Estimation of whole body muscle, adipose/fat mass, validation in health and during weight loss. Development of prediction equations using MRI as reference method

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. 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.

Exploring the role of miR-34a in regulating adipose inflammation during obesity

Background: Obesity is not a new disease, with roots that can be traced back to 400 BC. However, with the staggering increase in individuals that are overweight and obese since the 1980s, now over a quarter of individuals in Europe and the Americas are classed as obese. This presents a global health problem that needs to be addressed with novel therapies. It is now well accepted that obesity is a chronic, low-grade inflammatory condition that could predispose individuals to a number of comorbidities. Obesity is associated with cardiovascular diseases (CVDs) and type 2 diabetes (T2D) as part of “the metabolic syndrome,” and as first identified by Dr Vauge, central distribution of white adipose tissue (WAT) is an important risk factor in the development of these diseases. Subsequently, visceral WAT (vWAT) was shown to be an important factor in this association with CVDs and T2D, and increasing inflammation. As the obese WAT expands, mainly through hypertrophy, there is an increase in inflammation that recruits numerous immune cells to the tissue that further exacerbate this inflammation, causing local and systemic inflammatory and metabolic effects. One of the main types of immune cell involved in this pathogenic process is pro-inflammatory M1 adipose tissue macrophages (ATMs). MicroRNAs (miRNAs) are a species of small RNAs that post-transcriptionally regulate gene expression by targeting gene mRNA, causing its degradation or translational repression. These miRNAs are promiscuous, regulating numerous genes and pathways involved in a disease, making them useful therapeutic targets, but also difficult to study. miR-34a has been shown to increase in the serum, liver, pancreas, and subcutaneous (sc)WAT of patients with obesity, non- alcoholic fatty liver disease (NAFLD) and T2D. Additionally, miR-34a has been shown to regulate a number of metabolic and inflammatory genes in numerous cell types, including those in macrophages. However, the role of miR-34a in regulating vWAT metabolism and inflammation is poorly understood. Hypothesis: miR-34a is dysregulated in the adipose tissue during obesity, causing dysregulation of metabolic and inflammatory pathways in adipocytes and ATMs that contribute to adipose inflammation and obesity’s comorbidities, particularly T2D. Method/Results: The role of miR-34a in adipose inflammation was investigated using a murine miR-34a-/- diet-induced obesity model, and primary in vitro models of adipocyte differentiation and inflammatory bone marrow-derived macrophages (BMDMs). miR-34a was shown to be ubiquitously expressed throughout the murine epididymal (e)WAT of obese high-fat diet (HFD)-fed WT mice and ob/ob mice, as well as omental WAT from patients with obesity. Additionally, miR-34a transcripts were increased in the liver and brown adipose tissue (BAT) of ob/ob and HFD-fed WT mice, compared to WT controls. When miR-34a-/- mice were fed HFD ad libitum for 24 weeks they were significantly heavier than their WT counterparts by the end of the study. Ex vivo examinations showed that miR-34a-/- eWAT had a smaller adipocyte area on chow, which significantly increased to WT levels during HFD-feeding. Additionally, miR-34a-/- eWAT showed basal increases in cholesterol and fatty acid metabolism genes Cd36, Hmgcr, Lxrα, Pgc1α, and Fasn. miR-34a-/- iBAT showed basal reductions in Cebpα and Cebpβ, with increased Pgc1α expression during HFD- feeding. The miR-34a-/- liver additionally showed increased basal transcript expression of Pgc1α, suggesting miR-34a may broadly regulate PGC1α. Accompanying the ex vivo changes in cholesterol and fatty acid metabolism genes, in vitro miR-34a-/- white adipocytes showed increased lipid content. An F4/80high macrophage population was identified in HFD-fed miR-34a-/- eWAT, with increased Il-10 transcripts and serum IL-5 protein. Following these ex vivo observations, BMDMs from WT mice upregulated miR-34a expression in response to TNFα stimulation. Additionally, miR-34a-/- BMDMs showed an ablated CXCL1 response to TNFα. Conclusion: These findings suggest miR-34a has a multi-factorial role in controlling a susceptibility to obesity, by regulating inflammatory and metabolic pathways, potentially through regulation of PGC1α.