Population-level imaging and genetic analyses to determine how bone marrow adiposity impacts human health
Project Details - Population-level imaging and genetic analyses to determine how bone marrow adiposity impacts human health
Supervisor(s): Dr William Cawthorn, Prof Ewan Pearson & Prof Jimmy Bell
|Centre/Institute: Centre for Cardiovascular Science|
Bone marrow adipose tissue (MAT) accounts for up to 70% of total bone marrow volume and approximately 10% of total fat mass in lean, healthy humans . MAT further increases with ageing and in diverse clinical conditions, including osteoporosis, obesity, type 1 diabetes, oestrogen deficiency, radiotherapy and glucocorticoid treatment. In striking contrast to other adipose depots, MAT also increases during caloric restriction in animals and in humans with anorexia nervosa [1-3]. Thus, MAT is a major component of normal human anatomy; it is distinct to other types of adipose tissue; and it is altered in numerous clinical contexts. This suggests that MAT plays a role in normal physiological function and might also impact skeletal, metabolic and ageing-associated diseases. However, study of MAT has been extremely limited, and therefore the formation and function of MAT remains poorly understood.
Magnetic resonance imaging (MRI) is emerging as a key tool for non-invasively assessing the properties of MAT. For example, MRI can measure both the extent of bone marrow adiposity (BMA) and the degree of saturated and unsaturated lipids within the bone marrow. This approach has been applied in a handful of small-scale human cohort studies, revealing some insights into MAT’s association with human skeletal and metabolic health . However, MRI-based MAT analysis has never been done on a larger scale. Such population-level studies would have enormous potential to reveal fundamental knowledge of the formation and function of MAT, including the association with physiological, pathological and genetic variables. Such knowledge would provide new understanding about the factors that regulate MAT development, as well as highlighting how altered MAT content might impact human health and disease.
By pursuing these aims, the student will gain skills in image analysis (MRI), computational biology (machine learning algorithms), genomics (e.g. interrogation of UK Biobank genomic data, Mendelian Randomisation), bioinformatics and data management (e.g. programming for management and analysis of large-scale datasets), and a key understanding of human physiology and pathology. This project provides a unique opportunity to comprehensively establish how MAT impacts human health and disease.
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