Video Abstract
Development of cardiovascular disease in adults has been directly linked to an adverse metabolic phenotype. While there is evidence that development of these risk factors in childhood persists into adulthood and the development of cardiovascular disease, less is known about whether these risk factors are associated with target organ damage during adolescence.
We collected data from 379 adolescents (mean age 15.5, 60% male) with blood pressure between the 75th and 95th percentile to determine if there is a metabolic phenotype that predicts cardiovascular changes (left ventricular mass, systolic and diastolic function, pulse wave velocity, and renal function). We determined the number of risk factors for cardiovascular disease (hypertension, dyslipidemia, obesity, and insulin resistance) present in each participant. Generalized linear models were constructed to determine if the number of cardiovascular risk factors (CVRFs) were associated with measures of target organ damage.
The number of CVRFs present were associated with statistically significant differences in increased left ventricular mass index, increased pulse wave velocity, decreased peak longitudinal strain, urine albumin to creatine ratio and echocardiographic parameters of diastolic dysfunction. Generalized linear models showed that dyslipidemia and insulin resistance were independently associated with markers of diastolic dysfunction (P ≤ .05) while increased blood pressure was associated with all makers of target organ damage (P ≤ .03).
These data suggest the of the number of CVRFs present is independently associated with early changes in markers of target organ damage during adolescence.
Metabolic syndrome and risk factors that lead to cardiovascular disease in adults have their origins in childhood.
The number of cardiovascular risk factors present in adolescents is independently associated with early changes in markers of target organ damage, including left ventricular mass index, cardiac diastolic function, and pulse wave velocity.
Metabolic syndrome in adults, typically defined as a combination of central obesity, hypertriglyceridemia, hypertension, and elevated fasting blood glucose,1 has been directly linked to increased risk of target organ damage (TOD), cardiovascular disease, and all-cause mortality.2–4 Metabolic syndrome in children has the same core elements as in adults but is more difficult to define due to growth-related changes in its various components. While some work has been done to examine the effects of metabolic syndrome on cardiovascular function in youth,5–7 these studies tend to be small and focused on only a few markers of target organ damage. Most research involving metabolic syndrome in youth and adolescents has focused on its increasing prevalence persistence into adulthood. Many children with metabolic syndrome tend to appear otherwise healthy and there are low rates of overt cardiovascular disease in adolescents; thus, little attention has been given to the burden of subclinical TOD in youth with metabolic syndrome. Some work has identified subclinical cardiac dysfunction in youth with elevated blood pressure alone8 but did not include other risk factors for cardiovascular disease in their analyses.
To address this knowledge gap, we analyzed data from the Study of High Blood Pressure in Pediatrics, Adult Hypertension Onset in Youth (SHIP AHOY) to examine the relationship between blood pressure levels and TOD to identify the prevalence of subclinical TOD in youth with cardiovascular risk factors (CVRFs), typically considered part of the metabolic syndrome. To our knowledge, this study is one of the largest and most comprehensive evaluations of cardiovascular risk factors in youth to date. We hypothesized that an increasing number of adverse CVRFs would be significantly associated with TOD in otherwise healthy adolescents.
Methods
Study Design
The design and methods of SHIP AHOY have been published.9 Briefly, this cross-sectional study was designed to recruit otherwise healthy youth aged 11 to 18 years of age with blood pressure ranging from the 75th to 95th percentile to evaluate the relation of casual and ambulatory blood pressure to TOD. Three hundred ninety-seven participants were recruited at 6 clinical sites (Seattle, Cincinnati, Rochester, Houston, Philadelphia, and Boston). The study design was approved by institutional review boards at each of the 6 participating centers, and informed consent and/or assent was obtained from all study participants and their parents according to local requirements. The data that support the findings of the present analysis are available from the corresponding author upon request.
Study Assessments
Patient demographic data, including race and ethnicity, were self-reported by each participant. Laboratory analyses were performed on fasting serum samples and first morning urine samples.
Seated blood pressure measurements were obtained in the right arm by auscultation using a calibrated aneroid sphygmomanometer (Mabis MedicKit-5; Mabis Healthcare, Waukegan, IL) as previously described.10 All study personnel responsible for blood pressure measurement underwent standardized training and certification according to the study auscultatory blood pressure measurement protocol. Four blood pressure measurements at 30-second intervals were obtained on each of 2 separate study visits, with the average of the second, third, fourth and sixth, seventh, eighth measurements recorded as the study blood pressure value.
Methodology for evaluation of TOD has also been published.11 Standard measurements of left ventricular mass, systolic function, and diastolic function were obtained from echocardiograms. Images were read by an experienced sonographer using the Cardiology Analysis System (Digisonics, Houston, TX). Left ventricular mass was calculated using the Devereux equation12 from 2-dimensional M-Mode images of the left ventricle at end diastole.13,14 The left ventricular mass index (LVMI) was defined as left ventricular mass/ht2.7 as described by DeSimone15 to account for body size without overcompensating for obesity. Systolic function was evaluated by tracing the endocardium from the 4-chamber view at peak systole and end diastole (TOMTEC Corporation, Chicago, IL) to quantify global longitudinal strain, strain rate, time to peak longitudinal strain, time to peak longitudinal strain rate, and left ventricular ejection fraction (LVEF).16 Global longitudinal strain measurements were obtained at native echo frame rates (40–60 frames per second). Left ventricular shortening fraction was also obtained. Diastolic function was assessed using pulse wave Doppler of the mitral inflow velocity in the apical 4-chamber view to determine E/A ratio. Tissue Doppler imaging of the mitral annular inflow was recorded at the lateral and septal annulus with the e’/a’ and E/e’ ratios from both regions averaged. Higher LVMI, decreased LVEF, and global longitudinal strain and abnormal diastolic parameters (higher or lower E/A, higher E/e’, or lower e’/a’) are classically associated with cardiovascular disease and adverse outcomes.17 Supplemental Table 5 shows definitions, normal values, and expected changes for each of our markers of TOD.
Pulse wave velocity (PWV), a marker of vascular stiffness, was noninvasively measured using the SphygmoCor CPV System (AtCor Medical, Sydney, Australia). Carotid-femoral distance was calculated as the sum of the distance from the suprasternal notch to the femoral artery pulse (using a caliper) minus the distance from the carotid pulse to the suprasternal notch (measured with a tape measure) according to accepted guidelines.18 These measurements were made twice, averaged, and entered into the device. The pulse transit time is determined by the device as the difference in time between the peak of the R-wave (from the applied ECG leads) to the foot of the femoral pressure wave (obtained with a tonometer) minus the R-wave to the foot of the carotid pressure wave time. PWV is calculated as distance divided by difference in time. PWV is highly reproducible with coefficient of variation on replicate measures of 7%.19
Fasting glucose and insulin levels were used to estimate insulin resistance using the Homeostasis Model Assessment equation (HOMA-IR).20 An estimate of kidney damage was obtained by measuring urine albumin to creatinine ratio on a first-morning urine sample.
Cardiovascular Risk Factor Determination
Given the lack of consensus on a definition in adolescents, we chose not to define metabolic syndrome in our participants but rather to determine the number of CVRFs present. Risk factors were defined based on existing criteria for abnormal blood pressure,21 body mass index (BMI),22 dyslipidemia23–25 and insulin resistance26,27 in youth: Hypertension was defined as systolic blood pressure ≥95th percentile or >130 mmHg in children 13 and older; obesity as BMI ≥95th percentile adjusted for age and sex; dyslipidemia as LDL ≥155 mg/dL and/or triglyceride/HDL ≥3; and insulin resistance as HOMA-IR ≥2.5. Youth were divided into 3 groups based on the number of risk factors present: 0 (no risk factors), 1 to 2 (few risk factors), or more than 2 (many risk factors).
Statistical Analysis
Eleven participants were missing both HOMA-IR data as well as plasma lipid data and a 12th participant was missing HOMA-IR data alone. These 12 individuals were excluded from the present analysis, leaving 385 participants with complete data available for analysis. Patient characteristics were summarized using descriptive statistics. Continuous variables were assessed for normality using histograms and QQ-plots, and if normally distributed, were reported as means and standard deviations. Categorical variables were summarized as frequencies and percentages. Variables that were heavily skewed were log-transformed for analysis. Mean values for demographic and CVRFs were calculated with standard deviations after stratifying participants by number of CVRFs and between group differences were evaluated with ANOVA. Markers of TOD were evaluated similarly. To address the primary research question, generalized linear models were constructed to determine if CVRFs in combination with other known risk factors of end organ changes, including demographic, biometric and laboratory analyses, could predict TOD in our population. Multicollinearity was assessed using the variance inflation factor. All values were less than 10, indicating that multicollinearity was not problematic. Backward elimination with an inclusion criteria of P < .05 was performed to select the variables included in the final models. Results were summarized as β estimates with 95% confidence intervals. Analyses were performed using SAS 9.4 (Cary, NC) and the level of statistical significance was set to P ≤ .05.
Results
Characteristics of the study cohort are summarized in Table 1. After being stratified into groups by number of CVRFs (0 CVRFs, 1–2 CVRFs, >2 CVRFs), there were no significant differences in sex or height between the groups (Table 1). The mean age in the 0 CVRFs group was higher than the >2 CVRFs group. Weight, BMI, and blood pressure were higher in those in the >2 CVRFs group compared with the 1 to 2 CVRFs group and the 0 CVRFs group. Laboratory values including triglyceride levels, triglyceride to HDL ratio, uric acid, glucose, C-reactive protein, and HOMA-IR were also higher in the >2 CVRFs group compared with 1 to 2 CVRFs group and the 0 CVRFs group. There were also differences in diastolic blood pressure (higher in >2 CVRFs group vs 0 CVRFs group), HR and LDL (higher in >2 CVRFs group compared with 0 CVRFs group and 1–2 CVRFs group compared with 0 CVRFs group) and creatinine (lower in >2 CVRFs group compared with 0 CVRFs group).
Study Population Stratified by Numbers of Cardiovascular Risk Factors
| Parameters | 0 CVRFs, N = 90 | 1–2 CVRFs, N = 182 | >2 CVRFs, N = 113 | P | |||
|---|---|---|---|---|---|---|---|
| Mean | SD | Mean | SD | Mean | SD | ||
| Age, ya | 15.86 | 1.64 | 15.69 | 1.56 | 15.21 | 2.01 | .02 |
| Sex (% male) | 58.9 | — | 57.1 | — | 65.5 | — | .35 |
| Race (%) | .03 | ||||||
| White | 68.89 | — | 56.04 | — | 71.68 | — | — |
| Black or African American | 25.56 | — | 30.77 | — | 17.7 | — | — |
| Other | 5.56 | — | 13.19 | — | 10.62 | — | — |
| Ethnicity (% Hispanic) | 11.10 | — | 14.30 | — | 23.90 | — | .03 |
| Height (cm) | 169.73 | 9.14 | 168.71 | 9.67 | 170 | 10.67 | .50 |
| Wt (kg)b | 62.99 | 12.83 | 78.82 | 22.76 | 101.91 | 24.32 | <.001 |
| BMI percentileb,f | 62.9 | 36.82 | 92.18 | 18.73 | 98.94 | 1.43 | <.001 |
| Heart rate (BPM)d | 69.8 | 13.32 | 70.41 | 12.38 | 74.62 | 11.85 | .007 |
| Systolic BP percentileb,f | 78 | 53 | 86 | 26 | 95 | 11 | <.001 |
| Diastolic BP percentilee,f | 65 | 58 | 76 | 47 | 72 | 49 | .04 |
| Total cholesterol (mg/dL)d | 145.04 | 26.7 | 148.8 | 29.46 | 166.85 | 35.46 | <.001 |
| LDL (mg/dL)d | 81.29 | 22.41 | 85.59 | 25.6 | 103.24 | 29.96 | <.001 |
| HDL (mg/dL)c | 51.62 | 9.79 | 46.98 | 11.15 | 37.07 | 10.6 | <.001 |
| Triglyceride (mg/dL)b,f | 60.5 | 22 | 75 | 36 | 139 | 62.5 | <.001 |
| TG-HDLb,f | 1.19 | 0.54 | 1.65 | 1.03 | 3.98 | 2.27 | <.001 |
| Creatinine (mg/dL)a | 0.78 | 0.16 | 0.74 | 0.15 | 0.7 | 0.17 | .003 |
| Uric acid (mg/dL)b | 5.1 | 1.55 | 5.58 | 1.45 | 6.44 | 1.46 | <.001 |
| Glucose (mg/dL)b | 84.18 | 6.73 | 89.67 | 8.02 | 92.66 | 9.5 | <.001 |
| HOMA-IRb,f | 1.67 | 0.72 | 3.25 | 2.52 | 6.9 | 3.96 | <.001 |
| C-reactive protein(mg/dL)f,b | 0.34 | 0.72 | 0.54 | 1.12 | 1.98 | 2.93 | <.001 |
| Parameters | 0 CVRFs, N = 90 | 1–2 CVRFs, N = 182 | >2 CVRFs, N = 113 | P | |||
|---|---|---|---|---|---|---|---|
| Mean | SD | Mean | SD | Mean | SD | ||
| Age, ya | 15.86 | 1.64 | 15.69 | 1.56 | 15.21 | 2.01 | .02 |
| Sex (% male) | 58.9 | — | 57.1 | — | 65.5 | — | .35 |
| Race (%) | .03 | ||||||
| White | 68.89 | — | 56.04 | — | 71.68 | — | — |
| Black or African American | 25.56 | — | 30.77 | — | 17.7 | — | — |
| Other | 5.56 | — | 13.19 | — | 10.62 | — | — |
| Ethnicity (% Hispanic) | 11.10 | — | 14.30 | — | 23.90 | — | .03 |
| Height (cm) | 169.73 | 9.14 | 168.71 | 9.67 | 170 | 10.67 | .50 |
| Wt (kg)b | 62.99 | 12.83 | 78.82 | 22.76 | 101.91 | 24.32 | <.001 |
| BMI percentileb,f | 62.9 | 36.82 | 92.18 | 18.73 | 98.94 | 1.43 | <.001 |
| Heart rate (BPM)d | 69.8 | 13.32 | 70.41 | 12.38 | 74.62 | 11.85 | .007 |
| Systolic BP percentileb,f | 78 | 53 | 86 | 26 | 95 | 11 | <.001 |
| Diastolic BP percentilee,f | 65 | 58 | 76 | 47 | 72 | 49 | .04 |
| Total cholesterol (mg/dL)d | 145.04 | 26.7 | 148.8 | 29.46 | 166.85 | 35.46 | <.001 |
| LDL (mg/dL)d | 81.29 | 22.41 | 85.59 | 25.6 | 103.24 | 29.96 | <.001 |
| HDL (mg/dL)c | 51.62 | 9.79 | 46.98 | 11.15 | 37.07 | 10.6 | <.001 |
| Triglyceride (mg/dL)b,f | 60.5 | 22 | 75 | 36 | 139 | 62.5 | <.001 |
| TG-HDLb,f | 1.19 | 0.54 | 1.65 | 1.03 | 3.98 | 2.27 | <.001 |
| Creatinine (mg/dL)a | 0.78 | 0.16 | 0.74 | 0.15 | 0.7 | 0.17 | .003 |
| Uric acid (mg/dL)b | 5.1 | 1.55 | 5.58 | 1.45 | 6.44 | 1.46 | <.001 |
| Glucose (mg/dL)b | 84.18 | 6.73 | 89.67 | 8.02 | 92.66 | 9.5 | <.001 |
| HOMA-IRb,f | 1.67 | 0.72 | 3.25 | 2.52 | 6.9 | 3.96 | <.001 |
| C-reactive protein(mg/dL)f,b | 0.34 | 0.72 | 0.54 | 1.12 | 1.98 | 2.93 | <.001 |
BMI, body mass index; BP, blood pressure; CVRFs, cardiovascular risk factors; HDL, high density lipoprotein; HOMA-IR, Homeostasis Model Assessment equation; LDL, low density lipoprotein; TG-HDL, triglyceride/HDL ratio; —, not applicable.
Post hoc comparison significant, 0 CVRFs greater than >2 CVRFs.
Post hoc comparison significant, 0 CVRFs less than 1-2 CVRFs less than >2 CVRFs.
Post hoc comparison significant, 0 CVRFs greater than 1-2 CVRFs greater than >2 CVRFs.
Post hoc comparison significant, 0 CVRFs less than >2 CVRFs, 1-2 CVRFs less than >2 CVRFs.
Post hoc comparison significant, 1-2 CVRFs less than >2 CVRFs.
Median and interquartile range reported, P values are from Kruskal-Wallis Test. All other P values are from ANOVA if continuous or χ2 if categorical.
Mean values for TOD stratified by number of CVRFs are shown in Table 2. Analysis of variance showed increasing levels of TOD markers with higher numbers of CVRFs. For LVMI, PWV, and e’/a’ (a standard marker of diastolic dysfunction) there are statistically significant differences among all CVRFs groups (Fig 1). Other markers of diastolic dysfunction (E/A ratio and E/e’ ratio) as well as peak longitudinal strain percent (a marker of left ventricular systolic function) showed statistically significant differences between 0 CVRFs vs >2 CVRFs, as well as between 1 to 2 CVRFs vs >2 CVRFs. Urine albumin/ creatinine ratio showed a significant difference between the 0 CVRFs vs >2 CVRFs groups. For all significant differences, P ≤ .05.
Left ventricular mass index, cardiac diastolic function and pulse wave velocity stratified by number of cardiovascular risk factors. Each marker of target organ dysfunction differed in each comparison. P ≤ .001 for left ventricular mass index and pulse wave velocity. P = .03 for E/e’. CVRFs, cardiovascular risk factors
Left ventricular mass index, cardiac diastolic function and pulse wave velocity stratified by number of cardiovascular risk factors. Each marker of target organ dysfunction differed in each comparison. P ≤ .001 for left ventricular mass index and pulse wave velocity. P = .03 for E/e’. CVRFs, cardiovascular risk factors
Target Organ Damage Measures Stratified by Number of Cardiovascular Risk Factors
| Variable | 0 CVRFs (n = 90) | 1–2 CVRFs (n = 182) | >2 CVRFs (n = 113) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| N | Mean | SD | N | Mean | SD | N | Mean | SD | |
| Left ventricular mass index (g/m2.7)d | 84 | 29.69 | 6.76 | 173 | 32.67 | 6.66 | 108 | 35.10 | 6.73 |
| E/A ratioa,c | 83 | 2.47 | 0.75 | 171 | 2.25 | 0.63 | 107 | 2.08 | 0.60 |
| E/e' ratioa,c | 84 | 5.77 | 1.23 | 171 | 6.02 | 1.26 | 108 | 6.83 | 1.71 |
| e'/a' ratioa,c | 85 | 2.78 | 0.78 | 172 | 2.43 | 0.67 | 109 | 2.01 | 0.53 |
| Peak longitudinal strain (%)a | 71 | −20.97 | 3.47 | 145 | −20.72 | 3.12 | 94 | −19.06 | 3.40 |
| Pulse wave velocity (m/sec)c,d | 77 | 4.66 | 0.77 | 154 | 5.06 | 0.71 | 92 | 5.45 | 0.92 |
| Urine albumin or creatinine (mg/g)b,c | 86 | 0.01 | 0.04 | 170 | 0.01 | 0.02 | 106 | 0.01 | 0.01 |
| Variable | 0 CVRFs (n = 90) | 1–2 CVRFs (n = 182) | >2 CVRFs (n = 113) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| N | Mean | SD | N | Mean | SD | N | Mean | SD | |
| Left ventricular mass index (g/m2.7)d | 84 | 29.69 | 6.76 | 173 | 32.67 | 6.66 | 108 | 35.10 | 6.73 |
| E/A ratioa,c | 83 | 2.47 | 0.75 | 171 | 2.25 | 0.63 | 107 | 2.08 | 0.60 |
| E/e' ratioa,c | 84 | 5.77 | 1.23 | 171 | 6.02 | 1.26 | 108 | 6.83 | 1.71 |
| e'/a' ratioa,c | 85 | 2.78 | 0.78 | 172 | 2.43 | 0.67 | 109 | 2.01 | 0.53 |
| Peak longitudinal strain (%)a | 71 | −20.97 | 3.47 | 145 | −20.72 | 3.12 | 94 | −19.06 | 3.40 |
| Pulse wave velocity (m/sec)c,d | 77 | 4.66 | 0.77 | 154 | 5.06 | 0.71 | 92 | 5.45 | 0.92 |
| Urine albumin or creatinine (mg/g)b,c | 86 | 0.01 | 0.04 | 170 | 0.01 | 0.02 | 106 | 0.01 | 0.01 |
E/A, E/e’, and e’/a’ are markers of cardiac diastolic function. CVRFs, cardiovascular risk factors.
Differences significant (P ≤ .05) between 0 CVRFs vs >2 CVRFs and many, 1-2 CVRFs vs >2 CVRFs.
Differences significant (P ≤ .05) between 0 CVRFs and >2 CVRFs.
Variables log transformed.
Differences are significant (P ≤ .05) between all groups.
Final multivariable models (Supplemental Table 4) showed that each measure of TOD was independently associated with at least 1 measure of blood pressure (P ≤ .04 for all analyses). Systolic blood pressure was associated with LVMI, urine albumin/ creatinine ratio, and E/e’. Diastolic blood pressure was independently associated with all measures of diastolic function (e/a, E/A, and E/e’) and peak longitudinal strain (P ≤ .02 for all analyses). Mean arterial pressure (systolic and diastolic pressures were not assessed) was independently associated with PWV (P < .001). BMI was independently associated with LVMI, PWV, and urine albumin/ creatinine ratio (P ≤ .001 for all analyses). HOMA-IR was independently associated with all makers of diastolic function (P ≤ .001 for all analyses) but not LVMI, peak longitudinal strain, PWV, or urine albumin/ creatinine ratio. Other key determinants varied by measure of TOD and included age, sex, heart rate, triglyceride to HDL ratio, uric acid, and C-reactive protein (P ≤ .01 for all analyses). Tables showing full generalized linear models as well as final fully adjusted models are available in the supplemental online appendix (Supplemental Tables 3 and 4).
Discussion
This study demonstrates in otherwise health adolescents, a higher number of CVRFs were associated with markers of TOD (Table 2, Fig 1). Even adolescents without overt cardiovascular disease but with known risk factors for adult cardiovascular disease showed early changes in markers of TOD.
There was a significant difference in multiple markers of TOD between youth with fewer CVRFs compared with those with more CVRFs. This is a significant and new finding because we recruited participants who were by definition “healthy;” they did not have other chronic illnesses or risk factors for cardiovascular disease that would otherwise require treatment and included evaluation of a diverse set of markers of cardiovascular changes. Despite their good health status, we were able to identify risk factors for metabolic syndrome in a large percentage of participants. While even those participants with many CVRFs had mean TOD parameters still within age-defined normal ranges, the trend toward abnormal was evident. Based on current recommendations, most of our population did not meet criteria for treatment of hypertension, dyslipidemia, or hyperglycemia23,28 but still had evidence of early TOD.
In our generalized linear models, the number of CVRFs was significantly associated with higher LVMI, higher PWV, and alterations in several markers of diastolic function. Previous work evaluating LVMI,29 PWV,7,30,31 and diastolic function32 in children with obesity and metabolic syndrome have shown changes in these parameters. Analysis of the Babies substudy of the Bogalusa Heart Study5 examined the relationship between CVRFs and fertility. In this study, some analyses demonstrated alterations in cholesterol, blood pressure, and glucose as being associated with indicators of infertility. We were able to expand on these works by evaluating for multiple markers of cardiovascular changes simultaneously, including parameters not previously evaluated, such as cardiac diastolic function and peak longitudinal strain. The relative impact of each parameter is represented by the β estimate in our generalized linear models, with larger values representing larger influence.
Our generalized linear models identify a strong association between blood pressure and all makers of cardiovascular changes evaluated. We and others have previously shown that elevated blood pressure alone is a risk factor for increased LVMI.8,11,33 The current study advances this work by focusing not only on blood pressure as a risk factor for LVMI, but by also considering multiple components of the metabolic syndrome and their impact on the heart, vascular system, and kidneys.
Pacifico et al7 demonstrated that HOMA-IR is associated with carotid extra-media thickness, also a marker of cardiovascular disease. We did not evaluate arterial wall thickness but rather used pulse wave velocity as a surrogate for vascular stiffness, though both are markers of cardiovascular disease. We did not find an association between abnormal HOMA-IR and increased pulse wave velocity but did find a correlation with all 3 markers of abnormal cardiac diastolic function. Impaired cardiac relaxation may be difficult to detect clinically and recent work by Heiskanen et al34 has demonstrated that obesity in childhood is independently associated with left ventricular diastolic function in adulthood. Our study suggests that this correlation may begin even earlier in adolescence.
The development of cardiovascular disease in adults, the leading cause of death in North America, has been directly linked to a metabolic phenotype that includes hypertension, dyslipidemia, insulin resistance, and obesity. This metabolic phenotype is increasingly common among children and, when present, has been shown to persist into adulthood.35–37 Less is known about whether the presence of risk factors alone in youth contributes to TOD during childhood. The data from our study are compatible with the concept that the TOD in adults with cardiovascular disease has its origins in childhood and that subclinical TOD may begin to appear as risk factors for cardiovascular disease develop during adolescence. As the data on this topic grow, more aggressive screening, lifestyle intervention, and possible pharmacologic therapy may be warranted.
Our study had several limitations. It was cross-sectional and although statistically significant associations were found, these cannot be construed to imply causality. Although our cohort was recruited at multiple sites across the United States to enhance generalizability, the cohort was relatively small compared with other studies. There is ongoing debate about the best way to define metabolic syndrome in children. We chose to use HOMA-IR as a surrogate for insulin resistance in our analysis over more conventional markers such as fasting glucose levels or hemoglobin A1c levels, which may limit its ease of use in pediatric and general practice as well as the generalization of findings. We also did not evaluate other known confounding risk factors that could be contributing to TOD, such as tobacco use.
Conclusions
These data demonstrated that in a cohort of otherwise healthy youth, higher numbers of CVRFs were independently associated with increased risk of markers of early TOD. Future studies should address whether amelioration of these CVRFs in the young averts development of TOD and adult cardiovascular disease.
Acknowledgments
We thank all the SHIP AHOY participants and their families.
Dr Price collected data, conducted the initial analyses, drafted the initial manuscript, and reviewed and revised the manuscript; Drs Urbina, Flynn, Daniels, Falkner, Ferguson, Hooper, Lande, and Meyers served as site primary investigators and were involved in study conceptualization, design, data collection instrument design, data collection, and reviewed and revised the manuscripts; Ms Carlin conducted initial data analyses; Drs Becker, Hanevold, Martin, Mitsnefes, Samuels, and Rosner were involved in data collection and reviewed and revised the manuscript; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.
FUNDING: The project was supported by the following grants: American Heart Association SFRN 23680000; National Institute of Health UL1 TR001425 Clinical and Translational Sciences Award Program, and National Center For Advancing Translational Sciences UL1 TR002319. Funded by the National Institutes of Health (NIH).
CONFLICT OF INTEREST DISCLOSURES: The authors have indicated they have no financial relationships relevant to this article to disclose.
The number of cardiovascular risk factors in adolescents is independently associated with early changes in markers of target organ damage, including cardiac size and function.
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