A Metabolic Biomarker Panel for Congenital Heart Disease Assessment with Newborn Dried Blood Spots

Abstract

AbstractBackgroundCongenital heart disease (CHD) represents a significant contributor to both morbidity and mortality in neonates and children. The prompt recognition of CHD can facilitate timely and appropriate intervention, reducing the probability of complications and enhancing the prognosis for impacted newborns. However, unlike other rare conditions routinely identified through federal and state newborn screening (NBS) programs, there’s currently no analogous dried blood spot (DBS) screening for CHD immediately after birth.ObjectiveThis study was set to identify reliable metabolite biomarkers with clinical relevance, with the aim to assess feasibility of screening and subtype classification of CHD utilizing the DBS newborn screening method.MethodsWe assembled a cohort of DBS datasets from the California Department of Public Health (CDPH) Biobank, encompassing both normal controls and three pre-defined CHD categories (tetralogy of Fallot, inherited arrhythmia syndrome, neonatal cardiomyopathy). A robust, DBS-oriented metabolomic method, employing both global and targeted strategies based on liquid chromatography with tandem mass spectrometry (LC-MS/MS), was developed. To verify the reliability of this metabolic profiling, we conducted a correlation analysis comparing the absolute quantitated metabolite concentration in DBS against the CDPH NBS records. Additionally, for hydrophilic and hydrophobic metabolites, we executed significant pathway and metabolite analyses respectively. Finally, logistic and LightGBM models were established to aid in CHD discrimination and classification.ResultsOur metabolomic workflow demonstrated consistent and reliable quantification of metabolites in DBS samples stored at the California Department of Public Health (CDPH) for up to 15 years. Through this process, we discerned dysregulated metabolic pathways in CHD patients, including deviations in lipid and energy metabolism, as well as oxidative stress pathways. Furthermore, we identified three metabolites as potential biomarkers for CHD assessment, and an additional twelve metabolites as potential markers for classifying different CHD subtypes within DBS samples.ConclusionsThis study represents the first attempt to validate metabolite profiling results using long-term storage DBS samples procured from the high-quality conditions of the CDPH biobank. The results unveil distinct metabolic discrepancies between various CHD subtypes and healthy controls. Furthermore, our findings highlight the potential clinical applications of our DBS-based methods for CHD screening and subtype classification.