboratory at the Mayo Clinic for making this study possible.

Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

Funding Sources:

This work was supported by CTSA Grant Number UL1 TR002377 Dominium Foundation Career Development Award in Neurodegenerative Disease Research in memory of Jack W. Safar from the National Center for Advancing Translational Science (NCATS). This work was supported by National Institute of Health (NIH) grants U01 AG006786 (PI: Petersen/Mielke/Jack), R01 NS097495 (PI: Vemuri), R01 AG056366 (PI: Vemuri), P50 AG016574 (PI: Petersen), R37 AG011378 (PI: Jack), R01 AG041851 (PIs: Jack and Knopman), K76 AG057015 (PI: Graff-Radford), R01 AG034676 (Rochester Epidemiology Project PI: Rocca), RF1 AG55151 (PI: Mielke); the Gerald and Henrietta Rauenhorst Foundation grant, Alzheimer’s Drug Discovery Foundation (ADDF), the Millis Family, the Alexander Family Alzheimer’s Disease Research Professorship of the Mayo Foundation, Alzheimer’s Association (Zenith Fellows Award), Liston Award, Elsie and Marvin Dekelboum Family Foundation, Schuler Foundation, Opus building NIH grant C06 RR018898.

Footnotes

Financial Disclosure/Conflict of Interest: There is no support or financial issues from all authors relative to the research covered in the submitted manuscript.

E. A. Coon reports no disclosures relevant to the manuscript.

A. M. Castillo reports no disclosures relevant to the manuscript.

T. G. Lesnick reports no disclosures relevant to the manuscript.

M. M. Mielke reports no disclosures relevant to the manuscript.

R. I. Reid reports no disclosures relevant to the manuscript.

B. G. Windham reports no disclosures relevant to the manuscript.

R. C. Petersen reports no disclosures relevant to the manuscript.

C. R. Jack reports no disclosures relevant to the manuscript.

J. Graff-Radford reports no disclosures relevant to the manuscript.

P. Vemuri reports no disclosures relevant to the manuscript.

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