Early Gestational Diabetes Mellitus: Diagnostic Strategies and Clinical Implications.
Saptarshi BhattacharyaLakshmi NagendraAishwarya KrishnamurthyOm J LakhaniNitin KapoorBharti KalraSanjay KalraPublished in: Medical sciences (Basel, Switzerland) (2021)
Preexisting diabetes mellitus (DM) should be ruled out early in pregnancy in those at risk. During screening, a significant proportion of women do not reach the threshold for overt DM but fulfill the criteria used for diagnosing conventional gestational DM (cGDM). There is no consensus on the management of pregnancies with intermediate levels of hyperglycemia thus diagnosed. We have used the term early gestational DM (eGDM) for this condition and reviewed the currently available literature. Fasting plasma glucose (FPG), oral glucose tolerance test, and glycated hemoglobin (HbA1c) are the commonly employed screening tools in early pregnancy. Observational studies suggest that early pregnancy FPG and Hba1c correlate with the risk of cGDM and adverse perinatal outcomes. However, specific cut-offs, including those proposed by the International Association of the Diabetes and Pregnancy Study Group, do not reliably predict the development of cGDM. Emerging data, though indicate that FPG ≥ 92 mg/dL (5.1 mmol/L), even in the absence of cGDM, signals the risk for perinatal complication. Elevated HbA1c, especially a level ≥ 5.9%, also correlates with the risk of cGDM and worsened outcome. HbA1c as a diagnostic test is however besieged with the usual caveats that occur in pregnancy. The studies that explored the effects of intervention present conflicting results, including a possibility of fetal malnutrition and small-for-date baby in the early treatment group. Diagnostic thresholds and glycemic targets in eGDM may differ, and large multicenter randomized controlled trials are necessary to define the appropriate strategy.
Keyphrases
- pregnancy outcomes
- pregnant women
- glycemic control
- preterm birth
- randomized controlled trial
- type diabetes
- blood glucose
- weight gain
- systematic review
- cardiovascular disease
- emergency department
- polycystic ovary syndrome
- metabolic syndrome
- big data
- gestational age
- deep learning
- weight loss
- clinical practice
- combination therapy