Associations of Metabolomic Predictors of Fat Amount and Distribution with Ca

项目概述（见说明）：

PROJECT SUMMARY (See instructions): Notably, healthy, physically active adults display a fat distribution with a relatively low level of visceral adiposity - regardless of their overall adiposity. The heterogeneity in level and types of obesity that exists among the five ethnic groups in the MEC offers a unique research setting to better understand the ill effects of increases in BMI and shifts in distribution of fat, including its relationship to cancer. More specifically, we propose a general viewpoint in which: (i) Fat deposition in various body fat storage compartments carries different risks of cancer; (ii) Amount and body distribution of lean and fat tissue vary across the five ethnic groups and explain some of the observed ethnic differences in cancer risk (particularly for breast and colorectal cancer) observed in the MEC population; (iii) Body fat amount and distribution act on cancer promofion through the same biological mechanisms but to different extents in the five ethnic groups of the MEC. In the context of this view, we propose to test the hypothesis that biomarkers of fat distribution are sufficiently robust biomarkers of disease risk that they predict disease risk across ethnic groups. The Aims: Aim 1: To develop, optimize, and validate a defined series of nested plasma metabolomic biomarker profiles that reflect relative and absolute fat distribution in the context of overall body fat. Aim 2: To determine the similarities, differences, and interactions between the systemic metabolomic profiles of adiposity and body fat distribution and previously developed metabolomic profile(s) for caloric intake and dietary inter- and intra-class differences in macronutrient composition. Aim 3: To test the associations of these predictors of body fat amount and distribution with cancer risk in nested case-control studies using the prospectively collected biospecimens from the MEC (breast, colon) and the NHS (breast). Aim 4: To integrate results with those of the other projects in order to gain a better understanding of the underlying biology and better predict adiposity phenotypes and cancer risk. These data will improve public health by refining risk estimates of the links between adiposity and cancer risk. These data will improve public health by refining risk esfimates of the links between adiposity and cancer