Quantifying body shape in pediatric clinical research

量化儿科临床研究中的体形

• 批准号: 10641835
• 负责人: Steven Heymsfield
• 金额: $ 61.69万
• 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/10641835
• 关键词: 3-Dimensional; 6 year old; Address; Adolescent obesity; Adult; Age; Air; Anthropometry; Award; Biometry; Birth; Black race; Body Composition; Body Size; Body Water; Body mass index; Calibration; 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; 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; Qualifying; 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; Visualization; Weight; arm; cancer risk; cancer type; clinical practice; clinically relevant; demographics; disorder prevention; disorder risk; early childhood; ethnic diversity; experience; health determinants; high risk; human disease; metabolomics; muscle form; nutrition; obesity in children; obesity prevention; obesity risk; pediatrician; population stratification; 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。过度肥胖与显著增加的代谢变化有关 罹患13种癌症的风险。据估计，高达20%的癌症病例是由肥胖引起的 预防肥胖可以在降低癌症发病率方面发挥重要作用。在肥胖人群中 青少年中，体重增加最快的年龄出现在2至6岁之间。尽管很清楚 这些因素与肥胖风险之间的联系，儿童早期肥胖的研究受到缺乏的限制 适合这个年龄段的身体合成技术。长此以往的目标就是塑造起来！Keiki是1) 从高速的详细体型扫描中提供儿科健康表型描述符 和高深度分辨率3D摄像机，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 这一年龄段的研究包括参与者不能静止不动，不能遵循指令，体型较小，速度较快 流体发生了变化。为了发展我们的身体成分模型，我们将招募360名不同种族的儿童，从出生到 5年按性别和BMI-Z分层。我们的中心假设是，身体成分的光学估计适当 提出了一种用于幼儿肥胖与健康研究的5室(5C)身体成分模型 而且优于简单的人体测量学和人口学。我们的具体目标和次级目标如下 如下：1)从3DO扫描中确定最能代表5间隔身体的统计形状描述符 在不同种族的儿科人群中，1(A)确定最能联系3DO形状的关系 身体分区域(手臂、腿、躯干)的描述符，以及相匹配的体积和身体成分测量，1(B) 校准自动化3DO人体测量，以临床相关的腰围和长度，探索性)识别可访问 3DO和TBW的组合，可根据脂肪和水分的标准5C测量进行校准，2)确定 定义可访问3D光学体成分估计以监测变化的精度的因素 身体成分和代谢健康干预，3)对比体型、3DO和5C之间的关系 将身体成分作为儿科健康指标的标准，包括临床相关的暴露(SES，护理 持续时间、分娩方式、营养)和发育。这项研究的基本原理是，早期生命获得准确的 身体成分数据将有助于识别增加肥胖、代谢性疾病和癌症的因素 风险，并提供了一种手段，将干预的目标对准那些将受益的人。预期的结果是我们的 这一发现将立即适用于可访问的游戏和成像传感器 电脑。