CHD is the leading cause of death in western society (33). Factors associated with an increased risk of developing CHD include abdominal obesity, high blood pressure, insulin resistance and elevated triglycerides (TG), and lowered high-density lipoprotein cholesterol (HDL-c). These factors tend to cluster in some individuals. The constellation of these cardiovascular disease (CVD) and metabolic risk factors has been termed the metabolic syndrome (1). Although the clinical symptoms of CVD do not become apparent until later in life, it is now recognized that CVD is partly a pediatric problem because the onset of atherosclerosis occurs in early childhood (25). In addition, clustering of elevated levels of these risk factors has been observed in both children and adolescents (31) and tends to persist from childhood into adulthood (27).
In exercise science, the term fitness is often referred to as health-related fitness (12) and is defined as an ability to perform daily activities consisting of morphological, muscular, motor, cardiorespiratory, and metabolic components (9). In adults, low cardiorespiratory fitness is known to contribute to the early onset and progression of CVD and is associated with a doubling of the risk of premature death (7,26). Moreover, recent research findings have shown that cardiorespiratory fitness is a strong predictor for clustering of CVD risk factors in children and youth (5,14,29,32). However, in these studies, the term fitness mainly refers to cardiorespiratory fitness.
Recently, the role of muscular strength has been increasingly recognized in the prevention of chronic disease in adults (30), and features of the metabolic syndrome have also been negatively associated with muscle strength in men (20) and women (36). Consequently, inclusion of resistance training as part of an exercise program for promoting health in adults has been endorsed by several organizations. To date, however, few studies (6,15,19) have examined the association of the muscular component with CVD risk factors among children and adolescents, and they have been conducted with a limited number of subjects. Furthermore, two studies (6,19) included measurement of maximal strength only, and the associations are only analyzed with individual CVD risk factors and not clustered metabolic risk. By contrast, Garcia-Artero et al. (15) measured muscle endurance, explosive strength, and maximal strength and included a lipid-metabolic risk score in their analyses. However, they did not directly measure cardiorespiratory fitness. Hence, there is sparse knowledge about the independent association of muscle strength with both individual CVD risk factors and clustered metabolic risk in youth. To the best of our knowledge, ours is the first study that provides an opportunity to evaluate the independent association of both single and combined muscle strength measurements with clustered metabolic risk, compared with direct measurement of cardiorespiratory fitness in a national representative sample of children and adolescents. The aim of this study was to examine the independent associations of muscle fitness and cardiorespiratory fitness with clustered metabolic risk in youth. We hypothesized that muscle fitness is an equally important predictor for clustered metabolic risk as cardiorespiratory fitness.
This is a cross-sectional study of a randomly selected cohort of 9-yr-olds (4th grade) and 15-yr-olds (10th grade). A total of 63 schools were included in the study that included all regions of Norway. Of 2818 subjects invited to participate in the study, 2299 accepted, giving an overall participation rate of 82% (89% and 74% for the 9- and 15-yr-olds, respectively). Data were collected between March 2005 and October 2006. Tests were performed at the schools, and 10-15 children were examined per day. The study was carried out according to the Helsinki Declaration and was approved by the Regional Committee for Medical Research Ethics and the Norwegian Social Science Data Services. Each participant's parent or guardian provided written informed consent, and all subjects assented to participation.
After an overnight fast, venous serum blood samples were collected between 8:00 and 10:00 a.m. The samples were spun for 10 min at 2500g and separated within 30 min. Body height (nearest mm) and body weight (nearest 100 g) were measured in light clothing. Waist circumference was measured with anthropometric tape around the umbilicus at the end of a normal expiration. After being seated for at least 5 min, blood pressure was measured automatically five times at 2-min intervals (Omega™ Noninvasive blood pressure monitor; Invivo Research, Inc., Orlando, FL). The mean value of the last three measurements was used in the analyses. Identification of pubertal status was assessed by trained personnel according to Tanner's (34) classification. Analyses in the current study are based on breast development in girls and genitalia development in boys.
Upper limb strength was assessed by ahandgrip strength test using a hand dynamometer (Baseline® Hydraulic Hand Dynamometer, Elmsford, NY). The subject used the dominant hand, stood with the arm completely extended, and squeezed the dynamometer with maximum isometric effort for about 2-3 s. Explosive strength in the lower body was assessed with a standing broad jump. The participants stood behind a line, with feet slightly apart. They were instructed to perform a two-foot takeoff and landing and to jump as far as possible, landing on both feet without falling backward. The distance from the takeoff line to the nearest point of contact on the landing (back of the heels) was measured, and the better of two attempts was used for analyses. Abdominal muscular endurance was measured by a sit-up test. The subject started in a lying position with hands clasped behind the neck, knees bent at a 45° angle, and heels and feet flat on the mat. The subject had to rise to a position with the elbows pointed forward until they touched the knees. The total number of correctly performed and completed sit-ups within 30 s was counted. Endurance of the trunk extensor muscles was measured by how many seconds the subject was able to keep the unsupported upper body (from the upper border of the iliac crest) horizontal while placed prone, with the buttocks and legs fixed to a balance pad with the arms folded across the chest (modified Biering-Sørensen test).
To account for differences in body size, peak handgrip was adjusted for body weight (kg). Each of these variables was standardized as follows: standardized value = (value − mean)/SD. A muscle fitness score was computed by combining the standardized values of handgrip strength, standing broad jump, sit-ups, and the Biering-Sørensen test. The muscle fitness score was calculated as the mean of the four standardized scores by age and sex.
Cardiorespiratory fitness was assessed through a progressive cycle test to exhaustion using an electronically braked cycle ergometer (Ergomedic 839E; Monark, Varberg, Sweden). Initial and incremental work rates were 20 W for children weighing less than 30 kg and 25 W for children weighing 30 kg or more. For 15-yr-old girls and boys, the initial and incremental work rates were 40 and 50 W, respectively. The work rate increased every third minute until exhaustion. HR was recorded throughout the test using an HR monitor (Polar Vantage, Kempele, Finland). Expired air was measured with a portable oxygen and CO2 analyzer (MetaMax III X; Cortex Biophysics, Leipzig, Germany). The criteria for maximal exhaustion were met if there was a subjective judgment by the tester in that the subject showed signs of intense effort (e.g., facial flushing or difficulties in keeping up the pedal frequency) and if the HR was ≥185 bpm or if the respiratory exchange ratio was ≥0.99. Eight percent of the girls and boys failed to meet the inclusion criteria, whereas 4% were absent on the test day. Hence, 2027 (88%) children and adolescents had valid test.
HDL-c, TG, and glucose were analyzed by colorimetry on a Cobas Integra analyzer (F.Hoffmann-La Roche Ltd., Basel, Switzerland). The total analytic coefficients of variation were 4.0%, 4.0%, and 3.0% for HDL-c, TG, and glucose, respectively. Insulin was measured by fluoroimmunoassay using an automatic immunoassay system (AutoDELFIA® Insulin; PerkinElmer, Turku, Finland). The total analytic coefficients of variation for insulin were 6%-8%. Homeostasis model assessment (HOMA) was calculated as the product of fasting glucose (mmol·L−1) and insulin (μU·mL−1) divided by the constant 22.5 (24).
CVD risk score.
A continuous score representing a composite CVD risk factor profile was derived by computing standardized residuals (z-score) by age and sex for HOMA score, waist circumferences, TG, HDL-c, and systolic blood pressure. The z-scores of the individual risk factors were summed to create the metabolic risk score. These variables were chosen because they represent the same variables as used in the adult (1) and youth (37) clinical criteria for the metabolic syndrome. A lower metabolic risk score is indicative of a better overall CVD risk factor profile. To calculate the odds ratio, metabolic risk was dichotomized at the cutoff value plus 1 SD, identifying children with clustered risk. The cutpoint of 1SD above the mean was chosen to match the prevalence of clustering of risk factors in similar studies (2). When calculating the odds ratio, the cutoff value for the mean metabolic risk score was 0.63 (+1 SD); in addition, the mean metabolic risk score ranged from 3.33 to −1.50.
Analyses were executed with the Statistical Package for the Social Sciences (version 15; SPSS Inc., Chicago, IL). Means and SD for muscle fitness and components of the metabolic syndrome are presented by sex and age groups for the children with complete measurements. Of 2229 participants, a total of 1851 (81%) had valid blood samples. The reasons for exclusion were failing to provide a consent form for the blood sample (n=45), hemolyzed samples or not enough blood (n = 85), and not fasting or being absent on the day of blood sampling (n= 318). Moreover, in the present study, only participants (n = 1592) with valid measurement of muscle fitness, cardiorespiratory fitness, HDL-c, TG, HOMA (insulin and glucose), systolic blood pressure, and waist circumferences were included. Because only analyses on complete data are presented, we analyzed whether those with complete data differed from those without. There were no differences in mean age, sex distribution, body mass index (BMI), metabolic risk factors, or cardiorespiratory fitness between those with and those without complete measurements.
ANOVA was used to assess difference in metabolic risk across quartiles of muscle fitness, and the linear trend analysis was performed via polynomial contrast. Post hoc analyses were conducted with Tukey's least significant difference. Partial correlations adjusted for sex, age, and puberty were used to examine bivariate correlations of muscle fitness and cardiorespiratory fitness with single CVD risk factors and clustered metabolic risk. Furthermore, two separate multiple regression models were used to examine the association of muscle fitness and cardiorespiratory fitness with the clustered metabolic risk score. Model 1 contained muscle fitness or cardiorespiratory fitness and was adjusted for age, sex, and pubertal stage. In model 2, we additionally adjusted for the other predictor variable to test the independent associations of both muscle fitness and cardiorespiratory fitness with clustered metabolic risk. Moreover, the same two models were used in logistic regression to estimate odds ratios to examine the association of muscle fitness and cardiorespiratory fitness with risk of having clustered metabolic risk. The interval scaled variable (quartiles 1 to 4) was treated as if it was a continuous variable when we tested the slope for significance (test for trend). ANOVA was used to assess the difference in metabolic risk score across different body mass index (BMI) and muscle fitness groups (tertiles). The linear trend analysis was performed via polynomial contrast, and post hoc analyses were conducted with Tukey's least significant difference. On the basis of BMI, we classified children and adolescents as overweight and obese according to the age-adjusted cutoffs described by Cole et al. (10).
Descriptive statistics for girls and boys in the two age groups are shown in Table 1. In both age groups, there was a pattern of higher muscle fitness in boys than girls (P<0.001), except for the Biering-Sørensen test where the 15-yr-old girls performed better than the 15-yr-old boys (P< 0.001).
Table 2 shows partial correlations for muscle fitness and cardiorespiratory fitness with individual CVD risk factors and clustered metabolic risk. Weak to moderate associations were observed for individual muscle fitness tests with individual CVD risk factors; all r values being below r=0.3, except for the negative association between handgrip and waist circumference (r = −0.50, P < 0.001). Stronger associations (r = 0.14-0.55, all P values <0.001) were observed for cardiorespiratory fitness with single CVD risk factors.
Results showing the graded association of muscle fitness with the metabolic risk score are displayed in Figure 1. A main effect of muscle fitness was observed across quartiles (P < 0.001), with metabolic risk declining from Q1 (low fitness) to Q4 (high fitness). The same pattern was found in all sex and age subgroups (all groups P < 0.001), and there were no differences in sum of z-scores between the groups within each of the four quartiles. In all groups, participants in the lowest quartile of muscle fitness had significantly poorer metabolic risk scores compared with all other quartiles (all groups P < 0.05), whereas there were no differences among the other quartiles. The relationship was better described as an exponential function compared with the linear assumption, indicating that the largest difference was seen between the lowest quartiles (Q1 and Q2).
The regression analysis revealed that muscle fitness was negatively associated with clustered metabolic risk (P<0.001) after adjustment for age, sex, and pubertal stage (model 1) (Table 3). In addition, although adjustment for cardiorespiratory fitness (model 2) weakened the association, it remained significant (P < 0.001). An inverse association was found for cardiorespiratory fitness after adjustment for model 1 (P < 0.001), and these associations remained similar after additional adjustment for muscle fitness (P < 0.001).
Figure 2 shows the risk of having clustered risk in quartiles of muscle fitness and cardiorespiratory fitness. For muscle fitness, the odds ratio for having clustered risk was 7.2 (95% confidence interval (CI) = 4.3-12.0) in the least fit quartile (quartile 1) compared with the reference quartile (quartile 4). The test for trend was highly significant (P<0.001). Additional adjustment for cardiorespiratory fitness attenuated the odds ratio for having clustered risk to 1.73 (95% CI = 1.0-3.1) in the least fit quartile compared with the referent quartile. However, the test for trend was still significant (P = 0.003). For cardiorespiratory fitness, the odds ratio was 17.3 (95% CI = 9.2-32.7; P for trend <0.001) for the least fit group, and after additional adjustment for muscle fitness, the odds ratio declined to 9.4 (95% CI = 4.8-18.4; P for trend <0.001).
To further explore the association of muscle fitness with weight status and metabolic risk, both overweight and normal weight participants were divided into tertiles based on muscle fitness (low, moderate, and high). Significant differences in metabolic risk across muscle fitness groups existed among both normal weight (P = 0.013) and overweight participants (P < 0.001) (Fig. 3). In the normal weight group, post hoc analyses showed significant differences between tertiles 1 and 3 (P = 0.009), whereas in the overweight group, tertile 1 was significant different from tertile 2 (P = 0.001) and tertile 3 (P < 0.001). The overweight low muscle fitness group had the highest metabolic risk score (4.71 95% CI = 4.1-5.3), which presents a poorer CVD risk factor profile. Furthermore, the metabolic risk score in the overweight high fitness group was 1.10 (95% CI = −0.4 to 2.6), which was markedly lower than for the overweight low fitness group.
This study demonstrates that muscle fitness and cardiorespiratory fitness are independently and inversely associated with clustered metabolic risk after adjustment for confounding factors. Moreover, risk for having clustered risk was raised in the least fit quartile for both muscle fitness and cardiorespiratory fitness compared with the most fit quartile. We observed the poorest metabolic risk profile among the overweight children and adolescents with low muscle fitness.
In line with the present study, several previous reports have shown inverse associations between cardiorespiratory fitness and individual CVD risk factors (8), clustered CVD risk (5,15), and metabolic syndrome (18) in children and youth. So far, only one other study has reported the independent effect of muscle fitness on clustered CVD risk in youth. Garcia-Artero et al. (15) found an inverse association between muscular strength and lipid-metabolic profile, but the observation was only apparent in girls. In the present study, the association between both muscle fitness and cardiorespiratory fitness with clustered metabolic risk was significant after adjustment for the other fitness variable. There are some methodological differences that could partly explain discrepancies in sex difference between the two studies. First, Garcia-Artero et al. (15) have a smaller study sample, and second, they have included participants from a broader age range (13-18 yr). Cardiorespiratory fitness was more strongly associated with the clustered metabolic risk in comparison with muscle fitness, and additional adjustment for cardiorespiratory fitness weakened the associations between muscle fitness and clustered metabolic risk. Nonetheless, the associations remained significant after adjusting for cardiorespiratory fitness, suggesting that muscle fitness may already be an independent protective factor in thedevelopment of CVD risk in childhood. However, it is important to underline that the present study shows that cardiorespiratory fitness still appears to be the most important factor in predicting metabolic risk.
In adults, the central mechanisms through which high muscle fitness reduces CVD risk include improved TG (23), HDL-c levels (16), blood pressure (21), abdominal fat (35), and insulin sensitivity (17). It is possible that these biological pathways are transferable to the young population; however, the exact mechanisms that elicit the protective effect are not yet established in the young population. As sex hormone levels change and affect muscle mass during childhood, the apparent protective effect of muscle fitness in children and youth could be a function of puberty. Nevertheless, the associations were apparent already in prepubertal 9-yr-old children and in both sexes. Therefore, it is likely that the possible protective effect of muscle fitness is a function of participation in regular physical activity.
The protective association of muscle fitness was observed across both normal and overweight participants. The association was stronger in overweight subjects (P < 0.001). Several studies have shown that the poorest metabolic risk scores are observed among youth who are overweight and have low cardiorespiratory fitness (3,11,13). Findings in the present investigation suggest that additional benefits may be conferred with increased muscle fitness. Some overweight individuals may be averse to aerobic training, and therefore strength exercise may be a more attractive and better tolerated form of exercise.
The predictors found in this study are modifiable, highlighting the importance of identifying individuals at risk at an early age and the possible contribution cardiorespiratory fitness and muscle fitness can make when developing effective strategies for the prevention of CVD. Optimal health-related fitness might be a combination of cardiorespiratory fitness and muscle fitness. The American College of Sports Medicine (ACSM) recommends that the appropriate public health approach, in adults, is to promote regular participation in both strength and aerobic activities. On the basis of our findings, we additionally suggest applying these recommendations specific to children and adolescents.
In addition, the strong association of the two physical fitness components with metabolic risk observed in this study suggests the importance of including physical fitness testing in health-monitoring systems. We showed that a simple muscle fitness test battery may predict clustered metabolic risk comparable with direct measurement of cardiorespiratory fitness. Although measuring cardiorespiratory fitness directly is time consuming and expensive, determining muscle fitness is cheap and feasible. In the present study, muscle fitness was assessed by four tests that were easy to perform and required minimal equipment, taking only 15 min per child.
Strengths and limitations.
We used a continuous composite risk score for metabolic risk that is widely used for investigating associations between physical activity, cardiorespiratory fitness, and metabolic risk (2,5,12,14,28). This type of outcome may reflect health better than single risk factors and could to some extent compensate for the day-to-day fluctuation in the single risk factors (4). Although the children in this study did not suffer from clinical diseases, multiple risk factors of any type could be of concern. Additional strengths of this study include the availability of measures of insulin resistance, blood lipids, and objective measurement of both muscle fitness and cardiorespiratory fitness. Furthermore, along with a high participation rate, the inclusion of participants from all regions of the country made our sample a national representative of 9- and 15-yr-old Norwegians.
Our results must be interpreted with some limitations. First, we are aware of the fact that cross-sectional design does not permit explanations of causality. Second, the clustered risk score is sample specific and is based on the assumption that each component is weighted equally in predicting metabolic risk. Further, available evidence indicates that the risk accelerates from far below the so-called cut points. In our logistic regression to predict poor health, we defined individuals with a clustered risk score over 1 SD as being at risk. Thus, although we could have classified individuals who are not at risk as being at risk, we consider this a conservative choice. Finally, muscle fitness was measured by explosive strength, isometric strength, and endurance strength only. We could have selected other tests. The Eurofit test battery has been widely used with children and adolescents throughout Europe, and the tests are simple, practical, and reliable (22). Future prospective studies are needed to examine the independent and the combined effects of cardiorespiratory fitness and muscle fitness on the likelihood of having CVD later in life.
In summary, we found inverse and independent associations for muscle fitness and cardiorespiratory fitness with clustered metabolic risk in Norwegian children and youth. Cardiorespiratory fitness appears to be the strongest predictor for clustered metabolic risk and may be conferred with increased muscle fitness. From a public health perspective, this highlights the need to promote regular participation in both strength and aerobic activities. Finally, in the light of the strong and consistent associations between different physical fitness components and clustered metabolic risk, cardiorespiratory and muscle fitness testing should be included in health-monitoring systems.
The authors thank all the test personnel for their work during data collection and Professor Ingar Holme for statistical guidance. They also thank the Central Laboratory Ullevaal University Hospital and the Hormon Laboratory Aker University Hospital for using blood analysis. Financial support for this study was received from the Directorate for Health and the Norwegian School of Sport Sciences. The results of the current study do not constitute endorsement by the ACSM.
There is no conflict of interest.
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