Principal Investigator: Mrs Victoria Bland
Department: University of Arizona, Tucson, United StatesTags: 52264, adiposity, Bone, inflammation, Mendelian randomisation, obesity, osteoporosis
Obesity and osteoporosis remain two major public health concerns. While obesity was once considered protective against osteoporosis, more recent evidence has shown an association between obesity and increased osteoporotic fracture risk. Inconsistency in findings may be due to the use of non-specific measures for obesity, such as body mass index (BMI) or percent body fat, which do not account for the physiological differences between adipose tissue depots. Fat tissue located around the organs, known as visceral adipose tissue, has been associated with metabolic dysregulation (e.g., chronic inflammation, insulin resistance) often seen in obesity. Meanwhile, fat tissue located directly under the skin in the abdomen and lower body regions, known as subcutaneous adipose tissue, is thought to contribute less to the metabolic dysregulation. Recently genetic studies have identified “favorable adiposity” gene variants that are associated with less visceral adipose tissue, greater subcutaneous adipose tissue, and better metabolic health.
The proposed study aims to address the gaps in knowledge regarding the association between soft tissue and bone health by analyzing the relationship between fat distribution, lean mass, and bone outcomes (e.g., bone mineral density, fracture history). This study will utilize data available from individuals in the UK Biobank imaging sub-study, including magnetic resonance imaging (MRI), dual energy x-ray absorptiometry (DXA), genetic, and health outcomes data. We also propose to use Mendelian Randomization, a genetic epidemiological technique, to further determine if there is a potential causal relationship between genes for body composition and bone outcomes. The proposed aims will advance our understanding of the relationship between body composition and bone health with the overarching goal to better predict individual risk for osteoporosis based on body composition and genetic profile.