iScience (Sep 2023)

Similarity network fusion to identify phenotypes of small-for-gestational-age fetuses

  • Jezid Miranda,
  • Cristina Paules,
  • Guillaume Noell,
  • Lina Youssef,
  • Angel Paternina-Caicedo,
  • Francesca Crovetto,
  • Nicolau Cañellas,
  • María L. Garcia-Martín,
  • Nuria Amigó,
  • Elisenda Eixarch,
  • Rosa Faner,
  • Francesc Figueras,
  • Rui V. Simões,
  • Fàtima Crispi,
  • Eduard Gratacós

Journal volume & issue
Vol. 26, no. 9
p. 107620

Abstract

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Summary: Fetal growth restriction (FGR) affects 5–10% of pregnancies, is the largest contributor to fetal death, and can have long-term consequences for the child. Implementation of a standard clinical classification system is hampered by the multiphenotypic spectrum of small fetuses with substantial differences in perinatal risks. Machine learning and multiomics data can potentially revolutionize clinical decision-making in FGR by identifying new phenotypes. Herein, we describe a cluster analysis of FGR based on an unbiased machine-learning method. Our results confirm the existence of two subtypes of human FGR with distinct molecular and clinical features based on multiomic analysis. In addition, we demonstrated that clusters generated by machine learning significantly outperform single data subtype analysis and biologically support the current clinical classification in predicting adverse maternal and neonatal outcomes. Our approach can aid in the refinement of clinical classification systems for FGR supported by molecular and clinical signatures.

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