Abstract
Mean and pulsatile components of hemodynamic load are related to cardiovascular disease. Vascular growth factors play a fundamental role in vascular remodeling. The links between growth factors and hemodynamic load components are not well described.
In 3496 participants from the Framingham Heart Study Third Generation cohort (mean age 40±9 years, 52% women) we related 4 tonometry derived measures of central arterial load (carotid femoral pulse wave velocity and forward pressure wave, mean arterial pressure, and the global reflection coefficient) to circulating concentrations of angiopoietin 2, its soluble receptor; vascular endothelial growth factor, its soluble receptor; hepatocyte growth factor; insulin-like growth factor-1, and its binding protein3. Using multivariable linear regression models, adjusted for standard cardiovascular risk factors, serum insulin-like growth factor-1concentrations were negatively associated with carotid femoral pulse wave velocity, mean arterial pressure, and reflection coefficient (p≤0.01 for all), whereas serum vascular endothelial growth factor levels were positively associated with carotid femoral pulse wave velocity and mean arterial pressure (p≤0.02). Serum insulin-like growth factor binding protein −3 and soluble angiopoietin-2 receptor levels were positively related to mean arterial pressure and to forward pressure wave, respectively (p<0.05).
In our cross-sectional study of a large community-based sample, circulating vascular growth factor levels were related to measures of mean and pulsatile hemodynamic load in a pattern consistent with the known physiological effects of insulin-like growth factor-1 and vascular endothelial growth factor.
Keywords: Vasculature, Growth substances, angiogenesis, arteriosclerosis, elasticity
INTRODUCTION
The arterial tree consists of a serially connected system of large proximal elastic conduit arteries, medium sized muscular arteries, smaller arteries and arterioles, and the microcirculation. Consequently, arterial function and flow is determined by the complex integration of the physiological properties of these diverse segments of the vasculature. Large artery structure and function are powerful determinants of pulsatile hemodynamic load on the heart. Indeed, increased aortic stiffness is a major correlate of elevated blood pressure, cardiac hypertrophy and cardiovascular disease (CVD) outcomes.1-4 Therefore, evaluation of factors that influence conduit artery function may provide important insights into the pathogenesis of high blood pressure, target organ damage and CVD.
Numerous studies indicate that vascular structure and function across the arterial tree are influenced by several systemic risk factors and multiple biological pathways.5-7 More recently, attention has focused on vascular growth factors and their relations with endothelial cell 8, 9 and vascular smooth muscle function 10, arterial development and remodeling 11-13, and the balance between microvascular rarefaction and neovascularization.14-17 For example, vascular endothelial growth factor (VEGF) and angiopoietin pathways are separately responsible for vascular sprouting and maintenance of newly sprouted blood vessels. 15 Insulin-like growth factor 1 (IGF1) is known to stimulate elastin production in the developing aorta.18 Hepatocyte growth factor (HGF) is known to stabilize vascular endothelium and respond to injury by preventing neointimal proliferation and encouraging healthy endovascular healing in response to injury.19 Accordingly, we hypothesized that there are bidirectional relations between circulating levels of vascular growth factors and measures of large artery stiffness and hemodynamic load. We tested this hypothesis by examining the cross-sectional relations of a complementary panel of circulating vascular growth factors and their soluble receptors and binding proteins with measures of mean and pulsatile hemodynamic load in a large community-based sample.
METHODS
The sampling scheme and design of the Framingham Heart Study third generation cohort have been detailed.20 Briefly, children of the Offspring cohort (and grand children of the original Framingham cohort) who belonged to the largest families were recruited between 2002 and 2005. Participants who attended their first examination cycle (2002-2005) were eligible for the present investigation. At that examination, attendees underwent a medical history and physical examination, laboratory testing for standard CVD risk factors and assessment of arterial stiffness using applanation tonometry. In the present investigation, we evaluated 3496 participants (85% of attendees) who had available tonometry phenotypes, circulating measurements of all 7 vascular growth factors and available covariates for analysis. The study protocol was approved by the Boston University Medical Center Institutional Review Board; written informed consent was obtained from all participants.
Circulating Growth Factor Measurements
Fasting venous blood samples were drawn on the morning of the study visit for all attendees. Blood specimens were centrifuged and then stored at −80° C without any freeze-thaw cycles until vascular growth factor assays were performed. Circulating serum concentrations of VEGF, its soluble receptor (sFlt, soluble fms-like tyrosine kinase), angiopoietin 2 (Ang2) and its soluble receptor (sTie2, soluble tyrosine kinase with immunoglobulin-like and EGF-like domains-2), HGF, IGF1, and its primary binding protein, IGF binding protein 3 (IGFBP3) were assayed using commercial assay kits (R&D Systems Inc.; Minneapolis MN). Assays were conducted as previously described with use of serum VEGF due to its high correlation with plasma VEGF and intra-assay coefficients of variation ranging between 2.1% for VEGF to 9.1% for IGFBP3.21-23
Covariate Definitions
Covariates were assessed at the Heart Study examination. Continuous covariates included age, height, weight, body mass index, heart rate, alcohol use (ounces consumed per month), estimated glomerular filtration rate, fasting plasma total cholesterol and high density lipoprotein cholesterol (HDL-C) modeled as the ratio of total to HDL-C, triglycerides (TG), and glucose. Height, weight, and heart rate were specifically included in this analysis due to their effects on tonometric variables.7 Binary covariates included sex, self-report of smoking within the past year, treatment for hypertension, diabetes, treatment for hyperlipidemia, and prevalent CVD. We did not adjust the models for blood pressure because a goal of the analyses was to examine growth factor relations with individual components of the blood pressure waveform.
Noninvasive Hemodynamic Data Acquisition and Analysis by Applanation Tonometry
As previously described, participants underwent noninvasive hemodynamic assessment after 5 minutes of rest in a supine position.24 Four electrocardiographic leads were placed for timing of the cardiac cycle, and auscultatory blood pressures were obtained on the right arm of participants. Arterial tonometry was performed on right-sided carotid, brachial, radial and femoral arteries using a customized tonometer (Cardiovascular Engineering, Inc., Norwood, MA). Next, 2-dimensional echocardiographic images of the left ventricular outflow tract were obtained from a parasternal long axis view followed by pulsed Doppler of the left ventricular outflow tract from an apical 5-chamber view. Body surface measurements from suprasternal notch to pulse recording sites were obtained by using a fiberglass tape measure for carotid, brachial and radial sites and a caliper for the femoral site. Arterial waveform signals were digitized (1000 Hz) and transferred to the core laboratory (Cardiovascular Engineering Inc, Norwood MA) where they were analyzed blinded to all clinical and risk factor data.
Analysis of arterial waveforms began from the ECG-derived R wave as the referent timing point. Carotid pressure was used as a surrogate for central arterial pressure and was calibrated to diastolic and integrated mean brachial artery waveforms. The peak and trough of signal averaged brachial waveforms were calibrated to the brachial cuff measured systolic and diastolic blood pressures. The mean arterial pressure (MAP) was calculated by integration of the calibrated brachial pressure waveform. Forward (FPW) and reflected pressure waves were separated and the amplitude (peak minus trough) of each wave was assessed.25 The global reflection coefficient (RC) was computed as reflected wave amplitude divided by FPW and expressed as a percentage. Carotid-femoral pulse wave velocity (CFPWV) was calculated as described previously.26 We chose four specific vascular measures to capture different aspects of hemodynamic load: pressure pulsatility and matching between flow and diameter (FPW), aortic wall stiffness (CFPWV), global wave reflection (RC), and steady-flow load (MAP). These 4 variables collectively account for nearly all of the variance in pulsatile and steady state hemodynamic conditions.27 We chose to evaluate RC rather than a pressure-only measure of wave reflection such as augmentation index because augmentation is heavily dependent on a number of important confounders such as heart rate and left ventricular function.28 By focusing on FPW and RC, we are able to relate the primary elements of the backward wave to specific growth factors. The high reproducibility of hemodynamic measurement, interobserver consistency, and associations between hemodynamic parameters and CVD risk factors in this cohort have been previously described.7, 24
Statistical Analysis
All analyses were sex-pooled. The primary predictor variables of interest were the circulating vascular growth factor concentrations, which were natural logarithmically transformed to normalize their right-skewed distributions. Clinical covariates were modeled as continuous or binary variables as detailed above. The primary dependent variables of interest were MAP, FPW, RC, and CFPWV, which were considered in separate analyses. CFPWV was inverted (1000/CFPWV) to normalize right skewed variance. In order to preserve the directionality of growth factor associations with arterial stiffness, all effects for associations with inverse CFPWV are expressed per −1 SD increment. We evaluated the age- and sex-adjusted Pearson correlation coefficients relating growth factor levels to each other and to clinical covariates including standard CVD risk factors. Next, two multivariable linear regression models were constructed to determine associations of the 7 vascular growth factors with each of the 4 hemodynamic variables. First, we constructed separate age- and sex-adjusted linear regression models to assess the associations of the vascular growth factors with each tonometry variable. Then, we constructed a second set of models for each tonometry variable starting with the set of clinical covariates known to be associated with tonometry variables (including age, sex, height, weight, BMI, heart rate, smoking, hypertension treatment, diabetes, lipid therapy, total:HDL-C ratio, and triglycerides) followed by introduction of individual growth factors in a forward stepwise procedure using a significance criterion of < 0.1 as an entry criterion. These final models were estimated using generalized estimating equations with a compound symmetry correlation matrix to account for familial correlations. A p value of <0.05 was used to indicate statistical significance and all analyses were performed using SAS 9.2 (Cary, North Carolina). All authors have reviewed and take responsibility of the manuscript as written.
RESULTS
Baseline Characteristics
The clinical, biochemical, and hemodynamic characteristics of the study sample are summarized in Table 1. Mean circulating concentrations of HGF, VEGF, sFlt, Ang2, sTie2, IGF1, IGFBP3 were similar in men and women. For the distributions of measured values of the vascular growth factors please see http://hyper.ahajournals.org .
Table 1.
Characteristics of Study Sample
Clinical Variables |
Women (N=1816) |
Men (N=1680) |
---|---|---|
Age, years | 40±9 | 40±9 |
Height, cm | 164±6 | 178±7 |
Weight, kg | 68.5±14.7 | 87.4±14.9 |
BMI, kg/m2 | 25.4±5.2 | 27.6±4.4 |
Heart Rate, beats per minute | 63±10 | 60±10 |
Systolic BP, mm Hg | 113±14 | 120±12 |
Diastolic BP, mm Hg | 72±9 | 78±9 |
Hypertension diagnosis, % | 11 | 21 |
Hypertension Treatment, % | 6 | 10 |
Smoking, % | 16 | 18 |
Diabetes , % | 2 | 3 |
Alcohol intake, oz/month | 6±8 | 14±18 |
Lipid lowering therapy, % | 3 | 10 |
History of CVD, % | 1 | 2 |
Biochemical measures | ||
Total Cholesterol, mg/dL | 185±33 | 192±37 |
HDL-C, mg/dL | 62±16 | 47±12 |
Triglycerides, mg/dL | 94±60 | 133±104 |
Glucose, mg/dL | 92±18 | 98±17 |
eGFR, mL/min/1.73m2 | 99±19 | 99±17 |
Log-Circulating Growth Factors* |
||
Ang2 | 0.7±0.4 | 0.6±0.4 |
sTie2 | 2.7±0.3 | 2.7±0.3 |
HGF | 6.7±0.3 | 6.7±0.3 |
VEGF | 5.6±0.8 | 5.5±0.8 |
sFlt | 4.9±0.6 | 5.0±0.5 |
IGF1 | 4.8±0.4 | 4.8±0.3 |
IGFBP3 | 8.0±0.4 | 7.9±0.5 |
Tonometry measures | ||
Mean Arterial Pressure, mm Hg | 87±11 | 92±10 |
Forward Pressure Wave, mm Hg | 42.2±9.5 | 46.6±10.6 |
Reflection Coefficient, % | 34.75±6.42 | 33.30±6.1 |
Carotid Femoral Pulse Wave Velocity, m/s |
6.61±1.19 | 7.38±1.40 |
Inverse Carotid Femoral Pulse Wave Velocity, ms/m |
155.6+25.1 | 140.0±24.2 |
Values are Mean±Standard Deviation, unless indicated
Natural logarithmically-transformed nanograms/milliliter. See online supplementary Figure 1 for distributions of growth factors in original units.
Abbreviations: BMI, body mass index; bpm, beats per minute; BP, blood pressure; Oz, ounces; m/s, meter per second; GFR, glomerular filtration rate; CVD, cardiovascular disease; HDL-C, high density lipoprotein cholesterol; HGF, hepatocyte growth factor; sFlt, soluble fms-like tyrosine kinase; VEGF, vascular endothelial growth factor; Ang2, angiopoietin 2; sTie2, soluble tyrosine kinase with immunoglobulin-like and EGF-like domains-2; IGF1, insulin-like growth factor-1; IGFBP3, insulin like growth factor binding protein 3.
Covariate-adjusted Associations of Growth Factors with Tonometric Vascular Measurements
Modest relations were noted between the levels of the growth factors themselves, and between the growth factors and the clinical covariates (please see http://hyper.ahajournals.org). The results of age- and sex-adjusted and multivariable-adjusted regression models for each tonometry variable are displayed in Table 2. Serum HGF, VEGF, and IGFBP3 were positively whereas serum Ang2 and IGF1 were negatively related to MAP in age- and sex-adjusted models. Upon additional adjustment for standard CVD risk factors, VEGF, serum IGFBP3 and IGF1 remained related to MAP (p≤0.004 for all).
Table 2.
Relations of Vascular Growth factors to tonometry indices
Age- Sex-adjusted model | Multivariable model* | ||||
---|---|---|---|---|---|
Tonometry Variable |
Growth Factor |
Regression Coefficient† (95%CI) |
P value | Regression Coefficient† (95%CI) |
P value |
Mean Arterial Pressure (mm Hg) |
Ang2 | −0.36 (−0.69,−0.02) |
0.04 | ||
HGF | 1.09 (0.75,1.43) |
<0.0001 | |||
VEGF | 0.72 (0.38,1.05) |
<0.0001 | 0.47 (0.16,0.78) |
0.003 | |
IGF1 | −0.79 (−1.15,−0.42) |
<0.0001 | −0.53 (−0.89,−0.17) |
0.004 | |
IGFBP3 | 0.49 (0.15,0.83) |
0.004 | 0.47 (0.16,0.78) |
0.003 | |
Forward Pressure Wave (mm Hg) |
sTie2 | 0.44 (0.11,0.78) |
0.01 | 0.35 (0.01,0.69) |
0.046 |
HGF | 0.66 (0.31,1.01) |
0.0002 | |||
Reflection Coefficient (%) |
Ang2 | 0.37 (0.16,0.57) |
0.0004 | ||
sTie2 | −0.20 (−0.40, 0.002) |
0.048 | |||
HGF | −0.53 (−0.74,−0.32) |
<0.0001 | |||
IGF1 | −0.24 (−0.46,−0.01) |
0.04 | −0.31 (−0.51,−0.11) |
0.003 | |
Inverse Carotid femoral pulse wave velocity‡ (ms/m) |
Ang2 | −0.92 (−0.25,−1.59) |
0.007 | ||
sTie2 | 1.37 (2.04,0.70) |
<0.0001 | |||
HGF | 1.94 (2.63,1.25) |
<0.0001 | |||
VEGF | 1.09 (1.76,0.42) |
0.002 | 0.66 (1.28,0.04) |
0.04 | |
IGF1 | −1.83 (−1.09,−2.57) |
<0.0001 | −1.18 (−0.46,−1.90) |
0.001 |
All growth factors are natural logarithmically-transformed and standardized.
Multivariable models adjusted for age, sex, height, weight, body mass index, heart rate, Total:HDL-C, Triglycerides, Diabetes mellitus, hypertension treatment, lipid therapy, and smoking.
Standardized regression coefficients indicate change in hemodynamic variable per standard deviation change in log transformed-biomarker. For example, every standard deviation increase in VEGF (1 standard deviation=0.8 ln units = 2.22 ng/mL) is associated with a 0.47 mm Hg increase in MAP.
Effect sizes for growth factors in models of inverse CFPWV are −1 SD, which restores the directionality between growth factors and aortic stiffness, e.g., a positive beta indicates that higher growth factor levels are associated with higher CFPWV and greater aortic stiffness.
Abbreviations: HGF, hepatocyte growth factor; sFlt, soluble fms-like tyrosine kinase; VEGF, vascular endothelial growth factor; Ang2, angiopoietin 2; sTie2, soluble tyrosine kinase with immunoglobulin-like and EGF-like domains-2; IGF1, insulin-like growth factor-1; IGFBP3, insulin-like growth factor binding protein 3.
Serum HGF and sTie2 were positively related to FPW in age- and sex-adjusted models, while sTie2 remained associated upon multivariable adjustment. Serum Ang2 was positively but serum HGF and IGF1 negatively related to RC in age-sex-adjusted models; only serum IGF1 remained related upon adjustment for other risk factors. Finally, circulating sTie2, HGF and VEGF were positively related while IGF1 and Ang2 were negatively related to CFPWV in age- and sex-adjusted models; VEGF and IGF1 remained associated upon multivariable adjustment. Since MAP and CFPWV are related and IGF1 and VEGF were jointly associated with both CFPWV and MAP, we further adjusted the CFPWV model for MAP. With this additional adjustment, the association of IGF1 with CFPWV persisted (0.84; 95%CI 0.20 to 1.49, p=0.01) while the association with VEGF was no longer statistically significant (p=0.34).
DISCUSSION
Vascular growth factors play a fundamental role in arterial remodeling and microvascular homeostasis, which are key contributors to the pathophysiology of hypertension and target organ damage. In the present investigation, we related a panel of seven circulating vascular growth factors and their soluble receptors to four measures of mean and pulsatile hemodynamic load. Our principal findings are three-fold. First, circulating growth factors are associated with hemodynamic measures, with growth factors and their soluble receptors or binding proteins generally having opposite directionality of associations. Associations were observed across a concise but comprehensive panel of hemodynamic measures that capture different aspects of vascular function consistent with a broad influence of vascular growth factors. Second, adjustment for standard risk factors attenuated some of the associations observed. It is conceivable that such multivariable modeling may constitute overadjustment because standard risk factors may influence vascular growth factor levels (please see http://hyper.ahajournals.org), which in turn modulate vascular function.21-23, 29-31 Third, IGF1 and VEGF emerged as the two growth factors that were related to several measures of vascular function. Specifically, we observed a pattern of negative relations between circulating IGF1 and CFPWV, MAP, and RC, but a positive association of VEGF concentrations with CFPWV and MAP. After adjusting for MAP, the relation between VEGF and CFPWV was no longer significant whereas the inverse association between IGF1 and CFPWV persisted, suggesting a potentially favorable relation between IGF1 and aortic stiffness that is separate from the association with steady-flow load and MAP. Additional findings of interest include the positive relations observed between IGFBP3 with MAP, and sTie2 with FPW. Overall, our observations are consistent with the hypothesis that vascular growth factors modulate mean and pulsatile hemodynamic load, consistent with their panarterial effects.
Growth Factors in Vascular Remodeling
Central arterial stiffening is characterized histologically by intimal-medial thickening, with vascular smooth muscle cell (VSMC) proliferation and disorganization, and extracellular matrix remodeling, with an increased collagen I/III content and reduced elastin content.32-37 In parallel, peripheral arterial resistance is partly driven by arteriolar myogenic tone and a combination of microvascular remodeling and proliferation of VSMC.33-38
The negative association of IGF1 levels with MAP, CFPWV and RC are consistent with the known biological effects of this peptide hormone. IGF1 has vasodilatory effects via a nitric oxide-dependent mechanism involving endothelial cells and VSMC.9 IGF1 is anti-apoptotic and maintains endothelial health. 11 Our findings are also consistent with the graded negative relations between circulating IGF1 levels and hypertension in cohort studies.39 Additionally, genetic variation in an IGF1 promoter region causing low circulating IGF1 concentrations has been associated with greater carotid intimal thickening and higher pulse wave velocity in hypertensive participants.13 Similarly, growth hormone deficiency states with consequent low IGF1 levels are associated with greater vascular stiffness.40 In a cohort of older adults, IGF1 and IGFBP3 were negatively related to ankle brachial index.41 Likewise higher IGFBP3 levels have been associated with greater carotid intimal-medial thickening in hypertensive patients.42
The positive association of VEGF levels with MAP warrants comment. VEGF modulates vasculogenesis throughout the life course, both in physiologic and pathologic circumstances. Increased circulating VEGF concentrations have been associated with prevalent CVD and CVD risk factors including hypertension.22, 30, 43, 44 Another study reported that VEGF levels were negatively related to carotid distensibility, an index of arterial stiffness, in diabetic and nondiabetic patients.12 It has been postulated that VEGF levels may increase with increasing CVD risk factor burden to compensate for the effects of increased peripheral resistance, microvascular rarefaction and increased vascular stiffness.31, 45, 46 Conversely, VEGF inhibiting therapy is reported to have hypertension as a side effect.47 In vitro work has shown that VEGF stimulates vascular growth to counteract stiffening,48 inhibits VSMC apoptosis to maintain cell number,8 and induces VSMC proliferation.49 Other investigators have demonstrated VEGF production in pulmonary microvascular endothelial cells is related to the level of flow pulsatility, which is higher in the presence of stiffer large arteries.50 Relations between large artery stiffness and microvascular function on the one hand51 and between flow pulsatility and microvascular production of VEGF on the other50may contribute to the present observation that VEGF and CFPWV are related. However, those relations appear to be mediated by MAP, which is a measure of microvascular structure and function. VEGF is known to increase tissue vascularization, which should reduce peripheral resistance and MAP. Thus, the observation that higher VEGF levels are associated with higher MAP suggests a compensatory response rather than a primary effect of VEGF on the vasculature.
The contrast between IGF1 and VEGF, as well as other growth factors, are consistent with their respective roles in biology and the mechanisms of their dynamic regulation. IGF1 concentration is largely a function of the pituitary output of GH, and biologically regulates organ and organism size through growth effects on tissues. 52 The vascular effects of IGF1 are interpretable as a necessary process to facilitate nutrient flow to allow for tissue growth. Indirect effects on vasculature may stem from improved glucose homeostasis. 53 In contrast, VEGF is produced by tissues in response to reduced oxygen tension to promote increased vascular flow and reduce tissue starvation. 54
The positive associations of HGF with MAP, FPW, and CFPWV and the negative association with RC in age- and sex-adjusted models are consistent with the diverse effects of this peptide on vasculature.14, 55 The attenuation of these associations upon multivariable adjustment may reflect the moderate association of circulating HGF levels with CVD risk factors.22 The negative association of Ang2 with MAP and CFPWV, but the positive association of its soluble receptor, sTie2 with CFPWV in age-, sex-adjusted models are consistent with the complex role of the Ang2-Tie2 system on vasculature, in part dependent on the actions of Angiopoietin 1 and VEGF.15-17, 45, 46 Ang2 levels are known to be regulated by the competition of sTie2 versus cell bound Tie2. Ang 2 actions are facilitated by its competition with angiopoietin 1 and synergy with VEGF. Overall Ang2 promotes neovascular sprouting, enhances neovascular stability, and promotes adaptive remodeling.15, 56 Consistent with their antagonism, Ang2 and sTie2 had generally opposite associations with hemodynamic variables. Ang2’s opposite associations with FPW/CFPWV versus RC is consistent with the observation that as aortic stiffness increases impedance mismatch between central and peripheral arteries decreases, thereby decreasing RC.24 Also the inverse relation between MAP and Ang2 may be consistent with a positive role in neovascular number and stability.15 In parallel, mismatch of aortic material properties to loading conditions may be explained by inadequate Ang2 signaling given the direct relation between Ang2’s negative regulator sTie2 and FPW. However after multivariable adjustment, these cross-sectional associations are only marginally significant and will require further investigation.
Limitations
This study was conducted in a predominantly Caucasian, middle-aged population based cohort. These findings may not be generalizable to other ethnicities. Causation cannot be inferred from the modest associations seen in this cross-sectional study. We did not correct for multiple statistical testing and report all nominally significant relations. Consequently, several of the associations reported here should be viewed as hypothesis generating and warrant replication in other samples.
Perspectives
In this cross-sectional observational cohort of middle-aged adults, vascular growth factors were found to be associated with components of pulsatile and steady state hemodynamic load. Consistent with previous reports, circulating levels of IGF1 were generally inversely related to mean arterial pressure and pulse wave velocity whereas circulating VEGF concentrations were directly associated with these hemodynamic parameters. These data suggest that similar vasoactive pathways are at work in both large and small arteries. Future work may be able to discern the temporal relation of hemodynamic variables to growth factor alterations as well as illuminate causality.
Supplementary Material
Acknowledgments
Sources of Funding. This work was supported through National Institutes of Health/ National Heart, Lung, and Blood Institute contract NO1-HC 25195, R01-HL-70100, RO1-HL-077447 (RSV) and T32 HL-007572 (JPZ)
Footnotes
Disclosures. Dr Mitchell is owner of Cardiovascular Engineering Inc, a company that designs and manufactures devices that measure vascular stiffness. The company uses these devices in clinical trials that evaluate the effects of diseases and interventions on vascular stiffness. The remaining authors report no conflicts.
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