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A detection method for latent circadian rhythm sleep-wake disorder
Makoto Akashi1, Reimi Sogawa2, Ritsuko Matsumura1
1The Research Institute for Time Studies, Yamaguchi University, 1677-1 Yoshida, Yamaguchi 753-8511, Yamaguchi, Japan.
Ebiomedicine
|November 9, 2020
Summary
A new method using clock gene expression in hair follicles can detect latent circadian rhythm sleep-wake disorders (LCRSWD). This helps identify individuals with delayed sleep timing despite appearing to have normal sleep schedules.
Area of Science:
- Chronobiology
- Sleep Medicine
- Molecular Biology
Background:
- Circadian rhythm sleep-wake disorders (CRSWDs) disrupt habitual sleep timing relative to social schedules.
- Latent CRSWD (LCRSWD) presents with normal habitual sleep timing, masking underlying circadian desynchronization.
- Conventional diagnostic tools fail to detect LCRSWD due to its subtle presentation.
Purpose of the Study:
- To develop and validate a reliable method for detecting LCRSWD.
- To identify specific criteria for diagnosing LCRSWD based on circadian phase estimation.
- To confirm the reproducibility and effectiveness of the detection method.
Main Methods:
- Circadian phase was estimated using clock gene expression in hair follicles at three time points.
- Circadian and sleep parameters were compared between subjects with low and high Pittsburgh Sleep Quality Index (PSQI) scores.
- Reproducibility and sleep improvement after circadian intervention were assessed.
Main Results:
- Reliability of circadian phase estimation using clock gene expression was validated.
- Potential detection criteria for LCRSWD, particularly the interval between circadian phase and wake time, were identified.
- Screening reproducibility and sleep improvement post-intervention confirmed the detection criteria's reliability.
Conclusions:
- A promising prototype for detecting LCRSWD attributed to delayed circadian sleepiness was developed.
- The method shows potential for identifying individuals with masked circadian desynchronization.
- Further extensive trials are necessary for full validation of this diagnostic approach.
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