AMERICAN JOURNAL OF HUMAN BIOLOGY 26:29–35 (2014)

Original Research Article

Independent Associations Between Cardiorespiratory Fitness, Waist Circumference, BMI, and Clustered Cardiometabolic Risk in Adolescents DUNCAN S. BUCHAN,1* JOHN D. YOUNG,1 LYNNE M. BODDY,2 AND JULIEN S. BAKER1 Institute of Clinical Exercise and Health Science, School of Science, University of the West of Scotland, Hamilton ML3 0JB, Scotland, United Kingdom 2 The Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Tom Reilly Building, Byrom Street, Liverpool L3 3AF, United Kingdom

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ABSTRACT: Objectives: The purpose of this study was to examine the independent associations between measures of adiposity and cardiorespiratory fitness (CRF) with clustered cardiometabolic risk in adolescents. Methods: 209 adolescents (139 boys), aged 15–17.5 years participated. Participants completed anthropometric measurements [height, weight, waist circumference (WC)] whilst the 20 m fitness test was used to assess CRF. Additional measures included systolic blood pressure, triglycerides, ratio total cholesterol/high-density lipoprotein cholesterol, insulin resistance (HOMA), interleukin-6, C-reactive protein (CRP), and adiponectin. Results: Partial correlations revealed weak to moderate negative associations for body mass index (BMI) and WC with CRF (r 5 20.295 and –0.292, P < 0.001) and adiponectin (r 5 20.227 and 20.262, P < 0.05). Weak to moderate positive associations were evident for BMI with CRP, and cardiometabolic risk (r 5 0.274, and 0.283, P < 0.05, respectively). Weak to moderate positive associations were apparent for WC with CRP and triglycerides (r 5 0.240 and 0.254, P < 0.05), whilst moderate to large associations were evident for WC with clustered cardiometabolic risk (r 5 0.317, P < 0.05). Regression analyses revealed that BMI was positively associated with cardiometabolic risk (b 5 0.243, P < 0.001). Further analysis whilst additionally controlling for WC and CRF strengthened this association (b 5 0.352, P < 0.001). Finally, participants in the least-fit quartile for CRF had significantly poorer cardiometabolic risk scores than those in the other quartiles. Conclusion: BMI and not CRF was independently associated with cardiometabolic risk. Reducing BMI appears essential C 2013 Wiley Periodicals, Inc. to minimize cardiometabolic risk during adolescence. Am. J. Hum. Biol. 26:29–35, 2014. V

Whilst risk prediction was once only considered pertinent to adults, it is now well-established that the clustering of cardiometabolic risk factors is apparent in youth (Anderssen et al., 2007; Berenson et al., 1998; Ekelund et al., 2007; Martinez-Gomez et al., 2012). Whether the effects of long term exposure to cardiometabolic risk initiated in youth will result in earlier adulthood mortality in uncertain. Nonetheless, measurement of risk factors in youth is important since they may help identify individuals susceptible to developing cardiovascular disease (CVD) in later life. This is especially relevant as recent investigations have demonstrated that a number of risk factors associated with CVD in later life can track from adolescence, and predict the extent of atherosclerotic disease in adulthood (Juhola et al., 2011). Obesity in youth is a key contributor to poor risk profiles with many studies implicating poor weight status as an important predictor of cardiometabolic risk and of insulin resistance (IR) (Zimmet et al., 2007). Moreover, the clustering of cardiometabolic risk also appears to be associated with poor cardiorespiratory fitness (CRF) levels (Anderssen et al., 2007; Ekelund et al., 2007) and adiposity (Eisenmann et al., 2007). Given the interrelationships evident between IR and the clustering of cardiometabolic risk, the potential independent effects of adiposity and CRF on cardiometabolic risk are receiving increasing attention (Artero et al., 2011; Martinez-Gomez et al., 2012). Adipose tissue is known to secrete adipocytokines which have pro and anti-inflammatory activity and are linked to a number of biological functions such as vascular C 2013 Wiley Periodicals, Inc. V

function, lipid metabolism, inflammation and insulin sensitivity (Tam et al., 2010). Adipocytokines such as interleukin-6 (IL-6), adiponectin and inflammatory cytokines such as C-reactive protein (CRP) are all known to be associated with the development of atheroslcerosis, clustered cardiometabolic risk and Type 2 diabetes in youth (Herder et al., 2007; Punthakee et al., 2006). Whilst several studies have examined the link between adiposity and inflammation, most of our understanding comes from adults. Nonetheless, inflammation may provide a mechanism linking increased adiposity and the development of IR, Type 2 diabetes and CVD. Furthermore, the literature is scarce regarding the potential independent roles of both adiposity and CRF upon clustered cardiometabolic risk and markers of inflammation in youth. Improved understanding of these relationships is important to ensure that appropriate health promotion initiatives are implemented. Thus, the aim of this study was to examine the independent associations between measures of adiposity and CRF with individual CVD risk factors and clustered cardiometabolic risk in adolescents.

Contract grant sponsor: Chief Scientist Office (CSO) for Scotland; Contract grant number: CZG/2/541. *Correspondence to: Duncan S. Buchan, Institute of Clinical Exercise and Health Science, School of Science, University of the West of Scotland, Hamilton ML3 0JB, Scotland, United Kingdom. E-mail: [email protected] Received 16 July 2013; Revision received 10 September 2013; Accepted 10 September 2013 DOI: 10.1002/ajhb.22466 Published online 18 October 2013 in Wiley Online Library (wileyonlinelibrary.com).

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D.S BUCHAN ET AL.

METHODS Study population This cross-sectional study consisted of a cohort of school children (139 boys, 70 girls, 16.7 6 0.6 years) from the West of Scotland, recruited from a South Lanarkshire public school. All participants were part of a school-based physical activity intervention study. Informed consent was obtained for all volunteers and their parents. Ethical approval for the study was received from the University of the West of Scotland Ethics committee. All tests were undertaken between 9:00 am and 12:00 pm for all participants and measurements were taken by the same individuals. Physical and physiological measures Barefoot stature was measured to the nearest 1 mm (Seca Stadiometer, Seca, Birmingham, UK). Weight in light indoor clothing, without shoes, was measured to the nearest 0.1 kg using calibrated electronic weighing scales (Seca 880, Digital Scales, Seca, Birmingham, UK). Thereafter, body mass index (BMI) was calculated (weight/ height2, kg/m2). Waist circumference (WC), an index of central (abdominal) fat distribution, was measured at the midpoint between the lower ribs and the iliac crest in accordance with standard procedures (Ledoux et al., 1997). Sexual maturation status was determined using a self-report questionnaire based on the criteria of Tanner (Tanner and Whitehouse, 1976) for stage of pubic hair (PH) development. Systolic BP and diastolic BP was measured with an automated monitor (Omron M10-IT Blood Pressure Monitor HEM-7080IT-E, Omron Healthcare UK, Milton Keynes, UK) after each participant had sat quietly for a period of 10 min. The average of the second and third measures was used for analysis. Participants also completed a validated physical activity questionnaire for adolescents (PAQ-A) (Kowalski et al., 1997) which required them to recall their activity behaviours from the previous 7 days. The questionnaire provides a score for each individual ranging from 0 to 5, with 0 reflecting no physical activity with 5 being very physically active and is particularly useful as an indicator of general activity patterns that can discriminate between active and inactive individuals rather than trying to estimate the intensity and duration of activities. From previous experience, completion of the questionnaire required no longer than 30 min and was completed during scheduled class time. Completed questionnaires were inspected and if necessary, clarification of responses was confirmed with participants. CRF was measured using the 20 m multi-stage fitness test (20MSFT) (number of completed shuttles) in accordance with standard procedures and is viewed as a valid predictor of maximal CRF in young people with a greater number of shuttles completed indicative of a higher CRF (Leger et al., 1988). Participants were given verbal encouragement throughout the test and were instructed to exercise until exhaustion. Participants were instructed to run between two lines separated by 20 m, while keeping pace with the audio signals emitted from a CD produced by the National Coaching Foundation. The initial speed was set at 8.5 km/h which increased by 0.5 km/h each min (1 min equates to 1 level). All participants were instructed to continue for as long as possible until they reached their maximal effort. The test ended when the American Journal of Human Biology

participant failed to reach the end lines before the audio signal on two consecutive occasions. The CD was calibrated over a 1 min duration before its use. Blood sampling Blood samples were taken between 9:00 am and 11:00 am after an overnight fast in all participants. Fasting was verified before sampling. Qualified phlebotomists, experienced in paediatric sampling techniques obtained all blood samples. Blood samples were obtained from an antecubital vein and collected in a BD Vacutainer plasma tube (Becton, Dickinson and Company, Franklin Lakes, USA). Plasma was isolated by centrifugation at 3,500 rpm for 10 min and frozen at 280 C within 2 h of collection. Analyses were subsequently completed within 3 months of collection. Total cholesterol, insulin, high-density lipoprotein cholesterol (HDL), CRP, glucose, IL-6, adiponectin, the homeostasis model assessment (HOMA), and triglycerides (TG) were measured. All analyses were performed using standard procedures. Total cholesterol and TG were measured by enzymatic methods (TR210 and CH200 Randox, Antrim, UK) and a Camspec M107 spectrophotometer (Camspec, Leeds, UK). Concentration of HDL was determined after precipitation of very low density and lowdensity lipoproteins by the addition of phosphotungstic acid in the presence of magnesium ions. Glucose was measured using the glucose oxidase method (GL364, Randox, Antrim, UK) and analyzed with a Camspec M107 spectrophotometer (Camspec, Leeds, UK). Plasma insulin was analyzed with commercially available immunoassay kits (ALPCO, Salem, NH) and a Camspec M107 spectrophotometer (Camspec, Leeds, UK). Concentrations of IL-6 and CRP were measured with specific enzyme linked immune-sorbent assay (ELISA) kits (R & D Systems, Abingdon, UK) and a MRX microplate reader (Dynatech Laboratories, Cambridge, MA). The HOMA calculation was used to provide an indication of IR and was calculated as the product of fasting glucose (mmol/l) and insulin (lU/ ml) divided by the constant 22.5 (Matthews et al., 1985). Clustered cardiometabolic risk profile Since differences with individual risk markers between subjects may be difficult to identify in youth (Anderssen et al., 2007), a clustered cardiometabolic risk score for each participant was constructed from the following variables: the ratio of total cholesterol to HDL, TG, HOMA, SBP, CRP, IL-6 and inverted adiponectin (invAdiponectin). These variables were chosen as they are all wellestablished risk factors for cardiometabolic risk. Thus, a z-score [z 5 (value-mean)/SD] for each variable was constructed separately for boys and girls and by 1-yr age groups. The z-scores of the individual risk factors were then summed to create a clustered cardiometabolic risk score for each participant with a lower score being indicative of a healthier overall risk profile (Anderssen et al., 2007; Ekelund et al., 2007). The z-score approach is common in the paediatric literature given that there is not one accepted definition of cardiometabolic health currently available for youth populations since children rarely exhibit CVD and thus, trying to relate a criteria to a health outcome is challenging (McMurray and Andersen, 2010). However, there are also some limitations to the z-score approach too. For instance, the z-score approach is based on the premise that each selected variable is equally

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ASSOCIATIONS BETWEEN MEASURES OF ADIPOSITY & CRF TABLE 1. Participant characteristics

Age (yr) Maturation I/II/III/IV/V Height (cm) Physical activity (%)I/II/III/IV Weight (kg) BMI (kg m22) WC (cm) SBP (mm Hg) CRF (Shuttles) TC:HDL ratio TG (mMol l21) IL-6 (pg ml21) Adiponectin (mg l21) CRP(mg l21) HOMA

Boys (n 5 139)

Girls (n 5 70)

*P-value

16.75 6 0.64 0/4/8/53/73 175.58 6 7.30 9.4/60.9/26.8/2.9 67.04 6 9.33 21.73 6 2.40 75.71 6 5.52 119 6 8 91.84 6 17.29 2.70 6 0.83 1.01 6 0.37 3.10 6 1.74 9.36 6 4.23 2.48 6 1.93 1.34 6 0.65

16.70 6 0.58 2/12/31/18/7 164.28 6 6.55 27.1/48.6/21.4/2.9 61.69 6 9.69 22.90 6 3.53 72.34 6 7.80 119 6 8 48.71 6 14.93 2.22 6 0.83 1.03 6 0.36 2.43 6 1.78 12.67 6 5.39 2.40 6 2.12 1.23 6 0.54

0.136