Cell-free DNA in maternal blood and artificial intelligence: accurate prenatal detection of fetal congenital heart defects

Ray Bahado-Singh, Perry Friedman, Ciara Talbot, Buket Aydas, Siddesh Southekal, Nitish K. Mishra, Chittibabu Guda, Ali Yilmaz, Uppala Radhakrishna, Sangeetha Vishweswaraiah

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Background: DNA cytosine nucleotide methylation (epigenomics and epigenetics) is an important mechanism for controlling gene expression in cardiac development. Combined artificial intelligence and whole-genome epigenomic analysis of circulating cell-free DNA in maternal blood has the potential for the detection of fetal congenital heart defects. Objective: This study aimed to use genome-wide DNA cytosine methylation and artificial intelligence analyses of circulating cell-free DNA for the minimally invasive detection of fetal congenital heart defects. Study Design: In this prospective study, whole-genome cytosine nucleotide methylation analysis was performed on circulating cell-free DNA using the Illumina Infinium MethylationEPIC BeadChip array. Multiple artificial intelligence approaches were evaluated for the detection of congenital hearts. The Ingenuity Pathway Analysis program was used to identify gene pathways that were epigenetically altered and important in congenital heart defect pathogenesis to further elucidate the pathogenesis of isolated congenital heart defects. Results: There were 12 cases of isolated nonsyndromic congenital heart defects and 26 matched controls. A total of 5918 cytosine nucleotides involving 4976 genes had significantly altered methylation, that is, a P value of <.05 along with ≥5% whole-genome cytosine nucleotide methylation difference, in congenital heart defect cases vs controls. Artificial intelligence analysis of the methylation data achieved excellent congenital heart defect predictive accuracy (areas under the receiver operating characteristic curve, ≥0.92). For example, an artificial intelligence model using a combination of 5 whole-genome cytosine nucleotide markers achieved an area under the receiver operating characteristic curve of 0.97 (95% confidence interval, 0.87–1.0) with 98% sensitivity and 94% specificity. We found epigenetic changes in genes and gene pathways involved in the following important cardiac developmental processes: “cardiovascular system development and function,” “cardiac hypertrophy,” “congenital heart anomaly,” and “cardiovascular disease.” This lends biologic plausibility to our findings. Conclusion: This study reported the feasibility of minimally invasive detection of fetal congenital heart defect using artificial intelligence and DNA methylation analysis of circulating cell-free DNA for the prediction of fetal congenital heart defect. Furthermore, the findings supported an important role of epigenetic changes in congenital heart defect development.

Original languageEnglish (US)
Pages (from-to)76.e1-76.e10
JournalAmerican Journal of Obstetrics and Gynecology
Volume228
Issue number1
DOIs
StatePublished - Jan 2023

Keywords

  • DNA methylation
  • artificial intelligence
  • biomarkers
  • circulating cell-free DNA
  • congenital heart defect
  • precision cardiology

ASJC Scopus subject areas

  • Obstetrics and Gynecology

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