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Screening for Preterm Birth: Potential for a Metabolomics Biomarker Panel.

ABSTRACT: The aim of this preliminary study was to investigate the potential of maternal serum to provide metabolomic biomarker candidates for the prediction of spontaneous preterm birth (SPTB) in asymptomatic pregnant women at 15 and/or 20 weeks' gestation. Metabolomics LC-MS datasets from serum samples at 15- and 20-weeks' gestation from a cohort of approximately 50 cases (GA < 37 weeks) and 55 controls (GA > 41weeks) were analysed for candidate biomarkers predictive of SPTB. Lists of the top ranked candidate biomarkers from both multivariate and univariate analyses were produced. At the 20 weeks' GA time-point these lists had high concordance with each other (85%). A subset of 4 of these features produce a biomarker panel that predicts SPTB with a partial Area Under the Curve (pAUC) of 12.2, a sensitivity of 87.8%, a specificity of 57.7% and a p-value of 0.0013 upon 10-fold cross validation using PanelomiX software. This biomarker panel contained mostly features from groups already associated in the literature with preterm birth and consisted of 4 features from the biological groups of "Bile Acids", "Prostaglandins", "Vitamin D and derivatives" and "Fatty Acids and Conjugates".

SUBMITTER: Considine EC

PROVIDER: S-EPMC6572582 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Screening for Preterm Birth: Potential for a Metabolomics Biomarker Panel.

Considine Elizabeth C EC Khashan Ali S AS Kenny Louise C LC

Metabolites 20190507 5

The aim of this preliminary study was to investigate the potential of maternal serum to provide metabolomic biomarker candidates for the prediction of spontaneous preterm birth (SPTB) in asymptomatic pregnant women at 15 and/or 20 weeks' gestation. Metabolomics LC-MS datasets from serum samples at 15- and 20-weeks' gestation from a cohort of approximately 50 cases (GA < 37 weeks) and 55 controls (GA > 41weeks) were analysed for candidate biomarkers predictive of SPTB. Lists of the top ranked can ...[more]

PMID: 31067710

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Project description:Preterm birth (PTB) is defined as childbirth occurring at less than 37 completed weeks or 259 days of gestation. Premature babies have higher rates of cerebral palsy, sensory deficits, learning disabilities and respiratory illnesses that extend into adulthood. This lifelong morbidity results in high economic and social costs to families and communities. PTB is a syndrome initiated by multiple mechanisms, including infection or inflammation, uteroplacental ischaemia or haemorrhage, uterine overdistension, stress, and other immunologically mediated processes. Identifying and monitoring molecular signals in easily accessible body fluids that can diagnose or predict the risk of preterm labor in pregnant women will reduce or prevent PTBs. A number of studies reported the identification of putative biomarkers for PTB including protein, miRNA and hormone from different body fluids such as serum/plasma, cervical vaginal fluid, saliva and amniotic fluids. These putative biomarkers identified can largely be grouped into three main functional categories: inflammatory related molecules, placenta or fetal derived molecules and stress related molecules. In the past few years next generation sequencing (NGS) has become the major platform for miRNA analysis especially with body fluids. However, studies have shown significant sequence bias among different small RNA library preparation protocols. We have modified the small RNA library construction protocol which greatly reduces the sequence bias and increase miRNA coverage in sample. We also adapted a newly developed size exclusion chromatography (SEC) based EV purification protocol which can provide cleaner EVs compared to other methods. We are using these improved approaches to gain more reliable profile of circulating RNA in body fluid as well as its associated EVs. With these new approaches, we explore the possibility of using specific circulating miRNAs, specifically those encapsulated in EVs, as a noninvasive biomarker for PTB by comparing the miRNA profiles in maternal plasma, EV and EV-depleted plasma between individuals who had a spontaneous preterm birth and uncomplicated pregnancies.

Project description:Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in children, but diagnosis is challenging due to limited availability of noninvasive biomarkers. Machine learning applied to high-resolution metabolomics and clinical phenotype data offers a novel framework for developing a NAFLD screening panel in youth. Here, untargeted metabolomics by liquid chromatography-mass spectrometry was performed on plasma samples from a combined cross-sectional sample of children and adolescents ages 2-25 years old with NAFLD (n = 222) and without NAFLD (n = 337), confirmed by liver biopsy or magnetic resonance imaging. Anthropometrics, blood lipids, liver enzymes, and glucose and insulin metabolism were also assessed. A machine learning approach was applied to the metabolomics and clinical phenotype data sets, which were split into training and test sets, and included dimension reduction, feature selection, and classification model development. The selected metabolite features were the amino acids serine, leucine/isoleucine, and tryptophan; three putatively annotated compounds (dihydrothymine and two phospholipids); and two unknowns. The selected clinical phenotype variables were waist circumference, whole-body insulin sensitivity index (WBISI) based on the oral glucose tolerance test, and blood triglycerides. The highest performing classification model was random forest, which had an area under the receiver operating characteristic curve (AUROC) of 0.94, sensitivity of 73%, and specificity of 97% for detecting NAFLD cases. A second classification model was developed using the homeostasis model assessment of insulin resistance substituted for the WBISI. Similarly, the highest performing classification model was random forest, which had an AUROC of 0.92, sensitivity of 73%, and specificity of 94%. Conclusion: The identified screening panel consisting of both metabolomics and clinical features has promising potential for screening for NAFLD in youth. Further development of this panel and independent validation testing in other cohorts are warranted.

Dataset Information

Screening for Preterm Birth: Potential for a Metabolomics Biomarker Panel.

Publications

Screening for Preterm Birth: Potential for a Metabolomics Biomarker Panel.

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