The metabolomic profile of umbilical cord blood in neonatal hypoxic ischaemic encephalopathy

Brian H Walsh, David I Broadhurst, Rupasri Mandal, David S Wishart, Geraldine B Boylan, Louise C Kenny, Deirdre M Murray, Brian H Walsh, David I Broadhurst, Rupasri Mandal, David S Wishart, Geraldine B Boylan, Louise C Kenny, Deirdre M Murray

Abstract

Background: Hypoxic ischaemic encephalopathy (HIE) in newborns can cause significant long-term neurological disability. The insult is a complex injury characterised by energy failure and disruption of cellular homeostasis, leading to mitochondrial damage. The importance of individual metabolic pathways, and their interaction in the disease process is not fully understood. The aim of this study was to describe and quantify the metabolomic profile of umbilical cord blood samples in a carefully defined population of full-term infants with HIE.

Methods and findings: The injury severity was defined using both the modified Sarnat score and continuous multichannel electroencephalogram. Using these classification systems, our population was divided into those with confirmed HIE (n = 31), asphyxiated infants without encephalopathy (n = 40) and matched controls (n = 71). All had umbilical cord blood drawn and biobanked at -80 °C within 3 hours of delivery. A combined direct injection and LC-MS/MS assay (AbsolutIDQ p180 kit, Biocrates Life Sciences AG, Innsbruck, Austria) was used for the metabolomic analyses of the samples. Targeted metabolomic analysis showed a significant alteration between study groups in 29 metabolites from 3 distinct classes (Amino Acids, Acylcarnitines, and Glycerophospholipids). 9 of these metabolites were only significantly altered between neonates with Hypoxic ischaemic encephalopathy and matched controls, while 14 were significantly altered in both study groups. Multivariate Discriminant Analysis models developed showed clear multifactorial metabolite associations with both asphyxia and HIE. A logistic regression model using 5 metabolites clearly delineates severity of asphyxia and classifies HIE infants with AUC = 0.92. These data describe wide-spread disruption to not only energy pathways, but also nitrogen and lipid metabolism in both asphyxia and HIE.

Conclusion: This study shows that a multi-platform targeted approach to metabolomic analyses using accurately phenotyped and meticulously biobanked samples provides insight into the pathogenesis of perinatal asphyxia. It highlights the potential for metabolomic technology to develop a diagnostic test for HIE.

Trial registration: ClinicalTrials.gov NCT01498965.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1. Flow diagram detailing enrolment of…
Figure 1. Flow diagram detailing enrolment of study infants.
Figure 2. A comparison plot of percentage…
Figure 2. A comparison plot of percentage increase in metabolite concentration for metabolites that significantly differed in either the asphyxia vs. matched control comparison (squares), the HIE vs. matched controls (triangles), or both comparisons (circles).
Figure 3. Canonical Variate Analysis for the…
Figure 3. Canonical Variate Analysis for the combined data sets.
Squares = asphyxia cases; Circles = matched asphyxia controls; Triangles = HIE cases; Diamonds = matched HIE controls. Solid circles = 95% confidence intervals for each group population; Dashed circles = 95% confidence intervals for the mean of each group.
Figure 4. The predictive scores for a…
Figure 4. The predictive scores for a PLS-DA model built to discriminate between HIE versus all other outcomes (asphyxia and both the control groups) using the complete data set.
The PLS score box plot is grouped by Sarnat score. Here a Sarnat score of zero is equivalent to the “asphyxia” classification, and Sarnat grade of 1, 2 and 3 represent the 3 levels of increasing HIE severity. The model was optimally built using 2 latent factors. The model had an R2 = 0.32, Q2 = 0.22, and an AUC of 0.92 (95% CI: 0.84–0.97). For a fixed specificity of 0.95 the corresponding sensitivity for predicting HIE (at any level) is 0.75 (95% CI: 0.55–0.88), the corresponding decision boundary is indicated by a dashed line in the boxplot. Note: the Quality Control samples (repeated injection of serum from two control patients: QC1 & QC2) are projected through the PLS-DA model and the subsequent predictions give an estimation of model precision.
Figure 5. Variable importance plot for the…
Figure 5. Variable importance plot for the HIE versus ‘all other outcomes’ PLD-DA model.
A VIP score >1 indicates an important contribution to the model.
Figure 6. A ROC comparison of all…
Figure 6. A ROC comparison of all models produced in this study.
Triangle = PLS-DA: HIE versus matched controls, AUC = 0.96 (95% CI = 0.83–1.00); Square = PLS-DA: Asphyxia versus matched controls, AUC = 0.91 (0.83–0.96); Diamond = PLS-DA: HIE versus ‘all other outcomes’ (all metabolites), AUC = 0.91 (0.83–0.96); Circle = Logistic Regression: HIE versus ‘all other outcomes’ (5 metabolites), AUC = 0.92 (0.84–0.97). For clarity the convex-hull ROC curve approximations are shown. All AUC calculations were made on the actual predicted values.

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