Dataset Information

Maternal metabolic profiling to assess fetal gestational age and predict preterm delivery: a two-centre retrospective cohort study in the US.

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 (R²=0.98) and validated (R²=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 (R²=-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.

SUBMITTER: Sylvester KG

PROVIDER: S-EPMC7713207 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

Maternal metabolic profiling to assess fetal gestational age and predict preterm delivery: a two-centre retrospective cohort study in the US.

Sylvester Karl G KG Hao Shiying S You Jin J Zheng Le L Tian Lu L Yao Xiaoming X Mo Lihong L Ladella Subhashini S Wong Ronald J RJ Shaw Gary M GM Stevenson David K DK Cohen Harvey J HJ Whitin John C JC McElhinney Doff B DB Ling Xuefeng B XB

BMJ open 20201202 12

<h4>Objectives</h4>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.<h4>Study design</h4>A retrospective cohort study.<h4>Setting</h4>Two medical centres from the USA.<h4>Participants</h4>Thi ...[more]

PMID: 33268420

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Project description:BackgroundClinical recommendations to limit gestational weight gain (GWG) imply high GWG is causally related to adverse outcomes in mother or offspring, but GWG is the sum of several inter-related complex phenotypes (maternal fat deposition and vascular expansion, placenta, amniotic fluid and fetal growth). Understanding the genetic contribution to GWG could help clarify the potential effect of its different components on maternal and offspring health. Here we explore the genetic contribution to total, early and late GWG.Participants and methodsA genome-wide association study was used to identify maternal and fetal variants contributing to GWG in up to 10 543 mothers and 16 317 offspring of European origin, with replication in 10 660 mothers and 7561 offspring. Additional analyses determined the proportion of variability in GWG from maternal and fetal common genetic variants and the overlap of established genome-wide significant variants for phenotypes relevant to GWG (for example, maternal body mass index (BMI) and glucose, birth weight).ResultsApproximately 20% of the variability in GWG was tagged by common maternal genetic variants, and the fetal genome made a surprisingly minor contribution to explain variation in GWG. Variants near the pregnancy-specific beta-1 glycoprotein 5 (PSG5) gene reached genome-wide significance (P=1.71 × 10-8) for total GWG in the offspring genome, but did not replicate. Some established variants associated with increased BMI, fasting glucose and type 2 diabetes were associated with lower early, and higher later GWG. Maternal variants related to higher systolic blood pressure were related to lower late GWG. Established maternal and fetal birth weight variants were largely unrelated to GWG.ConclusionsWe found a modest contribution of maternal common variants to GWG and some overlap of maternal BMI, glucose and type 2 diabetes variants with GWG. These findings suggest that associations between GWG and later offspring/maternal outcomes may be due to the relationship of maternal BMI and diabetes with GWG.

Dataset Information

Maternal metabolic profiling to assess fetal gestational age and predict preterm delivery: a two-centre retrospective cohort study in the US.

Publications

Maternal metabolic profiling to assess fetal gestational age and predict preterm delivery: a two-centre retrospective cohort study in the US.

Similar Datasets

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