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

身体脂肪量和分布的营养和生化预测因子与癌症风险的关联

• 批准号: 8918531
• 负责人: Unhee Lim
• 金额: $ 83.95万
• 依托单位: UNIVERSITY OF HAWAII AT MANOA
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8918531
• 关键词: Abdomen; Accounting; African American; Anthropometry; Behavior; Behavior Therapy; Behavioral; Biochemical; Biochemical Markers; Biochemistry; Biological Markers; Blood; Blood Chemical Analysis; Body fat; Body mass index; Body measure procedure; Breast; Cessation of life; Cognitive; Cohort Studies; Colorectal; Colorectal Cancer; Cross-Sectional Studies; Data; Diabetes Mellitus; Dietary intake; Dimensions; Dual-Energy X-Ray Absorptiometry; Eating; Eating Behavior; Epidemiologic Studies; Estradiol; Ethnic group; Fasting; Fatty acid glycerol esters; Feasibility Studies; Female; Future; Genome; Genomics; Glucose; Gonadal Steroid Hormones; Hawaiian population; Height; Hepatic; Heterogeneity; Hormonal; Hormones; IGF1 gene; IGFBP1 gene; IL6 gene; Individual; Instruction; Insulin; Intra-abdominal; Japanese American; Latino; Leptin; Life Style; Lipids; Liver; Magnetic Resonance Imaging; Malignant Neoplasms; Measurement; Measures; Metabolic; Methods; Micronutrients; Modeling; Nested Case-Control Study; Nutritional; Obesity; Participant; Peripheral; Phenotype; Physical activity; Population; Proxy; Questionnaires; Race; Recontacts; Risk; SHBG gene; Testing; Testosterone; Variant; Visceral; Weight; abdominal fat; adiponectin; base; cancer epidemiology; cancer risk; cohort; design; ethnic difference; ethnic minority population; forest; high risk; imaging modality; improved; instrument; interest; malignant breast neoplasm; metabolomics; microbial; microbiome; predictive modeling; programs; psychologic; sex; subcutaneous; waist circumference

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.

项目摘要（参见说明）： 众所周知，与整体脂肪、外周脂肪或皮下脂肪相比，身体脂肪，特别是腹部、腹腔内和肝脏区域的脂肪具有更高的代谢风险。然而，在少数族裔中发现的高风险区室中脂肪量的影响更大，依赖于体重和腰围尺寸的替代测量的癌症流行病学研究可能低估了高风险区室中脂肪量的影响。我们假设可以通过人口统计、简单人体测量学和生活方式暴露变量（与暴露相关的血液生化和行为特征）相结合来预测身体脂肪分布。事实上，我们在使用多种族队列 (MEC) 研究的可行性研究中发现，预测 当考虑除 BMI 之外的暴露变量时，通过腹部 MRI 测量的腹部脂肪室可以得到改善。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等与能量相关的行为，并分析 2,000 名 MEC 受试者的空腹血液中脂肪细胞因子、胰岛素/IGF、性类固醇激素、脂质和脂溶性微量营养素的水平（10 个性别种族群体中每组 200 人）将被重新联系，以进行通过 DXA 和腹部测量的详细肥胖表型的横断面研究核磁共振成像。使用随机森林和其他类似的回归模型，我们将确定最佳的营养、生化和行为标记， 改进 BMI 对以下肥胖表型的预测：通过 DXA 测量的总脂肪量和躯干与外周脂肪比率，以及通过 MRI 测量的深部和浅表腹部皮下脂肪、内脏脂肪和肝脏脂肪。最后，我们将使用前瞻性的巢式病例对照研究来测试预测的肥胖表型以及主要预测因子与乳腺癌（n = 1,217 例）和结直肠癌（n = 1,379 例）风险的关联。在 MEC 中收集问卷数据和生物样本。这些数据对于研究暴露组、基因组、代谢组和肠道微生物组与身体脂肪分布和癌症风险之间的关系的 POT 综合方法至关重要，因为它们 可能有助于确定行为干预的新途径。