Fitness, body composition and metabolic syndrome score in children and adolescents: The UP&amp;DOWN study

doi:10.21203/rs.3.rs-1849920/v1

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Fitness, body composition and metabolic syndrome score in children and adolescents: The UP&DOWN study

https://doi.org/10.21203/rs.3.rs-1849920/v1

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Purpose: We aimed to analyse the longitudinal association between physical fitness (PF) and body composition (BC) with metabolic syndrome score (MetS) in children and adolescents, and to elucidate whether the association between PF and MetS differs when using relativized or absolute fitness variables.

Methods: A total of 188 children (86 females) and 195 adolescents (97 females) were included. Cardiorespiratory fitness (CRF) was determined by the 20-m shuttle run test and muscular fitness (MF) was determined by hand grip and standing long jump tests. Height and weight were measured, and body mass index (kg/m²) was calculated. Triceps and subscapular skinfolds were assessed to compute body fat percentage. MetS was computed from: waist circumference, systolic blood pressure, triglycerides, high-density lipoprotein cholesterol and glucose levels.

Results: Relative CRF was longitudinally and negatively associated with MetS in female children (β=-0.031,p=0.025), while absolute CRF was positively associated with MetS in male children and adolescents (β=0.000,p<0.05). Relative upper and lower-body MF were longitudinally and negatively associated with MetS in female adolescents (β=-1.347,β=-0.005,p<0.05), while absolute lower-body MF was positively associated with MetS in male children (β=0.000,p=0.019). BC was longitudinally and positive associated with MetS in male children (β-ranging from 0.011-0.055,all p<0.05) and male adolescents (β-ranging from 0.011-0.046,all p<0.05).

Conclusion: BC is more strongly associated with MetS than PF in children and adolescents. An optimal body weight status should be considered the main objective of health promoting programs at childhood and adolescence. Furthermore, the way of expressing the fitness variables determines the direction of the association with MetS.

cardiovascular disease

aerobic capacity

muscular strength

youth

adiposity

Physical fitness is an important health indicator in children and adolescents, with great amount of previous evidence supporting the preventive role of maintaining optimal levels of both cardiorespiratory and muscular fitness for future cardiometabolic issues.

What is new: The way of reporting physical fitness variables can affect the associations between physical fitness features and cardiometabolic outcomes. Since body composition variables have a great impact on both physical fitness and cardiometabolic health, relativizing physical fitness performance by body composition could lead to erroneous conclusions.

Cardiovascular diseases (CVD) are one of the leading causes of global mortality [1]. In 2015, CVD were responsible for 17.9 million deaths and, unfortunately, an increase in CVD mortality has been projected due to population aging and growth [1]. Although CVD occurs mainly after the fifth decade of life [2], its precursors are already present at early ages [3]. Interestingly, the cluster of CVD risk factor levels in childhood and adolescence track into adulthood [4, 5]. The well-known metabolic syndrome (MetS), which is the clustering of abdominal obesity, elevated systolic blood pressure (SBP), elevated triglycerides, decreased high-density lipoprotein cholesterol (HDL-C), and elevated fasting plasma glucose, remains a major public health burden with the prevalence of the syndrome increasing in concert with obesity and sedentary lifestyles [6]. MetS affects both youths and adults and has been linked with clinical manifestations in CVD and type 2 diabetes [7, 8].

Physical fitness (PF) is an important health marker in youth [9, 10]. Specifically having higher levels of cardiorespiratory fitness (CRF) [11, 12] and muscular fitness (MF) [9, 13] at early age is associated with lower levels of mortality and lower levels of CVD risk factors. On the other hand, adiposity is another fundamental aspect that has been strongly related with cardiovascular health, so that high levels of adiposity in youth are accompanied by a worse CVD profile [14, 15], and this outcome persists later in life [10, 16].

An essential aspect is that PF and adiposity are closely related [17], what makes it difficult to elucidate from longitudinal observational studies whether PF, adiposity or both are predictive of CVD risk. This problem, derived from the causal relationship between these both features, since this relationship become more difficult to solve when it treats with PF measures in absolute terms and/or relativized by anthropometric measures as equals [18]. In terms of MF, a recent systematic review and meta-analysis showed how the association between MF and several health outcomes can change and even reverse if relative MF (i.e., divided by body mass) or absolute MF are used [19].

Most of the studies including PF measures use their relativized form [18, 19], which could lead to misconceptions of the independent effect that PF exerts on the outcome, given the interferences produced by adiposity. Given this, we aimed to analyse the longitudinal association between PF (i.e., MF and CRF) and body composition with MetS in children and adolescents, and to elucidate whether the association between PF and MetS differs when using relativized or absolute PF variables.

2.1. Study design and population

Participants took part of the UP&DOWN study [20]. In brief, this 2-year longitudinal study aimed to assess the impact on health indicators of physical activity, sedentary behaviours and health-related PF in apparently healthy primary and secondary schoolchildren from Spain. The UP&DOWN study sample consisted of 2225 healthy children (6-11.9 years) and adolescents (12-17.9 years) enrolled from schools in Cádiz and Madrid, respectively. One-fourth of the total sample was selected randomly for blood sampling.

For the present longitudinal study, we used baseline data collected from September 2011 to June 2012, and follow-up data collected from September 2013 to June 2014. A total of 188 children (86 females) and 195 adolescents (97 females) with full data on body composition variables, biochemistry variables, and PF field tests were included. Participants' parents were informed about the purposes of the study and written informed consents were provided. The study protocol was accepted by The Ethics Committee of the Hospital Puerta del Hierro (Madrid, Spain), the Bioethics Committee of the National Research Council (Madrid, Spain) and the Committee for Research Involving Human Subjects of the University of Cádiz (Cádiz, Spain).

2.2. Tanner stage

Explicative drawings of breast and genial development for females and males, respectively, were given to participants for self-classification in one of the five stages of pubertal development according to Tanner & Whitehouse [21].

2.3. Systolic blood pressure

Systolic blood pressure was measured with a validated digital automatic blood pressure monitor (OMRON M6; OMRON HEALTH CARE Co., Ltd., Kyoto, Japan) according to the standardized and valid International Protocol of the European Society of Hypertension [22]. Two measurements were taken 1 to 2 minutes apart. If the first two readings differed in > 5 mm Hg, an additional measurement was taken, and the farthest value was removed. The average value of the two measurements was selected.

2.4. Blood sampling

After an overnight fast, 13.5 mL of blood was extracted from the cubital vein of each participant. About 3.5 mL of the blood sample was collected in ethylenediaminetetraacetic acid (EDTA) and analysed to acquire hemogram data. The remaining blood was collected in dried gel and sodium citrate and centrifuged to remove serum and plasma. Finally, serum was frozen at − 80ºC for future analyses. In the current study, enzymatic colorimetric methods (Olympus AU2700 Analyzer; Olympus UK Ltd, Watford, UK) were used to analyse serum lipid triglycerides, high-density lipoprotein cholesterol (HDL-c) and glucose.

2.5. Body composition

Weight and height were measured with an electronic scale (Type SECA 861; range, 0.05–130 Kg; precision, 0.05 kg; Hamburg, Germany) and a telescopic stature-measuring instrument (Type SECA 225; range, 60–200 cm; precision, 1 mm; Hamburg, Germany) respectively. These measurements were conducted with participants dressed in lightweight clothing and without shoes. Body mass index (BMI) was calculated as weight/height squared (kg/m²). Waist circumference (WC) was measured at the level of the narrowest part of the torso, using a non-elastic tape (SECA 200; range, 0 to 150 cm; precision, 1 mm; Hamburg, Germany). Triceps and subscapular skinfolds were assessed according to Lohman's anthropometric standardization reference manual [23] by trained professionals. Skinfolds were taken on the non-dominant side of the body using a holtain skinfold caliper (range, 0 to 40 mm; precision, 0.2 mm) and body fat percentage (%BF) was estimated with Slaughter equations [24]. Two non-consecutive measurements of all body composition variables were carried out, and the average was recorded.

2.6. Cardiorespiratory fitness

The 20-m shuttle run test was used to assess CRF [25]. Participants were asked to run between two lines 20 m apart at a pace marked by a prerecorded sound signal. The initial speed was 8.5 km/h, with 0.5 km/h increments each stage. The test finished when the participant could not reach the line twice in a row. The last half-stage attained was recorded and used to estimate relative maximal oxygen uptake (VO₂max [mL/kg/min]) through the Léger's equation [25]. This test has shown to be valid and reliable in children and adolescents [26, 27]. Finally, we obtained absolute VO₂max was obtained by multiplying relative VO₂max by body weight.

2.7. Muscular fitness

Upper and lower body strength was assessed by the handgrip strength and standing long jump tests, respectively. The handgrip strength test was conducted using a validated hand dynamometer with adjustable grip (TKK 5101 Grip D; Takey, Tokyo, Japan) [28]. After adjusting the grip-span to the hand size according to the equations specifically developed for children [29] and adolescents [30], participants were asked to squeeze gradually and continuously for at least 2 seconds. The test was performed twice, and the maximum score for each hand was recorded in kilograms. The average score of the left and right hands was used as absolute upper MF. Furthermore, to obtain relative upper MF, absolute upper MF was divided by body weight (handgrip strength/weight) to preclude body size influences [31].The standing long jump test (SLJ) was performed with participants standing behind a line with feet approximately shoulder’s width apart. From this position, participants jumped as far forwards as possible. The test was performed twice, and the best score was recorded in centimetres. This score was used as relative lower MF, and the product of centimetres obtained per body weight was used as absolute lower MF. A single muscular fitness score (i.e., global MF) was calculated as a standardized score by age groups (i.e., children and adolescents) and sex (i.e., male and female) composed of relative MF of the upper and lower body [32].

2.8. Overall fitness

An overall fitness score was computed as the mean of the global MF and the relative CRF values (paliers) standardized by age groups and by sex.

2.9. Metabolic syndrome score

A Metabolic syndrome score (MetS) was created from the mean of the standardized values of each individual CVD risk factor (i.e., WC, SBP, triglycerides, HDL-c, and glucose) by age groups (children and adolescents) and sex (males and females). This index has been previously used by the International Diabetes Federation to assess cardiovascular health in children and adolescents [33].

The standardized value for HDL-c was multiplied by (− 1) since higher HDL-c levels represent lower CVD risk. Given the close relationship between body composition variables and WC. Additionally MetS score was computed excluding WC (i.e., MetS4), given the close relationships between WC and the rest of body composition measures used as independent variables.

2.10. Data analyses

Significant interactions by sex (males and females) and age groups (children and adolescents) in the studied associations were observed. Consequently, all analyses were performed by sex and age groups. Descriptive statistics are presented as mean ± standard deviation. T tests were used to check differences in variables of interest between sex for both age groups at both time points. All variables were checked for normality, and values of triglycerides were transformed to the natural logarithm due to their skewed distribution.

To examine the cross-sectional association of PF and body composition variables with MetS scores, we used linear regression models, where PF variables (i.e., 20-m shuttle run test, relative VO₂max (ml/kg/min), absolute VO₂max (ml/min), absolute upper MF, relative upper MF, absolute lower MF, relative lower MF, global MF, overall fitness), and body composition variables (i.e., body weight, BMI, fat mass percentage and WC) at baseline were individually introduced as independent variables, and MetS and MetS4 scores at baseline were individually introduced as dependent variables. All analyses were controlled for age, educational centre and mother´s education level at baseline.

To study the longitudinal association between PF variables and MetS4, we used linear regression models where PF variables at baseline were individually introduced as independent variables, and MetS4 at 2-years follow-up as dependent variable. In model 1, we adjusted by age, educational centre, mother´s education level and MetS4 at baseline, model 2 was model 1 + WC at baseline and model 3 was model 1 + %BF at baseline.

To examine the longitudinal association between body composition variables and MetS4, linear regression models were used, where body composition variables (i.e., weight, BMI, %BF and WC) at baseline were individually introduced as independent variables, and MetS4 at 2-years follow-up as dependent variable. In model 1, we adjusted by age, educational centre, mother´s education level and MetS4 at baseline, model 2 was model 1 + relative CRF (paliers) at baseline, model 3 was model 1 + global MF at baseline and model 4 was model 1 + overall fitness score at baseline.

To analyse whether changes in PF variables are associated with future MetS4, linear regression models were used, where the change (follow-up value – baseline value) of 20-m shuttle run test, relative VO2max, absolute VO₂max, absolute upper MF, relative upper MF, absolute lower MF, relative lower MF, global MF and overall fitness score were individually introduced as independent variables, and MetS4 at 2-years follow-up were individually introduced as dependent variable. All models were adjusted by age, educational centre, mother´s education level and MetS4 at baseline. In addition, changes in PF variables were adjusted by changes in WC, while changes in PF variables were adjusted by changes in %BF. The same analyses were performed but introducing the changes in MetS4 as dependent variable in order to test whether changes in PF variables were associated with changes in MetS4. Analyses were controlled for the same variables except MetS4 at baseline.

To study whether changes in body composition variables are associated with future MetS4, linear regression models were used, where the change of weight, BMI, %BF and WC were individually introduced as independent variables and MetS4 at 2-years follow-up were individually introduced as dependent variable. All models were adjusted by age, educational centre, mother´s education level and MetS4 at baseline. Moreover, changes in PF variables (CRF, MF and overall fitness) were used individually as adjustment variables. The same analyses were performed but introducing the changes in MetS4 as dependent variable in order to test whether changes in body composition variables were associated with changes in MetS4. Analyses were controlled for the same variables mentioned above excepted MetS4 at baseline.

Finally, to evaluate how changes in body composition variables are associated with MetS4 at follow-up, cut points for WC [34], %BF [35] and BMI were used to create the different body composition groups at baseline and follow-up. WC and %BF groups were created as dichotomic variables (0 without risk, 1 with risk) in function of sex and age, and BMI was created as a dichotomic variable, where 0 (without risk) was assigned to underweight and normal weight and 1 (with risk) was assigned to overweight and obesity. Residuals of the model in which MetS4 at follow-up was a dependent variable, changes in overall fitness were considered the independent variable and age, educational center, mother's education level and MetS4 at baseline were adjustment variables, were used as dependent variable and changes in body composition groups were used as independent variable. All analyses were performed using the environment for statistical computing R [36].

Participant characteristics are shown in table 1. Overall, at baseline, female children and adolescents displayed higher levels in the sum of skinfolds and %BF, and lower levels of CRF and MF variables (all p<0.05) compared to male children and adolescents, except in absolute upper- and lower-body MF in children. At follow-up, female children had higher levels of %BF and lower levels of CRF and relative lower-body MF (all p<0.05) than males. In adolescents, females presented lower SBP, glucose levels, CRF and MF, and higher HDL-c, and higher body fatness indicators levels compared to males (all p<0.05).

Cross-sectional associations between PF and body composition variables with MetS scores at baseline are depicted in table 2. Relative CRF (i.e., paliers and relative VO₂max) was negatively associated with MetS4 in male children and in adolescents of both sexes (β ranging from -0.024 to -0.082, all p<0.05). Besides, absolute CRF (i.e., absolute VO₂max) was positively associated with MetS scores in children and adolescents of both sexes, although the effect size was small (β ranging from 0.0 to 0.001, all p<0.05). Absolute upper- and lower-body MF were positively associated with MetS4 in female children (β=0.072 and β=0.000, respectively, both p<0.001). In male adolescents, relative upper-body MF was negatively associated with MetS4 (β=-1.784, p=0.001), while absolute lower-body MF was positively associated with MetS4 (β=0.000, p=0.001). global MF and overall fitness score were negatively associated with MetS in both sexes and all age groups (β ranging from -0.195 to -0.419, all p<0.05). These scores were negatively associated with MetS4 in male children and adolescents (β ranging from -0.173 to -0.279, all p<0.05). All body compositions variables were positively associated with MetS4 in both sexes and all age groups (β ranging from 0.012 to 0.112, all p<0.05). Same results were found when controlled by tanner stage at baseline instead of age.

Table 3 shows longitudinal associations between PF variables at baseline and MetS4 at 2-years follow-up. Relative CRF expressed in paliers and VO₂max were negatively associated with MetS4 in female children (β =-0.073, β=-0.031, p=0.025 respectively). Contrary, absolute CRF was positively associated with MetS4 in male children and adolescents. Similar results were observed when %BF at baseline was included in the model (Model 3) (β =-0.106, β=-0.046, p=0.008 respectively). Overall PF was negatively associated with MetS4 when analyses were adjusted by %BF (model 3) (β =-0.283, p=0.025). In female adolescents relative upper- and lower-body MF, the global MF and overall PF were negatively associated with MetS4 (β ranging from -0.005 to -1.347, all p<0.05). Moreover, the global MF was negatively associated with MetS4 in model 2 and 3 (β =-0.216, p=0.03, β=-0.23, p=0.035 respectively); and absolute upper-body MF was negatively associated with MetS4 (β =-0.029, p=0.036) and absolute lower-body MF was positively associated with MetS4 (β =0.000, p=0.016) in model 2. Finally, in male children and adolescents not associations were found between PF variables and MetS4 score (p>0.05), except in male children where absolute lower-body MF was positively associated with MetS4 in model 1 (β =0.000, p=0.019).

The longitudinal associations between body composition variables at baseline and MetS4 are depicted in table 4. In male children all body composition variables (weight, BMI, %BF and WC) were positively associated with MetS4 (β=0.017, β=0.047, β=0.011, β=0.018, p<0.020 respectively). Similar results were found in model 2 (β=0.017, β=0.047, β=0.011, β=0.017, p<0.035 respectively), in model 3 (β=0.018, β=0.055, β=0.014, β=0.019, p<0.035 respectively) and in model 4 (β=0.017, β=0.051, β=0.013, β=0.018, p<0.038 respectively). In male adolescents, weight and BMI were positively associated with MetS4 in model 1 (β=0.011, β=0.037, p=0.04, p=0.026 respectively), in model 2 (β=0.013, β=0.046, p=0.025, p=0.011, respectively) and in model 4 (β=0.012, β=0.045, p=0.049, p=0.024 respectively). Moreover, %BF was positively associated with MetS4 in model 2 and model 4 (β=0.013, p=0.019, p=0.044, respectively). Finally, in female children and adolescents not associations were found between body composition variables and MetS4 score (p>0.05).

Table 5 shows the association between changes in PF variables and MetS4 at follow-up and its changes. Changes in absolute CRF were longitudinally positively associated with MetS4 only when %BF was included as covariable in the model. This association was also observed when changes in MetS4 levels were used as the dependent variable. Changes in absolute lower-body MF were positively associated with follow-up MetS4 in male children when adjusted by WC and %BF (β=0.000, p=0.037, p=0.031 respectively). In addition, changes in absolute upper-body MF were positively associated with follow-up MetS4 in female children (β=0.06, p=0.003). No associations were found between changes in PF variables and follow-up MetS4 in male and female adolescents as well as between changes in PF variables and changes in MetS4 in both sexes and age groups (p>0.05).

The associations between changes in body composition variables and MetS4 at follow-up and its changes are depicted in table 6. Changes in body weight and BMI were positively associated with follow-up MetS4 in male children when adjusted by CRF (β=0.045 and β=0.114, respectively, both p<0.01); by MF (β=0.044 and β=0.104, respectively, both p<0.01); and by overall fitness (β=0.045 and β=0.116, respectively, both p<0.01) at follow-up. Moreover, Changes in BMI was positively associated with changes in MetS4 when adjusted by CRF, MF and overall fitness (β=0.104, β=0.093, β=0.109, p=0.016, p=0.032, p=0.017 respectively). All changes in body composition variables were positively associated with MetS4 at follow-up and changes in MetS4 in female children, even when analyses were adjusted by changes in PF variables (all, p< 0.008). Changes in body weight were positively associated with changes in MetS4 when adjusted by CRF and overall fitness in male adolescents (β=0.023, p=0.038, p=0.044, respectively). All changes in body composition variables were positively associated with MetS4 at follow-up and changes in MetS4 in female adolescents (except for %BF), even when we adjusted by changes in PF variables (all, p<0.018).

Differences in MetS4 between changes in body composition groups are displayed in figure 1. Significant differences were found between participants groups who were persistent high %BF and those who were persistent low %BF (mean=0.09, mean=-0.08 respectively, p=0.014). Finally, significant differences were found between participants groups who were persistent high BMI and those who were persistent low BMI (mean=0.14, mean=-0.06, p=0.002) and there were significant differences between participants groups who were persistent high BMI and those who were decreasing BMI (mean=0.14, mean=-0.09, p=0.012).

Our results showed that most body composition variables were longitudinally and positively associated with the MetS4 in males children and adolescent. In contrast, some PF variables were negatively associated with MetS4 mainly in females children and adolescents. Specifically, relative CRF was negatively associated in female children, and MF was negatively associated in female adolescents. In contrast, absolute CRF showed a positive association with MetS4 in male children and adolescents. However, these associations were stronger for body composition variables than for PF variables, even when the changes produced after two years were included as exposures. The lack of association between body composition variables and MetS4 in females could be explained by the few changes that occurred in these variables after a 2-year follow-up period. On the contrary, males suffered greater changes in body composition variables than women. Therefore, from a statistical point of view, it is more likely to find a significant association when the independent variable has a greater range of values.

Regarding the association between PF and MetS, it is important to note that its direction and strength differs when using relativized or absolute PF variables. In regards to CRF, some authors have observed that when the VO2max is corrected by body size (i.e., body weight), the associations were negative. On the contrary, it would appear that this way of standardization coverts the positive correlation between the physiological performance variable and the body size variable into a negative one [37]. Our statistical analyses also showed differences in the direction of the association between CRF and MetS4 when using absolute or relative CRF. In this sense, some authors suggest that the association between VO2 relativized by body weight (ml/kg/min) could be highly influenced by the association between body weight and health parameters, reflecting their level of fat more than their level of PF [38]. Besides, in terms of MF, previous evidence show that when MF is relativized by body weight, there was a negatively association between MF and MetS [13, 39, 48–50, 40–47]. On the contrary, when MF was considered in absolute terms, a positive association between MF and MetS has been observed in other studies [43, 51]. In the current study, same results at cross-sectional and longitudinal analyses were found. The reason for this difference can be explained by the association between MF and some body composition variables as adiposity [17]. For instance in our sample, those children and adolescents who had higher body weight and higher BMI also had higher levels of absolute MF in the handgrip test and less distance in SLJ, being the latest a weight-bearing test (data not shown), in agreement with the results obtained by Artero et al [52]. One possible explanation could be that participants with a higher weight level followed a diet that promoted a caloric surplus diet. A positive energy balance is associated with an increased body weight [53] but also with an increases in absolute muscle mass [54], which is a clear determinant of MF [14, 55]. For this reason, those children and adolescents with higher body weight had more muscle mass and obtained better results in the handgrip test. On the contrary, these young people obtained worse results in the SLJ since they had to mobilize all their body weight to perform this test. In this sense, our cross-sectional results showed how the different indicators of relative or absolute MF were negatively or positively associated with MetS 4, respectively. This may be because the relative variables includes body composition in the measure, therefore this association between the relative PF and metabolic risk may be affected by the association between body composition and metabolic risk factors [43].

In fact, we observed that body composition measures were strongly and negatively associated with MetS, this result being explained because a substantial part of the pathophysiology of MetS is driven by the resistance to the metabolic effects of insulin. The major cause of insulin resistance in childhood is a typical lipid partitioning pattern characterized by increased deposition of lipids within insulin responsive tissues, such as the liver and skeletal muscle and within the viscera [56]. This lipid deposition pattern is also associated with infiltration of intra-abdominal tissues with cells of the immune system, inducing systemic, low-grade inflammation typically observed in insulin resistant obese children and adolescents [56]. All these physiological processes lead to a deterioration of the cardiovascular system, which increases the probability of future cardiovascular events[57].

In brief, it seems that body composition is more strongly associated with MetS than PF, as seen in previous review [58]. In fact, not only having excess body weight at a specific time is important, but also the progression or changes of this state of body weight is important. Therefore, given the relationship between PF and body composition, both variables should be the focus of preventive strategies to reduce the incidence of MetS in children and adolescents. On the one hand, evidence indicates that increments in CRF will ameliorate the level of adiposity and, ultimately, the MetS risk [59, 60]. On the other hand, increased MF through resistance training promotes increased insulin sensitivity, which also affects the risk of MetS [19]. Based on these results and considering the strong association between the body composition variables and MetS, we consider that the CRF and MF variables related to body composition are a better way to express the PF variables when the objective is to predict MetS. It is important to make a distinction between CRF and MF variables in absolute and relative terms in order to avoid terminological confusion and attribute benefits to MF that may come from the influence of body weight.

4.1 Limitations and strengths

Several limitations should be mentioned. First, %BF was derived from an indirect measure of body composition: skinfold thicknesses. The main disadvantage of using this technique is the need of expertise to carry out the measurements. Nonetheless, skinfold thicknesses were taken by trained professionals, and their validity has been previously established in the pediatric population [27]. Second, in the case of the 20-m shuttle run test, participants have to carry their weight over a series of 20-m shuttle runs, which adversely affects performance in the heavier participants [61]. Nonetheless, we used an equation to estimate CRF that was validated without correcting the test performance by any anthropometric measure [25]. Thus, the current results should be interpreted with caution, since no direct measure of absolute VO2 was available in our study. Third, since we used a longitudinal design, the causation of the associations could not be properly determined. Finally, the generalizability of these results should be cautiously considered cautiously because we could not determine the influence of ethnicity and country's economic development on these associations, given that only urban, Caucasian Spanish children and adolescents participated in this study. Otherwise, the current research presents some strengths. The longitudinal design and the relatively large sample, which allows us to conduct the analyses differentiating by sex and age groups, are major strengths of the present study. Moreover, the use of clustered MetS risk factors has been suggested as a good indicator of cardiovascular health, compared with individual MetS risk factors [5].

4.2 Conclusion

In conclusion, it seems that body composition is more strongly associated with MetS score than PF in children and adolescents. According to this, our results suggest that attaining/maintaining an optimal body weight status should be considered the main objective of health promoting programs at both, childhood and adolescence. Furthermore, the way of expressing the PF variables (absolute or relative) determines the direction of the association with MetS. For this reason, we suggest highlighting the type of PF used in the analyses in order not to attribute benefits to PF that may be due to body composition. Finally, in practical terms, when we want to predict the risk of MetS, using measures of PF relative to body composition seems a better strategy, since it shows a more precise knowledge about cardiovascular health status than just measuring absolute PF in youth.

%BF: body fat percentage

BMI: body mass index

CRF: cardiorespiratory fitness

CVD: cardiovascular disease

MetS: metabolic syndrome score

MetS4: MetS score excluding waist circumference

MF: muscular fitness

PF: physical fitness

SBP: systolic blood pressure

SLJ: standing long jump test

HDL-c: high-density lipoprotein cholesterol

WC: waist circumference

VO₂max: relative maximal oxygen uptake

Funding: This work was supported by the National Plan for Research, Development and Innovation (R+D+i) from the Spanish Ministry of Science and Innovation [DEP 2010-21662-C04-00 (DEP 2010-21662-C04-01: DEP 2010-21662-C04-02: DEP 2010-21662-C04-03: DEP 2010-21662-C04-04)]. The funding organizations had no role in data collection, analyses, interpretation or final report of the study.

Competing interests: No potential conflicts of interest relevant to this article were reported.

Author Contributions: Alejandro Sánchez-Delgado contributed to the acquisition of data, had full access to the database, contributed to the conception and design of the study, performed the statistical analyses, and drafted the manuscript. Alejandro Pérez-Bey contributed to the conception and design of the study, made critical revision of the statistical procedures and the draft of the manuscript, and approved the final manuscript as submitted. Rocío Izquierdo-Gómez, José Jimenez-Iglesias, Oscar L. Veiga, Ascensión Marcos, Sonia Gómez-Martínez and María José Girela-Rejón made critical revision of the draft of the manuscript and approved the final manuscript as submitted. José Castro-Piñero is the guarantor of this work, contributed to the acquisition of data, had full access to the database, handled funding, contributed to the conception and design of the study, made critical revision of the statistical procedures and of the draft of the manuscript, and approved the final manuscript as submitted. All authors approved the final manuscript as submitted.

Ethics approval: The study protocol was accepted by The Ethics Committee of the Hospital Puerta del Hierro (Madrid, Spain), the Bioethics Committee of the National Research Council (Madrid, Spain) and the Committee for Research Involving Human Subjects of the University of Cádiz (Cádiz, Spain).

Consent to participate: Participants' parents were informed about the purposes of the study and written informed consents were provided. The study protocol was accepted by The Ethics Committee of the Hospital Puerta del Hierro (Madrid, Spain), the Bioethics Committee of the National Research Council (Madrid, Spain) and the Committee for Research Involving Human Subjects of the University of Cádiz (Cádiz, Spain).

Acknowledgements: We thank the participation of children and adolescents in this study, as well as parents and headmasters and teachers of the participating schools

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Fitness, body composition and metabolic syndrome score in children and adolescents: The UP&DOWN study

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Abstract

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What Is Known

1. Introduction

2. Materials And Methods

2.1. Study design and population

2.2. Tanner stage

2.3. Systolic blood pressure

2.4. Blood sampling

2.5. Body composition

2.6. Cardiorespiratory fitness

2.7. Muscular fitness

2.8. Overall fitness

2.9. Metabolic syndrome score

2.10. Data analyses

3. Results

4. Discussion

4.1 Limitations and strengths

4.2 Conclusion

Abbreviations

Statements And Declarations

References

Tables

Additional Declarations

Supplementary Files

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Version 1