The Impact of Breatfeeding on FTO-Related BMI Growth Trajectories: An Application to the ALSPAC and RAINE Cohort Studies
Obesity in children and adolescents is a serious issue with many health and social consequences that continue into adulthood. Implementing early intervention programs and getting a better understanding of children's BMI growth is important for controlling the obesity epidemic. The Fat Mass and Obesity (FTO) gene has been linked with obesity in large populations of adults and children in a recent series of genome-wode association studies and a longer duration of breatfeeding has been found to be associated with a lower risk of being overweight later in life.
We studied 5,590 children from the Avon Longitudinal Studies of Parents and Children (ALSPAC, UK) cohort and 962 from the Western Australia Pregnancy (RAINE) cohort who were followed up from birth to 15 years of age. A mixed-effects model with cubic smooth splines was employed to investigate wether the genetic (FTO) and early environmental (breastfeeding) factors and associated with abnormal BMI growth. The proposed method provided a unified parametric statistical framework for the analysis of children's BMI growth trajectory, taking unbalanced, unevenly spaced repeated BMI measurements.
In this talk, I will show the novelties and flexibilities of our proposed model, the association of the risk allele (A) in the FTO gene and children's BMI growth curves and the long term impact of exclusive breastfeeding.
Mixed-effects Model is a powerful tool with a wide range of applications in public health research and genetic research. I will give a brief introduction to the theoretical aspect of my research in mixed-effects models at the end of my talk.
Yan Yan Wu is a Postdoctoral Fellow in Biostatistics at the Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital at University of Toronto. Her research is funded by the Canadian Institutes of Health Research's STAGE program (Strategic Traning for Advanced Genetic Epidemiology and Statistical Genetics). She obtained her PhD in Statistics from York University, Toronto in 2011. Dr. Wu's research interests focus on mixed-effects models for longitudinal data analysis, hierarchical/multilevel data analysis, survival analysis and development of statistical methodologies for genetic and epidemiological research. She has worked closely with epidemiologists and clinical researchers in Children's health studies, cancer research and social research.