Optical Body Composition and Health Assessment

 描述(申请人提供)：1在所有人类健康指标中，最直观的是体型，但以量化证据为基础。2人体测量和区域组成测量，如腰围(WC)、腰臀比(WHR)和内脏脂肪组织面积(VAT)比体重指数(BMI)更能预测肥胖相关疾病和死亡的风险。双能X射线吸收法(DXA)可以比上述方法更详细地量化区域肥胖症，但由于许多原因，包括电离辐射的潜在危害、成本和培训，DXA没有得到充分利用。需要进行一项研究，以利用“量化自我运动”方面的快速技术发展，更好地描述体型的表型及其与代谢风险的关系。该方案开发的候选方案是3D光学全身扫描。如果成功，可以构建复杂的9种肥胖表型特征，以阐明身体成分与疾病、遗传学、生活方式暴露、代谢组学的潜在关联，并使用自我评估11技术进行高度评估。全身3D成像技术已经非常容易获得，以至于可以用微软Xbox Kinect等视频游戏和消费相机来完成。13最佳体型与健康评估研究的长期目标是：1)利用体型提供健康的表型14描述符；2)在15项研究、临床实践和个人健康评估中提供形象化和量化体型的工具。我们的总体方法是首先推导出身体形态如何与局部和整体身体组成(皮下脂肪、内脏脂肪、17肌肉质量、瘦质量和脂肪百分比)相关的16个预测模型，然后展示我们的3DO身体组成估计是如何与重要的代谢风险因素相关联的。我们的中心假设是，与人体测量或DXA身体20成分相比，体型分类的3DO测量更能预测代谢危险因素。我们的具体目标是：1.在代表美国人群中发现的性别、年龄、BMI和种族差异的人群中，确定体型与身体21种成分指数的独特关联；2.描述3DO扫描的精确度和准确性，以监测身体23种成分的变化和代谢健康干预；以及3.按性别、种族、年龄和BMI估计3DO与24种常见健康指标(包括新陈代谢风险因素)的关联程度。在25个探索性目标中，我们使用统计形状模型和潜在类别27分析来研究整体的、高分辨率的3D身体形状描述符作为身体成分和代谢风险的直接预测因子。到这项研究结束时，我们希望有适合于自我28评估技术的形状和组成的模型，能够代表美国29个人口中超过95%的形状差异，以及这些模型如何与重要的代谢状态和身体组成相关。30的积极影响将是临床医生和个人立即适用于个性化风险评估。

 DESCRIPTION (provided by applicant):1 Of all markers of human health, the most intuitive is body shape but based on quantitative evidence. 2 Anthropometry and regional composition measures such as waist circumference (WC), waist to hip ratio 3 (WHR), and visceral adipose tissue area (VAT) are better predictors of obesity-related diseases and mortality 4 risk than body mass index (BMI). Dual-energy X-ray absorptiometry (DXA) can quantify regional adiposity in 5 more detail than the above measures but is underutilized for many reasons including potential harm from 6 ionizing radiation, cost, and training. A study is needed to take advantage of rapid technological developments 7 in the "quantified self movement" to better describe phenotypes of body shape and its relation to metabolic 8 risks. The candidate developed in this proposal is 3D optical whole body scanning. If successful, sophisticated 9 obesity phenotype profiles could be constructed to clarify the underlying associations of body composition with 10 disease, genetics, lifestyle exposures, metabolomics, and be highly assessable using self-assessment 11 technology. Whole body 3D imaging technology is already so accessible that it can be done with video games 12 such as the Microsoft Xbox Kinect, and consumer cameras. 13 The long term goal of the Optical Body Shape and Health Assessment Study is 1) to provide phenotype 14 descriptors of health using body shape, and 2) to provide the tools to visualize and quantify body shape in 15 research, clinical practice, and personal health assessment. Our overall approach is to first derive predictive 16 models of how body shape relates to regional and total body composition (subcutaneous fat, visceral fat, 17 muscle mass, lean mass, and percent fat), and then show how our 3DO body composition estimates are 18 associated to important metabolic risk factors. Our central hypothesis is that 3DO measures of body 19 composition with shape classification better predict metabolic risk factors than anthropometry or DXA body 20 composition alone. Our specific aims are: 1. Identify the unique associations of body shape to body 21 composition indices in a population that represents the variance of sex, age, BMI, and ethnicity found 22 in the US population; 2. Describe the precision and accuracy of 3DO scans to monitor change in body 23 composition and metabolic health interventions; and 3. Estimate the level of association of 3DO to 24 common health indicators including metabolic risk factors by gender, race, age, and BMI. In an 25 exploratory aim, we investigate holistic, high-resolution descriptors of 3D body shape as direct 26 predictors of body composition and metabolic risk using statistical shape models and Latent Class 27 Analysis. By the end of this study, we expect to have models of the shape and composition suitable for self- 28 assessment technologies that are capable of representing over 95% of the shape variance in the US 29 population, and how these models relate to important metabolic status and body composition. The positive 30 impact will be the immediate applicability to clinicians and individuals for personalized risk assessment.