Artificial intelligence and the analysis of multi-platform metabolomics data for the detection of intrauterine growth restriction.

Ray Oliver Bahado-Singh,Praveen Kumar,Anthony Odibo,Onur Turkoglu,Stewart F Graham,Eric Sherman,Halil Bisgin,Ali Yilmaz,Andrew Mrazik,Olivier Baud

doi:10.1371/journal.pone.0214121

Abstract

ObjectiveTo interrogate the pathogenesis of intrauterine growth restriction (IUGR) and apply Artificial Intelligence (AI) techniques to multi-platform i.e. nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) based metabolomic analysis for the prediction of IUGR.Materials and methodsMS and NMR based metabolomic analysis were performed on cord blood serum from 40 IUGR (birth weight < 10th percentile) cases and 40 controls. Three variable selection algorithms namely: Correlation-based feature selection (CFS), Partial least squares regression (PLS) and Learning Vector Quantization (LVQ) were tested for their diagnostic performance. For each selected set of metabolites and the panel consists of metabolites common in three selection algorithms so-called overlapping set (OL), support vector machine (SVM) models were developed for which parameter selection was performed busing 10-fold cross validations. Area under the receiver operating characteristics curve (AUC), sensitivity and specificity values were calculated for IUGR diagnosis. Metabolite set enrichment analysis (MSEA) was performed to identify which metabolic pathways were perturbed as a direct result of IUGR in cord blood serum.ResultsAll selected metabolites and their overlapping set achieved statistically significant accuracies in the range of 0.78–0.82 for their optimized SVM models. The model utilizing all metabolites in the dataset had an AUC = 0.91 with a sensitivity of 0.83 and specificity equal to 0.80. CFS and OL (Creatinine, C2, C4, lysoPC.a.C16.1, lysoPC.a.C20.3, lysoPC.a.C28.1, PC.aa.C24.0) showed the highest performance with sensitivity (0.87) and specificity (0.87), respectively. MSEA revealed significantly altered metabolic pathways in IUGR cases. Dysregulated pathways include: beta oxidation of very long fatty acids, oxidation of branched chain fatty acids, phospholipid biosynthesis, lysine degradation, urea cycle and fatty acid metabolism.ConclusionA systematically selected panel of metabolites was shown to accurately detect IUGR in newborn cord blood serum. Significant disturbance of hepatic function and energy generating pathways were found in IUGR cases.

Highlights

Fetal growth restriction (FGR) or alternatively Intrauterine growth restriction (IUGR) refers to inadequate fetal growth due to pathological reasons [1]
The model utilizing all metabolites in the dataset had an area under the curve (AUC) = 0.91 with a sensitivity of 0.83 and specificity equal to 0.80
Age, medical disorder, gravidity, and prior IUGR made up the clinical data available. These were mapped to the same scale as the metabolomic data and we evaluated their performance in combination with the optimal metabolite panel

Summary

Introduction

Fetal growth restriction (FGR) or alternatively Intrauterine growth restriction (IUGR) refers to inadequate fetal growth due to pathological reasons [1]. In the U.S.A. the diagnosis is based on an estimated fetal weight (EFW) less than the 10th percentile for gestational age. While other prenatal assessments are frequently used, for example Doppler, amniotic fluid volume, or a small abdominal circumference, they are currently not required to make the diagnosis. The gold standard is confirmation of a birth weight less that the 10th percentile for gender, ethnicity, and gestational age, termed small for gestational age (SGA). IUGR is associated with increased risk of stillbirth, and perinatal morbidity and mortality [2]. The increased risk extends into childhood and adulthood and include obesity and vascular disorders such as coronary artery disease [3]

Methods

Results

Conclusion