Vicky Goh1
1King's College London, London, United Kingdom
Synopsis
Normal adipose tissue is a dynamic
structure with endocrine, metabolic, haematological and immune functions in
addition to fat storage. Obesity (body
mass index ≥30 kg/m2) has doubled in prevalence since the 1980s and
is a risk to health. This lecture describes normal body fat distribution; the physiological
changes in fat and muscle distribution with ageing; the prevalence of obesity
globally and its impact on cancer risk and outcomes. Imaging techniques to
assess body composition (fat-muscle mass) are compared and how such imaging can
help the management of cancer patients is discussed.
Obesity is a growing threat to global health. More than 2 billion adults and 340 million children are now overweight (where body mass index is 25 kg/m2 or more) or obese (where body mass index is 30kg/m2 or more). Since 1980, the prevalence of obesity has doubled in over 70 countries. Obesity is a major risk
factor for developing cancer and for cancer death. In part, this may be related to the inter-relationship between obesity and metabolic syndrome. There is a strong association with some cancer types including endometrial, esophageal, renal, liver, pancreas, myeloma, gastric, colon and rectum, gall bladder, ovary, breast and thyroid cancers. For endometrial and breast cancer, higher circulating estrogen levels related to adipose tissue synthesis are also thought to be contributory.
Assessment of body composition (sarcopenic obesity and sarcopenia) may help in the management of cancer patients by identifying at-risk patients who might benefit from pre-habilitation or require some modifications to therapeutic approaches such as surgery or chemotherapy. Imaging techniques that are used in clinical practice include dual
energy X-ray absorptiometry, CT and MRI. Whole body MRI provides a comprehensive approach to assessing total body fat volume; subcutaneous and visceral fat volume; and lean body muscle volume; whole body workflow has been facilitated more recently with deep learning techniques. Acknowledgements
Not applicableReferences
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