Gestational diabetes mellitus (GDM) is the most common metabolic complication in pregnancy and is associated with substantial maternal and neonatal morbidity. The standard of care for GDM in most developed countries is universal mid- to late- pregnancy (24-28 weeks gestation) glucose testing. While earlier diagnosis and treatment could improve pregnancy outcomes, tools for early identification of risk for GDM are not commonly used in practice. Existing models for predicting GDM risk within the first trimester of pregnancy based on maternal risk factors perform only modestly in the clinical setting. Heavy reliance on history of GDM to predict GDM development in the current pregnancy prevents these tools from being applicable to nulliparous women (i.e., women who have never given birth). In order to offer timely preventive intervention and enhanced antenatal care to nulliparous women, we need to be able to accurately identify those at high risk for GDM early in pregnancy.
Data from the California Office of Statewide Health Planning and Development Linked Birth File was used to address three aims: 1) improve early pregnancy prediction of GDM risk in nulliparous women through development of a risk factor-based model, 2) conduct a systematic review and meta-analysis assessing the relationship between first trimester prenatal screening biomarker levels and development of GDM, and 3) determine if the addition of first and second trimester prenatal screening biomarkers to risk factor-based models will improve early prediction of GDM in nulliparous women.
We developed a clinical prediction model including five well-established risk factors for GDM (race/ethnicity, age at delivery, pre-pregnancy body mass index, family history of diabetes, and pre-existing hypertension). Our model had moderate predictive performance among all nulliparous women, and performed particularly well among Hispanic and Black women when assessed within specific racial/ethnic groups. Our risk prediction model also showed superior performance over the commonly used American College of Obstetricians and Gynecologists (ACOG) screening guidelines, encouraging the prompt incorporation of this tool into preconception and prenatal care.
Biomarkers commonly assessed in prenatal screening have been associated with a number of adverse perinatal and birth outcomes. However, reports on the relationship between first trimester measurements of prenatal screening biomarkers and GDM development are inconsistent. Our meta-analysis demonstrated that women who are diagnosed with GDM have lower first trimester multiple of the median (MoM) levels of both pregnancy associated plasma protein-A (PAPP-A) and free β-human chorionic gonadotropin (free β-hCG) than women who remain normoglycemic throughout pregnancy.
Findings from our meta-analysis suggested that incorporation of prenatal screening biomarkers in clinical risk prediction models could aid in earlier identification of women at risk of developing GDM. Upon linkage of California Office of Statewide Health Planning and Development Linked Birth File and California Prenatal Screening Program records, we found that decreased levels of first trimester PAPP-A, increased second trimester unconjugated estriol, and increased second trimester dimeric inhibin A were associated with GDM development in nulliparous women. However, the addition of these biomarkers in clinical models did not offer improvements to the clinical utility (i.e., risk stratification) of models including maternal risk factors alone.
Our findings demonstrate that incorporation of maternal risk factors in a clinical risk prediction model can more accurately identify nulliparous women at high risk for GDM early in pregnancy compared to current standard practice. The maternal characteristics model we developed is based on clinical history and demographic variables that are already routinely collected by clinicians in the United States so that it may be easily adapted into existing prenatal care practice and screening programs. Future work should focus on evaluating the clinical impact of model implementation on maternal and infant outcomes as well as financial costs to the health care system.