Accelerated Early Vascular Aging Among Adolescents With Obesity and/or Type 2 Diabetes Mellitus
Abstract
Background
The normal rate of subclinical vascular aging from adolescence to young adulthood has not been well‐characterized. We conducted a 5‐year longitudinal study among adolescents with normal‐weight, obesity, and/or type 2 diabetes mellitus to examine trajectories of early vascular aging.
Methods and Results
Adolescents (mean [SD] age 17.6 [3.5]; 35.3% male) had either normal weight (n=141), obesity (n=156), or type 2 diabetes mellitus (n=151) at baseline. Primary metrics used for early vascular aging included measures of vascular structure (carotid intima‐media thickness [cIMT]; common, internal, and bulb) and arterial stiffness (carotid‐femoral pulse wave velocity, and augmentation index). Longitudinal (5‐year) outcomes were examined using generalized estimating equations adjusting for baseline value, sex, race, and age. Compared with participants with normal weight, those with obesity had greater positive change in common cIMT (0.05 mm [0.03, 0.06]; P<0.001), bulb cIMT (0.02 mm [0.00, 0.05]; P=0.033), internal cIMT (0.03 mm [0.01, 0.05]; P<0.001), and pulse wave velocity carotid‐femoral (0.38 m/sec [0.14, 0.61]; P=0.001), and those with type 2 diabetes mellitus had greater positive change in common cIMT (0.05 mm [0.04, 0.07]; P<0.001), bulb cIMT (0.06 mm [0.04, 0.09]; P<0.001), internal cIMT (0.04 mm [0.02, 0.07]; P<0.001), augmentation index (4.67% [2.20, 7.13]; P<0.001), and pulse wave velocity carotid‐femoral (0.74 m/sec [0.46, 1.02]; P<0.001). Higher baseline systolic blood pressure was associated with greater positive change in common cIMT (0.007 mm [0.003, 0.011]; P<0.001), bulb cIMT (0.009 mm [0.002, 0.016]; P=0.01), internal cIMT (0.008 mm [0.003, 0.013]; P=0.001), and pulse wave velocity carotid‐femoral (0.066 m/sec [0.002, 0.130]; P=0.042).
Conclusions
These longitudinal data support the hypothesis that the presence of obesity, type 2 diabetes mellitus, and elevated baseline systolic blood pressure in early life accelerates the progression of risk factors key in the development of early vascular aging.
Clinical Perspective
What Is New?
•
This longitudinal study examines early vascular aging among adolescents with obesity and type 2 diabetes mellitus.
•
The presence of obesity and type 2 diabetes mellitus in adolescence accelerates the early vascular aging process associated with several key risk factors.
•
High systolic blood pressure is also a key risk factor for accelerated early vascular aging.
What Are the Clinical Implications?
•
Obesity and type 2 diabetes mellitus predispose adolescents to greater vascular disease risk and should warrant continued evaluation and treatment to reduce the associated risk.
Atherosclerosis is a progressive process beginning early in life and continuing through senescence. Longitudinal studies in adults clearly demonstrate that many cardiovascular risk factors (obesity, dyslipidemia, diabetes mellitus, hypertension) are associated with accelerated cardiovascular or early vascular aging.1, 2, 3, 4 The latter contributes to adverse cardiovascular outcomes and higher likelihood of early cardiovascular morbidity and mortality. While the association between risk factors and greater subclinical atherosclerosis is well‐defined in adults, data in youth are limited to cross‐sectional studies.
Seminal autopsy studies in children and adolescents demonstrated an association between individual cardiovascular risk factors (age, sex, race) and visible atherosclerotic development (eg, fatty streaks, lesions).5, 6, 7, 8 Non‐invasive imaging studies in youth have shown similar cross‐sectional associations between numerous risk factors (obesity, male sex, hypertension, insulin resistance) and higher carotid intima‐media thickness (cIMT) and arterial stiffness.9, 10, 11, 12, 13, 14, 15, 16 Moreover, autopsy and imaging studies showed clustering of key risk factors to be associated with more extensive visual and subclinical atherosclerosis.17, 18 However, the normal trajectory of subclinical vascular aging from adolescence into young adulthood has not been well‐documented because of relatively small sample sizes in longitudinal studies. Additionally, the key risk factors associated with accelerated early vascular aging are unknown, and need to be identified so that optimized prevention and treatment efforts can be initiated.
To address these gaps, we conducted a 5‐year longitudinal study among adolescents with normal weight, obesity, and/or type 2 diabetes mellitus to examine trajectories of early vascular aging and identify factors associated with accelerated subclinical atherosclerosis. We examined multiple non‐invasive measures of subclinical atherosclerosis and traditional cardiovascular risk factors (eg, age, sex, lipids, and blood pressure) at each time point. A priori, we hypothesized that accelerated early vascular aging, defined as a steeper slope of rise in carotid intima‐media thickness and arterial stiffness over time, would occur in youth with obesity and/or type 2 diabetes mellitus as compared with normal weight. Additionally, we hypothesized that male sex and higher systolic blood pressure (SBP) would be associated with accelerated early vascular aging.
Methods
The data that support the findings of this study are available from the corresponding author who will be responsible for maintaining availability upon reasonable request.
Study Design and Population
Adolescents with normal weight, obesity, and type 2 diabetes mellitus were identified through an existing cohort from a cross‐sectional study. The original cohort (n=775) was formed first by recruiting youth with type 2 diabetes mellitus (n=244). Then controls with normal weight (n=275) and obesity (n=256) were each recruited to be frequency matched to the set of type 2 diabetes mellitus youth on characteristics of sex, race, and age (within 3 years of median). All individuals in this original cross‐sectional cohort were invited to participate in a 5‐year follow‐up visit, regardless of their weight or disease status. These 3 baseline groups (type 2 diabetes mellitus, normal weight, obesity) were used to define adolescents at both time points. All participants with complete data at baseline and follow‐up were included for this analysis (n=448 of the original 775). Written informed consent was obtained from individuals aged ≥18 years or the parent or guardian for individuals aged <18 years. Written assent was also obtained for individuals aged <18 years according to the guidelines established by the Institutional Review Board at Cincinnati Children's Hospital Medical Center.
Participants who were examined as part of an ongoing study of the cardiac and vascular effects of obesity and type 2 diabetes mellitus were eligible. For the baseline examination, youth with type 2 diabetes mellitus were recruited who were islet cell antibody negative (glutamic acid decarboxylase, ICA 512, insulin autoantibodies), had no evidence of other types of diabetes mellitus, and did not require insulin in the basal state to prevent diabetic ketoacidosis. The BMI percentiles were obtained from the Centers for Disease Control and Prevention (Atlanta, GA) growth charts. All participants with obesity underwent a 2‐hour oral glucose tolerance test to rule out subclinical type 2 diabetes mellitus according to American Diabetes Association guidelines.19 Pregnant females and participants with preexisting cardiac diseases were excluded.
Anthropometrics, Diabetes Mellitus Status, Lipids, and Blood Pressure
At each assessment, height was measured on a wall‐mounted stadiometer (Holtain Ltd, Great Britain) and weight on a digital scale (SECA Inc, Hanover MD) and BMI (kg/m2) was calculated. Normal weight was defined as BMI‐percentile >5th to <85th and obesity as ≥95th. Diabetes mellitus was defined by using self‐reported diagnoses, medical record review, medication use for the treatment of diabetes mellitus, hemoglobin A1c (HbA1c) ≥6.5%, impaired fasting glucose (≥126 mg/dL), or 2‐hour oral glucose ≥200 mg/dL. Low‐density lipoprotein cholesterol (LDL‐C), high‐density lipoprotein cholesterol (HDL‐C), triglycerides, CRP (C‐reactive protein) were measured from fasting blood samples. Blood samples were obtained after a minimum 10‐hour fast. Plasma glucose was measured with a Hitachi glucose analyzer with intra‐assay and inter‐assay coefficients of variation of 1.2% and 1.6%, respectively.20 Plasma insulin was measured by radioimmunoassay with an anti‐insulin serum raised in guinea pigs, 125I‐labeled insulin (Linco, St Louis, MO), and a double antibody method to separate bound from free tracer. This assay has a sensitivity of 2 pmol and has intra‐assay and inter‐assay coefficients of variation of 5% and 8%, respectively.6 Lipid profile assays were performed in a laboratory standardized by the National Heart, Lung, and Blood Institute and Centers for Disease Control and Prevention. The low‐density lipoprotein cholesterol (LDL) concentration was calculated using the Friedewald equation. High‐sensitivity CRP was measured using an enzyme‐linked immunosorbent assay. Glycated hemoglobin A1c (HbA1c) was measured in red blood cells by high‐performance liquid chromatography methods. Average systolic blood pressure (SBP) was taken from ≥2 separate measurements obtained using an automated cuff system.
Measurement of Vascular Structure and Arterial Stiffness
Carotid intima‐media thickness (cIMT) was measured bilaterally in the common, internal, and bulb of the carotid artery. High‐resolution B‐mode carotid ultrasound was obtained with a linear array vascular 5.0 to 11.0 MHz probe with subjects in a supine position. Images were obtained at pre‐specified angles using a Meyer's arc.21 A continuous scan technique was used to find the maximal cIMT and the angle at which it is obtained is noted. Right and left carotid arteries in the distal 1.0 cm of the common carotid proximal to the bifurcation, the bifurcation itself (‘bulb’), and the proximal 1.0 cm of the internal carotid artery are imaged digitally for off‐line analyses. Depth and gain settings are adjusted to maximize resolution of the far wall lumen‐intima and media‐adventitia borders. M‐Mode measurements of the common carotid will also be performed 1 cm proximal to beginning of the carotid bulb.22 The maximum far wall cIMT of the 3 carotid artery segments are measured off‐line on both sides using an automatic edge detection software that reduces variability in measurement with the mean R and L used in analyses.23, 24 The maximal and minimal lumen diameters from M‐mode is measured for calculations of carotid stiffness including incremental elastic modulus (cIEM),25 Peterson elastic modulus,25, 26 beta stiffness index.25 Analyses of blind duplicate recordings from the vascular core laboratory demonstrate excellent reproducibility with coefficient of variability for automatic edge detection for all carotid segments of <4% compared with the older reading techniques such as manual trace (cardiovascular 5%) and point‐to‐point (cardiovascular 6%–7%) measures (Urbina, EM 2014).
Arterial stiffness was measured by carotid‐femoral pulse wave velocity (PWV) and Aix (SphygmoCor system, Sydney, Australia). Radial artery waveforms are recorded with a high‐fidelity micromanometer and calibrated with non‐invasive Mean Arterial Pressure (MAP) and diastolic blood pressure.27, 28, 29 A generalized transfer function validated from catheterization data is used to calculate central (ascending aortic) pressure waveforms28, 30, 31, 32, 33 from which central aortic pressure and Aix is calculated. Aix, is the pressure difference between the primary (main outgoing wave) and the reflected wave of the central arterial waveform, expressed as a percentage of the central pulse pressure.34, 35 The whole procedure is repeated 3 times per subject and the average value is used for the analysis. Reproducibility studies in our laboratory demonstrated intraclass correlation coefficients between 0.7 and 0.9.13
Statistical Analysis
Descriptive data at baseline and 5‐year follow‐up are presented as mean (SD) for continuous variables and n (%) for categorical variables. Measures of vascular structure and arterial stiffness are further visualized graphically at both timepoints with means and 95% CIs super‐imposed on top of scatter plots. Mean change in longitudinal outcomes were examined using multiple linear regression with robust SEs for CIs and P values adjusting for group (normal, obesity, and type 2 diabetes mellitus), baseline value, sex, race, and age. Additional models included adjustment for several additional risk factors (LDL‐C, triglycerides/HDL ratio, heart rate, CRP, SBP, and diastolic blood pressure) to examine the association of these baseline risk factors with change in measures of vascular structure and arterial stiffness. Each model was fit first with weight status (normal, obese, and type 2 diabetes mellitus) as categorical variables treating normal weight as the reference group to obtain obesity (versus normal) and type 2 diabetes mellitus (versus normal) contrasts. Models were then fit with the obesity group as the reference to obtain the type 2 diabetes mellitus (versus obesity) contrasts. Significance was set at an alpha level of P<0.05. All analyses were conducted using R version 3.4.0.36
Results
Four hundred and forty‐eight adolescents (mean [SD] age 17.6 [3.5]; 35.3% male; 36.4% white) completed a baseline and 5‐year follow‐up visit (Table 1). Baseline (Table 1) and 5‐year follow‐up (Table 2) visit demographics, clinical characteristics, and measures of vascular structure and arterial stiffness are presented without adjustment. At baseline, adolescents with obesity and type 2 diabetes mellitus were more likely to be female, non‐white, have higher SBP, and diastolic blood pressure, and higher lipids (total cholesterol, triglycerides, and LDL‐C) compared with adolescents with normal weight. Figure 1 displays change in common cIMT (A), bulb cIMT (B), and internal cIMT (C) in each group (normal weight, obesity, type 2 diabetes mellitus) over the 5‐year time period. Figure 2 displays changes in Aix (A), cIEM (B), and PWV carotid‐femoral (C) in each group (normal weight, obesity, type 2 diabetes mellitus) over the 5‐year time period.
| Measure | Normal‐Weight (n=141) | Obesity (n=156) | Type 2 Diabetes Mellitus (n=151) |
|---|---|---|---|
| Sex, Male | 60 (42.6%) | 43 (27.6%) | 52 (34.4%) |
| Race, non‐white | 78 (55.3%) | 108 (69.2%) | 99 (65.6%) |
| Age, y | 17.2 (3.7) | 17.6 (3.3) | 18.0 (3.4) |
| Height, cm | 165 (12.1) | 166 (10.4) | 168 (10.5) |
| Weight, kg | 57.5 (12.4) | 103 (21.5) | 106 (28.1) |
| BMI, kg/m2 | 20.8 (2.5) | 37.2 (6.9) | 37.0 (8.8) |
| SBP, mm Hg | 108 (10.4) | 116 (11.6) | 122 (12.6) |
| DBP, mm Hg | 58.8 (14.5) | 65.0 (13.0) | 68.0 (13.2) |
| HR, bpm | 62.9 (10.0) | 65.5 (9.9) | 71.0 (11.9) |
| Total cholesterol, mg/dL | 160 (26.6) | 171 (33.0) | 179 (38.8) |
| Triglycerides, mg/dL | 70.2 (31.3) | 102 (69.4) | 136 (93.6) |
| LDL‐C, mg/dL | 90.5 (22.9) | 104 (29.2) | 107 (31.8) |
| HDL‐C, mg/dL | 55.2 (12.3) | 47.1 (9.9) | 44.9 (11.0) |
| Glucose, mg/dL | 89.1 (6.7) | 91.6 (7.7) | 148 (76.2) |
| Insulin, μIU/mL | 10.6 (5.0) | 22.8 (11.6) | 27.3 (16.1) |
| HbA1c, % | 5.4 (0.51) | 5.5 (0.4) | 7.9 (2.6) |
| CRP, mg/L | 1.1 (2.0) | 4.5 (4.4) | 4.6 (4.0) |
| Common cIMT, mm | 0.5 (0.08) | 0.48 (0.07) | 0.53 (0.09) |
| Bulb cIMT, mm | 0.48 (0.09) | 0.49 (0.1) | 0.53 (0.11) |
| Internal cIMT, mm | 0.38 (0.08) | 0.4 (0.09) | 0.43 (0.1) |
| Augmentation Index, % | −1.2 (11.0) | 2.6 (11.6) | 6.6 (13.0) |
| PWV carotid femoral, m/sec | 5.3 (0.66) | 6.2 (1.1) | 6.7 (1.3) |
Values presented are mean (SD) or frequency (%) where indicated. BMI indicates body mass index; cIEM, carotid incremental elastic modulus; cIMT, carotid intima‐media thickness; DBP, diastolic blood pressure; HbA1c, glycated hemoglobin; HDL‐C, high‐density lipoprotein cholesterol; HR, heart rate; LDL‐C, low‐density lipoprotein cholesterol; PWV, pulse wave velocity; and SBP, systolic blood pressure.
| Measure | Normal‐Weight (n=141) | Obesity (n=156) | Type 2 Diabetes Mellitus (n=151) |
|---|---|---|---|
| Sex, Male | 60 (42.6%) | 43 (27.6%) | 52 (34.4%) |
| Race, non‐white | 78 (55.3%) | 108 (69.2%) | 99 (65.6%) |
| Age, y | 22.5 (4.1) | 23.1 (3.6) | 23.4 (4.1) |
| Height, cm | 170 (9.6) | 169 (10.0) | 170 (10.2) |
| Weight, kg | 67.3 (11.3) | 113 (26.8) | 107 (29.6) |
| BMI, kg/m2 | 23.1 (3.1) | 39.5 (9.2) | 36.6 (9.0) |
| SBP, mm Hg | 111 (11.3) | 117 (13.0) | 121 (15.4) |
| DBP, mm Hg | 69.1 (8.5) | 74.8 (10.6) | 76.3 (10.7) |
| HR, bpm | 64.5 (10.0) | 67.2 (10.8) | 76.4 (13.4) |
| Total cholesterol, mg/dL | 166 (33.3) | 168 (30.7) | 178 (41.4) |
| Triglycerides, mg/dL | 81.2 (38.6) | 102 (63.9) | 132 (80.5) |
| LDL‐C, mg/dL | 97.0 (28.2) | 105 (27.4) | 111 (36.2) |
| HDL‐C, mg/dL | 58.8 (16.0) | 49.7 (12.7) | 46.6 (13.4) |
| Glucose, mg/dL | 90.7 (7.9) | 92.4 (11.4) | 205 (118.4) |
| Insulin, μIU/mL | 8.9 (10.0) | 17.8 (8.3) | 22.2 (14.9) |
| HbA1c, % | 4.9 (0.55) | 5.1 (0.53) | 8.7 (3.1) |
| CRP, mg/L | 1.5 (2.2) | 4.5 (4.7) | 5.2 (4.7) |
| Common cIMT, mm | 0.47 (0.07) | 0.51 (0.09) | 0.53 (0.1) |
| Bulb cIMT, mm | 0.5 (0.1) | 0.53 (0.12) | 0.59 (0.16) |
| Internal cIMT, mm | 0.39 (0.09) | 0.43 (0.1) | 0.46 (0.13) |
| Augmentation Index, % | −3.1 (10.9) | −0.5 (12.1) | 17 (11.3%) |
| PWV carotid femoral, m/sec | 5.5 (0.81) | 6.4 (1.2) | 7.1 (1.4) |
Values presented are mean (SD) or frequency (%) where indicated. BMI indicates body mass index; cIEM, carotid incremental elastic modulus; cIMT, carotid intima‐media thickness; DBP, diastolic blood pressure; HbA1c, glycated hemoglobin; HDL‐C, high‐density lipoprotein cholesterol; HR, heart rate; LDL‐C, low‐density lipoprotein cholesterol; PWV, pulse wave velocity; and SBP, systolic blood pressure.
Figure 1. Change in common (A), bulb (B), and internal (C) carotid intima‐media thickness among adolescents with normal weight, obesity, and type 2 diabetes mellitus over 5 years.
cIMT indicates carotid intima‐media thickness.
Figure 2. Change in augmentation index (A), carotid stiffness including incremental elastic modulus (B), and carotid‐femoral pulse wave velocity (C) among adolescents with normal weight, obesity, and type 2 diabetes mellitus over 5 years.
Aix indicates augmentation index; cIEM, carotid stiffness including incremental elastic modulus; and PWV, pulse wave velocity.
Male sex was associated with greater positive change in bulb cIMT (0.05 mm [0.02, 0.08]; P<0.001) and cIEM (128.94 mm Hg [49.98, 207.91]; P=0.001) and reduced Aix (−3.40% [−5.42, −1.39]; P<0.001) compared with females (Table 3). Non‐white race was associated with greater positive change in bulb cIMT (0.03 mm [0.01, 0.05]; P=0.013) compared with whites. Age was associated with greater positive change in common cIMT (0.02 mm [0.00, 0.03]; P=0.007), bulb cIMT (0.04 mm [0.02, 0.06]; P<0.001), internal cIMT (0.03 mm [0.02, 0.05]; P<0.001), and Aix (3.94% [2.54, 5.34]; P<0.001). Participants with obesity had greater positive change in each of the following measures than normal weight: common cIMT (0.05 mm [0.03, 0.06]; P<0.001), bulb cIMT (0.02 mm [0.00, 0.05]; P=0.033), internal cIMT (0.03 mm [0.01, 0.05]; P<0.001), and PWV carotid‐femoral (0.38 m/sec [0.14, 0.61]; P=0.001). Participants with type 2 diabetes mellitus had greater positive change in each of the following measures than normal weight : common cIMT (0.05 mm [0.04, 0.07]; P<0.001), bulb cIMT (0.06 mm [0.04, 0.09]; P<0.001), internal cIMT (0.04 mm [0.02, 0.07]; P<0.001), Aix (4.67% [2.20, 7.13]; P<0.001), and PWV carotid‐femoral (0.74 m/sec [0.46, 1.02]; P<0.001). Participants with type 2 diabetes mellitus had greater change in each of the following measures than obesity: bulb cIMT (0.04 mm [0.01, 0.07]; P=0.007), Aix (4.83% [2.29, 7.36]; P<0.001), and PWV carotid‐femoral (0.36 m/sec [0.09, 0.63]; P=0.009).
| Outcome | Covariate | Difference in 5‐Y Change (95% CI) | P Value |
|---|---|---|---|
| Common cIMT, mm | Male (vs female) | 0.01 (0.00, 0.03) | 0.094 |
| Non‐white (vs white) | 0.01 (0.00, 0.03) | 0.078 | |
| Age (5 y) | 0.02 (0.00, 0.03) | 0.007 | |
| Obesity (vs normal) | 0.05 (0.03, 0.06) | <0.001 | |
| Type 2 diabetes mellitus (vs normal) | 0.05 (0.04, 0.07) | <0.001 | |
| Type 2 diabetes mellitus (vs obesity) | 0.01 (−0.01, 0.03) | 0.384 | |
| Bulb cIMT, mm | Baseline cIMT (per mm) | −0.66 (−0.79, −0.54) | <0.001 |
| Male (vs female) | 0.05 (0.02, 0.08) | <0.001 | |
| Non‐white (vs white) | 0.03 (0.01, 0.05) | 0.013 | |
| Age (5 y) | 0.04 (0.02, 0.06) | <0.001 | |
| Obesity (vs normal) | 0.02 (0.00, 0.05) | 0.033 | |
| Type 2 diabetes mellitus (vs normal) | 0.06 (0.04, 0.09) | <0.001 | |
| Type 2 diabetes mellitus (vs obesity) | 0.04 (0.01, 0.07) | 0.007 | |
| Internal cIMT, mm | Baseline cIMT (per mm) | −0.55 (−0.65, −0.44) | <0.001 |
| Male (vs female) | 0.02 (0.00, 0.04) | 0.060 | |
| Non‐white (vs white) | 0.00 (−0.02, 0.02) | 0.911 | |
| Age (5 y) | 0.03 (0.02, 0.05) | <0.001 | |
| Obesity (vs normal) | 0.03 (0.01, 0.05) | <0.001 | |
| Type 2 diabetes mellitus (vs normal) | 0.04 (0.02, 0.07) | <0.001 | |
| Type 2 diabetes mellitus (vs obesity) | 0.01 (−0.01, 0.03) | 0.440 | |
| Aix, % | Baseline AiX | −0.52 (−0.61, −0.44) | <0.001 |
| Male (vs female) | −3.40 (−5.42, −1.39) | <0.001 | |
| Non‐white (vs white) | 1.81 (−0.21, 3.82) | 0.079 | |
| Age (5 y) | 3.94 (2.54, 5.34) | <0.001 | |
| Obesity (vs normal) | −0.16 (−2.50, 2.18) | 0.892 | |
| Type 2 diabetes mellitus (vs normal) | 4.67 (2.20, 7.13) | <0.001 | |
| Type 2 diabetes mellitus (vs obesity) | 4.83 (2.29, 7.36) | <0.001 | |
| PWV carotid‐femoral, m/sec | Baseline PWV | −0.42 (−0.59, −0.24) | <0.001 |
| Male (vs female) | −0.16 (−0.34, 0.02) | 0.087 | |
| Non‐white (vs white) | −0.01 (−0.22, 0.19) | 0.899 | |
| Age (5 y) | 0.17 (0.00, 0.34) | 0.057 | |
| Obesity (vs normal) | 0.38 (0.14, 0.61) | 0.001 | |
| Type 2 diabetes mellitus (vs normal) | 0.74 (0.46, 1.02) | <0.001 | |
| Type 2 diabetes mellitus (vs obesity) | 0.36 (0.09, 0.63) | 0.009 | |
| Baseline cIEM | −0.70 (−0.80, −0.60) | <0.001 | |
| Male (vs female) | 128.94 (49.98, 207.91) | 0.001 | |
| Non‐white (vs white) | −3.04 (−79.38, 73.31) | 0.938 | |
| cIEM, mm Hg | Age (5 y) | −7.51 (−66.40, 51.38) | 0.803 |
| Obesity (vs normal) | −23.49 (−117.40, 70.41) | 0.624 | |
| Type 2 diabetes mellitus (vs normal) | 3.61 (−88.70, 95.93) | 0.939 | |
| Type 2 diabetes mellitus (vs obesity) | 27.11 (−65.44, 119.65) | 0.566 |
Each model adjusted for weight status, baseline value of measure, sex, and age. BMI indicates body mass index; cIEM, carotid incremental elastic modulus; cIMT, carotid intima‐media thickness; and PWV, pulse wave velocity.
After adjusting for baseline factors (baseline value, race, age, obesity, type 2 diabetes mellitus, LDL‐C, triglycerides/HDL ratio, heart rate, CRP, SBP, and diastolic blood pressure) male sex remained significantly associated with greater positive change in bulb cIMT (0.038 mm [0.010, 0.065]; P=0.008), and cIEM (188.16 mm Hg [100.97, 275.35]; P<0.001) and reduced Aix (−2.76% [−5.27, −0.25; P=0.031) compared with females (Table 4). Non‐white race was associated with greater positive change in common cIMT (0.018 mm [0.001, 0.034]; P=0.036), bulb cIMT (0.034 mm [0.007, 0.060]; P=0.013), and Aix (2.756% [0.534, 4.978; P=0.015) compared with whites. Age was associated with greater change in bulb cIMT (0.028 mm [0.007, 0.049]; P=0.009), internal cIMT (0.029 mm [0.014, 0.044]; P<0.001), and Aix (3.680% [1.932, 5.429]; P<0.001). Adjusting for all risk factors in the model, participants with obesity had greater positive change in common cIMT (0.024 mm [0.004, 0.043]; P=0.016) than normal weight. Participants with type 2 diabetes mellitus had greater positive change in each of the following measures than normal weight: common cIMT (0.032 mm [0.009, 0.055]; P=0.006), Aix (3.756% [0.221, 7.292]; P=0.037), and PWV carotid‐femoral (0.43 m/sec [0.107, 0.754]; P=0.009). Participants with type 2 diabetes mellitus had greater change in Aix (5.649% [2.751, 8.547]; P<0.001) and bulb cIMT (0.034 mm [0.002, 0.066]; P=0.039) than those with obesity. Higher baseline SBP was associated with greater positive change in common cIMT (0.007 mm [0.003, 0.011]; P<0.001), bulb cIMT (0.009 mm [0.002, 0.016]; P=0.010), internal cIMT (0.008 mm [0.003, 0.013]; P=0.001), and PWV carotid‐femoral (0.066 m/sec [0.002, 0.130]; P=0.042). Higher baseline LDL‐C was associated with greater positive change in PWV carotid‐femoral (0.069 m/sec [0.016, 0.122]; P=0.011) but reduced cIEM (−27.76 mm Hg [−51.24, −4.29]; P=0.02). No statistically significant associations were observed for heart rate, HDL‐C, triglycerides, or CRP with measures of early vascular aging.
| Outcome | Covariate | Difference in 5‐Y Change (95% CI) | P Value |
|---|---|---|---|
| Common cIMT, mm | Baseline cIMT (per mm) | −0.522 (−0.630, −0.414) | <0.001 |
| Male (vs female) | 0.004 (−0.014, 0.023) | 0.643 | |
| Non‐white (vs white) | 0.018 (0.001, 0.034) | 0.036 | |
| Age (per 5 y) | 0.008 (−0.005, 0.021) | 0.218 | |
| Obese (vs normal) | 0.024 (0.004, 0.043) | 0.016 | |
| Type 2 diabetes mellitus (vs normal) | 0.032 (0.009, 0.055) | 0.006 | |
| Type 2 diabetes mellitus (vs obese) | 0.008 (−0.013, 0.029) | 0.440 | |
| LDL‐C (per 15 mg/dL) | 0.004 (0.000, 0.009) | 0.068 | |
| Triglycerides/HDL ratio (per 0.25) | 0.001 (−0.001, 0.003) | 0.422 | |
| HR (10 bpm) | −0.002 (−0.010, 0.006) | 0.589 | |
| CRP (per 1 mg/L) | 0.002 (0.000, 0.004) | 0.056 | |
| SBP (per 5 mm Hg) | 0.007 (0.003, 0.011) | <0.001 | |
| DBP (per 5 mm Hg) | −0.001 (−0.005, 0.002) | 0.420 | |
| m | Baseline cIMT (per mm) | −0.735 (−0.879, −0.592) | <0.001 |
| Male (vs female) | 0.038 (0.010, 0.065) | 0.008 | |
| Non‐white (vs white) | 0.034 (0.007, 0.060) | 0.013 | |
| Age (per 5 y) | 0.028 (0.007, 0.049) | 0.009 | |
| Obese (vs normal) | −0.014 (−0.043, 0.014) | 0.329 | |
| Type 2 diabetes mellitus (vs normal) | 0.020 (−0.014, 0.054) | 0.258 | |
| Type 2 diabetes mellitus (vs obese) | 0.034 (0.002, 0.066) | 0.039 | |
| LDL‐C (per 15 mg/dL) | 0.008 (0.000, 0.015) | 0.052 | |
| Triglycerides/HDL ratio (per 0.25) | 0.001 (−0.001, 0.004) | 0.317 | |
| HR (10 bpm) | 0.003 (−0.009, 0.015) | 0.638 | |
| CRP (per 1 mg/L) | 0.003 (−0.001, 0.006) | 0.111 | |
| SBP (per 5 mm Hg) | 0.009 (0.002, 0.016) | 0.010 | |
| DBP (per 5 mm Hg) | 0.002 (−0.004, 0.007) | 0.586 | |
| Internal cIMT, mm | Baseline cIMT (per mm) | −0.578 (−0.703, −0.452) | <0.001 |
| Male (vs female) | 0.008 (−0.013, 0.028) | 0.471 | |
| Non‐white (vs white) | −0.005 (−0.028, 0.017) | 0.655 | |
| Age (per 5 y) | 0.029 (0.014, 0.044) | <0.001 | |
| Obese (vs normal) | 0.018 (−0.007, 0.043) | 0.154 | |
| Type 2 diabetes mellitus (vs normal) | 0.020 (−0.004, 0.044) | 0.099 | |
| Type 2 diabetes mellitus (vs obese) | 0.002 (−0.023, 0.026) | 0.894 | |
| LDL‐C (per 15 mg/dL) | 0.003 (−0.005, 0.010) | 0.442 | |
| Triglycerides/HDL ratio (per 0.25) | 0.000 (−0.002, 0.001) | 0.759 | |
| HR (10 bpm) | 0.006 (−0.005, 0.018) | 0.269 | |
| CRP (per 1 mg/L) | 0.001 (−0.002, 0.003) | 0.637 | |
| SBP (per 5 mm Hg) | 0.008 (0.003, 0.013) | 0.001 | |
| DBP (per 5 mm Hg) | −0.002 (−0.006, 0.002) | 0.333 | |
| Aix, % | Baseline Aix | −0.564 (−0.659, −0.470) | <0.001 |
| Male (vs female) | −2.762 (−5.271, −0.253) | 0.031 | |
| Non‐white (vs white) | 2.756 (0.534, 4.978) | 0.015 | |
| Age (per 5 y) | 3.680 (1.932, 5.429) | <0.001 | |
| Obese (vs normal) | −1.892 (−4.816, 1.031) | 0.205 | |
| Type 2 diabetes mellitus (vs normal) | 3.756 (0.221, 7.292) | 0.037 | |
| Type 2 diabetes mellitus (vs obese) | 5.649 (2.751, 8.547) | <0.001 | |
| LDL‐C (per 15 mg/dL) | 0.156 (−0.467, 0.779) | 0.624 | |
| Triglycerides/HDL ratio (per 0.25) | 0.087 (−0.098, 0.273) | 0.356 | |
| HR (10 bpm) | 0.419 (−0.658, 1.496) | 0.446 | |
| CRP (per 1 mg/L) | 0.055 (−0.244, 0.354) | 0.716 | |
| SBP (per 5 mm Hg) | 0.307 (−0.256, 0.870) | 0.286 | |
| DBP (per 5 mm Hg) | 0.283 (−0.101, 0.666) | 0.148 | |
| PWV carotid‐femoral, m/sec | Baseline PWV | −0.378 (−0.526, −0.231) | <0.001 |
| Male (vs female) | −0.216 (−0.439, 0.006) | 0.056 | |
| Non‐white (vs white) | 0.015 (−0.212, 0.242) | 0.896 | |
| Age (per 5 y) | 0.031 (−0.165, 0.226) | 0.759 | |
| Obese (vs normal) | 0.150 (−0.102, 0.403) | 0.244 | |
| Type 2 diabetes mellitus (vs normal) | 0.430 (0.107, 0.754) | 0.009 | |
| Type 2 diabetes mellitus (vs obese) | 0.280 (−0.011, 0.571) | 0.059 | |
| LDL‐C (per 15 mg/dL) | 0.069 (0.016, 0.122) | 0.011 | |
| Triglycerides/HDL ratio (per 0.25) | 0.007 (−0.010, 0.025) | 0.407 | |
| HR (10 bpm) | −0.018 (−0.141, 0.106) | 0.776 | |
| CRP (per 1 mg/L) | 0.007 (−0.024, 0.037) | 0.666 | |
| SBP (per 5 mm Hg) | 0.066 (0.002, 0.130) | 0.042 | |
| DBP (per 5 mm Hg) | −0.013 (−0.055, 0.029) | 0.538 | |
| Baseline cIEM | −0.69 (−0.79, −0.59) | <0.001 | |
| Male (vs female) | 188.16 (100.97, 275.35) | <0.001 | |
| Non‐white (vs white) | −23.89 (−105.89, 58.12) | 0.568 | |
| cIEM, mm Hg | Age (per 5 y) | 19.35 (−52.21, 90.91) | 0.596 |
| Obese (vs normal) | 40.62 (−62.12, 143.35) | 0.438 | |
| Type 2 diabetes mellitus (vs normal) | 54.24 (−72.36, 180.84) | 0.401 | |
| Type 2 diabetes mellitus (vs obese) | 13.62 (−94.47, 121.72) | 0.805 | |
| LDL‐C (per 15 mg/dL) | −27.76 (−51.24, −4.29) | 0.020 | |
| Triglycerides/HDL ratio (per 0.25) | −4.13 (−9.67, 1.43) | 0.145 | |
| HR (10 bpm) | 41.46 (−0.001, 82.92) | 0.050 | |
| CRP (per 1 mg/L) | −2.70 (−14.62, 9.21) | 0.657 | |
| SBP (per 5 mm Hg) | −16.59 (−36.03, 2.84) | 0.094 | |
| DBP (per 5 mm Hg) | 8.08 (−6.96, 23.12) | 0.293 |
Each model adjusted for weight status, baseline value of measure, sex, age, low‐density lipoprotein cholesterol, triglycerides/high‐density lipoprotein cholesterol ratio, heart rate, CRP, systolic blood pressure, and diastolic blood pressure. BMI indicates body mass index; cIEM, carotid incremental elastic modulus; cIMT, carotid intima‐media thickness; CRP, C‐reactive protein; DBP, diastolic blood pressure; HbA1c, glycated hemoglobin; HDL‐C, high‐density lipoprotein cholesterol; HR, heart rate; LDL‐C, low‐density lipoprotein cholesterol; PWV, pulse wave velocity; and SBP, systolic blood pressure.
Discussion
These longitudinal data collected over a 5‐year time period support the hypothesis that adolescents and young adults with obesity and/or type 2 diabetes mellitus experience accelerated early vascular aging evidenced by changes in subclinical vascular markers compared with normal weight. Accelerated early vascular aging is more prominent among youth with type 2 diabetes mellitus versus obesity. Importantly, risk factors for accelerated early vascular aging include SBP, age, male sex, and non‐white race, each appearing to be key independent risk factors for changes in cIMT and arterial stiffness. The influence of SBP at baseline was present in both obesity and type 2 diabetes mellitus. Of interest, the effects of different risk factors appeared to be location‐specific for cIMT. SBP was associated with adverse thickening in the common, bulb and internal carotid, while the effects of obesity and type 2 diabetes mellitus were only associated with higher cIMT in the common carotid after accounting for the effects of SBP.
Aging, even in our study of adolescents and young adults, was associated with adverse changes in cIMT and arterial stiffness. In adult cross‐sectional studies, age is a well‐established risk factor for increased IMT of both carotid and femoral arteries.37, 38 Furthermore, the effect of obesity/excess adiposity also appears to play an important role, even after accounting immutable cardiovascular risk factors (eg, age, sex, and race).39, 40 Our finding of male sex being associated with greater changes in cIMT and arterial stiffness was not unexpected, as males have a higher prevalence of cardiovascular disease than premenopausal women and cIMT increases more rapidly in age‐matched males before menopause.9, 41, 42 However, cross‐sectional data from the Young Finn's Study has shown that the sex difference in common cIMT in young and middle‐aged adults (aged 24–39 years) can be explained by differences in cardiovascular disease risk factors between sexes.43 When LDL‐C, smoking, SBP, carotid diameter (Note: our study did not adjust for this in analysis), and waist circumference were accounted for in the models, sex differences in common carotid cIMT were no longer present. In contrast, in our study male sex in younger individuals remained significantly associated with most cIMT and arterial stiffness measures. Our data support the concept that male sex is an independent and primary risk factor for accelerated early vascular aging.
We observed 3 consistent, potentially modifiable risk factors for adverse longitudinal changes in cIMT and arterial stiffness in adolescents and young adults: obesity, type 2 diabetes mellitus, and SBP. The role of obesity has been described previously;11, 12, 16, 44 however, our results meaningfully extend these findings by demonstrating that obesity and/or type 2 diabetes mellitus is associated with adverse changes in multiple cIMT vascular beds (common, bulb, internal) over a 5‐year time course. Previously,12 cross‐sectional data supported this accelerated process being most pronounced among adolescents with type 2 diabetes mellitus and our longitudinal data confirm these findings. Moreover, once accounted for in modeling, a 5 mm Hg change in SBP surfaces as a significant risk factor exhibiting potentially differential and additive effects beyond that of obesity and/or type 2 diabetes mellitus. The vascular consequences of pre‐hypertension and SBP are well‐documented among youth.9, 10, 45, 46, 47 However, our finding that the contribution of SBP is associated with longitudinal vascular bed changes after accounting for other cardiovascular risk factors, obesity, and/or type 2 diabetes mellitus is new. While each risk factor (obesity, type 2 diabetes mellitus, and SBP) is associated with significant longitudinal changes in common cIMT and carotid‐femoral PWV, once SBP and other cardiovascular disease risk factors are added to the modeling, only SBP is associated with changes in bulb and internal cIMT. In contrast, obesity and type 2 diabetes mellitus are associated with greater changes in Aix while SBP is not. Although these differences require further study, we hypothesize that SBP may promote vessel remodeling to reduce wall stress.
Our unique presentation of individual data points to accompany group statistics yield important additional findings. The heterogeneity among each of the subclinical vascular measures is substantial. This heterogeneity is present within each group and is consistent across time points. Heterogeneity could be explained by heritability with significant heritability estimates for carotid lumen diameter (range: 44%–55%) and common cIMT (21%–34%) found in multiple studies.48, 49, 50 This heterogeneity needs to be better understood in order for clinicians to identify high‐risk individuals, as these data clearly demonstrate, people at low and high cardiovascular disease risk are present within each of the 3 unique phenotypes studied.
Study Limitations
The strengths of this study include a relatively large sample size, particularly among adolescents with type 2 diabetes mellitus, a longitudinal design over a sufficient period of time to observe significant changes, and a robust panel of non‐invasive measures of subclinical cardiovascular risk. Despite these strengths this paper has the following limitations. We used non‐invasive measures rather than hard cardiovascular outcomes by virtue of our focus on a pediatric population. However, studies in adults demonstrate that both arterial stiffness and cIMT are strong predictors of cardiovascular disease mortality.4, 51, 52 The relatively low frequency of follow‐up visits (eg, only 2 visits over 5 years) limited our ability to evaluate intermediate changes, which may have been associated with or influenced by the trajectory of cardiovascular risk factors. It should also be noted that many of the youth with type 2 diabetes mellitus were on medications for glycemic control, lipids, and/or blood pressure regulation, despite this the vascular profiles worsened overtime.
Conclusions
The presence of obesity and especially type 2 diabetes mellitus in adolescence accelerates the early vascular aging process associated with several key risk factors. SBP is associated with changes in cIMT and arterial stiffness, which are comparable with the effects of obesity and/or type 2 diabetes mellitus. Immutable risk factors such as male sex, age, and race also hasten vascular aging independent of obesity and type 2 diabetes mellitus status. These data add further evidence underscoring the importance of efforts targeting prevention and treatment of obesity, type 2 diabetes mellitus, and elevated BP among youth with a goal of delaying and/or preventing the progression of early vascular aging.
Sources of Funding
This work was supported by National Institutes of Health R01 HL105591 (Urbina) and the National Center for Research Resources and the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, through Grant UL1 TR001425. Work is also supported in part by award UL1TR002494 of NCATS.
Disclosures
Dr Ryder receives support from Boehringer Ingelheim Pharmaceuticals in the form of drug/placebo. Dr Kelly serves as a consultant for Novo Nordisk, Vivus, and WW; he does not accept personal or professional income for his services. Dr Kelly also receives research support from Astra Zeneca Pharmaceuticals in the form of drug/placebo. The remaining authors have no disclosures to report.
Footnote
For Sources of Funding and Disclosures, please see page 10.
References
1.
Sharrett AR, Ding J, Criqui MH, Saad MF, Liu K, Polak JF, Folsom AR, Tsai MY, Burke GL, Szklo M. Smoking, diabetes, and blood cholesterol differ in their associations with subclinical atherosclerosis: the Multiethnic Study of Atherosclerosis (MESA). Atherosclerosis. 2006;186:441–447.
2.
Yeboah J, Folsom AR, Burke GL, Johnson C, Polak JF, Post W, Lima JA, Crouse JR, Herrington DM. Predictive value of brachial flow‐mediated dilation for incident cardiovascular events in a population‐based study: the multi‐ethnic study of atherosclerosis. Circulation. 2009;120:502–509.
3.
Andersson C, Quiroz R, Enserro D, Larson MG, Hamburg NM, Vita JA, Levy D, Benjamin EJ, Mitchell GF, Vasan RS. Association of parental hypertension with arterial stiffness in nonhypertensive offspring: the framingham heart study. Hypertension. 2016;68:584–589.
4.
Mitchell GF, Hwang S‐J, Vasan RS, Larson MG, Pencina MJ, Hamburg NM, Vita JA, Levy D, Benjamin EJ. Arterial stiffness and cardiovascular events: the Framingham heart study. Circulation. 2010;121:505–511.
5.
Epstein FHSR, Boas EP. The epidemiology of atherosclerosis among a random sample of clothing workers of different ethnic origins in New York City, II: associations between manifest atherosclerosis, serum lipid levels, blood pressure, overweight, and some other variables. J Chronic Dis. 1957;5:329–341.
6.
Strong J, McGill H. The natural history of coronary atherosclerosis. Am J Pathol. 1962;40:37–49.
7.
Strong J, McGill HC Jr. The natural history of aortic atherosclerosis: relationship to race, sex, and coronary lesions in New Orleans. Exp Mol Pathol. 1963;52(suppl 1):15–27.
8.
McGill HC Jr. Fatty streaks in the coronary arteries and aorta. Lab Invest. 1968;18:560–564.
9.
Urbina EM, Srinivasan SR, Tang R, Bond MG, Kieltyka L, Berenson GS. Impact of multiple coronary risk factors on the intima‐media thickness of different segments of carotid artery in healthy young adults (The Bogalusa Heart Study). Am J Cardiol. 2002;90:953–958.
10.
Li SCW, Srinivasan SR, Bond MG, Tang R, Urbina EM, Berenson GS. Childhood cardiovascular risk factors and carotid vascular changes in adulthood: the Bogalusa Heart Study. JAMA. 2003;290:2271–2276.
11.
Urbina EM, Williams RV, Alpert BS, Collins RT, Daniels SR, Hayman L, Jacobson M, Mahoney L, Mietus‐Snyder M, Rocchini A, Steinberger J, McCrindle B. Noninvasive assessment of subclinical atherosclerosis in children and adolescents: recommendations for standard assessment for clinical research a scientific statement from the American Heart Association. Hypertension. 2009;54:919–950.
12.
Urbina EMKT, McCoy CE, Khoury P, Daniels S, Dolan L. Youth with obesity and obesity‐related type 2 diabetes mellitus demonstrate abnormalities in carotid structure and function. Circulation. 2009;119:2913–2919.
13.
Urbina EM, Kimball TR, Khoury PR, Daniels SR, Dolan LM. Increased arterial stiffness is found in adolescents with obesity or obesity‐related type 2 diabetes mellitus. J Hypertens. 2010;28:1692–1698.
14.
Wadwa RP, Urbina EM, Anderson AM, Hamman RF, Dolan LM, Rodriguez BL, Daniels SR, Dabelea D. Measures of arterial stiffness in youth with type 1 and type 2 diabetes: the SEARCH for diabetes in youth study. Diabetes Care. 2010;33:881–886.
15.
Kelly AS, Barlow SE, Rao G, Inge TH, Hayman LL, Steinberger J, Urbina EM, Ewing LJ, Daniels SR. Severe obesity in children and adolescents: identification, associated health risks, and treatment approaches: a scientific statement from the American Heart Association. Circulation. 2013;128:1689–1712.
16.
Ryder JR, Dengel DR, Jacobs DR Jr, Sinaiko AR, Kelly AS, Steinberger J. Relations among adiposity and insulin resistance with flow‐mediated dilation, carotid intima‐media thickness, and arterial stiffness in children. J Pediatr. 2016;168:205–211.
17.
Smoak CG, Burke GL, Webber LS, Harsha DW, Srinivasan SR, Berenson GS. relation, of obesity, to clustering, of cardiovascular disease risk factors in children and young adults: the Bogalusa heart study. Am J Epidemiol. 1987;125:364–372.
18.
Mayer‐Davis EJ, Ma B, Lawson A, D'Agostino RB Jr, Liese AD, Bell RA, Dabelea D, Dolan L, Pettitt DJ, Rodriguez BL, Williams D. Cardiovascular disease risk factors in youth with type 1 and type 2 diabetes: implications of a factor analysis of clustering. Metab Syndr Relat Disord. 2009;7:89–95.
19.
Introduction: standards of medical care in diabetes—2019. Diabetes Care. 2019;42:S1–S2.
20.
Lakka H‐M, Laaksonen DE, Lakka TA, Niskanen LK, Kumpusalo E, Tuomilehto J, Salonen JT. The metabolic syndrome and total and cardiovascular disease mortality in middle‐aged men. JAMA. 2002;288:2709–2716.
21.
Stein JH, Korcarz CE, Hurst RT, Lonn E, Kendall CB, Mohler ER, Najjar SS, Rembold CM, Post WS. Use of carotid ultrasound to identify subclinical vascular disease and evaluate cardiovascular disease risk: a consensus statement from the American Society of Echocardiography Carotid Intima‐Media Thickness Task Force. Endorsed by the Society for Vascular Medicine. J Am Soc Echocardiogr. 2008;21:93–111; quiz 189–190.
22.
Roman MJ, Pickering TG, Schwartz JE, Pini R, Devereux RB. Association of carotid atherosclerosis and left ventricular hypertrophy. J Am Coll Cardiol. 1995;25:83–90.
23.
Crouse JR III, Craven TE, Hagaman AP, Bond MG. Association of coronary disease with segment‐specific intimal‐medial thickening of the extracranial carotid artery. Circulation. 1995;92:1141–1147.
24.
Terry JG, Tang R, Espeland MA, Davis DH, Vieira JL, Mercuri MF, Crouse JR III. Carotid arterial structure in patients with documented coronary artery disease and disease‐free control subjects. Circulation. 2003;107:1146–1151.
25.
Cavallini MC, Roman MJ, Blank SG, Pini R, Pickering TG, Devereux RB. Association of the auscultatory gap with vascular disease in hypertensive patients. Ann Intern Med. 1996;124:877–883.
26.
Riley WA, Freedman DS, Higgs NA, Barnes RW, Zinkgraf SA, Berenson GS. Decreased arterial elasticity associated with cardiovascular disease risk factors in the young Bogalusa heart study. Arteriosclerosis. 1986;6:378–386.
27.
Wilkinson IBMH, Flint L, Cockcroft JR, Newby DE, Webb DJ. The influence of heart rate on augmentation index and central arterial pressure in humans. J Physiol. 2000;525:263–270.
28.
Wilkinson IBFS, Jansen IM, Spratt JC, Murray GD, Cockcroft GD, Webb DJ. The reproducibility of pulse wave velocity and augmentation index measured by pulse wave analysis. J Hypertens. 1998;16:2079–2084.
29.
Wilkinson IBFS, Hall IR, Tyrrell S, Cockcroft JR. Pressure amplification explains why pulse pressure is unrelated to risk in young subjects. Hypertension. 2001;38:1461–1466.
30.
O'Rourke MF, Gallagher DE. Pulse wave analysis. J Hypertens. 1996;14:147–157.
31.
Takazawa K ORM, Fujita M, Tanaka N, Takeda K, Kurosu F, Ibukiyama C. Estimation of ascending aortic pressure from radial arterial pressure using a generalized transfer function. Z Kardiol. 1996;85(suppl 3):137–139.
32.
Karamanoglu MGD, Avolio AP, O'Rourke MF. Pressure wave propagation in a multibranched model of the human upper‐limb. Am J Physiol. 1995;269(pt 2):H1363–H1369.
33.
Chen C‐HNE, Fetics B, Pak PH, Yin FCP, Maughan WL, Kass DA. Estimation of central aortic pressure waveform by mathematical transformation of radial tonometry pressure. Circulation. 1997;95:1827–1836.
34.
Lurbe E TM, Carvajal E, Alvarez V, Redón J. Birth weight impacts on wave reflections in children and adolescents. Hypertension. 2003;41(part 2):646–650.
35.
London GMBJ, Pannier B, Guerin AP, Marchais SJ, Safar ME. Arterial wave reflections and survival in end‐stage renal failure. Hypertension. 2001;38:434–438.
36.
Team RC . R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing; 2015.
37.
Schmidt‐Trucksass A, Grathwohl D, Schmid A, Boragk R, Upmeier C, Keul J, Huonker M. Structural, functional, and hemodynamic changes of the common carotid artery with age in male subjects. Arterioscler Thromb Vasc Biol. 1999;19:1091–1097.
38.
Tanaka H, Dinenno FA, Monahan KD, DeSouza CA, Seals DR. Carotid artery wall hypertrophy with age is related to local systolic blood pressure in healthy men. Arterioscler Thromb Vasc Biol. 2001;21:82–87.
39.
Kotsis VT, Stabouli SV, Papamichael CM, Zakopoulos NA. Impact of obesity in intima media thickness of carotid arteries. Obesity. 2006;14:1708–1715.
40.
Burke GL, Bertoni AG, Shea S, Tracy R, Watson KE, Blumenthal RS, Chung H, Carnethon MR. The impact of obesity on cardiovascular disease risk factors and subclinical vascular disease: the Multi‐Ethnic Study of Atherosclerosis. Arch Intern Med. 2008;168:928–935.
41.
Bairey Merz CN, Shaw LJ, Reis SE, Bittner V, Kelsey SF, Olson M, Johnson BD, Pepine CJ, Mankad S, Sharaf BL, Rogers WJ, Pohost GM, Lerman A, Quyyumi AA, Sopko G. Insights from the NHLBI‐sponsored women's ischemia syndrome evaluation (WISE) StudyPart II: gender differences in presentation, diagnosis, and outcome with regard to gender‐based pathophysiology of atherosclerosis and macrovascular and microvascular coronary disease. J Am Coll Cardiol. 2006;47:S21–S29.
42.
Zannad F, Sass C, Visvikis S. Environmental and genetic determinants of intima‐media thickness of the carotid artery. Clin Exp Pharmacol Physiol. 2001;28:1007–1010.
43.
Juonala M, Kahonen M, Laitinen T, Hutri‐Kahonen N, Jokinen E, Taittonen L, Pietikainen M, Helenius H, Viikari JS, Raitakari OT. Effect of age and sex on carotid intima‐media thickness, elasticity and brachial endothelial function in healthy adults: the cardiovascular risk in Young Finns Study. Eur Heart J. 2008;29:1198–1206.
44.
Shah AS, Dolan LM, Khoury PR, Gao Z, Kimball TR, Urbina EM. Severe obesity in adolescents and young adults is associated with subclinical cardiac and vascular changes. J Clin Endocrinol Metab. 2015;100:2751–2757.
45.
Urbina EM, Khoury PR, McCoy C, Daniels SR, Kimball TR, Dolan LM. Cardiac and vascular consequences of pre‐hypertension in youth. J Clin Hypertens. 2011;13:332–342.
46.
Aggoun Y, Farpour‐Lambert NJ, Marchand LM, Golay E, Maggio ABR, Beghetti M. Impaired endothelial and smooth muscle functions and arterial stiffness appear before puberty in obese children and are associated with elevated ambulatory blood pressure. Eur Heart J. 2008;29:792–799.
47.
Doyon A, Kracht D, Bayazit AK, Deveci M, Duzova A, Krmar RT, Litwin M, Niemirska A, Oguz B, Schmidt BM, Sozeri B, Querfeld U, Melk A, Schaefer F, Wuhl E. Carotid artery intima‐media thickness and distensibility in children and adolescents: reference values and role of body dimensions. Hypertension. 2013;62:550–556.
48.
North KE, MacCluer JW, Devereux RB, Howard BV, Welty TK, Best LG, Lee ET, Fabsitz RR, Roman MJ. Heritability of carotid artery structure and function: the Strong Heart Family Study. Arterioscler Thromb Vasc Biol. 2002;22:1698–1703.
49.
Ryder JR, Pankratz ND, Dengel DR, Pankow JS, Jacobs DR Jr, Sinaiko AR, Gooty V, Steinberger J. Heritability of vascular structure and function. a parent‐child study. J Am Heart Assoc. 2017;6:e004757.
50.
Xiang AH, Azen SP, Buchanan TA, Raffel LJ, Tan S, Cheng LS, Diaz J, Toscano E, Quinonnes M, Liu CR, Liu CH, Castellani LW, Hsueh WA, Rotter JI, Hodis HN. Heritability of subclinical atherosclerosis in Latino families ascertained through a hypertensive parent. Arterioscler Thromb Vasc Biol. 2002;22:843–848.
51.
Blacher J, Asmar R, Djane S, London GM, Safar ME. Aortic pulse wave velocity as a marker of cardiovascular risk in hypertensive patients. Hypertension. 1999;33:1111–1117.
52.
Vlachopoulos C, Aznaouridis K, Stefanadis C. Prediction of cardiovascular events and all‐cause mortality with arterial stiffness: a systematic review and meta‐analysis. J Am Coll Cardiol. 2010;55:1318–1327.
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© 2020 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
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Received: 15 October 2019
Accepted: 14 February 2020
Published online: 6 May 2020
Published in print: 18 May 2020
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(J Am Heart Assoc. 2020;9:e014891. https://doi.org/10.1161/JAHA.119.014891.)
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National Institutes of Health R01: HL105591, UL1 TR001425
National Center for Research Resources and the National Center for Advancing Translational Sciences (NCATS): UL1TR002494
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