Denmark, Odense, Denmark

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1 This article was downloaded by:[dnl] On: 24 February 2008 Access Details: [subscription number ] Publisher: Informa Healthcare Informa Ltd Registered in England and Wales Registered Number: Registered office: Mortimer House, Mortimer Street, London W1T 3JH, UK International Journal of Pediatric Obesity Publication details, including instructions for authors and subscription information: Fitness, fatness and clustering of cardiovascular risk factors in children from Denmark, Estonia and Portugal: The European Youth Heart Study Lars B. Andersen ab ; Luis B. Sardinha c ; Karsten Froberg b ; Chris J. Riddoch d ; Angie S. Page e ; Sigmund A. Anderssen a a Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo, Norway b Institute of Sport Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark c Faculty of Human Movement, Technical University of Lisbon, Portugal d School for Health, University of Bath, UK e Department of Exercise and Health Sciences, University of Bristol, UK Online Publication Date: 01 January 2008 To cite this Article: Andersen, Lars B., Sardinha, Luis B., Froberg, Karsten, Riddoch, Chris J., Page, Angie S. and Anderssen, Sigmund A. (2008) 'Fitness, fatness and clustering of cardiovascular risk factors in children from Denmark, Estonia and Portugal: The European Youth Heart Study', International Journal of Pediatric Obesity, 3:1, To link to this article: DOI: / URL: PLEASE SCROLL DOWN FOR ARTICLE Full terms and conditions of use: This article maybe used for research, teaching and private study purposes. Any substantial or systematic reproduction, re-distribution, re-selling, loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.

2 International Journal of Pediatric Obesity. 2008; 3: 5866 ORIGINAL ARTICLE Fitness, fatness and clustering of cardiovascular risk factors in children from Denmark, Estonia and Portugal: The European Youth Heart Study LARS B. ANDERSEN 1,3, LUIS B. SARDINHA 2, KARSTEN FROBERG 3, CHRIS J. RIDDOCH 4, ANGIE S. PAGE 5 & SIGMUND A. ANDERSEN 1 1 Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo, Norway, 2 Faculty of Human Movement, Technical University of Lisbon, Portugal, 3 Institute of Sport Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark, 4 School for Health, University of Bath, UK, 5 Department of Exercise and Health Sciences, University of Bristol, UK Abstract Background. Levels of overweight have increased and fitness has decreased in children. Potentially, these changes may be a threat to future health. Numerous studies have measured changes in body mass index (BMI), but few have assessed the independent effects of low fitness, overweight and physical inactivity on cardiovascular (CVD) risk factors. Methods. A crosssectional multi-center study including children from Denmark, Estonia and Portugal. The main outcome was clustering of CVD risk factors. Independent variables were waist circumference, skinfolds, physical activity and cardiorespiratory fitness. Results. Both waist circumference and skinfolds were associated with clustered CVD risk. Odds ratios for clustered CVD risk for the upper quartiles compared with the lowest quartile were 9.13 (95% CI: ) and (95% CI: ) when systolic blood pressure, triglyceride, insulin resistance homeostasis assessment model (HOMA) score, cholesterol:hdl, and fitness were included in the score. When fitness was removed from the clustered risk variable, the association for fatness attenuated and after further adjustment for fitness, only the highest quartiles of the fatness parameters were significant. Fitness showed the same strength of association with the clustered risk score including systolic blood pressure, triglyceride, HOMA score, and cholesterol:hdl with odds ratio for the upper quartile of 4.97 (95% CI: ). Physical activity was associated with clustered risk even after adjustment for fitness and fatness with an odds ratio for the upper quartile of 1.81 (95% CI: ). Conclusion. Physical activity, fitness, skinfold and waist circumference were all independently associated with clustered CVD risk. Key words: Physical activity, fitness, fatness, clustered CVD risk Introduction The rise in overweight and obesity in most parts of the world is apparent to all. Some studies have also suggested that fitness levels in children have decreased at least in the less fit end of the population (13). The health consequences of these trends is relatively unstudied and there remains confusion about the role of inactivity, low fitness and overweight in the development of metabolic disorders in children, which may lead to type 2 diabetes and cardiovascular disease (CVD) later in life (46). It is important to assess the independent effects of physical activity and fitness as the first is a behavior and the second is a physical trait determining the ability to perform physical work. Higher intensity aerobic physical activity will affect aerobic fitness levels, while lower intensity physical activity acutely affects energy expenditure and insulin level, but may not affect fitness. We have reported earlier the association between physical activity, fitness and clustering of risk factors in the European Youth Heart Study (EYHS) (6,7). This clustering of risk factors in children with low physical activity and fitness is probably caused by a number of different Correspondence: Lars Bo Andersen, Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo, Norway. Box 4014 Ullevaal Stadion, 0806 Oslo. Fax: lars.bo.andersen@nih.no ISSN Print ISSN Online # 2008 Taylor & Francis DOI: /

3 Fitness, fatness and clustered CVD risk 59 physiological mechanisms, such as a decrease in insulin sensitivity, adrenalin sensitivity, lower insulin independent glucose uptake during muscle contraction and a decrease in lipoprotein lipase activity (8,9). Many studies have reported increased CVD risk factor levels in overweight children. However, not many have analyzed clustering of risk factors. Obesity could be a cause of clustering of CVD risk factors, as abdominal fat is an active tissue, which may cause insulin insensitivity (10). Some studies have analyzed fatness and fitness as predictors of CVD risk factor levels and fasting insulin and found that only fatness was an independent predictor (4,11). However, it is not possible from this type of analysis to conclude which parameter is more important, because the causal pathway is not known. It is likely that fitness/activity and fatness are competing risk factors, in which case, it makes sense to adjust for the other in the analysis, but they may also be parts of the same causal chain leading to high CVD risk factor levels. If the latter is the case, an adjustment for a link in the chain will remove the association between the real cause and the risk factor. The aim of the present study was firstly to analyze two measures of fatness as predictors of clustered CVD risk in a large cohort of children aged 9 and 15 years old. Secondly, the role of physical activity and fitness in this association was explored. Methods Setting Data from the European Youth Heart Study (EYHS) were used. The EYHS is a multi-center, international study addressing the prevalence and aetiology of cardiovascular disease risk factors in children aged 9 and 15 years. Data from Estonia, Denmark, and Portugal (Madeira) were used (12). Study protocols were similar in all countries and conformed to the international guidelines on biomedical research and each research team complied with the ethical procedures of that country. Written, informed consent was obtained from the child s parent or legal guardian after they were given a detailed written explanation of the aims of the study, its possible hazards, discomfort, and inconvenience, and the option to withdraw at any time. Participants Boys and girls aged 9 and 15 years old, representing children on either side of puberty, were randomly selected to participate. Samples of children were drawn in a similar fashion within each study location. At each study location, a sampling frame of schools using official lists was compiled. Schools were stratified by the socio-demographic characteristics of their local areas. Each school was given a weighting according to the number of children enrolled and a minimum of 20 schools were randomly selected using probability proportional to school size (12). Children of appropriate ages were sampled randomly (random number tables) within schools using the school register. The participation rate was similar in each country (76% in Estonia, 73% in Portugal, and 75% in Denmark). In total, girls and boys participated. There were no exclusion criteria, but only subjects with complete data on all risk factors are included (Tables I and II). Most of the excluded participants were excluded because of invalid accelerometer (physical activity) data. Initially, 421 children were excluded because criteria for a successful fitness test were not fulfilled. Another 762 lacked complete blood analyses or blood pressure measurements. Valid accelerometer data was available for subjects, were excluded because they did not fulfill the inclusion criteria, and 93 had worn broken instruments. Complete data was therefore available for subjects, and these subjects form the basis of the present investigation. No differences were found between subjects with complete data and those who were excluded with respect to age, height, weight, body mass index (BMI), cholesterol: HDL (highdensity lipoprotein), skinfolds and waist circumference. Differences between groups with complete and incomplete data were found in fitness (0.17 standard deviation (SD), p B0.0001), systolic blood pressure (0.06 SD, pb0.0001), glucose (0.07 SD, p0.0355), total cholesterol (0.11 SD, p ), triglyceride ( 0.10 SD, p0.0039), insulin ( 0.20 SD, p B0.0001) and HOMA score ( 0.19 SD, p B0.0001). All SD values are for participants compared with excluded subjects. Measurements Blood pressure was measured using a Dinamap paediatric/adult neonatal vital signs monitor (model XL, Critikron, Inc., Tampa, FL). Five measurements were taken at two-minute intervals with the mean of the final three measurements used in all analyses. Waist circumference was measured midway between the lower rib margin and the iliac crest at the end of gentle expiration. Weight was measured in light clothing to the nearest 0.1 kg using a calibrated beam balance scale. Height was measured without shoes to the nearest 0.5 cm using a transportable

4 60 L. B. Andersen et al. Harpenden stadiometer. Skinfold thicknesses were measured with Harpenden fat calipers at the biceps, triceps, subscapular, and suprailiac sites (13). Intratester reliability for skinfold was between 0.92 and 0.99 for the four sites, and inter-tester reliably was between 0.84 and Waist circumference was measured twice with a metal anthropometric tape midway between the lower rib margin and the iliac crest, at the end of gentle expiration. Pubertal status was assessed by trained personnel using Tanner s classification (14). Blood samples were collected after an overnight fast and stored at 80C before analysis. All samples were analyzed at Clinical Pathology Accredited laboratories (Bristol and Cambridge, UK). This is described in detail elsewhere (15). Insulin resistance was estimated according to HOMA as the product of fasting glucose (mmol l 1 ) and insulin (miu ml 1 ) divided by the constant 22.5 (16). Physical activity was assessed using the MTI accelerometer, Actigraph model 7164 (Manufacturing Technology Inc., Fort Walton Beach, FL). The MTI accelerometer measures the vertical acceleration of body movement, and the validity and reliability of the instrument has been tested in detail (17,18). Physical activity was monitored for 4 consecutive days: 2 weekdays and 2 weekend days. Instruments were attached tightly at the hip. Minute-by-minute data were stored in memory and subsequently downloaded to a computer. Data was reduced to derivative variables using customized macros. Our main derived physical activity variable was mean accelerometer counts per minute over the period of measurement. Time periods of at least 10 consecutive minutes of zero counts were considered to represent periods when the monitor was not worn and were thus disregarded before analysis. Criteria for a successful recording were a minimum of two days of 10 hours recording per day. Aerobic fitness (VO 2max ) was assessed as maximal power output in a cycle ergometer test with progressively increasing workload using an electronicallybraked cycle ergometer (Monark 839 Ergomedic). For details, see Riddoch et al. (12). Criteria defined for a maximal effort were HR]185 beats per minute and a subjective judgment by the observer that the participant could no longer continue, even after encouragement. Aerobic fitness was expressed as maximal power output relative to body weight (watts. kg 1 ). Statistical analysis SPSS version 15 was used for all analyses. Means and standard deviations for components of the metabolic syndrome are presented by country, sex, and age group of the children with complete measurements. Insulin, glucose, HOMA score, and triglyceride concentrations were positively skewed and were thus transformed (natural log). Z-scores by age, gender, and country were computed for all risk factor variables. In the logistic regression, z-scores of the individual risk factors were summed to construct a clustered risk score. Two clustered risk variables were constructed. Risk factors included in the first risk profile were systolic blood pressure, triglyceride, total cholesterol:hdl ratio, HOMA, and aerobic fitness. In the second variable, fitness was omitted in order to analyze the independent associations of fatness and physical activity on clustering of blood pressure and blood risk factors. We have earlier calculated that 15% of the children had a risk factor profile where risk factors clustered, i.e., risk factors were not independently distributed (19). We therefore defined children 1 SD in the sum of z-scores as being at risk. For the predictor variables, the sum of four skinfolds and waist circumference were used as measures of fatness instead of BMI. The reasoning for this is because physical fitness is included in our outcome variable, and is expressed relative to body weight. It is therefore a better solution to use skinfold and waist circumference in order to avoid body weight being accounted for twice. Results Descriptive statistics of the girls and boys from the three countries included in the analysis is shown in Tables I and II (only subjects with complete measures). In general, there was a pattern of higher risk in Denmark than in Portugal, and Estonia had the lowest risk in the 9-year-olds. In adolescents, Denmark also had the most adverse risk profile and Portugal had the lowest risk. There was a slight attenuation of the country differences in components of cardiovascular risk after adjustment for anthropometric measures but differences remained significant even following adjustment for body mass index, waist circumference, and height. Association between fatness parameters, fitness, physical activity and clustered CVD risk We first analyzed CVD risk as a continuous variable expressed as the summed z-score of the cardiovascular risk factors systolic blood pressure, triglyceride, total cholesterol:hdl ratio, HOMA score and aerobic fitness. In this analysis, fitness was included in the dependent variable because it is considered an independent risk factor. Mean z-score was calculated

5 Table I. Descriptive statistics for each sex and country in 9-year-olds. Denmark Portugal Estonia Girls (N198) Boys (N175) Girls (N177) Boys (N193) Girls (N161) Boys (N156) 9-year-olds Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD age (year) height (cm) weight (kg) BMI (kg/m 2 ) waist circumf (cm) skinfolds (mm) fitness (watts/kg) physical activity (mean count) diastolic blood pressure (mm Hg) systolic blood pressure (mm Hg) glucose (mmol/l) insulin (miu ml 1 ) cholesterol (mmol/l) HDL (mmol/l) triglyceride (mmol/l) SD: standard deviation; BMI: body mass index; HDL: high-density lipoprotein. Fitness, fatness and clustered CVD risk 61

6 Table II. Descriptive statistics for each sex and country in 15-year-olds. Denmark Portugal Estonia Girls (N132) boys (N117) girls (N89) boys (N194) girls (N168) Boys (N109) 15-year-olds Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD 62 L. B. Andersen et al. age (years) height (cm) weight (kg) BMI (kg/m 2 ) waist circumf (cm) skinfolds (mm) fitness (watts/kg) Physical activity (mean count) Diastolic blood pressure (mm Hg) systolic blood pressure (mm Hg) glucose (mmol/l) insulin (miu ml 1 ) cholesterol (mmol/l) HDL (mmol/l) triglyceride (mmol/l) SD: standard deviation; BMI: body mass index: HDL: high-density lipoprotein.

7 Fitness, fatness and clustered CVD risk 63 for each quartile of waist circumference and sum of 4 skinfolds (Figures 1 and 2). Afterwards the same calculation was done for summed z-score without the inclusion of fitness in the score to see if fatness was associated with blood pressure, HOMA score and lipids independent of fitness (Figures 1 and 2). Both waist circumference and skinfold were associated with risk, but the association was stronger when fitness was included in the z-score. When fitness was included in the z-score, the differences in z-score between the upper and lower quartile were 3.28 and 3.56, for waist circumference and skinfold, respectively. This means that the average difference for each risk factor was around 0.7 standard deviations. The differences in z-scores between the upper and lower quartiles when fitness was omitted from the risk score were 2.22 and 2.15, respectively. The modification of summed z-score resulting from the inclusion of fitness was apparent over the whole distribution of fatness parameters and not just in the high-risk groups. We then did a similar analysis as a logistic regression, because we expected the association between exposure variables and clustered risk was not linear. This type of analysis calculates the chance of having very high risk for different exposure groups instead of analyzing the whole distribution of risk factor levels. Odds ratios of having an adverse risk profile defined by 1 SD in the sum of z-scores for systolic blood pressure, triglyceride, total cholesterol:hdl ratio, HOMA, and aerobic fitness, were 0.89 (95% CI: ), 2.20 (95% CI: ) and 9.1 (95% CI: ) in the three upper quartiles compared with the lowest quartile of waist circumference, respectively. For skinfold, odds ratios were 1.61 (95% CI: ), 2.08 (95% CI: ) and (95% CI: ), respectively. In other words, risk was mainly elevated in the upper quartile of fatness parameters. The odds ratio in fatness parameters did not change when adjusted for objectively measured physical activity even if activity was independently associated to risk (Figure 3). Low physical fitness was as strongly associated with clustering of blood lipids, blood pressure and HOMA score as the fatness parameters (Figure 3). To further explore how fatness related to clustering of risk factors independently of fitness, we also analyzed fatness parameters without the inclusion of fitness in the risk score, which decreased odds ratios to 1.25 (95% CI: ), 1.83 (95% CI: ) and 5.94 (95% CI: ) for quartiles of waist circumference, and 1.77 (95% CI: ), 1.51 (95% CI: ) and 5.79 (95% CI: ) for quartiles of skinfold, respectively. Additional adjustment for fitness as a confounder in this analysis was done in order to see if the association of fatness to the other risk factors could be mediated by fitness. The associations were attenuated, but the upper quartile was still significantly higher than the lowest quartile, with odds ratios for the three upper quartiles of waist circumference of 1.17 (95% CI: 3 z-score of BP+Tg+C:HDL+HOMA+fitness z-score of BP+Tg+C:HDL+HOMA 2 z-score sum of quartiles of waist circumference Figure 1. Clustered risk and waste circumference.

8 64 L. B. Andersen et al. 3 z-score of BP+Tg+C:HDL+HOMA+fitness z-score of BP+Tg+C:HDL+HOMA z-score sum of Quartiles of skinfold Figure 2. Clustered risk and skinfold ), 1.57 (95% CI: ) and 3.92 (95% CI: ), respectively. The same analysis in quartiles of skinfolds gave odds ratios of 1.58 (95% CI: ), 1.16 (95% CI: ) and 3.29 (95% CI: ), respectively. Finally, quartiles of fitness was analyzed against the same clustered risk variable with adjustment for skinfold, and odds ratios of 1.54 (95% CI: ), 1.65 (95% CI: ) and 1.89 (95% CI: ) were found for quartiles three, two and one, respectively. Physical activity may be associated with CVD risk through the effect on fitness or fatness, but may also be independently associated through other physiological mechanisms. To elucidate this problem, we 10 clustered risk ratio of Odds Quartiles Figure 3. Clustered CVD risk in relation to physical activity (left bars) and fitness (right bars).

9 Fitness, fatness and clustered CVD risk 65 analyzed physical activity in different models. The association between physical inactivity and clustered risk without the inclusion of fitness in the risk variable was somewhat weaker than when fitness was included, but it was still significant with odds ratios of 1.30 (95% CI: ), 2.45 (95% CI: ) and 2.10 (95% CI: ) for quartiles three, two and one, respectively. After adjustment for both fitness and skinfold, physical activity was still associated with clustered risk, with odds ratios of 1.23 (95% CI: ), 2.28 (95% CI: ) and 1.81 (95% CI: ) for quartiles three, two and one, respectively. In a linear regression with sum of z-score including systolic blood pressure, triglyceride, total cholesterol:hdl and HOMA score as dependent variables, and physical activity, physical fitness and skinfold as independent variables, partial correlations of 0.13, 0.08 and 0.26 (all pb0.001), respectively, were found. Discussion The main findings of this study were that physical activity, fitness and fatness were independently associated with clustered CVD risk. The associations between the fatness variables (waist circumference and sum of 4 skinfolds) and CVD risk were strong and graded, but with a curvilinear shape, when fitness was included as a risk factor both in the linear and logistic regression analyses. When fitness was removed from the clustered risk variable and further adjusted for in the analysis, only the highest quartile of fatness had increased risk. The association between physical fitness and clustered risk had approximately the same strength as with fatness, and the lower quartiles of fitness had increased risk even after adjustment for fatness (skinfold). Physical activity showed an independent association to risk even after adjustment for both fitness and fatness. Study strengths and limitations The main strengths of this study were the availability of measures of insulin resistance and other CVD risk factors in a large population of children together with objectively measured physical activity and a maximal fitness test. We chose a composite score of the risk factors related to the metabolic syndrome as dependent variables for two reasons: 1) this type of outcome may reflect health better than single risk factors, and 2) a composite score may to some extent compensate for the day-to-day fluctuations in the single risk factors. Even if none of the participants suffer from clinical disease, clustered risk is certainly an undesirable condition, and it has been shown to track into young adulthood (20). Cut-off points in single risk factors for having metabolic syndrome are not established in children, but suggestions have been given (2123). It is possible we have classified subjects, who are not at risk, as at risk but such misclassification would only result in an underestimation of the true association between physical activity and clustered risk. The cross-sectional study design does not allow conclusions of which parameters are more important in the development of high risk factor levels. There are probably several more or less independent pathways, which is supported by our analyses. High intensity physical activity has other effects than larger volumes of low intensity exercise and this is not just in relation to fitness, but also to blood lipids, blood pressure and mean daily insulin level. It will be difficult to separate the effect of fitness, fatness and physical inactivity even in trials, because fatness develops over a long period of time in the normal population. Further, if trials include exercise training this will change metabolic regulatory processes, and it will be difficult to separate the effects of changes in body fat or fat distribution from the effects of physical activity and improved fitness. The main effect of training may not be a result of the energy expenditure per se, but rather the physiological changes related to increased insulin and adrenalin sensitivity, increased lipoprotein lipase activity, and contraction-mediated glucose uptake in the muscle cell (9,24,25). The physiological mechanisms causing the adverse risk factor profiles are similar for both fatness and physical inactivity. However, abdominal fat tissue may mainly change risk factor levels through the production of circulating cytokines, which can cause insulin resistance (26), while physical activity has both local effects in the trained muscles (related to hormone sensitivity and enzyme levels) and global effects caused by circulating hormones and changes in the sympathetic nervous system (9). Earlier studies have postulated that physical activity or fitness had no independent effect on risk factors beyond the effect mediated by decreased fatness (4). In this study we have found independent associations for physical activity, fitness and fatness, on clustered risk and there are plausible biological reasons for these associations to be independent. Our a priori expectation was that waist circumference would be more strongly related to clustered risk than skinfold measurements, because the abdominal fat is the metabolically active fat. However, we found similar associations for the two measures of fatness. Waist circumference is a surrogate measure of abdominal fat, and more precise assessment of abdominal fat may produce different results.

10 66 L. B. Andersen et al. Our findings suggest that interventions to improve CVD risk factor profiles in children should focus on both weight loss and increased physical activity. It may be particularly important to promote types of physical activity that are known to improve cardiorespiratory fitness. Many interventions focus exclusively on weight loss even if physical activity is a major part of the intervention (27). Our results suggest that increased physical activity and fitness may be equally important outcomes to assess. In conclusion, physical activity, fitness, skinfold and waist circumference were all independently associated with clustered CVD risk in these children. They were also related to each other, and the highest risk was found in children who were overweight and had low fitness. Physical activity was associated with clustered CVD risk even after adjustment for both fitness and fatness. References 1. Wedderkopp N, Froberg K, Hansen HS, Andersen LB. 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