Analysis of Biomarkers for Congenital Heart Disease Based on Maternal Amniotic Fluid Metabolomics

How Will Artificial Intelligence Shape the Future of Decision-Making in Congenital Heart Disease?

Improvements in medical technology have significantly changed the management of congenital heart disease (CHD), offering novel tools to predict outcomes and personalize follow-up care. By using sophisticated imaging modalities, computational models and machine learning algorithms, clinicians can experiment with unprecedented insights into the complex anatomy and physiology of CHD. These tools enable early identification of high-risk patients, thus allowing timely, tailored interventions and improved outcomes. Additionally, the integration of genetic testing offers valuable prognostic information, helping in risk stratification and treatment optimisation. The birth of telemedicine platforms and remote monitoring devices facilitates customised follow-up care, enhancing patient engagement and reducing healthcare disparities. Taking into consideration challenges and ethical issues, clinicians can make the most of the full potential of artificial intelligence (AI) to further refine prognostic models, personalize care and improve long-term outcomes for patients with CHD. This narrative review aims to provide a comprehensive illustration of how AI has been implemented as a new technological method for enhancing the management of CHD.

Amniotic fluid metabolic fingerprinting contributes to shaping the unfavourable intrauterine environment in monochorionic diamniotic twins

BACKGROUND & AIMS

Amniotic fluid (AF) is the primary intrauterine environment for fetal growth throughout gestation. Selective fetal growth restriction (sFGR) is an adverse complication characterized by unequal growth in twins with nearly identical genetic makeup. However, the influence of AF-mediated intrauterine environment on the development and progression of sFGR remains unexplored.

METHODS

High-throughput targeted metabolomics analysis (G350) was performed on AF samples collected from sFGR (n = 18) and MCDA twins with birth weight concordance (MCDA-C, n = 20) cases. Weighted correlation network analysis (WGCNA) was used to identify clinical features that may influence the metabolite composition in AF. Subsequently, partial least-squares discriminant analysis (PLS-DA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to compare the different types of sFGR and MCDA-C twins. Receiver operating characteristic (ROC) and multivariate ROC curves were utilized to explore potential AF markers in twins with sFGR.

RESULTS

In our study, 182 metabolites were quantified in 76 AF samples. WGCNA indicated that the metabolite composition in late AF may not be influenced by gestational age. PLSDA demonstrated distinct variations between the metabolite profiles of AF in the sFGR and MCDA-C twins, with a significant emphasis on amino acids as the primary differential metabolite. The dissimilarities observed in sFGR twins were predominantly attributed to lipid metabolism-related metabolites. In particular, the KEGG enrichment metabolic pathway analysis revealed significant associations of both types of sFGR twins with central carbon metabolism in cancer. The multivariate ROC curves indicated that the combination of carnosine, sarcosine, l-alanine, beta-alanine, and alpha-n-phenylacetylglutamine significantly improved the AUC to 0.928. Notably, the ROC curves highlighted creatine (AUC:0.934) may be a potential biomarker for severe sFGR.

CONCLUSION

The data presented in this study offer a comprehensive metabolic map of the AF in cases of sFGR, shedding light on potential biomarkers associated with fetal growth and development in MCDA twins.

Amniotic Fluid Mesenchymal Stromal Cells Derived from Fetuses with Isolated Cardiac Defects Exhibit Decreased Proliferation and Cardiomyogenic Potential

Amniotic fluid mesenchymal stromal cells (AF-MSCs) represent an autologous cell source to ameliorate congenital heart defects (CHDs) in children. The AF-MSCs, having cardiomyogenic potential and being of fetal origin, may reflect the physiological and pathological changes in the fetal heart during embryogenesis. Hence, the study of defects in the functional properties of these stem cells during fetal heart development will help obtain a better understanding of the cause of neonatal CHDs. Therefore, in the present study, we compared the proliferative and cardiomyogenic potential of AF-MSCs derived from ICHD fetuses (ICHD AF-MSCs) with AF-MSCs from structurally normal fetuses (normal AF-MSCs). Compared to normal AF-MSCs, the ICHD AF-MSCs showed comparable immunophenotypic MSC marker expression and adipogenic and chondrogenic differentiation potential, with decreased proliferation, higher senescence, increased expression of DNA-damaged genes, and osteogenic differentiation potential. Furthermore, the expression of cardiac progenitor markers (PDGFR-α, VEGFR-2, and SSEA-1), cardiac transcription factors (GATA-4, NKx 2-5, ISL-1, TBX-5, TBX-18, and MeF-2C), and cardiovascular markers (cTNT, CD31, and α-SMA) were significantly reduced in ICHD AF-MSCs. Overall, these results suggest that the AF-MSCs of ICHD fetuses have proliferation defects with significantly decreased cardiomyogenic differentiation potential. Thus, these defects in ICHD AF-MSCs highlight that the impaired heart development in ICHD fetuses may be due to defects in the stem cells associated with heart development during embryogenesis.

Insights and perspectives into clinical biomarker discovery in pediatric heart failure and congenital heart disease-a narrative review

Background and Objective

Heart failure (HF) in the pediatric population is a multi-factorial process with a wide spectrum of etiologies and clinical manifestations, that are distinct from the adult HF population, with congenital heart disease (CHD) as the most common cause. CHD has high morbidity/mortality with nearly 60% developing HF during the first 12 months of life. Hence, early discovery and diagnosis of CHD in neonates is pivotal. Plasma B-type natriuretic peptide (BNP) is an increasingly popular clinical marker in pediatric HF, however, in contrast to adult HF, it is not yet included in pediatric HF guidelines and there is no standardized reference cut-off value. We explore the current trends and prospects of biomarkers in pediatric HF, including CHD that can aid in diagnosis and management.

Methods

As a narrative review, we will analyze biomarkers with respect to diagnosis and monitoring in specific anatomical types of CHD in the pediatric population considering all English PubMed publications till June 2022.

Key Content and Findings

We present a concise description of our own experience in applying plasma BNP as a clinical biomarker in pediatric HF and CHD (tetralogy of fallot vs. ventricular septal defect) in the context of surgical correction, as well as untargeted metabolomics analyses. In the current age of Information Technology and large data sets we also explored new biomarker discovery using Text Mining of 33M manuscripts currently on PubMed.

Conclusions

(Multi) Omics studies from patient samples as well as Data Mining can be considered for the discovery of potential pediatric HF biomarkers useful in clinical care. Future research should focus on validation and defining evidence-based value limits and reference ranges for specific indications using the most up-to-date assays in parallel to commonly used studies.

Metabolomics: A New Tool in Our Understanding of Congenital Heart Disease

Although the genetic origins underpinning congenital heart disease (CHD) have been extensively studied, genes, by themselves, do not entirely predict phenotypes, which result from the complex interplay between genes and the environment. Consequently, genes merely suggest the potential occurrence of a specific phenotype, but they cannot predict what will happen in reality. This task can be revealed by metabolomics, the most promising of the "omics sciences". Though metabolomics applied to CHD is still in its infant phase, it has already been applied to CHD prenatal diagnosis, as well as to predict outcomes after cardiac surgery. Particular metabolomic fingerprints have been identified for some of the specific CHD subtypes. The hallmarks of CHD-related pulmonary arterial hypertension have also been discovered. This review, which is presented in a narrative format, due to the heterogeneity of the selected papers, aims to provide the readers with a synopsis of the literature on metabolomics in the CHD setting.

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

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.

The Relationship of Maternal Gestational Mass Spectrometry-Derived Metabolites with Offspring Congenital Heart Disease: Results from Multivariable and Mendelian Randomization Analyses

Background: It is plausible that maternal pregnancy metabolism influences the risk of offspring congenital heart disease (CHD). We sought to explore this through a systematic approach using different methods and data. Methods: We undertook multivariable logistic regression of the odds of CHD for 923 mass spectrometry (MS)-derived metabolites in a sub-sample of a UK birth cohort (Born in Bradford (BiB); N = 2605, 46 CHD cases). We considered metabolites reaching a p-value threshold <0.05 to be suggestively associated with CHD. We sought validation of our findings, by repeating the multivariable regression analysis within the BiB cohort for any suggestively associated metabolite that was measured by nuclear magnetic resonance (NMR) or clinical chemistry (N = 7296, 87 CHD cases), and by using genetic risk scores (GRS: weighted genetic risk scores of single nucleotide polymorphisms (SNPs) that were associated with any suggestive metabolite) in Mendelian randomization (MR) analyses. The MR analyses were performed in BiB and two additional European birth cohorts (N = 38,662, 319 CHD cases). Results: In the main multivariable analyses, we identified 44 metabolites suggestively associated with CHD, including those from the following super pathways: amino acids, lipids, co-factors and vitamins, xenobiotics, nucleotides, energy, and several unknown molecules. Of these 44, isoleucine and leucine were available in the larger BiB cohort (NMR), and for these the results were validated. The MR analyses were possible for 27/44 metabolites and for 11 there was consistency with the multivariable regression results. Conclusions: In summary, we have used complimentary data sources and statistical techniques to construct layers of evidence. We found that pregnancy amino acid metabolism, androgenic steroid lipids, and levels of succinylcarnitine could be important contributing factors for CHD.

Relationship between amniotic fluid metabolic profile with fetal gender, maternal age, and gestational week

BACKGROUND

Amniotic fluid (AF) provides vital information on fetal development, which is also valuable in identifying fetal abnormalities during pregnancy. However, the relationship between the metabolic profile of AF in the second trimester of a normal pregnancy with several maternal-fetal parameters remains poorly understood, which therefore limits its application in clinical practice. The aim of this study was to explore the association between the metabolic profile of AF with fetal gender, maternal age, and gestational week using an untargeted metabolomics method.

METHODS

A total of 114 AF samples were analyzed in this study. Clinical data on fetal gender, maternal age, and gestational week of these samples were collected. Samples were analyzed by gas chromatography/time-of-flight-mass spectrometry (GC-TOF/MS). Principal component analysis(PCA), orthogonal partial least square discrimination analysis(OPLS-DA) or partial least square discrimination analysis (PLS-DA) were conducted to compare metabolic profiles, and differential metabolites were obtained by univariate analysis.

RESULTS

Both PCA and OPLS-DA demonstrated no significant separation trend between the metabolic profiles of male and female fetuses, and there were only 7 differential metabolites. When the association between the maternal age on AF metabolic profile was explored, both PCA and PLS-DA revealed that the maternal age in the range of 21 to 40 years had no significant effect on the metabolic profile of AF, and only four different metabolites were found. There was no significant difference in the metabolic profiles of AF from fetuses of 17-22 weeks, and 23 differential metabolites were found.

CONCLUSIONS

In the scope of our study, there was no significant correlation between the AF metabolic profile and the fetal gender, maternal age and gestational week of a small range. Nevertheless, few metabolites appeared differentially expressed.