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Keywords:

  • child;
  • adiposity;
  • body composition;
  • plasma lipoproteins;
  • waist circumference

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Objective: Intra-abdominal fat has been identified as being the most clinically relevant type of fat in humans. Therefore, an assessment of body-fat distribution could possibly identify subjects with the highest risk of adverse lipid profile and hypertension. Few data on the relationship between body-fat distribution and cardiovascular risk factors are available in children, especially before puberty.

Research Methods and Procedures: This cross-sectional study was undertaken to explore the relationship between anthropometric variables, lipid concentrations, and blood pressure (BP) in a sample of 818 prepubertal children (ages 3 to 11 years) and to assess the clinical relevance of waist circumference in identifying prepubertal children with higher cardiovascular risk. Height, weight, triceps and subscapular skinfolds, waist circumference, and BP were measured. Plasma levels for triacylglycerol, total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein cholesterol, apolipoprotein A1 (ApoA1), and apolipoprotein B (ApoB) were determined.

Results: Females were fatter than males (5.8 [3.5] vs. 4.8 [3.3] kg of fat mass; p < 0.01). Males had higher HDL cholesterol and ApoA1/ApoB plasma concentrations than females (p < 0.001 and p < 0.01, respectively). Waist circumference had a higher correlation with systolic and diastolic BP (r = 0.40 and 0.29, respectively; p < 0.001) than triceps (r = 0.35 and 0.21, respectively; p < 0.001) and subscapular (r = 0.28 and 0.16, respectively; p < 0.001) skinfolds and relative body weight (0.33 and 0.23, respectively; p < 0.001). Multivariate linear model analysis showed that ApoA1/ApoB, HDL cholesterol, total cholesterol/HDL cholesterol, and systolic as well as diastolic BP were significantly associated with waist circumference and triceps and subscapular skinfolds, independently of age, gender, and body mass index.

Discussion: Waist circumference as well as subscapular and triceps skinfolds may be helpful parameters in identifying prepubertal children with an adverse blood-lipids profile and hypertension. However, waist circumference, which is easy to measure and more easily reproducible than skinfolds, may be considered in clinical practice. Children with a waist circumference greater than the 90th percentile are more likely to have multiple risk factors than children with a waist circumference that is less than or equal to the 90th percentile.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Several epidemiological studies support the hypothesis that the relationship between adiposity and risk of disease begins early in life (1) (2). Adipose tissue stores have different metabolic activity and relationships to disease risk as a function of their distribution in the body. In adults, intra-abdominal adipose tissue (IAAT) is the most clinically relevant type of body fat, apart from total body fat. Metabolic complications and adverse health effects of increased IAAT include high blood pressure (BP), hyperinsulinemia, type 2 diabetes, and dyslipidemia (3) (4) (5). In prepubertal children, the relationship between body-fat distribution and disease risk factors is not clear.

Accurate methods used to assess total body fat (DXA) and body-fat distribution (computed tomography and magnetic resonance imaging) in humans are not suitable for use in large population studies because of cost, irradiation exposure (i.e., computed tomography), and limited availability outside the research setting (6). To obtain a reasonable estimation of body-fat distribution in children, several anthropometric parameters have been proposed, such as subcutaneous skinfolds and body circumferences, which are easy to perform and have a sufficient degree of accuracy. Some anthropometric measures or indexes, such as body mass index (BMI) and waist circumference, have been used in a large number of studies on adults to analyze the association between adiposity and cardiovascular risk factors (7). Few studies have shown that waist circumference may be a better predictor of cardiovascular disease than BMI and waist-to-hip ratio (8). In fact waist circumference in adults is better correlated with visceral adipose tissue than BMI and waist-to-hip ratio (9). In contrast, the degree of association between cardiovascular risk factors and anthropometric parameters has not been studied extensively in prepubertal children. Therefore, the purposes of the present study were to explore the relationship between anthropometric variables, lipid profile, and BP in a group of 818 children of 3 to 11 years of age and to assess the clinical relevance of waist circumference in identifying prepubertal children with higher cardiovascular risks.

Research Methods and Procedures

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Subjects

A sample of 885 3- to 11-year-old children living in northeastern Italy was used. Selection of sampling areas and participant inclusion have been described previously (10). Briefly, children living in six areas of northeastern Italy were selected, and a number of school districts within each area were chosen to obtain a representative sample of children from different socioeconomic backgrounds and environmental conditions. Each child underwent a physical examination by pediatricians who took anthropometric measurements. All of the subjects were at the prepubertal stage, as verified by an expert pediatric endocrinologist. Pubertal development was clinically assessed based on Tanner stages (11). The parents of each child gave their informed consent to participate in the study.

A total of 67 subjects were not included in the results and statistical analysis because of missing data. We recorded complete data and statistical analysis results for 818 children (443 males and 375 females).

The protocol was in accordance with the Helsinki Declaration of 1975 as revised in 1983.

Physical Characteristics

Measurements of height, weight, triceps and subscapular skinfolds, waist circumference, and BP were carried out under fasting conditions. Body weight was determined to the nearest 0.5 kg on standard physician's beam scales with the child wearing only underwear and no shoes. Height was measured to the nearest 0.5 cm on standardized, wall-mounted height boards according to the following protocol: no shoes, heels together, and child's heels, buttocks, shoulders, and head touching the vertical wall surface with line-of-sight aligned horizontally. BMI was defined as weight/height2 and was expressed in kilograms per squared meter. Each of the standard physician's beam scales and wall-mounted height boards used to measure the children were calibrated previously, using three different weights and one reference tape. Skinfold thickness was measured to the nearest millimeter three times with a Harpenden skinfold caliper on both the right and left sides of the body (CMS; Weighing Equipment Ltd, London, UK). The triceps skinfold locus is halfway between the acromion and olecranon on the back of the arm measured with the elbow bent. We measured the triceps skinfold with the arm pendant, whereas the subscapular skinfold was measured just below the tip of the scapula (12). Readings were taken 3 seconds after the caliper jaws were released. In the present analysis, we used the mean of right- and left-sided measurements for each skinfold. Lohman's formulas were used to estimate relative body fat mass, based on the measurement of triceps and subscapular skinfolds (12). Waist circumference was measured to the nearest centimeter with a flexible steel tape measure while the subjects were in the standing position at the end of gentle expiration (12). The following anatomical landmarks were used: laterally, midway between the lowest portion of the rib cage and iliac crest, and anteriorly midway between the xiphoid process of the sternum and the umbilicus (12). Children were defined as obese on the basis of their BMI. In particular, BMI cutoff values were used, which were age- and gender-specific, according to Cole et al. (13). Relative body weight (RBW), calculated as the percentage of the ratio between weight and body weight at the 50th percentile for age and gender, was obtained in all the children. In accordance with the guidelines approved at the Italian Consensus Conference on Obesity (14), Tanner's growth tables were used to calculate the RBW% of each child (15). The physician made three BP measurements on the left arm over a period of 30 minutes with the subject supine, using a mercury sphygmomanometer. The cuffs used had bladders long enough to circle at least one-half of the upper arm without overlapping and widths that covered at least two-thirds of the upper arm. Systolic BP (SBP) (Korotkoff phase I) and diastolic BP (DBP) (Korotkoff phase V) were measured three times, and the average was used for analysis.

Lipids and Lipoproteins

Fasting venipuncture samples were drawn for lipid determinations. Plasma triacylglycerol (TG) and cholesterol were measured enzymatically (Abbott VP, Milan, Italy), using spectrophotometric methods in our Hospital Core laboratory (16) (17). The plasma high-density lipoprotein (HDL) cholesterol fraction was obtained after precipitation using phosphotungstic reagent. Apolipoprotein A1 (ApoA1) and apolipoprotein B (ApoB) were measured by radial immunodiffusion (18). A 10% sample was randomly chosen each day to assess measurement error, and intraclass correlation coefficients ranged from 0.94 (HDL cholesterol) to 0.99 (TG). Our Core laboratory monitored the accuracy of total cholesterol (TC), HDL cholesterol, TG, ApoA1, and ApoB measurements. The low-density lipoprotein (LDL) cholesterol level was calculated using the Friedewald formula (LDL cholesterol = TC − HDL cholesterol − TG/5) (19).

Statistical Analysis

All statistical analyses were carried out using the SPSS software version 9.0 for Windows (SPSS Inc., Chicago, IL) package for personal computers. Baseline variables are described as groups’ mean and SD. Differences between gender and between non-obese and obese subjects were analyzed using the Student's t test for unpaired samples. Zero-order correlations were performed first to assess unadjusted association between body composition parameters, plasma lipids, and BP. A χ2 test was run to compare the correlation coefficients of the relationships between cardiovascular risk factors and anthropometric variables (20). The degree of association between plasma lipids and BP, adjusted for age, gender (dummy variable), BMI (covariates), and anthropometric parameters, was calculated using a partial correlation multivariate linear model analysis. Plasma TG was not normally distributed; therefore, it was expressed as its logarithm, which normalized the distribution.

To assess the effects of waist circumference on the clustering of risk factors, the children were divided into normal and increased-risk groups for TC, LDL cholesterol, HDL cholesterol, SBP, and DBP. The cutoff points were 0.9 mM/L (35 mg/dL) for HDL cholesterol, 3.4 mM/L (130 mg/dL) for LDL cholesterol, 4.7 mM/L (180 mg/dL) for TC, and 90th percentile for SBP and DBP (21) (22). However, to include intercorrelated variables in the same category, we included LDL cholesterol and HDL cholesterol in the analysis; they were not correlated and excluded TC, which was highly correlated with LDL cholesterol and HDL cholesterol. Children with SBP or DBP higher than the 90th percentile were considered hypertensive. Therefore, three cardiovascular risk-factor categories were considered (LDL cholesterol, HDL cholesterol, and BP). The percentage of subjects with no, one, two, or three risk factors was calculated. The Mann–Whitney test for ordinal data was used to compare the positivity for risk-factor categories (none, one, two, or three) of the two groups of children: Group 1, children with a waist circumference less than the 90th percentile; Group 2, children with a waist circumference greater than the 90th percentile. To assess the prediction level of waist circumference on the probability of having cardiovascular risk factors (HDL cholesterol, LDL cholesterol, ApoA1/ApoB, and BP), we performed a multivariate logistic regression analysis with backward stepping of variables and an evaluation of the model using three goodness-of-fit χ2 statistics. In all the analyses, a probability level of p < 0.05 was used to indicate statistical significance.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

The physical characteristics of the 818 prepubertal children (443 males and 375 females) are shown in Table 1. Triceps and subscapular skinfold measurements and fat mass were significantly higher in females than in males (p < 0.01). RBW% was significantly higher in males than in females (p < 0.01). No other significant differences in the anthropometric parameters between genders were found. Regarding plasma lipids, TC and TG showed no differences between genders. HDL cholesterol and ApoA1/ApoB were significantly lower in females than males (p < 0.001 and p < 0.01, respectively). TC/HDL cholesterol was significantly higher in females than males (p < 0.01).

Table 1.  Physical characteristics and plasma lipids profile of the 845 prepubertal children
 Total sample (n = 818)Males (n = 443)Females (n = 375)
  • Data are shown as mean ± SD.

  • *

    p < 0.01.

  • p < 0.001.

Age (years)7.7 (2)7.8 (2)7.6 (2)
Weight (kg)27.7 (9)28.2 (9)27.2 (8)
Height (cm)126 (15)127 (15)125 (15)
BMI (kg/m2)17 (2)17 (2)17 (3)
RBW (%)104 (15)106 (14)102 (15)*
Triceps skinfold (mm)12 (5)11 (5)13 (5)*
Subscapular skinfold (mm)8 (5)7 (5)9 (5)*
Fat mass (kg)5 (3)5 (3)6 (4)*
Waist circumference (cm)57 (7)57 (7)56 (7)
Systolic blood pressure (mm Hg)107 (13)107 (14)106 (12)
Diastolic blood pressure (mm Hg)65 (11)66 (11)65 (11)
Total cholesterol   
(mmol/L)4.72 (0.80)4.74 (0.79)4.69 (0.82)
(mg/dL)182.3 (31)183.1 (30.6)181.2 (31.8)
LDL cholesterol   
(mmol/L)2.91 (0.69)2.88 (0.68)2.94 (0.72)
(mg/dL)112.4 (26.9)111.5 (26.2)113.5 (27.8)
HDL cholesterol   
(mmol/L)1.49 (0.37)1.54 (0.38)1.43 (0.35)
(mg/dL)57.5 (14.4)59.6 (14.8)55.1 (13.7)
TC/HDL cholesterol3.32 (0.90)3.22 (0.84)3.44 (0.88)*
TG   
(mmol/L)0.69 (0.31)0.68 (0.33)0.72 (0.27)
(mg/dL)61.5 (27)60 (29.3)63.4 (24.2)
ApoA1/ApoB2.13 (0.69)2.20 (0.76)2.04 (0.60)*

The physical characteristics of the children divided into obese and non-obese groups are shown in Table 2. As expected, all of the anthropometric variables and BP were significantly higher (p < 0.01) in the obese children than in the non-obese children. In the obese children, the LDL cholesterol and TC/HDL cholesterol were significantly higher (p < 0.05 and p < 0.01, respectively) and HDL cholesterol was significantly lower (p < 0.05) than in the non-obese children. No other differences were found in the lipids profile between obese and non-obese subjects.

Table 2.  Physical characteristics and plasma lipids profile of the children divided into two groups: obese children and non-obese children
 Obese (n = 43)Non-obese (n = 775)
  • Data are shown as mean ± SD.

  • *

    p < 0.001.

  • p < 0.01.

  • p < 0.05.

Age (years)8.1 (2)7.7 (2)
Weight (kg)41.4 (10)27.2 (8)*
Height (cm)132 (13)126 (15)
BMI (kg/m2)23 (2)17 (2)*
RBW (%)141 (10)102 (12)*
Triceps skin (mm)22 (5)12 (4)*
Subscapular skin (mm)19 (7)7 (4)*
Fat mass (kg)13 (4)5 (3)*
Waist circumference (cm)71 (8)56 (6)*
Systolic blood pressure (mm Hg)119 (14)106 (13)*
Diastolic blood pressure (mm Hg)72 (11)65 (11)*
Total cholesterol  
(mmol/L)4.83 (0.77)4.71 (0.8)
(mg/dL)186.6 (29.9)181.9 (31.1)
LDL cholesterol  
(mmol/L)3.11 (0.66)2.89 (0.69)
(mg/dL)120.5 (25.5)111.7 (26.9)
HDL cholesterol  
(mmol/L)1.37 (0.33)1.49 (0.37)
(mg/dL)52.9 (12.8)57.9 (14.4)
TC/HDL cholesterol3.66 (0.80)3.28 (0.86)
TG  
(mmol/L)0.75 (0.31)0.69 (0.30)
(mg/dL)67.8 (27.3)61.2 (27)
ApoA1/ApoB1.95 (0.49)2.15 (0.70)

Body composition parameters were significantly correlated with each other (p < 0.001; Table 3) as well as with SBP and DBP. The correlation between SBP and waist circumference (r = 0.40; p < 0.001) was significantly higher (χ2 = 9.08; p < 0.05) than those with triceps skinfold (r = 0.35; p < 0.001), subscapular skinfold (r = 0.28; p < 0.001), and RBW (r = 0.33; p < 0.001). The correlation between DBP and waist circumference (r = 0.29; p < 0.001) was higher, but not significantly higher (χ2 = 7.87; p = not significant), than those with triceps skinfold (r = 0.21; p < 0.001), subscapular skinfold (r = 0.16; p < 0.001), and RBW (r = 0.23; p < 0.001). Triceps and subscapular skinfolds were correlated with TG (r = 0.09 and 0.14, respectively; p < 0.001). Subscapular skinfold showed a negative correlation with HDL cholesterol (r = −0.11; p < 0.001). Subscapular skinfold was positively correlated with TC/HDL cholesterol (r = 0.11; p < 0.001). No other correlations were found with body composition parameters. ApoA1/ApoB was correlated with SBP (r = 0.13; p < 0.001) and DBP (r = 0.12; p < 0.001).

Table 3.  Correlation matrix of anthropometric parameters, BP, and plasma lipids in the 818 prepubertal children
 AgeRBWTriceps skinfoldSubscapular skinfoldWaistSBPDBPTCHDL cholesterolLDL cholesterolTC/HDL cholesterolTG
  • *

    p < 0.001.

  • p < 0.05.

Age           
RBW           
Triceps skinfold0.29*0.69*         
Subscapular skinfold0.21*0.67*0.72*        
Waist0.57*0.78*0.71*0.66*       
SBP0.35*0.33*0.35*0.28*0.40*      
DBP0.12*0.23*0.21*0.16*0.29*0.59*     
TC0.12*0.030.020.010.04−0.030.00    
HDL cholesterol0.30*−0.01−0.05−0.11*0.050.06−0.040.41*   
LDL cholesterol−0.0050.020.030.040.01−0.070.0040.90*0.008  
TC/HDL cholesterol0.27*0.010.050.11*−0.06−0.10*0.040.26*−0.71*0.58* 
TG0.09*0.09*0.09*0.14*0.060.020.10*0.17*−0.33*0.16*0.49*
ApoA1/ApoB0.23*0.03−0.06−0.050.060.13*0.12*−0.21*0.53*−0.49*−0.66*−0.22*

Eight sets of multivariate linear model analysis were run using ApoA1/ApoB, HDL cholesterol, TC/HDL cholesterol, LDL cholesterol, triacylglycerol logarithm, TC, SBP, and DBP as dependent variables, adjusted for age, gender, and BMI (covariates), and waist circumference and triceps and subscapular skinfolds as independent variables (Table 4). Waist circumference, as well as subscapular and triceps skinfolds, was significantly associated with ApoA1/ApoB, HDL cholesterol, TC/HDL cholesterol, SBP, and DBP (p < 0.01).

Table 4.  Multivariate linear model analysis using the total sample
VariablesFR2p
  1. Age, gender, and BMI were used as covariates.

  2. NS, Not significant.

Independent variable: waist circumference   
TC1.090.01NS
LDL cholesterol0.86−0.01NS
HDL cholesterol2.890.14<0.001
TC/HDL cholesterol2.200.09<0.001
LogTG1.330.03<0.05
ApoA1/ApoB2.080.08<0.001
SBP4.190.21<0.001
DBP2.630.12<0.001
Independent variable: subscapular skinfold   
TC1.250.03NS
LDL cholesterol1.210.03NS
HDL cholesterol2.090.13<0.001
TC/HDL cholesterol1.850.10<0.001
LogTG1.400.05<0.01
ApoA1/ApoB1.730.09<0.001
SBP3.470.25<0.001
DBP2.380.16<0.001
Independent variable: triceps skinfold   
TC1.120.02NS
LDL cholesterol0.99−0.001NS
HDL cholesterol2.210.16<0.001
TC/HDL cholesterol1.700.10<0.001
LogTG1.120.02NS
ApoA1/ApoB1.430.07<0.01
SBP3.010.25<0.001
DBP1.960.13<0.001

The effects of waist circumference on the clustering of risk factors in the total sample are shown in Table 5. The waist circumference (90th percentile) of the boys and girls at different ages is shown in Figure 1. Approximately 19% of children with a waist circumference that was greater than the 90th percentile had two or more risk factors, compared with 9% of children with a waist circumference that was less than or equal to the 90th percentile and 9.5% predicted by chance alone. Approximately 35% of the children with a waist circumference that was greater than the 90th percentile had no risk factors, compared with 80% of children with a waist circumference less than or equal to the 90th percentile and a predicted number of 48% (p < 0.01). Multivariate logistic regression analysis revealed that children with a waist circumference above the 90th percentile for sex and age have a higher probability of having cardiovascular risk factors. In particular, these children have a significantly greater risk of having lower HDL cholesterol (odds ratio = 0.97; 95% confidence interval: 0.96 to 0.99; p < 0.01) and higher BP (odds ratio = 2.3; 95% confidence interval: 1.41 to 3.72; p < 0.001) than subjects with a waist circumference that is less than the 90th percentile.

Table 5.  Number and percentage of subjects with no, one, two, or three risk factors by percentile of waist
 Waist
Number of risk factors≤90th percentile (n = 743)>90th percentile* (n = 75)
  • Risk factors included are HDL cholesterol, LDL cholesterol, and BP.

  • *

    p < 0.01.

0368 (79.6%)26 (35%)
1308 (41%)35 (47%)
264 (9%)14 (19%)
33 (0.4%)0 (0%)
image

Figure 1. Waist circumference (90th percentile) of males and females of different ages.

Download figure to PowerPoint

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

The results of this study show that the plasma lipids profile and BP in prepubertal children are significantly associated with anthropometric indexes of body-fat distribution. In particular, using multivariate linear model analysis with waist circumference and triceps and subscapular skinfolds as independent variables, we found a significant correlation between these variables and ApoA1/ApoB, HDL cholesterol, TC/HDL cholesterol, SBP, and DBP. The high degree of association between waist circumference and triceps and subscapular skinfolds suggests the use, with a negligible difference, of one of these three different anthropometric parameters. However, waist circumference has some relevant advantages compared with skinfolds: 1) the inter- and intraindividual reproducibility of the measurements is higher in the former than in the latter, and 2) in clinical practice, the measurement of waist circumference instead of skinfolds is easier and offers more accurate results for the pediatrician (3). Moreover, de Ridder et al. demonstrated that waist circumference is a good measure for truncal fat in girls (23). In addition, Goran et al. found that waist circumference was strongly correlated with subcutaneous adipose tissue (24). Based on the aforementioned consideration, we have discussed the results obtained using waist circumference as the dependent variable.

Chan et al. showed that in adults, the best determination of cardiovascular risk can be achieved by using waist circumference as a measure of body-fat distribution (25). In prepubertal children, waist circumference, adjusted for age and gender, significantly contributed the explanation of interindividual variability of HDL cholesterol and SBP. Including weight and height among the independent variables in the regression analyses did not affect the predictability of waist circumference. In our sample, LDL cholesterol did not show a correlation with waist circumference as found previously in studies on adults (26) (27). The difference between the findings for adults and children may be explained by several factors. First, the hormonal pattern in prepubertal children is different from adults, particularly regarding sex hormones. Testosterone and estradiol have been shown to affect body-fat distribution and lipid metabolism in humans (28). Another contributing factor may be the level of physical activity. BP and plasma lipids profile, in particular HDL cholesterol and LDL cholesterol, are associated with fitness (29). Modern lifestyles promote sedentary behavior and reduce the practice of sports or organized physical exercise in children and adults (30). In adults, a low level of fitness has been positively associated with mortality, apart from other risk factors such as obesity, smoking, alcohol intake, and a parental history of ischemic heart disease (31).

Few studies are available on the relationship between waist circumference and cardiovascular risk factors in prepubertal children (3) (32) (33). The findings of our study agree with the results of the Bogalusa Heart Study. Both studies found that waist circumference had a consistent association with cardiovascular risk factors. As in our subjects, the 5- to 9-year-old children of Bogalusa showed an inverse association between waist circumference and HDL cholesterol.

Few studies have discussed the clustering of risk factors among children and adolescents (22) (34) (35). In our study, the clustering was significantly higher in children with a waist circumference greater than the 90th percentile than in children with a waist circumference less than the 90th percentile. The choice of the 90th percentile was based on the association between truncal fat and waist circumference according to Taylor et al. (36). In our study, the 90th percentile of waist circumference is very similar to the 80th percentile in the study by Taylor et al. (36), chosen by those authors as the point closest to one on the corresponding receiver operating characteristic curve. Our analysis demonstrated that children with a waist circumference that is above the 90th percentile for sex and age are more likely to have multiple risk factors than children with a waist circumference that is equal to or below the 90th percentile. Children with a waist circumference greater than the 90th percentile are less likely to have no risk factors.

Epidemiological and clinical investigations have shown that the relationship between obesity and cardiovascular risk factors begins early in life (1) (2) (5). According to the criteria used to define obesity in the sample of children we recruited for this study, we analyzed the simple correlation between anthropometric indexes and plasma lipids profile in a subsample of 43 obese prepubertal children. In this sample, SBP and DBP showed a better correlation (r = 0.45 and 0.39, respectively; p < 0.01) with waist circumference than the total sample. Moreover, waist circumference showed a negative correlation with HDL cholesterol (r = −0.31; p < 0.05; data not shown). Prepubertal obese children could have a larger intra-abdominal fat store, which could explain our finding. Waist circumference measurement is not able to discriminate between IAAT and subcutaneous adipose tissue (2). However, Taylor et al. (36) found recently that waist circumference correctly identified a high proportion of children and adolescents with high trunk-fat mass as measured by a state-of-the-art measurement. They concluded that waist circumference is a simple technique that could be used to screen for high central obesity in children (36). Therefore, we cannot conclude that the relationship between waist circumference and cardiovascular risk factors in these children is due to IAAT or total fat. Despite the important effect of body fat location on the development of metabolic disturbances in prepubertal children, we confirmed that waist circumference is a sensitive marker of cardiovascular risk. This finding highlights the potential uses of waist measurement, using common anthropometric parameters, in identifying subgroups of obese children at higher metabolic risk (37).

In conclusion, waist circumference in prepubertal children adjusted for age, gender, and BMI is independently associated with cardiovascular risk factors. Measurement of waist circumference may be a good choice in clinical practice. Waist circumference may facilitate the detection of individuals with cardiovascular risk factors in childhood because it is easy to measure and has a good interindividual reproducibility. Longitudinal studies performed in children should verify whether, as in adults, changes in waist circumference will indicate changes in cardiovascular risk factors during growth.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

This study was supported by the National Research Council, Rome, Italy, contact no. 96.03441.CT04 and by Nestlè Italiana Spa, Italy.

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  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
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