Maternal metabolic profiling to assess fetal gestational age and predict preterm delivery: a two-centre retrospective cohort study in the US. Issue 12 (2nd December 2020)

• Record Type: Journal Article
• Authors: Sylvester, Karl G; Hao, Shiying; You, Jin; Zheng, Le; Tian, Lu; Yao, Xiaoming; Mo, Lihong; Ladella, Subhashini; Wong, Ronald J; Shaw, Gary M; Stevenson, David K; Cohen, Harvey J; Whitin, John C; McElhinney, Doff B; Ling, Xuefeng B
• Is Part Of: BMJ open. Volume 10:Issue 12(2020)
• Publication Date: 2020-12-02
• Subjects: health informatics -- risk management -- obstetrics; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2020-040647
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Objectives: The aim of this study was to develop a single blood test that could determine gestational age and estimate the risk of preterm birth by measuring serum metabolites. We hypothesised that serial metabolic modelling of serum analytes throughout pregnancy could be used to describe fetal gestational age and project preterm birth with a high degree of precision. Study design: A retrospective cohort study. Setting: Two medical centres from the USA. Participants: Thirty-six patients (20 full-term, 16 preterm) enrolled at Stanford University were used to develop gestational age and preterm birth risk algorithms, 22 patients (9 full-term, 13 preterm) enrolled at the University of Alabama were used to validate the algorithms. Outcome measures: Maternal blood was collected serially throughout pregnancy. Metabolic datasets were generated using mass spectrometry. Results: A model to determine gestational age was developed (R 2 =0.98) and validated (R 2 =0.81). 66.7% of the estimates fell within ±1 week of ultrasound results during model validation. Significant disruptions from full-term pregnancy metabolic patterns were observed in preterm pregnancies (R 2 =−0.68). A separate algorithm to predict preterm birth was developed using a set of 10 metabolic pathways that resulted in an area under the curve of 0.96 and 0.92, a sensitivity of 0.88 and 0.86, and a specificity of 0.96 and 0.92 during development and validation testing, respectively. Conclusions: In this study, metabolic profiling was used to develop and test a model for determining gestational age during full-term pregnancy progression, and to determine risk of preterm birth. With additional patient validation studies, these algorithms may be used to identify at-risk pregnancies prompting alterations in clinical care, and to gain biological insights into the pathophysiology of preterm birth. Metabolic pathway-based pregnancy modelling is a novel modality for investigation and clinical application development.