Biomarkers for isolated congenital heart disease based on maternal amniotic fluid metabolomics analysis

Abstract

Introduction: Congenital heart disease (CHD) is one of the most prevalent birth defects in the world. The pathogenesis of CHD is complex and unclear. With the development of metabolomics technology, variations in metabolites may provide new clues about the causes of CHD and may serve as a biomarker during pregnancy.

Methods: Sixty-five amniotic fluid samples (28 cases and 37 controls) during the second and third trimesters were utilized in this study. The metabolomics of CHD and normal fetuses were analyzed by untargeted metabolomics technology. Differential comparison and randomForest were used to screen metabolic biomarkers.

Results: A total of 2472 metabolites were detected, and they were distributed differentially between the cases and controls. Setting the selection criteria of fold change (FC) ≥ 2, P value < 0.01 and variable importance for the projection (VIP) ≥ 1.5, we screened 118 differential metabolites. Within the prediction model by random forest, PE(MonoMe(11,5)/MonoMe(13,5)), N-feruloylserotonin and 2,6-di-tert-butylbenzoquinone showed good prediction effects. Differential metabolites were mainly concentrated in aldosterone synthesis and secretion, drug metabolism, nicotinate and nicotinamide metabolism pathways, which may be related to the occurrence and development of CHD.

Conclusion: This study provides a new database of CHD metabolic biomarkers and mechanistic research. These results need to be further verified in larger samples.

Keywords: Biomarker; Congenital heart disease; Diagnosis; Metabolomics; Reproduction.

Fig. 1

Metabolomics analysis between the two groups. A, PCA of all samples, where the X-axis represents the first principal component and the Y-axis represents the second principal component. B, PCA 3D map of differential grouping. C, OPLS-DA score map. D, OPLS-DA model validation diagram

Fig. 2

The 10 features ranked by mean decrease accuracy for the CHD random forest model. meta_1461: PE(MonoMe(11,5)/MonoMe(13,5)) meta_1587: 4-[N-(p-Coumaroyl)serotonin-4’’-yl]-N-feruloylserotonin meta_901: 2,6-Di-tert-butylbenzoquinone meta_838: 3-Methylglutarylcarnitinemeta_1024: Mytilin B meta_1195: N-[(4E,8Z)-1,3-dihydroxyoctadeca-4,8-dien-2-yl] hexadecanamide 1-glucoside meta_2137: Medicagenic acid 28-O- [b-D-xylosyl-(1- > 4)-a-L-rhamnosyl-(1- > 2)-a-L-arabinosyl] ester meta_2373: C16:1-OH Sphingomyelin (SM(d18:0/16:1(9Z)(OH))) meta_1261: Glabrolide meta_1354: Lysophosphatidylcholine acyl C 10:0 (LysoPC10:0)

Fig. 3

The ROC curves for classifying the subjects. A, The ROC curves comparing the performances of meta_1461, meta_901 and meta_1587. B, The ROC curve of the logistic regression model for classifying the subjects

Fig. 4

The process and results for metabolite biomarker screening

Fig. 5

Differential metabolite enrichment pathways. A HMDB classification map of the differential metabolites in each group. B KEGG enrichment map of the differential metabolites