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Updated: Sep 8, 2025

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Genetic burden and multidimensional predictors in prenatal diagnosis of fetal congenital diaphragmatic hernia

Ruibin Huang1, Fang Fu1, Shanshan Mei2

  • 1Prenatal Diagnostic Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510620, Guangdong, China.

Human Genetics
|September 6, 2025
PubMed

Insights

Genetic testing and imaging accurately predict outcomes for fetuses with congenital diaphragmatic hernia (CDH). This combined approach aids in early diagnosis and personalized perinatal management for better infant health.

Area of Science:

  • Medical Genetics
  • Fetal Medicine
  • Neonatal Surgery

Background:

  • Congenital diaphragmatic hernia (CDH) presents a significant challenge in fetal medicine, necessitating improved diagnostic and prognostic tools.
  • Understanding the genetic basis and identifying reliable predictors are crucial for optimizing perinatal care and surgical interventions in CDH cases.

Purpose of the Study:

  • To assess the genetic burden in fetal CDH using advanced genetic analyses.
  • To identify prenatal, perinatal, and postnatal predictors of CDH outcomes.
  • To develop predictive models for improved early diagnosis, monitoring, and intervention strategies.

Main Methods:

  • Invasive prenatal diagnosis in 130 CDH fetuses.
  • Fetal prognosis evaluation using lung imaging parameters: observed-to-expected lung-to-head ratio (o/e LHR), observed-to-expected total lung volume (o/e TLV), and percent predicted lung volume (PPLV).
  • Genetic analyses including chromosomal microarray analysis (CMA) and exome sequencing (ES).
  • Logistic regression and receiver operating characteristic (ROC) curve analyses for prognostic indicator evaluation and predictive model construction.

Main Results:

  • CMA and ES identified pathogenic variants with diagnostic rates of 7.7% and 8.7%, respectively, revealing CDH's genetic heterogeneity.
  • Imaging parameters (o/e LHR, o/e TLV, PPLV) and liver herniation were significant predictors of neonatal outcomes, ECMO requirement, and post-neonatal prognosis.
  • Multivariable models incorporating genetic and imaging data demonstrated high predictive accuracy (AUCs > 0.85), with a neonatal outcomes model achieving an AUC of 0.929.

Conclusions:

  • CMA and ES are valuable tools for assessing the genetic burden in CDH fetuses.
  • A combination of genetic data, imaging parameters (o/e LHR, o/e TLV, PPLV), and clinical factors like liver herniation provides robust prognostic indicators.
  • Integrated genetic-imaging approaches enhance individualized risk assessment and guide perinatal management for CDH.

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