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.

Agency
National Institute of Health (NIH)
Institute
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Type
Research Project--Cooperative Agreements (U01)
Project #
1U01HD061968-01
Application #
7743327
Study Section
Special Emphasis Panel (ZHD1-DSR-M (23))
Program Officer
Esposito, Layla E
Project Start
2009-07-30
Project End
2014-06-30
Budget Start
2009-07-30
Budget End
2010-06-30
Support Year
1
Fiscal Year
2009
Total Cost
$394,605
Indirect Cost
Name
University of Southern California
Department
Public Health & Prev Medicine
Type
Schools of Medicine
DUNS #
072933393
City
Los Angeles
State
CA
Country
United States
Zip Code
90089
Zhang, Pingye; Lewinger, Juan Pablo; Conti, David et al. (2016) Detecting Gene-Environment Interactions for a Quantitative Trait in a Genome-Wide Association Study. Genet Epidemiol 40:394-403
Jerrett, Michael; McConnell, Rob; Wolch, Jennifer et al. (2014) Traffic-related air pollution and obesity formation in children: a longitudinal, multilevel analysis. Environ Health 13:49
Su, Jason G; Jerrett, Michael; McConnell, Rob et al. (2013) Factors influencing whether children walk to school. Health Place 22:153-61
Gauderman, W James; Zhang, Pingye; Morrison, John L et al. (2013) Finding novel genes by testing G × E interactions in a genome-wide association study. Genet Epidemiol 37:603-13
Lewinger, Juan Pablo; Morrison, John L; Thomas, Duncan C et al. (2013) Efficient two-step testing of gene-gene interactions in genome-wide association studies. Genet Epidemiol 37:440-51
Thomas, Duncan C; Lewinger, Juan Pablo; Murcray, Cassandra E et al. (2012) Invited commentary: GE-Whiz! Ratcheting gene-environment studies up to the whole genome and the whole exposome. Am J Epidemiol 175:203-7; discussion 208-9
Dunton, Genevieve; McConnell, Rob; Jerrett, Michael et al. (2012) Organized physical activity in young school children and subsequent 4-year change in body mass index. Arch Pediatr Adolesc Med 166:713-8