Quantifying body shape in pediatric clinical research

量化儿科临床研究中的体形

• 批准号: 10299250
• 负责人: Steven Heymsfield
• 金额: $ 65.93万
• 依托单位: UNIVERSITY OF HAWAII AT MANOA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-09 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10299250
• 关键词: 3-Dimensional; 6 year old; Address; Adolescent obesity; Adult; Age; Air; Anthropometry; Award; Biometry; Birth; Body Composition; Body Size; Body Water; Body mass index; Cardiovascular Diseases; Characteristics; Child; Childhood; Clinical Research; Computers; Data; Descriptor; Development; Diagnosis; Dietary Factors; Discipline of Nursing; Disease; Disease Resistance; Dual-Energy X-Ray Absorptiometry; Epidemic; Failure to Thrive; Fatty acid glycerol esters; Fluid Shifts; Goals; Grant; Health; Hydration status; Image; Incidence; Institutes; Insulin Resistance; International; Intervention; Investigation; Leg; Length; Life; Life Cycle Stages; Link; Machine Learning; Malignant Neoplasms; Manuals; Measurement; Measures; Medical Imaging; Metabolic; Metabolic Diseases; Methods; Mission; Modeling; Modernization; Monitor; Movement; National Institute of Diabetes and Digestive and Kidney Diseases; Obesity; Optics; Outcome; Participant; Personal Satisfaction; Phenotype; Play; Plethysmography; Population; Public Health; Research; Research Personnel; Research Support; Resolution; Resources; Risk; Role; Scanning; Scientist; Shapes; Spectrum Analysis; Speed; Study models; Technology; Thinness; Time; United States National Institutes of Health; Weight; arm; cancer risk; cancer type; clinical practice; clinically relevant; demographics; disorder prevention; disorder risk; early childhood; ethnic diversity; experience; high risk; human disease; metabolomics; muscle form; nutrition; obesity in children; obesity prevention; obesity risk; pediatrician; predictive modeling; rapid weight gain; recruit; sensor; sex; tool; whole body imaging

Project Summary/Abstract. Excess adiposity is associated with metabolic changes that significantly increase the risk of developing 13 types of cancer. It is estimated that up to 20% of cancer cases are caused by obesity and that obesity prevention can play a significant role in the reduction of cancer incidence. Among obese adolescents, the most rapid weight gain has been shown to occur between 2 and 6 years of age. Despite clear connections between these factors and obesity risk, the study of obesity in early childhood is limited by the lack of body composition technologies appropriate for this age range. The long term goal of the Shape Up! Keiki is 1) to provide pediatric phenotype descriptors of health derived from detailed body shape scans from high-speed and high depth resolution 3D cameras, and 2) to provide the tools to visualize and quantify body shape in research and clinical practice. Our approach addresses technology issues that have hindered body composition research in this age range including participants' inability to hold still, follow directions, small body size, and rapid fluid shifts. To develop our body composition models, we will recruit 360 ethnically-diverse children from birth to 5 years stratified by sex and BMI-Z. Our central hypothesis is that optical estimates of body composition suitably represent a 5-compartment (5C) body composition model for studies of adiposity and health in young children and are superior to that of simple anthropometry and demographics. Our specific aims and subaims are as follows: 1) identify the statistical shape descriptors from 3DO scans that best represent 5-compartment body composition in an ethnically-diverse pediatric population, 1(a) identify the relationships that best link 3DO shape descriptors of body subregions (arms, legs, trunk), and matching volumes and body composition measures, 1(b) calibrate automated 3DO anthropometry to clinically relevant girths and lengths, Exploratory) identify accessible combinations of 3DO and TBW that can be calibrated to criterion 5C measures of fat and hydration, 2) identify the factors that define the precision of accessible 3D optical body composition estimates to monitor change in body composition and metabolic health interventions, 3) contrast the association of body shape, 3DO, and 5C criterion body composition to pediatric health indicators including clinically relevant exposures (SES, nursing duration, birth method, nutrition) and development. The rationale for this study is that early life access to accurate body composition data will enable identification of factors that increase obesity, metabolic disease, and cancer risk, and provide a means to target interventions to those that would benefit. The expected outcome is that our findings would be immediately applicable to accessible gaming and imaging sensors found on modern computers.

项目摘要/摘要。过度肥胖与代谢变化有关 患有13种癌症的风险。据估计，多达20％的癌症病例是由肥胖引起的 肥胖预防可以在降低癌症发病率中发挥重要作用。肥胖 青少年，体重增加最快的人已显示出2至6岁之间的发生。尽管很清楚 这些因素与肥胖风险之间的联系，幼儿肥胖的研究受到缺乏的限制 适合此年龄范围的人体组成技术。形状的长期目标！ Keiki是1） 提供来自高速详细体形扫描的健康的儿科表型描述。 和高深度分辨率3D相机，以及2）提供可视化和量化身体形状的工具 研究和临床实践。我们的方法解决了阻碍身体组成的技术问题 在这个年龄范围内的研究，包括参与者无法保持静止，遵循指示，体积小和快速 流体移动。为了发展我们的身体成分模型，我们将从出生到 通过性和BMI-Z分层的5年。我们的中心假设是适当的身体组成的光学估计值 代表一个5室（5C）身体组成模型，用于研究幼儿的肥胖和健康 并且优于简单的人体测量法和人口统计。我们的具体目标和subiaim是 以下内容：1）从3DO扫描中确定最能代表5室体的统计形状描述符 种族多样性小儿种群中的组成，1（a）确定最佳链接3DO形状的关系 身体子区域（手臂，腿，躯干）的描述符和匹配的体积和身体组成措施，1（b） 校准自动3DO人体测量法至临床相关的腰围和长度，探索性）确定可访问的 3DO和TBW的组合可以校准为5C脂肪和水合的标准度量，2）识别 定义可访问的3D光学主体组成估算的精度以监视变化的因素 身体成分和代谢健康干预措施，3）对比体形，3DO和5C 小儿健康指标的标准体组成，包括临床相关的暴露（SES，护理） 持续时间，出生方法，营养）和发展。这项研究的理由是早期终身获得准确的访问 身体成分数据将鉴定出增加肥胖，代谢疾病和癌症的因素 风险，并提供一种将干预措施针对受益的方法的方法。预期的结果是我们 发现将立即适用于现代可访问的游戏和成像传感器 计算机。