Recent technological advances in genomic sequencing and bioinformatics have allowed researchers to examine the biological underpinnings of obesity through the lens of the human microbiome; however, the state of the science has yet to identify its exact mechanisms. With obesity being one of the most common complications of pregnancy, further work is necessary to establish the downstream maternal and child outcomes of body mass index (BMI) and composition of the maternal gut microbiome. With concurrent increases in maternal obesity, impaired glucose tolerance (GT) and gestational diabetes (GDM) and the known effects of obesity on neonatal birth outcomes, examining the role of the maternal intestinal microbiome and obesity to modulate these effects is an important area of exploration. Utilizing existing maternal fecal microbiome and medical record data from 150 participants currently enrolled in an ongoing pregnancy cohort, the Obstetrical and Neonatal Outcomes Study (ONOS) at the University of Virginia Medical Center, the specific aims for this study are: 1) to compare the microbial diversity of maternal intestinal microbiome samples at the GDM screening between normal weight (BMI 18.5-24.9), overweight (BMI 25.0-29.9), and obese participants (BMI >30) using 16s rRNA sequencing, 2) To compare the gene abundances for IR function of maternal intestinal microbes using a bioinformatics software, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt), 3) To examine the association between microbial diversity and function of the maternal microbiome with impaired GT while adjusting for confounders, and, 4) To explore how differences in microbial diversity and function of the maternal microbiome are associated with birth weight adjusted for gestational age. Samples will be randomly selected from completed cases based on inclusion into the maternal BMI grouping variables. In combining multi-source biological and clinical data, this study will utilize data mining methods to explore and define patterns in the influence of BMI and the maternal microbiome in insulin resistance, impaired GT, and neonatal outcomes. The findings from this study will deepen our scientific understanding of the complexities of the maternal microbiome to elucidate its role in adverse neonatal outcomes. A key strength of this proposal is the use of existing data from the ONOS biorepository and a strong multidisciplinary research team, committed to the applicant?s success, including nursing, nutrition, microbiology, genomics, and data science. This proposal has multiple additional strengths: 1) It addresses a significant maternal-child issue with substantial health implications, 2) An enhanced understanding of how intestinal microbiota composition and function differ by obesity and impaired GT may prove to be an effective target for interventions, and 3) It is highly responsive to NINR?s mission of advancing nursing research through data science by understanding the biological basis of illness.
Public health relevance: With over half of women entering pregnancy overweight or obese, there is a significant need to better understand the influence of maternal obesity on birth outcomes. As epidemiological trends confirm concurrent increases in the incidence of obesity, impaired glucose tolerance (GT) and gestational diabetes mellitus (GDM), it is important to consider the underlying mechanisms contributing to poor maternal and child outcomes. Examining the underpinnings of the maternal intestinal microbiome and insulin resistance, impaired GT and neonatal outcomes through data science methodology is essential to optimize biological and multi-source data in the clinical setting.
