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Characterization of craniofacial-based clinical phenotypes in children with suspected obstructive sleep apnea

Nathalia Carolina Fernandes Fagundes1, Prachi Loliencar1,2, Joanna E MacLean3

  • 1School of Dentistry, Faculty of Medicine and Dentistry, College of Health Sciences, University of Alberta, Edmonton, Alberta, Canada.

Journal of Clinical Sleep Medicine : JCSM : Official Publication of the American Academy of Sleep Medicine
|July 4, 2023
PubMed

Insights

This study found that distinct phenotypes of childhood obstructive sleep apnea (OSA) were not solely defined by facial features. Age and obesity appear to influence the impact of craniofacial characteristics on pediatric OSA risk.

Area of Science:

  • Pediatric Sleep Medicine
  • Craniofacial Biology
  • Obstructive Sleep Apnea Research

Background:

  • Obstructive sleep apnea (OSA) in children is a growing concern with complex contributing factors.
  • Understanding distinct phenotypes of pediatric OSA is crucial for targeted diagnosis and treatment.
  • Craniofacial morphology and lifestyle factors are potential contributors to OSA development in children.

Purpose of the Study:

  • To identify clinical phenotypes of pediatric obstructive sleep apnea (OSA).
  • To investigate the role of lifestyle, sleep habits, age, obesity, sex, soft tissue facial features, and craniofacial abnormalities in defining pediatric OSA phenotypes.
  • To analyze the relationship between craniofacial characteristics and OSA in children.

Main Methods:

  • Seventy-three children with suspected pediatric OSA underwent polysomnography.
  • Soft tissue facial features were assessed using 3D stereophotogrammetry.
  • Craniofacial abnormalities were evaluated, and data on lifestyle, sleep habits, age, obesity, and sex were collected. Fuzzy clustering with medoids was used for analysis.

Main Results:

  • Three clusters of pediatric OSA were identified based on craniofacial abnormalities and soft tissue facial features.
  • Cluster 1: Younger, non-obese children with smaller facial features. Cluster 2: Older, non-obese children with larger mandibular dimensions. Cluster 3: Older, obese children with excessive lower facial height and midface deficiency.
  • No significant differences in sleep features were observed across clusters; moderate OSA severity was present in all.

Conclusions:

  • Distinct pediatric OSA phenotypes were not solely identified by soft tissue facial features or craniofacial abnormalities.
  • Age and body mass index (BMI) likely modify the influence of craniofacial characteristics on OSA risk in children.
  • Further research is needed to elucidate the complex interplay of factors contributing to pediatric OSA phenotypes.
Abstract

Keywords:
childcluster analysisfacephotogrammetry

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