AMERICAN JOURNAL OF HUMAN BIOLOGY 28:534–538 (2016)
Original Research Article
Cardiorespiratory Fitness Is Related to Metabolic Risk Independent of Physical Activity in Boys but not Girls from Southern Brazil ^ O. WERNECK,1 PAUL COLLINGS,2,3 CRISIELI M. TOMELERI,1 ROMULO DANILO SILVA,1* ANDRE A. FERNANDES,4 ENIO RONQUE,1 DANIELLE VENTURINI,5 DECIO S. BARBOSA,5 MANUEL J. COELHO-E-SILVA,6 LUIS B. SARDINHA,7 AND EDILSON S. CYRINO1 1 Study and Research Group in Metabolism, Nutrition, and Exercise (GEPEMENE) State University of Londrina (UEL), Londrina, Brazil 2 MRC Epidemiology Unit, University of Cambridge, Cambridge, UK 3 Bradford Institute for Health Research, Bradford NHS Foundation Trust, Bradford, UK 4 Scientific Research Group Related to Physical Activity (GICRAF), Laboratory of InVestigation in Exercise (LIVE), Department of Physical Education, Universidade Estadual Paulista (UNESP), Presidente Prudente, Brazil 5 Department of Pathology, Clinical and Toxicological Analysis, Center of Health Sciences, University Hospital, State University of Londrina (UEL), Londrina, Brazil 6 Faculty of Sport Sciences and Physical Education, University of Coimbra, Coimbra, Portugal 7 Exercise and Health Laboratory, Faculty of Human Kinetics, University of Lisbon, Lisbon, Portugal
Objective: Our aim was to determine the relationship between cardiorespiratory fitness (CRF) and metabolic risk in adolescents from Southern Brazil. Methods: We performed a school-based cross-sectional study in 1,037 adolescents (436 boys) aged 10–16 years from Londrina, PR, Brazil. CRF was determined by 20-m shuttle run test. A continuous metabolic risk score was obtained from the mean of fasting glucose, triglycerides, high density lipoprotein, blood pressure, and waist circumference zscores. Age, physical activity (Baecke questionnaire), body mass index (BMI; weight/stature2), and somatic maturity (Mirwald method) were included as covariates in multiple linear regression analyses. Results: CRF was related to metabolic risk in boys (b 5 20.02, P < 0.01) and girls (b 5 20.01, P 5 0.02) after adjusting for chronological age, BMI, and somatic maturity. However, when adjusted for physical activity, CRF failed to explain metabolic risk in girls (b 5 20.01, P 5 0.24). Conclusion: We conclude that CRF is independently and inversely related to metabolic risk in boys, but physical activity either mediates or confounds the association between CRF and metabolic risk in girls. Am. J. Hum. Biol. C 2016 Wiley Periodicals, Inc. V 28:534–538, 2016. Secular changes in youth lifestyles have been accompanied by an increased prevalence of the metabolic syndrome, which now affects between 2.2 and 52.1% of children depending on the definition and the population studied (Barkai and Paragh, 2006; Moraes et al., 2009). The syndrome is characterized by a clustering of at least three of the following risk factors: low serum concentrations of high-density lipoprotein cholesterol (HDL-C), high serum concentrations of triglycerides, insulin resistance, high blood pressure, and high waist circumference (Alberti et al., 2005). However, there is no consensus about the specific cut points for youth for classifying each of these components of the syndrome. It has been suggested that a continuous metabolic risk score should be implemented in children, which has the advantage of also increasing statistical power (Eisenmann, 2008). In youth, cardiorespiratory fitness (CRF) is believed to protect against metabolic syndrome (Lobelo et al., 2010; Stabelini Neto et al., 2011). However, there are many potential confounders of the association between CRF and metabolic health, such as chronological age, body fat, biological maturation, and the level of physical activity (Lobelo et al., 2010; Shaibi et al., 2005). Few studies in low and middle income countries have investigated CRF and metabolic syndrome in adolescents while controlling for these factors. Machado-Rodrigues et al. (2014) found that CRF was associated with metabolic risk, independent of chronological age, gender, body mass index (BMI), physical activity, and parental education in 924 Brazilian youth aged 11–17 years. However, the authors did not adjust for biological maturation and performed a single C 2016 Wiley Periodicals, Inc. V
analysis for both genders combined. Elsewhere, Ekelund et al. (2007), in a large sample of European youth, observed that adiposity exerted an important confounding or mediating effect on the association between CRF and lower metabolic risk, whereas physical activity displayed an independent association. These data highlight the complexities of investigating CRF and metabolic syndrome, and specifically indicate that CRF and physical activity may affect metabolic health via different pathways. This study, however, assessed biological maturation by subjective methods, which has recognized limitations. Providing a clearer understanding of the relationship CRF and metabolic parameters could help to formulate specific strategies to promote the metabolic health of youth. The aim of this study was to analyze the independent associations between CRF and metabolic risk scores in boys and girls from Southern Brazil. Our hypothesis was that CRF would be independently and inversely related to metabolic risk in both genders.
Contract grant sponsor: Coordination for the Improvement of Higher Education Personnel (CAPES/BRAZIL [to DRPS and CMT]); Contract grant sponsor: National Council of Scientific and Technological Development (CNPq/BRAZIL [to ERVR and ESC]). *Correspondence to: Danilo Rodrigues Pereira da Silva, Universidade Estadual de Londrina, Rua Luiz Lerco, 399, Londrina, Paran a 86047-610, Brazil. E-mail:
[email protected] Received 23 April 2015; Revision received 22 July 2015; Accepted 9 December 2015 DOI: 10.1002/ajhb.22826 Published online 13 January 2016 in Wiley Online Library (wileyonlinelibrary.com).
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CARDIORESPIRATORY FITNESS AND METABOLIC RISK IN YOUTH
METHODS Sample This was a population-based study conducted within the school setting. Adolescents aged 10–16 years who were enrolled in public schools in the city of Londrina, PR, Brazil in 2011 were the population of interest. Located in northern Paran a State, Londrina has 515,707 inhabitants, and a Human Development Index and Gross Domestic Product of 0.824 and US $4,442,230, respectively (IBGE, 2010). Adolescents were recruited using a two-step procedure. Initially, all public schools were listed and grouped according to their geographical location (north, south, east, west, and center). Two schools were randomly selected from each region. Subsequently, two or three classes were randomly selected from each school and all students in those classes were invited to participate. Students who used prescription medication or those being treated for chronic diseases were excluded, as were students who did not return the consent form signed by parents. All of the study procedures were approved by the Ethics Research Committee of the State University of Londrina and the protocol was carried out in accordance with Declaration of Helsinki guidance for research involving humans. The current study is part of a project entitled “Prevalence of metabolic syndrome and cardiovascular risk factors in adolescents from Londrina” for which the sample size calculation was based on the following parameters: a prevalence of metabolic syndrome of 4%, an a of 0.05, a margin of error of two percentage points, and a design effect of 2.0. The sample size was further increased by 20% to compensate for any participant withdrawals. Of the 1,395 adolescents who were recruited, 358 did not have complete data to contribute to this study. The final sample consisted of 1,037 adolescents. Cardiorespiratory fitness The level of CRF was estimated by using a 20-m shuttle run test designed by Leger and Lambert (1982). The test was performed on a multisports indoor court. Based on the achieved testing time, VO2peak in ml/kg/min was calculated according to the equation proposed by Leger et al. (1988). Tests were conducted by a trained research team who encouraged participants during all assessment phases. The technical error of measurement (TEM) was 2.3% (n 5 62). Metabolic risk score Measurements of waist circumference, blood pressure, blood glucose, HDL-C, and triglycerides were combined to calculate a metabolic risk score. Waist circumference was measured to the nearest 0.1 cm using an anthropometric tape in accordance with recommended procedures (Katzmarzyk et al., 2004). Blood pressure was measured on the right arm after a rest period of 10 min using a validated automatic apparatus (OMRON, HEM-742) (Christofaro et al., 2009). Two measurements were performed at 2-min intervals, but if the difference between measurements exceeded 10 mm Hg for both systolic blood pressure (SBP) and diastolic blood pressure (DBP) then a third measurement was performed. Blood pressure was determined based on the arithmetic mean of the two closest measurements. All laboratory tests were performed in the Clinical Biochemistry Laboratory of the State University of Lon-
drina Hospital. The samples were collected after a 12-h fast, either in serum tubes (no anticoagulant) or, for the determination of glucose, a tube containing an anticoagulant fluoride as a preservative. Blood was collected from the antecubital vein when participants were seated. Following sample collection the tubes were centrifuged at 3,000 rpm for 5 min at 48C to separate the plasma and serum. All analyzes were performed immediately following separation of these materials using a biochemical autoanalyzer (Dimension RxL Max, Siemens DadeBehring). Covariates 2
BMI (kg/m ) was determined by anthropometry (weight [TEM 5 0.68%] and height measures [TEM 5 0.37%]). Somatic maturity was estimated by peak height velocity (PHV) as proposed by Mirwald et al. (2002). To indicate age at PHV, PHV was subtracted from chronological age. The Baecke questionnaire inquired about physical activity performed at school, in formal sports, and leisure time, and was used to provide an estimate of participants’ habitual physical activity level (Baecke et al., 1982). Completion of the questionnaire was by intervieweradministration; researchers read out and explained each question to participants prior to recording their responses. To examine the reliability of the Baecke questionnaire in Londrina population, the questionnaire was administered on two occasions with a time interval of 7 days in a representative portion of the sample (10%). The intraclass correlation coefficient (ICC) was 0.73, indicating high reliability. Finally, food pattern was self-reported by adolescents. The frequency (0 days, 1–3 days, 4–6 days, 7 days) of sausages and/or fast food consumption during the previous week was evaluated with one question (ICC 5 0.52). The cutoff used for inadequate diet was the consumption of these foods on 4 or more days per week. Statistical analysis Means and standard deviations were used to characterize the sample. Student t tests or Mann–Whitney tests (depending on the distribution of data as examined by Kolmogorov–Smirnov tests) were used to compare each of the distinct metabolic syndrome components between genders. A continuous metabolic risk score was subsequently calculated by summing the z-scores of each of the individual components of the metabolic syndrome. Multiple linear regression analysis was used to examine the independent relationship between CRF and this outcome, using four models specified with different covariates. Finally, metabolic risk score was compared across quartiles of CRF and physical activity by using one-way ANCOVA, adjusted for chronological age, BMI, and somatic maturation. ANCOVA was followed by the Scheffe post hoc test to identify differences between specific groups. All analyzes were performed using SPSS 17.0 software and the level of statistical significance was set at 5%. RESULTS General characteristics of the initial sample without (n 5 1,395) and the analytical sample with (n 5 1,037) complete data are presented in Table 1 according to gender. There were differences between participating boys and girls for the majority of parameters, except for BMI, American Journal of Human Biology
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D.R.P. SILVA ET AL. TABLE 1. General characteristics of the initial sample and the final analytical sample according to gender Initial sample
Chronological age (years) Age of PHV (years) BMI (kg/m2) Inadequate diet (% [95%CI]) Physical activity (score) CRF (ml/kg/min) Waist circumference (cm) Systolic BP (mmHg) Diastolic BP (mmHg) Fasting Glucose (mg/dL) HDL cholesterol (mg/dL) Triglycerides (mg/dL) Metabolic risk (z-score)
Final analytical sample
Boys (n 5 627)
Girls (n 5 768)
Boys (n 5 436)
Girls (n 5 601)
13.05 6 1.55 14.39 6 0.68 19.74 6 3.94 43.4 (40.2–45.9) 8.55 6 1.43 42.03 6 4.78 67.79 6 9.00 111.86 6 11.94 62.34 6 7.81 – – – –
12.87 6 1.50 12.38 6 0.70 19.95 6 4.06 53.1 (49.7–44.3) 7.69 6 1.26 38.25 6 3.94 65.53 6 7.93 109.91 6 10.09 63.86 6 7.72 – – – –
13.00 6 1.50 14.40 6 0.69 19.83 6 4.03 43.6 (39.7–47.6) 8.55 6 1.38 41.88 6 4.88 67.93 6 8.99 111.91 6 11.97 62.55 6 8.06 90.38 6 5.75 51.57 6 13.71 60.93 6 32.84 0.06 6 0.61
12.74 6 1.42* 12.35 6 0.69* 19.92 6 4.12 52.5 (48.8–56.2) 7.71 6 1.28* 38.38 6 3.88* 65.446 7.97* 109.90 6 9.93* 63.99 6 7.67* 88.56 6 6.41* 52.34 6 12.49 66.19 6 34.20* 20.04 6 0.52*
Note: *P < 0.05 vs. final sample boys. PHV, peak height velocity; BMI, body mass index; CRF, cardiorespiratory fitness; CI, confidence interval; BP, blood pressure; HDL, high density lipoprotein. Nonrespondents had no blood information.
TABLE 2. Spearman correlation between individual metabolic syndrome factors and independent variables according to gender Waist (cm)
Age (years) Age of PHV (years) BMI (kg/m2) PA (score) CRF (ml/kg/min)
SBP (mm Hg)
DBP (mm Hg)
Glucose (mg/dl)
HDL-C (mg/dl)
Triglycerides (mg/ dl)
Boys
Girls
Boys
Girls
Boys
Girls
Boys
Girls
Boys
Girls
Boys
Girls
0.396 20.316 0.934 0.025 20.379
0.240 20.293 0.915 20.041 20.375
0.401 20.071 0.369 0.010 20.156
0.185 20.145 0.247 20.092 20.093
0.401 20.124 0.297 20.050 20.239
0.132 20.122 0.238 20.173 20.202
0.020 0.023 0.029 20.025 20.070
20.181 20.108 20.084 20.123 0.107
20.216 0.167 20.392 0.099 0.201
0.001 0.148 20.268 0.022 0.160
0.006 20.196 0.271 20.052 20.261
0.008 20.132 0.242 20.004 20.157
Note: Bold, P < 0.05. SBP, systolic blood pressure; DBP, diastolic blood pressure; HDL-C, high density lipoprotein cholesterol; BMI, body mass index; PHV, peak height velocity; PA, physical activity; CRF, cardiorespiratory fitness.
TABLE 3. Associations between metabolic risk score and
cardiorespiratory fitness in boys and girls. Boys (n 5 436)
Model 1 Model 2 Model 3 Model 4
Adjusted R2 (%)
Beta
14.9 49.3 50.5 52.2
20.05 20.01 20.02 20.01
Girls (n 5 601) P
Adjusted R2 (%)
Beta
P
