Aims: Previous studies have found that a single liver enzyme may predict gestational diabetes mellitus (GDM), but the results have been inconsistent. This study aimed to explore the associations of liver enzymes in early pregnancy with risk of GDM, as well as to independently rank risk factors.
Methods: This prospective cohort study included 1295 women who underwent liver enzyme measurements during early pregnancy and completed GDM assessment in mid-pregnancy. Logistic regression and restricted cubic spline analyses were conducted to assess the relationship between liver enzymes and risk of GDM. Back-propagation artificial neural network was performed to rank independently risk factors of GDM.
Results: Women diagnosed with GDM exhibited significantly higher levels of liver enzymes than those without GDM (all p < 0.05). The highest quartile of liver enzymes was associated with higher risk of GDM compared with the lowest quartile, with adjusted odds ratio (ORs) ranging from 2.76 to 8.11 (all p < 0.05). Moreover, the ORs of GDM increased linearly with liver enzymes level (all P for overall association <0.001). Furthermore, Back-propagation artificial neural network identified γ-gamma-glutamyl transferase (GGT) as accounting for the highest proportion in the ranking of GDM risk prediction weights (up to 20.8%).
Conclusions: Single or total elevations of liver enzymes in early pregnancy could predict the GDM occurrence, in which GGT, alkaline Phosphatase, and aspartate aminotransferase were the three most important independent risk factors.
Keywords: back‐propagation artificial neural network; gestational diabetes mellitus; liver enzymes; risk factor.
© 2024 John Wiley & Sons Ltd.