Optical Body Composition and Health Assessment

光学身体成分和健康评估

基本信息

批准号：
9273519
负责人：
Steven Heymsfield
金额：
$ 65.18万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-05-15 至 2018-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9273519
关键词：
Adipose tissue Adult Age Animals Anorexia Nervosa Anthropometry Area Blood Pressure Body Composition Body Weight decreased Body mass index Body measure procedure Classification Computer Vision Systems Data Descriptor Development Devices Diabetes Mellitus Diagnosis Disease Dual-Energy X-Ray Absorptiometry Ethnic Origin Fatty acid glycerol esters Gender Genetic Glucose Goals Health High Density Lipoprotein Cholesterol Human Image Imaging technology Individual Insulin Resistance International Intervention Intuition Ionizing radiation Life Style Measures Medical Imaging Metabolic Metabolic Diseases Methodology Mission Modeling Monitor Movement National Health and Nutrition Examination Survey Obesity Obesity associated disease Optics Outcome Phenotype Population Public Health Race Research Research Personnel Research Support Resolution Risk Risk Assessment Risk Factors Sampling Scanning Self Assessment Shapes Technology Technology Assessment Thinness Three-Dimensional Imaging Training Triglycerides United States National Institutes of Health Video Games Visceral Waist-Hip Ratio bariatric surgery base clinical practice cost disorder prevention high risk human disease indexing insulin sensitivity metabolic profile metabolomics mortality muscle form optical imaging predictive modeling public health relevance sensor sex subcutaneous tool waist circumference whole body imaging

项目摘要

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.

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，但基于定量证据。 2 人体测量和区域构成测量，例如腰围 (WC)、腰臀比 3 (WHR)、和内脏脂肪组织面积 (VAT) 比双能 X 射线吸收测定法 (DXA) 更能预测肥胖相关疾病和死亡 4 风险。区域性肥胖的量化 5 比上述措施更详细，但由于许多原因而未得到充分利用，包括 6 电离辐射、成本和培训的潜在危害，需要进行一项研究，以利用“量化自我运动”中的快速技术发展 7 来实现这一目标。更好地描述身体形状的表型及其与代谢 8 风险的关系。如果成功，可以构建复杂的 9 肥胖表型概况，以阐明身体成分与 10 种疾病的潜在关联。全身 3D 成像技术已经非常容易使用，可以通过 Microsoft Xbox Kinect 等视频游戏 12 和消费者相机 13 来完成。光学体形和健康评估研究的长期目标是 1) 使用体形提供健康的表型 14 描述符，以及 2) 提供可视化和量化 15 体形的工具我们的总体方法是首先推导出 16 个体形与局部和全身成分（皮下脂肪、内脏脂肪、17 肌肉质量、瘦体重和脂肪百分比）之间关系的预测模型，然后显示我们的 3DO 身体成分估计值如何与重要的代谢风险因素相关 18 我们的中心假设是，与单独的人体测量或 DXA 身体成分相比，通过形状分类对身体 19 成分进行 3DO 测量可以更好地预测代谢风险因素。具体目标是： 1. 确定人群中体型与身体 21 组成指数的独特关联，这些指数代表了美国人口中性别、年龄、BMI 和种族的差异 22 2. 描述 3DO 的精度和准确度；扫描以监测身体 23 成分的变化和代谢健康干预措施；以及 3. 估计 3DO 与 24 项常见健康指标（包括按性别、种族、年龄和 BMI 划分的代谢风险因素）的关联水平。为了探索性目标，我们使用统计形状模型和潜在 27 类分析来研究 3D 身体形状的整体、高分辨率描述符，作为身体成分和代谢风险的直接预测因子。在本研究结束时，我们期望拥有形状模型。 28 适合自我评估的技术能够代表美国 29 人口 95% 以上的体型差异，以及这些模型如何与重要的代谢状态和身体成分相关联 30 将会产生积极的影响。立即适用于受惠者和个人进行个性化风险评估。