Association of Nutritional and Biochemical Predictors of Body Fat Amount and Distribution with Cancer Risk

项目总结（见说明）： 与整体、外周或皮下脂肪相比，已知特别是腹部、腹内和肝脏区域的身体肥胖具有更高的代谢风险。然而，在少数民族中发现的比例更大的高风险区间中脂肪量的影响可能被癌症流行病学研究低估了，这些研究依赖于身体质量和腰围的替代措施。我们假设体脂地形可以通过人口统计学，简单的人体测量学和生活方式的麻烦变量（与血压相关的血液生化和行为特征）相结合来预测。事实上，我们在一项使用多种族队列（MEC）研究的可行性研究中发现， 当考虑BMI以外的其他变量时，通过腹部MRI测量的腹部脂肪隔室的百分比可以得到改善。在拟议的研究中，我们将首先评估饮食摄入，体力活动和认知饮食的能量相关行为，并将分析脂肪细胞因子，胰岛素/IGF，性类固醇激素，脂质和脂溶性微量营养素的空腹血液水平，000名MEC受试者（10个性别-种族组各200例），将再次联系这些患者，以进行通过DXA和腹部MRI测量的详细肥胖表型的横断面研究。使用随机森林和其他类似的回归模型，我们将确定最好的营养，生化和行为标志物， 改善BMI对以下肥胖表型的预测：DXA测量的总脂肪量和躯干与外周脂肪比，以及MRI测量的深、浅腹部皮下脂肪、内脏脂肪和肝脏脂肪。最后，我们将使用MEC中前瞻性收集的问卷数据和生物标本，在巢式病例对照研究中检验预测的肥胖表型以及顶级预测因子与乳腺癌（n= 1，217例）和结直肠癌（n= 1，379例）风险的相关性。这些数据将是必不可少的这种POTs综合方法，研究的关系，基因组，代谢组和肠道微生物与身体脂肪分布和癌症风险，因为他们 可能有助于确定行为干预的新途径。

PROJECT SUMMARY (See Instructions): Body fatness, in particular, in the abdominal, intra-abdominal and hepatic regions is known to carry a higher metabolic risk, compared to overall, peripheral or subcutaneous fat. Yet, the effect of fat mass in high-risk compartments, which is found in greater proportions in ethnic minorities, is likely to have been underestimated by cancer epidemiology research that relied on proxy measures of body mass and waist size. We hypothesize that body fat topography can be predicted by a combination of demographic, simple anthropometry, and lifestyle exposome variables (exposure-related blood biochemistry and behavior profiles). Indeed, we found in a feasibility study using the Multiethnic Cohort (MEC) study that the prediction of the abdominal fat compartments measured by abdominal MRI can be improved when considering exposome variables in addition to BMI. In the proposed study, we will first assess energetics-related behaviors of dietary intake, physical activity and cognitive eating and will analyze fasting blood levels of adipocytokines, insulin/IGF, sex steroid hormones, and lipids and lipid-soluble micronutrients among 2,000 MEC subjects (200 in each of 10 sex-ethnic groups) who will be recontacted for a cross-sectional study of detailed adiposity phenotypes measured by DXA and abdominal MRI. Using Random Forest and other similar regression models, we will identify the best nutritional, biochemical and behavioral markers that improve the prediction from BMI of the following adiposity phenotypes: total fat mass and trunk-to-periphery fat ratio measured by DXA and deep and superficial abdominal subcutaneous fat, visceral fat and liver fat measured by MRI. Finally, we will test the associations of the predicted adiposity phenotypes, as well as top predictors, for association with the risk of breast (n=1,217 cases) and colorectal cancers (n=1,379 cases) in nested case-control studies using the prospectively collected questionnaire data and biospecimens in the MEC. These data will be essential to this POTs integrated approach to study the relationships of the exposome, genome, metabolome and gut microbiome with body fat distribution and cancer risk since they may help to identify new avenues for behavioral interventions.