(PQA2) Exploring the role of sarcopenia in obesity and breast cancer survival

描述（由申请人提供）：与最有利的乳腺癌结局相关的最佳体型尚未明确。最重要的是，并非所有超重和肥胖的女性都有更高的不良结局风险。肌肉质量（MM）在体内发挥强大的内分泌，免疫和激素影响。肌肉减少症（低肌肉质量或肌肉萎缩）和肌肉减少性肥胖”（即，肥胖和低肌肉质量）在乳腺癌幸存者中是常见的，并且可能由于增加脂肪质量和减少肌肉质量的肿瘤治疗而进一步恶化。在回答PQ 2“肥胖与癌症风险如何相关”时，我们建议除了单独的肥胖或脂肪量（FM）外，还检查MM的影响以及肥胖与肌肉发达对乳腺癌结局的关系。这将进一步加深我们对与最佳预后相关的理想身体成分的理解，并可能有助于解释超重和（轻度）肥胖预测乳腺癌风险升高的观察结果，但矛盾的是，与体重正常或体重不足的人相比，诊断后的死亡率并不总是预测更高。最近，已经开发并验证了使用腹部计算机断层扫描（CT）扫描来测量FM和MM的方法，以识别患有肌肉减少症的个体。在两组女性中，在2000年至2015年期间在Kaiser Permanente北方加州（KPNC）和Dana Farber癌症研究所（DCFI）诊断为I-III期浸润性乳腺癌的患者（总n= 3，250）中，我们将使用作为诊断时常规护理的一部分收集的CT扫描，并评估FM和MM，以了解身体成分、肌肉减少症、和肌肉减少性肥胖在乳腺癌预后中的作用，试图解释肥胖与生存风险的关系。具体来说，我们将检查乳腺癌幸存者在诊断时的肌肉萎缩（肌肉减少症）和肌肉减少性肥胖的水平，以及重要的医学和人口统计学特征（目标1）如何变化。我们还将研究FM、MM、肌肉减少症、肌肉减少性肥胖症和心脏代谢危险因素（目标2）、化学毒性（目标3）以及乳腺癌特异性死亡率和总体死亡率（目标4）之间的相关性。多元线性和逻辑回归将用于评价大多数相关性，考克斯模型将用于评价与死亡率的相关性。这项研究是第一个大规模的调查，以检查身体成分在诊断和治疗前对乳腺癌结果的作用。这项研究使用了一种新的、最先进的工具来测量脂肪/肌肉质量，这比身高和体重的标准测量在生物学上更相关，并且可以为身体成分在癌症生存中的作用提供重要的见解。这项研究有可能产生很大的影响，因为作为常规护理的一部分已经收集的CT扫描可以用来评估脂肪和肌肉质量，而几乎没有额外的成本。或者，其他方法，包括双能X射线吸收测定法（DXA）扫描或生物电阻抗分析（BIA）可以用于临床实践，为BMI增加一个重要的生理指标，以帮助目标和个性化体重控制策略和其他治疗，以防止肌肉质量损失，并提高生存率。FM和MM在乳腺癌预后中具有独立的代谢作用，这可能最终有助于解释肥胖和乳腺癌生存之间相关的机制途径。

DESCRIPTION (provided by applicant): 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.