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Deep phenotyping of insomnia: a multimodal assessment protocol
Yujin Lee1,2, Seonmin Kim3,4, Sujin Kim3
1Department of Psychiatry, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea.
Frontiers in Psychiatry
|December 1, 2025
Summary
This study used deep phenotyping to analyze insomnia, integrating clinical, digital, neuroimaging, and genomic data. Machine learning identified distinct insomnia subtypes, paving the way for personalized sleep disorder treatments.
Area of Science:
- Sleep Medicine
- Computational Biology
- Personalized Medicine
Background:
- Insomnia is common but complex, with varied causes and presentations.
- Current assessment methods struggle to capture this complexity, limiting personalized treatment development.
- A deep phenotyping approach is proposed to comprehensively characterize insomnia.
Purpose of the Study:
- To identify distinct biopsychosocial phenotypes of insomnia.
- To elucidate the heterogeneity of insomnia.
- To enable the development of targeted, personalized management strategies for sleep disorders.
Main Methods:
- A single-center prospective observational study of adults with insomnia and controls.
- A 4-week multimodal assessment including clinical, digital, neuroimaging (fNIRS), and genomic data.
- Analysis using machine learning and clustering to identify insomnia subtypes.
Main Results:
- Data collection completed between March 2023 and October 2024.
- Advanced computational methods applied to integrated datasets.
- Identification of distinct insomnia subtypes is anticipated.
Conclusions:
- Deep phenotyping offers a comprehensive approach to understanding insomnia's complexity.
- Machine learning analysis of multimodal data can reveal distinct insomnia phenotypes.
- This research aims to advance personalized treatment strategies for sleep disorders.
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