GLP-1 Receptor Agonists in Pregnancy Show No Clear Increase in Most Adverse OutcomesLarge study looks at GLP-1 medications and pregnancy outcomes

BACKGROUND: This systematic review and meta-analysis assesses whether exposure to glucagon-like peptide-1 receptor agonists (GLP-1 RAs) during pregnancy or the periconceptional period is associated with adverse maternal and perinatal outcomes. METHODS: Medline, Embase, and the Cochrane Library were searched for observational studies comparing pregnancies exposed and unexposed to GLP-1 RAs within 6 months before a positive pregnancy test. Outcomes included miscarriage, intrauterine death, congenital anomalies, preterm birth (PTB), hypertensive disorders of pregnancy (HDP), gestational diabetes mellitus (GDM), fetal growth restriction (FGR), small for gestational age (SGA), large for gestational age (LGA), and excess gestational weight gain. Random-effects meta-analyses and subgroup analyses by indication were performed. FINDINGS: Ten studies included 2,118,215 women, of whom 8,325 were exposed and 2,109,890 were unexposed. Pooled estimates did not show a clearly detectable increase in miscarriage or intrauterine death, congenital anomalies, preterm birth, HDP, gestational diabetes, abnormal fetal growth, or excess gestational weight gain. A lower pooled estimate for preeclampsia was observed (odds ratio [OR]: 0.87, 95% confidence interval [CI]: 0.78-0.98; p = 0.02), but this was based on two datasets and should be interpreted cautiously. Subgroup analyses showed no clearly detectable differences by indication. The exposure-window sensitivity analysis, covering 90 days before conception through the end of the first trimester, used the same four studies as the primary congenital-malformation analysis and therefore was not independent. CONCLUSION: Available observational evidence did not identify a clearly detectable increase in adverse maternal or perinatal outcomes following periconceptional exposure to GLP-1 RAs. However, these findings do not establish safety because of heterogeneity, residual confounding, and limited datasets. FUNDING: None.