This thesis is presented for the degree of Doctor of Philosophy of The University of Western Australia. Denise Le Demmer Bachelor of Science (Honours)

Transcription

1 The associations of fitness, adiposity and cardio-metabolic risk factors in a young population-based cohort: The Western Australian Pregnancy (Raine) Cohort Study This thesis is presented for the degree of Doctor of Philosophy of The University of Western Australia By Denise Le Demmer Bachelor of Science (Honours) Medical School University of Western Australia January 2018

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3 So, let me begin by urging you, particularly you on the youngsters side, on this path you ve chosen, to go as far as you can... The world needs you badly. Humanity is now fully into the techno-scientific age. There is going to be no turning back. Keep your eyes lifted and your head turning. The search for knowledge is in our genes. It was put there by our distant ancestors who spread across the world, and it s never going to be quenched. To understand and use it sanely, as a part of the civilization yet to evolve, requires a vastly larger population of scientifically trained people like you. The decades ahead will see dramatic advances in disease prevention, general heath, and quality of life. All of humanity depends on the knowledge, and practice of the medicine and the science behind it, you will master. You have chosen a calling that will come in steps to give you satisfaction, at its conclusion, of a life well lived. E. O. Wilson.

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5 Personal Contribution by the Candidate Personal Contribution by the Candidate Declaration I declare that the work presented in this thesis is wholly my own, except where referenced or acknowledged. I have written all chapters in this thesis. This thesis has not been previously submitted, either in part or in whole, for any other academic degree at this or any other institution. The conceptualisation of this thesis, including development of research questions, preparation of datasets and conducted statistical analyses, interpretation of results, and the writing of all chapters is wholly my own work, under the guidance of Professor Trevor Mori, Emeritus Professor Lawrence Beilin and Professor Beth Hands, with statistical advice from Assistant Professor Sally Burrows. The studies were part of a larger population cohort study named the Western Australian Pregnancy (Raine) Cohort Study, which was established in the late 1980s and is under the management of the Raine Executive. All data collection was performed by trained personnel employed under the Raine Study. This thesis contains published work, all of which have been co-authored. The bibliographical details of the work and the estimated contribution of each author are acknowledged. I

6 Personal Contribution by the Candidate Signed. Date. Denise Le Demmer, PhD Candidate Paper 1 Demmer, D.L. (80%), L.J. Beilin (5%), B. Hands (2%), S. Burrows (5%), K.L. Cox (2%), W.H. Oddy (1%), and T.A. Mori (5%). Relationships between fatness and fitness and cardio-metabolic risk factors in adolescents from the Western Australian Pregnancy (Raine) Cohort Study. Journal of Clinical Endocrinology and Metabolism 2017;102(12): Paper 2 Demmer, D.L. (80%), L.J. Beilin (5%), B. Hands (2%), S. Burrows (5%), K.L. Cox (2%), L.M. Straker (1%), and T.A. Mori (5%). Effects of muscle strength and endurance on blood pressure and related cardio-metabolic risk factors from childhood to adolescence. Journal of Hypertension 2016; 34(12): Paper 3 Demmer, D.L. (80%), L.J. Beilin (5%), B. Hands (2%), S. Burrows (5%), C.E. Pennell (1%), S.J. Lye (1%), J.A. Mountain (1%), K.L. Cox (5%), and T.A. Mori (5%). Dual energy X-ray absorptiometry compared with anthropometry in relation to cardio-metabolic risk factors in a young adult population: Is the gold standard tarnished? PLoS ONE 2016; 11(9):e II

7 Personal Contribution by the Candidate We confirm that permission has been obtained from all co-authors to include the manuscripts in this thesis. Signed 31st January Date. Professor Trevor Mori, Coordinating Supervisor, School of Medicine and Pharmacology, University of Western Australia Signed. Date. Denise Le Demmer, PhD Candidate III

8 Acknowledgements Acknowledgements My fascination with the inner workings of the human body began just over a decade ago; in hardly any time at all, especially when I contemplate how far I have come since those days in the high school classroom. To study at the Medical Research Foundation became an ambitious dream, and so writing this acknowledgment today as a contributor to this establishment is truly surreal. I would not have been able to accomplish this without efforts of my supervisors, Professor Trevor Mori and Emeritus Professor Lawrence Beilin. Thank you for your continual guidance and unwavering patience, for which I am deeply grateful. You have taught me the importance of resilience, and how to find joy in the process of perseverance. Many wonderful people have enriched my work ethic and academic experience since beginning my research, too many to mention; however, two extraordinary women deserve special thanks. Professor Beth Hands, your tireless positivity and advice kept me energised and driven. To Associate Professor Sally Burrows, I am indebted to you for your invaluable contribution to my research; the enthusiasm that you apply to your statistical work is truly inspiring. Thank you for being so generous with your wisdom and support. My deepest gratitude belongs to the Raine children, families and the Raine Study staff who have wholeheartedly dedicated themselves to this project. IV

9 Acknowledgements To my parents, George and Nga-Le Demmer; my every success is possible only by the sacrifices you have made for our family. Your unwavering love and support has allowed me to follow a long and challenging path, and I am forever thankful to have you in my life. Lastly, to my partner Vedran; your calm confidence in my every endeavour has encouraged me to endure, and to challenge my own fears. Thank you for believing in me. V

10 Publications, Presentations and Awards Publications, Presentations and Awards List of publications relevant to this thesis (* appear in Appendix B) 1. Demmer DL, Beilin LJ, Hands B, Burrows S, Cox KL, Oddy WH, and Mori TA. Relationships between fatness and fitness and cardio-metabolic risk factors in adolescents from the Western Australian Pregnancy (Raine) Cohort Study. Journal of Clinical Endocrinology and Metabolism. 2017;102(12): * 2. Demmer DL, Beilin LJ, Hands B, Burrows S, Cox KL, Straker LM, and Mori TA. Effects of muscle strength and endurance on blood pressure and related cardiometabolic risk factors from childhood to adolescence. Journal of Hypertension. 2016;34(12): * 3. Demmer DL, Beilin LJ, Hands B, Burrows S, Pennell CE, Lye SJ, Mountain JA, and Mori TA. Dual Energy X-Ray Absorptiometry Compared with Anthropometry in Relation to Cardio-Metabolic Risk Factors in a Young Adult Population: Is the Gold Standard Tarnished? PLoS ONE. 2016;11(9):e * VI

11 Publications, Presentations and Awards List of presentations related to this thesis : Annual Raine Study Scientific Day Meeting, Perth Oral presentation titled A comparison of dual energy x-ray absorptiometry and clinical anthropometry in relation to cardio-metabolic risk factors in the Raine Study : Young Investigator s Day, Royal Perth Hospital, Perth Oral presentation titled The relationships between measures of adiposity and cardio-metabolic risk factors in a young adult population : 8 th World Congress on Developmental Origins of Health and Disease, Singapore Poster presentation titled The relationships between measures of adiposity and cardio-metabolic risk factors in a young adult population : Australian Atherosclerosis Society and High Blood Pressure Research Council of Australia Joint Annual Scientific Meeting, Melbourne Moderated poster presentation titled The relationships between measures of adiposity and cardio-metabolic risk factors in a young adult population : Annual Raine Study Scientific Day Meeting, Perth Oral presentation titled Muscle strength and systolic blood pressure from childhood through to adolescence : State of the Heart Conference, Adelaide Poster presentation titled Muscle fitness and cardio-metabolic risk factors from childhood through to adolescence: The Raine Study. VII

12 Publications, Presentations and Awards : Science on the Swan Conference, Perth Poster presentation titled Muscle fitness and cardio-metabolic risk factors from childhood through to adolescence: The Raine Study : Annual Raine Study Scientific Day Meeting, Perth Oral presentation titled The relationships between fatness and fitness with cardio-metabolic risk factors at 17 years of age : Royal Perth Hospital Medical Research Foundation Research Day, Perth Oral presentation titled The relationships between fatness and fitness with cardio-metabolic risk factors at 17 years of age : Australian Atherosclerosis Society Meeting, Perth Moderated poster presentation titled Fitness, fatness and cardio-metabolic risk factors in adolescents : 9 th World Congress on Developmental Origins of Health and Disease, South Africa Poster presentation titled Muscle fitness and cardio-metabolic risk factors from childhood to adolescence: The Raine Study : Royal Perth Hospital Medical Research Foundation Research Day, Perth Oral presentation titled Effects of muscle strength and endurance on blood pressure and related cardio-metabolic risk factors from childhood through to adolescence. VIII

13 Publications, Presentations and Awards : Joint Annual Scientific Meeting, Australian Atherosclerosis Society, High Blood Pressure Research Council of Australia & Australian Vascular Biology Society, Hobart Oral presentation titled Relationships between fatness and fitness and cardiometabolic risk factors in adolescents from the Western Australian Pregnancy (Raine) Cohort Study. Awards related to this thesis University Postgraduate Award University of Western Australia Top-Up Scholarship Award 2013 High Blood Pressure Research Council of Australia Robert Vandongen Award 2013 Joint Annual Scientific Meeting: Australian Atherosclerosis Society and High Blood Pressure Research Council of Australia Student Travel Award 2014 State of the Heart Student Travel Award 2015 Royal Perth Hospital Medical Research Foundation Research Encouragement Award: Early Career Research Scientist 2015 Australian Atherosclerosis Society Best Student Poster Award 2016 Joint Annual Scientific Meeting: Australian Atherosclerosis Society, High Blood Pressure Research Council of Australia & Australian Vascular Biology Society Student Travel Award 2016 High Blood Pressure Research Council of Australia Robert Vandongen Award IX

14 Thesis Abstract Thesis Abstract Evidence suggests, the antecedents of cardiovascular disease (CVD) begin early in life. Therefore, childhood and adolescence are potential critical periods during the lifecourse where lifestyle influencing factors such as obesity, dietary patterns, alcohol use, smoking and physical activity habits which affect the development of CVD are established. The Developmental Origins of Health and Disease (DOHaD) hypothesis suggests negative environmental factors acting during the phase of developmental plasticity interact with genotypic variation to change the capacity of an organism s ability to cope with its environment in later life; which in turn may lead to an increased risk of CVD. To further explore this hypothesis, data were collected from children, adolescents and young adults from the Western Australian Pregnancy Cohort (Raine) Study, which is a populationbased pregnancy cohort. The first study explored the fat but fit hypothesis in the 17-year survey of the Raine Study. This hypothesis suggests that a moderate-to-high level of cardiorespiratory fitness (CRF) attenuates or eliminates the risk of cardiovascular and metabolic disease, independent of body fatness. Results showed that fatness had greater adverse effects on majority of cardio-metabolic risk factors examined compared to the beneficial effects of CRF. However, increasing fitness levels attenuated the adverse associations of fatness on diastolic blood pressure (BP) and the homeostatic model of insulin resistance (HOMA-IR) in both sexes and high density lipoprotein-cholesterol (HDL-C) in females only. Fitness did not attenuate the adverse effects of fatness on cholesterol, triglycerides, low density lipoprotein-cholesterol X

15 Thesis Abstract (LDL-C) or high sensitivity C-reactive protein (hs-crp). This study was conducted in late adolescence when potential confounders such as smoking, alcohol consumption and oral contraceptive (OC) use were becoming established behaviours and could be accounted for. Fatness and fitness were analysed as continuous variables which overcomes the use of arbitrary categories, that may obscure relative associations of fatness and fitness. Cardio-metabolic risk factors were examined as continuous and separate outcomes which overcome the limitation of an arbitrary CVD risk score to express the clustering of main components of adult CVD; as no clear definition of the metabolic syndrome exists within the paediatric population. The implications of these results demonstrate the predominant effect of adiposity and indicate that a reduction in obesity through caloric restriction and dietary changes are required in combination with increasing fitness levels, to alleviate the adverse effects of obesity on cardio-metabolic risk factors. The second study examined two modes of muscular fitness (handgrip strength and back muscular endurance) in relation to cardio-metabolic risk factors in children and adolescents aged between 10 to 17 years. The results surprisingly showed handgrip strength was positively associated with systolic BP, albeit the association attenuated with increasing age. In addition, the association between handgrip strength and systolic BP was stronger in males than females. This study also showed the anticipated inverse associations between both handgrip strength and back muscular endurance with HOMA-IR, fasting lipids and hs-crp. The strengths of this study include serial measures in a large cohort of children and adolescents, allowing for the employment of XI

16 Thesis Abstract longitudinal analyses to examine the evolution of associations between muscular fitness, with a wide range of cardio-metabolic risk factors. The third study compared dual energy X-ray absorptiometry (DXA) and clinical anthropometry derived measures of adiposity in young adults (20 years of age). Although, results showed that midriff fat mass (measured by DXA) was the best estimator of insulin sensitivity and triglycerides, overall clinical anthropometric measures were better or equivalent to various DXA measures in estimating majority of cardio-metabolic risk factors. These results suggest that clinical anthropometry is generally as useful as DXA in the evaluation of the individual cardio-metabolic risk factors in young adults. Strengths of this study include examination of a breadth of adiposity measures from a large population sample, within a narrow age range. Furthermore, a robust statistical approach of model selection was used to compare the performance of the adiposity measures within each outcome. In summary, various forms of fitness and adiposity are significantly associated with a wide range of cardio-metabolic risk factors in a contemporary young population. These findings are important as childhood and adolescence are periods where adverse lifestyle behaviours which influence long-term CVD are established. Therefore, its is suggested that individuals at risk should be identified at an early stage for management and intervention to prevent an adverse cardio-metabolic risk profile and reduce the onset of CVD in later life. XII

17 Organisation of the Thesis Organisation of the Thesis This thesis is being submitted as a series of papers and is organised into three sections. The first section covers a review of the literature related to the themes of this thesis and concludes with an outline of the scope and aims of the thesis. The second section presents the results in Chapters 2 to 4. Each chapter includes an abstract, introduction, methods, results, discussion, references with supporting information, all with uniform typeset. Each paper is preceded by a brief foreword linking the chapters. Section 3 presents the general discussion, interpreting the findings of Chapters 2-4 and drawing overall conclusions. The manuscripts of Chapter 2, Chapter 3 and 4 are published in the Journal of Clinical Endocrinology and Metabolism (impact factor= 5.45) the Journal of Hypertension (impact factor= 5.06) and PLoS One (impact factor= 3.54), respectively, and are included in Appendix B in their published typeset. XIII

18 Table of Contents Table of Contents Personal Contribution by the Candidate Acknowledgements Publications, Presentations and Awards Thesis Abstract Organisation of the Thesis Table of Contents List of Tables List of Figures Abbreviations and Acronyms I IV VI X XIII XIV XXI XXV XXVII CHAPTER 1 Literature review 1.1 Developmental origins of health and disease Barker s hypothesis Major studies providing evidence of the development of atherosclerosis in children and adolescents The Pathobiological Determinants of Atherosclerosis in Youth Study The Bogalusa Heart Study Population-based cohort studies examining the childhood origin of adult cardio-metabolic disease Cardiovascular Risk in Young Finns Study 6 XIV

19 Table of Contents Generation R Study Other cohort studies The Western Australian Pregnancy Cohort (Raine) Study Main findings from the Raine Study Cardiovascular risk factors Hypertension Dyslipidaemia Insulin resistance and type 2 diabetes Inflammatory biomarkers Obesity Measurement of obesity Dual energy x-ray absorptiometry Anthropometry Body mass index Waist circumference and waist to hip ratio Skinfold measurement Comparisons between DXA and clinical anthropometry with cardio- metabolic factors Lifestyle related factors influencing cardiovascular health Physical activity Measurement of physical activity Physical activity questionnaires 26 XV

20 Table of Contents Accelerometers Cardiorespiratory fitness Measurement of cardiorespiratory fitness Maximal exercise testing Submaximal exercise testing Cardiorespiratory fitness with mortality and CVD in adults Cardiorespiratory fitness and cardio-metabolic risk factors in children and adolescents The fat but fit hypothesis and mortality risk in adults The fat but fit hypothesis and CVD in adults The fat but fit hypothesis with cardio-metabolic risk factor in children and adolescents Muscular fitness Associations between muscular strength with mortality and CVD in adults Associations between muscular fitness and cardio-metabolic risk factors in children and adolescents Scopes and aims of the thesis References 56 XVI

21 Table of Contents CHAPTER 2 Relationships between Fatness and Fitness and Cardio-metabolic Risk Factors in Adolescents from the Western Australian Pregnancy (Raine) Cohort Study 2.1 Abstract Introduction Methods Participants Anthropometry Cardiorespiratory fitness Biochemistry Blood pressure Demographic and socio-behavioural features Statistical analysis Results Descriptive characteristics Independent associations between fatness and fitness with cardio-metabolic risk factors Blood pressure Fasting lipids HOMA-IR and hs-crp Discussion References 109 XVII

22 Table of Contents 2.7 Supporting information 112 CHAPTER 3 Effects of Muscular Strength and Endurance on Blood Pressure and Related Cardio-metabolic Risk Factors from Childhood through to Adolescence 3.1 Abstract Introduction Methods Participants Muscular strength Muscular endurance Cardiorespiratory fitness Anthropometry Puberty Biochemistry Blood pressure Statistical analysis Results Participant characteristics Interpretation of handgrip strength: Comparison of unadjusted and adjusted measures 124 XVIII

23 Table of Contents Longitudinal linear mixed models of handgrip strength, back muscular endurance, blood pressure and biochemical risk factors Longitudinal linear mixed models examining associations between handgrip strength and cardio-metabolic risk factors over time Longitudinal linear mixed models examining associations between back muscular endurance and cardio-metabolic risk factors over time Discussion References Supporting information 146 CHAPTER 4 Dual Energy X-Ray Absorptiometry Compared with Anthropometry in Relation to Cardio-metabolic Risk Factors in a Young Adult Population: Is the Gold Standard Tarnished? 4.1 Abstract Introduction Methods Participants Adult anthropometry and DXA measurements Biochemistry and blood pressure measurements Statistical analysis Results 173 XIX

24 Table of Contents Descriptive characteristics DXA and anthropometry measures with individual cardio-metabolic risk factors Comparisons within DXA and anthropometric measures Comparisons between the best DXA and best anthropometry measure Discussion References Supporting information 184 CHAPTER 5 General Discussion 5.1 Overall conclusions and discussions References 195 APPENDIX A 197 APPENDIX B 198 XX

25 List of Tables List of Tables Chapter 1 Table 1. Relative risk of all-cause mortality according to different fitness/fatness categories in 21,856 males aged years 35 Chapter 1 Table 2. Relative risk of all-cause mortality in 2,306 females (mean age 46.6 years) 36 Chapter 1 Table 3. Relative risk of all-cause and CVD mortality in 2,860 males (mean age 45.1 years) 36 Chapter 2 Table 1. Descriptive characteristics of participants at 17 years from the Raine Study at 17 years of age 96 Chapter 2 Table 2. Linear regression analyses examining the associations of fatness and CRF on BP at 17 years 98 Chapter 2 Table 3. Linear regression analyses examining the associations of fatness and CRF with HDL-C or triglycerides at 17 years 100 Chapter 2 Table 4. Linear regression analyses examining the associations of fatness and CRF with total cholesterol or LDL-C at 17 years 102 Chapter 2 Table 5. Linear regression analyses examining the associations of fatness and CRF with HOMA-IR or hs-crp at 17 years 103 Chapter 2 Supplementary Table 1. Comparing study sample participants to non-participants at 17-year survey 112 Chapter 2 Supplementary Table 2. Logistic regression analyses examining associations of fatness and CRF with prehypertension/hypertension status at 17 years (n= 1128) 112 XXI

26 List of Tables Chapter 3 Table 1. Descriptive characteristics of participants from the Raine Study by age and sex 129 Chapter 3 Table 2. Handgrip strength associations with BP generated from a longitudinal linear mixed model 131 Chapter 3 Table 3. Handgrip strength associations with HOMA-IR, triglycerides and hs-crp generated from a longitudinal linear mixed model or random effects tobit model 134 Chapter 3 Table 4. Handgrip strength associations with HDL-C generated from a longitudinal linear mixed model 135 Chapter 3 Table 5. Back muscular endurance with cardio-metabolic outcomes generated from a longitudinal linear mixed model or random effects tobit model 136 Chapter 3 Supplementary Table 1a. Raw handgrip strength with systolic BP 146 Chapter 3 Supplementary Table 1b. Height adjusted handgrip strength with systolic BP 147 Chapter 3 Supplementary Table 1c. Weight adjusted handgrip strength with systolic BP 148 Chapter 3 Supplementary Table 2: Longitudinal linear mixed model for the association between handgrip strength and systolic BP over time 149 Chapter 3 Supplementary Table 3: Longitudinal linear mixed model for the association between handgrip strength and diastolic BP over time 151 XXII

27 List of Tables Chapter 3 Supplementary Table 4: Longitudinal linear mixed model for the association between handgrip strength and log HOMA-IR over time 152 Chapter 3 Supplementary Table 5: Longitudinal linear mixed model for the association between handgrip strength and log triglycerides over time 153 Chapter 3 Supplementary Table 6: Longitudinal linear mixed model for the association between handgrip strength and HDL-C over time 154 Chapter 3 Supplementary Table 7: Random effects tobit model for the association between handgrip strength and log hs-crp over time 155 Chapter 3 Supplementary Table 8: Longitudinal linear mixed model for the association between back muscular endurance and systolic BP over time 156 Chapter 3 Supplementary Table 9: Longitudinal linear mixed model for the association between back muscular endurance and diastolic BP over time 157 Chapter 3 Supplementary Table 10: Longitudinal linear mixed model for the association between back muscular endurance and log HOMA-IR over time 158 Chapter 3 Supplementary Table 11: Longitudinal linear mixed model for the association between back muscular endurance and log triglycerides over time 159 Chapter 3 Supplementary Table 12: Longitudinal linear mixed model for the association between back muscular endurance and HDL-C over time 160 Chapter 3 Supplementary Table 13: Random effects tobit model for the association between back muscular endurance and log hs-crp over time 161 XXIII

28 List of Tables Chapter 4 Table 1. Descriptive characteristics of males and females from the Raine Study at 20 years of age 176 Chapter 4 Table 2. The Akaike Information Criterion differences between the best DXA and best anthropometry measures and individual cardio-metabolic risk factors in young adults 177 Chapter 4 Supplementary Table 1. Adjusted R squared and Akaike Information Criterion between DXA and anthropometry adiposity measures with lipid, lipoprotein and inflammatory markers in young adults 184 Chapter 4 Supplementary Table 2. Adjusted R squared and Akaike Information Criterion between DXA and anthropometry adiposity measures and insulin resistance measures in young adults 186 Chapter 4 Supplementary Table 3. Adjusted R squared and Akaike Information Criterion between DXA and anthropometry adiposity measures and blood pressure in young adults 187 XXIV

29 List of Figures List of Figures Chapter 1 Figure 1. Extent of fatty streaks and raised lesions present in the right coronary artery by age (mean and standard errors represented and values were adjusted for ethnicity and sex) 4 Chapter 1 Figure 2. CRF and fatness groups and a composite cardiovascular risk score of males and females 41 Chapter 1 Figure 3. Quartiles of muscular fitness with a clustered cardio-metabolic risk score (sum of z-scores) 52 Chapter 2 Figure 1. Flow diagram of Raine Study participants attending the 17-year follow up with complete fatness and CRF data 92 Chapter 2 Figure 2. Plot of fatness vs CRF with systolic BP for males, OC using females and non-oc using females 99 Chapter 2 Figure 3. Plot of fatness vs CRF with HDL-C for males and females 101 Chapter 2 Figure 4. Plot of fatness vs CRF with HOMA-IR for males and females. 104 Chapter 3 Figure 1. Flow diagram of Raine Study participants attending the 10, 14 and 17-year follow up with complete handgrip strength and back muscular endurance data 123 Chapter 3 Figure 2. Handgrip strength and systolic BP at 10, 14 and 17 years after adjustment for BMI generated from longitudinal linear mixed models (mean and 95% CIs represented) 132 XXV

30 List of Figures Chapter 3 Figure 3. Back muscular endurance and systolic BP at 14 and 17 years after adjustment for BMI generated from longitudinal linear mixed models (mean and 95% CIs represented) 133 Chapter 3 Supplementary Figure 1. Handgrip strength and systolic BP at 10, 14 and 17 years generated from longitudinal linear mixed models (mean and 95% CIs represented) 162 Chapter 3 Supplementary Figure 2. Back muscular endurance and systolic BP at 10, 14 and 17 years generated from longitudinal linear mixed models (mean and 95% CIs represented) 163 XXVI

31 Abbreviations and Acronyms Abbreviations and Acronyms ACLS BMI BP CI CRF CVD DOHaD DXA HDL-C HOMA-IR hs-crp LDL-C NHANES OC PDAY PWC 150 PWC 170 RM SD VLDL-C VO 2max Aerobics Centre Longitudinal Study body mass index blood pressure confidence interval cardiorespiratory fitness cardiovascular disease developmental origins of health and disease dual energy x-ray absorptiometry high density lipoprotein-cholesterol homeostatic model of insulin resistance high sensitivity C-reactive protein low density lipoprotein-cholesterol National Health and Nutrition Examination Surveys oral contraception Pathobiological Determinants of Atherosclerosis in Youth physical work capacity at a heart rate of 150 beats/minute physical work capacity at a heart rate of 170 beats/minute repetition-maximum standard deviation very low density lipoprotein-cholesterol maximal oxygen uptake XXVII

32 Abbreviations and Acronyms VO 2peak W peak oxygen consumption watts XXVIII

33 Chapter 1 CHAPTER 1 Literature Review 1.1 DEVELOPMENTAL ORIGINS OF HEALTH AND DISEASE CVD is one of the leading causes of global mortality (1). Although CVD events typically do not present until later adulthood, the antecedents of CVD (notably atherosclerosis) have their origins in childhood (2,3). The process of atherosclerosis is slow and involves the development of fatty streaks within blood vessels (4). During later years, these lesions may enlarge and result in complications such as calcification, haemorrhage, rupture or thrombosis (5). Therefore, the investigation of these factors that may predispose an individual to atherosclerosis in childhood and adolescence is of importance. The concept of adult disease having a foetal basis started with a focus on severe malnutrition in pregnancy and the susceptibility to type 2 diabetes, high BP and CVD in later adulthood (6,7). This concept is thought to be somewhat related to developmental factors in utero (8). It was previously thought developmental processes were primarily governed by genetics and its environmental influences were unknown (9). However, it is now evident that developmental processes are plastic in order to allow for the organism to respond to the surrounding environment. The ability to respond to environmental conditions can be evolutionarily advantageous by allowing the fine-tuning of gene expression, most likely through epigenetic mechanisms. Interference with these developmentally adaptive processes may have adverse consequences on physiological functions, thus disease risk later in life (10). 1

34 Chapter Barker s hypothesis Barker s original hypothesis emerged almost 25 years ago and encapsulates the DOHaD concept. It suggests that adverse environmental influences in utero leads to a permanent change in physiology, metabolism and structure, with consequent effects on CVD and metabolic disorders in adult life (9,11). Barker et al. (1989) correlated birth weight and environmental conditions, such as dietary influences, during early childhood with ischaemic heart disease of adults born at the start of the 20 th century (11). It was demonstrated that people who were born with low birth weights remained biologically different from those born with a normal birth weight, and these adverse effects persisted into adulthood (11). The former group were more likely to develop hypertension (odds ratio: 2.5) and type 2 diabetes in adulthood (odds ratio: 2.3) (11). Gluckman et al. (8,10) have suggested there is a strong epigenetic basis for the DOHaD model of disease pathogenesis. Findings have shown that minor alterations in the maternal diet during pregnancy can produce lasting changes in the physiology and metabolism of the offspring. Epigenetic DNA modification has been proposed as one of the molecular mechanisms to account for foetal programming of offspring for various adult-onset diseases including CVD, metabolic syndrome, obesity and atherosclerosis (2,7,12-16). 1.2 MAJOR STUDIES PROVIDING EVIDENCE OF THE EARLY DEVELOPMENT OF ATHEROSCLEROSIS IN CHILDREN AND ADOLESCENTS Early evidence suggests that atherosclerosis has its origins in childhood (17-23). From this, landmark studies such as Pathobiological Determinants of Atherosclerosis in Youth (PDAY) study and Bogalusa Heart Study were initiated to document the natural history of 2

35 Chapter 1 atherosclerosis, its interactions with cardiovascular risk factors and pathobiology of lesion development in young individuals (24) The Pathobiological Determinants of Atherosclerosis in Youth Study The PDAY study was established in 1985, to examine the progression of atherosclerosis and its relation to coronary heart disease in young individuals post-mortem. This study was a multicentre project and initially involved the assessment of atherosclerotic lesions obtained from autopsied individuals. Arteries, blood, and tissue data were collected from individuals aged 15 through to 34 years, who had died from external causes between 1987 and This allowed for post-mortem assessment of cardiovascular risk factors such as smoking, lipids (21-23) and BP (25). A total of 2,876 cases were collected with approximately 1,500 cases exhibiting adequate data to measure risk factors of interest (26). Reports have shown that atherosclerosis was evident in adolescence and the progression of atherosclerotic lesions is strongly influenced by the same cardiovascular risk factors that predict risk of clinical coronary disease in middle-aged adults (27). The prevalence of atherosclerotic lesions in the right coronary artery was 60% in the year old group and increased to >80% in males and >70% in females in the year old group, respectively (28) (Figure 1). The extent of fatty streaks and raised lesions within the right coronary artery and abdominal aorta was positively associated with total cholesterol, LDL-C, and very low density lipoprotein-cholesterol (VLDL-C) and was inversely associated with HDL-C. Hypertension was strongly associated with raised lesions in the right coronary artery. From this, PDAY risk scores have been developed which attempt to identify a young person at high likelihood of having advanced atherosclerosis and is suggested to be useful in selecting 3

36 Chapter 1 young individuals at extremely high risk of atherosclerotic lesions. Overall, the PDAY Study confirmed the origin of atherosclerosis within a paediatric population, and the progression towards clinically significant lesions in young adulthood (28). Figure 1. Extent of fatty streaks and raised lesions present in the right coronary artery by age (mean and standard errors represented, and values were adjusted for ethnicity and sex) (Mc Gill HC et al. Am J Clin Nutr 2000; 72(5):1307S-1315S) The Bogalusa Heart Study The Bogulusa Heart Study is a long term population study established in 1972, to examine predisposing factors and lifestyle behaviours related to the development of future coronary artery disease, hypertension and type 2 diabetes (24). The study participants included children and young adults from a biracial (65% Caucasian and 35% African American) semi-rural 4

37 Chapter 1 community (29). The first survey was conducted during and included 3,524 children (aged 5-14 years). Subsequently, nine series of cross-sectional surveys were conducted at two to three year intervals, which resulted in serial observations during childhood (30). Extensive cardiovascular risk factor data have been collected in approximately 16,000 individuals from birth to 40 years of age (24). Findings showed that 90% of autopsied individuals had atherosclerotic streaks and 70% showed prevalence of atherosclerotic lesions in coronary vessels (31). Coronary and aortic fatty streaks in 35 autopsied individuals were positively associated with LDL-C and inversely associated with HDL-C assessed during the life span (32). Furthermore, the severity of atherosclerotic disease increased with the number of cardiovascular risk factors (33). This finding again emphasises the early origin of atherosclerosis and the need for early screening of clinical risk factors in childhood and adolescence. Longitudinal data from the study also showed that children in the highest quintile of having multiple risk factors at 8 years of age remained in the highest quintile at 40 years of age (24). In addition, adverse lifestyle factors such as the early onset of smoking, alcohol use, and an unhealthy dietary pattern were associated with early stages of CVD (24). Subsequently, these findings have led to the conception of contemporary population studies worldwide. 5

38 Chapter POPULATION-BASED COHORT STUDIES EXAMINING THE CHILDHOOD ORIGIN OF ADULT CARDIO-METABOLIC DISEASE Cardiovascular Risk in Young Finns Study The Cardiovascular Risk in Young Finns Study was established in the late 1970s, with the objective to examine cardiovascular health and risk factors in a young population from Finland (34). The first baseline cross-sectional study conducted in 1980 included 3,596 children and adolescents aged 3-18 years (34). Subsequent surveys were conducted at 3-9 year intervals over the span of 30 years which have allowed comprehensive analyses of childhood predictors with adult cardiovascular risk factors (35,36). Like the Bogalusa Heart Study, findings showed later cardiovascular risk were significantly influenced by adverse lifestyle factors, such as smoking, physical inactivity and poor dietary habits established in youth (37,38). Furthermore, from the Young Finns cohort, Juonala et al (39) showed babies who were prematurely born and of low birth weight have increased systolic BP and diastolic BP levels (approximately and increase of 7 mmhg and 4 mmhg, respectively) and had a 50% increased risk of hypertension at 41 years of age, compared with those who were born at term. Children who were overweight or obese had 6 14 times increased risk of being overweight or obese in adulthood, and children with elevated childhood LDL-C levels had over 4 times increased risk of adult hypercholesterolemia (34). In addition, adverse lifestyle habits observed in childhood predicted adverse dietary and physical activity behaviours in adulthood (40,41). Higher childhood BP and adiposity predicted higher carotid intima medial thickness and lower arterial elasticity among year olds (34). In line with other 6

39 Chapter 1 prospective cohort studies, such as the Bogalusa Heart Study (42) and Muscatine Study (43), it was observed that several cardio-metabolic risk factors measured in childhood are predictive of subclinical atherosclerosis later in life (35,44,45). This cohort has provided strong evidence linking childhood influences to later CVD risk in adulthood Generation R Study The Generation R Study is a prospective cohort study, which included approximately 10,000 pregnant females in Rotterdam, Netherlands, and was established in The primary objective of the study was to investigate early environment and genetic causes of growth, development and health from foetal life to young adulthood (46,47). During pregnancy, assessments were conducted at <18 weeks, 18 to 25 weeks and >25 weeks gestation. Data included social and environmental aspects of which included pre- and postnatal diet, family function, substance abuse and behaviour (48). The main exposures of interest were environmental, endocrine, genetic and epigenetic lifestyle related nutritional and sociodemographic determinants, particularly in regard to cardio-metabolic disturbances. Gaillard et al. (2014) (48) observed that maternal and paternal BMI were associated with an adverse cardio-metabolic profile in offspring, with stronger associations observed with maternal prepregnancy BMI. Furthermore, low and high placental weight were found to be associated with adverse cardio-metabolic outcomes in later life. In addition, Gaillard et al. (2013) (49) have also shown that higher third trimester umbilical and uterine artery vascular resistance was associated with a smaller birth weight and an adverse cardiovascular profile in childhood. This cohort is valuable resource in determining parental influences on offspring development, which provides further evidence regarding the DOHaD hypothesis. 7

40 Chapter Other Cohort Studies Cohort studies Muscatine Study Year Study commenced participants ,377 children and adolescents aged 5-18 years. Length of follow up of each cohort Approximately 24 years. Key findings 1. Adult participants who had metabolic syndrome were observed to have significantly higher BMI (odds ratio: 2.2; p<0.001), systolic BP (odds ratio: 1.5; p<0.001) and triglyceride levels (odds ratio: 1.6; p<0.001) in childhood compared to adult participants without metabolic syndrome (50). 2. Childhood total cholesterol measured in participants aged 8-11 years was observed to be a significant risk factor for carotid IMT in young adulthood (odds ratio for males: 1.47; odds ratio for females: 1.71) (43). Childhood Determinants of Adult Health Study ,498 children aged 7-15 years. Approximately 20 years. 1. Childhood overweight or obesity was a predictor of hypertension (relative risk: 1.5; p=0.03) and high LDL-C levels (relative risk: 1.6; p=0.02) in adulthood (51). 2. Childhood adiposity is positively associated with left ventricular mass in adult males (coefficient: 0.41; p=0.003) and females (coefficient: 0.53; p<0.001) (52). Fels Longitudinal Study participants (generally enrolled at birth). Follow up examinations occurred semiannually until 18 years of age, biannually until 24 years if age, every 5 years until 40 years of age, then every 2 years. 1. Above average BP measured during childhood (baseline at 9 years of age) was observed to be a significant risk factor for elevated BP at 21 years of age in females (odds ratio; 1.9; p<0.05) (53). 2. Children at 9 years of age with elevated cholesterol levels (>180 mg/dl) were 6.67 (males) and 5 (females) times more likely to have elevated cholesterol at 21 years of age, compared to children with normal cholesterol levels (54). 8

41 Chapter 1 Princeton Lipid Research Clinics Follow Up Study 1999 Stage 1 of the survey included 6,775 students in grades 1-12 (aged 6-18 years). Approximately 30 years. 1. Of 31 children aged 6-19 years who had paediatric metabolic syndrome, 21 (68%) had adult metabolic syndrome at follow up (participants age: years) (X 2 =26.7; p<0.001). Furthermore, paediatric metabolic syndrome was a significant predictor of adult CVD (OR: 14.7; p<0/001) (55). 2. High glucose levels (OR: 4.43, 95% CI: ) and childhood cigarette smoking (OR: 1.64, 95% CI: 1.03, 2.61) in school children (aged 6-18 years of age) along with parental type 2 diabetes (OR: 2.2, 95% CI: ) were significant independent predictors for impaired fasting glucose and type 2 diabetes after 26 years of follow up (mean age 38 years) (56). Avon Longitudinal Study of Parents and Children (ALSPAC) ,867 pregnancies from 13,761 women. The offspring have undergone multiple examination between birth and 18 years of age. 1. In 7,589 children aged years, 13% of males and 18.8% of females were overweight, and 5.3% of males and 5% of females were obese. A 1kg/m 2 increase in BMI was associated with a 1.4 mmhg increase in systolic BP. In 5,002 children a 1 kg/m 2 increase of BMI was associated with a 0.05 mmol/l increase in non HDL-C levels and 0.03 mmol/l decrease in HDL-C (57). 2. In 5,235 children aged 9-12 years of age, a 1 standard deviation (SD) increase in BMI was associated with a greater risk of cardiometabolic risk factors (high systolic BP OR: 1.23, 95% CI: 1.10, 1.38; high LDL-C levels OR: 1.19, 95% CI: 1.03, 1.38; high triglyceride levels OR: 1.43, 95% CI: 1.08, 1.46) at age years in females. Similar trends were observed in males (high systolic BP OR: 1.24, 95% CI: 1.13 to 1.37; high LDL-C levels OR: 1.30, 95% CI: 1.07, 1.59; high triglyceride levels OR: 1.96, 95% CI: 1.51, 2.55) (58). 9

42 Chapter The Western Australian Pregnancy Cohort (Raine) Study The Western Australian Pregnancy Cohort (Raine) Study is a prospective observational study with the purpose of examining the lifecourse of health and well-being, from the prenatal period to adulthood (59). The Raine Study originally started as a randomised controlled trial that aimed to examine the effects of frequent and repeated ultrasound scans on pregnancy outcomes. The study involved 2,900 pregnant females recruited at or before 18 weeks of pregnancy from King Edward Memorial Hospital, in Perth, Australia, between May 1989 and November The criteria for participant enrolment included a gestational age between 16 and 20 weeks, sufficient proficiency in English to understand the implications of participation, an expectation to deliver at King Edward Memorial Hospital, and an intention to remain in Western Australia for long-term follow-up (60). Ultrasound and Doppler examinations were collected at enrolment (approximately 18 weeks gestation) and at 24, 28, 34 and 38 weeks gestation. Furthermore, questionnaire data pertaining to sociodemographic, substance use, medical history, stress and environmental influences were obtained. Additional information on recruitment and study findings are presented in Newnham et al. (1993) (42). From 2,900 females enrolled into the study, there were 2,868 live births from 2,826 mothers (60). From its original objectives, the Raine Study has evolved into a lifecourse study (61). The offspring have undergone subsequent surveys at 1, 2, 3, 5, 14, 17, 20 and most recently at 23 years of age (59). The number of participants in each review from birth to the 20-year survey is presented in Appendix A of this thesis. Several multidisciplinary information has been obtained and repeated in consecutive follow-up surveys, of which includes genetic, cardiometabolic, musculoskeletal, immunological, body composition and growth, and 10

43 Chapter 1 behavioural data such as dietary habits, sleep, risk-taking practices, physical activity and fitness assessments (59,62). Details of assessments obtained are further outlined by Straker et al. (43). Raine cohort characteristics of family structure, education, income level and socioeconomic status are representative of the Western Australian population (61). The Raine Study has the potential to provide important insights and the unique opportunity to study the development of lifestyle influences from childhood through to adulthood (63-65) Main findings from the Raine Study From the Raine Study, Chivers et al. (2010) showed that overweight/obese adolescents at 14 years of age had a different BMI trajectory pattern from birth compared to those of normal weight. BMI increased significantly over time with females having a faster rate of growth than males (66). Huang et al. (2012) (63) showed seven distinct adiposity trajectories by 14 years of age and three adiposity trajectories (two rising and one "chronic high" adiposity trajectory) were related to significantly higher insulin resistance when compared to the normal adiposity trajectory. These trajectories are characterised by either the early onset of elevated adiposity or a rising accelerated adiposity trajectory. Using the same approach, three adiposity trajectories (lifelong high adiposity trajectory, and rising trajectories from average and low birth weight) were also associated with an increased risk of prehypertension/hypertension in adolescence. Furthermore, elevated BP was detectable in individuals as early as 3 years (65). Using cluster analysis, Huang et al. (2009) (67) demonstrated that 29% of adolescents at 14 years fell into a high risk metabolic cluster group, that included higher BMI, systolic BP, triglycerides, inflammatory markers and lower HDL-C levels. Other findings from the Raine 11

44 Chapter 1 study have shown that at 17 years of age HDL-C levels were significantly lower in females exposed to passive smoking compared with females not exposed to passive smoking, since birth (68). Active smoking status at 17 years was associated with higher levels of hs-crp in females not using oral contraception (OC), with no associations being observed in females using oral contraception and males (69). The significance of these findings provides evidence of lifestyle influences and the progression of cardiovascular risk factors within the paediatric population. Continuing examination of the Raine cohort in early adulthood and beyond will provide critical information on the development of chronic health conditions and their impact in later life. 1.4 CARDIOVASCULAR RISK FACTORS The aetiology of CVD is complex and multifactorial in nature (70). CVD risk factors can be classified into non-modifiable factors such as age, sex and genetic predisposition (71), or modifiable factors related to lifestyle such as high BP, dyslipidaemia, obesity and physical fitness (72). The latter will be further discussed throughout this chapter Hypertension Multiple observational studies have shown hypertension is associated with premature mortality and morbidity in adults (73,74). Hypertension emerges from a complex inter-play of genetic, environmental, and behavioural factors (75,76). Complications detectable in children and adolescents with high BP include left ventricular hypertrophy (77,78), thickening of the carotid vessel wall (79,80), retinal vascular changes (81), and even subtle cognitive changes (82). Recent reports show that hypertension affects between 3-5% of the US paediatric population (83,84). Furthermore, longitudinal studies have shown that BP 12

45 Chapter 1 abnormalities during childhood translate into adult hypertension (4,85-88). Diagnostic criteria for elevated BP within the paediatric population are based on the concept that BP in children increases with age and body size (89). Normal BP in children is defined as systolic BP and diastolic BP less than the 90th percentile for age, sex and height, whereas hypertension is defined as systolic BP and/or diastolic BP persistently greater than the 95th percentile (89). Hypertension often clusters with other cardiovascular risk factors. Obesity is one of the main factors associated with elevated BP in childhood and accounts for more than half of the risk for developing hypertension (90). Birth size and postnatal growth have also been recently implicated in the development of high BP and adult CVD (91). The current childhood obesity epidemic and its strong relationship of BP indicate prevalence of high BP will increase in this young population (92). An analysis of childhood BP trends from the National Health and Nutrition Examination Surveys (NHANES) III group showed an overall increase in the prevalence of hypertension, from 2.7% in the survey to 3.7% in the survey (93). Sun et al. (2008) (94) examined serial data from the Fels Longitudinal Study and showed that high derived age and sex-specific BP levels in childhood predicted hypertension in adulthood. The identification and treatment of children with increased risk of high BP is crucial in reducing the excessive burden of CVD in adulthood Dyslipidaemia Abnormal plasma lipids levels are evident in childhood and adolescence (95). In 2011, the Expert Panel on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents concluded that early identification and control of dyslipidaemia throughout youth would substantially reduce the risk of clinical CVD in young adult life (96). Epidemiological observations have reported that low HDL-C and high triglycerides are 13

46 Chapter 1 present since childhood (97). This provides the rationale for identifying adverse lipid levels at younger ages (98). Furthermore, early identification of dyslipidaemia is justified by the fact that these lipid abnormalities in children and adolescents have been shown to promote the clinical manifestation of atherosclerosis (65). Dyslipidaemia strongly associates with central obesity and insulin resistance, which are, in turn, strongly associated with a low-grade inflammation and abnormalities in adipocyte production (99,100). Li et al. (2016) (101) found that approximately 13% of American youth aged 6 19 years participating in the NHANES study had high LDL-C, 16.5% had high total cholesterol, 30% had high triglycerides, and 40% of the population had low HDL-C levels. In addition, mean total cholesterol and LDL-C peak just before puberty, decrease during early adolescence, and increase again from late adolescence into young adulthood (102). Females have been shown to exhibit significantly higher mean total cholesterol and LDL-C than males (103), and adolescents with elevated LDL-C had an increased risk of high carotid intima thickness in adulthood (104). Furthermore, the Bogalusa Heart Study showed that 40% of children with cholesterol above the 80th percentile at baseline continued to have elevated levels 15 years later (104). These findings propose that early detection of dyslipidaemia in the paediatric population is warranted Insulin resistance and type 2 diabetes The early onset of type 2 diabetes is associated with increased risk of CVD morbidity and mortality (105,106). Type 2 diabetes is currently a widespread condition resulting from the growing obesity epidemic in the paediatric population, and accounts for 20% to 50% of newly diagnosed type 2 diabetic patients (107). Insulin resistance is a metabolic condition 14

47 Chapter 1 characterised by an impaired ability of plasma insulin at normal concentrations to promote peripheral glucose clearance, suppress gluconeogenesis within the liver, and inhibit VLDL-C output (108). Long term consequences of type 2 diabetes include damage to the kidneys (109), eyes (110) and vasculature (111). NHANES III data ( ) showed that females had higher insulin levels than boys and African American and Hispanics had higher insulin levels than non-hispanic whites (112). It has been shown that the transition from childhood through to adolescence is associated with increased insulin resistance, raised triglycerides and lower levels of HDL-C (113). In 5-9 year old Pima Indian children, fasting insulin was associated with obesity from 9 to 15 years of age (114). Furthermore, from the Cardiovascular Risk in Young Finns Study, a 1 SD increase of fasting insulin in 3-6 year old children was associated with a relative risk of 2.04 for type 2 diabetes in adulthood, independent of obesity and parental history of type 2 diabetes (115). The Bogalusa Heart Study has shown a strong relation over an 8-year period between persistent high fasting insulin levels and the subsequent development of cardiovascular risk factors in children and young adults (116). Studies have shown strong associations between obesity and insulin resistance in adolescents (117) and fat loss is associated with increased insulin sensitivity (118). Furthermore, a meta-analysis of 31 studies that assessed insulin resistance status in metabolically healthy obese adolescents aged years showed significantly higher circulating fasting insulin levels and higher HOMA-IR values in obese adolescents compared to their non-obese counterparts (119). Children and adolescents with insulin resistance/type 2 diabetes often have other CVD risk factors (120). 15

48 Chapter Inflammatory biomarkers Elevated levels of circulating inflammatory cytokines have been shown to be associated with atherosclerosis (121,122). One of the most commonly employed biomarkers for the assessment of systemic inflammation is hs-crp (123), as hs-crp has been found localised to atherosclerotic plaques and infarcted myocardium (124). Obesity is strongly associated with hs-crp, which suggests high levels of adiposity may represent a chronic state of low-grade inflammation (125). Ridker et al. (2002) (126) demonstrated that adults who fell into the highest category of hs-crp levels had a relative risk of 2.3 to 4.8 for future CVD events compared to those individuals within the lowest category. In youth, hs-crp levels have been shown to be strongly associated with obesity (127), increased insulin resistance (128) and vascular damage (129). In Raine study adolescents, hs- CRP levels were higher in the high-risk metabolic cluster group, compared to their low-risk cluster group counterparts (67). Guran et al. (2007) (130) found high hs-crp levels in 51 children were significantly correlated with BMI (r=0.41, p=0.003), diastolic BP (r=0.32, p=0.02) and HDL-C levels (r=-0.46, p=0.001), compared to individuals who exhibited low hs-crp levels. Martos et al. (2009) (131) reported that hs-crp levels were significantly higher in obese children than in controls. After 9 months of treatment, obese children who underwent substantial weight loss displayed a significant decrease in hs-crp levels (p=0.006), compared with obese children who did not undergo any weight loss. hs-crp levels have been shown to track from childhood through to adulthood (51). A large cohort study has shown hs-crp levels in childhood predicted the presence of the metabolic syndrome 21 years later (132). However, longitudinal studies between hs-crp relating to 16

49 Chapter 1 future CVD are lacking. Although data is limited, hs-crp has been considered as a potential biomarker to screen for CVD risk in children and adolescents (133) Obesity Obesity is defined as abnormal or excessive amounts of adipose tissue that has the potential to promote adverse health outcomes (134). Over the past 30 years, the NHANES survey found that childhood obesity rates had almost tripled, indicating that approximately 17% of children aged 2-19 years are obese (90). In 2010, the number of obese children was estimated to be greater than 42 million worldwide (135). In Australia, according to the Australian Bureau of Statistics National Health Survey , approximately 20% of children and adolescents were overweight and 7.5% were obese (aged 5-17 years) (136). From the Victorian Adolescent Health Cohort, 1,520 adolescents were tracked from the age of 14 years for a follow up period of 10 years to assess changes in overweight and obesity from adolescence to young adulthood (137). The proportion of overweight and obese individuals increased from 20% and 3.6% in adolescence to 33% and 6.7% in adulthood, respectively. These significant increases in obesity prevalence presents as an important public health concern. As obesity in childhood tends to persist into adulthood, this elevates the risk of many chronic diseases and adverse health conditions, including hypertension (138), dyslipidaemia (139), type 2 diabetes (140), arterial stiffening and vascular changes (141). Obesity status in childhood is a multifactorial condition ( ) and is attributed to genetic background, nutritional habits and physical activity behaviours (145). Furthermore, Intrauterine events and early developmental factors such as maternal gestational diabetes, low birth weight and infant feeding practices can contribute to a child s risk of obesity, type 2 17

50 Chapter 1 diabetes and the metabolic syndrome (146,147). Other factors thought to play an important role in the increased risk of obesity include genetic and obesogenic factors such as socioeconomic and environmental influences (148). Data from the Bogalusa Heart Study have shown that overweight children have higher levels of total cholesterol, LDL-C, triglycerides and lower HDL-C. It was also observed that obese children at 12 years had a BMI status 30 kg/m 2 in adulthood (149). The early identification and intervention of factors contributing to obesity is crucial to prevent CVD onset in adulthood (150,151) Measurement of obesity Accurate and valid body composition assessments are required to quantify adiposity levels of an individual and thus are critical for determining disease risk (152,153). Imaging methods, such as DXA, are considered as the most accurate approach for measuring body composition and determining body fat distribution (154) Dual energy x-ray absorptiometry DXA utilises X-rays to scan the body and divide it into three components (i) fat mass, (ii) fat free mass and (iii) bone mineral mass (155), which is useful in determining cardiovascular risk of an individual (156). DXA has been termed the gold standard for body composition measurement, as it is highly accurate, valid and requires little subject compliance (157,158). Other advantages of DXA include quick assessment (5-10 minutes), which is non-invasive, precise and operator independent (159). However, limitations of DXA include high cost, upper weight and height limits of the machine, which restricts the assessment of tall or obese individuals and involves a small amount of radiation (160). In addition, estimates of body composition are also affected by differences among manufacturers in type of technology, 18

51 Chapter 1 models, and software employed (161). Alternatively, clinical anthropometry measurements can be used as proxies to ascertain total body fat and its distribution within specific body depots, such as the abdomen (161,162). Anthropometry can provide insight into health risks associated with excess adiposity levels or specific patterns of adipose tissue distribution and practicality wise, are often used in clinical practice or large epidemiology studies. (163) Anthropometry Body mass index BMI is the most widely accepted measure for defining overweight and obesity in individuals (135). BMI is defined as body weight in kilograms divided by the square of the height in meters (kg/m 2 ) (134). For Caucasian adults, a BMI between 25.0 and 29.9 kg/m 2 is considered as overweight, while individuals with a BMI 30 kg/m 2 are regarded as obese (135). Although, BMI represents a simple, but crude index used to indirectly estimate overall or general adiposity. It lacks the discriminatory power to differentiate between body fat and lean mass and determine body fat composition ( ). Within the paediatric population, no international consensus on risk-based cut points of BMI exist to determine overweight and obese individuals. Therefore, it is unclear what criteria should be used (167). As BMI varies with age, BMI values are compared with reference distribution values that are age and sex specific in the form of Z-scores or percentiles (167). More recently, body fat distribution has been recognised as having greater importance on health impact, than total body fat mass (156). As, type of fat distribution in particular, such as excess abdominal fat, has been associated with increased metabolic impairment (168,169). Therefore, waist circumference, waist-to-hip ratio, and skinfold assessments are often used as surrogate markers to reflect 19

52 Chapter 1 central obesity or fat distribution patterns (170,171). These anthropometric measures may be more appropriate for assessing body fat and distribution instead of BMI, particularly in youth Waist circumference and waist to hip ratio There is growing evidence that central obesity (i.e., excess fat mass in the upper part of the body around the abdomen) is more strongly linked to metabolic abnormalities ( ) and is a key criterion for diagnosis of the metabolic syndrome (174,175). Cut off points for higher central obesity levels are a waist circumference of 94 cm and 80 cm for Caucasian males and females, respectively (176). In addition, increases of waist circumference can predict a 3.8- fold increase in CVD risk (177). Currently, no universally accepted thresholds for waist circumference exists for the identification of overweight/obese children and adolescents, as data are lacking on the effectiveness of waist circumference either combined with BMI (178,179) or as an alternative to BMI (179,180). Waist to hip ratio assesses fat distribution of an individual (181) through determining the ratio between central obesity (fat distribution around the waist) and gynoid obesity (fat distribution around the waist) (182). A waist ratio of more than 0.86 for females and more than 0.95 for males increases adverse health risks (176). In children and adolescents, it is suggested that a waist to-height ratio of 0.5 may be useful in predicting cardio-metabolic risk (183) Skinfold measurement Skinfold assessment measures the amount of subcutaneous fat of an individual from various anatomical sites (184,185). This method assumes that overall adipose tissue is related to the 20

53 Chapter 1 amount of subcutaneous adipose tissue (186). Therefore, as subcutaneous skinfold measurements increase, so does relative body fat content (187,188). Typically, a three or seven site formula is used to find body fat density (175). One of the main limitations of skinfold measurements include measurement error. Assessment of skinfolds by trained personnel has been shown to have an error rate of approximately ±3.5% (181). Furthermore, callipers limit the amount of subcutaneous fat that can be assessed in overweight/obese participants, as maximum jaw openings can only range between mm (186). It has been noted that it is often difficult to find appropriate landmarks for skinfold assessments in overweight/obese individuals (189). Therefore, skinfold measurements could potentially underestimate the amount of adiposity in these individuals (189) Comparisons between DXA and clinical anthropometry with cardio-metabolic risk factors Given the limitations of clinical anthropometry, DXA has been suggested to be more appropriate in determining an individual s adiposity level (190). Previous studies in youth have documented that BMI and DXA-assessed total body fat are strongly correlated (57,191,192) and are associated with various cardiovascular risk factors (57,193). However, it remains unclear whether DXA measures are superior or equivalent predictors of cardiometabolic risk when compared to clinical anthropometry indices in a young population. Barreira et al. (2012) (162) investigated associations between anthropometric measures (weight; hip circumference; waist circumference; waist-hip, waist-height, and BMI) and total body fat assessed by DXA with CVD risk factors in a large biracial sample. The cohort included 2,037 individuals aged 18 to 69 years: 488 African American females (24%),

54 Chapter 1 Caucasian females (34%), 196 African American males (9%), and 667 Caucasian males (33%). Elevated cardio-metabolic health risk was defined as the presence of two or more CVD risk factors (high BP, glucose and triglyceride levels or low HDL-C levels). Several anthropometric measures were either moderately, highly or equally correlated with total body fat and CVD risk factors. Bi et al. (2016) (194) compared the association strength of percent body fat to BMI, waist circumference and waist-to-hip ratio for the prediction of CVD risk factors in healthy Singaporeans (63 males and 62 females). They showed that percent body fat did not outperform simple anthropometric measurements of obesity in the prediction of CVD risk factors in healthy Asian adults. This is consistent with another study which found that percent fat mass did not perform better than BMI or waist circumference in predicting metabolic syndrome (195). In the NHANES survey, Sun et al (196) demonstrated that the magnitude of correlations of BMI and waist circumference were similar to the DXA measurements (total fat mass and percent fat mass of the whole body and trunk) in relation to BP, plasma lipids, hs-crp, fasting insulin and glucose among 8,773 adults. In the same survey, Cui et al. (2013) (197) aimed to evaluate the validity of anthropometric indices (BMI and waist circumference) compared to DXA adiposity measures (percent total body fat mass, percent trunk fat mass and fat mass index) in relation to cardio-metabolic risk factors in youth aged 8-19 years (n=7,013). DXA adiposity measures did not produce stronger correlations with cardiometabolic risk factors in youth than BMI or waist circumference. Another study compared fat mass index (determined by DXA) with BMI to identify the presence of the metabolic syndrome in 3,004 participants aged years. The use of fat mass index did not improve 22

55 Chapter 1 the identification of the metabolic syndrome compared to BMI, in this young population (198). Other population studies evaluating whether anthropometry and DXA measurements are similar in its effects with cardio-metabolic factors have been inconsistent ( ). Comparison of previous studies are difficult because studies have differed in many aspects, such as the age of the sample examined, statistical methods employed, type of adiposity assessed, sample size, and cardio-metabolic outcomes investigated. Of note, majority of comparison studies have used correlation coefficients as a comparative statistical method. It is important to highlight that correlation coefficients show relation but do not select which measure better estimates a particular outcome (203). The lack of robust validation studies, from large epidemiological studies, that compare a variety of anthropometric indices against DXA measures, limits our ability to conclude which adiposity indicators are most useful for estimating cardio-metabolic risk outcomes. From a public health perspective, this question is crucial given the obesity epidemic. 1.5 LIFESTYLE RELATED FACTORS INFLUENCING CARDIOVASCULAR HEALTH Lifestyle related factors such as physical activity, physical fitness and obesity are all recognised as important predictors of CVD morbidity and mortality (204). These behaviours are often established in childhood and persist into adulthood (204). Negative consequences of an unhealthy lifestyle has been shown to be potentially reversible (34) or mitigated by adopting healthy habits, which in turn reduces the risk of CVD (72). 23

56 Chapter Physical activity Physical activity is defined as any bodily movement that results in energy expenditure (205). It is a behavioural habit that may be further divided into seven different modes: autonomic/unaware movements, activity embedded in daily life, commuting, hobbies, exercise and sports training (206). Exercise and sports training are further defined as repetitive bodily movements that are planned, structured and are performed to improve or maintain physical fitness and/or health status (206). Physical activity was not considered as an important predictor of health outcomes until the landmark study of Morris and colleagues in 1953 (207). The epidemiological investigation into the associations between physical activity and health status were first studied. This study involved a large cohort of postal office employees and transport workers on London s double decker buses. It showed that more active conductors on buses had a significantly lower incidence of CVD mortality than sedentary bus drivers, with a yearly incidence of 1.9 and 2.7 per 1000 person-years, respectively. Similar observations were demonstrated in the early work of Paffenbarger and colleagues (208) in the College Alumni Study. This study consisted of more than 16,000 male Harvard alumni aged between years. Leisure-time physical activity and exercise were quantified as kilocalories expended per minute for self-reported activities such as stair climbing, walking, and various sports. Participants were subsequently divided into low (<2000 kilocalories per week) and high ( 2000 kilocalories per week) energy expenditure groups. After 8 years of follow-up, males in the low energy expenditure group at baseline were at 64% higher risk for a first-time CVD event than their classmates within the high energy expenditure group, independent of smoking and BMI. 24

57 Chapter 1 The 1976 Nurses Health Study was one of the first large prospective epidemiological initiatives to examine relationships between lifestyle and health outcomes in females. This study showed effects of physical activity on CVD health similar to those previously found in males (209,210). In Sweden, several studies have also shown important effects of physical activity on health. For example, in a cohort of middle-aged males, risk of CVD and all-cause mortality after 25 years of follow up was 50% higher in individuals with low physical activity levels compared to those with high physical activity levels at baseline (211). Males who increased their physical activity in middle-age showed the same reduction in all-cause mortality risk as those with constantly high physical activity levels in youth (212). In summary, accumulated epidemiological evidence supports a strong benefit of physical activity on CVD health and longevity in adults ( ). Physical activity guidelines have been proposed for recommended physical activity levels in children and adolescents for optimal health benefits (217). The Department of Health released the 2014 Physical Activity and Sedentary Behaviour Guidelines for the Australian population (218) which indicated children should engage in 60 or more minutes of moderate to vigorous-intensity physical activity per day, including (i) vigorous intensity physical activity at least 3 days per week and (ii) muscle-strengthening training at least 3 days per week. Based on self-reported data from the Australian Health Survey , only 29% of children (aged 5-11 years) and 8% of adolescents (aged years) undertake the recommended physical activity per day (219). 25

58 Chapter Measurement of Physical Activity Physical activity questionnaires As physical activity is a complex multidimensional behaviour (220,221), an accurate, reliable and objective assessment of physical activity has been a challenge to obtain in large scale epidemiological studies (222). Many population studies have used subjective self-reported methods e.g. questionnaires to assess physical activity levels, as it is inexpensive, easy to use and time-efficient (223). Self-administered or interview based questionnaires are commonly used to obtain a crude indication of activity status (sedentary vs active) or more detailed descriptions of activities (e.g. type, duration and frequency) (223). Issues pertaining to questionnaires such as recall bias and/or social desirability bias, limit the accuracy of selfreported physical activity exposure in estimating activity related expenditure (224). Differences among studies in the reported pattern and strength of association for physical activity in regards to health outcomes depend in part on the methods of assessment, as different questionnaires vary in accuracy, validity and reproducibility (225). Therefore, alternative methods are of interest to provide a more accurate, reliable and objective assessment of physical activity, particularly in large epidemiological studies Accelerometers Accelerometers are small and portable devices suitable for measuring activity in free-living conditions and can quantify physical activity frequency and duration ( ). Accelerometers have been increasingly used in the research setting and are suggested to be more advantageous than self-reported data ( ). However, there are methodological limitations with accelerometer data of which include employment of different recording 26

59 Chapter 1 protocols, criteria for valid days and number of days the accelerometer is worn (232,233). In addition, how overall wear time is defined is of importance, as non-wear time cannot differentiate between (i) being sedentary, (ii) not wearing due to sleeping or swimming, or (iii) poor compliance (229). As non-compliance is common, researchers have used imputational or probability methods to account for missing data, which may introduce systematic bias into analysis (234). Diversity of cut points used to define moderate to vigorous physical activity across studies can lead to issues in interpretation of findings (235). The lack of consensus as to what metabolic equivalent value defines moderate to vigorous physical activity levels in youth, adds to difficulty in accelerometer results between studies and undermines the value of using objective methods (236,237). Consensus on protocols for collecting, scoring and reporting of accelerometry data for children and adolescents are required (238). Another limitation is that certain accelerometer models are unable to distinguish between various postures and underestimate non-ambulatory activities that do not involve vertical movement of the trunk, such as cycling (239). These devices are limited in their inability to account for additional energy expended and intensity experienced when undertaking certain activities such as weight training (239). Accelerometers can only measure acceleration of the body part they are attached to; therefore, accelerometers mounted to the lower body have difficulty measuring upper body physical activity with accuracy (240). Furthermore, accelerometers do not provide information about the type of activity undertaken or the context in which it occur (241). Some additional considerations include methodological decisions around the required output frequency and filter settings to be used (241). Variations of the definitions of non-wear 27

60 Chapter 1 time, minimum wear time to define a valid day, minimum number of valid days adds to the difficulty of comparing findings between studies (242,243). An approach to overcome aforementioned limitations and accurately measure an individual s physical capacity is to assess an individual s CRF levels Cardiorespiratory fitness CRF refers to the ability of the circulatory and respiratory systems to supply oxygen to skeletal muscles during sustained physical activity (205). CRF is considered a direct measure of physiological status and can be improved by regular exercise training (244,245). CRF is determined by physical activity levels (244), and several non-modifiable factors, such as age, sex, ethnicity (246) and genetics (247). CRF assessments are less prone to biases experienced from the use of accelerometers and self-reported questionnaires. (248). The American Heart Association recognises CRF as an important cardiovascular health metric based on strong evidence that CRF independently predicts various health outcomes ( ). CRF levels are measured objectively by the employment of maximal or submaximal exercise testing protocols (181) Measurement of cardiorespiratory fitness Maximal exercise testing Assessment of CRF can determined through maximal oxygen uptake (VO 2max ) achieved by an individual during maximal intensity exercise (181,254,255), via direct gas analysis (256,257). VO 2max represents the highest physiologically attainable value and is considered the gold standard for assessing maximal aerobic capacity (257). Despite high validity and reliability of 28

61 Chapter 1 VO 2max, there are several disadvantages of which includes expensive cost, time constraints, need for sophisticated equipment and trained personnel. Furthermore, heavy physical burden is placed upon the participant during VO 2max testing, thus it is suggested this level of exertion may not be achievable for individuals with disability or disease (258). These tests are often impractical; therefore, submaximal exercise testing protocols are commonly employed in population-based studies Submaximal exercise testing Submaximal exercise testing requires lower levels of physical exertion and overcomes many maximal exercise testing limitations (259,260). In addition, it is the preferred exercise method for many individuals, particularly for those who are overweight/obese and/or are sedentary, as it ensures a greater probability of successful completion. A commonly used submaximal exercising testing method include the physical working capacity at a heart rate of 170 beats/minute (PWC 170 ) protocol. The PWC 170 test is designed to estimate the working capacity of an individual at a heart rate of 170 bpm. A heart rate of 170 bpm describes the beginning an optimal functioning zone of the cardio-respiratory system during physical activity (261). This protocol consists of a number of increasingly intense stages (based on the heart rate of the previous stage) and continues until the participant reaches a predetermined heart rate. It is assumed that the participant has reached a steady state of exercise in a given stage before continuing to the next stage. Stage lengths have varied from 6-min (261) to 3- min ( ). A major limitation of submaximal tests is their inability to pinpoint absolute CRF capacity of the individual as exercise is not performed to volitional exhaustion. However, studies have demonstrated PWC 170 indirectly determines VO 2max, as linear dependence between oxygen consumption and heart rate have been previously observed 29

62 Chapter 1 (266). Furthermore, PWC 170 has been shown to be highly correlated with VO 2max in children and in adolescents (267,268). Another commonly used submaximal testing method is the 20 metre shuttle test (269,270) (57, 58). This test requires subjects to run between 2 lines spaced 20 metres apart at a pace set by signals, with an increasing frequency. When the participant can no longer maintain the set pace, the last completed speed (i.e. stage) is used to predict VO 2max. Like PWC 170, the 20 metre shuttle run has also been shown to be highly correlated with VO 2max in both children and adults (271,272) Submaximal power output is normally adjusted for body weight in kilograms to eliminate the influences of body weight upon an individual s aerobic capacity. This enables individuals of different body masses to be compared, as there is a close dependence of workload output with body weight (273). Submaximal power output is moderately stable from childhood and adolescence into young adulthood (274) Cardiorespiratory fitness with mortality and CVD in adults Prospective studies in adults have shown low levels of CRF are strongly associated with increased risk of mortality (275,276), and developing hypertension (277), type 2 diabetes (278) and CVD (251,279). Based on Aerobics Centre Longitudinal Study (ACLS) data, Blair et al. (2009) (156) calculated the attributable fraction of CRF and different traditional risk factors for all-cause mortality in over 52,000 males and females. They concluded that low CRF accounted for about 16% of all deaths in males and females, substantially more than obesity (3%), smoking (8%), high cholesterol (2-4%), type 2 diabetes (2-4%), and hypertension in females (7%). The attributable fraction observed in hypertension in males was closer to the effects of low CRF (15%). Hassinen et al. (2008) (280) demonstrated that 30

63 Chapter 1 older males and females within the lowest tertile of CRF (determined by VO 2max ) had a 10- fold higher risk of developing the metabolic syndrome compared to individuals in the highest tertile of CRF. Sawada et al. (2003) (278) observed low CRF was significantly associated with type 2 diabetes after 14 years of follow up in 4,747 non-diabetic Japanese males, aged years at baseline (278). Individuals within the lowest quartile of CRF had a 4-fold greater risk of type 2 diabetes compared to those in the highest quartile of CRF (278). In a recent meta-analysis of 33 studies involving more than 100,000 participants, Kodama et al. (2009) (251) demonstrated that for every 1 metabolic equivalent increase in CRF, allcause mortality and cardiovascular events were reduced by 13% and 15%, respectively. Lavie et al. (2013) (281) reported CRF significantly improved 10 and 25 year risks of cardiovascular mortality. In a study of 3,148 healthy adults, increases in adiposity (BMI and body fat percentage) and low CRF levels predicted the development of hypertension, metabolic syndrome, and hypercholesterolaemia after 6 years of follow up. There is increasing evidence that high levels of CRF provide strong and independent prognostic information about the overall risk of cardiovascular illness and death in adulthood (282). Based on findings in adult populations, the relationships of CRF and cardio-metabolic risk factors have been increasingly studied within the paediatric population Cardiorespiratory fitness and cardio-metabolic risk factors in children and adolescents Clustering of cardio-metabolic risk factors in childhood predicts metabolic syndrome, type 2 diabetes and CVD development in adulthood ( ). Thus, it is crucial to identify potential determinants of cardio-metabolic risk factors in children and adolescents to reduce 31

64 Chapter 1 the risk of chronic diseases later in life (288). In youth, CRF is considered an independent marker of cardiovascular outcomes (284). In 3,000 European youths aged 9 or 15 years, those within the lowest quintile of CRF were 13 times more likely to have clustering of traditional CVD risk factors (total cholesterol/hdl-c ratio, triglycerides, HOMA-IR, sum of four skinfolds and systolic BP) than those within the highest quintile of CRF (289). Kwon et al. (2010) (290) showed that total cholesterol, triglycerides, insulin and hs-crp were significantly higher (p<0.05) in the not fit group compared to the fit group, independent of BMI and waist circumference in 1,629 males aged 12 to 19 years (NHANES ). Dencker et al. (2012) (291) observed low CRF levels were associated with an elevated composite CVD risk score (diastolic BP, systolic BP, pulse pressure, mean arterial pressure, resting heart rate, left ventricular mass, left atrial diameter, relative wall thickness, body fat percentage, abdominal fat mass and body fat distribution) in 136 males (r=0.48, p<0.05) and 107 females (r=0.42, p<0.05) aged 8-11 years. A life course analysis was performed on 647 individuals with a mean age of 12 years to 32 years from Australian Schools Health and Fitness Survey, which showed lower levels of CRF in childhood was associated with increased adult obesity (odds ratio: 3.0) and insulin resistance (odds ratio: 1.7). Furthermore, reduced CRF levels from childhood through to adulthood was associated with increased obesity (odds ratio: 4.5) and insulin resistance (odds ratio: 2.1) in adulthood. This study suggests that a decline in CRF is a stronger predictor of adult obesity and insulin resistance compared to lower levels of CRF in childhood (292). In contrast, a number of studies have found that associations of CRF disappear after accounting for adiposity. Bailey et al. (2015) (293) found the association between CRF and 32

65 Chapter 1 cardio-metabolic risk in children was mediated by central obesity. After subsequent adjustment for age, sex, ethnicity and central obesity, CRF was not independently associated with any individual or clustered cardio-metabolic risk. Jago et al. (294) reported effects in CRF over a 2-year period on clustered cardio-metabolic risk in 3,514 adolescents were negligible, once changes in body mass were included in the analyses. Their data suggest that the association between CRF and cardio-metabolic risk in year-old children was mediated by central adiposity This suggests central adiposity may be a more important determinant of adverse cardio-metabolic health in this age group. McMurray et al. (2008) (295) examined longitudinal relationships between CRF in childhood and metabolic syndrome among 389 adolescents after a follow up period of 7 years, using data from the Cardiovascular Health in Children and Youth Study. CRF level was determined by predicted VO 2max obtained from a submaximal cycle ergometry test. VO 2max was categorised into tertiles and BMI was grouped into two categories (BMI 95th percentile vs. < 95th percentile). This study showed that adolescents with the metabolic syndrome were 6 times more likely to be in the lowest tertile of VO 2max at 7 to 10 years of age (reference group: highest tertile) than those without the metabolic syndrome. In addition, a case-control study with 364 males and females from the Amsterdam Growth and Health Longitudinal Study, showed that young adults with the metabolic syndrome at 32 and 36 years of age had a lower CRF level at age 13 years than counterparts without the metabolic syndrome, independent of skinfold-measured adiposity (296). Consensus of the literature indicates that low CRF levels are associated with an adverse cardio-metabolic risk profile in the paediatric population. Although, improvements of low CRF levels may play an important role in 33

66 Chapter 1 cardiovascular risk factor prevention, what is lesser known are the interactions between CRF and adiposity on cardiovascular outcomes The fat but fit hypothesis and mortality risk in adults Low CRF and excess body fat often occur in combination, which poses the question of the relative importance of these factors on mortality and CVD risk. The fit but fat hypothesis suggests that moderate to high levels of CRF attenuate or potentially eliminate the risk of cardiovascular and metabolic disease independent of adiposity, even among the obese (297). This hypothesis was first postulated by Blair and colleagues (298) who examined the effects of CRF (assessed via a maximal treadmill test), with all-cause and cause-specific mortality risk outcomes in 10,224 males and 3,120 females. Over an 8-year period, there were a total of 240 and 243 deaths in males and females, respectively. Age-adjusted all-cause mortality rates in males declined from 64 per 10,000 person-years within the lowest CRF quintile to 18.6 per 10,000 person-years within the highest CRF quintile. In females, corresponding values were 39.5 per 10, 000-person years within the lowest CRF quintile to 8.5 per 10,000-person years within the highest CRF quintile. Blair et al. (1991) (297) further investigated the effects of CRF on all-cause mortality in 10,224 normotensive and 1,832 hypertensive males over an 8 year follow up period. Normotensive and hypertensive males who were in the higher CRF quintile had lower risk for all-cause mortality and these effects were still present after accounting for age, resting systolic BP, BMI, smoking status and length of follow up. Similar findings were documented in other male (299,300) and female populations (301,302). A large number of studies investigating the effects of CRF and fatness with mortality have since been derived from the 34

67 Chapter 1 prospective ACLS population of which consisted of approximately 80,000 individuals aged from years, followed over a period of 35 years (303). Baseline CRF examination included a maximal exercise testing protocol. Lee et al. (1998) (304) examined risk of mortality in 21,856 fit vs. unfit males aged years within three BMI categories (19.0 to <25.0, 25.0 to <27.8, 27.8). Unfit individuals had a significantly higher risk for all-cause mortality than fit individuals in each BMI category (Table 1). Table 1. Relative risk of all-cause mortality according to different CRF /fatness categories in 21,856 males aged years CRF/fatness categories Relative risk (95% CI) Unfit/Low BMI 2.25 (1.59, 3.17) Fit/Low BMI 1.00 (0.82, 2.34) Unfit/Middle BMI 1.68 (1.47, 2.02) Fit/Middle BMI 0.96 (0.54, 1.35) Unfit/High BMI 2.24 (1.76, 3.09) Fit/High BMI 1.08 (0.63, 1.89) Modified from Lee CD, et al. Int J Obes Relat Metab Disord 1998;22:S2-S7. These results contrasted with those from the Lipid Research Clinics Study. Stevens et al. (2002) (305) concluded that fatness and CRF were both risk factors for mortality and that being fit did not completely reverse the increased risks associated with adiposity (BMI <27.7 kg/m 2 ) in 2,506 females (Table 2) and 2,860 males (Table 3) approximately aged 45 years. 35

68 Chapter 1 Table 2. Relative risk of all-cause mortality in 2,306 females (mean age 46.6 years) CRF/fatness categories Relative risk (95% CI) Unfit/Normal-weight 1.30 (0.96, 1.85) Fit/Normal-weight 1.00 (0.76, 1.30) Unfit/fat 1.57 (1.02, 2.44) Fit/fat 1.32 (0.03, 1.52) Modified from Stevens et al. Am J Epidemiol 2002;156(9): Table 3. Relative risk of all-cause and CVD mortality in 2,860 males (mean age 45.1 years) CRF/fatness categories Relative risk (95% CI) Unfit/Normal-weight 1.25 (0.75, 2.08) Fit/Normal-weight 1.00 (1.88, 1.28) Unfit/fat 1.49 (0.90, 2.23) Fit/fat 1.44 (1.00, 2.15) Modified from Stevens et al. Am J Epidemiol 2002;156(9): A review by Fogelholm (2010) (306) reported that mortality risk, was significantly attenuated with higher levels of CRF in both obese and normal-weight individuals. These findings suggest that both fatness and CRF are predictors for mortality and that CRF did not completely reduce the increased risk associated with increased fatness. The relative risks of comparing unfit with fit males for all-cause mortality were 2.18 in healthy males and 2.01 in males who exhibited the metabolic syndrome. The relative risk for CVD mortality for unfit vs fit males were 3.21 in healthy males and 2.25 in males with the metabolic syndrome. Another review by Barry et al (307), examined 10 studies that included a follow-up period ranging from 7.7 to 16 years in adults. CRF levels predicted all-cause mortality rates to a greater degree than BMI itself. Unfit individuals, irrespective of their BMI, had twice the risk of all-cause mortality than their fit counterparts. Individuals who were fit and overweight or 36

69 Chapter 1 obese had similar mortality risks as their fit and normal weight counterparts. Overall the evidence, not only shows the importance of CRF in the prevention of all-cause and CVD mortality, but also suggest that, irrespective of fatness, individuals who improve their CRF can reduce their mortality risk The fat but fit hypothesis and CVD in adults The fat but fit hypothesis has also been investigated in relation to cardio-metabolic risk factors and CVD in adults. Since its original publication, Blair and colleagues have repeatedly shown that for adults within a high fatness category, CRF attenuates CVD risk (308,309). In the ACLS cohort of 4,057 healthy males aged 45 years, the metabolic syndrome score (HDL-C, fasting glucose, systolic BP, triglycerides) was lower in the high CRF/obese category (odds ratio 1.91; 95% CI: 1.14, 3.21) compared to the low CRF/obese category (odds ratio: 6.47; 95% CI: 4.42, 9.46) (310). Katzmarzyk et al. (2004) (311) examined the link between CRF and CVD in more than 20,000 Caucasian males. CRF was assessed using a graded treadmill exercise test. They showed that being overweight (odds ratio: 4.7) or obese (odds ratio: 30.6) significantly increased the probability of developing risk factors associated with the metabolic syndrome. Boule et al. (2005) (312) examined CRF and fatness in relation to the metabolic syndrome in 158 males and 198 females aged years. To ascertain CRF levels, participants performed a submaximal CRF test on a cycle ergometer. Body fatness was determined by hydrostatic weighing, and abdominal and visceral subcutaneous fat were assessed by computer tomography. In males, those in the lowest tertile of CRF were 6 times more likely to exhibit the metabolic syndrome compared to those in the highest tertile of CRF. In 37

70 Chapter 1 females, those in the lowest tertile were 4 times more likely to present with the metabolic syndrome than those in the highest tertile of CRF, and these effects were seen independent of adiposity. In addition, Lee et al (313) demonstrated high levels of CRF in obese middle -aged males were associated with a substantial reduction in cardio-metabolic risk (relative risk: 0.93; 95% CI: 0.65, 1.31), compared to their unfit and obese counterparts (relative risk: 1.92; 95% CI; 1.40, 262). Not all studies suggest that CRF is a better predictor of cardiometabolic risk factors than fatness. A study by Christou et al (314) showed that in 135 healthy males aged years, various measurements of fatness (BMI, percent body fat and waist circumference) were correlated with lipids, lipoproteins, BP, arterial stiffness, fasting insulin and glucose (r:-0.44 to 0.51; p<0.05). Whereas, CRF was only correlated with triglycerides and fasting insulin (r:-0.21 to 0.19, p<0.05). The effects of CRF on individual components of the metabolic syndrome were attenuated after accounting for total and central adiposity. Overall, controversy exists in relation to the fat but fit hypothesis and its effects on health outcomes, as conflicting findings exist. Some studies have suggested high CRF provides a protective effect which diminishes obesity-related risk for CVD and all-cause mortality (279,313,315,316). Others have claim that CRF may not completely eliminate the negative effects (305,317), or CRF and obesity are two independent risk factors (317,318), or are equivocal ( ). In view of the fact that, cardio-metabolic risk factors originate in childhood, it is important to ascertain whether CRF can reduce or eliminate deleterious effects of fatness in a younger population. 38

71 Chapter The fat but fit hypothesis with cardio-metabolic risk factors in children and adolescents The fit but fat hypothesis also has been examined within the paediatric population. Eisenmann et al. (2005) (322) examined the combined influence of CRF (assessed via a physical work capacity at a heart rate of 150 beats/minute protocol (PWC 150 )) and fatness on a metabolic syndrome score derived from total cholesterol, HDL-C, LDL-C, triglycerides, glucose and mean arterial pressure, in 416 males and 345 females (9-18 years) from the Quebec Family Study. Four fatness-crf categories were determined based on the median split of age-adjusted BMI and PWC 150. The results indicated differences between BMI categories, with the low fit participants having a higher metabolic risk score. Those in the high fit/low BMI group had the lowest metabolic syndrome risk factor score and those in the low fit/high BMI group had the highest metabolic syndrome risk factor score, for males and females. The same concept was investigated in 296 boys and 188 girls aged 8 18 years of age from the ACLS cohort. Fatness was assessed via BMI and CRF was determined by treadmill time to exhaustion. The metabolic syndrome score included waist circumference, HDL-C, triglycerides, glucose and mean arterial pressure. In males and females, the high BMI/low fit group had the highest metabolic syndrome score (0.87 and 0.41, respectively). In males, the metabolic syndrome score for the high fit/low BMI and the low fit/low BMI group were similar ( 0.87 and 0.86, respectively). These findings suggest body fatness was more important in relation to the metabolic syndrome score than CRF (323). In another study, Eisenmann et al. (2007) (324) examined differences in CVD risk score (age standardised Z-scores for waist circumference, mean arterial pressure, HDL-C and triglycerides) and individual risk factors in 860 males and 755 females (aged 9-15 years) 39

72 Chapter 1 from the Australian Schools Health and Fitness Survey. Unlike previous studies using a median split approach, CRF (VO 2max determined by a 1.6 run) and fatness categories were derived using clinical cut points. VO 2max and percentage fat (determined from skinfold thicknesses) were used to cross-tabulate the sample into four groups (low fat/low fit, low fat/high fit, high fat/low fit, high fat/high fit) based on health cut points for VO 2max, using FITNESSGRAM criteria (42 ml kg -1 min -1 for males and ml kg -1 min -1 (age dependent) for females) and percent fat (25% for males and 32% for females). As use of these criteria resulted in majority of participants being classified into the low fat/high fit group, these cut points were then adjusted to sample-specific values corresponding with the 75th percentile for percent fat and the 25th percentile for VO 2max. Subsequently, cut points were 48 ml kg -1 min -1 for males and 42 ml kg -1 min -1 for females) for VO 2max, and 18% for males and 30% for females for body fat percentage. Among females in the high fat category, those with high CRF had a significantly lower BP compared to their low fit counterparts. In males, there was a decreasing, but not significant trend. Among extreme groups (low fat/high fit and high fat/low fit) triglycerides, LDL-C, HDL-C and total cholesterol: HDL-C ratio were significantly different in males, whereas only HDL-C and the total cholesterol: HDL-C ratio were significantly different among females. CVD risk score (Figure 2) showed a significant trend across groups in both sexes (p<0.001 in males and p<0.001 in females). In males and females, the high-fat/low-fit group had the highest CVD risk scores (2.22 and 2.23, respectively), and the low-fat/high-fit group had the lowest CVD risk scores (-0.55 and -0.65, respectively). These differences between the two extreme groups were significant for males and females (p<0.05). Within a fatness group, the higher CRF group showed lower scores, except for the low fatness group in males. 40

73 Chapter 1 Figure 2. CRF and fatness groups and a composite cardiovascular risk score in males and females (Eisenmann et al. Med Sci Sports Exerc 2007;39(8): ) Overall in different populations, Eisenmann and colleagues ( ) have consistently shown in overweight or obese youth that an adverse CVD risk profile is attenuated by a high CRF level. The Physical Activity across the Curriculum study included 375 children aged 7-9 years (193 girls and 182 boys) (327). BMI was used to classify participants into normal weight, overweight and obese categories. CRF was assessed by PWC 170 and participants were categorised into low and high CRF groups by a median split. The metabolic syndrome score included waist circumference, HDL-C, triglycerides, mean arterial pressure and HOMA-IR. 41

74 Chapter 1 The results showed that the normal weight/high fit group had the better metabolic syndrome score and the obese/unfit group had the lowest metabolic syndrome score. The metabolic syndrome score was significantly lower in high fit children within the normal and obese groups compared to their low fit counterparts within the same BMI group (327). Martins et al (328) examined different categories of CRF and fatness with CVD risk factors in 173 boys and 219 girls aged years from Portugal. Participants were grouped into four categories; non-overweight and unfit (37.4%), non-overweight and fit (35%), overweight/obese and unfit (11%) and overweight/obese and fit (10%). They showed that irrespective of fatness, individuals with higher CRF levels had a lower prevalence of cardiometabolic risk factors. Boreham et al. (2002) (329) examined the independent and relative strengths of CRF and fatness with CVD risk factors in 1,015 participants (aged years) from the Northern Ireland Young Hearts Project. Fatness was assessed from skinfold thickness and CRF was determined from a 20-metre shuttle run test. The outcome examined included BP, non-fasting serum total, and HDL-C. Fatness had stronger associations with CVD risk factors compared to CRF. Furthermore, relationships found with CRF and CVD risk factors were mediated by fatness. Rizzo et al (330) examined the associations of CRF (maximal ergometer test) and the clustering of CVD risk factors (fasting insulin, glucose, triglycerides, total cholesterol, HDL-C and BP) in 273 children aged 9 years and 256 adolescents aged 15 years from the European Youth Heart Study. They observed significant inverse associations between CRF and the metabolic risk score. However, these associations were no longer significant after accounting for body fatness (sum of five skinfolds). 42

75 Chapter 1 Independent effects of low CRF and fatness (determined by waist circumference and the sum of four skinfolds) were examined with clustering of CVD risk factors in 1,769 children from Denmark, Estonia and Portugal. Odds ratios for clustered CVD risk for the upper quartile of fatness compared with the lowest quartile of fatness were 9.13 (95% confidence interval (CI): ) and (95% CI: ) when systolic BP, triglyceride, HOMA-IR, cholesterol: HDL-C, and CRF were included in the score. When CRF was removed from the clustered risk score and was accounted for as a confounder, only the highest quartile of fatness had an increased CVD risk score. CRF showed similar associations with the clustered risk score (score without CRF incorporated) with an odds ratio for the upper quartile of 4.97 (95% CI: ) (331). Associations of low-grade inflammation with CRF (maximal ergometer test) and fatness (sum of five skinfolds) were examined in pre-pubertal children (74 boys and 68 girls aged 9 10 years) from the European Youth Heart Study. CRF was inversely associated with hs-crp (regression coefficient: -0.25; p<0.05) and complement factors C3 (regression coefficient: ; p<0.05). However, when fatness was included as a confounder, the associations were no longer significant. This suggests fatness has a greater impact on inflammation when compared to CRF in children (332). Three-year changes in CRF (VO 2max ) and adiposity were examined with cardio-metabolic risk factors in 383 children aged 6-7 years of age (333). Change in sum of skinfolds was independently associated with change in total cholesterol (z =4.83, p 0.001), LDL-C (z =4.38, p 0.001), systolic BP (z= 3.45, p 0.001), and diastolic BP (z= 2.45, P = 0.014). Whereas, CRF change was only independently associated with change in total cholesterol (z=-3.86, p 0.001), HDL-C (z=3.85, p 0.001), and systolic blood pressure (z = 2.06, p 0.001). 43

76 Chapter 1 Limitations of the aforementioned studies investigating the fat but fit hypothesis within youth includes the use of CRF and fatness categories. This method could lead to the potential misclassification of individuals, as cut points used were generally derived from the population sample. Additionally, different methods used to assess fatness and CRF levels vary across studies which make comparisons between findings difficult. Furthermore, several studies created their own CVD risk scores to express clustering of the main components of adult metabolic syndrome. The use of different CVD risk scores with varying specific risk factors adds to the difficulty in comparing results between studies. These findings in children and adolescents contrast with findings from adult studies, which have, overall, shown that CRF and changes in CRF are strong predictors of CVD risk and mortality, largely independent of body fatness. At least part of the explanation for this discrepancy may lie in the different end points used in children and adult studies. Although the latter have mortality as the outcome, studies on children are restricted to the presence of cardio-metabolic risk factors. Thus, different independent relationships may exist between CRF and mortality in adults, and fatness and cardio-metabolic risk status in children. Furthermore, the potential confounding effects of puberty has not been considered in majority of paediatric studies, as puberty can influence body composition (334). In summary, there is no consensus regarding the interrelationships between fatness, CRF and cardio-metabolic risk factors in children and adolescents, in whether CRF can partially or wholly eliminate the effects of adiposity in a young population. These differing results can be partly explained by the complex multifactorial aetiology of CVD, but also by the large variation in methodology and CVD risk outcomes examined. Factors affecting precursors of 44

77 Chapter 1 CVD are of paramount importance, because atherosclerosis is lifelong degenerative process beginning in childhood (2). More data are required to better understand the roles that CRF and fatness play in determining cardiovascular health outcomes in youth, as it may be more feasible to identify individuals at risk and recommend lifelong health improvement strategies at a younger age. This issue is of particular public health importance as studies have shown substantial declines in CRF over the past few decades (335,336). Another aspect of fitness that is often overlooked with its effects on cardiovascular health is muscular fitness, of which includes muscular strength, muscular endurance and muscular power Muscular fitness Muscular fitness has been increasingly recognised in the prevention and treatment of chronic disease (337). Muscle-strengthening activities are included in most institutional recommendations of exercise, with the purpose to maintain and improve overall health status (338). Muscular fitness comprises of (i) muscular strength, which is the ability of a specific muscle or muscle group to generate force; (ii) muscular endurance which is defined as the ability to resist repeated contractions or maintain a contraction for a prolonged period of time; and (iii) muscular power which entails a maximal, dynamic and explosive contraction of a single muscle or muscle group in a short period of time (339). Maintaining muscular mass has been shown to be protective against the onset of insulin resistance and type 2 diabetes (340). Skeletal muscle plays a crucial role in the regulation of whole-body glucose homeostasis. Approximately 70-80% of ingested glucose is absorbed by skeletal muscle and is either stored as glycogen, oxidised for energy, or to a lesser extent stored as fat (341,342). Muscle of obese individuals and type 2 diabetics showed a lower capacity to uptake glucose, and a higher capacity to uptake fatty acids, due to increased levels of lipid availability (343). 45

78 Chapter 1 Lower BP and higher muscular fitness and mass, generally associate with better endothelial function (344), lower arterial stiffness (345), and lower vascular tone, which may contribute to a lower microvascular resistance at a given cardiac output (346). Amongst the components of muscular fitness, muscular strength has been increasingly studied in terms of its effects on mortality and chronic disease (347). Handgrip strength is influenced by many factors including sex, age, level of physical activity and handedness ( ). Several studies have shown that anthropometric variables such as body height, body weight ( ), hand length, hand width (354,355) and BMI (356) correlate with handgrip strength. One issue encountered in assessing handgrip strength is accounting for the effect of body size. This is an important factor in determining an individual s muscle strength, if and how strength measures should be normalised for differences in body size is controversial (357). Several studies have attempted to overcome this issue by using various scaling methods such as ratio scaling, linear regression and allometric scaling to remove the direct influence of body size or absolute values of muscular strength (358). Despite this, currently no consensus exists in whether and methods of how body size should be accounted for in measures of muscular fitness. Furthermore, employment of different anthropometric measures such as body height (359), weight (360), and fat free mass (361) can influence the reported findings, thus making comparisons between studies difficult Associations between muscular strength with mortality and CVD in adults Several adult population studies have shown that muscular strength is inversely associated with all-cause mortality and CVD-specific mortality (337). In the ACLS cohort, a higher 46

79 Chapter 1 level of muscular strength assessed as 1 repetition-maximum (1 RM) for bench and leg presses was inversely associated with all-cause mortality (hazard ratio: 0.68; 95% CI: 0.55, 0.84) in approximately 9,000 males (mean age 42.3 years), followed for an average of 19 years. These associations were independent of central and total adiposity (362). Aberg et al. (2015) (363) examined muscular strength (combination of knee extension, elbow flexion, and hand grip) on stroke risk (subarachnoidal haemorrhage, intracerebral haemorrhage and ischaemic stroke) in approximately 1.5 million Swedish male conscripts. After 42 years of follow up, low muscular strength was associated with subsequent stroke (hazard ratio 1.39), including fatal (hazard ratio, 2.16) and nonfatal (hazard ratio 1.30) strokes. Silventoinen et al. (2009) (364) examined the effects of muscular strength with CVD in 145,467 males (age at baseline 18.2 years). Muscular strength (elbow flexion, handgrip and knee extension) with incident CVD endpoints (fatal and non-fatal; CHD and three types of stroke (intracerebral haemorrhage, subarachnoid haemorrhage and intracerebral infarction) were examined after a follow up of 25 years. Analyses were adjusted for height, BMI, systolic BP, diastolic BP and social position. All muscular fitness measures were inversely associated with disease risk (p<0.05). Maslow et al. (2010) (365) examined the influence of muscular strength on incident hypertension among 4,147 males (mean age 43 years) in the ACLS cohort. In prehypertensive males (systolic BP of mm Hg or diastolic BP of mm Hg), the risk of developing hypertension was 29% and 28% lower in the middle muscular strength tertile and highest muscular strength tertile groups, respectively, compared to the lowest muscular strength. However, these associations were no longer significant after the inclusion of CRF. These aforementioned associations were not observed in normotensive males. 47

80 Chapter 1 The cross-sectional association of muscular strength (assessed via isometric knee extension and flexion peak torque) and CRF (assessed via peak oxygen uptake (VO 2peak )) with a metabolic syndrome risk score (waist circumference, triglycerides, BP, fasting plasma glucose, and HDL-C) was examined in Flemish adults, years of age. After adjusting for dietary intake and CRF, muscular strength was inversely associated with metabolic risk score in females. Independent of muscular strength, CRF was inversely and more strongly associated with metabolic risk score in both sexes. Significant associations of muscular strength and CRF with individual metabolic syndrome risk factors were only partially mediated by waist circumference and BMI (366). Jurca et al. (2005) (367) analysed the association of muscular strength with metabolic syndrome incidence in 3,233 males after 7 years of follow up. After adjusting for potential confounders (the number of risk factors at baseline, and family history of type 2 diabetes, hypertension, and premature CHD), the highest muscular strength category was associated with a 44% and 39% lower risk of metabolic syndrome in normal weight and overweight/obese men, respectively. Vaara et al. (2014) (368) showed that muscular endurance (push-ups, sit-ups and repeated squats) was inversely associated with a clustered cardiovascular risk factor score (plasma glucose, triglycerides, HDL-C, LDL-C, systolic BP and diastolic BP) (regression coefficient: 0.26; p<0.05). Whereas maximal strength (leg extension and bench press) was not associated with cardiovascular risk score in 846 young adult Finnish males at 25 years of age. In summary, considerable evidence from adult studies supports the protective role of muscular strength in relation to mortality risk, CVD and the metabolic syndrome. 48

81 Chapter Associations between muscular fitness and cardio-metabolic risk factors in children and adolescents Muscular strength increases as children mature due to increases in muscle mass, muscle fibre size and changes in hormonal influences ( ). Extensive evidence supports current physical activity recommendations for youth for the inclusion of muscle-strengthening activities in addition to aerobic exercise to maintain cardio-metabolic health in youth (337). However, declining levels of muscular fitness levels have been observed within the paediatric population ( ). The importance for promoting moderate to high levels of muscular fitness in children and adolescents are based on the growing body of evidence associating muscular fitness with an array of health benefits (337,377). Current literature suggests that many of these benefits are independent of CRF, providing a strong rationale for integrating different types of exercise training (378). Several studies have demonstrated that muscular strength (handgrip strength) is inversely associated with systolic BP ( ) and diastolic BP in youth ( ). Benson et al. (2006) (384) investigated cross-sectional associations of CRF and muscular strength (1RM for bench press and leg press) with estimated HOMA-IR in 126 children (mean age 12.1 years). Children within the middle and highest tertiles of upper body muscular strength were 98% less likely to have high insulin resistance than those within the lowest tertile. These effects were seen after accounting for puberty, waist circumference, and BMI. In 460 boys and girls aged 13 to 18.5 years, independent associations of muscular fitness and CRF (20 metre shuttle run) were examined with a continuous cardio-metabolic risk score based on triglycerides, LDL-C and HDL-C, and glucose. A muscular fitness index was derived based on handgrip, standing long jump, and bent arm hang tests. After adjusting for age, maturation 49

82 Chapter 1 and CRF, muscular fitness was inversely associated with cardio-metabolic risk score in females, with a similar trend towards an inverse association in males (385). In a study of 709 European adolescents (mean age 14.9 years) from 9 different countries, muscular fitness score (handgrip strength and standing long jump) was inversely associated with clustered metabolic risk score (waist circumference, systolic BP, triglycerides, total cholesterol/hdl-c ratio, and HOMA-IR ) in both sexes (regression coefficient: 0.26; p<0.001), independent of CRF (20m shuttle run test) (379). Furthermore, this inverse association between muscular fitness and cardio-metabolic risk score persisted in overweight/obese adolescents. Magnussen et al. (2012) (386) cross-sectionally examined the association between muscular strength (summed handgrip strength, shoulder and leg flexion and extension values), muscular power (standing long jump) and muscular endurance (pushups) with clustered CVD risk score (non HDL-C, HDL-C, triglycerides, mean arterial pressure) in 1,642 youth aged 9, 12 and 15 years from the Australian Schools Health and Fitness Survey. Muscular strength, endurance, and power were inversely associated with clustered CVD risk (p<0.05). After adjustment for BMI, this association remained for muscular endurance and power (p 0.001), but not strength. Muscular power was inversely related to prevalence of clustered CVD risk ( 80th percentile) within low (P trend < 0.001), moderate (P trend < 0.001), and high (P trend = 0.001) CRF categories. The Healthy Lifestyle in Europe by Nutrition in Adolescents study examined 346 boys and 363 girls at ages 12.5 to 17.5 years from 10 European centres and showed muscular fitness (measured by hand grip strength and standing long jump), was inversely associated with clustered CVD risk factors (waist circumference, systolic BP, triglycerides, total cholesterol: 50

83 Chapter 1 HDL-C ratio, HOMA-IR) (regression coefficient: -0.25; p<0.001), independent of CRF (387). Furthermore, the odds ratio for having a high CVD risk score (above or equal 1 SD) was 5.3% in the lowest muscular fitness quartile, compared to the highest muscular fitness quartile. In contrast, in a similar study by Steene-Johannessen et al. (2009) (388) showed that the positive influence of CRF (VO 2peak in a cycle ergometry test) on clustered cardio-metabolic risk was stronger than that of muscular fitness. Nevertheless, muscular fitness remained inversely and independently associated with cardio-metabolic risk after adjusting for CRF. This study comprised of 1,592 Norwegian youths aged 9 and 15 years, and muscular fitness included handgrip strength, standing long jump, sit-ups and a modified Biering-Sørensen test (measures endurance of the trunk extensor muscles). Cardio-metabolic risk factors included in the clustered cardio-metabolic risk score comprised of systolic BP, triglycerides, HDL-C, HOMA-IR and waist circumference. The protective role of muscular fitness was observed across both normal and overweight participants. Another study, examined the association between muscular strength and inflammatory biomarkers in 416 boys and girls at age 15.4±1.4 years. After controlling for sex, age, maturation, weight, height, socioeconomic status, and CRF (20 metre shuttle run), muscular fitness was inversely associated with hs- CRP (regression coefficient: -0.21; p=0.007) and complement factor C3 (regression coefficient: -0.16; p=0.04) (389). Moreover, muscular fitness was inversely associated with hs-crp in overweight adolescents after controlling for body fat and fat-free mass (390). Other studies in children and adolescents, have shown higher levels of muscular fitness were inversely and independently associated with insulin resistance (253) clustered cardio- 51

84 Chapter 1 metabolic risk ( ) and inflammatory proteins (258). Buchan et al (391) showed that muscular fitness was related with a continuous metabolic risk score (interleukin-6, hs- CRP, adiponectin, fibrinogen and plasminogen activator inhibitor-1). Across incremental groups of muscular fitness, participants in the first quartile (lowest muscular fitness group) had significantly greater cardio-metabolic risk scores than other quintiles (Figure 3). Figure 3. Quartiles of muscular fitness with a clustered cardio-metabolic risk score (sum of z- scores). A higher cardio-metabolic risk score indicates a less favourable risk profile after adjustment for age, sex, physical activity, pubertal status, waist circumference and CRF (Buchan et al. Res Sports Med 2015; 23(3): Additionally, muscular fitness in youth have been shown to track into adulthood (392,393) and are linked to future CVD risk (394). Fraser et al. (2016) (395) examined childhood fitness levels (baseline age 12 years) and metabolic syndrome in adulthood (follow up age 32 years) in 737 participants from the Childhood Determinants of Adult Health Study. Findings showed that higher childhood levels of muscular strength, muscular power, and a combined 52

85 Chapter 1 muscular fitness score (highest tertiles) were associated with a lower continuous metabolic syndrome score in adulthood (when compared to lowest tertiles), independent of CRF (muscular strength relative risk: 0.39; 95% CI: 0.19, 0.78; muscular power relative risk: 0.32; 95% CI: 0.15, 0.68; muscular fitness score relative risk: 0.30; 95% CI: 0.14, 0.63). No associations were observed between muscular endurance (number of correctly completed inclined push-ups in 30 seconds) with both metabolic syndrome scores. In another longitudinal study spanning from childhood to adulthood, data from the European Youth Heart Study suggested that higher childhood muscular strength (baseline age of 15 years) is associated with a favourable CVD risk score in young adulthood (follow-up age of years) (383). It is hypothesised, muscular fitness may prevent CVD at least partially through biological pathways different to those associated with CRF (347,362,396,397). Considerable evidence supports the crucial and independent role of muscular strength in the prevention of CVD. Despite these emerging associations, handgrip strength is rarely used as a marker of disease risk in primary care settings. Strategies should not only focus on enhancing CRF, but also improving muscular fitness, particularly in those who are at risk of developing an adverse cardio-metabolic risk profile. Especially as, overweight/obese youth tend to experience greater self-efficacy, adherence and satisfaction in resistance training exercises compared to activities that require a greater cardiorespiratory output (398,399). To date, the majority of youth studies have been cross-sectional in nature and has examined the clustering of cardio-metabolic risk factors, using different CVD risk scores. A better understanding of the longitudinal relationships of muscular fitness on individual cardiometabolic risk factors during youth, where significant maturational changes occur is crucial, considering the paediatric obesity epidemic. 53

86 Chapter SCOPE AND AIMS OF THE THESIS Evidence to date suggests that development of CVD has its origins in early life. Furthermore, the increase of adverse lifestyle habits and its influence on cardio-metabolic risk factors in childhood and adolescence, show that this is a crucial period during the life course (400,401). There is an urgent need to explore these relationships to develop strategies for prevention and implementation of interventions to reduce the onset of adult CVD. This thesis aims to examine CRF, muscular fitness and adiposity and its effects on cardiometabolic risk factors in a large Australian pregnancy cohort consisting of children, adolescents and young adults. This thesis utilised data collected from the Western Australian Pregnancy Cohort (Raine) Study. Childhood and adolescence are important periods where adverse health behaviours that can affect the later development of CVD, are established. Physical fitness, in particular CRF and muscular fitness, and its interactions with obesity are important determinants in long-term CVD risk. However, what is lesser known are the magnitude of CRF and fatness influences on cardio-metabolic risk factors, and the relationships between muscular fitness and cardio-metabolic risk factors throughout childhood and adolescence. Significant maturational changes occur during this period, therefore understanding the interrelationships between obesity and CRF and longitudinal effects of muscular fitness with cardio-metabolic related outcomes in children and adolescents will inform population-wide prevention strategies. In addition, the use of DXA is becoming more widespread within the clinical and research setting. However, what is uncertain is whether this technology is superior to clinical anthropometry for estimating current cardio-metabolic risk in a young adult population. 54

87 Chapter 1 This thesis is organised as a series of scientific papers, which have all have been published. The objectives and hypotheses of this thesis includes: (i) to examine whether CRF attenuates or eliminates the adverse effects of adiposity on a range of cardio-metabolic risk factors in adolescents at 17 years. It is hypothesised that CRF would either ameliorate or eliminate the adverse effects of fatness on a range of cardio-metabolic risk factors. (ii) to examine longitudinal effects of handgrip and back muscular endurance on a range of cardio-metabolic risk factors in children and adolescents from 10 to 17 years. It is hypothesised that moderate to higher levels of handgrip strength and back muscular endurance are inversely associated with individual cardiometabolic risk factors from childhood to adolescence. (iii) to identify which adiposity measure determined by either DXA or clinical anthropometry best estimate cardio-metabolic risk in a young adult population at 20 years. It is hypothesised that DXA measures are superior to clinical anthropometry in estimating individual cardio-metabolic risk factors in young adults. 55

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95 Chapter Sayin N, Kara N, Pekel G. Ocular complications of diabetes mellitus. World Journal of Diabetes. 2015;6(1): Cade WT. Diabetes-related microvascular and macrovascular diseases in the physical therapy setting. Physical Therapy. 2008;88(11): DeBoer MD, Wiener RC, Barnes BH, Gurka MJ. Ethnic differences in the link between insulin resistance and elevated ALT. Pediatrics. 2013;132(3):e718-e Fagot-Campagna A, Pettitt DJ, Engelgau MM, Burrows NR, Geiss LS, Valdez R, Beckles GLA, Saaddine J, Gregg EW, Williamson DF. Type 2 diabetes among North adolescents: An epidemiologic health perspective. The Journal of Pediatrics. 2000;136(5): Odeleye OE, de Courten M, Pettitt DJ, Ravussin E. Fasting hyperinsulinemia is a predictor of increased body weight gain and obesity in Pima Indian children. Diabetes. 1997;46(8): Sabin MA, Magnussen CG, Juonala M, Shield JPH, Kähönen M, Lehtimäki T, Rönnemaa T, Koskinen J, Loo B-M, Knip M. Insulin and BMI as predictors of adult type 2 diabetes mellitus. Pediatrics. 2015;135(1):e144-e Bao W, Srinivasan SR, Berenson GS. Persistent elevation of plasma insulin levels is associated with increased cardiovascular risk in children and young adults. Circulation. 1996;93(1): Arslanian S, Suprasongsin CH. Insulin sensitivity, lipids, and body composition in childhood: is" syndrome X" present? The Journal of Clinical Endocrinology and Metabolism. 1996;81(3): Perry RJ, Samuel VT, Petersen KF, Shulman GI. The role of hepatic lipids in hepatic insulin resistance and type 2 diabetes. Nature. 2014;510(7503): Thota P, Perez-Lopez FR, Benites-Zapata VA, Pasupuleti V, Hernandez AV. Obesityrelated insulin resistance in adolescents: a systematic review and meta-analysis of observational studies. Gynecological Endocrinology. 2017;33(3); Reinehr T. Metabolic syndrome in children and adolescents: a critical approach considering the interaction between pubertal stage and insulin resistance. Current Diabetes Reports. 2016;16(1): Galkina E, Ley K. Immune and inflammatory mechanisms of atherosclerosis. Annual Review of Immunology. 2009;27: Libby P, Ridker PM, Maseri A. Inflammation and atherosclerosis. Circulation. 2002;105(9): Heidari B. The importance of C-reactive protein and other inflammatory markers in patients with chronic obstructive pulmonary disease. Caspian Journal of Internal Medicine. 2012;3(2): Singh SK, Suresh MV, Voleti B, Agrawal A. The connection between Creactive protein and atherosclerosis. Annals of Medicine. 2008;40(2): Esser N, Legrand-Poels S, Piette J, Scheen AJ, Paquot N. Inflammation as a link between obesity, metabolic syndrome and type 2 diabetes. Diabetes Research and Clinical Practice. 2014;105(2): Ridker PM, Rifai N, Rose L, Buring JE, Cook NR. Comparison of C-reactive protein and low-density lipoprotein cholesterol levels in the prediction of first cardiovascular events. New England Journal of Medicine. 2002;347(20): de Ferranti S, Mozaffarian D. The perfect storm: obesity, adipocyte dysfunction, and metabolic consequences. Clinical Chemistry. 2008;54(6): DeFronzo RA. From the triumvirate to the ominous octet: a new paradigm for the treatment of type 2 diabetes mellitus. Diabetes. 2009;58(4):

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100 Chapter 1 relation to adiposity-related biologic factors. American Journal of Epidemiology. 2010;172(12): Cui Z, Truesdale K, Cai J, Koontz3 M, Stevens J. Anthropometric indices as measures of body fat assessed by DXA in relation to cardiovascular risk factors in children and adolescents: NHANES International Journal of Body Composition Research. 2013;11(3): Weber DR, Leonard MB, Shults J, Zemel BS. A comparison of fat and lean body mass index to BMI for the identification of metabolic syndrome in children and adolescents. The Journal of Clinical Endocrinology and Metabolism. 2014;99(9): Ito H, Nakasuga K, Ohshima A, Maruyama T, Kaji Y, Harada M, Fukunaga M, Jingu S, Sakamoto M. Detection of cardiovascular risk factors by indices of obesity obtained from anthropometry and dual-energy X-ray absorptiometry in Japanese individuals. International Journal of Obesity and Related Metabolic Disorders. 2003;27(2): Shen W, Punyanitya M, Chen J, Gallagher D, Albu J, PiSunyer X, Lewis CE, Grunfeld C, Heshka S, Heymsfield SB. Waist circumference correlates with metabolic syndrome indicators better than percentage fat. Obesity. 2006;14(4): Lee K, Song YM, Sung J. Which obesity indicators are better predictors of metabolic risk?: healthy twin study. Obesity. 2008;16(4): De Lorenzo F, Mukherjee M, Kadziola Z, Suleiman S, Kakkar VV. Association of overall adiposity rather than body mass index with lipids and procoagulant factors. Thrombosis and Haemostasis. 1998;80(4): Mukaka MM. A guide to appropriate use of correlation coefficient in medical research. Malawi Medical Journal. 2012;24(3): Booth FW, Roberts CK, Laye MJ. Lack of exercise is a major cause of chronic diseases. Comprehensive Physiology Caspersen CJ, Powell KE, Christenson GM. Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research. Public Health Reports. 1985;100(2): Howley ET. Type of activity: resistance, aerobic and leisure versus occupational physical activity. Medicine and Science in Sports and Exercise. 2001;33(6):S364- S Morris JN, Heady JA, Raffle PAB, Roberts CG, Parks JW. Coronary heart-disease and physical activity of work. The Lancet. 1953;262(6796): Paffenbarger RS, Wing AL, Hyde RT. Physical activity as an index of heart attack risk in college alumni. American Journal of Epidemiology. 1978;108(3): Manson JE, Stampfer MJ, Colditz GA, Willett WC, Rosner B, Hennekens CH, Speizer FE, Rimm EB, Krolewski AS. Physical activity and incidence of non-insulindependent diabetes mellitus in women. The Lancet. 1991;338(8770): Li TY, Rana JS, Manson JE, Willett WC, Stampfer MJ, Colditz GA, Rexrode KM, Hu FB. Obesity as compared with physical activity in predicting risk of coronary heart disease in women. Circulation. 2006;113(4): Kilander L, Berglund L, Boberg M, Vessby B, Lithell H. Education, lifestyle factors and mortality from cardiovascular disease and cancer. A 25-year follow-up of Swedish 50-year-old men. International Journal of Epidemiology. 2001;30(5):

101 Chapter Byberg L, Melhus H, Gedeborg R, Sundström J, Ahlbom A, Zethelius B, Berglund LG, Wolk A, Michaëlsson K. Total mortality after changes in leisure time physical activity in 50 year old men: 35 year follow-up of population based cohort. British Medical Journal. 2009;338:b Wannamethee SG, Shaper AG, Walker M. Changes in physical activity, mortality, and incidence of coronary heart disease in older men. The Lancet. 1998;351(9116): Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT, Lancet Physical Activity Series Working G. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. The Lancet. 2012;380(9838): Barengo NC, Hu G, Lakka TA, Pekkarinen H, Nissinen A, Tuomilehto J. Low physical activity as a predictor for total and cardiovascular disease mortality in middle-aged men and women in Finland. European Heart Journal. 2004;25(24): Löllgen H, Böckenhoff A, Knapp G. Physical activity and all-cause mortality: an updated meta-analysis with different intensity categories. International Journal of Sports Medicine. 2009;30(03): Colberg SR, Sigal RJ, Yardley JE, Riddell MC, Dunstan DW, Dempsey PC, Horton ES, Castorino K, Tate DF. Physical activity/exercise and diabetes: a Position Statement of the American Diabetes Association. Diabetes Care. 2016;39(11): Australian Institute of Health and Welfare. Insufficient physical activity; Australian Institute of Health and Welfare. Risk factors to health: Insufficient physical activity Haskell WL, Kiernan M. Methodologic issues in measuring physical activity and physical fitness when evaluating the role of dietary supplements for physically active people. The American Journal of Clinical Nutrition. 2000;72(2):541s-550s Trost SG. Objective measurement of physical activity in youth: current issues, future directions. Exercise and Sport Sciences Reviews. 2001;29(1): Warren JM, Ekelund U, Besson H, Mezzani A, Geladas N, Vanhees L. Assessment of physical activity a review of methodologies with reference to epidemiological research: a report of the exercise physiology section of the European Association of Cardiovascular Prevention and Rehabilitation. European Journal of Cardiovascular Prevention and Rehabilitation. 2010;17(2): Prince SA, Adamo KB, Hamel ME, Hardt J, Gorber SC, Tremblay M. A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. International Journal of Behavioral Nutrition and Physical Activity. 2008;5(1): Ainsworth BE, Caspersen CJ, Matthews CE, Mâsse LC, Baranowski T, Zhu W. Recommendations to improve the accuracy of estimates of physical activity derived from self report. Journal of Physical Activity and Health. 2012;9(s1):S76-S Matthews CE, Steven CM, George SM, Sampson J, Bowles HR. Improving selfreports of active and sedentary behaviors in large epidemiologic studies. Exercise and Sport Sciences Reviews. 2012;40(3): Cliff DP, Reilly JJ, Okely AD. Methodological considerations in using accelerometers to assess habitual physical activity in children aged 0 5 years. Journal of Science and Medicine in Sport. 2009;12(5):

102 Chapter Oliver M, Schofield GM, Kolt GS, Schluter PJ. Pedometer accuracy in physical activity assessment of preschool children. Journal of Science and Medicine in Sport. 2007;10(5): Pate RR, O'Neill JR, Mitchell J. Measurement of physical activity in preschool children. Medicine and Science in Sports and Exercise. 2010;42(3): Evenson KR, Terry Jr JW. Assessment of differing definitions of accelerometer nonwear time. Research Quarterly for Exercise and Sport. 2009;80(2): Troiano RP, McClain JJ, Brychta RJ, Chen KY. Evolution of accelerometer methods for physical activity research. British Journal of Sports Medicine. 2014;48(13): Trost SG. State of the art reviews: measurement of physical activity in children and adolescents. American Journal of Lifestyle Medicine. 2007;1(4): Corder K, Brage S, Ekelund U. Accelerometers and pedometers: methodology and clinical application. Current Opinion in Clinical Nutrition and Metabolic Care. 2007;10(5): Corder K, Ekelund U, Steele RM, Wareham NJ, Brage S. Assessment of physical activity in youth. Journal of Applied Physiology. 2008;105(3): Sterne JAC, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, Wood AM, Carpenter JR. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. British Medical Journal. 2009;338:b Lee IM, Shiroma EJ. Using accelerometers to measure physical activity in large-scale epidemiological studies: issues and challenges. British Journal of Sports Medicine. 2014;48(3): Hills AP, Mokhtar N, Byrne NM. Assessment of physical activity and energy expenditure: an overview of objective measures. Frontiers in nutrition. 2014;1: Butte NF, Ekelund U, Westerterp KR. Assessing physical activity using wearable monitors: measures of physical activity. Medicine and Science in Sports and Exercise. 2012;44(Suppl 1):S Chen KY, Bassett DR. The technology of accelerometry-based activity monitors: current and future. Medicine and Science in Sports and Exercise. 2005;37(11):S Altini M, Penders J, Vullers R, Amft O. Estimating energy expenditure using bodyworn accelerometers: a comparison of methods, sensors number and positioning. IEEE Journal of Biomedical and Health Informatics. 2015;19(1): Mannini A, Intille SS, Rosenberger M, Sabatini AM, Haskell W. Activity recognition using a single accelerometer placed at the wrist or ankle. Medicine and Science in Sports and Exercise. 2013;45(11): Atkin AJ, Gorely T, Clemes SA, Yates T, Edwardson C, Brage S, Salmon J, Marshall SJ, Biddle SJH. Methods of measurement in epidemiology: sedentary behaviour. International Journal of Epidemiology. 2012;41(5): Heil DP, Brage S, Rothney MP. Modeling physical activity outcomes from wearable monitors. Medicine and Science in Sports and Exercise. 2012;44(1 Suppl 1):S50-S Freedson P, Bowles HR, Troiano R, Haskell W. Assessment of physical activity using wearable monitors: recommendations for monitor calibration and use in the field. Medicine and Science in Sports and Exercise. 2012;44(1 Suppl 1):S Church TS, Earnest CP, Skinner JS, Blair SN. Effects of different doses of physical activity on cardiorespiratory fitness among sedentary, overweight or obese postmenopausal women with elevated blood pressure: a randomized controlled trial. Journl of the American Medical Association. 2007;297(19):

103 Chapter Duscha BD, Slentz CA, Johnson JL, Houmard JA, Bensimhon DR, Knetzger KJ, Kraus WE. Effects of exercise training amount and intensity on peak oxygen consumption in middle-age men and women at risk for cardiovascular disease. CHEST Journal. 2005;128(4): LaMonte MJ, Blair SN, Church TS. Physical activity and diabetes prevention. Journal of Applied Physiology. 2005;99(3): Bouchard C, An P, Rice T, Skinner JS, Wilmore JH, Gagnon J, Pérusse L, Leon AS, Rao DC. Familial aggregation of VO2 max response to exercise training: results from the HERITAGE Family Study. Journal of Applied Physiology. 1999;87(3): Robsahm TE, Falk RS, Heir T, Sandvik L, Vos L, Erikssen JE, Tretli S. Measured cardiorespiratory fitness and selfreported physical activity: associations with cancer risk and death in a longterm prospective cohort study. Cancer Medicine. 2016;5(8): Ross R, Blair SN, Arena R, Church TS, Després J-P, Franklin BA, Haskell WL, Kaminsky LA, Levine BD, Lavie CJ. Importance of Assessing Cardiorespiratory Fitness in Clinical Practice: A Case for Fitness as a Clinical Vital Sign: A Scientific Statement From the American Heart Association. Circulation. 2016:CIR Blair SN. Physical inactivity: the biggest public health problem of the 21st century. British Journal of Sports Medicine. 2009;43(1): Kodama S, Saito K, Tanaka S, Maki M, Yachi Y, Asumi M, Sugawara A, Totsuka K, Shimano H, Ohashi Y. Cardiorespiratory fitness as a quantitative predictor of allcause mortality and cardiovascular events in healthy men and women: a metaanalysis. Journal of the American Medical Association. 2009;301(19): Swift DL, Lavie CJ, Johannsen NM, Arena R, Earnest CP, O Keefe JH, Milani RV, Blair SN, Church TS. Physical activity, cardiorespiratory fitness, and exercise training in primary and secondary coronary prevention. Circulation Journal. 2013;77(2): Kokkinos P, Myers J. Exercise and physical activity. Circulation. 2010;122(16): Bruce RA, Kusumi F, Hosmer D. Maximal oxygen intake and nomographic assessment of functional aerobic impairment in cardiovascular disease. American Heart Journal. 1973;85(4): Patterson JA, Naughton J, Pietras RJ, Gunnar RM. Treadmill exercise in assessment of the functional capacity of patients with cardiac disease. The American Journal of Cardiology. 1972;30(7): de Silva CGS, Franklin BA, de Araújo CGS. Influence of central obesity in estimating maximal oxygen uptake. Clinics. 2016;71(11): Beltz NM, Gibson AL, Janot JM, Kravitz L, Mermier CM, Dalleck LC. Graded exercise testing protocols for the determination of VO2max: Historical perspectives, progress, and future considerations. Journal of Sports Medicine. 2016; Ekkekakis P, Parfitt G, Petruzzello SJ. The pleasure and displeasure people feel when they exercise at different intensities. Sports Medicine. 2011;41(8): Lambrick DM, Faulkner JA, Rowlands AV, Eston RG. Prediction of maximal oxygen uptake from submaximal ratings of perceived exertion and heart rate during a continuous exercise test: the efficacy of RPE 13. European Journal of Applied Physiology. 2009;107(1):

104 Chapter Klusiewicz A, Faff J, Starczewska-Czapowska J. Prediction of maximal oxygen uptake from submaximal and maximal exercise on a ski ergometer. Biology of Sport. 2011;28(1): Bengtsson E. The working capacity in normal children, evaluated by submaximal exercise on the bicycle ergometer and compared with adults. Acta Medica Scandinavica. 1956;154(2): Mocellin R, Rutenfranz J, Singer R. Normal values of physical performance capacity (PWC170) in children and adolescents. Zeitschrift fur Kinderheilkunde. 1971;110(2): Strong WB, Spencer D, Miller MD, Salehbhai M. The physical working capacity of healthy black children. American Journal of Diseases of Children. 1978;132(3): Pfeiffer KA, Dowda M, Dishman RK, Sirard JR, Pate RR. Cardiorespiratory fitness in girls-change from middle to high school. Medicine and Science in Sports and Exercise. 2007;39(12): Lohman TG, Ring K, Pfeiffer K, Camhi S, Arredondo E, Pratt C, Pate R, Webber LS. Relationships among fitness, body composition, and physical activity. Medicine and Science in Sports and Exercise. 2008;40(6): Klusiewicz A, Borkowski L, Sitkowski D, Burkhard-Jagodzińska K, Szczepańska B, Ładyga M. Indirect M=methods of assessing maximal oxygen uptake in rowers: Practical implications for evaluating physical fitness in a training cycle. Journal of Human Kinetics. 2016;50(1): Rowland TW, Rambusch JM, Staab JS, Unnithan VB, Siconolfi SF. Accuracy of physical working capacity (PWC170) in estimating aerobic fitness in children. The Journal of Sports Medicine and Physical Fitness. 1993;33(2): Heyman E, Briard D, Dekerdanet M, Gratas-Delamarche A, Delamarche P. Accuracy of physical working capacity 170 to estimate aerobic fitness in prepubertal diabetic boys and in 2 insulin dose conditions. Journal of Sports Medicine and Physical Fitness. 2006;46(2): Leger LA, Lambert J. A maximal multistage 20-m shuttle run test to predict VO2 max. European Journal of Applied Physiology and Occupational Physiology. 1982;49(1): Leger LA, Mercier D, Gadoury C, Lambert J. The multistage 20 metre shuttle run test for aerobic fitness. Journal of Sports Sciences. 1988;6(2): Mayorga-Vega D, Aguilar-Soto P, Viciana J. Criterion-related validity of the 20-m shuttle run test for estimating cardiorespiratory fitness: A meta-analysis. Journal of Sports Science and Medicine. 2015;14(3): Paradisis GP, Zacharogiannis E, Mandila D, Smirtiotou A, Argeitaki P, Cooke CB. Multi-stage 20-m shuttle run fitness test, maximal oxygen uptake and velocity at maximal oxygen uptake. Journal of Human Kinetics. 2014;41(1): Weisman IM, Zeballos RJ. Clinical exercise testing. Vol 32: Karger Medical and Scientific Publishers; Campbell PT, Katzmarzyk PT, Malina RM, Rao D, Perusse L, Bouchard C. Prediction of physical activity and physical work capacity (PWC 150) in young adulthood from childhood and adolescence with consideration of parental measures. American Journal of Human Biology. 2001;13(2): Laukkanen JA, Kurl S, Salonen R, Rauramaa R, Salonen JT. The predictive value of cardiorespiratory fitness for cardiovascular events in men with various risk profiles: a 72

105 Chapter 1 prospective population-based cohort study. European Heart Journal. 2004;25(16): Talbot LA, Morrell CH, Metter EJ, Fleg JL. Comparison of cardiorespiratory fitness versus leisure time physical activity as predictors of coronary events in men aged 65 years and > 65 years. The American Journal of Cardiology. 2002;89(10): Juraschek SP, Blaha MJ, Whelton SP, Blumenthal R, Jones SR, Keteyian SJ, Schairer J, Brawner CA, AlMallah MH. Physical fitness and hypertension in a population at risk for cardiovascular disease: the Henry Ford ExercIse Testing (FIT) Project. Journal of the American Heart Association. 2014;3(6):e Sawada SS, Lee IM, Muto T, Matuszaki K, Blair SN. Cardiorespiratory fitness and the incidence of type 2 diabetes. Diabetes Care. 2003;26(10): Sui X, LaMonte MJ, Laditka JN, Hardin JW, Chase N, Hooker SP, Blair SN. Cardiorespiratory fitness and adiposity as mortality predictors in older adults. Journal of the American Heart Association. 2007;298(21): Hassinen M, Lakka TA, Savonen K, Litmanen H, Kiviaho L, Laaksonen DE, Komulainen P, Rauramaa R. Cardiorespiratory fitness as a feature of metabolic syndrome in older men and women. Diabetes Care. 2008;31(6): Lavie CJ, Cahalin LP, Chase P, Myers J, Bensimhon D, Peberdy MA, Ashley E, West E, Forman DE, Guazzi M. Impact of cardiorespiratory fitness on the obesity paradox in patients with heart failure. Paper presented at: Mayo Clinic Proceedings LaMonte MJ, Blair SN. Physical activity, cardiorespiratory fitness, and adiposity: contributions to disease risk. Current Opinion in Clinical Nutrition and Metabolic Care. 2006;9(5): Morrison JA, Friedman LA, Wang P, Glueck CJ. Metabolic syndrome in childhood predicts adult metabolic syndrome and type 2 diabetes mellitus 25 to 30 years later. The Journal of Pediatrics. 2008;152(2): Ortega FB, Ruiz JR, Castillo MJ, Sjöström M. Physical fitness in childhood and adolescence: a powerful marker of health. International Journal of Obesity. 2008;32(1): Morrison JA, Glueck CJ, Woo JG, Wang P. Risk factors for cardiovascular disease and type 2 diabetes retained from childhood to adulthood predict adult outcomes: the Princeton LRC Follow-up Study. International Journal of Pediatric Endocrinology. 2012;2012(1): Magnussen CG, Koskinen J, Chen W, Thomson R, Schmidt MD, Srinivasan SR, Kivimäki M, Mattsson N, Kähönen M, Laitinen T. Pediatric metabolic syndrome predicts adulthood metabolic syndrome, subclinical atherosclerosis, and type 2 diabetes mellitus but is no better than body mass index alone. Circulation. 2010;122(16): Magnussen CG, Cheriyan S, Sabin MA, Juonala M, Koskinen J, Thomson R, Skilton MR, Kähönen M, Laitinen T, Taittonen L. Continuous and dichotomous metabolic syndrome definitions in youth predict adult type 2 diabetes and carotid artery intima media thickness: the Cardiovascular Risk in Young Finns Study. The Journal of Pediatrics. 2016;171: e Magnussen CG, Koskinen J, Juonala M, Chen W, Srinivasan SR, Sabin MA, Thomson R, Schmidt MD, Nguyen QM, Xu J-H. A diagnosis of the metabolic syndrome in youth that resolves by adult life is associated with a normalization of high carotid intima-media thickness and type 2 diabetes mellitus risk: the Bogalusa heart and cardiovascular risk in young Finns studies. Journal of the American College of Cardiology. 2012;60(17):

106 Chapter Anderssen SA, Cooper AR, Riddoch C, Sardinha LB, Harro M, Brage S, Andersen LB. Low cardiorespiratory fitness is a strong predictor for clustering of cardiovascular disease risk factors in children independent of country, age and sex. European Journal of Cardiovascular Prevention and Rehabilitation. 2007;14(4): Kwon S, Burns TL, Janz K. Associations of cardiorespiratory fitness and fatness with cardiovascular risk factors among adolescents: the NHANES Journal of Physical Activity and Health. 2010;7(6): Dencker M, Thorsson O, Karlsson MK, Lindén C, Wollmer P, Andersen LB. Aerobic fitness related to cardiovascular risk factors in young children. European Journal of Pediatrics. 2012;171(4): Dwyer T, Magnussen CG, Schmidt MD, Ukoumunne OC, Ponsonby A-L, Raitakari OT, Zimmet PZ, Blair SN, Thomson R, Cleland VJ. Decline in physical fitness from childhood to adulthood associated with increased obesity and insulin resistance in adults. Diabetes Care. 2009;32(4): Bailey DP, Savory LA, Denton SJ, Kerr CJ. The association between cardiorespiratory fitness and cardiometabolic risk in children is mediated by abdominal adiposity: The HAPPY Study. Journal of Physical Activity and Health. 2015;12(8): Jago R, Drews KL, McMurray RG, Baranowski T, Galassetti P, Foster GD, Moe E, Buse JB. BMI change, fitness change and cardiometabolic risk factors among 8th grade youth. Pediatric Exercise Science. 2013;25(1): McMurray RG, Bangdiwala SI, Harrell JS, Amorim LD. Adolescents with metabolic syndrome have a history of low aerobic fitness and physical activity levels. Dynamic Medicine. 2008;7(1): Ferreira I, Twisk JWR, van Mechelen W, Kemper HCG, Stehouwer CDA. Development of fatness, fitness, and lifestyle from adolescence to the age of 36 years: determinants of the metabolic syndrome in young adults: the Amsterdam Growth and Health Longitudinal Study. Archives of Internal Medicine. 2005;165(1): Blair SN, Kohl HW, Barlow CE, Gibbons LW. Physical fitness and all-cause mortality in hypertensive men. Annals of Medicine. 1991;23(3): Blair SN, Kohl HW, Paffenbarger RS, Clark DG, Cooper KH, Gibbons LW. Physical fitness and all-cause mortality: a prospective study of healthy men and women. Journal of the American Medical Association. 1989;262(17): Myers J, Prakash M, Froelicher V, Do D, Partington S, Atwood JE. Exercise capacity and mortality among men referred for exercise testing. New England Journal of Medicine. 2002;346(11): Kokkinos P, Myers J, Kokkinos JP, Pittaras A, Narayan P, Manolis A, Karasik P, Greenberg M, Papademetriou V, Singh S. Exercise capacity and mortality in black and white men. Circulation. 2008;117(5): Gulati M, Black HR, Shaw LJ, Arnsdorf MF, Merz CNB, Lauer MS, Marwick TH, Pandey DK, Wicklund RH, Thisted RA. The prognostic value of a nomogram for exercise capacity in women. New England Journal of Medicine. 2005;353(5): Mora S, Redberg RF, Cui Y, Whiteman MK, Flaws JA, Sharrett AR, Blumenthal RS. Ability of exercise testing to predict cardiovascular and all-cause death in asymptomatic women: a 20-year follow-up of the lipid research clinics prevalence study. Journal of the American Medical Association. 2003;290(12): Hainer V, Toplak H, Stich V. Fat or Fit: What Is More Important? Diabetes Care. 2009;32(Suppl 2):S392-S

107 Chapter Lee CD, Jackson AS, Blair SN. US weight guidelines: is it also important to consider cardiorespiratory fitness? International Journal of Obesity and Related Metabolic Disorders: Journal of the International Association for the Study of Obesity. 1998;22:S Stevens J, Cai J, Evenson KR, Thomas R. Fitness and fatness as predictors of mortality from all causes and from cardiovascular disease in men and women in the lipid research clinics study. American Journal of Epidemiology. 2002;156(9): Fogelholm M. Physical activity, fitness and fatness: relations to mortality, morbidity and disease risk factors. A systematic review. Obesity Reviews. 2010;11(3): Barry VW, Baruth M, Beets MW, Durstine JL, Liu J, Blair SN. Fitness vs. fatness on all-cause mortality: A Meta-Analysis. Progress in Cardiovascular Diseases. 2014;56(4): Lee S, Kuk JL, Katzmarzyk PT, Blair SN, Church TS, Ross R. Cardiorespiratory fitness attenuates metabolic risk independent of abdominal subcutaneous and visceral fat in men. Diabetes Care. 2005;28(4): Church TS, Cheng YJ, Earnest CP, Barlow CE, Gibbons LW, Priest EL, Blair SN. Exercise capacity and body composition as predictors of mortality among men with diabetes. Diabetes Care. 2004;27(1): Church TS, Finley CE, Earnest CP, Kampert JB, Gibbons LW, Blair SN. Relative associations of fitness and fatness to fibrinogen, white blood cell count, uric acid and metabolic syndrome. International Journal of Obesity. 2002;26(6): Katzmarzyk PT, Church TS, Blair SN. Cardiorespiratory fitness attenuates the effects of the metabolic syndrome on all-cause and cardiovascular disease mortality in men. Archives of Internal Medicine. 2004;164(10): Boulé NG, Bouchard C, Tremblay A. Physical fitness and the metabolic syndrome in adults from the Quebec Family Study. Canadian Journal of Applied Physiology. 2005;30(2): Lee CD, Blair SN, Jackson AS. Cardiorespiratory fitness, body composition, and allcause and cardiovascular disease mortality in men. The American Journal of Clinical Nutrition. 1999;69(3): Christou DD, Gentile CL, DeSouza CA, Seals DR, Gates PE. Fatness Is a better predictor of cardiovascular disease risk factor profile than aerobic fitness in healthy men. Circulation. 2005;111(15): McAuley PA, Blair SN. Obesity paradoxes. Journal of Sports Sciences. 2011;29(8): Farrell SW, Fitzgerald SJ, McAuley PA, Barlow CE. Cardiorespiratory fitness, adiposity, and all-cause mortality in women. Medicine and Science in Sports and Exercise. 2010;42(11): Katzmarzyk PT, Janssen I, Ardern CI. Physical inactivity, excess adiposity and premature mortality. Obesity Reviews. 2003;4(4): Hu G, Tuomilehto J, Silventoinen K, Barengo N, Jousilahti P. Joint effects of physical activity, body mass index, waist circumference and waist-to-hip ratio with the risk of cardiovascular disease among middle-aged Finnish men and women. European Heart Journal. 2004;25(24): Ekelund U, Franks PW, Sharp S, Brage S, Wareham NJ. Increase in physical activity energy expenditure is associated with reduced metabolic risk independent of change in fatness and fitness. Diabetes Care. 2007;30(8):

108 Chapter Borodulin K. Physical activity, fitness, abdominal obesity, and cardiovascular risk factors in Finnish men and women: The National FINRISK 2002 Study. Kansanterveyslaitoksen julkaisuja A Lee D-c, Sui X, Church TS, Lavie CJ, Jackson AS, Blair SN. Changes in fitness and fatness on the development of cardiovascular disease risk factors: Hypertension, metabolic syndrome, and hypercholesterolemia. Journal of the American College of Cardiology. 2012;59(7): Eisenmann JC, Katzmarzyk PT, Perusse L, Tremblay A, Despres JP, Bouchard C. Aerobic fitness, body mass index, and CVD risk factors among adolescents: the Quebec family study. International Journal of Obesity. 2005;29(9): Eisenmann JC, Welk GJ, Wickel EE, Blair SN. Combined influence of cardiorespiratory fitness and body mass index on cardiovascular disease risk factors among 8 18 year old youth: The Aerobics Center Longitudinal Study. International Journal of Pediatric Obesity. 2007;2(2): Eisenmann JC, Welk GJ, Ihmels M, Dollman J. Fatness, fitness, and cardiovascular disease risk factors in children and adolescents. Medicine and Science in Sports and Exercise. 2007;39(8): Eisenmann JC, Katzmarzyk PT, Perusse L, Tremblay A, Despres JP, Bouchard C. Aerobic fitness, body mass index, and CVD risk factors among adolescents: the Quebec family study. International Journal of Obesity and Related Metabolic Disorders. 2005;29(9): Eisenmann JC, Wickel EE, Welk GJ, Blair SN. Relationship between adolescent fitness and fatness and cardiovascular disease risk factors in adulthood: the Aerobics Center Longitudinal Study (ACLS). American Heart Journal. 2005;149(1): DuBose K, Eisenmann J, Donnelly J. Aerobic fitness attenuates the metabolic syndrome score in normal-weight, at-risk-for-overweight, and overweight children. Metabolic Syndrome and Related Disorders. 2008;6(1): Martins CL, Silva F, Gaya AR, Aires L, Ribeiro JC, Mota J. Cardiorespiratory fitness, fatness, and cardiovascular disease risk factors in children and adolescents from Porto. European Journal of Sport Science. 2010;10(2): Boreham C, Twisk J, Neville C, Savage M, Murray L, Gallagher A. Associations between physical fitness and activity patterns during adolescence and cardiovascular risk factors in young adulthood: the Northern Ireland Young Hearts Project. International Journal of Sports Medicine. 2002;23(S1): Rizzo NS, Ruiz JR, Hurtig-Wennlöf A, Ortega FB, Sjöström M. Relationship of physical activity, fitness, and fatness with clustered metabolic risk in children and adolescents: the European youth heart study. The Journal of Pediatrics. 2007;150(4): Andersen LB, Sardinha L, Froberg K, Riddoch CJ, Page AS, Anderssen SA. 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. 2008;3(S1): Ruiz JR, Ortega FB, Warnberg J, Sjöström M. Associations of low-grade inflammation with physical activity, fitness and fatness in prepubertal children; the European Youth Heart Study. International Journal of Obesity. 2007;31(10): Jago R, Froberg K, Cooper AR, Eiberg S, Andersen LB. Three-year changes in fitness and adiposity are independently associated with cardiovascular risk factors among young Danish children. Journal of Physical Activity and Health. 2010;7(1):

109 Chapter Loomba-Albrecht LA, Styne DM. Effect of puberty on body composition. Current Opinion in Endocrinology, Diabetes and Obesity. 2009;16(1): Tomkinson GR, Olds TS. Secular changes in pediatric aerobic fitness test performance: the global picture. Pediatric Fitness. Vol 50: Karger Publishers; 2007: Tomkinson GR, Lang JJ, Tremblay MS. Temporal trends in the cardiorespiratory fitness of children and adolescents representing 19 high-income and upper middleincome countries between 1981 and British Journal of Sports Medicine. 2017:bjsports Artero EG, Lee D-c, Lavie CJ, España-Romero V, Sui X, Church TS, Blair SN. Effects of Muscular Strength on Cardiovascular Risk Factors and Prognosis. Journal of cardiopulmonary rehabilitation and prevention. 2012;32(6): Landry BW, Driscoll SW. Physical activity in children and adolescents. PM&R Journal. 2012;4(11): Ruiz JR, Castro-Piñero J, Artero EG, Ortega FB, Sjöström M, Suni J, Castillo MJ. Predictive validity of health-related fitness in youth: a systematic review. British Journal of Sports Medicine Colberg SR, Albright AL, Blissmer BJ, Braun B, Chasan-Taber L, Fernhall B, Regensteiner JG, Rubin RR, Sigal RJ. Exercise and type 2 diabetes: American College of Sports Medicine and the American Diabetes Association: joint position statement. Exercise and type 2 diabetes. Medicine and Science in Sports and Exercise. 2010;42(12): Jensen J, Rustad PI, Kolnes AJ, Lai Y-C. The role of skeletal muscle glycogen breakdown for regulation of insulin sensitivity by exercise. Frontiers in Physiology. 2011;2: Stump CS, Henriksen EJ, Wei Y, Sowers JR. The metabolic syndrome: Role of skeletal muscle metabolism. Annals of Medicine. 2006;38(6): Guilherme A, Virbasius JV, Puri V, Czech MP. Adipocyte dysfunctions linking obesity to insulin resistance and type 2 diabetes. Nature Reviews Molecular Cell Biology. 2008;9(5): Loenneke JP, Fahs CA, Heffernan KS, Rossow LM, Thiebaud RS, Bemben MG. Relationship between thigh muscle mass and augmented pressure from wave reflections in healthy adults. Europeanc Journal of Applied Physiology. 2013;113(2): Fahs CA, Heffernan KS, Ranadive S, Jae SY, Bo F. Muscular strength is inversely associated with aortic stiffness in young men. Medicine and Science in Sports and Exercise. 2010;42(9): McCall GE, Byrnes WC, Dickinson A, Pattany PM, Fleck SJ. Muscle fiber hypertrophy, hyperplasia, and capillary density in college men after resistance training. Journal of Applied Physiology. 1996;81(5): Artero EG, Lee D-c, Ruiz JR, Sui X, Ortega FB, Church TS, Lavie CJ, Castillo MJ, Blair SN. A prospective study of muscular strength and all-cause mortality in men with hypertension. Journal of the American College of Cardiology. 2011;57(18): Hanten WP, Chen W-Y, Austin AA, Brooks RE, Carter HC, Law CA, Morgan MK, Sanders DJ, Swan CA, Vanderslice AL. Maximum grip strength in normal subjects from 20 to 64 years of age. Journal of Hand Therapy. 1999;12(3): Niempoog S, Siripakarn Y, Suntharapa T. An estimation of grip strength during puberty. Journal of the Medical Association of Thailand. 2007;90(4):

110 Chapter Koley S, Singh AP. Effect of hand dominance in grip strength in collegiate population of Amritsar, Punjab, India. Anthropologist. 2010;12(1): Chatterjee S, Chowdhuri BJ. Comparison of grip strength and isometric endurance between the right and left hands of men and their relationship with age and other physical parameters. Journal of Human Ergology. 1991;20(1): Aghazadeh F, Lee K, Waikar A. Impact of anthropometric and personal variables on grip strength. Journal of Human Ergology. 1993;22(2): Schmidt RT, Toews JV. Grip strength as measured by the Jamar dynamometer. Archives of Physical Medicine and Rehabilitation. 1970;51(6): HägerRoss C, Rösblad B. Norms for grip strength in children aged 4 16 years. Acta Paediatrica. 2002;91(6): Clerke AM, Clerke JP, Adams RD. Effects of hand shape on maximal isometric grip strength and its reliability in teenagers. Journal of Hand Therapy. 2005;18(1): Jürimäe T, Hurbo T, Jürimäe J. Relationship of handgrip strength with anthropometric and body composition variables in prepubertal children. Journal of Comparative Human Biology. 2009;60(3): Jaric S. Muscle strength testing. Sports Medicine. 2002;32(10): Hazir T, Kosar NS. Assessment of gender differences in maximal anaerobic power by ratio scaling and allometric scaling. Isokinetics and Exercise Science. 2007;15(4): Maranhao GAN, Oliveira AJ, Pedreiro RC, Pereira-Junior PP, Machado S, Marques SN, Farinatti PT. Normalizing handgrip strength in older adults: An allometric approach. Archives of Gerontology and Geriatrics. 2017;70: Peterson MD, Krishnan C. Growth charts for muscular strength capacity with quantile regression. American Journal of Preventive Medicine. 2015;49(6): Sartorio A, Lafortuna CL, Pogliaghi S, Trecate L. The impact of gender, body dimension and body composition on hand-grip strength in healthy children. Journal of Endocrinological Investigation. 2002;25(5): Ruiz JR, Sui X, Lobelo F, Morrow JR, Jackson AW, Sjöström M, Blair SN. Association between muscular strength and mortality in men: prospective cohort study. British Medical Journal. 2008;337: a Åberg ND, Kuhn HG, Nyberg J, Waern M, Friberg P, Svensson J, Torén K, Rosengren A, Åberg MAI, Nilsson M. Influence of cardiovascular fitness and muscle strength in early adulthood on long-term risk of stroke in swedish men. Stroke. 2015;STROKEAHA Silventoinen K, Magnusson PKE, Tynelius P, Batty GD, Rasmussen F. Association of body size and muscle strength with incidence of coronary heart disease and cerebrovascular diseases: a population-based cohort study of one million Swedish men. International Journal of Epidemiology. 2009;38(1): Maslow AL, Sui X, Colabianchi N, Hussey J, Blair SN. Muscular strength and incident hypertension in normotensive and prehypertensive men. Medicine and Science in Sports and Exercise. 2010;42(2): Wijndaele K, Duvigneaud N, Matton L, Duquet W, Thomis M, Beunen G, Lefevre J, Philippaerts RM. Muscular strength, aerobic fitness, and metabolic syndrome risk in Flemish adults. Medicine and Science in Sports and Exercise. 2007;39(2): Jurca R, Lamonte MJ, Barlow CE, Kampert JB, Church TS, Blair SN. Association of muscular strength with incidence of metabolic syndrome in men. Medicine and Science in Sports and Exercise. 2005;37(11):

111 Chapter Vaara JP, Fogelholm M, Vasankari T, Santtila M, Häkkinen K, Kyröläinen H. Associations of maximal strength and muscular endurance with cardiovascular risk factors. International Journal of Sports Medicine. 2014;35(4): Jaric S. Role of body size in the relation between muscle strength and movement performance. Exercise and Sport Sciences Reviews. 2003;31(1): Markovic G, Jaric S. Movement performance and body size: the relationship for different groups of tests. European Journal of Applied Physiology. 2004;92(1-2): Rauch F, Neu CM, Wassmer G, Beck B, Rieger-Wettengl G, Rietschel E, Manz F, Schoenau E. Muscle analysis by measurement of maximal isometric grip force: new reference data and clinical applications in pediatrics. Pediatric Research. 2002;51(4): Wind A, Takken T, Helders P, Engelbert R. Is grip strength a predictor for total muscle strength in healthy children, adolescents, and young adults? European Journal of Pediatrics. 2010/03/ ;169(3): Cohen DD, Voss C, Taylor MJD, Delextrat A, Ogunleye AA, Sandercock GRH. Ten year secular changes in muscular fitness in English children. Acta Paediatrica. 2011;100(10):e Matton L, Duvigneaud N, Wijndaele K, Philippaerts R, Duquet W, Beunen G, Claessens AL, Thomis M, Lefevre J. Secular trends in anthropometric characteristics, physical fitness, physical activity, and biological maturation in Flemish adolescents between 1969 and American Journal of Human Biology. 2007;19(3): Aires L, Andersen LB, Mendonça D, Martins C, Silva G, Mota J. A 3year longitudinal analysis of changes in fitness, physical activity, fatness and screen time. Acta Paediatrica. 2010;99(1): Westerstahl M, BarnekowBergkvist M, Hedberg G, Jansson E. Secular trends in body dimensions and physical fitness among adolescents in Sweden from 1974 to Scandinavian Journal of Medicine and Science in Sports. 2003;13(2): Padilla-Moledo C, Ruiz JR, Ortega FB, Mora J, Castro-Piñero J. Associations of muscular fitness with psychological positive health, health complaints, and health risk behaviors in Spanish children and adolescents. The Journal of Strength and Conditioning Research. 2012;26(1): Myer GD, Faigenbaum AD, Chu DA, Falkel J, Ford KR, Best TM, Hewett TE. Integrative training for children and adolescents: techniques and practices for reducing sports-related injuries and enhancing athletic performance. The Physician and Sports Medicine. 2011;39(1): Artero EG, Ruiz JR, Ortega FB, España-Romero V, Vicente-Rodríguez G, Molnar D, Gottrand F, González-Gross M, Breidenassel C, Moreno LA, Gutiérrez A, on behalf of the HSG. Muscular and cardiorespiratory fitness are independently associated with metabolic risk in adolescents: the HELENA study. Pediatric Diabetes. 2011;12(8): Cohen DD, Gómez-Arbeláez D, Camacho PA, Pinzon S, Hormiga C, Trejos-Suarez J, Duperly J, Lopez-Jaramillo P. Low muscle strength is associated with metabolic risk factors in Colombian children: The ACFIES Study. PLoS ONE. 2014;9(4):e Peterson MD, Saltarelli WA, Visich PS, Gordon PM. Strength capacity and cardiometabolic risk clustering in adolescents. Pediatrics. 2014;133(4):e896-e Cohen DD, López-Jaramillo P, Fernández-Santos JR, Castro-Piñero J, Sandercock GRH. Muscle strength is associated with lower diastolic blood pressure in schoolchildren. Preventive Medicine. 2017;95:

112 Chapter Grøntved A, Ried-Larsen M, Møller NC, Kristensen PL, Froberg K, Brage S, Andersen LB. Muscle strength in youth and cardiovascular risk in young adulthood (the European Youth Heart Study). British Journal of Sports Medicine. 2015;49(2): Benson AC, Torode ME, Fiatarone Singh MA. Muscular strength and cardiorespiratory fitness is associated with higher insulin sensitivity in children and adolescents. International Journal of Pediatric Obesity. 2006;1(4): García-Artero E, Ortega FB, Ruiz JR, Mesa JL, Delgado M, González-Gross M, García-Fuentes M, Vicente-Rodríguez G, Gutiérrez Á, Castillo MJ. Lipid and metabolic profiles in adolescents are affected more by physical fitness than physical activity (AVENA Study). Revista Española de Cardiología (English Version). 2007;60(06): Magnussen CG, Schmidt MD, Dwyer T, Venn A. Muscular fitness and clustered cardiovascular disease risk in Australian youth. European Journal of Applied Physiology. 2012;112(8): Artero EG, Ruiz JR, Ortega FB, EspañaRomero V, VicenteRodríguez G, Molnar D, Gottrand F, GonzálezGross M, Breidenassel C, Moreno LA. Muscular and cardiorespiratory fitness are independently associated with metabolic risk in adolescents: the HELENA study. Pediatric Diabetes. 2011;12(8): Steene-Johannessen J, Anderssen SA, Kolle E, Andersen LB. Low Muscle Fitness Is Associated with Metabolic Risk in Youth. Medicine and Science in Sports and Exercise. 2009;41(7): Ruiz JR, Ortega FB, Wärnberg J, Moreno LA, Carrero JJ, Gonzalez-Gross M, Marcos A, Gutierrez A, Sjöström M. Inflammatory proteins and muscle strength in adolescents: the Avena study. Archives of Pediatrics and Adolescent Medicine. 2008;162(5): Lavie CJ, Church TS, Milani RV, Earnest CP. Impact of physical activity, cardiorespiratory fitness, and exercise training on markers of inflammation. Journal of Cardiopulmonary Rehabilitation and Prevention. 2011;31(3): Buchan DS, Boddy LM, Young JD, Cooper S-M, Noakes TD, Mahoney C, Shields JP, Baker JS. Relationships between cardiorespiratory and muscular fitness with cardiometabolic risk in adolescents. Research in Sports Medicine. 2015;23(3): Ortega FB, Ruiz JR, Castillo MJ, Sjostrom M. Physical fitness in childhood and adolescence: a powerful marker of health. International Journal of Obesity. 2007;32(1): Fraser BJ, Schmidt MD, Huynh QL, Dwyer T, Venn AJ, Magnussen CG. Tracking of muscular strength and power from youth to young adulthood: Longitudinal findings from the Childhood Determinants of Adult Health Study. Journal of Science and Medicine in Sport. 2017;20(10): Ortega FB, Silventoinen K, Tynelius P, Rasmussen F. Muscular strength in male adolescents and premature death: cohort study of one million participants. British Medical Journal. 2012;345:e Fraser BJ, Huynh QL, Schmidt MD, Dwyer T, Venn AJ, Magnussen CG. Childhood muscular fitness phenotypes and adult metabolic syndrome. Medicine and Science in Sports and Exercise. 2016;48(9): Ruiz JR, Sui X, Lobelo F, Lee D-c, Morrow JR, Jackson AW, Hébert JR, Matthews CE, Sjöström M, Blair SN. Muscular strength and adiposity as predictors of adulthood 80

113 Chapter 1 cancer mortality in men. Cancer Epidemiology and Prevention Biomarkers. 2009;18(5): Mason C, Brien SE, Craig CL, Gauvin L, Katzmarzyk PT. Musculoskeletal fitness and weight gain in Canada. Medicine and Science in Sports and Exercise. 2007;39(1): Schranz N, Tomkinson G, Olds T. What is the effect of resistance training on the strength, body composition and psychosocial status of overweight and obese children and adolescents? A systematic review and meta-analysis. Sports Medicine. 2013;43(9): McGuigan MR, Tatasciore M, Newton RU, Pettigrew S. Eight weeks of resistance training can significantly alter body composition in children who are overweight or obese. The Journal of Strength and Conditioning Research. 2009;23(1): Bundy DAP, de Silva N, Horton S, Patton GC, Schultz L, Jamison DT. Investment in child and adolescent health and development: key messages from Disease Control Priorities. The Lancet The Lancet. The next phase for adolescent health: from talk to action. The Lancet. 2017;390(10106):

114 Chapter 2 CHAPTER 2 Relationships between Fatness and Fitness with Cardio-metabolic Risk Factors in Adolescents from the Raine Study FOREWORD The 'fat but fit' hypothesis has been investigated in adults and its findings are controversial. As the development of cardio-metabolic risk factors can emerge during early life and be influenced by modifiable lifestyle factors such as obesity and physical CRF, as well as adopted risk taking behaviours such as smoking and alcohol intake; it is important to ascertain whether the 'fat but fit' hypothesis is evident within a paediatric population. To date, little is known about the concurrent influences of CRF and fatness and whether CRF can be considered as an important factor in alleviating individual cardio-metabolic risk factors in this young age group. This chapter examines the 'fat but fit' hypothesis in Raine Study post-pubertal adolescents at 17 years of age. It demonstrates the relative relationships of fatness and CRF on individual cardio-metabolic risk factors i.e. BP, HOMA-IR, fasting lipids and hs-crp and the degree to which CRF counteracts the adverse associations of fatness on these risk factors. This chapter was published online ahead of print in the Journal of Clinical Endocrinology and Metabolism in October 2017;102(12):

115 Chapter ABSTRACT Background: Independent effects of CRF and obesity on cardiovascular risk factors are well established in adults. However, the relative importance of CRF and fatness with cardiometabolic risk factors are uncertain during the crucial developmental stage of late adolescence. This study aimed to compare the concurrent influences of CRF and fatness in relation to cardio-metabolic risk factors in adolescents from the Western Australian Pregnancy (Raine) Cohort Study. Methods: Cross-sectional analysis was performed on 1128 participants with complete blood pressure data and 963 participants with complete blood biochemistry at 17 years of age. Fatness (waist circumference) and CRF (PWC 170 ) were assessed as continuous measures to avoid the use of arbitrary cut points. Cardio-metabolic risk factor outcomes included BP, fasting lipids, HOMA-IR and hs-crp. Analyses used linear regression models adjusted for sex and potential lifestyle confounders. Results: Fatness was positively associated with systolic BP (coefficient: 0.19; p<0.001; beta coefficient: 0.20), triglycerides (log coefficient: 0.009; p<0.001; beta coefficient: 0.24), LDL- C (coefficient: 0.005; p=0.007; beta coefficient: 0.10) and hs-crp (log coefficient: 0.05; p<0.001; beta coefficient: 0.35). There were no significant effects of CRF on any of these measures. A positive association between HOMA-IR and fatness (log coefficient: 0.02; p<0.001; beta coefficient: 0.33) was attenuated by CRF (log coefficient: ; p<0.001; beta coefficient: -0.18). Fatness was inversely associated with HDL-C in both sexes (coefficient: ; p<0.001; beta coefficient: -0.23), while CRF was positively associated with HDL-C only in females (coefficient: 0.08; p=0.03; beta coefficient: 0.15). 83

116 Chapter 2 Conclusion: The adverse effects of central adiposity seen across a broad range of cardiometabolic risk factors were only partially ameliorated by CRF in this adolescent population. Strategies to avoid excessive weight gain will need to focus on both eating patterns and physical activity in earlier childhood to minimise the risk of cardiovascular and metabolic disease in later life. 84

117 Chapter INTRODUCTION The prevalence of all-cause mortality, CVD and type 2 diabetes among overweight adults is attenuated in individuals with a moderate-to-high level of CRF suggesting that CRF offers a degree of protection in overweight individuals (1, 2). The fat but fit hypothesis as expressed by Blair et al. (3) stated that a moderate-to-high level of CRF may attenuate or eliminate the risk of cardiovascular and metabolic disease, independent of BMI. Obesity is a major health problem that affects children and adolescents. Adults who were overweight in childhood have a higher BP, fasting lipids and insulin levels, and increased risk of CVD, compared to adults who were not overweight as children (4). Childhood and adolescence are crucial periods during which dynamic changes in various metabolic systems, including hormonal regulation, body fat content and body fat distribution occur during puberty and growth (5). Similarly, lifestyle and healthy or unhealthy behaviours are established during adolescence, which may persist into adult life and influence later health status (6). Understanding the influence of CRF on the development of obesity-associated cardio-metabolic risk factors is important for developing strategies for the prevention of CVD in adulthood. Low levels of CRF and increased adiposity often occur in combination. However, the relative importance of CRF and fatness on cardio-metabolic risk factors is unclear, particularly during adolescence. Previous studies examining the effect of CRF and fatness on cardio-metabolic risk factors in children and adolescents have used different methodologies and outcomes and have generally categorised fatness and CRF using arbitrary cut points, which can lead to the misclassification of individuals (7-10). Moreover, most studies have used BMI as a measure of fatness, which may have inherent limitations as it does not consider lean body mass, nor does it specify the degree of central adiposity, which has been linked to increased 85

118 Chapter 2 risks of metabolic disease (11). In this regard, a measure of central obesity, such as waist circumference may be more relevant. The aim of the present study was to examine CRF and fatness, as continuous variables, in relation to cardio-metabolic risk factors in a large population of from the Western Australian Pregnancy (Raine) Cohort Study at age 17 years. 2.3 METHODS Participants The Western Australian Pregnancy Cohort (Raine) Study is a prospective population study that recruited 2900 pregnant women between 16 to 20 weeks gestation from King Edward Memorial Hospital and closely located practices, in Perth, Western Australia from 1989 to 1992 (12). The mothers gave birth to 2868 live infants. The offspring have been followed at approximately three-year intervals with a range of anthropometry measurements, BP, lifestyle and psychosocial factors and detailed biochemistry related to cardio-metabolic risk. Ethics approval for the 17-year assessments was obtained from the Human Research Ethics Committee at the University of Western Australia, Curtin University and Princess Margaret Hospital. Written informed consent was obtained from the participant's parent and the participant. This analysis uses data from the 17-year follow-up of the offspring conducted from The 17-year survey included participants with complete measures of blood pressure, waist circumference and CRF (n=1128) or complete measures of blood biochemistry, waist circumference and CRF (n=963) (Figure 1) Anthropometry Waist circumference was measured at the umbilicus level with a metal tape measure (to the nearest 0.1 cm). Obese individuals were identified as having a waist circumference >90 th 86

119 Chapter 2 percentile as defined by the National Health and Nutritional Examination Survey (13). Height was measured by a wall mounted Stadiometer (to the nearest 0.1 cm) and body weight was measured using a Wedderburn Chair Scale (to the nearest 100g) with participants dressed in light clothes. Individuals with a high BMI as defined by the Centres for Disease Control and Prevention Growth Charts were identified as having a BMI >90 th percentile (14) Cardiorespiratory fitness CRF was estimated from heart rate recordings during sub-maximal cycle ergometry using the Physical Work Capacity Protocol (PWC 170 ) (Monark 818e Ergomedic, Nordic Health Care, Sweden). PWC 170 is the maximal steady state power attained for a heart rate of 170 beats per minute on a cycle ergometer (15). Participants cycled at an initial workload of 25 W and aimed to work at a rate of rpm for 2 minutes each at 2 successively increasing but submaximal workloads of 25 W increments. The ergometer s workload was independent of cadence. PWC170 was assessed by linear regression with extrapolation of the line of best fit to a heart rate of 170 beats per minute. CRF was divided by body weight to normalise the effects of body size on workload output. To identify those with low, middle and high CRF levels, participants were grouped into tertiles of CRF groups Biochemistry Venous blood samples were taken after an overnight fast of 12 hours (fasting status was confirmed by trained personnel) and analysed in the PathWest Laboratory at Royal Perth Hospital. Serum samples were analysed for total cholesterol, HDL-C, triglycerides and glucose, determined enzymatically on an Architect c16000 Analyser (Abbott Laboratories, Lake Forest, Illinois). Intra- assay coefficients of variation were 0.87% for total cholesterol, 87

120 Chapter 2 2.0% for HDL-C, 1.92% for triglycerides and 1.04% for glucose. LDL-C was calculated using the Friedewald formula (16). An adverse lipid profile as defined by the National Cholesterol Education Program Expert Panel on Cholesterol Levels in Children was identified as having triglycerides > 1.13 mmol/l, HDL-C <1.04 mmol/l and LDL-C > 3.36 mmol/l (17). hs-crp was measured by an immunoturbidimetric method on Architect c16000 Analyser (intra-assay coefficient of variation 15.96%). Insulin was determined on an Architect i2000sr Analyser (intra-assay coefficient of variation 1.78%). HOMAIR was calculated using the formula: fasting insulin (µu/ml) x fasting glucose (mmol/l) / 22.5 (18) Blood pressure Systolic BP and diastolic BP were measured using an oscillometric sphygmomanometer (DINAMAP vital signs monitor 8100, DINAMAP XL vital signs monitor or DINAMAP ProCare 100; GE Healthcare) after resting in a supine position for 5 minutes and using the appropriate cuff size on the right arm. Six BP readings were obtained every 2 minutes and the average BP was calculated using the last five readings. BP was recorded on a separate day to the PWC 170 assessment. Prehypertensive and hypertensive individuals were grouped together and defined as non-sex-specific systolic BP 90th percentile Demographic and socio-behavioural features Socio-behavioural data were assessed via a computer-based questionnaire. Alcohol consumption included the type (beer, spirits or wine) and amount (can, stubby, nip, glass or standard drink) of alcoholic beverages consumed daily during the previous 7 days. Alcohol consumption was defined as the average number of standard drinks consumed per day, where 88

121 Chapter 2 1 standard drink equates to 10 g of ethanol. A drinker was a consumer of alcohol at any level during the previous week. Smoking status was assessed from the number of cigarettes smoked each day during the previous 7 days. OC use in girls was defined by a Yes or No answer to the question, In the last 6 months, have you taken any prescription medication(s) e.g. the Pill? and if yes, which medication(s), and are you still taking it? ). Dietary data were collected from a validated 212-item food frequency questionnaire developed by the Commonwealth Scientific and Industrial Research Organisation, Adelaide, Australia. Two dietary patterns ( Western and Healthy ) were identified using factor analysis (19). Details of the methodology, the reliability and the validity of this food frequency questionnaire have been previously published (20). Annual family income was categorised as Australian dollars (low family income), to (middle family income) and (high family income) at 17 years from 2006 to Statistical analyses Descriptive data were summarised by sex using means, geometric means, percentages and 95% CIs. Comparisons between study participants with data used in any of the analyses (i.e. with waist circumference and physical activity, and either blood pressure or biochemistry), with individuals that did not participate in the 17-year follow-up were assessed using linear regression. Waist circumference was assessed as the fatness measure and PWC 170 as the CRF measure. These two measures were assessed as continuous variables to avoid the use of arbitrary cut points. Risk factors were assessed for normality and log transformed if substantial departures from normality were found. For the outcome of hs-crp, values >10 mg/l were excluded as being likely due to incidental illness. In view of the potential effect of OC use on blood pressure and lipids a 3-level sex variable (females not using OC, females 89

122 Chapter 2 using OC and males) was created to assess the effect of OC use in the final multivariable models. If no significant difference was detected between the two female groups, a 2-level sex variable (females, males) was included. Potential confounding factors included alcohol use, smoking, dietary pattern and family income. Associations with fatness and CRF were assessed using linear regression or Tobit regression if the outcome was censored at the lower limit of an assay (21). Logistic regression was employed examining the outcome of prehypertension and hypertension. Potential non-linearity of associations was investigated initially by Lowess plots and then using fractional polynomials. With the exception of Tobit regression, all models included a variance adjustment for siblings within a family. Maximum likelihood estimation was used to retain individuals with missing confounder data in the analysis, resulting in unbiased estimates when missing data was missing at random. Tobit regression models were adjusted for confounding variables but only for those participants with complete data on all confounders. We also investigated the interactions between sex and the effects of fatness or CRF on each cardio-metabolic risk factor. Interactions were excluded from the model if p>0.05. Initial models were adjusted for sex and extended models further adjusted for potential lifestyle confounders, unless Tobit regression was employed. Standardised beta coefficients for fatness and CRF were used to provide comparable measures of the effect size. Beta coefficients were only provided for significant associations. Three-dimension surface plots were generated using SAS software (V9.4 SAS Institute Inc., Cary. NC, USA) for those models where both CRF and fatness were significant or where significant interactions with sex were detected. Stata MP Version 13 (Stata Corp, College Station, TX) was used for statistical analysis. All reported p values are 2-tailed, and significance was set at α=

123 Chapter RESULTS Descriptive characteristics At 17 years of age, compared with females, males were heavier, had a larger waist circumference, were fitter and had higher systolic BP and mean blood glucose (p<0.001) (Table 1). Mean BMI was similar between the sexes. Females had significantly higher mean cholesterol, HDL-C, LDL-C, insulin, hs-crp and diastolic BP than males. Approximately 14% of females used OC and 50% of participants consumed alcohol at any level within the previous week. A comparison of the 1,131 study participants with data used in any of the analyses (i.e. with waist circumference and physical activity, and either blood pressure or biochemistry), with those 1,737 individuals that did not participate in the 17-year follow-up, shows that those who did not participate were more likely to have a lower family income (46.3% vs 34.3%), a shorter gestational age (273.3 days vs days), a shorter birth length (48.7 cm vs 49 cm) and lower birth weight (3256.8g vs g), but similar maternal weight and height at the 18 th week of pregnancy, (Supplementary Table 1). The original cohort comprised of 88% Caucasians, which is representative of the Western Australian population. 91

124 Chapter 2 Figure 1. Flow diagram of Raine Study participants attending the 17 year follow up with complete fatness and CRF data 92

125 Chapter Independent associations between fatness and CRF with cardio-metabolic risk factors Blood Pressure In model 1 adjusting for sex and OC use, fatness was positively associated with systolic BP (coefficient: 0.19; p<0.001) (Table 2, Model 1, Figure 2). Fatness beta coefficient of 0.20 indicates for a change of fatness of 1 standard deviation (11.2 cm), systolic BP will change by 2.0 mmhg. After adjusting for additional potential confounders, systolic BP remained positively associated with fatness (coefficient: 0.18; p<0.001; beta coefficient: 0.2) (Table 2, Model 2). CRF was not associated with systolic BP in the unadjusted or the confounder adjusted model. Fatness was not significantly associated with diastolic BP whereas CRF was inversely associated with diastolic BP (coefficient: -1.57; p<0.001; beta coefficient: -0.16) (Table 2, Model 1). CRF beta coefficient of indicates for a change of CRF of 1 standard deviation (0.6 W/kg), diastolic BP will change by -1.0 mmhg. Further adjustment for lifestyle factors did not substantially change the coefficients nor did they vary by sex (Table 2, Model 2). Fatness was significantly associated with pre-hypertension/hypertension status in both sexes (odds ratio: 1.03, p=0.001) (Supplementary Table 2), whereas CRF was not associated with pre-hypertension/hypertension status in either sex Fasting lipids Fatness was inversely associated with HDL-C in both males and females (coefficient: p<0.001) (Table 3, Model 1, Figure 3). CRF was positively associated with HDL-C in females only (coefficient: 0.08; p=0.03). The standardised beta coefficients showed that in females, fatness had a greater effect on HDL-C (beta coefficient: -0.23) compared with CRF (beta coefficient: 0.15). In other words, the fatness beta coefficient of indicates for a change of fatness of 1 standard deviation (11.2 cm), HDL-C will change by mmol/l, 93

126 Chapter 2 whereas the CRF beta coefficient of 0.15 indicates for a change of CRF of 1 standard deviation (0.6 W/kg), HDL-C will change by 0.04 mmol/l. The differential association between fatness and CRF relative to HDL-C in males and females are illustrated in Figure 2. These findings did not change after the inclusion of potential confounders (Table 3, Model 2). Fatness was positively associated with triglycerides (log coefficient: 0.009, p<0.001; beta coefficient: 0.24) (Table 3, Model 1) and LDL-C in both sexes (coefficient: 0.005, p=0.007; beta coefficient: 0.10) (Table 4, Model 1). Fatness beta coefficients of 0.24 and 0.10 for triglycerides and LDL-C respectively, indicates for a change of fatness of 1 standard deviation (11.2 cm), triglycerides will change by 0.10 mmol/l and LDL-C by 0.06 mmol/l. These associations were not altered after the inclusion of potential confounders (Table 3, Model 2: Table 4, Model 2). CRF was not associated with either triglycerides or LDL-C in unadjusted or adjusted analyses. Neither fatness nor CRF was significantly associated with cholesterol in both unadjusted and adjusted models (Table 4), although fatness almost reached significance HOMA-IR and hs-crp Fatness was positively associated with HOMA-IR (log coefficient: 0.02; p<0.001) after adjusting for sex (Table 5, Model 1, Figure 4), whereas CRF was inversely associated with HOMA-IR (log coefficient: -0.18; p<0.001). The standardised beta coefficients showed that fatness had a greater effect on HOMA-IR (beta coefficient: 0.33) compared with CRF (beta coefficient: -0.16). The fatness beta coefficient of 0.33 indicates for a change of fatness of 1 standard deviation (11.2 cm), HOMA-IR will change by Whereas the CRF beta coefficient of indicates for a change of CRF of 1 standard deviation (0.6 W/kg), HOMA-IR will change by These effects did not vary by sex. The differential 94

127 Chapter 2 associations between log HOMA-IR and CRF and fatness are illustrated in Figure 4. After further adjustment for lifestyle factors, these effects were unchanged (Table 5, Model 2). Fatness was positively associated with hs-crp in both sexes (log coefficient: 0.05, p<0.001; beta coefficient: 0.35) (Table 5, Model 1). Fatness beta coefficient of 0.35 indicates for a change of fatness of 1 standard deviation (11.2 cm), hs-crp will change by 0.63 mg/l. This association was not altered after the inclusion of confounders (Table 5, Model 2). CRF was not associated with hs-crp in either model. 95

128 Chapter 2 Table 1. Descriptive characteristics of participants from the Raine Study at 17 years Measure Females N=544 Males n= 587 P value Anthropometry Waist circumference (cm) 77.7 (76.8, 78.7) 80.5 (79.6, 81.4) <0.001 Waist circumference 9.6 (7.3, 12.4) 10.7 (8.5, 13.5) 0.38 ( 90 th percentile) (%) Weight (kg) 63.4 (62.3, 64.5) 72.0 (70.9, 73.2) <0.001 Height (m) 1.7 (1.6, 1.7) 1.9 (1.8, 1.8) <0.001 BMI (kg/m 2 ) 22.9 (22.6, 23.4) 22.6 (22.3, 22.9) 0.12 BMI ( 90 th percentile) (%) 9.2 (6.9, 11.9) 8.4 (6.4, 10.9) 0.44 CRF (PWC 170 ) (W) (98.1, 102.2) (151.6, 158.6) <0.001 CRF/body weight (W/kg) 1.6 (1.5, 1.7) 2.2 (2.1, 2.2) <0.001 CRF/body weight (lowest 0.8 (0.6, 0.9) 1.3 (1.1, 1.4) <0.001 tertile) Biochemistry Cholesterol (mmol/l) 4.3 (4.2, 4.4) 3.9 (3.8, 4.0) <0.001 Triglycerides (mmol/l) 1.0 (0.9, 1.1) 1.1 (1.0,1.1) 0.23 Triglycerides ( 1.47 mmol) 12.5 (9.7, 15.9) 18.3 (15.2, 21.9) 0.02 (%) HDL-C (mmol/l) 1.4 (1.3, 1.5) 1.2 (1.1, 1.3) <0.001 HDL-C ( 1.04 mmol/l (%) 9.7 (7.5, 12.6) 22.8 (19.6, 26.4) <0.001 LDL-C (mmol/l) 2.4 (2.4, 2.5) 2.2 (2.2, 2.3) <0.001 LDL-C ( 3.36 mmol/l) (%) 8.5 (6.2, 11.4) 5.1 (3.5, 7.3) 0.05 Glucose (mmol/l) 4.6 (4.6, 4.7) 4.8 (4.8, 4.9) <0.001 Insulin (mu/l) 9.5 (8.8, 10.2) 8.5 (8.0, 9.1) 0.03 HOMA-IR 2.1 (1.9, 2.2) 1.9 (1.7, 2.0) 0.12 hs C-reactive protein (mg/l) 2.3 (1.7, 2.9) 1.4 (1.0, 1.7) Blood Pressure Systolic BP (mmhg) (107.6, 109.1) (117.0, 118.5) <0.001 Diastolic BP (mmhg) 59.3 (58.8, 59.9) 58.1 (57.6, 58.7) Pre-hypertension/hypertension status (n) Lifestyle factors (%) Alcohol drinker & 49 (46, 54) 51 (47.1, 55) 0.71 Oral contraceptive use 13.9 (12.1, 16) Smoker^ 15.3 (12.5, 18.6) 15.4 (12.6, 18.6) 0.96 Healthy dietary pattern 0.1 (0.03, 0.2) -0.1 (-0.2, -0.02) <0.001 Western dietary pattern -0.5 (-0.6, -0.4) 0.09 (-0.01, 0.2) <0.001 Annual family income A$ 35, (10.3, 16.4) 12.2 (9.6, 15.3) A$ 35,001 78, (29.1, 37.7) 33.6 (29.6, 27.9) > A$ 78, (49.0, 58.2) 54.2 (49.8, 58.5) Data are based on participants present in either set of analyses and presented as means or for categorical variables as a percentage (95% CI) or n. Abbreviations: BMI: Body mass index; 96

129 Chapter 2 BP: blood pressure; HDL-C: high density lipoprotein cholesterol; LDL-C: low density lipoprotein cholesterol; HOMA-IR: Homeostatic model of assessment of insulin resistance; W: watts; Consumer of alcohol at any level over the last 7 days; ^ Smoking 1 cigarettes in a week 97

130 Chapter 2 Table 2. Linear regression analyses examining the associations of fatness and CRF on BP at 17 years Systolic blood pressure n=1128 Diastolic blood pressure n=1128 Model 1 Regression Coefficient 95% CI P value Beta Coefficient Regression Coefficient 95% CI P value Beta Coefficient OC using females , 5.18 < , Males , < , Fatness (cm) , 0.24 < , N/A CRF (W/kg) , N/A , 0.79 < Constant , < , <0.001 Model 2 OC using females , 5.21 < , Males , < , Fatness (cm) , 0.24 < , N/A CRF (W/kg) , N/A , 0.80 < Alcohol , , Smoking , , Diet- Healthy , , Diet- Western , , Middle family , , income High family , , income Constant , < , <0.001 Model 1: Base model adjusting for 3 level sex variable only Model 2: Model 1 with further adjustment for potential lifestyle confounders of alcohol use, smoking, dietary pattern and family income Abbreviations: CI: confidence interval; OC: oral contraceptives. The regression coefficient represents a change in the outcome for a one-unit increase of the predictor variable 98

131 Chapter 2 Figure 2. Plot of fatness vs CRF with systolic BP for males, OC using females and non- OC using females 99

132 Chapter 2 Table 3. Linear regression analyses examining the associations of fatness and CRF with HDL-C or triglycerides at 17 years HDL-C n=963 Log triglycerides n=936* Model 1 Regression Coefficient 95% CI P value Beta Coefficient Regression Coefficient 95% CI P value Beta Coefficient Males , , Fatness (cm) , < , 0.01 < CRF (W/kg) , , N/A CRF*sex , N/A N/A N/A Constant , 1.93 < , <0.001 Model 2 Males , , Fatness (cm) , < , 0.01 < CRF (W/kg) , , N/A CRF*sex , N/A N/A N/A N/A Alcohol , , Smoking , , Diet- Healthy , , Diet-Western , , Middle family income , , High family , , income Constant , 1.93 < , <0.001 Model 1: Base model adjusting for sex only Model 2: Model 1 with further adjustment for potential lifestyle confounders of alcohol use, smoking, dietary pattern and family income *Non-fasting individuals removed Abbreviations: CI: confidence interval; HDL-C: high density lipoprotein-cholesterol. The regression coefficient represents a change in the outcome for a one-unit increase of the predictor variable 100

133 Chapter 2 Figure 3. Plot of fatness vs CRF with HDL-C for males and females 101

134 Chapter 2 Table 4. Linear regression analyses examining the associations of fatness and CRF with total cholesterol or LDL-C at 17 years Total cholesterol n=963 LDL-C n=963 Model 1 Regression Coefficient 95% CI P value Beta Coefficient Regression Coefficient 95% CI P value Beta Coefficient Males , < , Fatness (cm) , N/A , CRF (W/kg) , N/A , N/A Constant , 4.45 < , 2.45 <0.001 Model 2 Males , < , Fatness (cm) , N/A , CRF (W/kg) , N/A , N/A Alcohol , , Smoking , , Diet- Healthy , , Diet-Western , , Middle family income , , High family , , income Constant , 4.51 < , 2.51 <0.001 Model 1: Base model adjusting for sex only Model 2: Model 1 with further adjustment for potential lifestyle confounders of alcohol use, smoking, dietary pattern and family income Abbreviations: CI: confidence interval; LDL-C: low density lipoprotein-cholesterol. The regression coefficient represents a change in the outcome for a one-unit increase of the predictor variable 102

135 Chapter 2 Table 5. Linear regression analyses examining the associations of fatness and CRF with HOMA-IR or hs-crp at 17 years Log HOMA-IR n=936* Log hs-crp n=950 & Model 1 Regression Coefficient 95% CI P value Beta Coefficient Regression Coefficient 95% CI P value Beta Coefficient Males , , <0.001 Fatness (cm) , 0.02 < , 0.06 < CRF (W/kg) , < , N/A Constant , < , <0.001 Model 2 Males , , <0.001 Fatness (cm) , 0.02 < , 0.05 < CRF (W/kg) , , N/A Alcohol , N/A N/A N/A Smoking , , Diet- Healthy , N/A N/A N/A Diet-Western , N/A N/A N/A Middle family , N/A N/A N/A income High family income , N/A N/A N/A Constant , < , <0.001 Model 1: Base model adjusting for sex only Model 2: Model 1 with further adjustment for potential lifestyle confounders of alcohol use, smoking, dietary pattern and family income *Non-fasting individuals removed & Tobit regression Abbreviations: CI: confidence interval; HOMA-IR: homeostatic model of insulin resistance; hs-crp: high sensitivity C-reactive protein. The regression coefficient represents a change in the outcome for a one-unit increase of the predictor variable 103

136 Chapter 2 Figure 4. Plot of fatness vs CRF with HOMA-IR for males and females 104

137 Chapter DISCUSSION Our results have shown that the adverse effects of fatness, measured as waist circumference, are substantially greater than any beneficial effects of CRF, on a wider range of cardiometabolic risk factors, in this adolescent population. Fatness had a positive effect on systolic BP, triglycerides, LDL-C, HOMA-IR, hs-crp and was inversely associated with HDL-C. In contrast, CRF was inversely associated with diastolic BP, and HOMA-IR in both sexes, and was positively associated with HDL-C only in females. Although CRF attenuated the adverse effects of fatness on HOMA-IR and HDL-C, fatness had generally stronger effects on most cardio-metabolic risk factors. The lack of an effect of CRF on systolic BP is unexpected but perhaps explained by the dominant effect of fatness. However, the inverse association between CRF and diastolic BP is relevant in view of the fact that diastolic BP may be more predictive of cardiovascular risk in younger individuals (22). Possible mechanisms for the moderating effect of CRF on adiposity related insulin resistance and dyslipidemia include increased muscle mass leading to improved insulin sensitivity and lipoprotein profiles, while reduced diastolic BP is likely to result from exercise/fitness related adaptations of preresistance and resistance vessels (23). Thus, our findings provide an alternative viewpoint of the fat but fit hypothesis at this age of development. In view of the fact that adolescent activity-related behaviours and obesity track to adult life (24) and predict adult diabetes and coronary disease (25), the significant positive effects of CRF, particularly on adiposity related insulin sensitivity, HDL-C and diastolic BP, would be relevant to reducing the development of type 2 diabetes and hypertensive CVD (26). Results from the CARDIA (Coronary Artery Risk Development in Young Adults) study demonstrated increasing CRF over a 15-year period was associated with reduced risk for 105

138 Chapter 2 developing type 2 diabetes diabetes and the metabolic syndrome, but was not associated with hypertension or hypercholesterolemia (27). Previous studies examining the fat but fit hypothesis in paediatric populations have used diverse measures of fatness, differing categories of CRF and fatness groups and different CVD risk scores determined from arbitrarily selected risk factors. For example, Jago et al (2010) (28) examined quintiles of CRF (shuttle run tests) and fatness (BMI) separately for males and females, in U.S. children aged 12 years. They showed fatness had stronger associations with an adverse cardiovascular profile. Ondrak et al. (2007) (29) also reported that fatness (as percentage body fat using sum of skinfolds) was a stronger predictor of an adverse cardio-metabolic risk score than CRF among a cohort of 1,824 white and African youth (aged 8-10, 11-13, and years). A higher BMI level was associated with a greater risk of being hypertensive in both unfit and fit, in 5,464 males and 8,093 females aged years (30). The European Youth Heart Study showed that individuals within the high CRF group attenuated the negative consequences of total and central adiposity on BP in 873 children aged 9-10 years (31), and HOMA-IR and fasting insulin in 873 girls aged 9.6 years (32). Reports from the ACLS cohort (8, 10) have examined associations between CRF and fatness on cardiovascular risk factors by categorising adolescents using median split, tertiles, quartiles, or clinical cut points, separately for males and females. Overall the results show significant differences in cardio-metabolic risk factors at the extremes of low vs. high CRF or normal weight vs. overweight/obese. Within BMI categories, the low fit individuals exhibited a higher metabolic syndrome score, compared to the high fit subjects. 106

139 Chapter 2 Strengths of our study include the large population-based sample within a narrow age band that has examined sex differences in post pubertal individuals. These analyses were conducted at 17 years of age when potential confounders such as smoking, drinking and OC use were becoming established behaviours, and the multiplicative effects of these potentially confounding lifestyle factors could be accounted for. As BMI cannot differentiate between lean and fat mass, the use of waist circumference in our study may be more discriminating Indices of central adiposity better estimate a wide range of cardio-metabolic risk factors in other paediatric populations (33, 34). Fatness and CRF were analysed as continuous variables which overcomes arbitrary categories that may obscure relative effects of fatness and CRF on cardiovascular risk. In addition, the potential misclassification bias of individuals into obesity and CRF groups is reduced, as cut points used to define these categories are often derived from the study populations investigated. The examination of cardio-metabolic risk factors as separate outcomes overcomes the limitation of an arbitrary CVD risk score used to express the clustering of main components of adult CVD; as no clear definition of the metabolic syndrome exists within the paediatric population. Limitations of our study include the cross-sectional study design which cannot infer causal relationships. The use of a submaximal exercise test may be a limitation as it is less accurate than measuring the VO 2 max of an individual using a maximal exercise test. The latter is considered the gold standard, particularly at lower and higher levels of physical activity (35). The use of a food frequency questionnaire to determine dietary habits has limitations such as under-reporting, reporter bias due to confusion about serving sizes and reliance on memory (36). A further limitation may be some of the relatively small regression coefficients. However, the fatness and CRF coefficients for HDL-C in particular, are likely to be 107

140 Chapter 2 underestimates of true effects due to regression dilution bias. The coefficients for HOMA- IR, systolic BP and hs-crp are more substantial and show the relative contribution of fatness and CRF to each cardio-metabolic risk factor. Our findings have significance in view of the obesity epidemic during adolescence. This study has shown that CRF partially ameliorated the adverse effects of adiposity on selected cardio-metabolic risk factors, thus providing some support for the fat but fit hypothesis. Nevertheless the importance of physical activity and CRF at this age should not be downplayed in view of their known contribution to prevention and management of obesity (37), and the wide ranging benefits of physical activity for physical and mental health over and beyond measures of cardio-metabolic risk factors (38-40). As CRF alone cannot completely reduce the adverse effects of central obesity, it is crucial that healthy eating patterns conducive to avoiding adiposity along with physical activity continue to be the two pillars of public health programs, to reduce the risk of obesity-related co-morbidities in adolescence and later life. 108

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143 Chapter Sun SS, Liang R, Huang TTK, Daniels SR, Arslanian S, Liu K, et al. Childhood obesity predicts adult metabolic syndrome: the Fels Longitudinal Study. The Journal of Pediatrics. 2008;152(2): e Daniels SR, Greer FR. Lipid screening and cardiovascular health in childhood. Pediatrics. 2008;122(1): Heyman E, Briard D, Dekerdanet M, Gratas-Delamarche A, Delamarche P. Accuracy of physical working capacity 170 to estimate aerobic fitness in prepubertal diabetic boys and in 2 insulin dose conditions. Journal of Sports Medicine and Physical Fitness. 2006;46(2): Ventura AK, Loken E, Mitchell DC, SmiciklasWright H, Birch LL. Understanding reporting bias in the dietary recall data of 11yearold girls. Obesity. 2006;14(6): Redwine KM, Acosta AA, Poffenbarger T, Portman RJ, Samuels J. Development of hypertension in adolescents with pre-hypertension. The Journal of Pediatrics. 2012;160(1): Knapen J, Vancampfort D, Moriën Y, Marchal Y. Exercise therapy improves both mental and physical health in patients with major depression. Disability and Rehabilitation. 2015;37(16): Berchicci M, Pontifex MB, Drollette ES, Pesce C, Hillman CH, Di Russo F. From cognitive motor preparation to visual processing: The benefits of childhood fitness to brain health. Neuroscience. 2015;298: Robsahm TE, Falk RS, Heir T, Sandvik L, Vos L, Erikssen JE, et al. Measured cardiorespiratory fitness and selfreported physical activity: associations with cancer risk and death in a longterm prospective cohort study. Cancer Medicine. 2016;5(8):

144 Chapter SUPPORTING INFORMATION Supplementary Table 1. Comparing study sample participants to non-participants at 17-year survey Participants N= 1,131 Non-participants N= 1,737 P value Family income at 1 st year follow-up < A$ 24, 000 (%) A$ 24, 000 (%) <0.001 Gestational age (days) (2.0) (2.3) Mother s weight (kg)* 59.6 (11.6) 60.1 (12.7) 0.25 Mother s height (cm)* (6.7) (6.5) 0.06 Mother s BMI (kg/m 2 ) 22.3 (4.2) 23.5 (6.2) 0.34 Birth length (cm) 49.0 (2.7) 48.7 (3.1) Birth weight (g) (588.9) (640.3) Sex Female Male Data are shown as percentages for categorical variables and mean for continuous variables with SD s in brackets. *At 18 th week of pregnancy Abbreviations: BMI: Body mass index. Supplementary Table 2. Logistic regression analyses examining the associations of fatness and CRF with prehypertension/hypertension status at 17 years (n= 1128) Odds ratio 95% CI P value Males , <0.001 Fatness (cm) , CRF (W/kg) , Constant , 0.03 <0.001 Abbreviation: CI: Confidence interval. The odds ratio represents a change in the outcome for a one-unit increase of the predictor variable 112

145 Chapter 3 CHAPTER 3 Effects of Muscular Strength and Endurance on Blood Pressure and Related Cardio-metabolic Risk Factors from Childhood through to Adolescence FOREWORD In the previous chapter, the beneficial effects of CRF partially alleviated the adverse relationships of fatness, using waist circumference as a measure of central obesity, on some cardio-metabolic risk factors in post-pubertal individuals at 17 years of age. Another important aspect of physical fitness includes muscular fitness which has been increasingly investigated in relation to CVD, all-cause and CVD mortality in middle-aged to older adults. However, a lack of data exists on whether muscular fitness can influence cardiometabolic risk factors in the paediatric population. This period of life is where substantial behavioural and maturational changes occur which may have a long-term impact on cardiometabolic health. This chapter focuses on the longitudinal associations between handgrip strength and back muscular endurance, used as proxies of muscular fitness, with individual cardio-metabolic risk factors in childhood through to late adolescence. This chapter was published online ahead of print in the Journal of Hypertension in August 2016, (printed version, December 2016; 34 (12): ). 113

146 Chapter ABSTRACT Background: Muscular function as assessed by strength and endurance is an important aspect of physical fitness. Low levels of muscular strength have been associated; insulin resistance and dyslipidaemia in adults but the effects on blood pressure are less clear. Moreover, there is a lack of data examining the evolution of relationships between muscular function and cardio-metabolic risk factors in childhood and adolescence. This study aimed to examine the evolution of relationships between measures of muscular strength and endurance with individual cardio-metabolic risk factors from childhood to late adolescence in a prospective population-based cohort. Methods: Participants from the Western Australian Pregnancy Cohort (Raine) Study at ages 10, 14 and 17 were analysed, using longitudinal linear mixed model analyses. Results: Handgrip strength after adjusting for the confounding effects of BMI was positively associated with systolic BP, but not diastolic BP. The association between handgrip strength and systolic BP was stronger in males than females at all time points (coefficient (females): 0.18, p<0.001; sex*handgrip strength coefficient: 0.09, p=0.002). The association was strongest at 10 years and significantly attenuated over time (year*handgrip coefficient from 10 to 14 years: -0.11, p=0.003; year*handgrip coefficient from 10 to 17 years: -0.19, p<0.001). After the inclusion of BMI as a confounder, handgrip strength was significantly negatively associated with HOMA-IR and hs-crp over time in both sexes. Back muscular endurance was positively associated with systolic BP, but not diastolic BP, after adjustment for the confounding effects of BMI (coefficient: 0.01, p=0.002). There were small, albeit 114

147 Chapter 3 significant inverse associations between back muscular endurance and log HOMA-IR and log hs-crp. Conclusion: The positive association between handgrip strength and back muscular endurance with systolic BP throughout childhood and adolescence contrasts with beneficial effects on other related traditional cardio-metabolic risk factors. Mechanisms underlying these paradoxical effects on systolic BP warrant further investigation. 115

148 Chapter INTRODUCTION Moderate to high levels of CRF have been associated with lower mortality rates (1) and lower levels of hypertension, dyslipidaemia and insulin resistance (2, 3). Muscular strength and endurance are key contributors to measures of physical fitness. Muscular strength is defined as the ability to generate a maximal force during a single contraction, whereas muscular endurance is the ability to maintain a repeated exertion of force over an extended period of time (4). The handgrip test acts as a proxy for general muscular strength and is associated with total body strength measures in adults (5) and children (6). In middle-aged adults, low muscular strength has been associated with high morbidity and mortality rates (7). At least part of this effect is suggested to be mediated by beneficial influences on standard cardio-metabolic risk factors. For example, increased muscular mass due to resistance training has been shown to be protective against the onset of insulin resistance (8). Furthermore, large epidemiological studies have shown high levels of handgrip strength are associated with lower levels of undiagnosed hypertension (9) and to be a moderately strong predictor of incident cardiovascular disease in adults (10). However, there are limited data in children and adolescents. Adolescence is a critical period of substantial physiological, psychological and lifestyle changes which can impact on future adult behaviours and health outcomes (11). Muscular strength and endurance increase as children age, due to growth in muscle mass and muscle fibre size (12). In addition, BP has been shown to linearly increase throughout adolescence (13). With a view to understanding the ontogeny of effects of muscular strength and endurance on adolescent risk factors for cardio-metabolic disease, two modes of muscular 116

149 Chapter 3 function, specifically handgrip strength and back muscular endurance were examined. These measures were analysed with a range of individual cardio-metabolic risk factors in participants from the population based Western Australian Pregnancy Cohort (Raine) Study. It is hypothesised that higher levels of handgrip strength and back muscular endurance would be associated with lower levels of cardio-metabolic risk factors. 3.3 METHODS Participants The Raine Study is a prospective population study that recruited 2900 pregnant females between 16 to 20 weeks gestation from King Edward Memorial Hospital and closely located practices, in Perth, Western Australia, Australia. The mothers gave birth to 2868 live infants. The offspring have been followed at approximately three-year intervals with a comprehensive range of anthropometry, BP, lifestyle, psychosocial and biochemical measures recorded. Details on methods of the Raine Study have previously been reported (14). Ethics approvals for the 10, 14 and 17-year assessments were obtained from the Human Research Ethics Committees at the University of Western Australia, Curtin University and Princess Margaret Hospital. Written informed consent was obtained from the participant's parent and the participant at 17 years of age. Handgrip strength measures, BMI and BP recordings were recorded at 10, 14 and 17 years. Back muscular endurance, CRF, puberty and biochemistry measures were recorded at 14 and 17 years Muscular strength Muscular strength was assessed using a handgrip dynamometer (Dynamometer, Therapeutic Instruments Clifton N.J.) following the protocol developed by McCarron (15). Participants 117

150 Chapter 3 were asked to extend and hold the hand dynamometer at shoulder length and pull hard on the device to record grip force (kg). Measurements were assessed with the right hand and the left hand with the process being repeated. The best score of the two trials for each hand was recorded. These were combined to create a total handgrip strength score (kg) Muscular Endurance The sustained back muscular endurance test was employed to evaluate dynamic back muscular endurance by testing the postural trunk muscles using a procedure described by Biering-Sorensen (16). Participants were instructed to lie in the prone position with their lower body strapped to a bed and the upper body extended beyond the bed. Subjects were required to maintain the horizontal position isometrically for as long as possible. The endurance time was recorded (seconds) Cardiorespiratory fitness CRF was estimated from heart rate recordings during sub-maximal cycle ergometry using the PWC 170 protocol (Monark 818e Ergomedic, Nordic Health Care, Sweden). PWC 170 is the maximal steady state power attained for a heart rate of 170 beats per minute on a cycle ergometer (17). Participants cycled at an initial workload of 25 W and aimed to work at a rate of rpm for 2 minutes each at 2 successively increasing but submaximal workloads of 25 W increments. The ergometer s workload was independent of cadence. PWC170 was assessed by linear regression with extrapolation of the line of best fit to a heart rate of 170 beats per minute. 118

151 Chapter Anthropometry Height was measured using a mounted stadiometer (to the nearest 0.1 cm) and weight was measured (to the nearest 100g) with participants dressed in light clothes. BMI was calculated as weight (kg) divided by the square of height (m 2 ) Puberty Puberty was assessed using the Tanner Stages of physical development. This scale asks participants to rate their development based on several physical sexual characteristics, such as development of breasts, genitalia and pubic hair. The five stages range from 1 (prepubertal) to 5 (fully developed) (18). At 14 years of age, misreporting occurred when approximately 330 individuals were shown the wrong developmental stage chart. To account for this, these individuals were omitted from the analyses when using puberty as a confounder Biochemistry Venous blood samples were taken after an overnight fast of 12 hours (fasting status was confirmed by trained personnel) and analysed in the PathWest Laboratory at Royal Perth Hospital (19). Serum samples were analysed for total cholesterol, HDL-C, triglycerides and glucose, determined enzymatically on an Architect c16000 Analyser (Abbott Laboratories, Lake Forest, Illinois). Intra- assay coefficients of variation were 0.87% for total cholesterol, 2.0% for HDL-C, 1.92% for triglycerides and 1.04% for glucose. LDL-C was calculated using the Friedewald formula (20). hs-crp was measured by an immunoturbidimetric method on Architect c16000 Analyser (intra-assay coefficient of variation 15.96%). Insulin was determined on an Architect i2000sr Analyser (intra-assay coefficient of variation 119

152 Chapter %). HOMA-IR was calculated using the formula: fasting insulin (µu/ml) x fasting glucose (mmol/l) / 22.5 (21) Blood pressure Systolic and diastolic BP were measured by trained personnel using an oscillometric sphygmomanometer (DINAMAP vital signs monitor 8100, DINAMAP XL vital signs monitor or DINAMAP ProCare 100; GE Healthcare, Soma Technology, Bloomfield, Connecticut, USA) after resting for 5 minutes and using the appropriate cuff size on the right arm. Three cuff sizes were available. At 10 years, two BP readings were obtained every 2 minutes in the seated position and the average calculated. At 14 years, six BP readings were obtained every 2 minutes within a 10-minute period in the seated position, whereas at 17 years, the same protocol was employed in the supine position. The average BP was calculated using the last five readings at 14 and 17 years. BP was recorded on a separate day to handgrip strength and back muscular endurance assessments Statistical analysis Descriptive data were summarised by year and sex using means, geometric means and their 95% confidence intervals. There is no consensus in the literature regarding the need to adjust handgrip strength for body size or how this might be achieved. To explore this issue, handgrip strength was calculated at each year by regressing handgrip strength on height or weight respectively, we then added the residuals from these analyses to the mean handgrip strength, to obtain a height adjusted and weight adjusted handgrip strength. Potential nonlinearity was accommodated by using fractional polynomials. Hierarchal linear mixed 120

153 Chapter 3 models of a selection of cardio-metabolic outcomes (systolic BP, diastolic BP, log HOMA- IR, log triglycerides, HDL-C, hs-crp) were then used to compare the performance of raw handgrip strength, height adjusted handgrip strength and weight adjusted handgrip strength. A simple model was adopted for this purpose, containing time, sex and subsequently an adjustment for adiposity, using BMI. Models were assessed using Akaike Information Criterion (AIC) where the lowest value indicates the better model. Longitudinal patterns of raw handgrip strength and back muscular endurance over time as well as individuals cardio-metabolic risk factors were investigated using hierarchical linear mixed models, to account for nesting of time within an individual and siblings within a family. In these models, maximum likelihood estimation was utilised to retain those with outcome data from at least one-time point in the analysis, resulting in unbiased estimates when missing data is missing at random. Random effects Tobit regression (for censored data) was utilised for hs-crp due to the lower boundary of the test (but does not make any adjustment for siblings) (22). HOMA-IR, triglycerides and hs-crp were not normally distributed and hence were log transformed. Initial associations between cardio-metabolic risk factors and handgrip strength and back muscular endurance were investigated after adjusting for sex and year. A further adjustment for adiposity was investigated in a subsequent model using BMI. Interactions between handgrip strength or back muscular endurance with time or sex were tested to determine whether the relationship varied over time or sex. In addition, a time and sex interaction was included to determine whether an outcome varied over time and sex. Interactions were excluded from the model if p CRF was included as a confounder in both handgrip strength and back muscular endurance models with either systolic BP or diastolic BP as the outcome. Data were analysed using STATA 121

154 Chapter 3 (StataCorp, Stata Statistical Software: release 13. College Station, Texas, USA). All reported P values are 2-tailed, and significance was set at α= RESULTS Participant characteristics At 10, 14 and 17 years, 1632, 1560 and 1234 participants, respectively had complete data for handgrip strength and systolic BP (Figure 1). Handgrip strength and biochemistry at 14 and 17 years was available for 1346 and 1048 participants, respectively. At 14 and 17 years, 1554 and 1158 participants, respectively, had complete data for back muscular endurance and systolic BP, whereas 1341 and 983 participants, respectively, had complete data for back muscular endurance and biochemistry. 122

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156 Chapter Interpretation of handgrip strength: Comparison of unadjusted and adjusted measures For the determination of the better handgrip strength measure, identical models of raw handgrip strength, height adjusted handgrip strength and weight adjusted handgrip strength were compared (Supplementary Table 1a, 1b and 1c,). Raw handgrip strength in unadjusted and BMI confounder adjusted models exhibited the lowest AIC compared to height and weight adjusted handgrip strength. Therefore, raw handgrip strength was deemed a better fit for the outcome of systolic BP and hence was employed as the preferred handgrip strength measure in the final analyses. The same pattern was observed for other cardio-metabolic risk factors investigated (only systolic BP data shown as supplementary material) Longitudinal linear mixed models of handgrip strength, back muscular endurance, blood pressure and biochemical risk factors Handgrip strength increased between 10 to 17 years in both sexes (p<0.001), but to a greater extent in males (p<0.001) (Table 1). Back muscular endurance was higher in females than in males at 14 years (p=0.021) but by 17 years there was no difference between the sexes (p=0.200) (Table 1). Back muscular endurance increased between 14 and 17 years in females (p<0.001) and in males but to a greater extent (p=0.001) (Table 1). Handgrip strength and back muscular endurance were weakly correlated (r= 0.07, p=0.008 at 14 years; r= 0.08, p=0.004 at 17 years). Systolic BP increased between 10, 14 and 17 years in both sexes but to a greater extent in males (p<0.001) (Table 1). Diastolic BP increased between years 10 and 14 in both sexes but to a greater extent in females (p<0.001). There was no significant change in diastolic BP between 14 and 17 years for either sex. Log HOMA-IR decreased between

157 Chapter 3 and 17 years in both sexes to a similar extent (p<0.001) (Table 1). Cholesterol decreased in males (p<0.001) with no change in females over time (p=0.281). Log triglycerides increased in males (p<0.001) with no change in females over time (p=0.547). LDL-C increased in females (p=0.004) with no change in males over time (p=0.818). HDL-C decreased in both sexes but to a greater extent in males (p<0.001). Log hs-crp increased from 14 to 17 years in females (p<0.001) but decreased in males (p<0.001) Longitudinal linear mixed models examining associations between handgrip strength and cardio-metabolic risk factors over time Handgrip strength was positively associated with systolic BP at all time points in both sexes. The association was strongest at 10 years and attenuated over time (Table 2, Supplementary Table 2 and Supplementary Figure 1). After adjustment for the confounding effects of BMI, similar associations were detected (Table 2, Supplementary Table 2 and Figure 2). The association between handgrip strength and systolic BP was stronger in males than females at all time points (sex*handgrip strength coefficient: 0.09, p=0.002). In both sexes, handgrip strength was positively associated with systolic BP at 10 years of age and attenuated over time, (year*handgrip for 10 to 14 years coefficient: -0.11, p=0.003; year*handgrip for 10 to 17 years coefficient: -0.19, p=<0.001) resulting in a non-significant association at 17 years of age in females. The change in systolic BP resulting from a 1 SD change (noted in brackets) in females was 1.07 mm Hg (5.94 kg) and 0.72 mm Hg (9.02 kg) for years 10 and 14 respectively. The corresponding changes in males were 1.78 mm Hg (6.37 kg), 2.50 mm Hg (14.70 kg) and 1.43 mm Hg (15.86 kg) for years 10, 14 and 17 respectively. To investigate whether these associations may be 125

158 Chapter 3 attributable to CRF (only available at 14 and 17 years); PWC 170 was included as a further confounder with BMI (Supplementary Table 2). The same pattern was observed after the additional adjustment for PWC 170 between 14 and 17 years when BMI was the sole confounder in adjusted models. There were no associations between handgrip strength and diastolic BP at any time point in the unadjusted, BMI-adjusted (Table 2) and the PWC 170 and BMI-adjusted models (data not shown). Handgrip strength was positively associated with HOMA-IR in both sexes at all time points (coefficient (log HOMA-IR): 0.003, p=0.013) (Table 3 and Supplementary Table 3). After the inclusion of BMI as a confounder in the model, handgrip strength was negatively associated with HOMA-IR in both sexes at all time points, (coefficient (log HOMA-IR): , p<0.001). A 1 SD change in handgrip strength at 14 years (13.40 kg) and 17 years (20.58 kg) equated to a decrease in HOMA-IR of 3.9% and 6 %, respectively. Handgrip strength was positively associated with triglycerides at all time point in both sexes in the unadjusted model (coefficient (log triglycerides): 0.002, p=0.006) (Table 3). After the inclusion of BMI as a confounder, the aforementioned association was no longer significant (coefficient (log triglycerides): , p=0.302). Handgrip strength was negatively associated with HDL-C and this association attenuated from 14 to 17 years in both sexes, (coefficient at 14 years: , p<0.001; year*handgrip coefficient: 0.003, p<0.001) (Table 4 and Supplementary Table 4). After, the inclusion of BMI as a confounder, handgrip strength remained negatively associated with HDL-C at 14 years (coefficient: , p<0.001) but the association was no longer significant at 17 years (year*handgrip coefficient: 0.003, p<0.001). A 1 SD change in handgrip strength at 14 years (13.40 kg) equated to a 0.04 mmol/l decrease in HDL-C. There was a positive association between handgrip strength and hs-crp 126

159 Chapter 3 (coefficient (log hs-crp): 0.005, p=0.013) (Table 3 and Supplementary Table 5). After the inclusion of BMI as a confounder, there was a small negative, albeit significant, association between handgrip strength and hs-crp (coefficient (log hs-crp): , p=0.007). These associations did not vary with sex or time. A 1 SD change in handgrip strength at 14 years (13.40 kg) and 17 years (20.58 kg) equated to a decrease in hs-crp of 7.7% and 11.3%, respectively Longitudinal linear mixed models examining associations between back muscular endurance and cardio-metabolic risk factors over time There were no interactions for back muscular endurance and time or back muscular endurance and sex for any of the cardio-metabolic risk factors investigated. The associations reported therefore apply to both time points in both sexes. Back muscular endurance was not associated with systolic BP in unadjusted analysis (coefficient: , p=0.723) (Table 5, Supplementary Table 6 and Supplementary Figure 2). After the inclusion of BMI as a confounder in the model, back muscular endurance was positively associated with systolic BP (coefficient: 0.01, p=0.002) (Table 5, Supplementary Table 6 and Figure 3). A 1 SD (14 years: seconds; 17 years: seconds) increase in back muscular endurance equated to a 0.06 mm Hg increase in systolic BP. After the inclusion of CRF as a further confounder, these associations were not altered (Supplementary Table 6). Back muscular endurance was not associated with diastolic BP in unadjusted, BMI adjusted (Table 5) and the PWC 170 and BMI-adjusted models (data not shown). Back muscular endurance was inversely associated with HOMA-IR (coefficient (log HOMA- IR): , p<0.001) (Table 5 and Supplementary Table 7). With BMI as a confounder in the 127

160 Chapter 3 model, back muscular endurance remained negatively associated with HOMA-IR (coefficient (log HOMA-IR): , p<0.001). A 1 SD (14 years: seconds; 17 years: seconds) increase in back muscular endurance equated to a 6% and 5.5% decrease in HOMA- IR at 14 and 17 years, respectively. Back muscular endurance was inversely associated with triglycerides (coefficient (log triglycerides): , p<0.001) and remained significant after the inclusion of BMI as a confounder (coefficient (log triglycerides): , p=0.007) (Table 5 and Supplementary Table 8). A 1 SD (14 years: seconds; 17 years: seconds) increase in back muscular endurance equated to a 2.5% and 2.2% decrease in triglycerides at 14 and 17 years, respectively. Back muscular endurance was positively associated with HDL-C (coefficient: , p<0.001), but this was no longer significant after the inclusion of BMI as a confounder (coefficient: , p=0.209) (Table 5). Back muscular endurance was negatively associated with hs-crp (coefficient (log hs-crp): , p<0.001) and the relationship remained significant after inclusion of BMI in the model (coefficient (log hs-crp): , p=0.018) (Table 5 and Supplementary Table 9). A 1 SD (14 years: seconds; 17 years: seconds) increase in back muscular endurance equated to a 6% and 5.5% decrease in hs-crp at 14 and 17 years, respectively. 128

161 Chapter 3 Table 1. Descriptive characteristics of participants from the Raine Study by age and sex Males 10 years n= years n=815 Height (cm) (143.25, ) (165.65, ) Weight (kg) (57.66, (38.00, 39.14) 59.59) Body mass index (kg/m 2 ) (19.86, 21.18) (20.78, 21.35) Waist circumference (cm) N/A (75.52, 77.09) Handgrip strength (kg) (31.60, 32.45) (55.98, 58.02) Back muscular endurance (sec) N/A (73.86, 82.28) 17 years n= (177.74, ) (71.13, 73.41) (22.36, 23.02) (79.67, 81.04) (79.93, 82.43) (98.55, ) CRF PWC 170 (watts) N/A (122.14, ) (151.08, ) Systolic BP (mm Hg) (106.2, 107.5) ( ) (117.07, ) Diastolic BP (mm Hg) (56.2, 57.1) (58.0, 59.0) (57.7, 58.7) Glucose (mmol) N/A 4.72 (4.64, 4.92) 4.65 (4.82, 4.92) Insulin (mu/l) N/A (11.29, 13.30) (8.36, 9.57) HOMA-IR* N/A (0.61, 0.70) (0.46, 0.55) Females 10 years n= years n= (143.20, ) (161.58, ) (38.13, 39.31) (55.82, 57.52) (19.60, 21.22) (21.24, 21.83) N/A (73.88, 75.31) (28.59, 29.43) (45.64, 46.93) N/A (81.26, 90.39) N/A (95.79, 98.63) (105.54, ) (108.15, ) (56.3, 57.2) (58.8, 59.7) N/A 4.88 (4.68, 4.76) N/A (12.06, 13.36) N/A 0.72 (0.68, 0.77) 17 years n= (165.39, ) (62.61, 64.71) (22.76, 23.48) (76.63, 78.47) (48.95, 50.53) (94.03, ) (97.60, ) (107.94, ) 59.5 (58.9, 60.0) 4.87 (4.61, 4.70) 9.91 (8.93, 10.89) 0.52 (0.47, 0.56) 129

162 Chapter 3 Triglycerides (mmol)* Total cholesterol (mmol) N/A N/A 0.22 (0.20, 0.26) 4.06 (4.01, 4.11) HDL-C (mmol) N/A 1.35 (1.33, 1.37) LDL-C (mmol) N/A 2.25 (2.21, 2.30) hs-crp (mg/l)* N/A 0.61 (0.52, 0.71) 0.27 (0.24, (3.90, 4.01) 1.20 (1.18, 1.22) 2.25 (2.20, 2.31) 0.65 (0.55, 0.76) N/A 0.19 (0.17, 0.22) N/A 4.28 (4.23, 4.34) N/A 1.43 (1.41, 1.45) N/A 2.38 (2.36, 2.43) N/A 0.54 (0.42, 0.68) 0.21 (0.18, 0.24) 3.96 (4.24, 4.36) 1.38 (1.36, 1.41) 2.44 (2.38, 2.49) 0.74 (0.64, 0.84) Descriptive characteristics are presented as means, geometric means and 95% CI. CI, confidence interval; PWC 170, physical work capacity protocol; BP, blood pressure; HOMA-IR, homeostatic model assessment of insulin resistance; HDL-C, high density lipoprotein-cholesterol; LDL- C, low density lipoprotein-cholesterol; hs-crp, high-sensitivity C-reactive protein. BP was measured in the seated position at ages 10 and 14 years, and in the supine position at 17 years *Variables were log transformed 130

163 Chapter 3 Table 2. Handgrip strength associations with BP generated from a longitudinal linear mixed model Unadjusted model P value Coefficient (95% CI) Systolic BP (mmhg) Females 10 years 0.29 (0.21, 0.37) 14 years 0.15 (0.09, 0.20) 17 years 0.07 (0.01, 0.12) Males 10 years 0.35 (0.28, 0.42) 14 years 0.21 (0.17, 0.25) 17 years 0.13 (0.09, 0.17) Diastolic BP (mmhg) All years (-0.02, 0.01) N= 4425 observations from 1916 participants BMI adjusted model Coefficient P value (95% CI) < (0.11, 0.26) < (0.02, 0.13) (-0.06, 0.05) < (0.20, 0.35) < (0.13, 0.20) < (0.05, 0.13) (-0.03, 0.01) < <0.001 <0.001 < Coefficients reported were generated from longitudinal linear mixed models (shown in Supplementary tables). CI, confidence interval; BP, blood pressure The regression coefficient represents a change in the outcome for a one-unit increase of the predictor variable 131

164 Chapter 3 Figure 2. Handgrip strength and systolic BP at 10, 14 and 17 years after adjustment for BMI generated from longitudinal linear mixed models (mean and 95% CIs represented) 132

165 Chapter 3 Figure 3. Back muscular endurance and systolic BP at 14 and 17 years after adjustment for BMI generated from longitudinal linear mixed models (mean and 95% CIs represented) 133

166 Chapter 3 Table 3. Handgrip strength associations with HOMA-IR, triglycerides and hs-crp generated from a longitudinal linear mixed model or random effects tobit model Log HOMA-IR Coefficient (95% CI) Unadjusted model BMI adjusted model (0.0006, 0.005) P value Log Triglycerides (mmol/l) Coefficient (95% CI) (0.0006, 0.003) (-0.005, ) (-0.002, ) P value Log hs-crp* (mg/l) Coefficient (95% CI) (0.001, 0.01) (-0.01, ) P value N= 4425 observations from 1916 participants *N= 1967 observations from 1363 participants as participants with values >10 were excluded Coefficients reported were generated from longitudinal linear mixed models or random effects tobit model (shown in Supplementary tables). CI, confidence interval; HOMA-IR, homeostatic model assessment insulin resistance; hs-crp, high-sensitivity C-reactive protein The regression coefficient represents a change in the outcome for a one-unit increase of the predictor variable 134

167 Chapter 3 Table 4. Handgrip strength associations with HDL-C generated from a longitudinal linear mixed model Unadjusted model BMI adjusted model Coefficient (95% CI) 14 years (-0.006, ) 17 years (-0.003, ) P value Coefficient (95% CI) < (-0.004, ) (-0.001, 0.001) P value < N= 2396 observations from 1506 participants Coefficients reported were generated from longitudinal linear mixed models (shown in Supplementary tables). CI, confidence intervals; HDL-C, high density lipoprotein-cholesterol The regression coefficient represents a change in the outcome for a one-unit increase of the predictor variable 135

168 Chapter 3 Table 5. Back muscular endurance with cardio-metabolic outcomes generated from a longitudinal linear mixed model or random effects tobit model Systolic BP (mmhg)^ Coefficient (95% CI) Unadjusted model BMI adjusted model (-0.007, 0.005) (0.004, 0.02) P value Diastolic BP (mmhg)^ Coefficient (95% CI) (-0.002, 0.006) (-0.002, 0.007) P value Log HOMA-IR Coefficient (95% CI) (-0.002, ) (-0.001, ) P value <0.001 <0.001 Log Triglycerides (mmol/l) Coefficient (95% CI) (-0.001, ) ( , ) P value < HDL-C (mmol/l) Coefficient (95% CI) (0.0003, ) ( , ) P value < Log hs-crp (mg/l)* Coefficient (95% CI) (-0.004, ) (-0.002, ) P value < N= 2325 observations from 1491 participants ^N= 2712 observations from 1687 participants *N= 1895 observations from 1337 participants as participants with values >10 were excluded Coefficients reported were generated from longitudinal linear mixed models or random effects tobit model (shown in Supplementary tables). The regression coefficient represents a change in the outcome for a one-unit increase of the predictor variable 136

169 Chapter DISCUSSION To our knowledge, this the first study examining the relationship between measures of muscular performance i.e. muscular strength and endurance, with BP and other cardiometabolic risk factors from childhood to late adolescence. The study has shown unexpectedly, handgrip strength was positively associated with systolic BP and attenuated over time. The association between handgrip strength and systolic BP was stronger in males than females, with a non-significant association at 17 years of age in females. More predictably, handgrip strength and back muscular endurance were inversely associated with HOMA-IR and hs-crp after the inclusion of BMI as a confounder. Additionally, back muscular endurance was inversely associated with plasma triglycerides. Previous paediatric studies investigating the effects of muscular strength on cardiovascular risk factors have examined cardio-metabolic risk factors as components of a clustered metabolic risk score. These studies have shown an inverse relationship between muscular strength and clustered metabolic risk (23-25). Artero et al. (2011) (23) used a muscular fitness score computed using the standardised values of handgrip strength and standing long jump (separately for males and females) and showed a negative association with a clustered metabolic risk score which included systolic BP, HOMA-IR, total cholesterol/hdlcholesterol and triglycerides in 12 to 17 year old participants of the HELENA study. In addition, the ACFIES study observed that poorer handgrip strength were associated with a worse cardio-metabolic risk in Columbian schoolchildren, aged 8-14 years (26). However, the aforementioned studies did not examine BP and related cardio-metabolic risk factors individually. More recently, Zaqout et al. (2016) (27) examined the longitudinal associations 137

170 Chapter 3 between individual components of physical fitness (CRF, handgrip strength and lower limb strength and speed/agility) and the combined sum of these individual physical fitness components (using z-scores) with metabolic risk markers, in 6-11-year-old European participants in the IDEFICS study. No longitudinal associations were observed between handgrip strength or the sum of physical fitness with clustered metabolic risk (sum of age and sex specific z-scores of waist circumference, systolic BP, diastolic BP, triglycerides, HDL-C and HOMA-IR) or with systolic BP, HOMA-IR and blood lipids. Lower limb strength (measured by standing long jump) showed longitudinal and inverse associations with clustered metabolic risk, blood lipids and HOMA-IR but there were no associations with systolic BP. A longitudinal study from childhood to adulthood have shown that 737 children (aged 9, 12 or 15 years) categorised in the highest tertile of muscular fitness score (combined muscular strength (maximum voluntary contractile force in kg of handgrip, shoulder and leg flexion, and shoulder and leg extension, muscular endurance (push-ups) and muscular power (standing long jump)) had significantly lower relative risk (RR: 0.30, 95% CI: 0.14, 0.63) for adult metabolic syndrome, compared to those in the lowest tertile of muscle fitness score at 20 year follow up (28). However, no studies to date have examined BP or related cardiometabolic risk factors individually or longitudinally from childhood through to adolescence where significant maturational changes occur. Additionally, no studies have examined the association between isometric endurance of the trunk extensor muscles and the individual risk factors. Adult population studies present conflicting results with muscular fitness and BP. Sayer et al. (2007) (29) have shown a lower handgrip strength was associated with higher systolic BP in a population based sample of 2677 males and females aged years from the 138

171 Chapter 3 Hertfordshire study. In that study, a 1 SD decrease in handgrip strength associated with high BP (odds ratio 1.13; 95% CI: 1.04, 1.24). Vaara et al. (2014) (30) found that muscular endurance was inversely associated with both systolic and diastolic BP in young males (25±years). The younger age of these participants and the use of dynamic assessment of muscular endurance may explain the observational differences seen in this study. In contrast, Viitasalo et al. (1979) (31) found positive correlations between muscular strength of the trunk extensor muscles with systolic BP in young healthy males (mean age 19.7 years). Takaema et al. (2011) (32) reported higher levels of handgrip strength associated with higher resting BP in a large population based sample of Japanese males and females (aged 66 years). In contrast to this study of muscular strength and muscular endurance per se, a study of self-reported aerobic exercise in young adults (<30 years), showed an association with lower diastolic BP and higher pulse pressure (33). Aerobic physical activity was self-reported via questionnaire (International Physical Activity Questionnaire, 2002) and individuals who reported regular resistance training were excluded. There were no differences in central or brachial systolic BP between exercising and non-active individuals. This study contrasts with these results, as a positive association between handgrip strength and back muscular endurance with systolic BP, even after adjustment for CRF. Additionally, no associations were observed between muscular strength or muscular endurance with diastolic BP in this young population. Discrepancies between the two studies are likely due to the different measurement methods, types of physical fitness assessed, and different ages of the populations. Population data are in contrast to resistance training studies which have consistently shown improvements in handgrip strength are associated with reductions in systolic BP (34-36). Owen et al. (2010) (37) suggested handgrip exercise might increase forearm blood flow 139

172 Chapter 3 and/or improve local flow-mediated dilation (FMD). Enhanced brachial FMD response as well as endothelium dependent vasodilatation were observed after isometric handgrip training in older males, elderly hypertensives and chronic heart failure subjects (38). Similarly, forearm blood flow was increased with regular handgrip exercise training in young males and in middle-aged males (39). However, it is important to note various factors, such as exercise intensity, different populations and exercise modality can have differing influences on BP. The lack of effect of muscular strength and muscular endurance on diastolic BP in this study is of interest given that systolic BP and more predominantly diastolic BP in young adults are predictive of subsequent cardiovascular events and mortality (40). These findings of a positive relationship between handgrip strength and back muscular endurance with systolic BP across periods of the survey may be related to the fact that both muscular strength and BP increase in magnitude during adolescence. Nevertheless, similar associations were observed cross-sectionally at each time point in subjects that were predominantly pre-pubertal at 10 years to post-puberty at 17 years. Thus it is possible that changes in androgen levels related to puberty influenced muscular mass and strength (41, 42) and thereby the positive association with systolic BP (43). Increases in testosterone could increase muscular mass and may have an additive effect on BP, although the associations in this study were seen in females as well as males making this explanation unlikely. This critical period of growth in the Raine cohort is associated with other hormonal changes such as growth hormone, which might also contribute to parallel changes in muscular strength (12) and left ventricular mass and contractility (44, 45) and hence BP. The association between handgrip strength and back muscular endurance with systolic BP in this study could be explained by an increase in 140

173 Chapter 3 arterial stiffness and/or vascular hypertrophy with a higher degree of muscular fitness in childhood and adolescents. In accordance with these findings with HOMA-IR and triglycerides, adult studies have consistently shown inverse associations between different measures of muscular strength and measures of insulin resistance and lipids (46, 47). Vaara et al. (30) showed that muscular endurance (sit-ups, push-ups and repeated squats) was inversely associated with blood triglycerides and glucose in young males. Furthermore, similar results have been shown longitudinally, where isometric muscle strength (317 children aged 15 years of age) was inversely associated with markers of insulin resistance and β-cell function in young adulthood (adults aged 27 years of age) (2). Increased muscle mass is the most likely explanation for the effects on glucose homeostasis and lipids which appear to persist through childhood and adolescence into adult life. Skeletal muscle is one of the primary tissue sites for glucose and lipid metabolism and is considered a determinant of resting metabolic rate. Thus changes in muscle mass may influence multiple cardio-metabolic risk factors (48). These results are likely to be generalisable as Raine study participants were derived from a cohort broadly representative of the general population and were not a selected group of athletes. Strengths of this study include serial measures of detailed phenotypes in a large cohort of population-based children and adolescents, allowing the study of the evolution of associations between different aspects of muscular function and a panel of cardio-metabolic risk factors. Limitations include the use of BMI in the analyses which does not distinguish between fat and lean mass. In addition, not all measures were repeated at every time point. BP recordings varied across the three-time points in terms of posture and number of 141

174 Chapter 3 measurements obtained. This could have influenced the associations observed. Relationships with home BP or 24-hour ambulatory monitoring would be of further interest. In conclusion, paradoxically handgrip strength and back muscular endurance were positively associated with higher systolic BP in children and adolescents despite inverse associations with HOMA-IR, lipids and hs-crp. These BP findings warrant replication in other populations and further evaluation to the possible underlying mechanisms. In view of the fact grip strength increases from childhood through to years of age (49, 50), it will be of particular interest to ascertain whether the continuous associations between handgrip strength and back muscular endurance, with systolic BP, are maintained or reversed in later adulthood. Equally important will be to determine the optimal period to gain a substantial benefit from resistance strength training for the early prevention of type 2 diabetes, dyslipidaemia and hypertension. 142

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178 Chapter SUPPORTING INFORMATION Supplementary Table 1a. Raw handgrip strength with systolic BP Unadjusted for adiposity model Adjusted for adiposity model Systolic BP (mmhg) Coefficient P Value 95% CI Coefficient P value 95% CI Year (ref 10) Handgrip strength 0.30 < < Year*handgrip strength Sex (ref females) 1.58 < < BMI N/A N/A N/A N/A 0.52 < Constant < < AIC N= 4425 observations from 1916 participants. CI, confidence interval; BP, blood pressure; BMI, body mass index; AIC, Akaike Information Criterion The regression coefficient represents a change in the outcome for a one-unit increase of the predictor variable 146

179 Chapter 3 Supplementary Table 1b. Height adjusted handgrip strength with systolic BP Unadjusted for adiposity model Adjusted for adiposity model Systolic BP (mmhg) Coefficient P Value 95% CI Coefficient P value 95% CI Year (ref 10) Handgrip strength Year*handgrip strength Sex (ref females) 3.34 < < BMI N/A N/A N/A N/A 0.60 < Constant < < AIC N= 4425 observations from 1916 participants. CI, confidence interval; BP, blood pressure; BMI, body mass index; AIC, Akaike Information Criteria The regression coefficient represents a change in the outcome for a one-unit increase of the predictor variable 147

180 Chapter 3 Supplementary Table 1c. Weight adjusted handgrip strength with systolic BP Unadjusted for adiposity model Adjusted for adiposity model Systolic BP (mmhg) Coefficient P Value 95% CI Coefficient P value 95% CI Year (ref 10) < < Handgrip strength Year*handgrip strength Sex (ref females) 2.94 < < BMI N/A N/A N/A N/A 0.77 < Constant < < AIC N= 4425 observations from 1916 participants. CI, confidence interval; BP, blood pressure; BMI, body mass index; AIC, Akaike Information Criterion The regression coefficient represents a change in the outcome for a one-unit increase of the predictor variable 148

181 Chapter 3 Supplementary Table 2: Longitudinal linear mixed model for the association between handgrip strength and systolic BP over time Unadjusted model BMI adjusted model BMI and PWC 170 adjusted model Coefficient (95% CI) Year 14 years 3.35 (0.70, 6.00) 17 years 6.69 (3.87, 9.52) Sex Male (-4.22, -0.22) Year*sex 14 years 2.24 (0.65, 3.83) 17 years 4.40 (2.08, 6.72) Handgrip strength 0.29 (0.21, 0.37) Year*handgrip strength 14 years (-0.21, -0.07) P value Coefficient (95% CI) (-0.62, 4.61) < (1.72, 7.32) (-4.82, -0.90) (0.63, 3.75) < (2.68, 7.22) < (0.11, 0.26) < (-0.18, -0.04) P value Coefficient (95% CI) Reference group (1.25, 6.14) (-3.67, 2.99) Reference group < (1.49, 5.00) < (0.06, 0.19) Reference group P value Reference group Reference group <0.001 <0.001 Reference group 17 years (-0.29, -0.15) Sex*handgrip strength < (-0.26, -0.11) < (-0.15, -0.06) <0.001 Males 0.06 ( , 0.12) (0.03, 0.15) (0.02, 0.15)

182 Chapter 3 BMI PWC 170 N/A N/A N/A N/A 0.58 (0.50, 0.67) N/A <0.001 N/A 0.50 (0.40, 0.59) (-0.03, ) Constant < < (88.08, 93.13) (88.08, 93.13) (90.79, 97.40) N (unadjusted and BMI adjusted model) = 4425 observations from 1916 participants; (BMI and PWC 170 adjusted model) =2617 observations from 1649 participants.ci, confidence interval; BP, blood pressure; BMI, body mass index; PWC 170, physical work capacity protocol The regression coefficient represents a change in the outcome for a one-unit increase of the predictor variable <

183 Chapter 3 Supplementary Table 3: Longitudinal linear mixed model for the association between handgrip strength and diastolic BP over time Coefficient (95% CI) Year 14 years 2.75 (2.14, 3.37) 17 years 3.14 (2.45, 3.82) Sex Male 0.01 (-0.62, 0.64) Year*sex 14 years Unadjusted model P value < (2.02, 3.26) < (2.22, 3.62) (-0.59, 0.67) (-1.33, 0.16) (-1.23, 0.26) 17 years (-2.22, -0.32) (-2.00, -0.07) Handgrip strength (-0.02, 0.01) (-0.03, 0.01) BMI N/A N/A 0.08 (0.02, 0.14) Constant < (55.99, 57.40) (54.16, 56.55) BMI adjusted model Coefficient P value (95% CI) <0.001 < <0.001 N= 4425 observations from 1916 participants. CI, confidence interval; BP, blood pressure; BMI, body mass index The regression coefficient represents a change in the outcome for a one-unit increase of the predictor variable 151

184 Chapter 3 Supplementary Table 4: Longitudinal linear mixed model for the association between handgrip strength and log HOMA-IR over time Coefficient (95% CI) Year 17 years (-0.44, -0.31) Sex Male (-0.19, -0.05) Year*sex 17 years Unadjusted model P value < (-0.52, -0.40) (0.08, -0.05) (-0.16, 0.04) (-0.03, 0.15) Handgrip strength (0.0006, 0.005) (-0.005, ) BMI N/A N/A 0.06 (0.05, 0.07) Constant 0.72 < (0.61, 0.83) (-0.53, -0.24) BMI adjusted model Coefficient P value (95% CI) N= 2392 observations from 1505 participants. CI, confidence interval; HOMA-IR, homeostatic model assessment of insulin resistance; BMI, body mass index The regression coefficient represents a change in the outcome for a one-unit increase of the predictor variable < <0.001 <

185 Chapter 3 Supplementary Table 5: Longitudinal linear mixed model for the association between handgrip strength and log triglycerides over time Coefficient (95% CI) Year 17 years (-0.04, 0.03) Sex Male (-0.15, -0.05) Year*sex 17 years 0.04 Unadjusted model P value (0.08, ) < (-0.10, -0.01) (-0.02, 0.10) (0.03, 0.15) Handgrip strength (0.0006, 0.003) (-0.002, ) BMI N/A N/A 0.03 (0.02, 0.03) Constant < (-0.20, -0.06) (-0.74, -0.54) BMI adjusted model Coefficient P value (95% CI) N= 2396 observations from 1506 participants. CI, confidence interval; BMI, body mass index The regression coefficient represents a change in the outcome for a one-unit increase of the predictor variable <0.001 <

186 Chapter 3 Supplementary Table 6: Longitudinal linear mixed model for the association between handgrip strength and HDL-C over time Coefficient (95% CI) Year 17 years (-0.22, -0.08) Sex Male (-0.06, 0.01) Year*sex 17 years (-0.15, -0.06) Handgrip strength (-0.006, ) Year*handgrip strength 17 years Unadjusted model P value < (-0.18, -0.05) (-0.09, -0.02) < (-0.18, -0.02) < (-0.004, ) < (0.001, 0.004) (0.001, 0.004) BMI N/A N/A (-0.02, -0.01) Constant 1.64 < (1.58, 1.70) (1.94, 2.09) BMI adjusted model Coefficient P value (95% CI) <0.001 <0.001 <0.001 <0.001 <0.001 N= 2396 observations from 1506 participants. CI, confidence interval; HDL-C, high density lipoprotein-cholesterol; BMI, body mass index The regression coefficient represents a change in the outcome for a one-unit increase of the predictor variable 154

187 Chapter 3 Supplementary Table 7: Random effects tobit model for the association between handgrip strength and log hs-crp over time Coefficient (95% CI) Year 17 years 0.43 (0.30, 0.56) Sex Male 0.06 (-0.09, 0.22) Year*sex 17 years Unadjusted model P value < (0.23, 0.48) (0.08, 0.37) < (-0.94, -0.53) (-0.70, -0.31) Handgrip strength (0.001, 0.01) (-0.01, ) BMI N/A N/A 0.11 (0.10, 0.13) Constant < (-1.06, -0.62) (-3.18, -2.59) BMI adjusted model Coefficient P value (95% CI) < < <0.001 <0.001 N= 1967 observations from 1363 participants. CI, confidence interval; hs-crp, high-sensitivity C-reactive protein; BMI, body mass index The regression coefficient represents a change in the outcome for a one-unit increase of the predictor variable 155

188 Chapter 3 Supplementary Table 8: Longitudinal linear mixed model for the association between back muscular endurance and systolic BP over time Unadjusted model BMI adjusted model BMI and PWC 170 adjusted model P value Coefficient P value Coefficient P value (95% CI) (95% CI) Coefficient (95% CI) Year* 17 years (-1.17, 0.50) Sex Male 4.99 (4.05, 5.95) Year*sex 17 years (-2.18, -0.51) < (4.47, 6.31) (-2.27, -0.52) < (4.54, 6.57) < < (3.17, 5.49) (2.93, 5.21) (2.67, 5.14) Back muscular endurance (-0.007, 0.005) (0.004, 0.02) (0.003, 0.02) BMI N/A N/A 0.61 < (0.52, 0.71) (0.52, 0.72) PWC 170 N/A N/A N/A N/A (-0.01, 0.02) Constant < < (107.99, ) (92.32, 97.07) (91.66, 96.76) N (unadjusted and BMI adjusted model) = 2712 observations from 1687 participants; (BMI and PWC 170 adjusted model) =2557 observations from 1638 participants.ci, confidence interval; BP, blood pressure; BMI, body mass index; PWC 170, physical work capacity protocol The regression coefficient represents a change in the outcome for a one-unit increase of the predictor variable <0.001 < < <

189 Chapter 3 Supplementary Table 9: Longitudinal linear mixed model for the association between back muscular endurance and diastolic BP over time Unadjusted model P value Coefficient (95% CI) Year 17 years 0.17 (-0.41, 0.74) Sex Male (-1.31, -0.01) Year*sex 17 years (-0.47, 0.71) (-1.29, 0.007) (-1.56, 0.05) (-1.57, 0.04) Back muscular endurance (-0.002, 0.006) (-0.002, 0.007) BMI N/A N/A 0.03 (-0.04, 0.10) Constant < (58.46, 59.64) (56.69, 60.06) BMI adjusted model Coefficient P value (95% CI) N= 2712 observations from 1687 participants. CI, confidence interval; BP, blood pressure; BMI, body mass index The regression coefficient represents a change in the outcome for a one-unit increase of the predictor variable <

190 Chapter 3 Supplementary Table 10: Longitudinal linear mixed model for the association between back muscular endurance and log HOMA-IR over time Unadjusted model P value Coefficient (95% CI) Year 17 years (-0.43, -0.30) Sex Male (-0.17, -0.04) Year*sex 17 years 0.04 < (-0.52, -0.39) (-0.12, ) (-0.05, 0.12) (-0.07, 0.10) Back muscular endurance < (-0.002, ) (-0.001, ) BMI N/A N/A 0.06 (0.05, 0.10) Constant 1.02 < (0.97, 1.08) (-0.44, -0.14) BMI adjusted model Coefficient P value (95% CI) N= 2325 observations from 1491 participants. CI, confidence interval; HOMA-IR, homeostatic model assessment of insulin resistance; BMI, body mass index The regression coefficient represents a change in the outcome for a one-unit increase of the predictor variable < <0.001 <0.001 <

191 Chapter 3 Supplementary Table 11: Longitudinal linear mixed model for the association between back muscular endurance and log triglycerides over time Unadjusted model P value Coefficient (95% CI) Year 17 years (-0.04, 0.04) Sex Male (-0.13, -0.04) Year*sex 17 years (-0.08, ) < (-0.11, -0.03) < (0.04, 0.15) (0.03, 0.14) Back muscular endurance < (-0.001, ) ( , ) BMI N/A N/A 0.03 (0.02, 0.03) Constant (-0.007, 0.07) (-0.69, -0.47) BMI adjusted model Coefficient P value (95% CI) N= 2325 observations from 1491 participants. CI, confidence interval; BMI, body mass index The regression coefficient represents a change in the outcome for a one-unit increase of the predictor variable <0.001 <

192 Chapter 3 Supplementary Table 12: Longitudinal linear mixed model for the association between back muscular endurance and HDL-C over time Unadjusted model P value Coefficient (95% CI) Year 17 years (-0.06, -0.01) Sex Male (-0.11, -0.04) Year*sex 17 years (-0.02, 0.03) < (-0.12, -0.06) < (-0.16, -0.09) (-0.15, -0.08) Back muscular endurance < (0.0003, ) ( , ) BMI N/A N/A (-0.02, -0.01) Constant 1.39 < (1.36, 1.42) (1.83, 1.99) BMI adjusted model Coefficient P value (95% CI) <0.001 < <0.001 <0.001 N= 2325 observations from 1491 participants. CI, confidence interval; HDL-C, high density lipoprotein-cholesterol; BMI, body mass index The regression coefficient represents a change in the outcome for a one-unit increase of the predictor variable 160

193 Chapter 3 Supplementary Table 13: Random effects tobit model for the association between back muscular endurance and log hs-crp over time Unadjusted model P value Coefficient (95% CI) Year 17 years 0.45 (0.32, 0.59) Sex Male 0.10 (-0.04, 0.25) Year*sex 17 years < (0.21, 0.46) (0.02, 0.29) < (-0.76, -0.39) (-0.78, -0.43) Back muscular endurance < (-0.004, ) (-0.002, ) BMI N/A N/A 0.10 (0.09, 0.12) Constant < (-0.44, -0.18) (-3.14, -2.49) BMI adjusted model Coefficient P value (95% CI) < < <0.001 <0.001 N= 1895 observations from 1337 participants. CI, confidence interval; hs-crp, high-sensitivity C-reactive protein; BMI, body mass index The regression coefficient represents a change in the outcome for a one-unit increase of the predictor variable 161

194 Chapter 3 Supplementary Figure 1. Handgrip strength and systolic BP at 10, 14 and 17 years generated from longitudinal linear mixed models (mean and 95% CIs represented) 162

195 Chapter 3 Supplementary Figure 2. Back muscular endurance and systolic BP at 14 and 17 years generated from longitudinal linear mixed models (mean and 95% CIs represented) 163

196 CHAPTER 4 Dual Energy X-Ray Absorptiometry Compared with Anthropometry in Relation to Cardiometabolic Risk Factors in a Young Adult Population: Is the Gold Standard Tarnished? Chapter 4 FOREWORD The previous two chapters focused on different aspects of physical fitness and its impact on cardio-metabolic risk factors, alongside adiposity. In the light of the obesity epidemic, most studies in youth have used BMI as a measure of obesity, despite there being no universally accepted BMI cut point definition for normal weight, overweight and obese individuals in this population. In addition, inherent limitations exist as previously discussed in Chapter 1 which could potentially misclassify individuals as being overweight or obese, when this is not the case. Nonetheless, other anthropometric measures such as waist circumference and waist to height ratio have been increasingly used in young populations, in an attempt to overcome the shortcomings of BMI. Furthermore, the emergence of technologically advanced methods that determine body composition with a higher degree of accuracy such as DXA have been increasingly employed in the clinical and research setting. It is crucial to identify which method can better detect individuals with an adverse cardio-metabolic risk profile to allow for the early implementation of lifestyle interventions. 164

197 Chapter 4 In this chapter, different measures of adiposity using DXA and anthropometry were compared at the 20-year survey of the Raine Study. The 20-year survey was selected for this study as DXA assessments were only performed at this time point. These measures were examined in relation to an extensive range of cardio-metabolic risk factors. Findings from this study formed the basis for selecting the adiposity measures used in analyses presented in the previous two chapters of this thesis. This chapter was published online in PLoS One in September 2016;11(9):e

198 Chapter ABSTRACT Background: Assessment of adiposity using DXA has been considered more advantageous in comparison to anthropometry for predicting cardio-metabolic risk in the older population, by virtue of its ability to distinguish total and regional fat. Nonetheless, there is increasing uncertainty regarding the relative superiority of DXA and little comparative data exist in young adults. This study aimed to identify which measure of adiposity determined by either DXA or anthropometry is optimal within a range of cardio-metabolic risk factors in young adults. Methods: 1138 adults aged 20 years were assessed by DXA and standard anthropometry from the Western Australian Pregnancy Cohort (Raine) Study. Cross-sectional linear regression analyses were performed. Results: Waist to height ratio was superior to any DXA measure with HDL-C. BMI was the superior model in relation to BP than any DXA measure. Midriff fat mass (measured by DXA) and waist circumference were comparable in relation to glucose. For all other cardiometabolic variables, anthropometric and DXA measures were comparable. DXA midriff fat mass compared with BMI or waist hip ratio was the superior measure for triglycerides, insulin and HOMA-IR. Conclusion: Although midriff fat mass (measured by DXA) was the superior measure with insulin sensitivity and triglycerides, the anthropometric measures were better or equal with various DXA measures for majority of the cardio-metabolic risk factors. These findings 166

199 Chapter 4 suggest, clinical anthropometry is generally as useful as DXA in the evaluation of the individual cardio-metabolic risk factors in young adults. 167

200 Chapter INTRODUCTION The prevalence of obesity is increasing worldwide. In 2014 more than 1.9 billion adults, 18 years and older, were overweight and approximately 600 million adults were obese (1). Excess body fat is an established risk factor for numerous chronic diseases and premature death (2, 3). Most studies seeking to increase the understanding of the negative influence of obesity have been based on BMI. However, BMI does not reflect total body adiposity because it cannot differentiate between lean and fat mass of an individual. Alternative measures such as waist circumference or waist-height ratio may be better clinical indicators of adiposity (4). DXA is the gold standard for the diagnosis of osteoporosis (5) and has increasingly been used for the diagnosis/management of overweight and obesity and in clinical research in cardiovascular disease (6-8). The question arises as to whether the use of DXA in the management of overweight and obesity is justified in terms of cost and/or complexity, in return for any clear-cut scientific or clinical value. There are commercial drivers for the use of DXA; for example in the USA a patient can expect to pay approximately $100 US in the public health setting or $250 US within the private health sector for a DXA scan without rebate (9). There has been a 65% decline in Medicare reimbursement of DXA bone mineral density testing in the non-facility setting, from approximately $140 in 2006 to $50 in 2014 (10). At this lower level, providers of DXA scans are finding it more difficult to cover the operating costs, due to funding cuts (11). Therefore, there has been some motivation to find another use for DXA that may be clinically relevant to health professionals, particularly to allow for early identification and intervention of individuals in relation to obesity and cardiometabolic health. 168

201 Chapter 4 The dominance and differential effects of DXA over anthropometry for estimating the presence of cardio-metabolic risk has not been clearly established. Studies in middle-aged adults, which compare anthropometric and DXA adiposity measurements, have been inconsistent with respect to the strength of associations with cardio-metabolic risk factors, most likely due to methodological inconsistencies in defining adiposity (2, 12). Moreover, many of these reports only consider estimates of total body fat percentage and not fat distribution or abdominal fat mass (10-14). In addition, there is also a lack of reported data comparing DXA against cardio-metabolic risk factors solely in young adult populations (4, 12). Obesity in young adults is an important predictor for subsequent coronary disease and type 2 diabetes in middle to old age (13, 14). Australia follows worldwide trends showing increasing levels of obesity and type 2 diabetes in early adulthood (15). The early identification of individuals who are of increased risk of coronary disease and type 2 diabetes in later life has the potential to implement lifestyle modifications that could reduce this risk. This study therefore aimed to identify which measure of adiposity determined by either DXA (total body fat percentage, Fat Distribution Index and midriff fat mass) or anthropometry (abdominal skinfold, waist circumference, waist to height ratio, weight and BMI), is optimal within a range of cardio-metabolic risk factors in a sample of healthy young adults from the Western Australian Pregnancy Cohort (Raine) Study. 169

202 Chapter METHODS Participants The Western Australian Pregnancy Cohort (Raine) Study is a prospective population study where pregnant females between 16 to 20 weeks gestation were recruited from King Edward Memorial Hospital and closely located practices. The mothers gave birth to 2868 live infants. Detailed information on the methods of the Raine Study has previously been reported (16). The present population comprised 1273 adults from the Raine Study who attended the 20- year survey. Written informed consent was obtained from the participants. Ethics approval for the 20-year assessment was obtained from the Human Research Ethics Committee at the University of Western Australia Adult anthropometry and DXA measurements At the 20 year follow up, height was measured by a wall mounted Stadiometer (to the nearest 0.1 cm) and weight was measured (to the nearest 100g) with participants dressed in light clothes. BMI was calculated as weight (kg) divided by the square of height (m 2 ). Waist circumference was evaluated at the umbilicus level and hip circumference at the level of the maximum posterior extension of the buttocks with a non-stretch plastic (flexible) tape measure (to the nearest 0.1 cm). The abdominal skinfold was measured from a vertical skinfold immediately to the left of the umbilicus, the suprailiac skinfold was assessed from a diagonal fold located 1 cm above the anterior superior iliac crest and the tricep skinfold was measured from a vertical skinfold along the midline on the back of the triceps of the right arm using a skinfold caliper (Holtain, Crosswell, United Kingdom) (17). The maximum measurement possible with skinfold callipers was 40 cm. If participants exceeded the maximum measurement, 40 cm was noted in addition to a separate written measurement. The 170

203 Chapter 4 smallest increment of the callipers is to the nearest 0.2 mm. Callipers were routinely calibrated and 4-5 staff members performed skinfold assessments at the 20-year survey. All skinfold measurements were assessed twice with the average of the skinfolds calculated. The sum of three skinfolds was calculated from the abdominal, suprailiac and tricep skinfold sites and this characterises the subcutaneous fat thickness at different regions of the body (18). Waist to hip and waist to height ratios were derived from the division of waist circumference (cm) by the hip circumference (cm) and height (m), respectively. The study used a Norland XR-36 densitometer (Norland Medical Systems, Inc., Fort Atkinson, WI, USA) to provide estimates of whole body fat mass (g), lean mass (g), midriff fat mass (g) (vertebrae L1 - L4). Total body fat percentage was estimated as total body fat mass (g) / total mass x 100. The Fat Distribution Index was calculated from the formula chest fat mass (g) + midriff fat mass (g) / pelvis fat mass (g) + left leg fat mass (g) + right leg fat mass (g) (19). All measurements were performed by trained research personnel (20) Biochemistry and blood pressure measurements Venous blood samples taken after an overnight fast f 12 hours (fasting status was confirmed by trained personnel) were analysed in the PathWest Laboratory at Royal Perth (21). Serum samples were analysed for total cholesterol, HDL-C, triglycerides and glucose, determined enzymatically on an Architect c16000 Analyser (Abbott Laboratories, Lake Forest, Illinois). Intra- assay coefficients of variation were 0.87% for total cholesterol, 2.0% for HDL-C, 1.92% for triglycerides and 1.04% for glucose. LDL-C was calculated using the Friedewald equation (22). hs-crp was measured by an immunoturbidimetric method on Architect c16000 Analyser (intra-assay coefficient of variation 15.96%). Insulin was determined on an Architect i2000sr Analyser (intra-assay coefficient of variation 1.78%). HOMA-IR was 171

204 Chapter 4 calculated using the formula: fasting insulin (µu/ml) x fasting glucose (mmol/l) / 22.5 (23). BP was measured using an oscillometric sphygmomanometer using the appropriate cuff size on the right arm (DINAMAP vital signs monitor 8100, DINAMAP XL vital signs monitor or DINAMAP ProCare 100; GE Healthcare, Soma Technology, Bloomfield, Connecticut, USA). Six BP readings were obtained every 2 minutes within a 10-minute time period in the supine position, after a 5-minute resting period. The average BP value was calculated using the last five readings to obtain systolic and diastolic BP values (24) Statistical analysis Characteristics of the sample were summarised using means and standard deviations (SD) separately for males and females. Differences between the sexes were tested using t-tests. All reported P values are 2-tailed, and significance was set at α=0.05. Linear regression analyses examined the relationship between each adiposity measure and cardio-metabolic risk factors. All models included and tested the interaction between sex and the adiposity indicator. For each outcome, comparisons were made within the set of models, using each adiposity measure to identify the best DXA measure related to a given cardiometabolic risk factor and similarly for the anthropometric measures. The measures identified as best from each of these two sets (DXA and anthropometry) were then compared. For example, the model of DXA midriff fat mass and triglycerides was compared with the model of waist/height and triglycerides. Given that these models were not nested within each other; likelihood ratio tests were not appropriate to formally compare models. Thus the Akaike Information Criterion (AIC) was utilised (25). As required, all models for a particular outcome were constructed on a static sample determined by complete data on all adiposity measures. 172

205 Chapter 4 For any given outcome, the model with the minimum AIC was deemed to be the best model. Differences from the minimum AIC provide a strength of evidence comparison and the ranking of models with respect to the best model. By calculating differences between AIC, arbitrary scaling constants are removed allowing the application of guidelines to interpret the difference. Models where the difference in AIC is 2 indicates substantial support that they are not different, while support for this claim decreases as the difference in AIC increases (AIC values above 10 are considered to have no support for equivalence). The adjusted R 2 for each model was also reported. In addition to models with a single adiposity measure, combinations of anthropometric and DXA measures were explored in a similar manner. The combinations included midriff fat mass and waist circumference, fat distribution index and BMI, and midriff fat mass and BMI. Tobit regression (for censored data) was utilised for fasting insulin and hs-crp due to the lower boundary of the test (26). All other regressions performed were ordinary linear regression. Variables were log transformed if the values were not normally distributed (triglycerides, HDL-C, insulin, hs-crp). Data were analysed using STATA (StataCorp, Stata Statistical Software: release 12. College Station, Texas, USA). 4.4 RESULTS Descriptive characteristics General characteristics of body composition using DXA, anthropometry and the individual cardio-metabolic risk factors are shown separately for females and males in Table 1. Ethnicity in the cohort was predominantly Caucasian (93%). At 20 years of age, females had a higher total body fat percentage, fat distribution index and midriff fat mass compared with males (p<0.001) (Table 1). Males were taller, heavier and had a greater waist circumference 173

206 Chapter 4 than females (all p<0.001) but had a significantly lower abdominal skinfold. Males and females were not statistically different for waist to height ratio and BMI. All cardio-metabolic risk factors were statistically different between males and females except for HOMA-IR and diastolic BP. Females had lower systolic BP, triglycerides and glucose, and higher total cholesterol, HDL-C, LDL-C, insulin and hs-crp, than males. The number of individuals in the sample with metabolic syndrome according to the revised National Cholesterol Education Program Adult Treatment Panel III guidelines(27) was 26 females (1.8%) and 47 males (3.3%) DXA and anthropometry measures with individual cardio-metabolic risk factors Comparisons within DXA and anthropometric measures Table 2 summarises the DXA and anthropometry measures that produced the lowest AIC for each of the individual cardio-metabolic risk factors (Supplementary Table 1, Supplementary Table 2 and Supplementary Table 3). No statistically significant differences between the sexes in the relationship between the adiposity measures and the cardio-metabolic risk factors were found for any outcome. The interaction term was therefore removed and a single adiposity coefficient applicable to both males and females was estimated. Within the DXA measures, midriff fat mass was the superior model for all risk factors with the exception of HDL-C, hs-crp and systolic BP. In contrast, there was considerable variation in the best anthropometric measure across the cardio-metabolic risk factors: BMI was the best measure for systolic BP, hs-crp, insulin and HOMA-IR; waist to height ratio was best for triglycerides, HDL-C and LDL-C; and abdominal skin fold was best for cholesterol and diastolic BP. Sum of skinfolds, as anthropometry measurements, were not evident to have the lowest AIC with any of the risk factors. None of the combinations of DXA with 174

207 Chapter 4 anthropometry measures were superior to the individual DXA and anthropometry measures with any of the cardio-metabolic risk factors Comparisons between the best DXA and best anthropometry measure Differences between the best DXA and anthropometry measure for individual cardiometabolic risk factors either clearly indicated the dominance of one measure (delta AIC >10) or suggested equivalence (delta AIC 2) (Table 2, Supplementary Table 1, Supplementary Table 2 and Supplementary Table 3)). The model that incorporated midriff fat mass was superior for triglycerides, insulin and HOMA-IR. In contrast, waist-to-height ratio was the superior model in relation to HDL-C and BMI was superior to any DXA measure in relation to systolic BP. For all the other cardio-metabolic variables, anthropometric and DXA measures were comparable. The adjusted R-squared values show marginal differences between the best DXA and anthropometry measures. 175

208 Chapter 4 Table 1. Descriptive characteristics of males and females from the Raine Study at 20 years of age Females n= 532 Males n= 606 P value DXA Total body fat percentage (%) 39.3 (8.9) 21.8 (8.7) <0.001 Fat distribution index (g) ( ) ( ) <0.001 Midriff fat mass (g) (932.9) (887.6) <0.001 Anthropometry Abdominal skinfold (mm) 25.4 (8.6) 21.5 (10.2) <0.001 Waist circumference (cm) 77.2 (13.0) 83.0 (12.2) <0.001 Waist/height ratio 46.7 (8.0) 46.4 (6.6) 0.44 Height (m) 1.66 (0.1) 1.78 (0.1) <0.001 Weight (kg) 67.1 (15.8) 78.7 (16.4) <0.001 BMI (kg/m 2 ) 24.4 (5.6) 24.5 (4.5) 0.71 Biochemistry Cholesterol (mmol/l) 4.5 (0.8) 4.2 (0.8) <0.001 Triglycerides (mmol/l) 1.0 (0.5) 1.1 (0.6) 0.03 HDL-C (mmol/l) 1.4 (0.3) 1.2 (0.2) <0.001 LDL-C (mmol/l) 2.6 (0.6) 2.4 (0.7) <0.001 hs-crp (mg/l) 3.0 (5.5) 2.1 (5.8) Glucose (mmol/l) 4.8 (0.4) 5.1 (0.4) <0.001 Insulin (mu/l) 1.3 (0.7) 1.2 (0.7) HOMA-IR 1.1 (1.2) 1.0 (1.4) 0.69 Blood Pressure Systolic BP (mmhg) (10.2) (11.8) <0.001 Diastolic BP (mmhg) 65.4 (7.2) 65.2 (7.8) 0.62 Descriptive characteristics are presented as means and SD. BMI, body mass index; HDL, high-density lipoprotein; LDL, low-density lipoprotein; hs-crp; high-sensitivity C-reactive protein; HOMA-IR, homeostatic model assessment of insulin resistance; BP, blood pressure. 176

209 Chapter 4 Table 2. The Akaike Information Criterion differences between the best DXA and best anthropometry measures and individual cardio-metabolic risk factors in young adults Adiposity indices Cholesterol N= 1021 Triglycerides N= 1021 HDL-C N= 1021 LDL-C N= 1021 hs-crp N= 1021 Glucose N= 1021 Insulin N= 1021 HOMA-IR N= 1021 Systolic BP N= 1180 Diastolic BP N= 1180 Best DXA AIC R2 Midriff fat mass Midriff fat mass* Fat distribution index Midriff fat mass Total body fat percentage Midriff fat mass Midriff fat mass* Midriff fat mass* Fat distribution index Midriff fat mass Best Anthropometry AIC R2 Abdominal skinfold AIC diff (DXA- Anthropometry) Waist/height ratio Waist/height ratio* Waist/height ratio BMI Waist circumference BMI BMI BMI* Abdominal skinfold *Adiposity measure with the lowest AIC for a given outcome AIC and R 2 values were derived from linear or Tobit regression models adjusted for sex (Supplementary Tables 1-3). AIC differences were derived from the best DXA AIC value - the best anthropometry AIC value. Differences in AIC between models of approximately 2 were considered to indicate equivalent adiposity measures. Otherwise the model with the lowest AIC was considered superior. AIC, Akaike information criterion; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; HOMA-IR, homeostatic model assessment of insulin resistance; hs C-reactive protein; high-sensitivity C-reactive protein; BP, blood pressure; BMI, body mass index; WC, waist circumference. 177

210 Chapter DISCUSSION In this population cohort of young adults, clinical anthropometry measures either outperformed or were equivalent to DXA for majority of the cardio-metabolic risk factors. The exception was DXA midriff fat mass as it was superior for triglycerides, insulin and HOMA-IR. Combinations of various DXA and anthropometry measures were no better than the individual DXA or anthropometry. While not reported here, no additional value of lean body mass measurements or a fat/lean mass ratio compared with clinical anthropometry were found. These results show the utility of anthropometry as a simple, cost-effective primary screening tool for the identification of individuals at risk. The data, however, do not preclude the use of DXA in relation to more focussed research questions or for a further detailed clinical assessment of patients. It is difficult to justify high equipment and scanning costs, burden on the individual and reduced accessibility imposed by DXA used as a primary adiposity screening tool in a clinical setting or for population level research into cardio-metabolic disease. In addition, several factors affecting the efficacy of DXA include the interpretation of DXA scans which require specialised training, technician error operating equipment, the type of clothing and positioning of the patient on the table. With worldwide health costs rapidly increasing, the use of DXA for fat assessment is hard to justify, except in relation to a much more focussed research question and for a more detailed clinical assessment of the patient, as a secondary screening tool. In this latter setting, other direct imaging methods such as magnetic resonance imaging, computer tomography, ultrasound and possibly bioelectrical impedance analysis techniques can be useful for the quantification and differentiation of subcutaneous and visceral fat tissue (28-30). 178

211 Chapter 4 In this study, the much-criticised BMI was the best measure with systolic BP and was superior to any DXA derived measure. Height and weight were also important individual determinants of BP; however, neither was better than BMI in relation to systolic BP. In other studies, BMI was significantly related to systolic BP among US adolescents (4) (31, 32), the NHANES study in children found BMI was more strongly correlated with systolic BP than DXA fat mass percentage (33). Ito et al. (2003) (34) reported the accuracy of detecting hypertension and dyslipidaemia was comparable between BMI, waist circumference and waist to height ratio measures and the DXA measures of total percentage fat mass and midriff fat mass percentage in adults. No other comparisons of DXA and anthropometry were observed in relation to wide range of cardio-metabolic risk factors in young adults from population-based studies. Studies in middle-aged adults and children have also shown lack of dominance of DXA over BMI. Krachler et al. (2013) (31) found BMI had similar predictive power compared to DXA fat mass percentage and bio-impedance analysis for hypertension, impaired fasting glucose, dyslipidaemia and the metabolic syndrome, in middle-aged adults. BMI and waist circumference were similarly correlated with DXA fat mass and fat mass percentage in relation to hs-crp, BP and fasting lipids, glucose and insulin in adults (32) and in youth aged 8 18 years of age (33). Percentage body fat (DXA) did not produce stronger associations in estimating components of the metabolic syndrome (35) and cardiovascular risk factors than BMI and skinfold thickness in youth (36). These findings on hs-crp contrast with those of Vega et al. (2006) (37) who observed that DXA total body fat percentage was better correlated with hs-crp than BMI, in middle-aged US adults. 179

212 Chapter 4 The strengths of this study include data from a large sample population within a narrow age range and a breadth of measures evaluating adiposity using DXA and anthropometry. In addition, an array of cardio-metabolic risk factors was examined, in contrast to most other studies which had a narrower focus. A robust statistical approach was used to compare the performance of the adiposity measures within each outcome. Limitations of this study include its cross-sectional nature and the DXA instrument utilised cannot distinguish between visceral adipose tissue and subcutaneous fat adipose tissue. Therefore, these two components of adiposity were not analysed separately. Additionally, the Norland software used in this study does not differentiate between abdominal visceral and subcutaneous fat. However, as the Hologic DXA software can differentiate between these compartments and these findings may not be applicable to all DXA machine models. Overall this study adds to the weight of evidence suggesting that anthropometry measures are generally as useful as DXA in the evaluation of the individual cardio-metabolic risk factors. Anthropometry offers the advantages of technical simplicity, convenience, and a lower cost compared with DXA. Anthropometry has a great utility as a cost effective primary screening tool of excess adiposity and allows for the early identification of individuals at risk. 180

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215 Chapter 4 adolescents: NHANES International Journal of Body Composition Research. 2013;11(3): Ito H, Nakasuga K, Ohshima A, Maruyama T, Kaji Y, Harada M, et al. Detection of cardiovascular risk factors by indices of obesity obtained from anthropometry and dualenergy X-ray absorptiometry in Japanese individuals. International Journal of Obesity and Related Metabolic Disorders. 2003;27(2): Shen W, Punyanitya M, Chen J, Gallagher D, Albu J, Pi-Sunyer X, et al. Waist circumference correlates with metabolic syndrome indicators better than percentage fat. Obesity. 2006;14(4): Freedman DS, Ogden CL, Kit BK. Interrelationships between BMI, skinfold thicknesses, percent body fat, and cardiovascular disease risk factors among U.S. children and adolescents. BMC Pediatrics. 2015;15(1): Vega GL, Adams-Huet B, Peshock R, Willett D, Shah B, Grundy SM. Influence of body fat content and distribution on variation in metabolic risk. The Journal of Clinical Endocrinology and Metabolism. 2006;91(11):

216 Chapter SUPPORTING INFORMATION Supplementary Table 1. Adjusted R squared and Akaike Information Criterion between DXA and anthropometry adiposity measures with lipid, lipoprotein and inflammatory markers in young adults Adiposity indices DXA Total body fat percentage Cholesterol N= 1021 Triglycerides N= 1021 HDL-C N= 1021 LDL-C N= 1021 C-reactive protein N= 1021 R 2 AIC R 2 AIC R 2 AIC R 2 AIC R 2 AIC Fat distribution index Midriff fat mass Anthropometry Abdominal skinfold Waist circumference Waist/height ratio Weight BMI Combination Midriff fat mass & waist circumference Fat distribution index & BMI Midriff fat mass & BMI Best DXA model Midriff fat mass* Midriff fat mass # Fat distribution index Midriff fat mass * Total body fat percentage # Best anthropometry model Abdominal skinfold* Waist height ratio Waist height ratio # Waist height ratio* BMI 184

217 Chapter 4 Adjusted R 2 and AIC values were derived from linear or Tobit regression models and was adjusted for sex. AIC, Akaike information criterion; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; hs C-Reactive Protein; high sensitivity C-Reactive Protein; BMI, body mass index; WC, waist circumference. # Best adiposity measure * Equivalent adiposity measure 185

218 Chapter 4 Supplementary Table 2. Adjusted R squared and Akaike Information Criterion between DXA and anthropometry adiposity measures and insulin resistance measures in young adults Adiposity indices Glucose Insulin HOMA-IR N= 1021 N= 1021 N= 1021 R 2 AIC R 2 AIC R 2 AIC DXA Total body fat percentage Fat distribution index Midriff fat mass Anthropometry Abdominal skinfold Waist circumference Waist/height ratio Weight BMI Combination Midriff fat mass & waist circumference Fat distribution index & BMI Midriff fat mass & BMI Best DXA model Midriff fat mass * Midriff fat mass # Midriff fat mass # Best anthropometry model Waist circumference* BMI BMI Adjusted R 2 and AIC values were derived from linear or Tobit regression models and was adjusted for sex. AIC, Akaike information criterion; HOMA-IR, homeostatic model assessment of insulin resistance; BMI, body mass index; WC, waist circumference. # Best adiposity measure * Equivalent adiposity measure 186

219 Chapter 4 Supplementary Table 3. Adjusted R squared and Akaike Information Criterion between DXA and anthropometry adiposity measures and blood pressure in young adults Adiposity indices Systolic BP Diastolic BP N= 1180 N= 1180 R 2 AIC R 2 AIC DXA Total body fat percentage Fat distribution index Midriff fat mass Anthropometry Abdominal skinfold Waist circumference Waist/height ratio Weight BMI Combination Midriff fat mass & WC Fat distribution index & BMI Midriff fat mass & BMI Best DXA model Fat distribution index Midriff fat mass * Best anthropometry model BMI # Abdominal skinfold * Adjusted R 2 and AIC values were derived from linear regression models and was adjusted for sex. AIC, Akaike information criterion; BP, s blood pressure; BMI, body mass index; WC, waist circumference. # Best adiposity measure * Equivalent adiposity measure 187

220 CHAPTER 5 General Discussion Chapter OVERALL CONCLUSIONS AND DISCUSSIONS Although CVD does not clinically manifest until later in life, behaviours and risk factors that increase the onset of atherosclerotic CVD begin early in childhood (1). Furthermore, due to the high prevalence of obesity within the paediatric population, it is important to understand the determinants that affect this urgent public health concern (2). This thesis sought to add understanding by examining relationships between different aspects of physical fitness and obesity with a wide range of cardio-metabolic risk factors in a young population, from the Raine cohort, using cross-sectional and longitudinal analyses. Childhood and adolescence are critical stages of life where significant biological and psychological changes occur, and health behaviours are established (3, 4). Adverse behavioural patterns will likely persist into later adulthood, therefore early lifestyle interventions are needed to be implemented in youth, to prevent atherosclerotic and CVD development (3, 5). The Raine Study cohort has provided an excellent opportunity to investigate cardio-metabolic risk factors from a behavioural and developmental perspective. CVD is multifactorial in nature and is affected by dynamic inter-relations between protective and promoting factors that include biological, behavioural, psychological and social influences (6). Using a contemporary cohort of children, adolescents and young adults with comprehensive phenotypic, clinical and socio-behavioural data that were prospectively collected, enabled the employment of longitudinal analytical methods. Furthermore, adjustments for a range of lifestyle and socio-economic confounders in cross-sectional analyses could be utilised. The 188

221 Chapter 5 employment of longitudinal analyses was not feasible for Studies 1 and 3; as important lifestyle confounders were not available in different examination surveys and DXA measurements were only available at the 20-year survey. The cross-sectional design of these two studies, limits causal inferences from its findings. Lastly, the large and healthy sample population has enabled precise estimation and generalisation of these findings to the wider population, as Raine cohort characteristics were similar to the Western Australian population (7). Methodological limitations of the studies include sample attrition, which is more common in the socially disadvantaged families of the Raine Study. Bias could be introduced, as behavioural and emotional factors are prevalent in socially and economically disadvantaged groups. Therefore, these factors could be underestimated, as these factors were not statistically accounted for in the cohort. Another limitation included the lack of consistent data collection of all variables over consecutive surveys which limited the time range and the inclusion of lifestyle and socio-economic confounders in longitudinal analyses. Overall my thesis provides important evidence in relation to CRF and fatness relationships on cardio-metabolic risk factors in an adolescent population, where CVD endpoints have not yet been established. If the observed associations are causal, these findings suggest that fatness has greater adverse effects on cardio-metabolic risk factors compared to the beneficial effects of CRF. Moderate to high levels of muscular strength is favourable for a wide range of cardio-metabolic risks factors, with the exception of BP. Muscular strength and endurance showed unexpected inverse associations with BP from childhood through to adolescence. Furthermore, clinical anthropometry is just as useful as DXA in estimating cardio-metabolic 189

222 Chapter 5 risk at a population level in young adults. These studies need to be replicated in ethnic populations to allow for the application of these findings in a more diverse population. The beneficial effects of CRF on cardio-metabolic risk have been well-established in children and adults (8-11). The fit but fat hypothesis suggests that a moderate-to-high level of CRF substantially attenuates or eliminates the risk of cardiovascular and metabolic disease, in overweight or obese individuals (12). This hypothesis is controversial and little information exists within the paediatric population. To explore this, the first study aimed to examine the relative importance of CRF and fatness, as continuous variables, in relation to cardiometabolic risk factors in Raine Study participants at age 17 years. The results showed independent associations of CRF and fatness with cardio-metabolic risk factors, such that fatness had a positive association on systolic BP, triglycerides, LDL-C, HOMA-IR and hs- CRP, and was inversely associated with HDL-C. CRF only partially attenuated the adverse associations of fatness on HOMA-IR in both sexes and HDL-C in females only. Results showed that overall, fatness had a greater impact and was more closely associated with all cardio-metabolic risk factors examined in post pubertal adolescents, compared to CRF, even after adjustment for a range of lifestyle confounders. When fatness and CRF were analysed as continuous variables, the beneficial effects of CRF on HOMA-IR and HDL-C were seen across the whole spectrum of fatness, not just in overweight or obese individuals. These results suggest that a reduction in weight through caloric restriction to reduce the adverse effects of obesity has more importance for cardiovascular health, than just increasing CRF at this age. Nevertheless, moderate to high levels of physical activity and CRF has a wide range of benefits for physical and mental 190

223 Chapter 5 health outcomes over and above cardio-metabolic risk factors (13-15). In addition, these findings are important in view of the obesity epidemic (16). Follow up of the Raine cohort into adulthood will provide a greater insight as to whether these findings regarding the relative importance of fatness and CRF are maintained and how they relate to major CVD events in later adulthood. The second study aimed to understand the ontogeny of effects of muscular strength (handgrip strength) and endurance (back muscular endurance) on adolescent risk factors for cardiometabolic disease. Muscular fitness is a component of physical fitness that is often overlooked in regards to cardio-metabolic health, particularly in the paediatric population (17). The results showed that two different measures of muscular fitness i.e. handgrip strength and back muscular endurance were both associated with multiple cardio-metabolic risk factors in the paediatric population. After adjustment for BMI, unexpectedly, handgrip strength was positively associated with systolic BP with greater effects in males compared to females. However, these associations attenuated over time from childhood through to adolescence, and the association was no longer evident in females at 17 years of age. Similarly, back muscular endurance was positively associated with systolic BP during adolescence with constant effects through time in both males and females. The possible mechanisms for these associations are unclear and are discussed in Chapter 3. With regards to the unexpected observations on systolic BP, it will be of interest to ascertain whether the positive associations with measures of muscular strength are maintained or reversed into late adulthood, as muscular strength reaches its peak from 20 to 30 years of age (18, 19). In contrast to the effects of muscular fitness on systolic BP, insulin sensitivity and inflammatory 191

224 Chapter 5 markers were inversely related with handgrip strength and back muscular endurance as expected. This finding is of high importance in individuals at risk of type 2 diabetes. This study emphasises the importance of considering specific aspects of physical fitness such as muscular strength when examining individual cardio-metabolic risk factors, not just clusters of CVD risk in childhood and adolescence. In view, future studies are required to determine the optimal period to gain a substantial benefit from resistance strength training for improved health outcomes. As adiposity has a high degree of variance in terms of its influence on cardio-metabolic disturbances, accurate measurements of fatness are crucial in determining individuals and populations at risk. DXA has increasingly been used in the healthcare setting and clinical research to assess body composition of adiposity with regard to cardio-metabolic disease risk (20). However, the dominance and differential effects of DXA over clinical anthropometry for estimating the presence of cardio-metabolic risk has not been clearly established using robust statistical methods. In addition, there is a lack of comparison data between DXA and clinical anthropometry within a young adult population. Therefore, the aim of this study was to identify which measures of adiposity determined by either DXA or clinical anthropometry, are optimal in relation with a wide range of cardio-metabolic risk factors in a sample of healthy young adults. In the 20-year survey of the Raine Study clinical anthropometry measures were equivalent to DXA for majority of the cardio-metabolic risk factors examined. With the exception of DXA midriff fat mass, as it had the lowest AIC value in comparison to all clinical anthropometry indices with serum triglycerides, insulin and HOMA-IR. Combinations of 192

225 Chapter 5 various DXA and anthropometry measures were no better than individual measures of DXA or anthropometry. Furthermore, neither lean body mass or a fat/lean mass ratio provided any additional value to clinical anthropometry. Overall, clinical anthropometry was just as useful in estimating a wide range of cardio-metabolic risk factors compared to DXA in a population where CVD end points are not yet established. The use of a tape measure and scales offer the advantages of technical simplicity and convenience at a lower cost, which is favourable in large epidemiological studies. To our knowledge, this is the first comprehensive study comparing adiposity measures utilising DXA and anthropometry with a wide range of cardiometabolic risk factors using robust statistical methods, in a young population. The findings from the third study formed the basis of adiposity measurements employed in Studies 1 and 2. Determining which mode of adiposity assessment better estimates different cardio-metabolic risk factors should provide a basis for future population studies regarding optimal risk stratification in research and for the management of obesity. Potential future directions include the investigation of DXA and anthropometry measures using similar robust statistical methods in children and adolescents. This is important, particularly as no definitive and universal definition and cut points for paediatric obesity exist. Therefore, it will be of interest to see if these relationships are also observed within this population. These novel findings provide further understanding of the determinants of cardio-metabolic health within a young population. This information should provide as a valuable resource to assist with developing innovative strategies for health promotion programs, to promote healthy eating and physical activity behaviours, particularly within the paediatric context (4, 21, 22). In addition, these findings should be useful in educating individuals who work 193

226 Chapter 5 alongside children and adolescents, to allow for lifestyle modifications to alleviate the consequences of obesity, thereby reducing the development of CVD in later life. 194

227 Chapter REFERENCES 1. Juonala M, Viikari JSA, Raitakari OT. Main findings from the prospective cardiovascular risk in young Finns study. Current Opinion in Lipidology. 2013;24(1): Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, Journal of the American Medical Association. 2014;311(8): Patton GC, Sawyer SM, Santelli JS, Ross DA, Afifi R, Allen NB, et al. Our future: a Lancet commission on adolescent health and wellbeing. The Lancet.387(10036): Patton GC, Sawyer SM, Santelli JS, Ross DA, Afifi R, Allen NB, et al. Our future: a Lancet commission on adolescent health and wellbeing. The Lancet. 2016;387(10036): Maahs DM, Daniels SR, de Ferranti SD, Dichek HL, Flynn J, Goldstein BI, et al. Cardiovascular disease risk factors in youth with diabetes mellitus. Circulation. 2014;130(17): Lynch J, Smith GD. A life course approach to chronic disease epidemiology. Annual Review of Public Health. 2005;26: Straker L, Mountain J, Jacques A, White S, Smith A, Landau L, et al. Cohort Profile: The Western Australian Pregnancy Cohort (Raine) Study Generation 2. International Journal of Epidemiology. 2017:46(5): j. 8. Kodama S, Saito K, Tanaka S, Maki M, Yachi Y, Asumi M, et al. Cardiorespiratory fitness as a quantitative predictor of all-cause mortality and cardiovascular events in healthy men and women: a meta-analysis. Journal of the American Medical Association. 2009;301(19): Hassinen M, Lakka TA, Savonen K, Litmanen H, Kiviaho L, Laaksonen DE, et al. Cardiorespiratory fitness as a feature of metabolic syndrome in older men and women. Diabetes Care. 2008;31(6): Anderssen SA, Cooper AR, Riddoch C, Sardinha LB, Harro M, Brage S, et al. Low cardiorespiratory fitness is a strong predictor for clustering of cardiovascular disease risk factors in children independent of country, age and sex. European Journal of Cardiovascular Prevention and Rehabilitation. 2007;14(4): Dencker M, Thorsson O, Karlsson MK, Lindén C, Wollmer P, Andersen LB. Aerobic fitness related to cardiovascular risk factors in young children. European Journal of Pediatrics. 2012;171(4): Blair SN, Kohl HW, Paffenbarger RS, Clark DG, Cooper KH, Gibbons LW. Physical fitness and all-cause mortality: a prospective study of healthy men and women. Journal of the American Medical Association. 1989;262(17): Ekelund U, Franks PW, Sharp S, Brage S, Wareham NJ. Increase in physical activity energy expenditure is associated with reduced metabolic risk independent of change in fatness and fitness. Diabetes Care. 2007;30(8): Knapen J, Vancampfort D, Moriën Y, Marchal Y. Exercise therapy improves both mental and physical health in patients with major depression. Disability and Rehabilitation. 2015;37(16): Ruggero CJ, Petrie T, Sheinbein S, Greenleaf C, Martin S. Cardiorespiratory fitness may help in protecting against depression among middle school adolescents. Journal of Adolescent Health. 2015;57(1): Biro FM, Wien M. Childhood obesity and adult morbidities. The American Journal of Clinical Nutrition. 2010;91(5):1499S-505S. 195

228 Chapter Grøntved A, Ried-Larsen M, Møller NC, Kristensen PL, Froberg K, Brage S, et al. Muscle strength in youth and cardiovascular risk in young adulthood (the European Youth Heart Study). British Journal of Sports Medicine Granacher U, Muehlbauer T, Gruber M. A Qualitative Review of Balance and Strength Performance in Healthy Older Adults: Impact for Testing and Training. Journal of Ageing Research. 2012;2012: Dodds RM, Syddall HE, Cooper R, Benzeval M, Deary IJ, Dennison EM, et al. Grip strength across the life course: Normative data from twelve British studies. PLoS ONE. 2014;9(12):e Wells JCK, Fewtrell MS. Measuring body composition. Archives of Disease in Childhood. 2006;91(7): Bundy DAP, de Silva N, Horton S, Patton GC, Schultz L, Jamison DT. Investment in child and adolescent health and development: key messages from Disease Control Priorities. The Lancet The Lancet. The next phase for adolescent health: from talk to action. The Lancet. 2017;390(10106):

229 Appendix A APPENDIX A The Western Australian Pregnancy Cohort (Raine) Study Participants from birth to the 20- year review Deferred: Remaining in the cohort but declined to participate in the current review Lost: Lost to follow up Withdrawn: Withdrawn from cohort, no further contact Deceased: Participant is deceased 197

230 Appendix B APPENDIX B Published Manuscripts Related to this Thesis 198

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238 Appendix B 206

239 Appendix B 207

240 Appendix B 208

241 Appendix B 209

242 Appendix B 210

243 Appendix B 211

244 Appendix B 212

245 Appendix B 213

246 Appendix B 214

247 Appendix B 215

248 Appendix B 216

249 Appendix B 217

250 Appendix B 218

251 Appendix B 219

252 Appendix B 220

253 Appendix B 221

254 Appendix B 222

255 Appendix B 223

256 Appendix B 224

257 Appendix B 225

258 Appendix B 226

259 Appendix B 227

260 Appendix B 228