Clinically Oriented Subtyping of Chronic Insomnia of Childhood.

OBJECTIVES: To identify different profiles of pediatric insomnia, based on the most frequent clinical presentations (nocturnal awakenings, difficulty in falling asleep, nocturnal restlessness, early morning awakenings). STUDY DESIGN: A structured parent interview was conducted in 338 children (mean age 21.29 months, SD 10.56) referred by pediatricians because of insomnia resistant to behavioral approaches and common drug treatments. The aim was to assess the characteristics of insomnia in children, together with family sleep-related history. A latent class analysis was run to identify profiles of insomnia. ANOVA and the χ2 test were used to examine differences between profiles. RESULTS: A 3-class model was built by latent class analysis: 17% (n = 58) of children constituted the first class, characterized by difficulties in falling asleep, with restlessness, nocturnal restlessness, and awakenings during the night; the second class, characterized by early morning awakenings, comprised 21% (n = 71) of children; 62% (n = 209) of children fell within the third class because of their high frequency of nocturnal awakenings and difficulties in falling asleep. The first class reported longer sleep latency and the presence of restless legs syndrome and anemia in the family history; depression and/or mood disorders were more frequent in class 2 and allergies and/or food intolerance were more frequent in class 3. CONCLUSIONS: Our study suggests the existence of 3 different phenotypes of insomnia in children, based on clinical, personal, and familial data. The identification of these different phenotypes might help to optimize the assessment and treatment of insomnia in young children.

Nocturnal sleep phenotypes in idiopathic hypersomnia - A data-driven cluster analysis.

INTRODUCTION: The diagnostic process for idiopathic hypersomnia (IH) is complex due to the diverse aetiologies of daytime somnolence, ambiguous pathophysiological understanding, and symptom variability. Current diagnostic instruments, such as the multiple sleep latency test (MSLT), are limited in their ability to fully represent IH's diverse nature. This study endeavours to delineate subgroups among IH patients via cluster analysis of polysomnographic data and to examine the temporal evolution of their symptomatology, aiming to enhance the granularity of understanding and individualized treatment approaches for IH. METHODS: This study included individuals referred to the Uppsala Centre for Sleep Disorders from 2010 to 2019, who were diagnosed with IH based on the International Classification of Sleep Disorders-3 (ICSD-3) criteria, following a thorough diagnostic evaluation. The final cohort, after excluding participants with incomplete data or significant comorbid sleep-related respiratory conditions, comprised 69 subjects, including 49 females and 20 males, with an average age of 40 years. Data were collected through polysomnography (PSG), MSLT, and standardized questionnaires. A two-step cluster analysis was employed to navigate the heterogeneity within IH, focusing on objective time allocation across different sleep stages and sleep efficiency derived from PSG. The study also aimed to track subgroup-specific changes in symptomatology over time, with follow-ups ranging from 21 to 179 months post-diagnosis. RESULTS: The two-step cluster analysis yielded two distinct groups with a satisfactory silhouette coefficient: Cluster 1 (n = 29; 42 %) and Cluster 2 (n = 40; 58 %). Cluster 1 exhibited increased deep sleep duration, reduced stage 2 sleep, and higher sleep maintenance efficiency compared to Cluster 2. Further analyses of non-clustering variables indicated shorter wake after sleep onset in Cluster 1, but no significant differences in other sleep parameters, MSLT outcomes, body mass index, age, or self-reported measures of sleep inertia or medication usage. Long-term follow-up assessments showed an overall improvement in excessive daytime sleepiness, with no significant inter-cluster differences. CONCLUSION: This exploratory two-step cluster analysis of IH-diagnosed patients discerned two subgroups with distinct nocturnal sleep characteristics, aligning with prior findings and endorsing the notion that IH may encompass several phenotypes, each potentially requiring tailored therapeutic strategies. Further research is imperative to substantiate these findings.

The Two Fundamental Shapes of Sleep Heart Rate Dynamics and Their Connection to Mental Health in College Students.

Introduction: Wearable devices are rapidly improving our ability to observe health-related processes for extended durations in an unintrusive manner. In this study, we use wearable devices to understand how the shape of the heart rate curve during sleep relates to mental health. Methods: As part of the Lived Experiences Measured Using Rings Study (LEMURS), we collected heart rate measurements using the Oura ring (Gen3) for over 25,000 sleep periods and self-reported mental health indicators from roughly 600 first-year university students in the USA during the fall semester of 2022. Using clustering techniques, we find that the sleeping heart rate curves can be broadly separated into two categories that are mainly differentiated by how far along the sleep period the lowest heart rate is reached. Results: Sleep periods characterized by reaching the lowest heart rate later during sleep are also associated with shorter deep and REM sleep and longer light sleep, but not a difference in total sleep duration. Aggregating sleep periods at the individual level, we find that consistently reaching the lowest heart rate later during sleep is a significant predictor of (1) self-reported impairment due to anxiety or depression, (2) a prior mental health diagnosis, and (3) firsthand experience in traumatic events. This association is more pronounced among females. Conclusion: Our results show that the shape of the sleeping heart rate curve, which is only weakly correlated with descriptive statistics such as the average or the minimum heart rate, is a viable but mostly overlooked metric that can help quantify the relationship between sleep and mental health.

No bidirectional relationship between sleep phenotypes and risk of proliferative diabetic retinopathy: a two-sample Mendelian randomization study.

This study aimed to investigate the probable existence of a causal relationship between sleep phenotypes and proliferative diabetic retinopathy (PDR). Single nucleotide polymorphisms associated with sleep phenotypes were selected as instrumental variables at the genome-wide significance threshold (P < 5 × 10-8). Inverse-variance weighted was applied as the primary Mendelian randomization (MR) analysis method, and MR Egger regression, weighted median, simple mode, and weighted mode methods were used as complementary analysis methods to estimate the causal association between sleep phenotypes and PDR. Results indicated that genetically predicted sleep phenotypes had no causal effects on PDR risk after Bonferroni correction (P = 0.05/10) [Chronotype: P = 0.143; Daytime napping: P = 0.691; Daytime sleepiness: P = 0.473; Insomnia: P = 0.181; Long sleep duration: P = 0.671; Morning person:P = 0.113; Short sleep duration: P = 0.517; Obstructive sleep apnea: P = 0.091; Sleep duration: P = 0.216; and snoring: P = 0.014]. Meanwhile, there are no reverse causality for genetically predicted PDR on sleep phenotypes [Chronotype: P = 0.100; Daytime napping: P = 0.146; Daytime sleepiness: P = 0.469; Insomnia: P = 0.571; Long sleep duration: P = 0.779; Morning person: P = 0.040; Short sleep duration: P = 0.875; Obstructive sleep apnea: P = 0.628; Sleep duration: P = 0.896; and snoring: P = 0.047]. This study's findings did not support the causal effect of between sleep phenotypes and PDR. Whereas, longitudinal studies can further verify results validation.

Chronic insomnia of early childhood: Phenotypes and pathophysiology.

This paper aims to review the limitations of the current classification of insomnia of early childhood and propose a new conceptual model allowing a better understanding of its pathophysiology. Our hypothesis is that chronic insomnia of childhood has different phenotypical expressions, associated to different pathophysiological mechanisms. Based on a long-lasting experience in evaluating a very large number of children with specific insomnia symptoms (nocturnal awakenings, difficulty in falling asleep, nocturnal restlessness, early morning awakenings) and on published data, we hypothesize that different phenotypes of insomnia might exist with different therapeutic implications. We describe three phenotypes of insomnia in early childhood: a) insomnia with motor restlessness; b) insomnia characterized without difficulties in falling asleep but with long-lasting early morning awakenings; c) insomnia with multiple night awakenings and falling asleep difficulty. This type of categorization might have important implications for treatment, based on the different hypothetical neurotransmitter dysfunctions. The early identification of a phenotype of insomnia might guide to specific behavioral and/or pharmacological interventions with the aim to prevent chronic insomnia.

Cardiovascular risks and sociodemographic correlates of multidimensional sleep phenotypes in two samples of US adults.

Study Objectives: Sleep is a modifiable risk factor for cardiovascular conditions. Holistic examination of within-person, multidimensional sleep patterns may offer more detailed information about the sleep-cardiovascular condition link, including who is more vulnerable to both. This study aimed to identify common sleep phenotypes in adulthood, establish the validity of the phenotypes in relation to cardiovascular conditions, and explore sociodemographic and background characteristics of the phenotypes. Methods: Across two independent samples of adults (N 1 = 4600; N 2 = 2598) from the Midlife in the United States Study, latent class analysis (LCA) extracted sleep phenotypes using five key self-reported sleep dimensions. Log-binomial regression was used to determine whether sleep phenotypes differentially predicted cardiovascular conditions, adjusting for known risk factors. LCA with covariates was used to compare sociodemographic characteristics of the identified sleep phenotypes. Results: Four sleep phenotypes were identified consistently across the two samples: good sleepers, nappers, dissatisfied/inefficient sleepers, and irregular sleepers. Compared to good sleepers (reference), dissatisfied/inefficient sleepers exhibited a higher risk of cardiovascular conditions in both samples (RR Sample1: 29%, RR Sample2: 53%) and consisted of relatively more racial/ethnic minorities. Nappers exhibited a higher risk of cardiovascular conditions in one sample (RR Sample1: 38%) and consisted of more women and older adults. Irregular sleepers exhibited no significantly different cardiovascular risk and were relatively younger. Conclusions: Common sleep phenotypes in adulthood exhibit differential risks for cardiovascular conditions. Cooccurring sleep dissatisfaction and inefficiency, in particular, may relate to increased risk of cardiovascular conditions. Certain sociodemographic groups (racial minorities, women, older adults) disproportionately fit within high-risk sleep phenotypes.

Associations of sleep phenotypes with severe intentional self-harm: a prospective analysis of the UK Biobank cohort.

STUDY OBJECTIVES: We aimed to investigate the prospective associations of sleep phenotypes with severe intentional self-harm (ISH) in middle-aged and older adults. METHODS: A total of 499,159 participants (mean age: 56.55 ± 8.09 years; female: 54.4%) were recruited from the UK Biobank between 2006 and 2010 with follow-up until February 2016 in this population-based prospective study. Severe ISH was based on hospital inpatient records or a death cause of ICD-10 codes X60-X84. Patients with hospitalized diagnosis of severe ISH before the initial assessment were excluded. Sleep phenotypes, including sleep duration, chronotype, insomnia, sleepiness, and napping, were assessed at the initial assessments. Cox regression analysis was used to estimate temporal associations between sleep phenotypes and future risk of severe ISH. RESULTS: During a follow-up period of 7.04 years (SD: 0.88), 1,219 participants experienced the first hospitalization or death related to severe ISH. After adjusting for demographics, substance use, medical diseases, mental disorders, and other sleep phenotypes, short sleep duration (HR: 1.50, 95% CI: 1.23-1.83, p < .001), long sleep duration (HR: 1.56, 95% CI: 1.15-2.12, p = .004), and insomnia (usually: HR: 1.57, 95% CI: 1.31-1.89, p < .001) were significantly associated with severe ISH. Sensitivity analyses excluding participants with mental disorders preceding severe ISH yielded similar results. CONCLUSION: The current study provides the empirical evidence of the independent prediction of sleep phenotypes, mainly insomnia, short- and long-sleep duration, for the future risk of severe ISH among middle-aged and older adults.

Phenotypes of obstructive sleep apnea in the Hispanic Community Health Study/Study of Latinos.

STUDY OBJECTIVES: Recent work on US Whites from clinical samples used obstructive sleep apnea (OSA) symptoms to generate phenotypes for individuals with moderate-severe OSA which suggested 3 to 5 symptom classes. However, it is unknown whether similar classes generalize to diverse Hispanics/Latino adults. Therefore, we sought to fill this gap by empirically deriving sleep phenotypes among a large sample of diverse Hispanics/Latinos. METHODS: We used data from The Hispanic Community Health Study/Study of Latinos (HCHS/SOL; 2008-2011), a prospective cohort study designed using a multisite multistage probability sample of adults 18-74 years old. The subpopulation of interest included participants with moderate-severe OSA symptoms (≥15 respiratory event index (REI) events per hour; n = 1,605). We performed latent class analysis for complex survey data using 15 common OSA symptoms (e.g. Epworth Sleepiness Scale) and 4 comorbidities to identify phenotype classes. RESULTS: Average age was 52.4 ± 13.9 years and 34.0% were female. Mean REI was 33.8 ± 22.5 events per hour. Fit statistics and clinical significance suggested that a three-class solution provided the best fit to the data. The three phenotypes were: (1) Minimally Symptomatic (47.7%), (2) Excessive sleepiness (37.1%), and (3) Disturbed Sleep (15.2%). Sensitivity models were consistent with the main proposed solution. CONCLUSIONS: Derived sleep phenotypes among diverse Hispanic/Latinos were consistent with recent findings from the Sleep Apnea Global Interdisciplinary Consortium, but we found notable differences in class prevalence relative to Whites. Further research is needed to link derived sleep phenotypes to health comorbidities in diverse populations.

FAAH and CNR1 Polymorphisms in the Endocannabinoid System and Alcohol-Related Sleep Quality.

Sleep disturbances are common among individuals with alcohol use disorder (AUD) and may not resolve completely with short-term abstinence from alcohol, potentially contributing to relapse to drinking. The endocannabinoid system (ECS) is associated with both sleep and alcohol consumption, and genetic variation in the ECS may underlie sleep-related phenotypes among individuals with AUD. In this study, we explored the influence of genetic variants in the ECS (Cannabinoid receptor 1/CNR1: rs806368, rs1049353, rs6454674, rs2180619, and Fatty Acid Amide Hydrolase/FAAH rs324420) on sleep quality in individuals with AUD (N = 497) and controls without AUD (N = 389). We assessed subjective sleep quality (from the Pittsburgh Sleep Quality Index/PSQI) for both groups at baseline and objective sleep efficiency and duration (using actigraphy) in a subset of individuals with AUD at baseline and after 4 weeks of inpatient treatment. We observed a dose-dependent relationship between alcohol consumption and sleep quality in both AUD and control groups. Sleep disturbance, a subscale measure in PSQI, differed significantly among CNR1 rs6454674 genotypes in both AUD (p = 0.015) and controls (p = 0.016). Only among controls, neuroticism personality scores mediated the relationship between genotype and sleep disturbance. Objective sleep measures (sleep efficiency, wake bouts and wake after sleep onset), differed significantly by CNR1 rs806368 genotype, both at baseline (p = 0.023, 0.029, 0.015, respectively) and at follow-up (p = 0.004, p = 0.006, p = 0.007, respectively), and by FAAH genotype for actigraphy recorded sleep duration at follow-up (p = 0.018). These relationships suggest a significant role of the ECS in alcohol-related sleep phenotypes.