FLEXIBLE MULTI-LEVEL MODELS FOR LONGITUDINAL ANALYSIS OF CHILDHOOD OBESITY

用于儿童肥胖纵向分析的灵活多级模型

• 批准号: 8301579
• 负责人: Kiros T Berhane
• 金额: $ 36.8万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-30 至 2014-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8301579
• 关键词: Acute; Address; Adult; Affect; Age; Area; Asthma; Biological Markers; Body mass index; California; Candidate Disease Gene; Centers for Disease Control and Prevention (U.S.); Characteristics; Child; Child health care; Childhood; Chronic; Clinical; Cohort Studies; Communities; Complex; Data; Development; Diet; Dietary intake; Eating; Environment; Environmental Risk Factor; Epidemic; Epidemiologic Studies; Epidemiology; Equation; Equilibrium; Etiology; Family; Food; Frequencies; Future; Gender; Gene-Modified; Genes; Genetic; Genetic screening method; Genomics; Growth; Health; Health behavior; Height; Individual; Internet; Intervention; Joints; Knowledge; Leptin; Measurement; Measures; Mediating; Mediator of activation protein; Metabolic; Methods; Modeling; Neighborhoods; Obesity; Organ; Overweight; Parents; Pathway interactions; Pattern; Performance; Physical activity; Physiological; Play; Population; Population Study; Positioning Attribute; Predisposition; Prevention; Preventive; Procedures; Property; Puberty; Public Health; Questionnaires; Relative (related person); Research; Research Personnel; Resources; Risk; Risk Factors; Role; Running; Sampling; Schools; Skin; Statistical Methods; Statistical Models; Techniques; Testing; Time; Weight; Work; base; cohort; community planning; effective intervention; energy balance; family influence; flexibility; genetic risk factor; genome wide association study; genome-wide; innovation; knowledge base; longitudinal analysis; novel; obesity in children; obesity prevention; obesity risk; offspring; prospective; psychosocial; public health relevance; rapid growth; response; sex; simulation; social; stem; transmission process

DESCRIPTION (provided by applicant): The alarming rise in childhood obesity and its health consequences are serious public health challenges. Our ability to address these challenges depends on understanding the multi-factorial etiology of obesity which involves factors which can be intrinsic (e.g., metabolic, cellular, genetic) or extrinsic (e.g. environmental, demographic). Data are available at several levels (e.g., temporal, individual, school, neighborhood, and community). Existing methods may not suffice for characterizing complex inter-level relationships of these factors with obesity during the rapid growth period of puberty. To address this gap, we propose new methods that incorporate both age and sex related changes in the dynamic relationship between weight, height and obesity as well as the complex multi-level relationships of determinants of obesity. The research is motivated by needs identified in the USC based Children's Health Study and the Smart Growth Study. In Aim 1, we develop new flexible multi-level quantile regression models based on our work on functional-based multi-level growth curves. Bayesian inference is conducted for concurrent assessment of effects of various factors, across several levels, on important features of the BMI trajectories of each child, relative to an appropriate reference. This model is further extended via hierarchical modeling of regression coefficients to assess patterns of effect estimates across reference quantiles and to integrate prior characteristics among risk factors. Advances in genomics present opportunities for assessing joint effects of genetics and the environment to the development of obesity, but new methods are needed to fully exploit family based and genome wide data. In Aim 2, we develop methods for analysis of genes and gene-environment (GxE) interactions, using parent-offspring data in the context of growth curves (including quantile regression) with focus on longitudinal versions of the quantitative transmission disequilibrium test (L-QTDT). We also develop a method to find obesity-related genes involved in a GxE interaction (e.g. genes that modify the effect of dietary intake) in the context of a candidate gene or genome wide association study. For intervention purposes, one needs to understand the role of mediating factors on development of obesity in the context of causal pathways. In Aim 3, we develop structural equation models and latent growth curve models to incorporate such mediational effects via a new multi-level quantile regression approach. We finally develop a latent variable approach for integrating information across all environmental, genetic and biomarker factors. Theoretical work on estimation and inferential procedures will be followed by extensive simulation studies to evaluate their performance and to investigate statistical properties of model parameters. We anticipate that the new methods will play a significant role in our capacity to understand the impact of risk factors on childhood obesity, and the use of information gained from analysis of risk factors to inform future obesity prevention efforts. PUBLIC HEALTH RELEVANCE: The development of the proposed novel statistical modeling techniques will play a significant role in enhancing our ability to understand the multifactorial etiology of childhood obesity, potentially leading to development of effective management and preventive measures against the rising tide of childhood obesity and related health consequences.

描述（由申请人提供）：儿童肥胖症及其健康后果的令人震惊的增长是严重的公共卫生挑战。我们应对这些挑战的能力取决于理解肥胖的多因素病因，这些因素涉及可能是内在的（例如，代谢，细胞，遗传）或外在的因素（例如环境，人口统计学）。数据可在多个级别（例如时间，个人，学校，社区和社区）获得。在青春期快速增长期间，现有方法可能不足以表征这些因素与肥胖的复杂关系之间的关系。为了解决这一差距，我们提出了新的方法，这些方法在体重，身高和肥胖症之间的动态关系以及肥胖决定因素的复杂多层次关系中都包含了年龄和性别相关的变化。这项研究是由基于USC的儿童健康研究和智能增长研究中确定的需求激励的。在AIM 1中，我们根据基于功能性多层增长曲线的工作开发了新的灵活的多级分位数回归模型。相对于适当的参考，进行了贝叶斯推断，以同时评估各个层次的各个层次的效果，对每个孩子的BMI轨迹的重要特征。通过对回归系数的层次建模进一步扩展该模型，以评估参考分位数范围内的效果估计模式，并在风险因素之间整合先前的特征。基因组学的进步为评估遗传学和环境对肥胖发展的关节作用的机会提供了机会，但是需要新的方法来充分利用基于家庭和基因组的数据。在AIM 2中，我们开发了用于分析基因和基因环境（GXE）相互作用的方法，该方法使用生长曲线（包括分数回归）的母体向外弹簧数据，重点关注定量传输不平衡测试（L-QTDT）的纵向版本。我们还开发了一种在候选基因或基因组广泛关联研究的背景下，找到参与GXE相互作用的肥胖相关基因（例如改变饮食摄入量的效果）的方法。出于干预目的，人们需要了解中介因素在因果途径背景下的肥胖发展的作用。在AIM 3中，我们开发了结构方程模型和潜在生长曲线模型，以通过新的多级分位回归方法结合此类中介作用。我们最终开发了一种潜在变量方法，用于整合所有环境，遗传和生物标志物因素。关于估计和推论程序的理论工作将进行大量的模拟研究，以评估其性能并研究模型参数的统计特性。我们预计，新方法将在我们了解危险因素对儿童肥胖的影响的能力中发挥重要作用，以及从分析风险因素中获得的信息，以告知未来的肥胖预防工作。 公共卫生相关性：拟议的新型统计建模技术的发展将在增强我们了解儿童肥胖的多因素病因学的能力方面发挥重要作用，这有可能导致开发有效的管理和预防措施，以抵抗儿童肥胖和相关健康后果的上升。