Associations between self‐reported parasomnias and psychiatric illness in 370,000 patients with sleep disorders

Aim To assess self‐reported parasomnias in patients with sleep disorders and explore relationships with psychiatric illness, comorbidities, subjective sleep assessments, and polysomnographic study results. Methods Results from intake questionnaires and polysomnographic assessments, collected from 240 sleep centers across 30 US states between 2004 and 2019, were analyzed retrospectively. Of 540,000 total patients, 371,889 who answered parasomnia‐specific questions were included. Patients responding “often” or “always” to parasomnia‐specific questions were considered “symptom‐positive,” whereas a “few times” or “never” were considered “symptom‐negative” (controls). Results The study sample was 54.5% male with mean age 54 years (range, 2–107 years). Frequencies for the different parasomnias were 16.0% for any parasomnia, 8.8% for somniloquy, 6.0% for hypnagogic hallucinations, 4.8% for sleep‐related eating disorder, 2.1% for sleep paralysis, and 1.7% for somnambulism. Frequent parasomnias were highly associated with diagnosed depression (odds ratio = 2.72). All parasomnias were associated with being younger and female and with symptoms of depression, anxiety, insomnia, restless legs, pain, medical conditions, fatigue, and sleepiness. Associations with objective sleep metrics showed characteristics of consolidated sleep and differentiated weakly between nonrapid eye movement sleep and rapid eye movement sleep parasomnias. Machine learning accurately classified patients with parasomnia versus controls (balanced accuracies between 71% and 79%). Benzodiazepines, antipsychotics, and opioids increased the odds of experiencing parasomnias, while antihistamines and melatonin reduced the odds. Z‐drugs were found to increase the likelihood of a sleep‐related eating disorder. Conclusion Our findings suggest that parasomnias may be clinically relevant, yet understudied, symptoms of depression and anxiety. Further investigation is needed to quantify the nature of multimorbidity, including causality and implications for diagnosis and treatment.