Clusters of Insomnia Disorder: An Exploratory Cluster Analysis of Objective Sleep Parameters Reveals Differences in Neurocognitive Functioning, Quantitative EEG, and Heart Rate Variability

Miller, Claramae H.; Bartlett, Delwyn J.; Mullins, Anna; Dodds, Kirsty L.; Gordon, Christopher J.; Kyle, Simon D.; Kim, Jong Won; D'Rozario, Angela L; Lee, Rico S.C.; Comas, Maria; Marshall, Nathaniel S.; Yee, Brendon J.; Espie, Colin A.; Grunstein, Ronald R.

doi:10.5665/sleep.6230

Cited by 47 publications

(63 citation statements)

References 55 publications

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“…Insomnia with NNSD is related with an anxious‐ruminative profile (Vgontzas et al., ), whereas insomnia with OSSD is linked with physiological hyperarousal (Vgontzas et al., ), increased risk of type 2 diabetes, hypertension, as well as concurrent and future cerebrovascular and cardiovascular events (Fernandez‐Mendoza et al., ; Vgontzas et al., ). Despite data on the link between insomnia and OSSD with cardiometabolic disease (Fernandez‐Mendoza et al., ; Kalmbach, Pillai, Arnedt, & Drake, ; Vgontzas et al., ), information on potential underlying pathophysiological mechanisms, such as systematic inflammation (Fernandez‐Mendoza et al., ), hormonal and metabolic dysregulation (D'Aurea et al., ; Fernandez‐Mendoza et al., ), or alterations in the autonomic nervous system (Miller et al., ; Spiegelhalder et al., ) have been scant.…”

Section: Introductionmentioning

confidence: 99%

Cardiovascular autonomic dysfunction in insomnia patients with objective short sleep duration

Jarrin

Ivers

Lamy

et al. 2018

Journal of Sleep Research

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Two phenotypes have been proposed: insomnia with objective near-normal sleep duration, related to increased psychological symptoms, and insomnia with objective short sleep duration, associated with cardiometabolic morbidity. Reduced heart rate variability has also been implicated in the pathophysiology of cardiometabolic disease; however, there are little data on whether cardiovascular function differs between patients with objective short sleep duration and near-normal sleep duration. Participants (M = 49.9 ± 11.3 years; 62.8% female) were 180 adults with chronic insomnia (M = 15.7 ± 13.6). Objective sleep duration was based on total sleep time averaged across two consecutive nights of polysomnography and subjective sleep duration was based on 2-week sleep diaries. The sample was divided into two groups, with sleep duration shorter (polysomnography-total sleep time: n = 46; sleep diary: n = 95) or equal/longer (polysomnography-total sleep time: n = 134; sleep diary: n = 85) than 6 hr. Electrocardiogram data derived from polysomnography were used to obtain heart rate and heart rate variability during stage 2 (N2) and rapid eye movement sleep. Heart rate variability measures included absolute and normalized high-frequency component, an index of parasympathetic activation, and the ratio of low- to high-frequency (LF/HF ratio), an index of sympathovagal balance. After controlling for covariates (e.g., co-morbidity), patients with objective short sleep duration had reduced high-frequency (p < .05) and elevated low-frequency/high-frequency ratio (p = .036) and heart rate (p = .051) compared with patients with near-normal sleep duration. No differences were observed between phenotypes when subjective sleep duration was used. Insomnia patients with objective short sleep duration showed significantly dampened parasympathetic activation and increased sympathovagal imbalance relative to their counterparts with near-normal sleep duration. These findings highlight the importance of treating insomnia, as treatment may reduce the risk of cardiovascular disease.

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Section: Introductionmentioning

confidence: 99%

Cardiovascular autonomic dysfunction in insomnia patients with objective short sleep duration

Jarrin

Ivers

Lamy

et al. 2018

Journal of Sleep Research

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“…There have been previous attempts to identify nighttime and daytime symptom cluster profiles in patients with insomnia disorder using data-driven approaches. [8][9][10][11][12][13] Similar attempts have been published characterizing the heterogeneity of obstructive sleep apnea. 14,15 However, most of these studies used cluster analysis to characterize this heterogeneity.…”

Section: Introductionmentioning

confidence: 66%

“…A number of studies have highlighted differential outcomes associated with short versus long objective sleep. [38][39][40][41][42]59 In contrast to these top-down approaches, data-driven methods, such as cluster analysis, have been applied to this area and have revealed that daytime symptoms such as sleepiness, fatigue, mood and sleep hygiene practices, 9 nighttime symptoms such as objective sleep parameters, 10 night-to-night variability and longitudinal development of subjective sleep variables, 11,17 and dysfunctional beliefs about sleep 12 uniquely fall together in identifiable and meaningful clusters. Others have used both sleep and psychiatric history, and daytime and nighttime symptoms to identify symptom clusters.…”

Section: Discussionmentioning

confidence: 99%

“…We used TST derived from the PSG rather than the sleep diary because of mounting evidence that insomnia with objective short sleep may form a distinct subtype of insomnia. 10,[38][39][40][41][42] The optimal number of symptom cluster profiles was determined after examination of the following fit indices: the Akaike information criteria (AIC), the Bayesian information criteria (BIC), the sample-size adjusted BIC (aBIC), log-likelihood (LL), entropy, the adjusted likelihood ratio test (ALRT), and the parametric bootstrapped likelihood ratio test (BLRT). 43 Important advantages of LPA over standard cluster techniques have been identified in the literature.…”

Section: Discussionmentioning

confidence: 99%

“…61 The average objective TST for this symptom cluster profile-just above 6 hours-was below the sample's overall average; thus, individuals with this symptom profile might benefit from therapeutic approaches that lead to rapid sleep consolidation-for example, sleep restriction, stimulus control, or sedative-hypnotics. Whether some of these individuals present with similar characteristics and sequelae as the symptom cluster profile "insomnia with objective short sleep" 10,[38][39][40]42,59 remains to be elucidated in future studies.…”

Section: "High Subjective Wakefulness" Symptom Cluster Profilementioning

confidence: 99%

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Characterization of Patients Who Present With Insomnia: Is There Room for a Symptom Cluster-Based Approach?

Crawford

Chirinos

Iurcotta

et al. 2017

Journal of Clinical Sleep Medicine

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Study Objectives: This study examined empirically derived symptom cluster profiles among patients who present with insomnia using clinical data and polysomnography. Methods: Latent profile analysis was used to identify symptom cluster profiles of 175 individuals (63% female) with insomnia disorder based on total scores on validated self-report instruments of daytime and nighttime symptoms (Insomnia Severity Index, Glasgow Sleep Effort Scale, Fatigue Severity Scale, Beliefs and Attitudes about Sleep, Epworth Sleepiness Scale, Pre-Sleep Arousal Scale), mean values from a 7-day sleep diary (sleep onset latency, wake after sleep onset, and sleep efficiency), and total sleep time derived from an in-laboratory PSG. Results:The best-fitting model had three symptom cluster profiles: "High Subjective Wakefulness" (HSW), "Mild Insomnia" (MI) and "Insomnia-Related Distress" (IRD). The HSW symptom cluster profile (26.3% of the sample) reported high wake after sleep onset, high sleep onset latency, and low sleep efficiency. Despite relatively comparable PSG-derived total sleep time, they reported greater levels of daytime sleepiness. The MI symptom cluster profile (45.1%) reported the least disturbance in the sleep diary and questionnaires and had the highest sleep efficiency. The IRD symptom cluster profile (28.6%) reported the highest mean scores on the insomnia-related distress measures (eg, sleep effort and arousal) and waking correlates (fatigue). Covariates associated with symptom cluster membership were older age for the HSW profile, greater obstructive sleep apnea severity for the MI profile, and, when adjusting for obstructive sleep apnea severity, being overweight/obese for the IRD profile. Conclusions:The heterogeneous nature of insomnia disorder is captured by this data-driven approach to identify symptom cluster profiles. The adaptation of a symptom cluster-based approach could guide tailored patient-centered management of patients presenting with insomnia, and enhance patient care. Keywords: insomnia disorder, latent profile analysis, symptom profile, symptom clusters Citation: Crawford MR, Chirinos DA, Iurcotta T, Edinger JD, Wyatt JK, Manber R, Ong JC. Characterization of patients who present with insomnia: is there room for a symptom cluster-based approach? J Clin Sleep Med. 2017;13(7):911-921. I NTRO DUCTI O NInsomnia is the experience of the difficulty falling asleep, difficulty staying asleep, or early morning awakenings. About one-third to one-half of adults complains of these symptoms. Frequently, other complaints, such as sleepiness, fatigue, and hyperarousal, will occur with nocturnal sleep disturbance. An insomnia disorder is defined in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) 2 as the combination of the nocturnal sleep disturbance with one of these waking complaints at least 3 nights a week for at least 3 months. Approximately 8% to 10% of the adult population meets these criteria. 3,4Insomnia disorder is a heterogeneous condition.5 This can pose a chall...

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Insomnia Disorder Detection Using EEG Sleep Trajectories

McCloskey

Jeffries

Koprinska

et al. 2022

Advances in Knowledge Discovery and Data Mining

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Clusters of Insomnia Disorder: An Exploratory Cluster Analysis of Objective Sleep Parameters Reveals Differences in Neurocognitive Functioning, Quantitative EEG, and Heart Rate Variability

Abstract: Insomnia 100 sleep study: Australia New Zealand Clinical Trials Registry (ANZCTR) identification number 12612000049875. URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=347742.

Cited by 47 publications

References 55 publications

Cardiovascular autonomic dysfunction in insomnia patients with objective short sleep duration

Cardiovascular autonomic dysfunction in insomnia patients with objective short sleep duration

Characterization of Patients Who Present With Insomnia: Is There Room for a Symptom Cluster-Based Approach?

Insomnia Disorder Detection Using EEG Sleep Trajectories

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