Abstract
INTRODUCTION
Central arterial stiffness and cerebral blood flow (CBF) are inversely related. Poor cardiorespiratory fitness (CRF) and low physical activity (PA) are related to both higher arterial stiffness and lower CBF. The present study examined (i) whether CRF or PA moderate the relationship between arterial stiffness and CBF, and (ii) whether the intensity or the type of PA needs to be considered.
METHODS
Participants (N = 78, MeanAGE = 64.2±6.14, 72% female) from the Wisconsin Registry for Alzheimer's Prevention and the Wisconsin Alzheimer's Disease Research Center were categorized into low, average, and high fitness groups based on maximal graded exercise treadmill test performance. PA was assessed using the CHAMPS (Community Health Activities Model Program for Seniors) questionnaire. Based on hours/week, participants were classified as meeting the recommended 2.5 h of moderate intensity PA per week (PA Rec Met). Weekly hours of moderate and low intensity PA were calculated as activities of > 3 or < 3 metabolic equivalents, respectively. Activity type was categorized as exercise‐, sports/leisure‐ and work‐related. Arterial stiffness was measured as aortic pulse wave velocity (aoPWV) by 2D phase contrast magnetic resonance imaging (MRI). CBF was assessed by 4D flow MRI in the internal carotid arteries (ICAs), cavernous ICAs, middle cerebral arteries (MCAs), and via two composite measures of total and global flow.
RESULTS
The association between aoPWV and CBF differed by fitness levels, with a negative relationship in the low fitness group and positive relationships in the average and high fitness groups (all Ps<0.05). Significant moderating effects on the relationships between aoPWV and CBF were also observed for PA Rec Met (all Ps<0.05), moderate intensity (p = 0.05), and exercise‐related (all p’s < 0.02) PA.
DISCUSSION
Average or high fitness, meeting the PA guidelines, and more specifically, moderate intensity and exercise‐related PA seem to attenuate the negative relationship between aoPWV on CBF.
Highlights
Higher cardiorespiratory fitness (CRF) reduces the negative impact of aortic pulse wave velocity (aoPWV) on cerebral blood flow (CBF)
150 min of moderate physical activity (PA) also mitigates this impact, depending on activity type
Innovative methods: use of cardiac magnetic resonance imaging (MRI) for aoPWV assessment
Innovative methods: use of free‐breathing acquisition during cardiac MRI
Innovative methods: use of cranial 4D flow MRI for CBF assessment
Keywords: Alzheimer's disease, aortic stiffness, cardiorespiratory fitness, cerebral hemodynamics, physical activity
1. BACKGROUND
Age‐related arterial stiffening is associated with higher incidence of cardiovascular disease (CVD) 1 , cerebral small vessel disease 2 , unfavorable structural brain changes 3 , and cognitive impairment 4 . The literature also suggests a significant relationship between higher arterial stiffness and lower cerebral blood flow (CBF) 5 . A supporting theory posits that higher central arterial stiffness contributes to harmful pulsative blood flow to the cerebral microvasculature, triggering vasoconstriction as a protective compensatory mechanism, thereby increasing resistance and decreasing CBF 6 , 7 . CBF is essential for maintaining brain function and cognitive health, with cerebral hypoperfusion and cerebrovascular stiffening recognized as important risk factors contributing to cognitive decline and Alzheimer's disease (AD) 6 , 8 , 9 . Individuals at higher risk for cognitive decline or dementia stand to benefit from strategies aimed at improving vascular health or lifestyle factors known to contribute to both CVD and AD.
Exercise and physical activity (PA) are known to improve outcomes related to cardiovascular and cerebrovascular health 10 , including arterial stiffness 11 , 12 and cerebral hemodynamics 13 , 14 . Greater cardiorespiratory fitness (CRF), attained through regular aerobic exercise, seems to attenuate age‐related central arterial stiffening 15 , 16 . Indeed, several exercise intervention studies have demonstrated beneficial changes in arterial stiffness with concurrent increases in maximal oxygen consumption (VO2MAX), indicating improvement in CRF 17 . Moreover, both high CRF and PA are associated with higher CBF 10 , 18 , 19 , 20 .
Although CRF is determined by a number of nonmodifiable factors such as age, sex, and genetic factors 21 , it is generally considered an objective indicator of habitual PA, with CRF and PA often used interchangeably 22 . Although CRF is partly a consequence of regular PA, PA‐related health benefits can occur independent of changes to CRF 21 , 23 . It is currently unknown whether each makes a unique contribution to CBF alterations related to central arterial stiffness. Given the beneficial relationship between CRF, PA, and vascular health (both cardiovascular and cerebrovascular), the present study examines whether CRF or PA moderate the relationship between arterial stiffness and CBF, and whether the intensity or the type of PA impacts this relationship differentially. We hypothesize that greater CRF and PA will attenuate the adverse relationship between arterial stiffness and CBF, and furthermore, that the intensity and type of PA (moderate intensity, exercise‐related and sports/leisure‐related activity) will play a significant role in this attenuation.
2. MATERIALS AND METHODS
2.1. Participants
The sample consisted of 78 cognitively unimpaired adults from the Wisconsin Registry for Alzheimer's Prevention (WRAP) 24 or the Wisconsin Alzheimer's Disease Research Center (WADRC) 9 , both longitudinally followed cohorts enriched at enrollment for family history of dementia and apolipoprotein E (APOE) ε4 allele carriage 9 , 24 . Participants for the present study were selected based on completion of a graded exercise treadmill test and cardiac and cranial magnetic resonance imaging (MRI) scans within 1 yr of each other. Socio‐demographic and health characteristics are detailed in Table 1. All study procedures were approved by the University of Wisconsin Institutional Review Board, and signed written consent was obtained from all participants.
TABLE 1.
Participant characteristics.
| Parameter | Total | Low fitness (L) | Average fitness (A) | High fitness (H) | p‐value | Post hoc |
|---|---|---|---|---|---|---|
| (N = 78) | (N = 14) | (N=20) | (N = 44) | |||
| Demographic and health characteristics | ||||||
| Age M (SD) | 64.2 (6.14) | 64.9 (6.01) | 62.1 (7.07) | 64.9 (5.63) | 0.22 | |
| Female N (%) | 56 (72) | 10 (71) | 14 (70) | 32 (73) | 0.1 | |
| Education (years; M (SD)) | 16.3 (2.21) | 15.0 (2.42) | 16.9 (1.87) | 16.5 (2.17) | 0.04 | A > L |
| APOE ε4 N (%) | 38 (49) | 7 (50) | 7 (35) | 24 (55) | 0.35 | |
| Family Hx Dementia N (%) * | 39 (50) | 6 (43) | 8 (40) | 25 (57) | 0.18 | |
| WHR M (SD) | 0.90(0.08) | 0.91 (0.06) | 0.92 (0.08) | 0.88 (0.08) | 0.2 | |
| Hypertension N (%) | 18 (23) | 3 (21) | 8 (40) | 7 (16) | 0.1 | |
| aoPWV (m/s; M (SD)) | 7.62 (3.57) | 7.86 (4.82) | 7.59 (3.06) | 7.56 (3.42) | 0.96 | |
| Elevated PWV (>10 m/s; N (%)) | 13 (17) | 3 (21) | 4 (20) | 6 (14) | 0.71 | |
| VO2predicted (ml/kg/min; M (SD)) | 25.9 (6.13) | 18.9 (3.58) | 24.1 (3.90) | 29.0 (5.44) | <0.001 | H > A > L |
| Physical Activity | ||||||
| PA Rec Met (yes; N (%))) | 52 (67) | 7 (50) | 11 (55) | 34 (77) | 0.07 | |
| Low Intensity PA (hrs/week; M (SD)) | 7.94 (4.94) | 7.61 (4.97) | 7.53 (4.81) | 8.23 (5.08) | 0.84 | |
| Moderate Intensity >PA (hrs/week; M (SD)) | 9.26 (7.01) | 7.34 (9.14) | 7.55 (6.79) | 10.7 (6.15) | 0.14 | |
| Exercise‐Related PA (hrs/week; M (SD)) | 5.59 (4.74) | 3.18 (4.44) | 4.98 (4.96) | 6.64 (4.50) | 0.04 | H > A |
| Leisure/Sports‐Related PA (hrs/week; M (SD)) | 0.73 (1.94) | 0.45 (1.53) | 0.44 (1.05) | 0.10 (2.34) | 0.51 | |
| Work‐Related PA (hrs/week; M (SD)) | 3.06 (3.63) | 3.71 (5.45) | 2.33 (2.93) | 3.19 (3.23) | 0.52 | |
| Average CBF (mL/min; M (SD)) | ||||||
| Total Q | 542 (99.1) | 522 (95.7) | 567 (84.0) | 538 (106) | 0.39 | |
| Global Q | 126 (22.6) | 120 (21.4) | 131 (18.2) | 126 (24.6) | 0.34 | |
| ICA | 211 (37.0) | 200 (34.3) | 225 (29.4) | 208 (39.6) | 0.12 | |
| Cav. ICA | 220 (37.6) | 200 (31.7) | 236 (30.9) | 219 (39.6) | 0.02 | A > L |
| MCA | 131 (22.8) | 126 (20.9) | 137 (22.9) | 130 (23.3) | 0.32 | |
Family Hx Dementia had 22% missingness, 28% negative, and 50% positive.
Post hoc: H > A > L = high fitness mean is greater than means for average fitness and low fitness and average fitness mean is greater than low fitness mean; H>A = high fitness mean is greater than average fitness mean; A>L = average fitness mean is greater than low fitness mean.
Abbreviations: APOE ε4+, APOE ε4 allele carriage; aoPWV, aortic pulse wave velocity; BA, basilar artery; Cav. ICA, cavernous ICA; CHAMPS, Community Health Activities Model Program for Seniors; Exercise‐Related PA, exercise‐related physical activity (hours/week); Fitness, Low, average, and high fitness groups were defined based on their calculated VO2predicted falling within the fitness percentile ranges for age and sex: <49%, 50%–60%, and > 70%, respectively; Global Q, global flow; the average of seven cerebral arteries; HTN, hypertension; ICA, internal carotid arteries; Low Intensity PA, all physical activity less than 3.0 METs from the CHAMPS questionnaire; MCA, middle cerebral artery; Moderate Intensity PA, all physical activity greater than 3.0 METs from the CHAMPS questionnaire (hours/week); M (SD), mean (standard deviation); PA Rec Met, physical activity recommendations of 150 min of moderate‐intensity activity were met (yes/no); Sports‐Related PA, sports/leisure related physical activity (hours/week); Total Q, total flow; the sum of the ICAs and the BA; VO2predicted, predicted maximal oxygen consumption based on the oxygen uptake efficiency slope; WHR, waist to hip ratio; Work‐Related PA, work related physical activity (hours/week).
2.2. Cardiorespiratory fitness and physical activity
Participants performed a continuous graded exercise treadmill test (ETT; Trackmaster TMX428CP, Full Vision Inc., Newton, KS); full methods have been previously described 25 . Continuous measurements of oxygen uptake (VO2), carbon dioxide production (CO2), and minute ventilation (VE) were obtained using a metabolic cart and two‐way non‐rebreathing valve (TrueOne 2400, ParvoMedics, Sandy, UT). As previously published, a predicted peak VO2 (VO2predicted; mL/kg/min) was calculated from metabolic data collected during the ETT based on the oxygen uptake efficiency slope (OUES) equation 26 to determine CRF. For analyses purposes, participants were categorized into low (N = 14), average (N = 20), and high (N = 44) fitness groups based on age and sex specific reference standards for VO2 established through normative data from the “FRIEND” (Fitness Registry and the Importance of Exercise) National Data Base 27 (see Table S1 for details).
RESEARCH IN CONTEXT
Systematic review: The authors reviewed the literature using PubMed. Previous research supports that elevated central arterial stiffness negatively impacts cerebral blood flow (CBF) and independently links poor cardiorespiratory fitness (CRF) and low physical activity (PA) to higher arterial stiffness and lower CBF. However, no studies have assessed the combined impact of CRF, PA, and arterial stiffness on CBF.
Interpretation: In a late middle‐aged cohort enriched for Alzheimer's disease (AD) risk, average or high CRF attenuated the adverse relationship between arterial stiffness and CBF compared to the low CRF group. The relationship was similarly mitigated by 150 min of weekly PA, depending on activity type and intensity.
Future directions: Longitudinal studies that assess concurrent changes in CRF, PA, arterial stiffness, and CBF are needed to better elucidate directionality of the relationships. Studies measuring arterial stiffness as aortic pulse wave velocity (aoPWV) via cardiac magnetic resonance imaging (MRI) should consider the effects of stroke volume, cardiac output, and heart rate as confounding variables whenever possible.
At the time of ETT assessment, participants also completed the CHAMPS physical activity questionnaire (Community Health Activities Model Program for Seniors). CHAMPS is designed for older adults and has an established reliability, sensitivity, and construct validity 28 . For analyses purposes, PA was assessed as meeting the PA recommendations (PA Rec Met; yes/no) as set by the World Health Organization (WHO) 29 and as hours/ week of moderate and low intensity PA, and hours/week of exercise‐, sports/leisure‐ and work‐related type of activity. Instructions and further details on scoring PA from the questionnaire for type and intensity are detailed in Figure 1.
FIGURE 1.
Physical activity scoring instructions. The community health activities model program for seniors (CHAMPS) Questionnaire items for each category are reflected in the figure. The intensity for each activity (METs) as well as total physical activity (hours/week) was calculated as the sum of all activities according to the original reference 28 . Meeting the Physical Activity Recommendations (PA Rec Met; yes/no) was calculated as achieving ≥ 2.5 h (150 min) of moderate intensity activity in a week. Moderate‐intensity PA (hours/week) was defined as any PA ≥ 3 METs, while low‐intensity PA (hours/week) was defined as any PA < 3 METs 28 . Of 52 participants who were classified as physically active, determined by moderate intensity activities, 25 also completed some level of vigorous intensity activity (≥6 METs). We opted to combine them under the moderate intensity grouping for analysis purposes because the low variation in vigorous activity rendered subgroups underpowered when modeling vigorous PA as a separate group. PA type (hours/week) was investigated as exercise, leisure/sports, and work‐related activities, and were classified as such based on the World Health Organization's “Global Recommendations on Physical Activity for Health” 29 . Exercise‐related PA was defined as activities traditionally aimed at increasing a component of physical fitness, like cycling, swimming, running, walking, or strength training. Leisure/sports‐related activities were defined as leisure or recreational related sporting activities, like dancing, golf, basketball, soccer, racquetball, and gentle swimming, and work‐related activities were defined as tasks performed to maintain and manage a home or as part of an occupation.
2.3. Cardiovascular MRI for arterial stiffness
Central arterial stiffness was assessed with aortic pulse wave velocity (aoPWV; meters/second) measurements obtained with MRI. MRI data were acquired as previously described in detail 30 on clinical 3T scanners (Discovery MR 750 and Signa Premier, GE Healthcare, Waukesha, WI) using an eight‐channel torso coil. An ungated, breath‐hold, 2D multi‐slice balanced steady‐state free‐precession (bSSFP) product sequence generated anatomical images to guide plane placement of the flow measurement planes and for aortic centerline tracing and subsequent distance measurements between the planes. A 2D radially undersampled, velocity‐sensitive MRI sequence with retrospective cardiac and respiratory gating was used to enable free‐breathing acquisitions 30 . Flow data were acquired in two axial planes: one over the aortic arch, including the ascending and descending aorta, and one in the abdominal aorta above the renal arteries, allowing for three flow measurements. Photoplethysmography (PPG) data were recorded using a pulse oximeter on the index finger, and respiratory motion was recorded using a bellows on the chest. Time‐resolved and time‐averaged velocity and magnitude datasets were reconstructed from data during expiration with a respiratory acceptance window of 50%. A customized analysis package with a graphical user interface (GUI) was developed for PWV calculations using MATLAB (The Mathworks, Natwick, MA) and is publicly available (https://github.com/tnaren97/PWV_2DPC ). The processing pipeline includes computation of centerlines and flow plane distances from the anatomical bSSFP images and determines time shifts between the flow waveforms using the Time‐to‐Point (TTP) PWV method 31 . Flow waveforms were smoothed with a Gaussian filter (width = 7 pixels) and linearly temporally interpolated (20x).
2.4. 4D flow MRI and analysis
As previously published 9 , a cranial MRI scan acquisition was performed on one of the two GE Healthcare 3T clinical systems: Discovery MR750 with a 32‐channel head coil or Signa Premier with a 48‐channel coil. Cerebral artery flow was assessed using a radial 4D Flow MRI sequence 32 , 33 . Automatic vessel segmentation and flow quantification were performed in a customized post‐processing tool (MATLAB, Mathworks, Natick, MA). Bilateral cycle‐averaged volumetric flow rates (mL/min) were obtained in the distal cervical internal carotid arteries (ICAs), cavernous ICAs (Cav. ICAs), middle cerebral arteries (MCAs), vertebral arteries (VAs), anterior cerebral arteries (ACAs), posterior cerebral arteries (PCAs), and the basilar artery (BA). Total cerebral inflow (Total Q) reflected the sum of the left and right ICAs and BA. A composite score for global flow (Global Q) was created by averaging flows from the bilateral ICAs, Cav. ICAs, MCAs, VAs, ACAs, PCAs, and the BA. CBF outcomes utilized for analysis purposes were CBF in the ICA, Cav. ICA, MCA, Total Q and Global Q; and were selected based on the existing literature 18 , 34 and by the distance from the carotid artery to inform future investigations and identify areas more susceptible to pulsatile flow from arterial stiffness, given that the information is currently grossly lacking in the literature.
2.5. Covariate definitions
All models were adjusted for age, sex, APOE ε4 allele presence, waist‐to‐hip ratio (WHR), and hypertension. Covariates were determined a priori based on the existing literature 5 , 35 , 36 , 37 . WHR was calculated as waist circumference (cm) divided by hip circumference (cm) and was selected over body mass index to better represent central adiposity/obesity. Hypertension was defined as self‐report or based on medication use. Being a carrier of at least one APOE ε4 allele (APOE4+) was treated as a categorical variable (positive/negative).
2.6. Statistical analyses
Analyses were performed using R Statistical Software 4.4.0 (www.r‐project.org). For all regression models, assumptions of linearity, normality of residuals, homoscedasticity, and absence of multicollinearity (variance inflation factor less than 5) were assessed for all regression models. Sample characteristics were evaluated using one‐way analysis of variance (ANOVA) for continuous or χ 2 test for categorical measures. Tukey honestly significant difference (HSD) post hoc tests were conducted to compare participant characteristics between low, average, and high fitness groups.
Covariate‐adjusted, linear regression models with CBF outcomes (Total Q, Global Q, and mean bilateral blood flow in the ICA, Cav. ICA, and MCA) were used to address the aims. Analyses were first performed with CRF as the predictor while covarying for PA Rec Met to investigate the moderating effect of CRF (low, average, high) on the relationship between aoPWV and CBF; the interaction between CRF and aoPWV was the term of interest. Then, the analyses were repeated with PA Rec Met as the predictor while covarying for CRF (VO2predicted) to parse out the contribution of PA to overall CRF, in moderating the relationship between aoPWV and CBF; the interaction between PA Rec Met (yes/no) and aoPWV was the term of interest. We further examined whether the moderating effect of PA on the relationship between aoPWV and CBF was dependent on PA intensity (moderate or low) and type (exercise, sports/leisure, and work‐related). Significance level with stringent Bonferroni correction for multiple comparisons is set at p < 0.01; however, we report the conventional p < 0.05 to help guide future investigations.
3. RESULTS
3.1. Sample characteristics
Table 1 details sociodemographic and health characteristics of study participants. The sample was predominantly white (94%) and female (72%), with an average age of 64.2 ± 6.14. The sample was enriched for AD risk at enrollment, with 49% carrying at least one APOE ε4 allele and 50% having a parental history of dementia. Overall, the sample had a low to moderate prevalence of hypertension (23%), aoPWV ranged from 3.02 to 23.2 m/s, and 16% of the sample had what would clinically (>10 m/s) be considered elevated aoPWV 38 .
Based on reference standards for VO2predicted, 18% of the sample was classified as low, 26% as average, and 56% as highly fit. There were no significant differences in age (p = 0.22), sex (p = 0.1), hypertension (p = 0.1), APOE4+ status (p = 0.35), or aoPWV (p = 0.96) among fitness groups. The low fitness group was significantly less educated compared to the average (p = 0.04), but not the high (p = 0.07) fitness group. No group differences were found in hours/week engaged in low (p = 0.84) or moderate (p = 0.14) intensity PA. There were also no significant group differences in hours/week engaged in leisure/sports (p = 0.51) or work‐related (p = 0.52) PA. The high fitness group averaged significantly more exercise‐related activity compared to the low fitness group (p = 0.04); no differences were observed between average and high (p = 0.4) or between average and low (p = 0.5) fitness groups. The low fitness group had a significantly lower CBF only in the Cav. ICA (p = 0.02) compared to the average fitness group.
3.2. CRF‐ and PA‐related alterations in CBF
Results for the joint effect of CRF and aoPWV on CBF, adjusted for PA Rec Met, are presented in Table 2 and Figure S1. There were significant positive interactions between the high fitness group and aoPWV for all CBF outcomes [Total Q (p = 0.02), Global Q (p = 0.04), ICA (p = 0.01), Cav. ICA (p = 0.03), MCA (p = 0.02)], as well as between the average fitness group and aoPWV for Total Q (p = 0.02) and in the ICA (p = 0.01), Cav. ICA (p = 0.02), and MCA (p = 0.01), where for each 1 unit increase in aoPWV, those in the low fitness group had decreased CBF while those in the high and average fitness groups increased CBF. In the low fitness group, higher aoPWV was related to lower CBF [Total Q (p = 0.04); Global Q (p = 0.05); ICA (p = 0.03); Cav. ICA (p = 0.06); MCA (p = 0.05)]. Age was negatively associated with CBF for Global Q (p = 0.01), and in the Cav. ICA (p = 0.03) and MCA (p = 0.01). Hypertension was positively associated with CBF for Total Q (p = 0.03), and in the ICA (p = 0.02), and MCA (p = 0.03).
TABLE 2.
Associations between arterial stiffness and cardiorespiratory fitness on cerebral blood flow, adjusting for physical activity.
| Outcome | Exposure | Estimate | Std. error | p‐value |
|---|---|---|---|---|
| Total Q | Age | −3.73 | 2.01 | 0.07 |
| WHR | −321.65 | 204.41 | 0.12 | |
| HTN | 64.13 | 28.54 | 0.03 | |
| aoPWV | −12.14 | 5.64 | 0.04 | |
| PA Rec Met (yes) | −35.25 | 24.61 | 0.16 | |
| aoPWV x average fitness | 22.63 | 9.61 | 0.02 | |
| aoPWV x high fitness | 17.09 | 7.16 | 0.02 | |
| Global Q | Age | −1.17 | 0.46 | 0.01 |
| WHR | −52.49 | 47.01 | 0.27 | |
| HTN | 12.41 | 6.56 | 0.06 | |
| aoPWV | −2.59 | 1.30 | 0.05 | |
| PA Rec Met (yes) | −6.93 | 5.66 | 0.23 | |
| aoPWV x average fitness | 3.97 | 2.21 | 0.08 | |
| aoPWV x high fitness | 3.54 | 1.65 | 0.04 | |
| ICA | Age | −1.02 | 0.73 | 0.17 |
| WHR | −134.65 | 73.82 | 0.07 | |
| HTN | 24.81 | 10.31 | 0.02 | |
| aoPWV | −4.55 | 2.04 | 0.03 | |
| PA Rec Met (yes) | −17.01 | 8.89 | 0.06 | |
| aoPWV x average fitness | 9.53 | 3.47 | 0.01 | |
| aoPWV x high fitness | 6.85 | 2.59 | 0.01 | |
| Cav. ICA | Age | −1.57 | 0.72 | 0.03 |
| WHR | −138.61 | 73.31 | 0.06 | |
| HTN | 18.48 | 10.24 | 0.08 | |
| aoPWV | −3.86 | 2.02 | 0.06 | |
| PA Rec Met (yes) | −17.37 | 8.83 | 0.06 | |
| aoPWV x average fitness | 8.37 | 3.45 | 0.02 | |
| aoPWV x high fitness | 5.82 | 2.57 | 0.03 | |
| MCA | Age | −1.21 | 0.45 | 0.01 |
| WHR | −83.43 | 45.67 | 0.07 | |
| HTN | 14.44 | 6.38 | 0.03 | |
| aoPWV | −2.52 | 1.26 | 0.05 | |
| PA Rec Met (yes) | −4.83 | 5.50 | 0.38 | |
| aoPWV x average fitness | 5.41 | 2.15 | 0.01 | |
| aoPWV x high fitness | 3.87 | 1.60 | 0.02 |
Covariates: PA Rec Met, age, sex, WHR, HTN, and APOE ε4+. Only significant covariates and the term(s) of interest (interaction) were included in the table.
Abbreviations: APOE ε4+, APOE ε4 allele carriage; aoPWV, aortic pulse wave velocity; BA, basilar artery; Cav. ICA. cavernous ICA; Fitness. Low, average, and high fitness groups were defined based on their calculated VO2predicted falling within the fitness percentile ranges for age and sex: <49%, 50%–60%, and > 70%, respectively; Global Q, global flow; the average of seven cerebral arteries; HTN, hypertension; ICA, internal carotid arteries; MCA, middle cerebral artery; PA Rec Met, physical activity recommendations of 150 min of moderate‐intensity activity were met (yes/no); PA, physical activity; Total Q, total flow; the sum of the ICAs and the BA; VO2predicted, predicted maximal oxygen consumption based on the oxygen uptake efficiency slope; WHR, waist‐to‐hip ratio.
Results for the joint effect of PA Rec Met and aoPWV on CBF, adjusted for CRF, are presented in Table 3 and Figure S2. There were significant positive interactions between PA Rec Met and aoPWV on CBF for Total Q (p = 0.03), Global Q (p = 0.05) and in the MCA (p = 0.03), such that the adverse relationship between aoPWV and CBF was attenuated in those meeting PA recommendations compared to those who did not. Age (p = 0.03) and WHR (p = 0.05) were negatively associated with CBF in the MCA.
TABLE 3.
Associations between arterial stiffness and physical activity on cerebral blood flow, adjusting for cardiorespiratory fitness.
| Outcome | Exposure | Estimate | Std. error | p‐value |
|---|---|---|---|---|
| Total Flow | Age | −2.41 | 2.06 | 0.25 |
| WHR | −349.65 | 211.76 | 0.10 | |
| VO2predicted | −0.52 | 2.41 | 0.83 | |
| aoPWV | −8.00 | 4.91 | 0.11 | |
| PA Rec Met (yes) | −143.63 | 54.75 | 0.01 | |
| aoPWV x PA Rec Met (yes) | 14.61 | 6.38 | 0.03 | |
| Global Flow | Age | −0.87 | 0.47 | 0.07 |
| WHR | −63.94 | 48.57 | 0.19 | |
| VO2predicted | −0.02 | 0.55 | 0.97 | |
| aoPWV | −1.80 | 1.13 | 0.12 | |
| PA Rec Met (yes) | −27.98 | 12.56 | 0.03 | |
| aoPWV x PA Rec Met (yes) | 2.89 | 1.46 | 0.05 | |
| ICA | Age | −0.65 | 0.78 | 0.41 |
| WHR | −131.50 | 80.27 | 0.11 | |
| VO2predicted | 0.04 | 0.91 | 0.96 | |
| aoPWV | −1.63 | 1.86 | 0.39 | |
| PA Rec Met (yes) | −45.08 | 20.76 | 0.03 | |
| aoPWV x PA Rec Met (yes) | 3.81 | 2.42 | 0.12 | |
| Cav. ICA | Age | −1.24 | 0.78 | 0.12 |
| WHR | −141.00 | 80.28 | 0.08 | |
| VO2predicted | 0.18 | 0.91 | 0.84 | |
| aoPWV | −1.58 | 1.86 | 0.40 | |
| PA Rec Met (yes) | −43.02 | 20.76 | 0.04 | |
| aoPWV x PA Rec Met (yes) | 3.65 | 2.42 | 0.14 | |
| MCA | Age | −1.00 | 0.46 | 0.03 |
| WHR | −95.75 | 47.51 | 0.05 | |
| VO2predicted | −0.38 | 0.54 | 0.49 | |
| aoPWV | −1.48 | 1.10 | 0.18 | |
| PA Rec Met (yes) | −28.43 | 12.28 | 0.02 | |
| aoPWV x PA Rec Met (yes) | 3.27 | 1.43 | 0.03 |
Covariates: VO2predicted, age, sex, WHR, HTN, and APOE ε4+. Only significant covariates and the term(s) of interest (interaction) were included in the table.
Abbreviations: APOE ε4+, APOE ε4 allele carriage; aoPWV. aortic pulse wave velocity; BA, basilar artery; Cav. ICA, cavernous ICA; Global Q, global flow; the average of seven cerebral arteries; HTN, hypertension; ICA, internal carotid arteries; MCA, middle cerebral artery; PA Rec Met, physical activity recommendations of 150 min of moderate‐intensity activity were met (yes/no); PA, physical activity; Total Q, total flow; the sum of the ICAs and the BA; VO2predicted, predicted maximal oxygen consumption based on the oxygen uptake efficiency slope; WHR, waist‐to‐hip ratio.
3.3. Alterations in CBF based on PA intensity and type
Results for PA intensity are presented in Table 4 and Table S2. An investigation into the intensity of PA, categorized as moderate or low, revealed that low‐intensity PA did not significantly modify any of the relationships between aoPWV and CBF (all Ps ≥0.30; Table S2). A significant positive interaction between moderate intensity PA and aoPWV was observed for Global Q (p = 0.05; Table 4). Hypertension was also positively associated with CBF (Total Q: p = 0.03; ICA: p = 0.02; MCA: p = 0.03), whereas age was negatively associated with CBF (Global Q: p = 0.03; Cav. ICA: p = 0.04; MCA: p = 0.02).
TABLE 4.
Associations between arterial stiffness and moderate intensity physical activity on cerebral blood flow.
| Outcome | Exposure | Estimate | Std. error | p‐value |
|---|---|---|---|---|
| Total Q | Age | −3.06 | 1.95 | 0.12 |
| WHR | −321.38 | 203.06 | 0.12 | |
| HTN | 61.96 | 27.93 | 0.03 | |
| aoPWV | −6.02 | 5.23 | 0.25 | |
| Moderate Intensity PA | −4.31 | 3.53 | 0.23 | |
| aoPWV x Moderate Intensity PA | 0.83 | 0.45 | 0.07 | |
| Global Q | Age | −0.99 | 0.44 | 0.03 |
| WHR | −60.68 | 45.81 | 0.19 | |
| HTN | 12.13 | 6.30 | 0.06 | |
| aoPWV | −1.76 | 1.18 | 0.14 | |
| Moderate Intensity PA | −1.09 | 0.80 | 0.18 | |
| aoPWV x Moderate Intensity PA | 0.20 | 0.10 | 0.05 | |
| ICA | Age | −0.96 | 0.74 | 0.20 |
| WHR | −132.04 | 77.17 | 0.09 | |
| HTN | 24.78 | 10.61 | 0.02 | |
| aoPWV | −1.07 | 1.99 | 0.59 | |
| Moderate Intensity PA | −1.27 | 1.34 | 0.35 | |
| aoPWV x Moderate Intensity PA | 0.24 | 0.17 | 0.17 | |
| Cav. ICA | Age | −1.56 | 0.74 | 0.04 |
| WHR | −144.26 | 76.42 | 0.06 | |
| HTN | 20.55 | 10.51 | 0.06 | |
| aoPWV | −1.48 | 1.97 | 0.45 | |
| Moderate Intensity PA | −1.44 | 1.33 | 0.28 | |
| aoPWV x Moderate Intensity PA | 0.28 | 0.17 | 0.11 | |
| MCA | Age | −1.04 | 0.43 | 0.02 |
| WHR | −78.21 | 45.03 | 0.09 | |
| HTN | 13.71 | 6.19 | 0.03 | |
| aoPWV | −1.12 | 1.16 | 0.34 | |
| Moderate Intensity PA | −0.85 | 0.78 | 0.28 | |
| aoPWV x Moderate Intensity PA | 0.19 | 0.10 | 0.07 |
Covariates: Age, sex, WHR, HTN, and APOE ε4+. Only significant covariates and the term(s) of interest (interaction) were included in the table.
Abbreviations: APOE ε4+, APOE ε4 allele carriage; aoPWV, aortic pulse wave velocity; BA, basilar artery; Cav. ICA, cavernous ICA; CHAMPS, Community Health Activities Model Program for Seniors; Global Q, global flow; the average of seven cerebral arteries; HTN, hypertension; ICA, internal carotid arteries; MCA, middle cerebral artery; Moderate Intensity PA, all physical activity greater than 3.0 METs from the CHAMPS questionnaire (hours/week); PA, physical activity; Total Q, total flow; the sum of the ICAs and the BA; WHR, waist to hip ratio.
We subsequently investigated whether different types of moderate intensity PA (exercise, sports/leisure, and work‐related activities) modify the aoPWV‐CBF relationship. Results are displayed in Table 5 and Tables S3 and S4. There were significant positive interactions between exercise‐related PA and aoPWV on CBF (Table 5) for Total Q (p = 0.02), Global Q (p = 0.02), and in the MCA (p = 0.02). Hypertension was positively associated with CBF (ICA: p = 0.05), while age was negatively associated with CBF (Global Q: p = 0.04; MCA: p = 0.04). Neither sports/leisure (all Ps>0.12; Table S3) nor work‐related PA (all Ps > 0 1; Table S4) modified the effect of aoPWV on CBF. A positive main effect was found between sports/leisure‐related PA and CBF for Total Q (p = 0.04), Global Q (p = 0.03), and in the MCA (p = 0.04), indicating higher CBF with higher amounts of sports/leisure‐related PA achieved. Hypertension was also positively associated with CBF in the ICA (p = 0.04), while age was negatively associated with CBF for Global Q (p = 0.01), and in the Cav. ICA (p = 0.02) and MCA (p = 0.01).
TABLE 5.
Associations between arterial stiffness and exercise‐related physical activity on cerebral blood flow.
| Outcome | Exposure | Estimate | Std. error | p‐value |
|---|---|---|---|---|
| Total Q | Age | −2.53 | 1.91 | 0.19 |
| HTN | 51.72 | 27.93 | 0.07 | |
| aoPWV | −8.15 | 4.96 | 0.11 | |
| aoPWV x Exercise‐Related PA | 1.54 | 0.63 | 0.02 | |
| Global Q | Age | −0.89 | 0.43 | 0.04 |
| HTN | 9.91 | 6.35 | 0.12 | |
| aoPWV | −2.05 | 1.13 | 0.07 | |
| aoPWV x Exercise‐Related PA | 0.34 | 0.14 | 0.02 | |
| ICA | Age | −0.81 | 0.73 | 0.27 |
| HTN | 21.43 | 10.67 | 0.05 | |
| aoPWV | −1.65 | 1.90 | 0.39 | |
| aoPWV x Exercise‐Related PA | 0.43 | 0.24 | 0.07 | |
| Cav. ICA | Age | −1.43 | 0.73 | 0.05 |
| HTN | 17.16 | 10.65 | 0.11 | |
| aoPWV | −1.72 | 1.89 | 0.37 | |
| aoPWV x Exercise‐Related PA | 0.43 | 0.24 | 0.07 | |
| MCA | Age | −0.92 | 0.43 | 0.04 |
| HTN | 11.62 | 6.26 | 0.07 | |
| aoPWV | −1.52 | 1.11 | 0.18 | |
| aoPWV x Exercise‐Related PA | 0.33 | 0.14 | 0.02 |
Covariates: age, sex, WHR, HTN, and APOE ε4+. Only significant covariates and the term(s) of interest (interaction) were included in the table.
Abbreviations: APOE ε4+, APOE ε4 allele carriage; aoPWV, aortic pulse wave velocity; BA, basilar artery; Cav. ICA, cavernous ICA; Exercise‐Related PA, exercise‐related physical activity (hours/week); Global Q, global flow; the average of seven cerebral arteries; HTN, hypertension; ICA, internal carotid arteries; MCA, middle cerebral artery; PA, physical activity; Total Q, total flow; the sum of the ICAs and the BA; WHR, waist‐to‐hip ratio.
3.4. Sensitivity analyses
Although there is no universally agreed‐upon threshold for physiologically implausible aoPWV values, the range of 19–24 m/s was selected for sensitivity analysis, as values above 25 m/s are typically considered potential artifacts due to errors in transit time estimation, poor temporal resolution, flow turbulence, or segmentation inaccuracies. For reference, normal aoPWV values in healthy adults typically range from 5 to 10 m/s, while older adults or those with atherosclerosis may exhibit values between 14 and 18 m/s 39 , 40 , 41 . The aoPWV*High Fitness interaction for the MCA (β = 3.82; p = 0.14), Total (β = 18.77; p = 0.09) and Global (β = 3.15; p = 0.23) Q, as well as the main effect of aoPWV for the MCA (β = −3.63; p = 0.09) and Global Q (β = −3.43; p = 0.13) were no longer significant after sensitivity analyses were performed excluding aoPWV ranging between 19–24 m/s. The main effect of aoPWV in the Cav. ICA (β = −7.22; p = 0.04) was significant and the results for aoPWV*Average Fitness interaction were unchanged in the ICA (β = 14.18; p = 0.02), Cav. ICA (β = 11.55; p = 0.01), MCA (β = 6.43; p = 0.02), and for Total Q (β = 30.84; p = 0.01). Analyses that covaried for treatment with vasodilators or excluded participants who were taking vasodilators did not alter our findings (results not reported but available upon request).
4. DISCUSSION
The overreaching goal of this study was to characterize the relationships between CRF or PA, aoPWV, and CBF in a cognitively unimpaired, late middle‐aged cohort enriched for AD risk. Our findings suggest that having average or high CRF or meeting PA recommendations attenuates the adverse relationship between arterial stiffness and CBF. Importantly, the beneficial impact of average or high CRF on the aoPWV–CBF relationship remained significant even after adjusting for PA status, and vice versa, suggesting that CRF and PA account for unique variance in outcomes (i.e., they are not entirely overlapping constructs); we hence caution against using CRF and PA interchangeably. Further investigations into the intensity and type of PA suggest that the PA‐related findings were likely driven by individuals with greater CRF, as only moderate intensity and exercise‐related PA modified the aoPWV–CBF relationship, both of which are activities with high potential to increase CRF (compared to low intensity or sports/leisure related PA).
In contrast to well‐established negative relationships between hypertension and CBF 42 , 43 , hypertension was related to better CBF in our sample. Further investigation revealed that 12 of 18 participants with hypertension were currently receiving treatment (angiotensin‐converting enzyme inhibitors [ACE], n = 3; angiotensin receptor blockers [ARBs], n = 1; calcium channel blockers [CCBs], n = 4; hydrochlorothiazide diuretic [HCTZs], n = 1; or a combination of beta‐blockers with one of the previously mentioned treatments, n = 3). Adherence to and the length of treatment are unknown. Of the 12 hypertensive participants receiving treatment, 11 were on vasodilators (ACE, ARBs, CCBs) known to increase blood flow in the brain 44 , which may have at least partly contributed to this unexpected finding.
We did not observe significant differences in aoPWV across the three CRF groups (Table 1), which contrasts with several published reports 15 , 16 , 45 , 46 . The average aoPWV (7.53 m/s) in the current study is slightly higher than what has been reported in the literature to date (6.8 m/s) 39 , 40 . Although there could be a number of contributing factors, it is also theoretically possible that increased stroke volume may register as a higher velocity if arterial remodeling (e.g., diameter or extensibility) has reached its ceiling. According to the principle of continuity in fluid dynamics, if the arterial diameter cannot increase further, then increased stroke volume will result in higher blood velocity 47 . Evidence supports that regular exercise triggers structural adaptations to the heart and the arterial system, lowering resting heart rate, 48 and increasing stroke volume 49 ; therefore, higher velocities could result from a greater blood volume load versus higher velocities due to arterial stiffening. Future work should therefore take cardiac output, stroke volume, and heart rate into consideration. Although outside the scope of the current study, we will be able to interrogate this possibility in our cohort in the future.
This study is not without limitations. Our sample is predominantly white, female, and highly educated. Moreover, the majority of our cohort would be considered highly physically fit. This distribution is not entirely unexpected, as the requirement of maximal exercise testing likely biased our results toward more highly fit individuals. The use of the CHAMPS questionnaire, which is based on self‐report and designed for adults aged 50 and above (https://cadc.ucsf.edu/champs), may be seen as a potential limitation; two participants in our sample were younger than 50. Although outside the scope of the present study, we acknowledge that both the APOE gene and hypertension have been independently linked to cerebrovascular function 5 and could act as potential moderators of the relationships examined herein. Further stratification of an already small sample size and the extent of multiple comparisons would greatly limit our statistical power to detect modest associations needed for three‐way interactions with any certainty. Lastly, the cross‐sectional nature of this study may be seen as a potential limitation. Given the prospective nature of the parent studies (WRAP and WADRC) and the concerted effort to increase the enrollment of underrepresented populations, we should be able to address some of these limitations in the future, as well as increase the generalizability of our findings.
Potential limitations notwithstanding, strengths are evident in our rigorous approach to measurement. The use of cardiac MRI to assess central arterial stiffness provides more precise measurement of aortic geometry and length, thereby increasing reproducibility and reliability of measurements 41 , though the absence of carotid‐femoral PWV measurements to validate our cardiovascular magnetic resonance (CMR) measurements may be considered a limitation. Furthermore, our newly developed CMR sequence allows for free‐breathing acquisitions to enable measurement in subjects that cannot hold their breath. The 4D flow MRI with radial undersampling captures dynamic changes in blood flow over the cardiac cycle and enables blood flow measurement in all major cerebral arteries 9 . Lastly, utilization of the VO2predicted measure, calculated from metabolic data collected during an ETT and based on the OUES equation 26 , represents a highly accurate and reliable VO2 measure since results are derived directly from individual‐specific metabolic gas exchange data, providing precise and individualized results.
Overall, our results suggest that average to high CRF or meeting PA recommendations positively modify the otherwise adverse relationship between arterial stiffness and CBF, and that the intensity and type of activity do play a role in the PA‐related modifications. Given the evidence supporting a vascular contribution in cognitive decline and AD progression 6 , 8 , 9 and the need to identify modifiable lifestyle factors targeting the preclinical phase of AD, it follows that vascular health should be targeted. While speculative in nature, it appears that the prevention of central arterial aging may attenuate its detrimental impact on cerebral hemodynamics and thereby confer protection from or at least slow the progression of AD‐related neuropathological processes or clinical manifestation thereof. Additional research is necessary to further elucidate the exact mechanisms underlying this modifiable and potentially protective risk factor.
AUTHOR CONTRIBUTIONS
B.M.B., M.P.G, and O.C.O. designed the experiments. B.M.B., G.S.R., T.L.B., M.M.J., L.E.S., A.Y.G., T.N., and S.R.L. collected the data. B.M.B. analyzed the data, wrote the manuscript. and prepared figures. All authors edited, revised, and approved the final version of the manuscript.
CONFLICT OF INTEREST STATEMENT
Dr. Ozioma Okonkwo serves as the treasurer of the International Neuropsychological Society. He is also a guest editor of this journal but was not involved in the peer‐review process of this article nor had access to any information regarding its peer‐review. Dr. Sterling Johnson serves as a consultant and on advisory boards for ALZPath and Enigma Biosciences. Dr. Sanjay Asthana receives royalty as an editor of a textbook entitled, Hazzard's Geriatrics and Gerontology, McGraw Hill, Publisher. All other authors have no relevant disclosures to report.
CONSENT STATEMENT
Written informed consent was provided by all participants prior to study participation and approval was obtained from the institutional review board.
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ACKNOWLEDGMENTS
We acknowledge and thank the staff and study participants of the Wisconsin Registry for Alzheimer's Prevention and the Wisconsin Alzheimer's Disease Research Center. This work was supported by National Institute of Aging grants: R01 AG062167 (OCO), R01 AG077507 (ID), R01 AG085592 (OCO), R01 AG027161 (SCJ), R01 AG021155 (SCJ), and P30 AG062715 (SA). Portions of this research were supported by the Clinical and Translational Science Award (UL1TR002373) to the University of Wisconsin, Madison, and by a NIH High‐End Instrumentation grant (S10 OD030415) (BTC)
Breidenbach BM, Driscoll I, Glittenberg MP, et al. Cardiorespiratory fitness modifies the relationship between arterial stiffness and cerebral blood flow independent of physical activity. Alzheimer's Dement. 2025;11:e70130. 10.1002/trc2.70130
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