The Relationship between the Waist Circumference and Increased Carotid Intima Thickness in Obese Children

Abstract

Background:

This study aimed to evaluate the cardiometabolic risk factors in normotensive obese and hypertensive obese (HT-obese) children by comparison of anthropomorphic measurements, fat distribution, carotid artery intima-media thickness (CIMT), and inflammatory markers.

Methods:

Fifty-three obese patients 10–18 years of age with a BMI-for-age/gender >95th percentile and 20 age- and gender-matched healthy volunteers enrolled in the study. Obese patients were divided into two groups according to the presence of hypertension (HT), as follows: HT-obese subgroup (n = 30) and nonhypertensive obese (non-HT-obese) subgroup (n = 23).

Results:

Weight standard deviation score (SDS), BMI-SDS, waist circumference (WC) SDS, and the fat tissue z-score were significantly higher (p < 0.001 for all) in the obese patients than the control groups. Obese patients had higher 24-hour systolic blood pressure (SBP) SDS and leptin, high-sensitivity C-reactive protein, tumor necrosis factor-alpha, and interleukin-6 levels. Furthermore, CIMT and CIMT-SDS were significantly higher in them. HT-obese patients (n = 30) had significantly higher WC-SDS and lower serum leptin and adiponectin levels than those of non-HT-obese group (n = 23). Finally, an association between increased CIMT-SDS and WC-SDS (β = 0.399, p = 0.002) and 24-hour SBP-SDS (β = 0.272, p = 0.009) was shown.

Conclusions:

Association between increased WC and HT implies the importance of central obesity in atherosclerosis. We concluded that WC measurement could be used to define risk groups since it is related to cardiometabolic complications.

Introduction

As all over the world, prevalence of obesity has been increasing in Turkey and is reported to be 6.5%–8.9%.^1,2 Changes in nutritional habits and decrease in physical activity have made obesity an endemic disease of modern life. Dyslipidemia, insulin resistance (IR), hypertension (HT), and atherosclerosis are now more commonly observed in children as the prevalence of obesity increases.³ Although coronary artery disease as a complication of obesity has still been seen only in adults, atherosclerosis has been reported to begin during childhood with an increase in fatty tissue.^4,5 Obesity is associated with inflammation that may play a major role in the development of atherosclerosis.^6–8 HT, dyslipidemia, and IR are other risk factors that contribute to the development of atherosclerosis in obesity.^9–11 Clinical studies emphasize the role of obesity in children and adolescents in the development of atherosclerosis in early childhood. Carotid artery intima-media thickness (CIMT) has recently become a well-known marker of the atherosclerotic process. Measurement of CIMT may be valuable for long-term follow-up in children with an increased risk of atherosclerosis.^9–11 Increase in CIMT have previously been shown among obese children and researches claimed that the CIMT measurement may be an additional tool to improve risk stratification of atherosclerosis in these children.^9–11

This study aimed to determine the presence of traditional (measures of obesity, HT, dyslipidemia, IR) and novel (adipokines) cardiometabolic risk factors and put forth the potential predictors of subclinical atherosclerosis (CIMT) in children and adolescents with obesity.

Materials and Methods

The study included 53 children and adolescents with obesity (age- and gender-specific BMI ≥95th percentile)¹² between 10 and 18 years of age. Control group consisted of 20 age- and gender-matched healthy children and adolescents. Patients with syndromic obesity (children and adolescents with mental retardation, dysmorphic features, organ-specific abnormalities, hyperphagia, and/or other signs of hypothalamic dysfunction), any kind of chronic disease including allergic diseases, or type 2 diabetes were excluded.

Anthropometric Measurements and Body Composition Analysis

All anthropometric measurements and body composition analyses were carried out at the same study visit and obtained in the morning after fasting at least 4 hours, after 15 minutes of rest. Weight (kg), height (cm), and waist circumference (WC) (cm) were measured and BMI was calculated (kg/m²)—measurements were performed by the same physician. The naked weights were obtained on an electronic digital scale, accurate to 5 g. Standing height was measured using a Leicester Height Measure. WC was measured using a nonelastic tape with the subject in a standing position. Standard deviation scores (SDS) of weight, height, WC, and BMI were computed using the least mean squares (LMS) method and the references for Turkish children.^13–15 The Tanner scale was used to evaluate pubertal status.^16,17

Fat mass (kg) was estimated by multiple frequency bioimpedance analysis (BIA). Measurements were performed by the same physician using a portable bioimpedance device [the Body Composition Monitor (BCM); Fresenius Medical Care, Germany]. Electrodes were placed on the wrist and on the ipsilateral ankle, and then connected to the BCM monitor. Adipose tissue (%) was measured and then z-score of the adipose tissue was calculated [z-score = (fat % – mean fat %)/standard deviation (SD)].¹⁸

Laboratory Measurements

Blood samples were collected in the morning after a 12-hour fasting for measurements of the following biochemical parameters: triglycerides (TG), total cholesterol, low-density lipoprotein (LDL)-cholesterol, high-density lipoprotein (HDL)-cholesterol, glucose, and insulin. The lipid profile and glucose were analyzed using routine laboratory methods. Insulin was measured by the chemiluminescence immunoassay method. The homeostasis model assessment of insulin resistance (HOMA-IR) was calculated to estimate insulin resistance using the following formula: (HOMA-IR = fasting plasma glucose (mg/dL) × fasting plasma insulin (μU/m)/405).¹⁹ In addition, serum samples were stored at −80°C and subsequently studied for high-sensitivity C-reactive protein (hs-CRP) using the nephelometric method and interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), adiponectin, and leptin using enzyme-linked immunosorbent assay.

Blood Pressure Assessment

Office blood pressure (BP) was measured in both arms by an oscillometric device and then averaged. Indexed systolic blood pressure (SBP) and diastolic blood pressure (DBP) were calculated by dividing the observed BP to the age, gender, and height-specific 95th percentile of BP. Furthermore, 24-hour ambulatory blood pressure monitoring (ABPM) was performed using a Spacelabs 90217 device (Spacelabs Healthcare, Snoqualmie, WA). SBP and DBP, mean arterial pressure, and dipping were recorded during daytime, nighttime, and 24 hours. The height-specific SDSs of all BP parameters were calculated.²⁰ HT was defined according to the American Heart Association criteria.^21,22 In addition, all patients using antihypertensive drug were considered as hypertensive regardless of their BP values.^21,22 Patients were divided into two subgroups according to the presence or absence of HT: hypertensive obese (HT-obese) and nonhypertensive obese (non-HT-obese) subgroups. Patients were also classified as having metabolic syndrome according to International Diabetes Federation consensus criteria.^23,24

Vascular Assessment

Carotid intima-media thickness was measured using a B-mode (2D) high-resolution ultrasonography (Siemens Acuson P50 Ultrasound System, Munich, Germany). Measurements were performed in the supine position and the neck slightly hyperextended. Three manual measurements were obtained in both carotid arteries, 1 to 2 cm proximal to the bifurcation,⁹ and then these were averaged for each patient. The SDSs for CIMT were calculated using the LMS method and height-specific normative values.²⁵

Statistical Analysis

Statistical analyses were performed using the SPSS software version 21. The variables were investigated using visual (histogram, probability plots) and analytic methods (Kolmogorov–Smirnov) to determine whether they are normally distributed. The data of descriptive analysis were expressed as mean ± SD or median; interquartile range where appropriate. Categorical variables were compared with the chi-square test or Fisher's exact test where appropriate. The Student's t-test or Mann–Whitney U-test was used to compare the continuous data between two groups. Associations between variables were assessed by Spearman's correlation analysis. The variables that showed a p-value of 0.05 in the univariate analysis were tested in a multivariate regression analysis for assessment of risk factors. The variance inflation factor (VIF) was used to reduce multicollinearity. The level of statistical significance was defined as two-tailed p ≤ 0.05.

Results

Clinical and Laboratory Findings

This cross sectional, case–control study included 53 obese children and adolescents and 20 healthy controls. There were no significant differences between the groups regarding age, gender, or Tanner pubertal status. The median SDSs of weight, BMI, and WC, and the fat tissue z-score were significantly higher in the obese group compared with the control group (Table 1).

Table 1.

Demographic and Anthropometric Findings of the Obese and Control Groups

	Patient group (n = 53)	Control group (n = 20)	p
Age, ^* years	14.1 ± 2.14	14.7 ± 1.73	0.62
Female/male	29/31	9/11	0.42
BMI-SDS^**	2.60; 0.82	0.21; 1.07	<0.001
Weight SDS^**	2.80; 0.77	0.47; 0.12	<0.001
Height SDS^**	0.35; 1.21	0.35; 1.29	0.52
WC-SDS^**	3.44; 0.94	0.87; 1.23	<0.001
Fat tissue z-score,^** %	3.09; 1.15	0.01; 1.51	<0.001

Data are given as mean ± SD and Student's t-test was used to compare two groups.

Data are presented as median; IQR and Mann–Whitney U-test was used to compare two groups.

IQR, interquartile range; SDS, standard deviation scores; WC, waist circumference.

HOMA-IR, TG, LDL-cholesterol, all inflammatory markers (hs-CRP, TNF-α, and IL-6), and leptin levels were significantly higher but HDL-cholesterol and adiponectin levels were significantly lower in the obese group compared with the control group. Laboratory findings are given Table 2.

Table 2.

Laboratory Findings of the Obese and Control Groups

	Patient group (n = 53)	Control group (n = 20)	p
TG,^* mg/dL	109 ± 52.1	60.6 ± 15.7	<0.001
TC,^* mg/dL	172 ± 33.7	156 ± 44.7	0.09
LDL-cholesterol,^* mg/dL	100 ± 31.8	83.4 ± 19.7	0.007
HDL-cholesterol,^* mg/dL	47.0 ± 10.6	62.0 ± 16.4	0.001
HOMA-IR^*	4.22 ± 1.92	2.0 ± 1.03	<0.001
hs-CRP,^** mg/dL	0.31; 0.12	0.07; 0.33	<0.001
TNF-α,^** pg/mL	4.05; 8.73	1.7; 2.52	0.001
IL-6,^* pg/mL	3.21 ± 0.92	2.04 ± 0.81	<0.001
Adiponectin,^** μg/mL	12.3; 3.61	15.0; 4.31	0.009
Leptin,^** ng/mL	23.8; 33.9	3.82; 5.12	<0.001

Data are presented as mean ± SD and Student's t-test was used to compare two groups.

Data are presented as median; IQR and Mann–Whitney U-test was used to compare two groups.

HDL, high-density lipoprotein; HOMA-IR, homeostasis model assessment of insulin resistance; hs-CRP, high-sensitivity C-reactive protein; IL-6, interleukin-6; LDL, low-density lipoprotein; TC, total cholesterol; TG, triglycerides; TNF-α, tumor necrosis factor-alpha.

BP and Vascular Findings

Office BP indexes and ambulatory SBP-SDSs during daytime and nighttime were significantly higher in the obese group than in the control group. Office BP and ABPM findings are given in Table 3. A total of 30 obese patients (57%) were HT, whereas none of the controls was HT. Of them, 25 obese patients were receiving anti-HT drugs and classified as HT. The remaining five obese patients (9.4%) were not diagnosed as having HT previously, but according to ABPM findings, they were classified as having HT. Among patients treated with anti-HT drugs, 22 of patients had controlled HT. Anti-HT drugs were angiotensin-converting enzyme inhibitor/angiotensin receptor blockers (ACEI/ARBs) (n = 12), calcium channel blockers (CCBs) (n = 6), or the combination of ACEI/ARB and CCB (n = 7).

Table 3.

Blood Pressure and Vascular Findings of the Patient and Control Groups

	Patient group (n = 53)	Control group (n = 20)	p
Office SBP index	1.06; 0.16	0.86; 0.41	<0.001
Office DBP index	0.99; 0.26	0.80; 0.07	0.003
24-hour SBP-SDS	0.14; 1.52	−0.55; 0.57	0.003
24-hour DBP-SDS	−0.87; 1.36	−0.39; 0.69	0.29
24-hour MAP-SDS	−0.17; 1.17	−0.26; 0.71	0.43
Daytime SBP-SDS	−0.07; 1.36	−0.71; 0.79	0.008
Daytime DBP-SDS	1.14; 1.47	1.55; 0.17	0.23
Nighttime SBP-SDS	0.75; 1.11	0.23; 0.83	0.002
Nighttime DBP-SDS	0.22; 1.31	−0.04; 1.03	0.84
CIMT, mm	0.44; 0.56	0.35; 0.71	0.001
CIMT SDS	1.33; 1.21	−0.62; 1.85	0.001

All data are presented as median; IQR and Mann–Whitney U-test was used to compare two groups.

CIMT, carotid artery intima-media thickness; DBP, diastolic blood pressure; MAP, mean arterial pressure; SBP, systolic blood pressure.

As given in Table 3, the median SDS of CIMT in the obese group was significantly higher compared with the control group (p = 0.001). Fifteen of 53 patients (28%) had an increased CIMT (>2 SDS).

Comparison of the HT-Obese and Non-HT-Obese Patients

A total of 30 patients (57%) were classified as HT-obese subgroup, whereas 23 patients (43%) were non-HT obese. As given in Table 4, there were no significant differences between the two subgroups regarding age, gender, median SDSs of weight, height, BMI, or fat tissue z-score, whereas the median SDS of WC was significantly higher in the HT-obese subgroup (p = 0.02). The lipid profile, HOMA-IR, or inflammatory markers (hs-CRP, IL-6, and TNF-α) did not differ between the two subgroups; however, median levels of serum leptin and adiponectin were significantly lower in the HT-obese subgroup (Table 4). Furthermore, leptin levels were significantly lower in the patients using ACEI/ARBs compared with the patients using CCBs (13.1 ± 13.7 vs. 18.3 ± 37.9, p = 0.02).

Table 4.

Comparison of Clinical and Laboratory Findings between Hypertensive-Obese and Nonhypertensive-Obese Groups

	HT-obese group (n = 23)	Non-HT-obese group (n = 30)	p
Clinical findings
Age,^* years	13.97 ± 2.37	14.29 ± 1.89	0.99
Weight SDS^**	2.81; 1.07	2.67; 0.32	0.95
Height SDS^**	0.13; 0.72	0.64; 1.55	0.34
WC SDS^**	3.52; 1.11	3.42; 0.97	0.02
BMI SDS^**	2.62; 0.77	2.54; 0.97	0.55
Fat tissue z-score^**	3.12; 1.02	2.93; 1.22	0.16
Inflammatory and vascular findings
hs-CRP,^** mg/dL	0.30; 0.03	0.33; 0.43	0.06
TNF-α,^** pg/mL	3.20; 9.28	4.40; 8.48	0.18
IL-6,^* pg mL	3.40 ± 1.05	3.10 ± 0.85	0.65
Adiponectin,^** μg/mL	11.2; 3.35	13.7; 3.13	0.006
Leptin,^** ng/mL	20.6; 16.8	42.3; 35.5	<0.001
Carotid IMT,^** mm	0.45; 0,09	0.43; 0.30	0.35
Carotid IMT SDS^**	1.44; 1.32	1.06; 1.01	0.06

Data are presented as mean ± SD and Student's t-test was used to compare two groups.

Data are presented as median; IQR and Mann–Whitney U-test was used to compare two groups.

HT-obese, hypertensive obese; IMT, intima-media thickness; non-HT obese, nonhypertensive obese.

Median SDS of CIMT was higher in HT-obese patients but the difference did not reach statistical significance (p = 0.06). Ten HT-obese patients (33%) and five non-HT obese patients (22%) had an increased CIMT.

Comparison of the Metabolically Healthy and Metabolically Unhealthy Obese Patients

Twenty-four patients were classified as having metabolic syndrome. There were no significant differences between the obese patients with and without metabolic syndrome regarding age, median SDSs of weight, height, BMI, or fat tissue z-score, whereas the median SDS of WC was slightly higher in the patients with metabolic syndrome than the metabolically healthy obese patients (3.69; 1.25 vs. 3.32; 0.74, p = 0.04). Patients with metabolic syndrome had significantly higher levels of insulin (21.63; 15.54 vs. 15.15; 9.58, p = 0.001) and TG (125.5; 112 vs. 84.5; 38.3, p = 0.07). HDL levels were significantly lower in patients with metabolic syndrome than others (50.25; 20.9 vs. 41.6; 8.2, p = 0.01). The other laboratory findings did not differ between these two subgroups. Patients with metabolic syndrome had higher median SDS of CIMT than patients without metabolic syndrome (1.58; 1.57 vs. 1.12; 0.64, p = 0.06), but the difference was not statistically significant. Furthermore, patients with metabolic syndrome had significantly higher 24-hour SBP-SDS (0.72; 1.47 vs. 0.03; 1.48, p = 0.003).

Evaluation of the Risk Factors of Subclinical Atherosclerosis within Obese Patients

Patients who had an increased CIMT (n = 15) showed higher SDS of WC (3.6; 1.06 vs. 2.9; 0.37, p = 0.02), higher levels of hs-CRP (0.6; 0.12 vs. 0.3; 0.19, p = 0.03), and TNF-α (4.8; 10.7 vs. 3.2; 10.4, p = 0.02) than those with normal CIMT. However, there were no differences between the obese patients with and without increased CIMT in terms of other anthropometric measures, fat mass, BP values, or laboratory findings.

To identify cardiometabolic risk factors for subclinical atherosclerosis (increase in CIMT) in obese patients, all clinical and laboratory results were analyzed by univariate analysis. The median SDS of CIMT was positively correlated with SDSs of weight, BMI, WC, fat mass z-score, office SBP index, 24-hour SBP-SDS, hs-CRP, IL-6, and leptin (Table 5). In a multivariate regression analysis, a high CIMT-SDS was independently associated with higher SDS of WC [p = 0.002, 95% confidence interval (CI) = 0.333–0.699] and higher SDS of 24-hour SBP (p = 0.009, 95% CI = 0.053–0.641). The SDS of WC was correlated with only IL-6 level (r = 0.399, p = 0.001). Thus, the variables including SDS of weight, BMI, WC, and fat mass z-score were highly correlated to each other. We use the VIF to reduce multicollinearity. We removed the high VIF from the multivariate regression model. Because these variables supply redundant information, removing one of the correlated factors usually does not drastically reduce the R-squared. Finally, we found an association between high CIMT and SDS of WC (p = 0.002, 95% CI = 0.333–0.699).

Table 5.

Risk Factors for Carotid Intima-Media Thickness

	Carotid IMT-SDS
	r	p
BMI-SDS	0.555	<0.001
WC-SDS	0.583	<0.001
Fat tissue z-score	0.559	<0.001
Office SBP index	0.551	<0.001
24-hour SBP-SDS	0.441	<0.001
hs-CRP, mg/dL	0.328	0.005
IL-6, pg/mL	0.332	0.001
Leptin, ng/mL	0.472	0.001

Spearman's correlation analysis.

Discussion

This study demonstrates that WC in obese children and adolescents is an important predictor for increased CIMT. Furthermore, systolic HT detected by ABPM seems to be another important risk factor for atherosclerosis and WC in this population.

There are controversial results regarding the increase in CIMT in obese children and adolescents. Some researchers have reported elevated CIMT level in obese patients,^26,27 whereas others reported that it is normal.^28,29 Our results showed that obese patients had significantly higher CIMT-SDS compared with the healthy controls and 28% of obese patients had an increased CIMT. This result suggested that obese patients have increased risk for increased CIMT as a subclinical cardiovascular disease. In this study, increased CIMT was independently associated with WC, which was related to inflammation (higher levels of IL-6).

WC measurement is an easy-to-use method that provides reliable information about fat distribution^30,31; however, BMI is the most known and widely used method of measuring obesity. BIA is another accessible and safe method to estimate body fat mass but studies on its prediction of cardiometabolic risk in the pediatric obese population are limited. A multicenter study including 3327 overweight and obese children and adolescents has reported that BIA-derived body fat was not superior to BMI to assess the cardiometabolic risk.³² On the contrary, WC has been linked to atherosclerosis in children with obesity.^33,34 In this study, patients with increased CIMT had higher SDS of WC, but not higher BMI-SDS or z-score of fat tissue. It is also important to note that WC-SDS was an independent predictor of an increased CIMT, whereas neither BMI-SDS nor fat tissue z-score was not associated with CIMT-SDS. Taken together WC seems to be a valuable indicator for predicting subclinical cardiovascular disease in obese patients.

ABPM provides a more accurate assessment than office BP and enables a more precise diagnosis of HT compared with the office measuring. Pediatric studies have consistently described an association between obesity and higher BP in children.^35,36 High SBP plays a major role in changing the carotid intima-media complex thickness. Our results demonstrated an independent association of increased CIMT with 24-hour SBP-SDS, but not with office measurements. ABPM seems to be superior to office measuring of BP among obese children who have increased risk of atherosclerosis. Early recognition and primary prevention of HT in such children may help to protect against the development of atherosclerosis. Thus, ABPM might be used for routine follow-up of obese children. However, we did not demonstrate any association between DBP and CIMT. Most of HT patients were receiving anti-HT drugs. This may influence the results.

Some studies have confirmed that children with metabolic syndrome had a higher mean value of CIMT.^37,38 Abdominal obesity and the accompanying components of metabolic syndrome may lead to increase thickness of carotid artery wall in children and adolescents. In this study, we demonstrated that patients with metabolic syndrome had slightly higher median SDS of CIMT compared with patients without metabolic syndrome; however, the difference did not reach statistical significance. Along with an increase in adipose tissue, obesity is also associated with an increase in adipokines (including leptin, TNF-α, IL-6, and CRP). Adipokines play an important role in inflammation, atherosclerosis, and glucose metabolism.³⁵ Some researchers consider that obesity may cause inflammation, with a slow and progressive course. This inflammatory process is also thought to be responsible for the endothelial damage and atherosclerosis commonly observed in patients with obesity.⁷ It is reported that hs-CRP and IL-6 levels are significantly higher in the obese children compared with children with normal weight, and also hs-CRP level is correlated with WC and fat mass.³⁹ In this study, hs-CRP, IL-6, and TNF-α levels were significantly higher in the obese group and higher IL-6 was significantly correlated with higher WC-SDS. This result suggests that increased WC has a strengthened role of central obesity in inflammatory process.

Leptin level has been linked to body weight, BMI, WC, and fat mass in a study that included 833 pubertal children.⁴⁰ As stated previously, in this study, the leptin level was significantly higher in the obese group compared with the control group. Of interest, the leptin level was significantly lower in the HT-obese subgroup than in those in the non-HT-obese subgroup. In a study on overweight men, the leptin level was significantly higher in men who were HT, independent of BMI and fat distribution.⁴¹ They suggested that an elevated leptin level might play a causative role in HT. Administration of an ACEI has been reported to reduce food intake and decrease levels of angiotensin 2 and leptin in normotensive mice.⁴² It has also reported that ACE activity increases the leptin level in obese HT patients and ramipril treatment significantly decreases ACE activity and leptin level.⁴³ In this study, 19 patients (76%) in the HT-obese subgroup were using an ACEI or ARB. Compared with the non-HT-obese subgroup, lower leptin levels in the HT-obese subgroup might have been because of the positive effects of ACEI treatment.

Adiponectin is an adipose tissue-specific peptide that is abundantly present in plasma, and has antiinflammatory, antiatherogenic, and insulin-sensitizing effect. Serum levels of adiponectin decrease with obesity and are associated with IR.^44,45 Furthermore, an association between HT and serum adiponectin concentration has been reported by several groups.^46–48 It ameliorates endothelial function and stimulates nitric oxide (NO) production.⁴⁹ Animal studies confirmed that adiponectin-deficient mice display impaired endothelial vasodilation response and NO production.^50,51 Lower adiponectin levels were found to be lower in HT patients, independent of BMI and fat distribution.^45–48 Litwin et al. have also reported that serum adiponectin levels negatively correlated with increased CIMT in children with primary HT.⁵² In this study HT-obese subgroup, the adiponectin level was significantly lower than in the non-HT-obese subgroup, which is in agreement with the literature. However, no association was found between adiponectin and CIMT-SDS.

In this single-center case–control study, the main strength is to analyze many risk factors including BIA-based fat mass, ABPM, and adipokines that may affect cardiometabolic risk in obese children. Our study is limited by the confounding factors associated with small sample size and its cross-sectional design. It is now well established that observational studies assessing a risk factor for a disease have several limitations including causality, reverse causality, and generalizability. We cannot generalize our findings based on an observational and single-centered study. Furthermore, it is still matter of debate whether inflammation causes atherosclerotic diseases or elevated inflammatory cytokines is a result of atherosclerosis. An association between a laboratory marker such as adipokines and cardiovascular risk may represent a causal relationship (causation) or a result of the disease (reverse causation). In addition, we could not ignore confounding factors such as genetic susceptibility, nutrition habits, and psychological state that may lead to atherosclerotic diseases. Furthermore, fat and inflammation-related variables may cause multicollinearity in the regression models. We believe that further studies with a large cohort are required to verify our findings and a larger cohort may help to reduce multicollinearity. In addition, using anti-HT medicine may influence our results or change the effect of BP on CIMT. Another limitation of the study was that it was not feasible to discuss the influence of obesity duration on findings. As the patients were not regularly followed-up by a physician, history of weight status is lacking. However, birth weights of all the patients were normal.

In conclusion, we have demonstrated a relationship between WC and increased CIMT based on vascular risk analysis. WC seems to be a valuable and easy predictor of early cardiovascular disease in obese children. Large-scale multicentric prospective studies including long-term follow-up of obese children may provide better insight to the early screening of risk factors for atherosclerosis and its morbidity.

Footnotes

Acknowledgments

The authors sincerely thank all dialysis nurses for their help during the data collection. The study was funded by scientific research unit of Istanbul University, (Project ID: 5308, project code: 24263).

Author Disclosure Statement

The authors declare they have no conflict of interest.

The study protocol was approved by the local ethical committee of Istanbul University Cerrahpaşa Medical Faculty and written informed consent was received from all the participants or their parents.

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