An Interdisciplinary Study to Examine Obesity and Inactivity as Early Predictors of Gestational Diabetes

Gestational Diabetes (GD) is a serious condition that affects approximately 9% of pregnancies and is characterized by an insufficient response of blood glucose to the actions of insulin. In early pregnancy, GD can result in birth defects, over-nutrition, and large babies that increase risks of childbirth. The offspring are more likely to develop obesity and diabetes within their lifetimes. The risks of these disorders perpetuate a transgenerational cycle of obesity and diabetes, underscoring the need for effective interventions that reduce maternal obesity and GD. The current standard of care involves testing for GD during month six of pregnancy, however earlier prediction of GD could lead to earlier lifestyle intervention and a better outcome for both mother and baby.

Obesity and inactivity are major modifiable risk factors for GD. It is unknown which of these risk factors is most predictive of insulin resistance during pregnancy. This interdisciplinary study involving nursing and exercise science examines which early measures of obesity and inactivity can serve as early clinical predictors of gestational insulin resistance or GD.

First time pregnant women, aged 18 to 34, are recruited during their first prenatal visit. Participants receive body composition measurements in the exercise science laboratory and complete a physical activity questionnaire. Participants also wear an accelerometer to collect motion data. At 24-28 weeks, fasting plasma glucose and insulin levels are collected to compute the homeostasis model assessment of insulin resistance (HOMA-IR).

Preliminary data on the impact of early measures of obesity and inactivity on gestational insulin resistance and GD in primigravida women will be presented. Predicting GD early in pregnancy will provide healthcare workers an opportunity to intervene in order to prevent or delay the onset of this disease. The goal of this collaborative project is to create a predictive model for early identification and prevention of GD.