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Abstract

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Limitations and Strengths
  7. Conclusions
  8. Acknowledgments
  9. References

J Clin Hypertens(Greenwich). 2010;12:636–644. © 2010 Wiley Periodicals, Inc.

The objective of this study was to determine independent and joint association of body mass index (BMI) percentile and leisure time physical activity (LTPA) with continuous metabolic syndrome (cMetS) risk score in 12- to 17-year-old American children. The 2003 to 2004 US National Health and Nutrition Examination Survey data were used for this investigation. LTPA was determined by self-report. cMetS risk score was calculated using standardized residuals of arterial blood pressure, triglycerides, glucose, waist circumference, and high-density lipoprotein cholesterol. Multiple linear regression analysis was used to evaluate association of BMI percentile and LTPA with cMetS risk score, adjusting for confounders. Increased BMI percentile and LTPA were each associated with increased and decreased cMetS risk score, respectively ((P<.01). There was a gradient of increasing cMetS risk score by BMI percentile cutpoints, from healthy weight (−0.77) to overweight (3.43) and obesity (6.40) ((P<.05). A gradient of decreasing cMetS risk score from sedentary (0.88) to moderate (0.17) and vigorous (−0.42) LTPA levels was also observed (P<.01). The result of this study suggests that promoting LTPA at all levels of weight status may help to reverse the increasing trends of metabolic syndrome in US children.

The metabolic syndrome (MetS) is characterized by a constellation of metabolic factors that confers an increased risk for cardiovascular disease–associated morbidity and mortality as well as all-cause mortality.1–3 In adults, the core variables in MetS are well defined and include central obesity, elevated blood pressure (BP), dyslipidemia (elevated triglycerides and low levels of high-density lipoprotein cholesterol [HDL-C]) and hyperglycemia.1–3

In children, MetS is poorly defined, and in the United States, the prevalence of MetS in 12- to 19-year-old adolescents (using a modified adult definition) is 4%, and an increasing trend has been noted.4–6 The association of MetS with obesity and other factors has been studied extensively in adults. However, the association of MetS with obesity among children remains poorly understood. Because of the increasing prevalence of obesity and MetS in US children,6,7 and the link of the two comorbidities with risks of cardiovascular diseases, diabetes, and other chronic diseases, understanding the epidemiology of MetS in children is imperative in developing strategies for forestalling its increasing prevalence in the United States. Indeed, some studies have shown that MetS and components of MetS track from childhood to adulthood.8,9 In addition, cohort studies have shown that a pronounced increase in body mass index (BMI) during adolescence persists into adulthood and that overweight in early childhood is associated with a greater risk of MetS in adulthood.10

One fundamental methodologic pitfall in the study of the association of MetS in children is the lack of unanimity in the definition of MetS in this population. The lack of consensus in the definition may be responsible for varying findings across epidemiologic studies.4,5,7 Due to the low prevalence of MetS in children,4,5 a large sample size is necessary for association studies using clinical cutpoints of factors that have been proposed for adults. Using the current prevalence estimate of 4% in adolescents, 40 patients would meet the criteria for MetS in a random sample of 1000 US children. Thus, the ability to determine association between MetS (defined as a categoric variable) and risk factors (eg, diet, physical activity) using discriminate analysis or multivariate logistic regression analysis is limited. The American Diabetes Association and the European Association for the Study of Diabetes has consequently recommended using a continuous value of MetS risk score for investigating the association of MetS with potential risk factors in children and adolescents.11

In children, the relative importance of the joint occurrence of obesity and leisure time physical inactivity in the development of MetS is unclear. However, in adults, some evidence suggests that MetS is modified by physical activity.12,13 In this study, MetS is defined using a continuous metabolic syndrome (cMetS) risk score in a representative sample of 12- to 17-year-old American children. The objective is to clarify the association of BMI percentile and leisure time physical activity (LTPA) with MetS in children. We hypothesize that increase in BMI percentile and LTPA is associated with high and low cMetS risk scores, respectively. We also hypothesize that LTPA will attenuate the association of BMI percentile with cMetS risk score in 12- to 17-year-old American children.

Methods

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Limitations and Strengths
  7. Conclusions
  8. Acknowledgments
  9. References

Patients and Study Design

The 2003 to 2004 National Health and Nutrition Examination Survey (NHANES) data that were used for this investigation were obtained from the US National Center for Health Statistics (NCHS). The NHANES was a complex cross-sectional multistage sampling design obtained in a representative sample of civilian noninstitutionalized individuals within the US population. Descriptions of the plan and operation of the survey have been described by other investigators.14,15 Briefly, the NHANES sample included oversampling of youth. The NHANES study protocols were approved by the institutional review board of NCHS. Assent was obtained from patients under the age of 16 after oral and written informed consent from their parents.

Overall, approximately 10,000 persons, including adolescents, completed the 2003 to 2004 NHANES. However, in this study, only 12- to 17- (n=655) year-old children who had values for age, height, weight, and waist circumference and who had assays for triglycerides, glucose, and HDL-C were eligible for investigation. Patients with diastolic and systolic BP values were also included in this study. Because MetS and components of MetS track from childhood to adulthood,8,9 we restricted this study to early and middle adolescents. This age is appropriate for many physical, mental, emotional, cognitive, and social changes for preventing and delaying the onset of MetS.

In NHANES, height was measured with a fixed stadiometer with a vertical backboard and a moveable headboard. Weight was measured at a standing position using a Toledo digital weight scale (Seritex, Carlstadt, NJ). Waist circumference was measured between bony landmark, the lateral border of the ilium, and uppermost lateral border of the right ilium. The measurement was made at the end of a normal expiration and to the nearest 0.1 cm.16 Other variables included in this study are BMI, sex, race/ethnicity, education, and household income. BMI was calculated by dividing measured weight in kilograms by measured height in meters squared. Classifications of overweight and obesity were based on the 2000 US Centers for Disease Control and Prevention recommended cutpoints.17 Overweight was defined as a BMI percentile at or above the 85th percentile and lower than the 95th percentile for age and sex. Obesity was defined as BMI percentile at or above the 95th percentile for age and sex.17 In this study, healthy weight was defined using a value below the 85th percentile.

Participants for this study were examined in the morning after 8 hours of overnight fasting and assayed for triglycerides in NHANES. Blood samples were analyzed for triglycerides and glucose using standard methods.16 Three consecutive BP readings were obtained, using the same arm. Appropriate BP cuff sizes were used for participants based on measurement of midarm circumference. All BP readings were obtained at a single examination visit using a standard protocol. The average of the 3 systolic and diastolic BP readings were used as the participants’ systolic and diastolic BP values.16

In 2003 to 2004 NHANES, racial/ethnic status and household income status of young people were provided by parents. In this study, household income was categorized into 3 groups: <$25,000, 25,000 to 74,999, and $75,000 or greater. In the survey, parents were asked to provide information on the highest grade or level of education completed by the child. The responses were recoded into 3 educational attainment categories: (1) elementary school, (2) middle school, and (3) high school education. Due to uneven distribution of education and lack of participants with high school education in healthy-weight and overweight groups, we classified education into elementary school and middle school and higher.

Leisure Time Physical Activities

In NHANES, participants were asked the following questions: (1) During the past 30 days, did you do any vigorous activities for at least 10 minutes that caused heavy sweating or large increases in breathing or heart rate? Some examples of activities are running, lap swimming, aerobics classes, or fast bicycling; and (2) During the past 30 days, did you do any tasks in or around your home or yard for at least 10 minutes that required moderate or greater physical effort? Moderate physical effort was defined as tasks that caused moderate sweating or a slight to moderate increase in heart rate or breathing such as raking leaves, mowing the lawn, or heavy cleaning. Respondents who answered yes to vigorous activities but did not give at least one vigorous activity or reported duration of less than 10 minutes were recorded as sedentary. The same was done for moderate activities. No participants reported having both vigorous and moderate intensity activity with more time spent in one or the other. In this study, participants were therefore classified on the basis of LTPA levels as sedentary, moderate, and vigorous.

Components and Calculation of cMetS Score

cMetS score was computed using standardized residuals (z score) of mean arterial BP (MAP), triglyceride, glucose, waist circumference, and HDL-C.18,19 MAP was calculated by using the formula MAP=([(systolic BP – diastolic BP)/3] + diastolic BP). Computation of z score for the individual MetS variables were developed by regressing them onto age and sex to account for age- and sex-related differences.18,19 Because the standardized HDL-C is inversely related to metabolic risk it was multiplied by −1. cMetS risk score was calculated as the sum of standardized residuals of MAP, triglycerides, glucose, waist circumference, and HDL-C. A higher score indicates a less favorable MetS profile.18,19

Non-Hispanic whites, non-Hispanic blacks, and Mexican Americans were the primary categories of this study. Due to small sample size, other races/ethnicities, including Asians and multiracial groups, were excluded for this study. Overall, participants who were excluded due to missing variables of interests were not different from those who were examined in terms of age, weight, BMI, and other important anthropometric and clinical factors.

Statistical Analysis

Statistical programs available in SAS for Windows (release 9.1; SAS Institute, Cary, NC) and SUDAAN20 were used in this analysis. All variables were checked for normality. Differences between healthy-weight, overweight, and obese patients for continuous variables including age, weight, height, and standardized scores of MAP, triglycerides, glucose, waist circumference, and HDL-C were assessed by one-way analysis of variance, and Tukey’s post hoc method was used for pair-wise comparisons. Differences between categoric variables, including race/ethnicity, education, sex, and annual household incomes across BMI percentile cutpoints were assessed using Pearson chi-square tests. Analyses of linear trends were performed for cMetS across BMI percentile cutpoints and LTPA levels.

Three multiple regression models were performed for each dependent variable (cMetS and components of cMetS) in this study. In model I, analyses of the association between BMI percentile and cMetS risk score and association between BMI percentile and components of cMetS were performed. In model 2, we included LTPA in addition to BMI percentile in order to evaluate the effect of the joint occurrence of BMI percentile and LTPA. In model 3, we included BMI percentile, LTPA, and interaction term (BMI percentile × LTPA) as independent variables. We also fitted BMI percentile with cMetS risk score (dependent variable) across LTPA levels. In each analysis, adjustment was made for race/ethnicity, household income, and child education. The customary P value of <.05 was used to specify statistical significance.

Results

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Limitations and Strengths
  7. Conclusions
  8. Acknowledgments
  9. References

The basic characteristics of eligible children (n=655) by levels of BMI percentile are presented in Table I. Overall, 32.1%, 44.8%, and 23.1% of participants reported vigorous, moderate, and sedentary LTPA, respectively. There were statistically significant differences between healthy-weight, overweight, and obese children in age, weight, height, waist circumference, and educational attainment. Analysis of post hoc tests indicated pair-wise differences between healthy-weight, overweight, and obese children with respect to these variables (P<.05). Obese children were older, heavier, taller, and had larger waist circumference compared with healthy-weight and overweight children (P<.01). Distribution of race/ethnicity, sex, and annual household income did not vary across levels of BMI percentile.

Table I.   Basic Anthropometric and Demographic Characteristics of Studied Population
VariablesHealthy WeightOverweightObeseP Value
  1. Values with different superscripts differ from each other at P<.05 using Tukey’s post hoc pairwise comparison.

No. (BMI percentile)466 (<85)107 (85–94.9)82 (≥95) 
Age, y14.3±1.6a14.9±1.6b15.2±1.7c<.001
Weight, kg55.7±10.2a76.6±10.1b96.4±17.1c<.001
Height, cm164.4±9.9a167.6±10.2b166.7±11.2b.003
Waist circumference, cm73.0±6.9a90.6±6.9b107.5±12.2c<.001
Race/ethnicity, %   .500
 Non-Hispanic white26.025.224.4 
 Non-Hispanic black43.337.448.8 
 Mexican American30.737.426.8 
Education, %     .001
 Elementary school91.586.581.5 
 Middle school+8.513.518.5 
Sex, %   .667
 Male56.455.151.2 
 Female43.644.948.8 
Annual household income, %     .675
 <$25,00038.738.432.5 
 $25,000–$74,00040.741.146.3 
 >$75,00020.620.521.2 
Leisure time physical activity, %     .002
 Sedentary8.220.760.5 
 Moderate31.847.120.1 
 Vigorous60.032.219.4 

Table II shows average standardized scores of individual components of cMetS according to BMI percentile cutpoints. Participants who were obese presented with higher standardized scores of MAP, triglycerides, glucose, waist circumference, and HDL-C compared with healthy-weight and overweight children (P<.01).

Table II.   Mean Values of Components of Continuous Metabolic Syndrome Score in US Children Stratified by Adiposity Level
VariablesAdiposity Level
Healthy WeightOverweightObeseP Value
  1. Abbreviations: HDL-C, high-density lipoprotein cholesterol; MAP, mean arterial pressure; Z, standardized components of a continuous metabolic syndrome risk score. Values for continuous variables are means ± standard error.

Z_MAP−0.071±0.0460.103±0.0910.267±0.092.009
Z_Triglycerides−0.182±0.0370.413±0.1240.489±0.137<.001
Z_Glucose−0.316±0.0240.376±0.1071.303±0.174<.001
Z_Waist−0.485±0.0230.691±0.0541.850±0.097<.001
Z_HDL-C−0.156±0.0460.322±0.0950.467±0.083<.001

cMetS risk score stratified by BMI percentile and LTPA levels are presented in the Figure. Test of linear trend showed a gradient of increasing cMetS risk score by BMI percentile cutpoints, from healthy weight, to overweight, and obesity (P<.05). The mean cMetS risk scores were −0.77, 3.45, and 6.40 for healthy weight, overweight, and obese children, respectively. A gradient of increasing cMetS risk score from sedentary, to moderate, to vigorous LTPA levels was also observed (P<.001). The mean cMetS risk scores were 0.88, 0.17, and −0.42 for sedentary LTPA, moderate, and vigorous LTPAs, respectively. Assessment of cMetS risk scores by BMI percentile cutpoints across LTPA levels showed that vigorous LTPA was associated with significantly lower cMetS risk score at each level of BMI percentile defined by healthy weight, overweight, and obesity. In healthy-weight children, mean cMetS risk scores were −0.45, −0.63, and −0.96, for sedentary, moderate, and vigorous physical activities, respectively. The corresponding values for overweight children were 3.87, 3.33, and 1.87, respectively. A similar gradient of decreasing mean cMetS risk score with increasing LTPA was also observed in obese children, with values of 5.73, 5.12, and 4.67 for children reporting sedentary, moderate, and vigorous LTPAs, respectively.

image

Figure Figure.  Continuous metabolic syndrome (CMetS) risk score by (A) body mass index (BMI) percentile cut points, (B) leisure time physical activity levels, and (C) across BMI percentile by leisure time physical activity levels.

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In order to determine whether BMI percentile was associated with cMetS risk score and components of cMetS independent of other factors, BMI percentile, race/ethnicity household income, and child education were tested in multiple linear regression models using cMetS risk score and each component of cMetS as dependent variables. We compared model I and model II, representing unadjusted and LTPA-adjusted models, respectively (Table III). In both models, BMI percentile was positively associated with cMetS risk score and components of cMetS, independent of other variables (P<.01). Comparative analysis of model I and model II indicated that adjustment for LTPA significantly attenuated the association of BMI percentile with cMetS risk score as well as components of cMetS. There was a negative interaction between BMI percentile and LTPA (model III) (P<.01).

Table III.   Effect of LTP Activity in the Association of BMI Percentile with Standardized Components of a Continuous Metabolic Syndrome Score in US Children
VariablesZ_MapZ_TriglycerideZ_GlucoseZ_WaistZ_HDL-CcMetS
  1. Abbreviations: BMI percentile×leisure time physical (LTP) activity, interaction term between body mass index percentile and leisure time physical activity; MAP, mean arterial pressure; Z, standardized components of a continuous metabolic syndrome (cMetS) risk score. Values are betas from multiple linear regression analysis. aP<.01. bP<.05.

Model 1: unadjusted for LTP activity
 BMI percentile0.179a0.257a0.628a0.922a0.289a0.691a
 Race/ethnicity−0.047−0.216a0.022−0.124a−0.104a−0.143a
 Household income0.0490.0680.068b0.0800.0240.068b
 Child education−0.111a0.0600.0460.0400.030−0.084
Model 2: adjusted for LTP activity
 BMI percentile0.162a0.262a0.619a0.912a0.271a0.687a
 Race/ethnicity−0.045−0.219a0.026−0.124a−0.104a−0.147a
 Household income0.0500.0700.069b0.0800.0200.081b
 Child education−0.102b0.0200.0400.030−0.031−0.041
 LTP activity−0.152a−0.165a−0.162a−0.192a−0.157a−0.151a
Model 3: adjusted for LTP activity, and BMI×LTP activity
 BMI percentile−0.157a0.347a0.449a0.851a0.249b0.567a
 Race/ethnicity0.050−0.221a0.022−0.125−0.102a−0.145a
 Household income0.0490.0710.057b0.0600.0240.070
 Child education−0.104a0.0080.068b0.0900.050−0.016
 Physical activity−0.339b−0.168a−0.298b−0.111b−0.073−0.130b
 BMI percentile×LTP activity−0.382b−0.118b−0.280b−0.126b−0.056−0.227a

Finally, to determine the effect of LTPA on the associations of BMI percentile with cMetS risk score, we performed LTPA level–specific multivariable regression analyses (Table IV). As shown, BMI percentile was positively associated with increase in cMetS risk score adjusting for race/ethnicity, household income, and child education in participants with sedentary, moderate, and vigorous LTPAs (P<.01). Adjusting for race/ethnicity, household income, and education, a 1-kg/m2 increase in BMI percentile was associated with 0.701, 0.691, and 0.652 increases in cMetS risk score in children reporting sedentary, moderate, and vigorous LTPAs, respectively (P<.01). The results of the regression model showed that the proportion of the variance of cMetS risk score explained by the model that included BMI percentile, age, race/ethnicity, household income, and child education were 46.4%, 48.8%, 59.1% in children reporting sedentary, moderate, and vigorous leisure physical activities, respectively.

Table IV.   Association of BMI Percentile With a Continuous Metabolic Syndrome Across Leisure Time Physical Activity Levels in US Children
VariablesLeisure Time Physical Activity Intensity Levels
SedentaryModerateVigorous
  1. Abbreviation: BMI, body mass index. Values are betas from multivariable linear regression, aP<.01. bP<.05.

BMI percentile0.701a0.691a0.652a
Race/ethnicity−0.132b−0.130a−0.124b
Household income0.0540.1240.074b
Child education−0.2200.0460.018
Model summary (R2)0.4640.4880.591

Discussion

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Limitations and Strengths
  7. Conclusions
  8. Acknowledgments
  9. References

The role of LTPA in the association between BMI and MetS in children has not been well defined. To our knowledge, we are unaware of published studies that examined the association of BMI and LTPA with MetS using a cMetS risk score in a nationally representative sample of American children. The emphasis of this study is on the role of LTPA in modifying the association between the BMI and cMetS.

Although it is well-known that BMI is related to MetS in youth,10 many studies evaluating the relationship between BMI and MetS in children have methodological problems, including lack of unified definition of MetS and the use of adult clinical cutpoints for the definition of childhood MetS. Increasing evidence supports the use of continuous values such as cMetS risk score, instead of the binary classification for epidemiologic analyses.11,18,19 As a continuous variable, cMetS risk score is a more robust measure of MetS than a categoric measure, with these advantages: (1) since cardiovascular risk is a progressive function of several MetS risk factors, the use eliminates the need to dichotomize these factors,21,22 (2) cMetS risk score is more sensitive and less error prone compared with categoric measures of MetS, and (3) statistical power is increased with the use of cMetS.21 The use of cMetS risk score has been validated in adults against MetS defined using International Diabetes Federation guidelines.23 However, we do not yet know which cMetS component confers increased risk of cardiovascular disease in youth.

The use of NHANES for this study is appropriate because the sampling scheme is representative of the national population of non-Hispanic whites, non-Hispanic blacks, and Mexican Americans. The training program and quality-control procedures instituted in the surveys give additional credibility to the data.

The result of this study clearly demonstrates significant independent and combined influences of BMI percentile and LTPA in cMetS in children. The results of this study indicate that BMI percentile is positively associated with elevated cMetS risk score in American children, controlling for concomitant variables including race/ethnicity, household income, and child education. The results of this study show a gradient of increasing cMetS risk score by increasing BMI percentile. The mean cMetS risk scores increased as BMI percentile category increased by more than 4 times for healthy-weight compared with obese children. The mean cMetS risk scores decreased by more than 4 times as LTPA increased from sedentary to vigorous LTPAs. Sedentary obese children had much higher mean cMetS risk scores compared with sedentary overweight and sedentary healthy-weight children. A similar gradient was observed for both moderate and vigorous leisure time physically active obese, overweight, and healthy-weight children.

The result of this study also shows that LTPA modifies the relationship between BMI percentile and CMetS. LTPA was inversely associated with cMetS risk score. Indeed, vigorous LTPA attenuated cMetS risk score within BMI percentile categories. Our finding of an inverse association between LTPA and cMetS score is consistent with findings of other investigators using maximum cycle-ergometer and aerobic fitness as measures of physical activity.24,25 Findings from the European Youth Heart Study26 also showed a similar inverse relationship between fitness and the clustering of cardiovascular disease risk factors in children.

Another important finding of this study is that at a fixed level of BMI percentile, increase in LTPA was associated with reduced cMetS risk score as well as each component of cMetS (P<.01). Overall, levels of LTPA (moderate and vigorous) were associated with a decreased association of BMI percentile with cMetS risk scores compared with sedentary LTPA. The difference was most prominent in the obese group. This finding is similar to the Québec Family Study in which adolescents with low BMI and high fitness levels were found to have the lowest MetS score, whereas those with high BMI and low fitness levels had the highest MetS score.27 The significant association of race/ethnicity with cMetS in this study is noteworthy and was driven by racial/ethnic differences in triglyceride level. Triglyceride levels in this study were much lower in non-Hispanic blacks compared with non-Hispanic whites and Mexican American participants.

Limitations and Strengths

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Limitations and Strengths
  7. Conclusions
  8. Acknowledgments
  9. References

Some important limitations must be taken into account in the interpretation of the results from this study. One, this study did not differentiate between unfit healthy-weight, fit-fat, and unfit-fat participants. Several studies have shown that the association between cardiovascular risk factors vary by fat and fitness status.27–29 In those studies, fat/low-fit participants had the highest MetS risk scores while obese-unfit participants possessed the worst MetS risk score. Two, as a cross-sectional study, causal inferences must be made with caution. However, improvement in metabolic risk profile due to LTPA is biologically plausible. Three, in this study, physical activity status in children was based on self-report compared with more objective measures such as pedometers or accelerometers. Self-report of physical activity suffers from reporting bias attributable to social desirability and the cognitive problem associated with estimating frequency and duration of physical activity in children.30 However, a recent study of physical activity in 6- to 19-year-old US children and adolescents from the survey for this study indicates that subjective measures of physical activity give qualitatively similar results as objective measures using accelerometers.31 It is also worth mentioning that it would have been more appropriate if the volume of physical activity was used in this study since many studies report that health and function outcomes are related to the total moderate to vigorous physical activity. Estimating health status based on a minimum of 10 minutes of activity per month is less than ideal. In the interpretation of the role of physical activity, there is also the need to recognize differences between physical activity and fitness. Certainly these two variables influence each other and both can be modified. However, fitness (peak aerobic capacity) is determined by physical activity, genetics, and other factors. There are situations where exercise training and physical activity level are weakly related to peak oxygen uptake. Thus, the terms fitness and physical activity cannot be used interchangeably. Four, although we controlled for the confounding effect of race/ethnicity, income, and education, other unmeasured factors such as genetics and dietary factors could explain our findings.

The mechanism linking LTPA to cMetS is unclear but may be related to association of physical activity with fitness. Although physical activity and fitness are a separate construct that may have separate mechanisms for influencing MetS, they both have an effect on health and thus are interrelated in a reciprocal manner.32 Some studies have shown that glucose transport can be improved by physical fitness,33,34 while other studies in adults have shown that increased physical fitness can delay or prevent diabetes.35,36 Also it has been shown that physical fitness is associated with increased capillarization, leading to increased blood flow and oxygen supply to the muscle tissue and ultimately improved fat metabolism, higher HDL-C concentration, and decreased BP.29,37 Additionally, physical fitness is associated with a much improved overall sympathetic tone, leading to reduced BP through efficient use of the motor units in the muscle.38

In agreement with other studies,39,40 our results showed that the association of BMI percentile with MetS is stronger in sedentary children and may represent a subgroup of youths who carry a greater risk for cardiovascular disease and type 2 diabetes. Our findings of the effects of LTPA in the association of BMI percentile with cMetS risk score have clinical and public health significance. From a clinical standpoint, this information could be helpful to health care providers when determining treatment options for at-risk children. The results of this study suggest that children who are sedentary and obese are those in need of aggressive lifestyle modifications, and recommending increased LTPA may be a significant modifier of disease. Children should be encouraged to engage in healthy lifestyle habits, including LTPA and obtaining or maintaining a healthy weight in order to improve their metabolic health. Promoting LTPAs during childhood is a vital public health agenda in reducing the risk of clustering cardiovascular disease risk factors. Some studies have shown that physical inactivity may independently contribute to metabolic disease risk in youth independent of total body fat in children.38–40 Clear-cut claims about the effect of physical inactivity on MetS must be supported by fitness data.

Conclusions

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Limitations and Strengths
  7. Conclusions
  8. Acknowledgments
  9. References

LTPA and healthy weight programs should be promoted in childhood to reduce the risk of MetS and other obesity-related comorbidities. Promotion of LTPAs in children, especially for those who are at-risk, should be a core clinical and public health function. Overweight children should receive the greatest level of behavior modification efforts to increase their volume of moderate/vigorous activity to help reduce the risk for MetS in children.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Limitations and Strengths
  7. Conclusions
  8. Acknowledgments
  9. References

Acknowledgments:  We are thankful to the US National Center for Health Statistics for the National Health and Nutrition Examination Surveys.

References

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Limitations and Strengths
  7. Conclusions
  8. Acknowledgments
  9. References