Optimal gestational weight gain in Taiwan: A retrospective cohort study

Objective: Maternal body mass index (BMI) and gestational weight gain (GWG) are believed to be associated with fetal conditions and maternal complications. The aim of this study was investigating the influence of maternal prepregnancy BMI and GWG on pregnancy outcomes and exploring the optimal GWG in Taiwanese singleton women. Materials and methods: Retrospective records of 8146 singleton pregnant women between 2017 and 2019, obtained from a tertiary center in Taiwan were reviewed, and the associations between maternal prepregnancy BMI, GWG, and perinatal outcomes were analyzed. The patients were divided into five groups based on prepregnancy BMIs, defined by Taiwan Health Promotion Administration, as underweight (BMI <18.5 kg/m(2)), normal-weight (18.5 <= BMI <24 kg/m(2)), overweight (24.0 <= BMI <27.0 kg/m(2)), mild obese (27.0 <= BMI <30 kg/m(2)) and moderate-to-severe obese (BMI >= 30.0 kg/m(2)), and the lowest risks of different complications were summarized in each group. Also, using the Institute of Medicine and Japanese guidelines as reference, the Taiwanese recommendations for GWG were revised. Results:<bold> </bold>The rate of cesarean section, hypertensive disorder, preeclampsia, preeclampsia with severe features, and preterm birth were higher in prepregnancy overweight and obese women than in normal-weight women (p < 0.05). In the Taiwanese population, the recommended GWG for underweight pregnant women is 11-15 kg, for normal-weight pregnant women, it is 9.4-13.5 kg, for overweight pregnant women, it is 7.9-11.4 kg, for mild obese pregnant women, it is 7.3-10 kg, and for moderate-to-severe obese pregnant women, it is 4.9-9 kg with lowest perinatal complications. Conclusions:<bold> </bold>Unsatisfactory controlled prepregnancy weight (BMI >= 24) and inappropriate body weight gain during pregnancy can increase the risk of adverse pregnancy complications. Taiwanese GWG guidelines should be revised based on the characteristics and clinical data of the local population.