Biomarkers for congenital ventricular outflow tract malformations based on maternal serum lipid metabolomics analysis

Abstract

Abstract Background The congenital ventricular outflow tract malformations (CVOTMs) is a major congenital heart diseases (CHDs) subtype, and its pathogenesis is complex and unclear. Lipid metabolic plays a crucial role in embryonic cardiovascular development. However, due to the limited types of detectable metabolites in previous studies, findings on lipid metabolic and CHDs are still inconsistent, and the possible mechanism of CHDs remains unclear. Methods The nest case-control study obtained subjects from the multicenter China Teratology Birth Cohort (CTBC), and maternal serum from the pregnant women enrolled during the first trimester was utilized. The subjects were divided into a discovery set and a validation set. The metabolomics of CVOTMs and normal fetuses were analyzed by targeted lipid metabolomics. Differential comparison, random forest and lasso regression were used to screen metabolic biomarkers. Results The lipid metabolites were distributed differentially between the cases and controls. Setting the selection criteria of P value 1.2 or < 0.833, we screened 70 differential metabolites. Within the prediction model by random forest and lasso regression, DG (14:0_18:0), DG (20:0_18:0), Cer (d18:2/20:0), Cer (d18:1/20:0) and LPC (0:0/18:1) showed good prediction effects in discovery and validation sets. Differential metabolites were mainly concentrated in glycerolipid and glycerophospholipids metabolism, insulin resistance and lipid & atherosclerosis pathways, which may be related to the occurrence and development of CVOTMs. Conclusion Findings in this study provide a new metabolite data source for the research on CHDs. The differential metabolites and involved metabolic pathways may suggest new ideas for further mechanistic exploration of CHDs, and the selected biomarkers may provide some new clues for detection of COVTMs.

Keywords

• Congenital heart defects
• Lipid metabolomics
• Biomarker
• Machine learning