The optimal body size associated with the most favorable breast cancer outcomes is not well established. Most importantly, not all overweight and obese women are at higher risk of poor outcomes. Muscle mass (MM) exerts powerful endocrine, immune, and hormonal influences within the body. Sarcopenia (low muscle mass or muscle wasting) and sarcopenic obesity"""""""" (i.e., obesity and low muscle mass) are common in breast cancer survivors and may be further exacerbated by oncological treatments which increase fat mass and reduce muscle mass. In response to PQ2, """"""""How does obesity relate to cancer risk"""""""", we propose to examine in addition to adiposity or fat mass (FM) alone, the effect of MM and the relationship of adiposity to muscularity on breast cancer outcomes. This will further our understanding of the ideal body composition associated with the best prognosis and may help explain the observation that overweight and (mild) obesity predict elevated risk of breast cancer, but paradoxically do not always predict higher mortality after diagnosis compared with those who are normal-weight or underweight. Recently, methods to measure FM and MM using abdominal Computerized Tomography (CT) scans have been developed and validated to identify individuals with sarcopenia. In two cohorts of women (total n=3,250) diagnosed between 2000 and 2015 with Stage I-III invasive breast cancer at the Kaiser Permanente Northern California (KPNC) and the Dana Farber Cancer Institute (DCFI), we will use CT scans collected as part of routine care at diagnosis, and assess FM and MM in order to understand the role of body composition, sarcopenia, and sarcopenic obesity in breast cancer prognosis in an attempt to explain how obesity relates to survival risk. Specifically, we will examine the level of muscle wasting (sarcopenia) and sarcopenic obesity in breast cancer survivors at diagnosis and how that varies by important medical and demographic characteristics (Aim 1). We will also examine associations between FM, MM, sarcopenia, sarcopenic obesity, and cardiometabolic risk factors (Aim 2);chemotoxicity (Aim 3);and breast cancer-specific and overall mortality (Aim 4). Multiple linear and logistic regression will be used to evaluate most associations, and Cox models will be used to evaluate associations with mortality. The proposed study is the first large-scale investigation to examine the role of body composition at diagnosis and before treatment on breast cancer outcomes. This study uses a novel, state-of-the art tool to measure fat/muscle mass that is biologically more relevant than standard measures of height and weight, and could provide important insight into the role of body composition in cancer survival. This study has the potential for high impact since CT scans already collected as a part of regular care could be used to assess fat and muscle mass with little added extra cost. Alternatively, other methods including dual energy X-ray absorptiometry (DXA) scans or bioelectrical impedance analyses (BIA) could be adopted into clinical practice, adding an important physiologic measure to BMI to help target and personalize weight control strategies and other treatments to prevent muscle mass loss, and improve survival. Both FM and MM have independent metabolic roles in breast cancer prognosis that may ultimately help to explain the mechanistic pathways underlying the association between obesity and breast cancer survival.
The optimal body size associated with the most favorable breast cancer outcomes is not well established. Most importantly, not all overweight and obese women are at higher risk of poor outcomes. The importance of muscle mass in breast cancer survival has not been adequately studied. This study will examine the extent of body fat and muscle mass as well as sarcopenia (low muscle mass) and sarcopenic obesity (low muscle mass and obesity) among women diagnosed with breast cancer to evaluate how these conditions influence prognosis. Study findings may provide important insight into the role of body composition in cancer survival and may lead to more tailored treatment strategies.
