ORIGINAL
ARTICLE
Severe Obesity in Adolescents and Young Adults is Associated with Sub-Clinical Cardiac and Vascular Changes Amy S Shah, MD, MS,1 Lawrence M Dolan, MD,1, Philip R Khoury, MS1, Zhiqan Gao, PhD,1, Thomas R Kimball1, Elaine M Urbina, MD, MS1 1
Cincinnati Children’s Hospital Medical Center, The University of Cincinnati
Context: Severe obesity is the fastest growing subgroup of obesity in youth. Objective: We sought to explore the association between severe obesity and sub-clinical measures of cardiac and vascular structure and function in adolescents and young adults. Design, Setting, Participants: Cross sectional comparison of 265 adolescents and young adults with severe obesity (defined as body mass index (BMI) ⱖ120% of the 95th percentile) to 182 adolescents and young adults with obesity defined as BMI ⱖ100 –119th of the 95th percentile at tertiary medical center. Main Outcomes: Noninvasive measures of cardiac and vascular structure and function. Results: Participants were a mean age of 17.9 years, 62% were Non- Caucasian and 68% were female. Systolic blood pressure, fasting insulin, C reactive protein, interleukin-6 and frequency of type 2 diabetes was higher in participants with severe obesity (all p⬍0.05). Arterial thickness and stiffness, cardiac structure and diastolic function were also significantly worse in youth with severe obesity as measured by higher left ventricular mass index, worse diastolic function, higher carotid intima media thickness and pulse wave velocity and lower brachial distensibility (all p⬍0.05). Regression modeling showed that severe obesity (compared to obesity) was independently associated with each of the above outcomes after adjustment for age, race, sex, blood pressure, lipids and inflammatory markers (p⬍0.05). Conclusions: Adolescents and young adults with severe obesity have a more adverse cardiovascular risk profile and worse cardiac and vascular structure and function. More importantly, severe obesity is independently associated with these sub-clinical cardiac and vascular changes.
evere obesity (defined as (ⱖ120% of the 95th percentile) is the fastest growing subcategory of overweight and obesity in the United States and currently affects 4%– 6% of all youth (1, 2). Youth with severe obesity have a worse cardiometabolic risk profile including increased numbers of risk factors (3), and a more extreme risk profile including higher blood pressure (BP) (4), more dyslipidemia (5) and more inflammation (5). Using noninvasive cardiovascular imaging techniques, our group has previously demonstrated that obesity is associated with adverse changes in cardiac and vascular
S
structure and function (6 – 8). Similar changes have also been described in youth with severe obesity but comparisons have been made to normal weight controls (9 –12). Therefore, the extent to which severe obesity (compared to less severe forms of obesity) imparts additional cardiovascular risk subclinical cardiac and vascular changes has not been established. In this study we sought to compare noninvasive cardiac and vascular structure and function in adolescents and young adults with severe obesity to an obese control group. Additionally, we sought to evaluate the indepen-
ISSN Print 0021-972X ISSN Online 1945-7197 Printed in U.S.A. Copyright © 2015 by the Endocrine Society Received December 30, 2014. Accepted May 11, 2015.
Abbreviations:
doi: 10.1210/jc.2014-4562
J Clin Endocrinol Metab
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Severe Obesity
dent contributions of severe obesity to subclinical cardiac and vascular changes that are known to predict future myocardial infarction (MI) and stroke (13).
Materials and Methods Participants Participants included in this analysis were recruited as part of the type 2 Cardiovascular Disease study, a cross sectional study conducted in Cincinnati, Ohio USA which was designed to compare cardiac and vascular structure and function in adolescents with type 2 diabetes to lean (⬍85th percentile) and obese (ⱖ95th percentile) controls. Details of the larger study population have been previously published (6 – 8). In the present manuscript, we sought to evaluate the effects of severe obesity on cardiac and vascular structure and function. Thus, we divided the larger study group by age- and genderspecific body mass index (BMI) percentiles derived from Centers for Disease Control and Prevention growth charts. Here, only obese (BMI ⱖ 100 –119th of the 95th percentile or class I obesity) and severe obese [ⱖ120% of the 95th percentile or class II and III obesity (14)] participants are compared. Classification of BMI as a percentage of the 95th percentile was chosen over waist circumference or waist to height ratio as a measure of adiposity because the latter two measures have limited use in persons with severe obesity due to difficulty in obtaining anatomic landmarks (15). We also chose this classification methodology over traditional BMI percentiles because a BMI percentile ⱖ 99th performs poorly to define the severity of obesity (16). Lean and overweight controls are not included here since we have previously published their cardiac and vascular data (6 – 8). Written informed consent was obtained from subjects ⱖ 18 years or from a parent/ guardian with written assent for subjects ⬍ 18 years of age, according to the guidelines established by the local institutional review board (IRB) and in accordance with the Declaration of Helsinki.
Clinical Parameters Demographics, anthropometrics, BP, Tanner pubertal staging, fasting blood (⬎8 hours) and cardiac and vascular measurements were obtained as previously described (6 – 8). Briefly, height, weight, and waist circumference were obtained twice and averaged (6). Body mass index was calculated as weight in kilograms divided by the square of height in meters and BMI percentile was obtained using the Centers for Disease Control and Prevention data table of BMI-for-age percentile charts. Blood pressure was measured manually with a mercury sphygmomanometer (Baum Desktop model with V-Lok cuffs, Copiague, NY) three times and averaged according to the Fourth Report (17). Mean arterial pressure was calculated as two thirds times diastolic BP plus one third systolic BP (in mmHg). Self-report puberty data for breast (females) and pubic hair (males and females) was collected.
Laboratory Fasting blood was drawn to measure lipids, glucose, insulin, glycosylated hemoglobin (A1c), C reactive protein (CRP) and interleukin 6 (IL-6). Total cholesterol, high density lipoprotein cholesterol (HDL-C) and triglycerides were measured in a Na-
J Clin Endocrinol Metab
tional Heart Lung and Blood Institute standardized laboratory with low-density lipoprotein cholesterol (LDL-C) calculated by using the Friedewald equation. If triglycerides were ⱖ 400 mg/dl, LDL-C was measured directly. Glucose was measured using a Hitachi model 704 glucose analyzer (Roche Hitachi, Indianapolis, IN) and insulin was measured by radioimmunoassay (RIA) 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. A1c was measured in red blood cells by using high-performance liquid chromatography (HPLC). CRP and IL-6 were measured using high sensitivity enzymelinked immunoabsorbent assays. Diagnosis of type 2 diabetes was based on American Diabetes Association criteria which included an elevated fasting plasma glucose levels of ⱖ 126 mg/dl, or symptoms of hyperglycemia and random plasma glucose of at least 200 mg/dl, or 2-hour plasma glucose of at least 200 mg/dl during an oral glucose tolerance test (OGTT) (18). Negative islet cell antibody titers (glutamic acid decarboxylase, islet cell antigen 512, insulin autoantibodies) were also confirmed in individuals with type 2 diabetes from the time of diagnosis (Barbara Davis Center for Childhood Diabetes, Denver CO).
Physical Activity Physical activity was assessed using a multidirectional accelerometer (Actical, Phillips Respironics, Bend, OR). Participants were instructed to wear the accelerometer on the right hip at waist level for 7 consecutive days, except when sleeping, bathing, or playing contact sports. Mean counts per minute (cpm) were calculated per day. A valid day included 10 –20 hours of wear time and at least 10 cpm.
Cardiac Structure and Function The heart was noninvasively assessed with either a GE Vivid 5 or 7 (Milwaukee, Wisconsin) or Philips Sonos 5500 (Andover, Massachusetts) ultrasound system with the participant in the left decubitus position to acquire parasternal long and short axis and apical 4 chamber views. Left ventricular (LV) mass (a measure of hypertrophy) was calculated on each participant using the formula by Devereaux et al (19) and then indexed by dividing LV mass by height in meters raised to the power of 2.7 to give a measure of LV mass index which standardizes the measure between different age, gender and race groups (20). Doppler measurements of the mitral inflow velocities were obtained to assess peak early filling (E wave: E) and late (A wave: A) left ventricular (LV) filling with the ratio (E/A) used to assess diastolic function. Tissue Doppler analyses of the myocardial wall velocities at both the septal and lateral annuli were also performed to obtain peak (Ea) and late (Aa) velocities, with the ratio (Ea/Aa) representing mitral annular flow. The ratio of mitral to myocardial early filling E/Ea lateral and septal were calculated as a measure of a diastolic function (21). These particular noninvasive measures of diastolic function have been shown to correlate well with invasive measures of diastolic function and LV end-diastolic pressure (22). A total of 3 cardiac cycles were measured and averaged per subject. All measurements were read off-line using Cardiology Analysis System (Digisonics, Houston, Texas).
Vascular structure and function Vascular structure was measured as carotid intima media thickness in bilateral carotid arteries using high-resolution B-
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doi: 10.1210/jc.2014-4562
mode ultrasonography (GE Vivid 7 ultrasound imaging system) with a 7.5 MHz linear array transducer. For each subject, the common, internal and bulb far wall carotid segments were examined to identify the thickest area of intima media thickness (IMT) with the average value from the right and left artery used in the analysis. A trace technique using a Camtronic Medical System was employed to ascertain the maximum carotid thickness from leading edge of the lumen-intima to leading edge of the media-adventitia. Coefficients of variability for all carotid sites is ⱕ 5.5% (23). The SphygmoCor SCOR-PVx System (AtcorMedical, Sydney Australia) was used to obtain pulse wave velocity and augmentation index. The pulse wave velocity measurement is based on the principle that the pressure pulse generated by the LV ejection travels at a speed determined by the size, shape and properties of the artery (24). A tonometer is used to collect proximal (carotid) and distal (femoral) arterial waveforms gated by the R-wave on a simultaneously recorded electrocardiogram (ECG). Pulse wave velocity is then calculated as the distance from the carotid-tofemoral artery divided by the time delay measured between the feet of the two waveforms reported in m/sec (13). A higher pulse wave velocity indicates higher peripheral vascular stiffness. Three measurements were obtained and averaged. Repeat measures show a coefficient of variation of ⬍ 7% (25). Augmentation index a mixed measure of central and peripheral vascular stiffness was measured by placing the SphygmoCor tonometer over the right radial artery. The device analyzes pulse waves using a generalized transfer function validated in catheterization laboratory to calculate a central aortic pressure wave (13). Augmentation index is derived from the central pressure waveform by calculating the difference between the main outgoing wave and the reflected wave of the central arterial waveform, expressed as a percentage of the central pulse pressure. The magnitude of reflected wave represents the increased afterload that left ventricle (LV) copes with each cardiac cycle. A higher augmentation index indicates increased vessel stiffness. A negative number indicates reflections that happen late in cardiac cycle and are consistent with more pliable (less stiff) arteries. Augmentation index is influenced by heart rate therefore all values were adjusted to a standard heart rate of 75 beats per minute. Three measurements of augmentation were obtained per participant and averaged. Reproducibility studies demonstrated intraclass correlation coefficient of 0.9 (25). The DynaPulse pathway instrument (Pulse Metric, San Diego California) was used to measure brachial distensibility. Brachial distensibility assesses resting vascular function in a medium muscular artery (26). Brachial artery distensibility is derived from pressure curves generated from arterial pressure signals obtained from a standard BP cuff sphygmomanometer. A lower brachial destensibility indicates increased vascular stiffness. Three measures were averaged. Repeat measures show coefficients of variation of ⬍ 9% (25).
Statistics All analyses were performed with SAS 9.3 (SAS, Cary, NC). Variance stabilizing transformations were applied to continuous variables, as appropriate. Group differences were evaluated using t-tests. Regression modeling was performed to determine whether severe obesity was associated with additional or independent risk beyond that of traditional cardiovascular risk factors. Therefore, the full model contained severe obesity group
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(vs. obese group) and was adjusted for age, race, sex, mean arterial pressure, triglyceride/HDL-C ratio [representing small dense LDL particles (27)], CRP, IL-6 and diabetes status. HbA1c, fasting glucose and insulin were not included in the models as they were collinear with diabetes status. Strength of all models was assessed by R2. For all analysis, values of P ⬍ .05 were considered significant.
Results The mean age of the study population was 17.9 years (age range 10.2 – 23.9 years), 62% were Non-Caucasian and 68% were female. Details of the demographic and metabolic variables by group are presented in Table 1. There were more Non-Caucasians and females in the severe obesity group (P ⬍ .05). Thirty eight percent of participants in the obese group had type 2 diabetes compared to half of the participants in the severe obesity group (P ⬍ .05). Systolic BP, fasting insulin, CRP and IL-6 were higher in youth with severe obesity (all P ⬍ .05). There were no significant differences in diastolic BP, lipids, fasting glucose, A1c, and pubertal staging or physical activity counts per minute between the two groups. All participants were pubertal (Tanner 2 or higher). Twenty four participants with severe obesity and eight participants with obesity, all which had type 2 diabetes, reported taking an ACE inhibitor. Four participants also reported taking lipid lowering medication. Table 2 lists the cardiac structure and diastolic function data by group. Vascular structure (thickness) and function (stiffness) measures are shown at the bottom of Table 2 and in Figure 1 for those that were significant. Cardiac structure, diastolic function and vascular thickness and stiffness was worse in the severe obesity group compared to the obese group as measured by a higher LV mass index, E/Ea lateral, internal carotid, PWV femoral and lower BrachD, respectively (all P ⬍ .05). Regression modeling was performed to determine whether severe obesity was independently associated with cardiac and vascular outcomes after adjustment for traditional cardiovascular risk factors including age, race, sex, mean arterial pressure, triglyceride/HDL-C ratio, CRP, IL-6 and diabetes status. Pubertal staging and physical activity cpm were omitted from the models since they were not different between groups. Only outcomes that were significantly different between the two groups were modeled. After adjustment for the above, severe obesity was a significant independent risk factor for LV mass index and E/Ea lateral, internal carotid IMT, PWV femoral and BrachD, (Table 3). Each model was repeated excluding participants with type 2 diabetes and the results were unchanged. Severe
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Severe Obesity
Table 1.
J Clin Endocrinol Metab
Description of the study population Obese, n ⴝ 182
Age (years) Race n (%) Non Caucasian Sex n (%) Female Type 2 diabetes n(%) Height (cm) Weight (kg) Body mass index (kg/m2) Body mass index z score Systolic blood pressure (mmHg) Diastolic blood pressure(mmHg) Total cholesterol (mg/dl) LDL cholesterol (mg/dl) HDL cholesterol (mg/dl) Triglycerides (mg/dl) Fasting Glucose (mg/dl) Fasting Insulin (mIU/mL) Hemoglobin A1c (%) C reactive protein (mg/liter) Interleukin-6 (pg/ml)
Severe Obesity, n ⴝ 265
p value (t test or X2)
18.4 ⫾ 3.3 102 (56%) 119 (65%) 69 (38%) 168 ⫾ 11 91.9 ⫾ 15.0 32.5 ⫾ 2.9 1.92 ⫾ 0.16 117 ⫾ 11 63 ⫾ 11
17.6 ⫾ 3.1 176 (66%) 184 (69%) 133 (50%) 167 ⫾ 10 120.0 ⫾ 22.4 42.7 ⫾ 6.9 2.46 ⫾ 0.26 121 ⫾ 12 68 ⫾ 13
0.022 0.029 0.041 0.012 0.780 ⬍0.001 ⬍0.001 ⬍0.001 ⬍0.001 0.084
173 ⫾ 36 106 ⫾ 32 46 ⫾ 10 111 ⫾ 54 112 ⫾ 56 20.9 ⫾ 16.5 6.3 ⫾ 2.1 3.5 ⫾ 3.7 1.3 ⫾ 1.2
175 ⫾ 35 105 ⫾ 29 45 ⫾ 11 122 ⫾ 73 119 ⫾ 55 31.9 ⫾ 32.3 6.6 ⫾ 2.0 5.8 ⫾ 4.5 2.3 ⫾ 1.5
0.794 0.613 0.246 0.395 0.222 ⬍0.001 0.184 ⬍0.001 ⬍0.001
Obese ⫽ BMI ⱖ100 –119% of the 95th %ile; Severe Obese ⫽ BMI ⱖ120% of the 95th %ile. Data are mean and standard deviation.
Table 2.
Cardiac and Vascular Measurements
LVM index (g/m2.7) E/A Ea/Aa lateral Ea/Aa septal E/Ea lat E/Ea septal Common carotid IMT (mm) Bulb carotid IMT (mm) Internal carotid IMT (mm) Pulse Wave Velocity (m/s) Augmentation Index (%) Brachial Distensibility (mm/mmHg)
Obese
Severe Obesity
p value by
n ⫽ 182 35.9 ⫾ 7.20 2.0 ⫾ 0.06 11.5 ⫾ 1.9 9.8 ⫾ 1.4 6.0 ⫾ 1.5 8.0 ⫾ 1.9 0.50 ⫾ 0.09
n ⫽ 265 42.1 ⫾ 9.6 1.9 ⫾ .5 11.2 ⫾ 1.9 10.0 ⫾ 1.6 6.6 ⫾ 1.5 8.2 ⫾ 1.7 0.51 ⫾ 0.09
t test ⬍0.001 0.592 0.150 0.096 0.002 0.383 0.254
0.51 ⫾ 0.10 0.52 ⫾ 0.12 0.615 0.04 ⫾ 0.10 0.43 ⫾ 0.10 0.002 6.17 ⫾ 1.00 6.7 ⫾ 1.15 ⬍0.001 2.54 ⫾ 11.9 4.87 ⫾ 11.6 0.045 5.72 ⫾ 0.98 5.02 ⫾ 0.91 ⬍0.001
Obese ⫽ BMI ⱖ100 –119% of the 95th %ile; Severe Obese ⫽ BMI ⱖ120% of the 95th %ile. Data are mean and standard deviation.
obesity was still an independently associated with a higher LVM index, E/Ea lateral, internal carotid IMT, PWV femoral, and lower BrachD.
Discussion This study demonstrates two novel findings: Adolescents and young adults with severe obesity have a worse cardiac and vascular structure and function abnormalities com-
pared to those with obesity and after adjustment for demographic and cardiovascular risk factors severe obesity is independently associated with adverse changes in the heart and vasculature. These data suggest that adolescents and young adults with severe obesity are at higher risk for early cardiovascular complications. Prior work has shown that severely obese youth have higher blood pressures (3–5, 9) and insulin levels (4, 5) and more dyslipidemia most commonly with elevated triglycerides and low HDL-C (3, 4, 9) but comparisons have been made to normal weight youth. Norris et al was the first group to utilize an obese control group and found that youth with severe obesity also have higher inflammatory markers (IL-6 and CRP) and oxidized LDL levels (5). The present study is concordant with the latter and shows higher IL-6 and CRP levels as well as higher insulin levels and systolic BP in severely obese youth. Prior work evaluating cardiac and peripheral vascular structure and function in the severely obese youth has utilized normal weight controls. Specifically, Obert et al has shown that severely obese youth mean age 14 years have higher indexed LV mass, diastolic filling pressure and cardiac strain compared to their lean counterparts (10). Tounian et al also documented lower common carotid artery distensibility and compliance as well as decreased brachial artery reactivity (12), while Kaplotis found severely obese youth have higher mean carotid thickness and more endothelial dysfunction (measured by flow mediated dilation) (9). By using normal weight controls, the extent to which severe obesity (compared to less severe forms of
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doi: 10.1210/jc.2014-4562
obesity) imparts additional cardiovascular risk on early cardiac and vascular changes has not been established. Thus, the present study adds to the growing literature in this area but is able to document novel findings by using an obese control group. We show that the degree (or extent) of obesity is significant and specifically severe obesity is associated with additional risk for more advanced preclinical cardiac and peripheral vascular changes. It is also important to note that for some measures, including the internal and bulb IMT, augmentation index and several measures of diastolic function there were no statistical differences between groups. We know atherosclerosis develops in a nonuniform fashion (28, 29) so it is possible that these are later sites of development. Additionally, each of the vascular outcomes, while all reliable, represent a different aspect of the arterial tree or the heart. PWV, a measure of central arterial stiffness, is considered the gold standard measure of subclinical arterial stiffness in both adults and children and has been shown to predict future cardiovascular events and mortality (30, 31). AIx is a mixed measure of arterial stiffness that is influenced by central stiffness (PWV) and peripheral wave reflections (13) and has also been shown to predict all-cause mortality in adults with end stage renal disease (32) and hypertension (33). BrachD is a nonultrasound measure of stiffness (arterial compliance) in a medium muscular artery (34) and is highly correlated with cardiovascular risk factors (34). Indexed LVM is a measure of cardiac hypertrophy and has been shown to predict future cardiovascular mor-
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tality (35). For the diastolic measures there is not one single noninvasive measurement that is proven reliable to predict future diastolic dysfunction though E/Ea lateral best correlates with the reference standard LV end diastolic pressure measured invasively (22). Thus, given the differences between the various outcome measurements, it is not unexpected that each may be differentially affected by the extent of obesity and different cardiovascular risk factors. Another subtle point of this study is that despite establishing severe obesity is an independent risk factor for cardiac and peripheral vascular changes, we were at best only able to explain 10%–20% of the variance in the preclinical cardiac measures and 20%–50% of the variance in the preclinical peripheral vascular measures. This suggests there are there are likely other risk factors in youth with obesity that contribute to subclinical cardiac and vascular disease not identified. While we are not able to examine these risks in this study, adipokines and insulin resistance are important areas for further work. This paper has limitations. First, the cross-sectional design of the present study prevents the assessment of temporal sequence of events. However, since adiposity tracks from childhood to adulthood (36) and childhood adiposity increases future cardiovascular disease risk (37– 40), it is reasonable to hypothesize that severe obesity contributes to our findings. Second, we have self-report pubertal staging and in males only pubic hair (not testicular staging). For this reason, age was included in the models to
Figure 1. Comparisons of the Arterial Measurements in Youth with Obesity and Severe Obesity. Legend: Data are mean and standard deviation. White indicates obese group, black indicates the severe obesity group.
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Severe Obesity
Table 3.
J Clin Endocrinol Metab
Independent Determinants of Vascular and Cardiac Outcomes LVM index ht2.7
Variable Intercept Age (years) Sex (female) Race (Non Caucasian) Mean arterial pressure (mmHg) TG/HDL ratio IL-6 (pg/ ml) Diabetes Status (Yes vs. No) Severe obesity group (vs. obese group) Model R2
(g/m) 3.375 ⫾ 0.068 0.015 ⫾ 0.003
E/EA lateral 1.538 ⫾ 0.133
⫺0.043 ⫾ 0.023
Internal Carotid (mm)
PWV femoral (meters/sec)
BrachD
⫺1.240 ⫾ 0.060 0.021 ⫾ 0.003
0.930 ⫾ 0.074 0.024 ⫾ 0.002
(mm/mmHg) 1.845 ⫾ 0.971 0.006 ⫾ 0.003
⫺0.101 ⫾ 0.021
0.031 ⫾ 0.014
0.077 ⫾ 0.019
0.064 ⫾ 0.014 0.003 ⫾ 0.001
0.004 ⫾ 0.001
⫺0.003 ⫾ 0.001
0.006 ⫾ 0.003 0.012 ⫾ 0.005 0.043 ⫾ 0.022
0.074 ⫾ 0.026
0.043 ⫾ 0.020
0.039 ⫾ 0.013
⫺0.036 ⫾ 0.018
0.153 ⫾ 0.023
0.080 ⫾ 0.026
0.079 ⫾ 0.020
0.078 ⫾ 0.013
⫺0.116 ⫾ 0.018
0.20
0.09
0.18
0.46
0.21
PWV- pulse wave velocity; BrachD-brachial distensibility. Data are parameter estimate and standard error. All of the variables were included in each of the models but only significant parameters (P ⬍ 0.05) are listed. Blanks indicate that parameter was P ⬎ 0.05.
serve as a surrogate. Third, because of small numbers and an unclear the indication for the use of ACE inhibitors (BP lowering or microalbuminuria), we are unable to assess the effects of ACE inhibitors on our outcomes. Finally, though we accounted for the presence of type 2 diabetes in our regression models, these analyses were performed in an established group recruited to study the effects of type 2 diabetes, not severe obesity, so it is possible that the results may differ depending on the prevalence of diabetes in the cohort. In summary, this study establishes that adolescents and young adults with severe obesity have a worse cardiovascular risk profile and a greater degree of preclinical cardiac and peripheral vascular changes than obese youth. In addition, severe obesity is an independent risk factor for preclinical cardiac and peripheral vascular disease. Thus, youth with severe obesity appear to be at a greater risk for subclinical cardiovascular complications. These findings support the need for more aggressive management and intervention in this group.
Acknowledgments We would like to thank the participants who participated in the T2CVD study in Cincinnati Ohio. Address all correspondence and requests for reprints to: Amy Sanghavi Shah MD MS. Cincinnati Children’s Hospital Medical Center. 3333 Burnet Ave ML 7012. Cincinnati OH 45 229. Email: [email protected]. Phone: 513– 636-4744. Fax: 513– 636-7486. This work was supported by Funding Sources: NIH grant R01 HL076269 (Cardiovascular Disease in Adolescents with type 2 Diabetes). It was also supported in part by Grants UL1 RR026314 and K23HL118132. Disclosure: The authors have nothing to disclose.
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the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents. Pediatrics 2004;114: 555–576. American Diabetes A. Standards of medical care in diabetes–2014. Diabetes Care. 2014;37 Suppl 1:S14 – 80. Devereux RB, DR Alonso, EM Lutas, GJ Gottlieb, E Campo, I Sachs and N. Reichek Echocardiographic assessment of left ventricular hypertrophy: comparison to necropsy findings. Am J Cardiol. 1986; 57:450 – 458. de Simone G, SR Daniels, RB Devereux, RA Meyer, MJ Roman, O de Divitiis and MH. Alderman Left ventricular mass and body size in normotensive children and adults: assessment of allometric relations and impact of overweight. J Am Coll Cardiol. 1992;20:1251– 1260. De Boeck BW, MJ Cramer, JK Oh, RP van der Aa and W Jaarsma. Spectral pulsed tissue Doppler imaging in diastole: a tool to increase our insight in and assessment of diastolic relaxation of the left ventricle. Am Heart J. 2003;146:411– 419. Kasner M, D Westermann, P Steendijk, R Gaub, U Wilkenshoff, K Weitmann W Hoffmann, W Poller, HP Schultheiss, M Pauschinger and C. Tschope Utility of Doppler echocardiography and tissue Doppler imaging in the estimation of diastolic function in heart failure with normal ejection fraction: a comparative Doppler-conductance catheterization study. Circulation. 2007;116:637– 647. Urbina EM, D Dabelea, RB D’Agostino Jr, AS Shah, LM Dolan, RF Hamman, SR Daniels, S Marcovina and RP.med Wadwa Effect of type 1 diabetes on carotid structure and function in adolescents and young adults: the SEARCH CVD study. Diabetes Care. 2013;36: 2597–2599. London GM, and AP Guerin. Influence of arterial pulse and reflected waves on blood pressure and cardiac function. Am Heart J. 1999; 138:220 –224. Urbina EM, PR Khoury, C McCoy, SR Daniels, TR Kimball and LM. Dolan Cardiac and vascular consequences of pre-hypertension in youth. J Clin Hypertens (Greenwich). 2011;13:332–342. Urbina EM, L Kieltkya, J Tsai, SR Srinivasan and GS Berenson. Impact of multiple cardiovascular risk factors on brachial artery distensibility in young adults: the Bogalusa Heart Study. Am J Hypertens. 2005;18:767–771. Tsimihodimos V, I Gazi, C Kostara, AD Tselepis and M Elisaf. Plasma lipoproteins and triacylglycerol are predictors of small, dense LDL particles. Lipids 2007;42:403– 409. Mackinnon AD, P Jerrard-Dunne, M Sitzer, A Buehler, S von Kegler and HS Markus. Rates and determinants of site-specific progression of carotid artery intima-media thickness: the carotid atherosclerosis progression study. Stroke. 2004;35:2150 –2154. Solberg LA, and DA Eggen. Localization and sequence of development of atherosclerotic lesions in the carotid and vertebral arteries. Circulation. 1971;43:711–724. Mancia G, G De Backer, A Dominiczak, R Cifkova, R Fagard, G Germano, G Grassi, AM Heagerty, SE Kjeldsen, S Laurent, K Narkiewicz, L Ruilope, A Rynkiewicz, RE Schmieder, HA Boudier, A Zanchetti, A Vahanian, J Camm, R De Caterina, V Dean, K Dickstein, G Filippatos, C Funck-Brentano, I Hellemans, SD Kristensen, K McGregor, U Sechtem, S Silber, M Tendera, P Widimsky, JL Zamorano, S Erdine, W Kiowski, E Agabiti-Rosei, E Ambrosioni, LH Lindholm, M Viigimaa, S Adamopoulos, E Agabiti-Rosei, E Ambrosioni, V Bertomeu, D Clement, S Erdine, C Farsang, D Gaita, G Lip, JM Mallion, AJ Manolis, PM Nilsson, E O’Brien, P Ponikowski, J Redon, F Ruschitzka, J Tamargo, P van Zwieten, B Waeber, B Williams, H. Management of Arterial Hypertension of the European Society of, and C. European Society of 2007 Guidelines for the Management of Arterial Hypertension: The Task Force for the Management of Arterial Hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). J Hypertens. 2007;25:1105–1187. Urbina E.M RV Williams, BS Alpert, RT Collins, SR Daniels, L Hayman, M Jacobson, L Mahoney, M Mietus-Snyder, A Rocchini, J Steinberger, B McCrindle, H. American Heart Association Ath-
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erosclerosis, and Y. Obesity in Youth Committee of the Council on Cardiovascular Disease in the 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. London GM, J Blacher, B Pannier, AP Guerin, SJ Marchais and ME Safar. Arterial wave reflections and survival in end-stage renal failure. Hypertension. 2001;38:434 – 438. Williams B, PS Lacy, SM Thom, K Cruickshank, A Stanton, D Collier, AD Hughes, H Thurston and M O’Rourke. Differential impact of blood pressure-lowering drugs on central aortic pressure and clinical outcomes: principal results of the Conduit Artery Function Evaluation (CAFE) study. Circulation. 2006;113:1213–1225. Urbina EM, TJ Brinton, A Elkasabany and GS Berenson. Brachial artery distensibility and relation to cardiovascular risk factors in healthy young adults (The Bogalusa Heart Study). Am J Cardiol. 2002;89:946 –951. Koren MJ, RB Devereux, PN Casale, DD Savage and JH Laragh. Relation of left ventricular mass and geometry to morbidity and
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mortality in uncomplicated essential hypertension. Ann Intern Med. 1991;114:345–352. Freedman DS, Z Mei, SR Srinivasan, GS Berenson and WH Dietz. Cardiovascular risk factors and excess adiposity among overweight children and adolescents: the Bogalusa Heart Study. J Pediatr 2007; 150:12–17 e12. Baker JL, LW Olsen and TI. Sorensen Childhood body-mass index and the risk of coronary heart disease in adulthood. N Engl J Med. 2007;357:2329 –2337. Franks PW, RL Hanson, WC Knowler, ML Sievers, PH Bennett and HC Looker. Childhood obesity, other cardiovascular risk factors, and premature death. N Engl J Med. 2010;362:485– 493. Morrison JA, LA Friedman and C Gray-McGuire. Metabolic syndrome in childhood predicts adult cardiovascular disease 25 years later: the Princeton Lipid Research Clinics Follow-up Study. Pediatrics. 2007;120:340 –345. Tirosh A, I Shai, A Afek, G Dubnov-Raz, N Ayalon, B Gordon, E Derazne, D Tzur, A Shamis, S Vinker and A Rudich. Adolescent BMI trajectory and risk of diabetes versus coronary disease. N Engl J Med. 2011;364:1315–1325.
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ARTICLE
Severe Obesity in Adolescents and Young Adults is Associated with Sub-Clinical Cardiac and Vascular Changes Amy S Shah, MD, MS,1 Lawrence M Dolan, MD,1, Philip R Khoury, MS1, Zhiqan Gao, PhD,1, Thomas R Kimball1, Elaine M Urbina, MD, MS1 1
Cincinnati Children’s Hospital Medical Center, The University of Cincinnati
Context: Severe obesity is the fastest growing subgroup of obesity in youth. Objective: We sought to explore the association between severe obesity and sub-clinical measures of cardiac and vascular structure and function in adolescents and young adults. Design, Setting, Participants: Cross sectional comparison of 265 adolescents and young adults with severe obesity (defined as body mass index (BMI) ⱖ120% of the 95th percentile) to 182 adolescents and young adults with obesity defined as BMI ⱖ100 –119th of the 95th percentile at tertiary medical center. Main Outcomes: Noninvasive measures of cardiac and vascular structure and function. Results: Participants were a mean age of 17.9 years, 62% were Non- Caucasian and 68% were female. Systolic blood pressure, fasting insulin, C reactive protein, interleukin-6 and frequency of type 2 diabetes was higher in participants with severe obesity (all p⬍0.05). Arterial thickness and stiffness, cardiac structure and diastolic function were also significantly worse in youth with severe obesity as measured by higher left ventricular mass index, worse diastolic function, higher carotid intima media thickness and pulse wave velocity and lower brachial distensibility (all p⬍0.05). Regression modeling showed that severe obesity (compared to obesity) was independently associated with each of the above outcomes after adjustment for age, race, sex, blood pressure, lipids and inflammatory markers (p⬍0.05). Conclusions: Adolescents and young adults with severe obesity have a more adverse cardiovascular risk profile and worse cardiac and vascular structure and function. More importantly, severe obesity is independently associated with these sub-clinical cardiac and vascular changes.
evere obesity (defined as (ⱖ120% of the 95th percentile) is the fastest growing subcategory of overweight and obesity in the United States and currently affects 4%– 6% of all youth (1, 2). Youth with severe obesity have a worse cardiometabolic risk profile including increased numbers of risk factors (3), and a more extreme risk profile including higher blood pressure (BP) (4), more dyslipidemia (5) and more inflammation (5). Using noninvasive cardiovascular imaging techniques, our group has previously demonstrated that obesity is associated with adverse changes in cardiac and vascular
S
structure and function (6 – 8). Similar changes have also been described in youth with severe obesity but comparisons have been made to normal weight controls (9 –12). Therefore, the extent to which severe obesity (compared to less severe forms of obesity) imparts additional cardiovascular risk subclinical cardiac and vascular changes has not been established. In this study we sought to compare noninvasive cardiac and vascular structure and function in adolescents and young adults with severe obesity to an obese control group. Additionally, we sought to evaluate the indepen-
ISSN Print 0021-972X ISSN Online 1945-7197 Printed in U.S.A. Copyright © 2015 by the Endocrine Society Received December 30, 2014. Accepted May 11, 2015.
Abbreviations:
doi: 10.1210/jc.2014-4562
J Clin Endocrinol Metab
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dent contributions of severe obesity to subclinical cardiac and vascular changes that are known to predict future myocardial infarction (MI) and stroke (13).
Materials and Methods Participants Participants included in this analysis were recruited as part of the type 2 Cardiovascular Disease study, a cross sectional study conducted in Cincinnati, Ohio USA which was designed to compare cardiac and vascular structure and function in adolescents with type 2 diabetes to lean (⬍85th percentile) and obese (ⱖ95th percentile) controls. Details of the larger study population have been previously published (6 – 8). In the present manuscript, we sought to evaluate the effects of severe obesity on cardiac and vascular structure and function. Thus, we divided the larger study group by age- and genderspecific body mass index (BMI) percentiles derived from Centers for Disease Control and Prevention growth charts. Here, only obese (BMI ⱖ 100 –119th of the 95th percentile or class I obesity) and severe obese [ⱖ120% of the 95th percentile or class II and III obesity (14)] participants are compared. Classification of BMI as a percentage of the 95th percentile was chosen over waist circumference or waist to height ratio as a measure of adiposity because the latter two measures have limited use in persons with severe obesity due to difficulty in obtaining anatomic landmarks (15). We also chose this classification methodology over traditional BMI percentiles because a BMI percentile ⱖ 99th performs poorly to define the severity of obesity (16). Lean and overweight controls are not included here since we have previously published their cardiac and vascular data (6 – 8). Written informed consent was obtained from subjects ⱖ 18 years or from a parent/ guardian with written assent for subjects ⬍ 18 years of age, according to the guidelines established by the local institutional review board (IRB) and in accordance with the Declaration of Helsinki.
Clinical Parameters Demographics, anthropometrics, BP, Tanner pubertal staging, fasting blood (⬎8 hours) and cardiac and vascular measurements were obtained as previously described (6 – 8). Briefly, height, weight, and waist circumference were obtained twice and averaged (6). Body mass index was calculated as weight in kilograms divided by the square of height in meters and BMI percentile was obtained using the Centers for Disease Control and Prevention data table of BMI-for-age percentile charts. Blood pressure was measured manually with a mercury sphygmomanometer (Baum Desktop model with V-Lok cuffs, Copiague, NY) three times and averaged according to the Fourth Report (17). Mean arterial pressure was calculated as two thirds times diastolic BP plus one third systolic BP (in mmHg). Self-report puberty data for breast (females) and pubic hair (males and females) was collected.
Laboratory Fasting blood was drawn to measure lipids, glucose, insulin, glycosylated hemoglobin (A1c), C reactive protein (CRP) and interleukin 6 (IL-6). Total cholesterol, high density lipoprotein cholesterol (HDL-C) and triglycerides were measured in a Na-
J Clin Endocrinol Metab
tional Heart Lung and Blood Institute standardized laboratory with low-density lipoprotein cholesterol (LDL-C) calculated by using the Friedewald equation. If triglycerides were ⱖ 400 mg/dl, LDL-C was measured directly. Glucose was measured using a Hitachi model 704 glucose analyzer (Roche Hitachi, Indianapolis, IN) and insulin was measured by radioimmunoassay (RIA) 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. A1c was measured in red blood cells by using high-performance liquid chromatography (HPLC). CRP and IL-6 were measured using high sensitivity enzymelinked immunoabsorbent assays. Diagnosis of type 2 diabetes was based on American Diabetes Association criteria which included an elevated fasting plasma glucose levels of ⱖ 126 mg/dl, or symptoms of hyperglycemia and random plasma glucose of at least 200 mg/dl, or 2-hour plasma glucose of at least 200 mg/dl during an oral glucose tolerance test (OGTT) (18). Negative islet cell antibody titers (glutamic acid decarboxylase, islet cell antigen 512, insulin autoantibodies) were also confirmed in individuals with type 2 diabetes from the time of diagnosis (Barbara Davis Center for Childhood Diabetes, Denver CO).
Physical Activity Physical activity was assessed using a multidirectional accelerometer (Actical, Phillips Respironics, Bend, OR). Participants were instructed to wear the accelerometer on the right hip at waist level for 7 consecutive days, except when sleeping, bathing, or playing contact sports. Mean counts per minute (cpm) were calculated per day. A valid day included 10 –20 hours of wear time and at least 10 cpm.
Cardiac Structure and Function The heart was noninvasively assessed with either a GE Vivid 5 or 7 (Milwaukee, Wisconsin) or Philips Sonos 5500 (Andover, Massachusetts) ultrasound system with the participant in the left decubitus position to acquire parasternal long and short axis and apical 4 chamber views. Left ventricular (LV) mass (a measure of hypertrophy) was calculated on each participant using the formula by Devereaux et al (19) and then indexed by dividing LV mass by height in meters raised to the power of 2.7 to give a measure of LV mass index which standardizes the measure between different age, gender and race groups (20). Doppler measurements of the mitral inflow velocities were obtained to assess peak early filling (E wave: E) and late (A wave: A) left ventricular (LV) filling with the ratio (E/A) used to assess diastolic function. Tissue Doppler analyses of the myocardial wall velocities at both the septal and lateral annuli were also performed to obtain peak (Ea) and late (Aa) velocities, with the ratio (Ea/Aa) representing mitral annular flow. The ratio of mitral to myocardial early filling E/Ea lateral and septal were calculated as a measure of a diastolic function (21). These particular noninvasive measures of diastolic function have been shown to correlate well with invasive measures of diastolic function and LV end-diastolic pressure (22). A total of 3 cardiac cycles were measured and averaged per subject. All measurements were read off-line using Cardiology Analysis System (Digisonics, Houston, Texas).
Vascular structure and function Vascular structure was measured as carotid intima media thickness in bilateral carotid arteries using high-resolution B-
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doi: 10.1210/jc.2014-4562
mode ultrasonography (GE Vivid 7 ultrasound imaging system) with a 7.5 MHz linear array transducer. For each subject, the common, internal and bulb far wall carotid segments were examined to identify the thickest area of intima media thickness (IMT) with the average value from the right and left artery used in the analysis. A trace technique using a Camtronic Medical System was employed to ascertain the maximum carotid thickness from leading edge of the lumen-intima to leading edge of the media-adventitia. Coefficients of variability for all carotid sites is ⱕ 5.5% (23). The SphygmoCor SCOR-PVx System (AtcorMedical, Sydney Australia) was used to obtain pulse wave velocity and augmentation index. The pulse wave velocity measurement is based on the principle that the pressure pulse generated by the LV ejection travels at a speed determined by the size, shape and properties of the artery (24). A tonometer is used to collect proximal (carotid) and distal (femoral) arterial waveforms gated by the R-wave on a simultaneously recorded electrocardiogram (ECG). Pulse wave velocity is then calculated as the distance from the carotid-tofemoral artery divided by the time delay measured between the feet of the two waveforms reported in m/sec (13). A higher pulse wave velocity indicates higher peripheral vascular stiffness. Three measurements were obtained and averaged. Repeat measures show a coefficient of variation of ⬍ 7% (25). Augmentation index a mixed measure of central and peripheral vascular stiffness was measured by placing the SphygmoCor tonometer over the right radial artery. The device analyzes pulse waves using a generalized transfer function validated in catheterization laboratory to calculate a central aortic pressure wave (13). Augmentation index is derived from the central pressure waveform by calculating the difference between the main outgoing wave and the reflected wave of the central arterial waveform, expressed as a percentage of the central pulse pressure. The magnitude of reflected wave represents the increased afterload that left ventricle (LV) copes with each cardiac cycle. A higher augmentation index indicates increased vessel stiffness. A negative number indicates reflections that happen late in cardiac cycle and are consistent with more pliable (less stiff) arteries. Augmentation index is influenced by heart rate therefore all values were adjusted to a standard heart rate of 75 beats per minute. Three measurements of augmentation were obtained per participant and averaged. Reproducibility studies demonstrated intraclass correlation coefficient of 0.9 (25). The DynaPulse pathway instrument (Pulse Metric, San Diego California) was used to measure brachial distensibility. Brachial distensibility assesses resting vascular function in a medium muscular artery (26). Brachial artery distensibility is derived from pressure curves generated from arterial pressure signals obtained from a standard BP cuff sphygmomanometer. A lower brachial destensibility indicates increased vascular stiffness. Three measures were averaged. Repeat measures show coefficients of variation of ⬍ 9% (25).
Statistics All analyses were performed with SAS 9.3 (SAS, Cary, NC). Variance stabilizing transformations were applied to continuous variables, as appropriate. Group differences were evaluated using t-tests. Regression modeling was performed to determine whether severe obesity was associated with additional or independent risk beyond that of traditional cardiovascular risk factors. Therefore, the full model contained severe obesity group
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(vs. obese group) and was adjusted for age, race, sex, mean arterial pressure, triglyceride/HDL-C ratio [representing small dense LDL particles (27)], CRP, IL-6 and diabetes status. HbA1c, fasting glucose and insulin were not included in the models as they were collinear with diabetes status. Strength of all models was assessed by R2. For all analysis, values of P ⬍ .05 were considered significant.
Results The mean age of the study population was 17.9 years (age range 10.2 – 23.9 years), 62% were Non-Caucasian and 68% were female. Details of the demographic and metabolic variables by group are presented in Table 1. There were more Non-Caucasians and females in the severe obesity group (P ⬍ .05). Thirty eight percent of participants in the obese group had type 2 diabetes compared to half of the participants in the severe obesity group (P ⬍ .05). Systolic BP, fasting insulin, CRP and IL-6 were higher in youth with severe obesity (all P ⬍ .05). There were no significant differences in diastolic BP, lipids, fasting glucose, A1c, and pubertal staging or physical activity counts per minute between the two groups. All participants were pubertal (Tanner 2 or higher). Twenty four participants with severe obesity and eight participants with obesity, all which had type 2 diabetes, reported taking an ACE inhibitor. Four participants also reported taking lipid lowering medication. Table 2 lists the cardiac structure and diastolic function data by group. Vascular structure (thickness) and function (stiffness) measures are shown at the bottom of Table 2 and in Figure 1 for those that were significant. Cardiac structure, diastolic function and vascular thickness and stiffness was worse in the severe obesity group compared to the obese group as measured by a higher LV mass index, E/Ea lateral, internal carotid, PWV femoral and lower BrachD, respectively (all P ⬍ .05). Regression modeling was performed to determine whether severe obesity was independently associated with cardiac and vascular outcomes after adjustment for traditional cardiovascular risk factors including age, race, sex, mean arterial pressure, triglyceride/HDL-C ratio, CRP, IL-6 and diabetes status. Pubertal staging and physical activity cpm were omitted from the models since they were not different between groups. Only outcomes that were significantly different between the two groups were modeled. After adjustment for the above, severe obesity was a significant independent risk factor for LV mass index and E/Ea lateral, internal carotid IMT, PWV femoral and BrachD, (Table 3). Each model was repeated excluding participants with type 2 diabetes and the results were unchanged. Severe
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Severe Obesity
Table 1.
J Clin Endocrinol Metab
Description of the study population Obese, n ⴝ 182
Age (years) Race n (%) Non Caucasian Sex n (%) Female Type 2 diabetes n(%) Height (cm) Weight (kg) Body mass index (kg/m2) Body mass index z score Systolic blood pressure (mmHg) Diastolic blood pressure(mmHg) Total cholesterol (mg/dl) LDL cholesterol (mg/dl) HDL cholesterol (mg/dl) Triglycerides (mg/dl) Fasting Glucose (mg/dl) Fasting Insulin (mIU/mL) Hemoglobin A1c (%) C reactive protein (mg/liter) Interleukin-6 (pg/ml)
Severe Obesity, n ⴝ 265
p value (t test or X2)
18.4 ⫾ 3.3 102 (56%) 119 (65%) 69 (38%) 168 ⫾ 11 91.9 ⫾ 15.0 32.5 ⫾ 2.9 1.92 ⫾ 0.16 117 ⫾ 11 63 ⫾ 11
17.6 ⫾ 3.1 176 (66%) 184 (69%) 133 (50%) 167 ⫾ 10 120.0 ⫾ 22.4 42.7 ⫾ 6.9 2.46 ⫾ 0.26 121 ⫾ 12 68 ⫾ 13
0.022 0.029 0.041 0.012 0.780 ⬍0.001 ⬍0.001 ⬍0.001 ⬍0.001 0.084
173 ⫾ 36 106 ⫾ 32 46 ⫾ 10 111 ⫾ 54 112 ⫾ 56 20.9 ⫾ 16.5 6.3 ⫾ 2.1 3.5 ⫾ 3.7 1.3 ⫾ 1.2
175 ⫾ 35 105 ⫾ 29 45 ⫾ 11 122 ⫾ 73 119 ⫾ 55 31.9 ⫾ 32.3 6.6 ⫾ 2.0 5.8 ⫾ 4.5 2.3 ⫾ 1.5
0.794 0.613 0.246 0.395 0.222 ⬍0.001 0.184 ⬍0.001 ⬍0.001
Obese ⫽ BMI ⱖ100 –119% of the 95th %ile; Severe Obese ⫽ BMI ⱖ120% of the 95th %ile. Data are mean and standard deviation.
Table 2.
Cardiac and Vascular Measurements
LVM index (g/m2.7) E/A Ea/Aa lateral Ea/Aa septal E/Ea lat E/Ea septal Common carotid IMT (mm) Bulb carotid IMT (mm) Internal carotid IMT (mm) Pulse Wave Velocity (m/s) Augmentation Index (%) Brachial Distensibility (mm/mmHg)
Obese
Severe Obesity
p value by
n ⫽ 182 35.9 ⫾ 7.20 2.0 ⫾ 0.06 11.5 ⫾ 1.9 9.8 ⫾ 1.4 6.0 ⫾ 1.5 8.0 ⫾ 1.9 0.50 ⫾ 0.09
n ⫽ 265 42.1 ⫾ 9.6 1.9 ⫾ .5 11.2 ⫾ 1.9 10.0 ⫾ 1.6 6.6 ⫾ 1.5 8.2 ⫾ 1.7 0.51 ⫾ 0.09
t test ⬍0.001 0.592 0.150 0.096 0.002 0.383 0.254
0.51 ⫾ 0.10 0.52 ⫾ 0.12 0.615 0.04 ⫾ 0.10 0.43 ⫾ 0.10 0.002 6.17 ⫾ 1.00 6.7 ⫾ 1.15 ⬍0.001 2.54 ⫾ 11.9 4.87 ⫾ 11.6 0.045 5.72 ⫾ 0.98 5.02 ⫾ 0.91 ⬍0.001
Obese ⫽ BMI ⱖ100 –119% of the 95th %ile; Severe Obese ⫽ BMI ⱖ120% of the 95th %ile. Data are mean and standard deviation.
obesity was still an independently associated with a higher LVM index, E/Ea lateral, internal carotid IMT, PWV femoral, and lower BrachD.
Discussion This study demonstrates two novel findings: Adolescents and young adults with severe obesity have a worse cardiac and vascular structure and function abnormalities com-
pared to those with obesity and after adjustment for demographic and cardiovascular risk factors severe obesity is independently associated with adverse changes in the heart and vasculature. These data suggest that adolescents and young adults with severe obesity are at higher risk for early cardiovascular complications. Prior work has shown that severely obese youth have higher blood pressures (3–5, 9) and insulin levels (4, 5) and more dyslipidemia most commonly with elevated triglycerides and low HDL-C (3, 4, 9) but comparisons have been made to normal weight youth. Norris et al was the first group to utilize an obese control group and found that youth with severe obesity also have higher inflammatory markers (IL-6 and CRP) and oxidized LDL levels (5). The present study is concordant with the latter and shows higher IL-6 and CRP levels as well as higher insulin levels and systolic BP in severely obese youth. Prior work evaluating cardiac and peripheral vascular structure and function in the severely obese youth has utilized normal weight controls. Specifically, Obert et al has shown that severely obese youth mean age 14 years have higher indexed LV mass, diastolic filling pressure and cardiac strain compared to their lean counterparts (10). Tounian et al also documented lower common carotid artery distensibility and compliance as well as decreased brachial artery reactivity (12), while Kaplotis found severely obese youth have higher mean carotid thickness and more endothelial dysfunction (measured by flow mediated dilation) (9). By using normal weight controls, the extent to which severe obesity (compared to less severe forms of
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doi: 10.1210/jc.2014-4562
obesity) imparts additional cardiovascular risk on early cardiac and vascular changes has not been established. Thus, the present study adds to the growing literature in this area but is able to document novel findings by using an obese control group. We show that the degree (or extent) of obesity is significant and specifically severe obesity is associated with additional risk for more advanced preclinical cardiac and peripheral vascular changes. It is also important to note that for some measures, including the internal and bulb IMT, augmentation index and several measures of diastolic function there were no statistical differences between groups. We know atherosclerosis develops in a nonuniform fashion (28, 29) so it is possible that these are later sites of development. Additionally, each of the vascular outcomes, while all reliable, represent a different aspect of the arterial tree or the heart. PWV, a measure of central arterial stiffness, is considered the gold standard measure of subclinical arterial stiffness in both adults and children and has been shown to predict future cardiovascular events and mortality (30, 31). AIx is a mixed measure of arterial stiffness that is influenced by central stiffness (PWV) and peripheral wave reflections (13) and has also been shown to predict all-cause mortality in adults with end stage renal disease (32) and hypertension (33). BrachD is a nonultrasound measure of stiffness (arterial compliance) in a medium muscular artery (34) and is highly correlated with cardiovascular risk factors (34). Indexed LVM is a measure of cardiac hypertrophy and has been shown to predict future cardiovascular mor-
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tality (35). For the diastolic measures there is not one single noninvasive measurement that is proven reliable to predict future diastolic dysfunction though E/Ea lateral best correlates with the reference standard LV end diastolic pressure measured invasively (22). Thus, given the differences between the various outcome measurements, it is not unexpected that each may be differentially affected by the extent of obesity and different cardiovascular risk factors. Another subtle point of this study is that despite establishing severe obesity is an independent risk factor for cardiac and peripheral vascular changes, we were at best only able to explain 10%–20% of the variance in the preclinical cardiac measures and 20%–50% of the variance in the preclinical peripheral vascular measures. This suggests there are there are likely other risk factors in youth with obesity that contribute to subclinical cardiac and vascular disease not identified. While we are not able to examine these risks in this study, adipokines and insulin resistance are important areas for further work. This paper has limitations. First, the cross-sectional design of the present study prevents the assessment of temporal sequence of events. However, since adiposity tracks from childhood to adulthood (36) and childhood adiposity increases future cardiovascular disease risk (37– 40), it is reasonable to hypothesize that severe obesity contributes to our findings. Second, we have self-report pubertal staging and in males only pubic hair (not testicular staging). For this reason, age was included in the models to
Figure 1. Comparisons of the Arterial Measurements in Youth with Obesity and Severe Obesity. Legend: Data are mean and standard deviation. White indicates obese group, black indicates the severe obesity group.
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Table 3.
J Clin Endocrinol Metab
Independent Determinants of Vascular and Cardiac Outcomes LVM index ht2.7
Variable Intercept Age (years) Sex (female) Race (Non Caucasian) Mean arterial pressure (mmHg) TG/HDL ratio IL-6 (pg/ ml) Diabetes Status (Yes vs. No) Severe obesity group (vs. obese group) Model R2
(g/m) 3.375 ⫾ 0.068 0.015 ⫾ 0.003
E/EA lateral 1.538 ⫾ 0.133
⫺0.043 ⫾ 0.023
Internal Carotid (mm)
PWV femoral (meters/sec)
BrachD
⫺1.240 ⫾ 0.060 0.021 ⫾ 0.003
0.930 ⫾ 0.074 0.024 ⫾ 0.002
(mm/mmHg) 1.845 ⫾ 0.971 0.006 ⫾ 0.003
⫺0.101 ⫾ 0.021
0.031 ⫾ 0.014
0.077 ⫾ 0.019
0.064 ⫾ 0.014 0.003 ⫾ 0.001
0.004 ⫾ 0.001
⫺0.003 ⫾ 0.001
0.006 ⫾ 0.003 0.012 ⫾ 0.005 0.043 ⫾ 0.022
0.074 ⫾ 0.026
0.043 ⫾ 0.020
0.039 ⫾ 0.013
⫺0.036 ⫾ 0.018
0.153 ⫾ 0.023
0.080 ⫾ 0.026
0.079 ⫾ 0.020
0.078 ⫾ 0.013
⫺0.116 ⫾ 0.018
0.20
0.09
0.18
0.46
0.21
PWV- pulse wave velocity; BrachD-brachial distensibility. Data are parameter estimate and standard error. All of the variables were included in each of the models but only significant parameters (P ⬍ 0.05) are listed. Blanks indicate that parameter was P ⬎ 0.05.
serve as a surrogate. Third, because of small numbers and an unclear the indication for the use of ACE inhibitors (BP lowering or microalbuminuria), we are unable to assess the effects of ACE inhibitors on our outcomes. Finally, though we accounted for the presence of type 2 diabetes in our regression models, these analyses were performed in an established group recruited to study the effects of type 2 diabetes, not severe obesity, so it is possible that the results may differ depending on the prevalence of diabetes in the cohort. In summary, this study establishes that adolescents and young adults with severe obesity have a worse cardiovascular risk profile and a greater degree of preclinical cardiac and peripheral vascular changes than obese youth. In addition, severe obesity is an independent risk factor for preclinical cardiac and peripheral vascular disease. Thus, youth with severe obesity appear to be at a greater risk for subclinical cardiovascular complications. These findings support the need for more aggressive management and intervention in this group.
Acknowledgments We would like to thank the participants who participated in the T2CVD study in Cincinnati Ohio. Address all correspondence and requests for reprints to: Amy Sanghavi Shah MD MS. Cincinnati Children’s Hospital Medical Center. 3333 Burnet Ave ML 7012. Cincinnati OH 45 229. Email: [email protected]. Phone: 513– 636-4744. Fax: 513– 636-7486. This work was supported by Funding Sources: NIH grant R01 HL076269 (Cardiovascular Disease in Adolescents with type 2 Diabetes). It was also supported in part by Grants UL1 RR026314 and K23HL118132. Disclosure: The authors have nothing to disclose.
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