Transdisciplinary approaches for maternal-child health: a focus on gestational weight gain

Vanderbilt University Medical Center, Nashville TN

Investigators

Abstract

PROJECT SUMMARY/ABSTRACT Gestational weight gain has profound consequences for mothers and their children. However, roughly 70% of pregnant individuals in the United States do not fall within the recommended range for gestational weight gain. Despite the significant public health burden of insufficient or excessive gestational weight gain, intervention strategies have had limited success and are often difficult for individuals to adhere to outside of clinical trials. Additional opportunities for prevention, early modification, and/or reduction of adverse outcomes may be gained with a better understanding of the causal pathways leading to and resulting from gestational weight gain. Novel research approaches, adding biological and clinical perspectives, delving further into upstream determinants, and dissecting periods of gestational weight gain that affect later outcomes, are necessary to curb the multigenerational adverse effects of gestational weight gain outside of recommendations. The research objective of this proposal is to discover novel risk factors and biologic pathways underlying gestational weight gain as well as outcomes associated with patterns of weight gain during pregnancy; thereby enabling targeted interventions and further refinement of current guidelines. We will pursue the following specific aims: 1) apply machine learning models to electronic health records (EHRs) to predict excessive and inadequate gestational weight gain, 2) examine the causal effect of prepregnancy glucose on gestational weight gain using Mendelian randomization, and 3) investigate the impact of gestational weight gain trajectories on risk for adverse pregnancy and maternal outcomes using longitudinal latent profile analysis. To achieve these aims, we will utilize data from individuals with documented pregnancies in Vanderbilt University Medical Center’s (VUMC) Synthetic Derivative, a large EHRs database, as well as a DNA DataBank linked to deidentified EHRs at VUMC (BioVU), and publicly available summary statistics from a genome-wide association study of gestational weight gain. The proposed research will advance clinical care by identifying modifiable prepregnancy risk factors, characterizing at-risk individuals prior to pregnancy, and pinpointing concerning patterns of GWG, resulting in increased opportunities for prevention and intervention. This research is part of a K01 award for Dr. Elizabeth A. Jasper, Ph.D. Through a comprehensive career development plan, under the guidance of an exceptional mentor panel, and with access to unique resources, Dr. Jasper will gain training in biomedical informatics and advanced epidemiologic and statistical methodologies. Dr. Jasper will acquire the knowledge, skills, and resources necessary to achieve her long- term career goal of becoming a leading maternal-child health genetic epidemiologist who focuses on identifying the prepregnancy, prenatal, and perinatal origins of adverse health outcomes.