Metabolomic analysis to predict the onset and severity of necrotizing enterocolitis

Abstract

Background: Necrotizing enterocolitis (NEC) is the most devastating gastrointestinal (GI) emergency in preterm neonates. Untargeted metabolomics may allow the identification of biomarkers involved in NEC pathophysiology.

Methods: We conducted a prospective study including preterm infants born at < 34 gestational weeks (GWs) whose urine was longitudinally collected at birth (< 48 h, T0) and at 14 (T1) and 28 days (T2). Neonates were followed for their development of NEC, spontaneous intestinal perforation (SIP), or other GI conditions and compared to those of matched healthy controls. Urine samples were investigated by untargeted metabolomic analysis based on mass-spectrometry.

Results: Thirty-five patients with NEC, 5 patients with SIP, 14 patients with other GI diseases and 113 controls were enrolled and selected for metabolomic analysis on the basis of their clinical characteristics and available samples. Considering urine samples at T0, the one-class classification approach was able to correctly classify 16/20 subjects (80%) who developed NEC, 3/3 (100%) who developed SIP and 5/7 subjects (71.4%) with other GI pathologies as not belonging to the control group. Neonates with surgical NEC had higher N-acetylaspartic acid, butyrylcarnitine and propionylcarnitine levels than did those with medical NEC. Considering the time evolution of the urinary metabolome, the NEC and control groups showed differences independently of the time point.

Conclusions: The urinary metabolome is closely associated with the underlying GI disease from birth. Urinary metabolic features characterize NEC patients from healthy controls until 28 days of life. The early urinary metabolome has the potential to predict surgical NEC. Future studies are needed to validate our results.

Keywords: Biomarker; Mass-spectrometry; Metabolome; Necrotizing enterocolitis; Preterm neonate.

Fig. 1

Diagram flow of the study design:from each enrolled patient, samples of plasma, urine and stools were collected at the timepoints T0, T14 and T28; patients were then followed for their possible development of NEC, SIP or other GI conditions until discharge or transfer to other hospital; urine samples were analysed by untargeted metabolomics using UPLC-MS; statistical data analysis of clinical and metabolomic data derived from mass-to-charge spectra identified potential relevant metabolites of NEC development and severity. Images from Freepik and BioRender.com

Fig. 2

Metabolomics analysis of urine collected at birth (T0): volcano plot (panel A) and relevant score plot (panel B) obtained for the NEG dataset, and volcano plot (panel C) and relevant score plot (panel D) obtained for the POS dataset. The features discovered as relevant are colored in red. In the volcano plot, p is the p-value of the Mann–Whitney test and FC[NEC/CTRL] is the fold change calculated as ratio between the median in the NEC group and the median in the control group; the dashed black line represents the threshold used to control the false discovery rate

Fig. 3

PLS-doe: score scatter plots of the models obtained with the NEG (panel A) and the POS (panel B) datasets. Samples of the NEC group (blue) and those of the controls (green) clusterise according to the group at all time points along tp[2], while the time increases from left to right along tp[1]. Circles are used for samples at T0, diamonds for samples at T1 and triangles for samples at T2

Fig. 4

LME models for longitudinal data: NEG dataset (panel A) and POS dataset (panel B); p[time] and p[group] are the p-values of the fixed effects “time” and “group”, respectively. Features significantly relevant to distinguish NEC cases and controls are in red. The dashed black lines indicate the thresholds used to control the FDR at level 0.05

Fig. 5

Boxplots representing the distributions of the most significant metabolites discovered in the comparison of medical vs surgical NEC at T0