Objective: To characterize the metabolic variation in neonatal hair samples associated with intrauterine growth discordance in dichorionic-diamniotic (DCDA) twins and to evaluate the effects of specific metabolic alterations on later neurobehavioural outcomes in infancy. Design: Cohort-based case-control study Setting: Peking University Third Hospital Population: DCDA twins with birth weight discordance(DCDA-D) and birthweight concordance (DCDA-C) within a twin cohort recruited between September 2017 and December 2018 in Beijing, China. Methods: A specific hair metabolic profile of 14 pairs of DCDA-D twins was revealed using gas chromatography-mass spectrometry by comparing that of 28 pairs of DCDA-C twins. Pearson’s correlation was used to assess the relationship between the neonatal hair metabolome and neurocognitive outcomes, assessed using the Ages and the Infant’s Stages Questionnaires, third edition (ASQ-3) at 2 or 3 years of age. Main outcome measure: neonatal hair metabolome and long-term neurodevelopment. Results: A total of seventeen hair metabolites were significantly different within DCDA-D twin pairs compared to DCDA-C twins. Particularly, reduced levels of cysteine, threonine, and leucine were identified in both the larger and smaller DCDA-D twins compared with DCDA-C twins. The deregulated metabolic pathways including cysteine, methionine, aminoacyl-tRNA, nicotinate, and nicotinamide metabolism biosynthesis pathways in DCDA-D groups were positively correlated with infant neurocognitive development at 2 or 3 years of age, especially in problem-solving domains. Conclusion: Neonatal hair metabolic variations in utero of growth discordance in DCDA twins may be associated with poor neurocognitive development. Metabolome profiles of hair may be novel predictors of infant neurodevelopment longitudinally.

Objective: The aim of this study was to determine the factors predicting the probability of severe postpartum hemorrhage in women undergoing repeat cesarean delivery. Design: This multicenter, retrospective cohort study based on data from 11 public tertiary hospitals within 7 provinces of China. Setting: 11 public tertiary hospitals within 7 provinces of China. Population: 11074 eligible pregnant women who had a history of cesarean delivery and undergo cesarean delivery again after 28 weeks of gestation. Methods: The cohort was divided into the development and validation sets. The all-variables model and the multivariable logistic regression model (simple model) were fitted to estimate the probability of severe postpartum hemorrhage. Results: Six independent risk factors of severe postpartum hemorrhage in the simple model were selected from 40 clinical information features including a history of endometrial injury, complications with placenta previa or placenta accreta, lower gestational age at delivery, pelvic adhesion, and previous uterine incision status. Our final simple model showed excellent discrimination and calibration, with areas under the ROC curve of more than 0.90 in the validation set. Conclusions: Predictive tools based on patient clinical characteristics can be used to accurately estimate the probability of severe postpartum hemorrhage in patients undergoing repeat cesarean delivery. Funding National Key R&D Program of China (No. 2016YFC1000405 and 2017YFC1001402) and the National Natural Science Foundation (No. 81830045, 81671533 and 81571518). Keywords repeat cesarean deliveries; severe postpartum hemorrhage; placenta previa; placenta accrete; pelvic adhesion; prediction; obstetrics