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Estimation of Whole Body Muscle, Adipose/Fat Mass, Validation in Health and During Weight Loss Development of Prediction Equations Using MRI As Reference Method

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Estimation of Whole Body Muscle, Adipose/Fat Mass, Validation in Health and During Weight Loss Development of Prediction Equations Using MRI As Reference Method UNIVERSITY OF GLASGOW Estimation of whole body muscle, adipose/fat mass, validation in health and during weight loss Development of prediction equations using MRI as reference method Yasmin Yussuf Algindan 9/22/2016 A thesis submitted for the degree of Doctor of Philosophy To Department of Human Nutrition School of Medicine 1 Abstract: Background: Body composition is affected by diseases, and affects responses to medical treatments, dosage of medicines, etc., while an abnormal body composition contributes to the causation of many chronic diseases. While we have reliable biochemical tests for certain nutritional parameters of body composition, such as iron or iodine status, and we have harnessed nuclear physics to estimate the body’s content of trace elements, the very basic quantification of body fat content and muscle mass remains highly problematic. Both body fat and muscle mass are vitally important, as they have opposing influences on chronic disease, but they have seldom been estimated as part of population health surveillance. Instead, most national surveys have merely reported BMI and waist, or sometimes the waist/hip ratio; these indices are convenient but do not have any specific biological meaning. Anthropometry offers a practical and inexpensive method for muscle and fat estimation in clinical and epidemiological settings; however, its use is imperfect due to many limitations, such as a shortage of reference data, misuse of terminology, unclear assumptions, and the absence of properly validated anthropometric equations. To date, anthropometric methods are not sensitive enough to detect muscle and fat loss. Aims: The aim of this thesis is to estimate Adipose/fat and muscle mass in health disease and during weight loss through; 1. evaluating and critiquing the literature, to identify the best-published prediction equations for adipose/fat and muscle mass estimation; 2. to derive and validate adipose tissue and muscle mass prediction equations; and 3.to evaluate the prediction equations along with anthropometric indices and the best equations retrieved from the literature in health, metabolic illness and during weight loss. Methods: a Systematic review using Cochrane Review method was used for reviewing muscle mass estimation papers that used MRI as the reference method. Fat mass estimation papers were critically reviewed. 2 Mixed ethnic, age and body mass data that underwent whole body magnetic resonance imaging to quantify adipose tissue and muscle mass (dependent variable) and anthropometry (independent variable) were used in the derivation/validation analysis. Multiple regression and Bland-Altman plot were applied to evaluate the prediction equations. To determine how well the equations identify metabolic illness, English and Scottish health surveys were studied. Statistical analysis using multiple regression and binary logistic regression were applied to assess model fit and associations. Also, populations were divided into quintiles and relative risk was analysed. Finally, the prediction equations were evaluated by applying them to a pilot study of 10 subjects who underwent whole-body MRI, anthropometric measurements and muscle strength before and after weight loss to determine how well the equations identify adipose/fat mass and muscle mass change. Results: The estimation of fat mass has serious problems. Despite advances in technology and science, prediction equations for the estimation of fat mass depend on limited historical reference data and remain dependent upon assumptions that have not yet been properly validated for different population groups. Muscle mass does not have the same conceptual problems; however, its measurement is still problematic and reference data are scarce. The derivation and validation analysis in this thesis was satisfactory, compared to prediction equations in the literature they were similar or even better. Applying the prediction equations in metabolic illness and during weight loss presented an understanding on how well the equations identify metabolic illness showing significant associations with diabetes, hypertension, HbA1c and blood pressure. And moderate to high correlations with MRI-measured adipose tissue and muscle mass before and after weight loss. Conclusion: Adipose tissue mass and to an extent muscle mass can now be estimated for many purposes as population or groups means. However, these equations must not be used for assessing fatness and categorising individuals. Further exploration in different populations and health surveys would be valuable. 3 Acknowledgments I would like to thank all who helped in making my dream a reality. The process of earning a doctorate is certainly not done singlehandedly. I would like to thank people who have journeyed with me as I have worked on this thesis. First, I would like to thank the University of Dammam and the University of Glasgow for giving me this amazing educational opportunity. Professor Lean, you are inspiring; you knew how to lead me to the right sources, ideas and perspectives. With the guidance and support of Dr Hankey and Dr Govan, I am blessed with the best supervising team a PhD student could ask for; their encouragement, trust and wealth of information throughout is what has kept me going. Dr Wilma, although not my supervisor, you were there whenever I needed your help, and Maureen, thank you for your administrative support. Naomi and George, we are a great team. It was an amazing final year for me, and I am looking forward to collaborations with Dr Brosnahan and Dr Thom. During the past five years I have been half a daughter, sister, wife and mother. Despite that you showered me with love, support and patience. Mom and Dad, thank you for being the best role models and being available whenever I needed you. Reem, Mohanna, Najla, Nora and Sara, thank you for your heartfelt support and love. Deem, Haneen and Leen, my three beautiful angels, you inspire me. I hope this journey inspires you too. Hazim… none of this is possible without you by my side… love you. I am forever grateful…. Yasmin 4 Publications Arising from this PhD Thesis Al-Gindan YY, Hankey C, Leslie W, Govan L, Lean MEJ .2013. Predicting muscle mass from anthropometry, using magnetic resonance imaging (MRI) as reference: a systematic review.Proceedings of the nutrition society.72(OCE2),E102(Appendix 1) AL-GINDAN, Y. Y., HANKEY, C. R., LESLIE, W., GOVAN, L. & LEAN, M. E. 2014b. Predicting muscle mass from anthropometry using magnetic resonance imaging as reference: a systematic review. Nutr Rev, 72, 113- 26.http://www.ncbi.nlm.nih.gov/pubmed/24447263 AL-GINDAN, Y. Y., HANKEY, C., GOVAN, L., GALLAGHER, D., HEYMSFIELD, S. B. & LEAN, M. E. 2014a. Derivation and validation of simple equations to predict total muscle mass from simple anthropometric and demographic data. Am J Clin Nutr, 100, 1041-51. http://www.ncbi.nlm.nih.gov/pubmed/25240071 Al-Gindan YY, Hankey C, Govan L, Gallagher D, Heymsfield SB, Lean MEJ. 2014. Derivation and validation of simple anthropometric equations to predict adipose tissue fat mass, using MRI as reference method. ICO T8:S37.06 (Appendix 2) AL-GINDAN, Y. Y., HANKEY, C. R., GOVAN, L., GALLAGHER, D., HEYMSFIELD, S. B. & LEAN, M. E. 2015. Derivation and validation of simple anthropometric equations to predict adipose tissue mass and total fat mass with MRI as the reference method. Br J Nutr, 114, 1852-67.http://www.ncbi.nlm.nih.gov/pubmed/26435103 Gonzalez MRL, Al-Gindan YY, Brosnahan N, Thom G, Govan L, Hankey C, Roditi G and Lean M EJ. 2015. Quantification of whole body fat and muscle using Magnetic Resonance Imaging. ESMRMB (Appendix 4) Al-Gindan YY, Brosnahan N, Thom G, McCombie L, Hankey C, Lean M (Chief Investigator), Govan L, Gerasimidis K, Rizou E, Dombrowski S, Banger M, Roditi G, Lopez-Gonzalez R.2015. Body Composition and Energy Expenditure with Total Diet Replacement during weight loss and maintenance (BEYOND): study protocol. UKCO 2015 (Glasgow) (Appendix 5) 5 Author’s Declaration I hereby declare that I am the author of this thesis literature review, the study designs and statistical analysis with the guidance of my supervisors Professor Michael Lean, Dr Catherine Hankey and Dr Lindsay Govan. Dr Wilma Leslie was the second reviewer in the systematic review (Chapter3). Professor Steven Heymsfield and Professor Dympna Gallagher contributed to the data collection studies presented in Chapters 4 and 5. The BEYOND weight loss study (Chapter 6) was designed by George Thom, Naomi Brosnahan, and I, with the guidance of our supervisors. The study is divided to three work packages: I am the author and designer of work package 1 (body composition); George is the author and designer of work package 2 (metabolic adaptation and psychological experience during weight loss); and Naomi is the author and designer of work package 3 (binge eating disorder and weight loss maintenance). MRI measurements in work package 1 were led by Dr Giles Rodditi and analysed by Dr Gonzalez Marie, while strength measurements were measured by Dr Matthew Banger. 6 Table of Content Abstract: ........................................................................................ 2 Acknowledgments .............................................................................. 4 Publications Arising from this PhD Thesis .................................................. 5 Author’s Declaration .......................................................................... 6 Table of Content ..............................................................................
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