Abstract
OBJECTIVE. The purpose of this study was to examine the combined influence of aerobic fitness and BMI on the metabolic syndrome score in children.
METHODS. A total of 375 children (193 girls and 182 boys) aged 7 to 9 years were categorized as being normal weight, at risk for overweight, and overweight on the basis of BMI and aerobic fitness (high or low based on median split) via a submaximal physical working capacity test. Participants were cross-tabulated into 6 BMI fitness categories. High-density lipoprotein cholesterol and triglyceride levels, homeostasis assessment model of insulin resistance, mean arterial pressure, and waist circumference were used to create a continuous metabolic syndrome score.
RESULTS. Both BMI and fitness were associated with the metabolic syndrome score. In general, the metabolic syndrome score increased across the cross-tabulated groups with the normal-weight, high-fit group possessing the lowest metabolic syndrome score and the overweight, unfit group possessing the highest metabolic syndrome score. Children who were at risk for overweight and had high fitness had a lower metabolic syndrome score compared with those at-risk-for-overweight, less-fit children, and the score was similar to that of the less-fit, normal-weight children. Furthermore, a high fitness level resulted in a lower metabolic syndrome score in overweight children compared with overweight children with low fitness.
CONCLUSIONS. High fitness levels modified the impact that BMI had on the metabolic syndrome score in children. Increasing a child's fitness level could be one method for reducing the risk of obesity-related comorbidities.
The metabolic syndrome consists of a clustering of specific cardiovascular disease risk factors that include elevated blood pressure, dyslipidemia, hyperglycemia or insulin resistance, and central obesity.1 Depending on the definition used for the metabolic syndrome, ∼35% to 39% of adults in the United States have the metabolic syndrome.2 Given the epidemic of pediatric obesity and emergence of type 2 diabetes among adolescents, it should not be surprising that the metabolic syndrome has also been reported to exist in children and adolescents. Estimated prevalence rates range from 5% to 39% in the general population of youth and overweight adolescents, respectively.3–7
As mentioned above, obesity has reached epidemic proportions in youth.8 This excess amount of body weight (ie, BMI) is positively related to the metabolic syndrome in adults and youth.3,4,9,10 Furthermore, a prospective study reported that a pronounced increase in BMI during adolescence was associated with a greater likelihood of possessing the metabolic syndrome in adulthood,11 which is especially problematic for youth, because the metabolic syndrome and individual components track from adolescence to adulthood.12
In adults, high fitness levels are inversely associated with the metabolic syndrome, even among overweight and obese individuals.13,14 High fitness levels are also related to a better metabolic syndrome profile among adolescents.15–17 Moreover, adolescents who maintain a high fitness level through early adulthood are less likely to develop the metabolic syndrome in young adulthood.11
In adults, research has shown that even among those who are obese there is a reduced risk for chronic disease and mortality if they have a high fitness level or participate in regular physical activity.18–20 In adolescents and children, studies have shown that BMI is positively related to the metabolic syndrome,3–5,7 whereas fitness levels are inversely related to the metabolic syndrome.15–17 Until recently, the combined influence of BMI and fitness level on metabolic syndrome components in youth was unknown. Eisenmann et al21–23 examined this relationship in adolescence and reported that a high aerobic fitness level and a high BMI resulted in a lower metabolic syndrome score compared with the low-fitness and high-BMI group. However, these studies included mainly normal-weight adolescents.
Therefore, the purpose of this study was to examine the independent and combined influences of BMI and aerobic fitness on the metabolic syndrome in children. Furthermore, we examined whether the metabolic syndrome score varied among normal-weight, at-risk-for-overweight, and overweight children by aerobic fitness level. It was hypothesized that aerobic fitness would attenuate the metabolic syndrome score within BMI categories.
METHODS
Participant Recruitment
The participants used for this study were part of a 3-year physical activity intervention called Physical Activity Across the Curriculum. However, only baseline measures (ie, before random assignment into the intervention) are included in this report. A subsample of second- and third-grade children (aged 7–9 years old) from 22 elementary schools was recruited for additional baseline testing. Inclusion into the subsample consisted of the following criteria: (1) both the parent and child gave written consent and assent, respectively, to participate in the testing in accordance with the human subjects committee at the university; (2) the child had to participate in all of the tests (ie, the child could not choose which tests to complete); and (3) the child did not have insulin-dependent diabetes, cardiovascular disease, or any other disease that limited physical activity participation.
A total of 852 children volunteered for the baseline testing from a possible 2494 children. Because of time constraints, all of the children could not participate, so a random sample of 499 second- and third-graders was selected to participate in the baseline testing. Approximately equal numbers of boys and girls participated, and 27% of the sample was a race other than non-Hispanic white. Baseline testing included the following procedures: height, weight, circumference measurements, skinfold measurements, resting blood pressure, fasting blood draw, aerobic fitness and academic achievement tests, and a physical activity and nutritional surveys. For this analysis, the variables of interest included height, weight, waist circumference, blood pressure, blood chemistry, and aerobic fitness.
Anthropometric Measures
Height was measured to the nearest 0.1 cm using a portable stadiometer (model IP0955, Invicta Plastics Limited, Leicester, England). Weight was measured to the nearest 0.1 kg using a portable electronic scale (model 68987, Befour Inc, Saukville, WI). Both height and weight were measured in duplicate with shoes off but while subjects were wearing lightweight clothing. BMI was calculated as weight in kilograms divided by height in meters squared, and the children were grouped into 1 of 3 BMI categories: (1) normal weight (<85th percentile); (2) at risk for overweight (85th–94th percentile); or (3) overweight (≥95th percentile) according to the age- and gender-specific reference values of the Centers for Disease Control and Prevention.24
Waist circumference was measured in duplicate to the nearest 0.1 cm using a Gullick tape at the smallest girth around the trunk in the horizontal plane underneath the participant's clothing.25 There was no difference for intertester reliability for waist circumference (P = .55), and the coefficient of variation was 1.65%. Skinfold measurements were taken in duplicate on the right side of the body using procedures outlined by the American College of Sports Medicine at the calf and triceps.26 Percentage of body fatness was estimated from skinfold measurements using the equation by Lohman and Going.27 The correlation between BMI and percentage of body fat was .80; therefore, BMI categories were used given their clinical and epidemiologic use.
Blood Pressure
Resting blood pressure was measured in duplicate by trained personnel using a random-0 sphygmomanometer (Hawksley & Sons Ltd, Lancing, United Kingdom) according to standard methods.28 To determine the appropriate cuff size, the child's arm circumference was measured. Children rested quietly for 5 minutes before measurement. The first and fifth Korotkoff sounds were recorded as systolic and diastolic blood pressure, respectively. Mean arterial pressure (MAP) was calculated by using the formula MAP = [(systolic blood pressure − diastolic blood pressure)/3] + diastolic blood pressure. Intertester reliability for systolic or diastolic blood pressure was similar (P = .14 and .11, respectively), and the coefficient of variation for both measures was 5.3%.
Blood Collection and Storage
Blood samples were collected using standard venipuncture methods by a trained phlebotomist after an 8-hour fast. Blood samples were processed at the study site by centrifuging the samples, placing the serum in prelabeled vials, storing the samples at −70°C, and shipping the samples to the University of Colorado Health Sciences Center (Denver, CO) for further processing. Blood samples remained stored at −70°C until analyses were conducted.
Blood Analyses
Glucose, total cholesterol, and triglyceride concentrations were measured enzymatically using a Cobas Mira Chemistry System (Roche Diagnostic Systems, Indianapolis, IN). High-density lipoprotein cholesterol (HDL-C) concentrations were also measured enzymatically using a Cobas Mira Chemistry System (Diagnostic Chemicals Ltd, Oxford, CT). Insulin levels were measured using a radioimmunoassay (Diagnostic Systems Laboratory, Webster, TX). Homeostasis model assessment (HOMA), an insulin resistance indicator, was calculated as fasting insulin (units per milliliter) × fasting glucose (milligrams per deciliter)/22.5. The coefficient of variation for all of the blood measurements was <5% for both interassay and intraassay quality control.
Physical Working Capacity
A modified physical working capacity (PWC) 170-cycle ergometer test assessed aerobic fitness. The PWC has been shown to correlate well with maximal oxygen consumption in boys and girls (r = 0.70 and 0.71, respectively).29 Before beginning the test, participants were acclimated to the pedaling cadence. During the graded exercise test, participants pedaled on a cycle ergometer until their heart rate reached ≥85% of heart rate reserve or until the participant could no longer maintain a cadence of 60 rpm. The PWC test had a total of four 2-minute stages, and after each stage, the load was increased based on the participant's heart rate. Heart rate was recorded every minute using a Polar heart rate monitor (Polar Accurex Plus; Polar Electro, Inc, Woodbury, NY). The maximum workload (watts) was recorded and divided by body weight (kilograms) for each participant. Because PWC varies by age and gender, it was regressed onto age and gender. The standardized residuals (z scores) were used to create high and low fitness categories based on the median split.
Statistical Analysis
Of the 499 children randomly selected to participate in the baseline testing, 34 did not participate because of absences on the testing day, 10 ate the morning of the blood draw, 1 did not have waist circumference, 76 had incomplete blood data, and 3 did not complete the PWC, leaving 375 children (193 girls and 182 boys; 272 white, 42 Hispanic, 23 black, 9 Asian, 5 Native American, 1 Pacific Islander, 16 >1 race, and 7 unknown or not reported) with complete data for statistical analysis. Sample bias was not present, because demographic characteristics (ie, age, gender, race, and BMI) were similar between those children who were either included or excluded from the data analyses.
Currently, there is no universally accepted definition for the metabolic syndrome in children. Therefore, a continuous metabolic syndrome score was created. The metabolic syndrome score was derived by first standardizing the individual metabolic syndrome variables (waist circumference, MAP, HOMA, and HDL-C and triglyceride levels) by regressing them onto age, gender, and race to account for age-, gender- and race-related differences. The standardized HDL-C level was multiplied by −1, because it is inversely related to the metabolic syndrome risk. The standardized residuals (z scores) for the individual variables were summed to create the continuous metabolic syndrome score. These variables were chosen because they represent the same variables (except blood glucose) used in the adult clinical criteria for the metabolic syndrome.30 HOMA was chosen instead of glucose because most children have normal fasting glucose, and HOMA is related to insulin resistance.31 MAP was used instead of systolic and diastolic blood pressure to ensure that blood pressure was represented similar to the other factors, and MAP includes both systolic and diastolic pressures in its calculation. To date, it is unknown whether one factor is more important than another for the development of the metabolic syndrome; thus, each factor was weighted equally. A higher metabolic syndrome score indicates a less favorable metabolic profile.
Descriptive statistics were calculated for the total sample and by gender. Gender differences in descriptive variables were determined by an independent t test. Pearson's correlations examined the associations among BMI, PWC, and the metabolic syndrome. An analysis of covariance evaluated independent differences between BMI and PWC categories on the metabolic syndrome score, controlling for age, gender, and race. Because BMI was used as a predictor variable, and BMI and waist circumference are highly correlated (r = 0.87 in this sample), the analyses were conducted with and without including waist circumference in the derivation of the score. To test the main hypothesis, 6 BMI-PWC (fat-fit) groups were created: (1) normal weight, high PWC; (2) normal weight, low PWC; (3) at risk for overweight, high PWC; (4) at risk for overweight, low PWC; (5) overweight, high PWC; and (6) overweight, low PWC. Differences in the metabolic syndrome score among the fat-fit groups were examined by analysis of covariance, controlling for age, gender, and race. Posthoc comparisons were made using Tukey least-significant difference. Statistical significance was set at P < .05.
RESULTS
Descriptive statistics for the total sample and by gender are shown in Table 1. Approximately 21% of the children were at risk for overweight, and an additional 23% were overweight. Girls had higher BMI and body fat percentage and lower PWC values compared with boys (P < .05). The metabolic variables were similar between the genders.
Descriptive Statistics of the Sample According to Gender
Univariate analyses examining the relationships between fatness and fitness (as measured by BMI) and the metabolic syndrome score showed that the correlations were stronger between BMI and the metabolic syndrome score when waist circumference was included in the score (r = 0.70) compared with when it was excluded from the score (r = 0.51). In contrast, the correlations between PWC and the metabolic syndrome score were similar when waist circumference was included (r = −0.46) and excluded (r = 0.41) in the metabolic syndrome score. There were significant differences in the metabolic syndrome score between BMI groups (Table 2) and fitness groups (Table 3). There was a graded relationship across the BMI groups when the normal weight group had the lowest metabolic syndrome score and the overweight group had the highest metabolic syndrome score (P < .05). The metabolic syndrome score was significantly lower in the low-fitness group compared with the high-fitness group (P < .05).
Metabolic Syndrome Score According to BMI Category
Metabolic Syndrome Score According to PWC Category
Figure 1 shows the differences in the metabolic syndrome score across the BMI aerobic fitness groups. It is noteworthy to mention the percentages of children in each category were as follows: 36% normal weight, high fitness; 19% normal weight, low fitness; 8% at risk for overweight, high fitness; 13% at risk for overweight, low fitness; 5% overweight, high fitness; and 18% overweight, low fitness. Furthermore, 35% of children in the normal-weight group possessed low fitness, whereas 30% of the at-risk-for-overweight and overweight children possessed high fitness. In general, the metabolic syndrome score increased across groups, with the normal-weight, high-fit group possessing the lowest metabolic syndrome score and the overweight, unfit group possessing the highest metabolic syndrome score. Several significant differences existed between the 6 fat-fit groups. Of particular note are the differences within BMI groups by fitness level and the comparison of values between the children in the normal-weight, low-fit group and the at-risk-for-overweight children with high fitness. The metabolic syndrome score was significantly lower in children in the high-fitness group in all 3 of the BMI groups compared with their low-fitness counterparts within the same BMI group (P < .05). Furthermore, the metabolic syndrome score was not significantly different between the normal-weight, low-fitness group and the at-risk-for-overweight, high-fitness group or the at-risk-for-overweight, low fitness group and the overweight, high-fitness group.
The relationship between aerobic fitness and the metabolic syndrome risk score according to BMI categories. Values are reported as the mean and SE. aP < .05 in high versus low fitness within BMI category. bP < .05 in low fitness, normal weight versus low fitness, at risk for overweight; high fitness, overweight; and low fitness, overweight. cP < .05 in low fitness, at risk for overweight versus low fitness, overweight.
DISCUSSION
The purpose of this study was to examine the independent and combined influences of aerobic fitness and fatness (measured by BMI) on the metabolic syndrome in children. Fatness was positively related to the metabolic syndrome score, indicating that excess weight is associated with the metabolic syndrome score. In addition, high fitness was inversely associated with the metabolic syndrome score. Moreover, the metabolic syndrome score varied by BMI fitness groups, where high fitness attenuated the metabolic syndrome score within BMI categories.
Previous studies have reported a relatively high prevalence of the metabolic syndrome in overweight adolescents (eg, 35%–40%).4,6 In the present study, the metabolic syndrome was positively related to BMI and clinical BMI groups (normal weight, at risk for overweight, and overweight), and this relationship was observed with and without the inclusion of waist circumference in the risk score. Fatness and the metabolic syndrome score have been shown to track from early childhood to adulthood12,32; therefore, interventions should target children who have an increased obesity risk.
Aerobic fitness is inversely related to the metabolic syndrome in adults.13,14,33 Furthermore, there is mounting evidence for a similar relationship between fitness and the metabolic syndrome in adolescents15,16,21; however, limited research exists in children <10 years old. The results from the present study showed that fitness was inversely associated with the metabolic syndrome score. Results from the European Youth Heart Study have also shown a similar inverse relationship between aerobic fitness and the clustering of cardiovascular disease risk factors in children.16,34 These results indicate that promoting the development of aerobic fitness during childhood is important to reduce the risk of developing adverse cardiovascular disease risk factors.
Although a growing body of evidence is being established for the independent associations between fitness and fatness on the metabolic syndrome in children, the combined influence of fitness and fatness on the metabolic syndrome has received limited attention. The present study showed that the metabolic syndrome score varied by clinical cut points of BMI and fitness category. More specifically, high fitness attenuated the metabolic syndrome score within the BMI categories, and this difference was most pronounced in the overweight group. These results are similar to previous studies among adolescents.21,22 Data from the Québec Family Study showed that adolescents with low BMI and high fitness had the lowest metabolic syndrome score, whereas those with high BMI and low fitness had the highest metabolic syndrome score.21 Eisenmann et al22 studied the combined influence of aerobic fitness and percentage of body fat on cardiovascular disease risk factors in Australian youth (9- to 15-year-olds). They found that the high-fat and low-fitness group had a higher metabolic syndrome score compared with the low-fat and high-fitness group. Previous studies examined the relationship among fitness, fatness, and the metabolic syndrome in children and adolescences together; thus, it was unclear whether the relationship would exist in prepubescent children only. Considering the available evidence, it is becoming clear that when children and adolescents are cross-tabulated into fat-fit categories, fitness attenuates the metabolic syndrome score among overweight children and adolescents. Although the reasons for these observations have not been fully explored, they possibly involve genetics, adipocytokines, and oxidative capacity of skeletal muscle.35–37
A finding of particular interest in the present study was the magnitude of the difference in the metabolic syndrome score between the high-fit and low-fit overweight children. Furthermore, the lack of statistical difference between the normal-weight, low-fit and at-risk-of-overweight, high-fit groups also is important. These findings, along with the attenuation of the metabolic syndrome score within obesity levels as a result of being aerobically fit, further support the idea of being “fat but fit” and having a reduced chronic disease risk compared with those who are fat but unfit.
CONCLUSIONS
The results of this study show that the metabolic syndrome is a complex phenotype associated with both fatness and fitness. In addition, the metabolic syndrome score varies within clinical BMI categories (ie, normal weight, at risk for overweight, and overweight) by fitness level. Therefore, aerobic fitness level should be considered when interpreting the metabolic syndrome profile in children. For example, a child who is overweight but participates in activities (eg, swimming, soccer, etc) that positively impact physical fitness may have fewer risk factors compared with a child who is overweight but does not engage in activities that improve physical fitness. This information would be helpful to health care providers when determining treatment options. Aerobic fitness should also be promoted to reduce the risk of obesity-related comorbidities. Longitudinal studies of the fat-fit phenotype during childhood and adolescence and into adulthood are necessary to examine the risk of developing the metabolic syndrome, atherosclerosis, and type-2 diabetes.
Acknowledgments
This work was supported by National Institute of Diabetes and Digestive and Kidney Diseases (National Institutes of Health, Bethesda, MD) grant NIHN20 DK 061489.
Footnotes
- Accepted April 19, 2007.
- Address correspondence to Katrina D. DuBose, PhD, Department of Exercise and Sport Science, 153 Minges Coliseum, East Carolina University, Greenville, NC 27858. E-mail: dubosek{at}ecu.edu
The authors have indicated they have no financial relationships relevant to this article to disclose.
REFERENCES
- Copyright © 2007 by the American Academy of Pediatrics
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