Association Between Carotid Intima Media Thickness, Age, and Cardiovascular Risk Factors in Children and Adolescents

Abstract

Background: Measures of carotid intima media thickness (cIMT) in adults are correlated with adiposity and the metabolic syndrome (MetS) and predict cardiovascular (CV) events. Relations in children are not as well studied. Our objective was to determine the relations of cIMT with body mass index (BMI) and CV risk score in children.

Methods: The study included 291 children (158 M/133 F) 6–18 years of age (140 aged 6–11/151 aged 12–18) with measurements of height, weight, waist circumference; fasting lipids, glucose, insulin, and cIMT. A CV risk cluster score was developed from sum of the z-scores of the five MetS components (waist circumference, blood pressure, serum triglycerides, high-density lipoprotein cholesterol, and insulin). Partial Pearson correlation coefficients were adjusted for age, sex, and race.

Results: There was no significant age difference in cIMT from 6 to 18 years of age. BMI and CV risk score were significantly correlated (P < 0.0001), and both were correlated with cIMT (r = 0.14, P = 0.02 and r = 0.16, P = 0.006, respectively). Slight age-related differences in associations of cIMT with CV risk score and BMI were explained by unusual values in a few children.

Conclusions: These cross-sectional data in normal children show that cIMT was stable from childhood into adolescence. However, both BMI and CV risk score had small, but significant positive correlations with cIMT. Therefore, maintaining normal levels of adiposity and other risk variables may be useful in preventing early changes associated with preclinical atherosclerosis.

Introduction

Subclinical atherosclerosis begins in early childhood and progresses into adulthood. An early manifestation of the atherosclerotic process is the accumulation of lipid in the intimal layer of arteries forming fatty streaks,¹ leading to morphological changes,² a potential impact on vascular function, and eventually leading to coronary artery disease and stroke.³ The development of atherosclerosis is accelerated in the presence of risk factors in youth, such as obesity,^4–6 hypertension, dyslipidemia,⁷ and insulin resistance.^4,7

Evidence from longitudinal adult studies has confirmed a significant relation between carotid intima media thickness (cIMT) and development of myocardial infarction and stroke.^8–10 Data on cIMT are less clear in pediatric populations. Cross-sectional studies in children published by our group and others have shown significant relations for cIMT with obesity, blood pressure, metabolic syndrome (MetS), visceral adipose tissue, and insulin resistance,^11–13 and persistence of cardiovascular (CV) risk factors in childhood has been associated with cIMT in adulthood.^14–20 Others have shown increased cIMT in high-risk adolescents with diabetes and chronic kidney disease.²¹

Despite these prior studies, there continues to be a lack of information on cIMT during development of healthy children and adolescents and conflicting information on cIMT differences between healthy lean adolescents and adolescents with obesity or MetS.^11,13 Defining the age course of cIMT and cIMT relations with presumed risk factors before adulthood is relevant to the early clinical approach to preventive cardiology and longitudinal studies of cIMT from childhood into adulthood. In this study, we focus on healthy children and adolescents to provide new data on the cIMT growth curve and on the association of cIMT with a CV risk cluster score and body mass index (BMI).

Materials and Methods

The study protocol was approved by the University of Minnesota Institutional Review Board, and consent/assent was obtained from parents/participants, respectively. Data were collected in 2007–2012 from participants in a community-based study evaluating cardiometabolic risk in healthy children who were the offspring of parents recruited when they were children in the 1970s.²² Participants included were nondiabetic, free from chronic diseases, and were not taking medications known to influence vascular function and/or glucose metabolism; no potential participants were excluded for these reasons. All testing were performed in the morning after an overnight fast (including no caffeine consumption) of at least 8 hr. Height was measured using a wall-mounted stadiometer and weight with an electronic scale. BMI was calculated as body weight in kilograms divided by height in meters squared. BMI percentiles were determined using age and sex-based Centers for Disease Control definitions.²³ Blood pressure was measured twice on the right arm using a digital blood pressure monitor after participants were sitting in a quiet room for at least 5 min, and an average of the two values was reported for systolic blood pressure and diastolic blood pressure. A fasting lipid profile [total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol, triglycerides (TG)], and fasting insulin and glucose were measured using standard procedures in the Fairview Diagnostic Laboratories at the Fairview University Medical Center (Minneapolis, Minnesota), a Center for Disease Control and Prevention-certified laboratory.

As previously reported,²⁴ we developed a CV risk cluster score, consisting of the average of standardized deviates of the primary components of the MetS (i.e., the sum of the z scores of waist circumference, SBP, TG, inverse HDL-C, and fasting glucose divided by 5. As base values, we used means and standard deviations for the five MetS components at mean age 11.7 and 22 years, namely the mean age of the cohort. Thus, the score at any age can be computed as: 1/5*[(waist circumference-77.7)/11.4 − (HDL-C-44.6)/10.2 + (TG-90.5)/52.9 + (SBP-107.6)/9.2 + (glucose-88.2)/7.4]. The concept allows the score to grow with age and changes in the MetS components by inserting the observed values of waist circumference, HDL-C, TG, SBP, and fasting glucose for that age into the formula. A higher z-score indicates that the components tend to cluster in the higher sections of the distributions, that is, represent overall higher risk. While it is theoretically possible that a high cluster score could arise from an extreme value of a single component, we verified that high scores are almost always the result of high values in two or more components.

Images for determining cIMT were obtained at end diastole (gated by R-wave on electrocardiography) using B-mode images obtained at the distal 10 mm of the left common carotid artery, as recommended by pediatric guidelines.¹¹ An electronic wall-tracking software program was used for the analysis of cIMT. Our laboratory has previously documented satisfactory reproducibility for measurement of cIMT, with the mean difference for repeated measurements on separate days in the same subject of 0.02 ± 0.03 mm.²⁵

Statistical analyses

Demographic, anthropometric, and cardiometabolic differences across age group were compared by paired t-test. Data were examined using both continuous and categorical analyses. Variables that were not normally distributed (i.e., fasting insulin, TG) were log transformed. However, because results were not qualitatively different from those using nontransformed data, nontransformed data were presented for simplicity and were used for the calculation of the CV cluster score.

cIMT was analyzed by ANCOVA using linear mixed models to adjust for sex, age, and race. We studied the linearity assumption made in computing correlations by examining data in categories. The data analysis for this article was generated using SAS software. Copyright^© SAS Institute, Inc., Cary, NC. Significance was set at an alpha level of P < 0.05.

Results

Demographic and clinical characteristics of the study population are displayed in Table 1. A total of 291 children (158 males), 55% Caucasian, 31% Black, 14% other, with age range 6–18 years, mean age 11.7 years, were included in the study. Given the wide age range, participants were divided into childhood (younger children) (age 6–11) and adolescent (older children) (age 12–18) groups. The gender distribution was similar between the two age groups. While the mean BMI percentile was slightly higher in adolescents (with borderline significance), the mean waist circumference, systolic blood pressure, fasting insulin, CV risk cluster score, and cIMT were significantly greater in the adolescents than younger children. As shown in Table 2, there were significant differences in cIMT between ages 6–18 years, however, there was no clear progression with age and there was no significant age effect (P = 0.08).

Table 1.

Descriptive Characteristics of the Cohort

Participants: age variables	All (6–18 years) n = 291, mean ± SD	Younger (6–11 years) n = 140, mean ± SD	Older (12–18 years) n = 151, mean ± SD	P-value (where appropriate)
Caucasian, n (%)	161 (55)	98 (70)	63 (42)
Black, n (%)	89 (31)	27 (19)	62 (41)
Native American, n (%)	11 (4)	5 (4)	6 (4)
Asian, n (%)	10 (3)	4 (3)	6 (4)
Hispanic, n (%)	2 (1)	0 (0)	2 (1)
Other, n (%)	18 (6)	6 (4)	12 (8)
Gender, n (%)	Male: 158 (54)	Male: 78 (56)	Male: 158 (54)
	Female: 133 (46)	Female: 62 (44)	Female: 133 (46)
BMI (kg/m2)	21.3 ± 6.3	17.9 ± 3.4	24.4 ± 6.6	<0.0001
BMI percentile	64.7 ± 29.7	61 ± 29.2	68 ± 29.9	0.047
Overweight (BMI 85th percentile–95th percentile), n (%)	52 (17.9)	24 (17.1)	28 (18.5)
Obese (BMI ≥95th percentile), n (%)	54 (18.6)	16 (11.4)	38 (25.2)
Waist (cm)	72.7 ± 16.1	63.5 ± 10.1	81.1 ± 15.9	<0.0001
SBP (mm Hg)	104 ± 10	100 ± 9.1	107 ± 9.7	<0.0001
DBP (mm Hg)	57 ± 7	56 ± 6.8	59 ± 7.7	0.003
Fasting insulin (μU/L)	7.7 ± 7.4	4.6 ± 5.5	10.5 ± 7.8	<0.0001
Total-C (mg/dL)	154.4 ± 28.1	160 ± 28.3	149 ± 26.9	0.0006
LDL-C (mg/dL)	88.5 ± 23.7	93 ± 23.2	84 ± 23.3	0.0008
HDL-C (mg/dL)	51.1 ± 11.8	54 ± 10.9	48 ± 12.1	<0.0001
TG (mg/dL)	73.9 ± 38.9	65 ± 28.4	82 ± 45	0.0001
Fasting glucose (mg/dL)	76.9 ± 13.8	69 ± 13.1	84 ± 10.2	<0.0001
CV risk cluster score with waist	0.03 ± 0.6	−0.4 ± 0.5	0.38 ± 0.5	<0.0001
cIMT (mm)	0.45 ± 0.04	0.44 ± 0.04	0.45 ± 0.04	0.9

SD, standard deviation; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; Total-C, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; cIMT, carotid intima media thickness; CV, cardiovascular.

Table 2.

Carotid Intima Media Thickness by Age in Children and Adolescents

Age (years)	N	cIMT mean (mm) ± SE	P
6	31	0.44 ± 0.007	<0.0001
7	24	0.44 ± 0.008	<0.0001
8	22	0.46 ± 0.009	<0.0001
9	22	0.44 ± 0.008	<0.0001
10	23	0.44 ± 0.008	<0.0001
11	18	0.48 ± 0.009	<0.0001
12	18	0.45 ± 0.009	<0.0001
13	22	0.44 ± 0.008	<0.0001
14	28	0.44 ± 0.007	<0.0001
15	26	0.44 ± 0.008	<0.0001
16	20	0.44 ± 0.009	<0.0001
17	17	0.44 ± 0.010	<0.0001
18	20	0.44 ± 0.009	<0.0001

SE, standard error.

There was a significant correlation between BMI and the CV risk score (r = 0.61, P < 0.0001). Although deleting waist from the CV risk score attenuated the correlation with BMI, it remained highly significant (r = 0.43, P < 0.0001).

The correlations of cIMT with BMI and the CV cluster score are shown in Table 3. In analyses of the total cohort, cIMT had a weak but significant association with BMI (r = 0.14, P = 0.02; Table 3 and Fig. 1) and the CV risk cluster (r = 0.16, P = 0.006). Due to the wide age range (6–18 years) of the cohort, we examined the relations among cIMT, adiposity, and CV risk scores separately in the younger (age 6–11 years, n = 140) and older (age 12–18 years, n = 151) children (Table 3). In interpreting the apparent lack of correlation in BMI with cIMT in 6–11 year olds we should exercise caution, since it might have been influenced by the eight children with mean BMI = 27.2 and with surprisingly low cIMT, relative to the linear trends observed in children with lower BMI.

Table 3.

Relation of Carotid Intima Media Thickness to Body Mass Index and Cardiovascular Risk Cluster Score by Age Group

Total cohort N = 291	cIMT	BMI r = 0.14; P = 0.02	CV risk cluster score r = 0.16; P = 0.01
Age 6–11 years, N = 140 (48%)	cIMT	r = 0.04; P = 0.65*	r = 0.20; P = 0.02
Age 12–18 years, N = 151 (52%)	cIMT	r = 0.21; P = 0.01	r = 0.14; P = 0.10

CV risk cluster score included the sum of the z scores of waist circumference, SBP, triglycerides, inverse HDL-C and fasting glucose divided by 5. Analyses were adjusted for sex, race, and age.

^*This was considered unreliable due to outliers. After elimination of the outliers, the correlation coefficient r improved (r = 0.16), but P-value was still not significant (P = 0.07).

FIG. 1.

Filled–BMI, unfilled–CVR cluster score with waist. BMI, body mass index; CV risk, cardiovascular risk; cIMT, carotid intima media thickness.

Discussion

In this cross-sectional study in healthy youth we found that cIMT did not change significantly between childhood and adolescence. The literature is inconsistent regarding cIMT changes in healthy children. The age trend of cIMT in a cohort of 1100 healthy children from the largest currently available pediatric study²⁶ was positive but small, supporting our hypothesis that changes in vascular morphology progress slowly. Conversely, a report in 280 healthy Brazilian children, in whom cIMT measured at the distal 10 mm of the far wall of both the right and left common carotid artery, was constant in children younger than 10 years, but increased significantly between ages 10–15 years.²⁷ This variability, although small, may be related to variations in cIMT measurement site and method or to differences in population characteristics.

Studies in obese and diabetic youth have found that the earliest atherosclerotic changes in cIMT occur at the carotid bulb and internal carotid artery,¹¹ suggesting that measurements in these carotid locations in the Brazilian study, rather than only at the left common cIMT used in our and previous studies²⁵ in healthy children, may have resulted in the age-related increases in cIMT. Increased cIMT has been reported in children with traditional CV risk factors, such as obesity, hypertension, and chronic kidney disease.^28–31 Contrary to a previous study showing lack of association between cIMT and BMI in healthy children less than 15 years of age,²⁷ we observed an association between cIMT and both BMI and CV risk cluster score. While the association with BMI was stronger in adolescents (age 12–18) than in young children, cIMT showed stronger association with the CV risk score in younger children (age 6–11). These findings persisted after excluding waist circumference from the CV risk score analyses, suggesting that a CV risk score of traditional CVD risk factors, independent of adiposity, is related to cIMT. However, we acknowledge that all CV risk factors may be confounded by waist, and simply excluding waist from the CV risk score may not remove its total influence on CV risk.

Notably, in the majority of the existing literature in children, associations of cIMT and adiposity are compared between lean and obese youth.³² In the current study, despite the overwhelming majority of youth being nonobese and generally healthy, the fact that both major concepts of what might constitute adult risk (BMI and CV risk score) correlated with cIMT is important in the notion of primary prevention of CVD. Therefore, maintaining normal levels of adiposity and other risk variables may be useful in preventing early changes associated with preclinical atherosclerosis.

The most unique finding is the lack of growth in cIMT during childhood and adolescence in healthy youth, despite growth in body size. Although the idea that childhood obesity and MetS features are somewhat predictive of cIMT is less novel, the study provides insight in important features of growth and biology.

Some limitations of this study should be considered. Differences among the younger and older children in the acquisition of data did not allow complete comparisons of some variables, such as Tanner stage and socioeconomic status. By virtue of this being an observational study, we cannot exclude residual confounding or make definitive statements regarding causality. Nevertheless, data are consistent with the notion that excess adiposity in childhood and adolescence is related to higher levels of MetS-related variables and with concurrently measured cIMT throughout childhood and adolescence. These concepts are also consistent with recommendations to maintain normal BMI during these critical periods of growth.

Acknowledgments

National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Diseases R01DK072124 (J.S.), the National Center for Research Resources Grants 1UL1RR033183 to the University of Minnesota Clinical and Translational Science Institute, and the General Clinical Research Center Program M01-RR00400, and T32DK65519)–Dr. J.S., MD, MS.

Author Disclosure Statement

No conflicting financial interests exist. No honorarium, grant, or other form of payment was given to anyone to produce the article.