Optical Body Composition and Health Assessment

光学身体成分和健康评估

• 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
• 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

 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在所有人体健康标志中，最直观的是体型，但基于定量证据。 2人体测量学和区域组成测量，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风险。双能X线吸收测定法（DXA）可以比上述方法更详细地量化局部肥胖，但由于许多原因，包括电离辐射的潜在危害，成本和培训，未得到充分利用。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。该提案中开发的候选人是3D光学全身扫描。如果成功，可以构建复杂的肥胖表型谱，以澄清身体组成与疾病、遗传学、生活方式暴露、代谢组学的潜在关联，并使用自我评估技术进行高度评估。全身3D成像技术已经如此普及，以至于可以通过视频游戏12（如Microsoft Xbox Kinect）和消费者相机来实现。13光学体形与健康评估研究的长期目标是1）使用体形提供健康的表型描述符，以及2）在研究，临床实践和个人健康评估中提供可视化和量化体形的工具。我们的总体方法是首先推导出身体形状如何与区域和全身组成（皮下脂肪，内脏脂肪，肌肉质量，瘦体重和脂肪百分比）相关的预测模型，然后显示我们的3DO身体组成估计值如何与重要的代谢风险因素相关。我们的中心假设是，3DO措施的身体19组成的形状分类更好地预测代谢危险因素比人体测量或DXA身体20组成单独。我们的具体目标是：1.确定独特的协会的身体形状的身体21组成指数在人口中，代表的方差的性别，年龄，体重指数，和种族发现22在美国人口; 2。描述3DO扫描的精确度和准确度，以监测身体成分和代谢健康干预措施的变化;以及3.评估3DO与24种常见健康指标的关联水平，包括性别、种族、年龄和BMI的代谢风险因素。在一个探索性的目标中，我们使用统计形状模型和潜在类别27分析研究了3D体型的整体，高分辨率描述符作为身体成分和代谢风险的直接预测因素。到本研究结束时，我们希望有适合自我评估技术的形状和组成模型，这些模型能够代表美国29人口中超过95%的形状变化，以及这些模型如何与重要的代谢状态和身体组成相关。积极的影响将是立即适用于临床医生和个人进行个性化的风险评估。