Associated factors and a risk prediction model for comorbid circadian rhythm sleep-wake disorders in patients with chronic fatigue syndrome: implications for early screening and sleep health management - Report - MDSpire

Identifying Factors and Developing a Risk Assessment Model for Comorbid Circadian Rhythm Sleep-Wake Disorders in Chronic Fatigue Syndrome Patients: Insights for Early Detection and Sleep Health Strategies

  • By

  • Xiao Shao

  • Fan Yang

  • Le-le Qin

  • Jia-ning Shi

  • Min Chen

  • Wen-jin Ge

  • Tian-jun Jiang

  • Jing-wen Yue

  • Wen Feng

  • Jing-han Wang

  • Zhen-xian Zhang

  • July 20, 2026

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Clinical Report: Identifying Factors and Developing a Risk Assessment Model for Comorbid Circadian Rhythm Sleep-Wake Disorders in Chronic Fatigue Syndrome Patients

Overview

This study characterizes comorbid circadian rhythm sleep-wake disorders (CRSWDs) in chronic fatigue syndrome (CFS) patients and identifies key factors associated with these disorders.

Background

Chronic fatigue syndrome (CFS) is a debilitating condition characterized by persistent fatigue and various multisystem symptoms, including sleep disturbances. The relationship between CFS and circadian rhythm sleep-wake disorders (CRSWDs) is significant, as these disorders can exacerbate the overall symptom burden and impact quality of life.

Data Highlights

FactorOdds Ratio (OR)95% Confidence Interval (CI)
12-hour rotating shifts6.9812.603-18.720
24-hour shifts5.3162.197-12.863
8-hour rotating shifts3.9821.698-9.339
Fatigue Severity (FS-14)1.9841.442-2.728
Female sex1.8921.178-3.037
Slow-wave sleep (N3%)0.9820.971-0.992

Key Findings

  • 48.7% of CFS patients in the training cohort had comorbid CRSWDs.
  • Gender, BMI, fatigue severity, slow-wave sleep, screen use before bedtime, and shift-work schedule were significantly associated with CRSWDs.
  • Multivariable analysis identified 12-hour rotating shifts, 24-hour shifts, and 8-hour rotating shifts as risk factors for CRSWDs.
  • Increased fatigue severity and female sex were also independently associated with higher incidence of CRSWDs.
  • Higher slow-wave sleep (N3%) was linked to decreased odds of CRSWDs.
  • The developed risk prediction model demonstrated good performance in both training and validation cohorts.

Clinical Implications

The identification of specific factors associated with CRSWDs in CFS patients can inform screening strategies.

Conclusion

The study highlights the importance of recognizing comorbid CRSWDs in CFS patients.

Related Resources & Content

  1. Frontiers in Psychiatry, 2026 -- Circadian dimensions in insomnia disorder: mechanistic evidence, candidate phenotypes, and a phenotype-stratified research framework
  2. Frontiers in Digital Health, 2026 -- Exploring the feasibility of modeling next-day fatigue and sleepiness using digital sleep tracker data in neurodegenerative and immune-mediated inflammatory diseases
  3. Clinical Rheumatology, 2020 -- Morning Chronotype Observed in Individuals with Rheumatoid Arthritis
  4. Practice Guidelines | American Academy of Sleep Medicine | AASM
  5. Overview | Myalgic encephalomyelitis (or encephalopathy)/chronic fatigue syndrome: diagnosis and management | Guidance | NICE
  6. conexiant — One Night's Sleep May Predict 130 Diseases
  7. Practice Guidelines | American Academy of Sleep Medicine | AASM
  8. Overview | Myalgic encephalomyelitis (or encephalopathy)/chronic fatigue syndrome: diagnosis and management | Guidance | NICE
  9. Sleep and circadian rhythm alterations in myalgic encephalomyelitis/chronic fatigue syndrome and post-COVID fatigue syndrome and its association with cardiovascular risk factors: A prospective cohort study

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