Quantifying body shape in pediatric clinical research

量化儿科临床研究中的体形

基本信息

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
项目类别：
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

项目摘要

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，代谢变化显着增加患 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。肥胖者中对于青少年来说，体重增加最快的时期是 2 至 6 岁。尽管明确这些因素与肥胖风险之间存在联系，但对儿童早期肥胖的研究因缺乏相关研究而受到限制。适合该年龄段的身体成分技术。 Shape Up 的长期目标！惠辉是1) 提供源自高速详细体形扫描的儿科健康表型描述符和高深度分辨率 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成分的技术问题该年龄段的研究包括参与者无法保持静止、遵循指示、体型较小和快速流体变化。为了开发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合代表用于研究幼儿肥胖和健康的 5 室 (5C) 身体成分模型并且优于简单的人体测量学和人口统计学。我们的具体目标和子目标如下如下：1) 从 3DO 扫描中识别最能代表 5 室体的统计形状描述符种族多样化儿科人群的组成，1(a) 确定与 3DO 形状最相关的关系身体分区域（手臂、腿、躯干）的描述符以及匹配的体积和身体成分测量，1(b) 将自动 3DO 人体测量校准到临床相关的周长和长度，探索性）识别可访问的 3DO 和 TBW 的组合，可根据脂肪和水合标准 5C 测量进行校准，2) 确定定义可访问的 3D 光学身体成分估计精度的因素，以监测身体成分的变化身体成分和代谢健康干预措施，3) 对比体型、3DO 和 5C 之间的关联儿科健康指标的标准身体成分，包括临床相关暴露（SES、护理持续时间、出生方式、营养）和发育。这项研究的基本原理是，生命早期就能够获得准确的信息身体成分数据将有助于识别增加肥胖、代谢疾病和癌症的因素风险，并提供一种针对受益者进行干预的手段。我们的预期结果是研究结果将立即适用于现代游戏和成像传感器电脑。