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
Affiliations
Jezid Miranda
BCNatal – Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain; Department of Obstetrics and Gynecology, Faculty of Medicine, Universidad de Cartagena, Cartagena de Indias, Colombia
Cristina Paules
BCNatal – Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain; Aragon Institute of Health Research (IIS Aragon), Obstetrics Department, Hospital Clínico Universitario Lozano Blesa, Zaragoza, Spain
Guillaume Noell
University of Barcelona, Biomedicine Department, IDIBAPS, Centre for Biomedical Research on Respiratory Diseases (CIBERES), Barcelona, Spain
Lina Youssef
BCNatal – Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
Angel Paternina-Caicedo
Faculty of Medicine, Universidad del Sinu, Cartagena de Indias, Colombia
Francesca Crovetto
BCNatal – Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
Nicolau Cañellas
Metabolomics Platform, IISPV, DEEiA, Universidad Rovira i Virgili, Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), Tarragona, Spain
María L. Garcia-Martín
BIONAND, Andalusian Centre for Nanomedicine and Biotechnology, Junta de Andalucía, Universidad de Málaga, Málaga, Spain
Nuria Amigó
Biosfer Teslab, Reus, Spain
Elisenda Eixarch
BCNatal – Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
Rosa Faner
University of Barcelona, Biomedicine Department, IDIBAPS, Centre for Biomedical Research on Respiratory Diseases (CIBERES), Barcelona, Spain
Francesc Figueras
BCNatal – Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
Rui V. Simões
BCNatal – Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain; Institute for Research & Innovation in Health (i3S), University of Porto, Porto, Portugal; Corresponding author
Fàtima Crispi
BCNatal – Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain; Corresponding author
Eduard Gratacós
BCNatal – Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
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.
