Contributions of circadian tendencies and behavioral problems to sleep onset problems of children with ADHD

BACKGROUND: Children with attention-deficit/hyperactivity disorder (ADHD) are two to three times more likely to experience sleep problems. The purpose of this study is to determine the relative contributions of circadian preferences and behavioral problems to sleep onset problems experienced by children with ADHD and to test for a moderation effect of ADHD diagnosis on the impact of circadian preferences and externalizing problems on sleep onset problems. METHODS: After initial screening, parents of children meeting inclusion criteria documented child bedtime over 4 nights, using a sleep log, and completed questionnaires regarding sleep, ADHD and demographics to assess bedtime routine prior to PSG. On the fifth night of the study, sleep was recorded via ambulatory assessment of sleep architecture in the child’s natural sleep environment employing portable polysomnography equipment. Seventy-five children (26 with ADHD and 49 controls) aged 7–11 years (mean age 8.61 years, SD 1.27 years) participated in the present study. RESULTS: In both groups of children, externalizing problems yielded significant independent contributions to the explained variance in parental reports of bedtime resistance, whereas an evening circadian tendency contributed both to parental reports of sleep onset delay and to PSG-measured sleep-onset latency. No significant interaction effect of behavioral/circadian tendency with ADHD status was evident. CONCLUSIONS: Sleep onset problems in ADHD are related to different etiologies that might require different interventional strategies and can be distinguished using the parental reports on the CSHQ

Variability of sleep stage scoring in late midlife and early old age

peer reviewedSleep stage scoring can lead to important inter-expert variability. Although likely, whether this issue is amplified in older populations, which show alterations of sleep electrophysiology, has not been thoroughly assessed. Algorithms for automatic sleep stage scoring may appear ideal to eliminate inter-expert variability. Yet, variability between human experts and algorithm sleep stage scoring in healthy older individuals has not been investigated. Here, we aimed to compare stage scoring of older individuals and hypothesized that variability, whether between experts or considering the algorithm, would be higher than usually reported in the literature. Twenty cognitively normal and healthy late midlife individuals’ (61 ± 5 years; 10 women) night-time sleep recordings were scored by two experts from different research centres and one algorithm. We computed agreements for the entire night (percentage and Cohen's κ) and each sleep stage. Whole-night pairwise agreements were relatively low and ranged from 67% to 78% (κ, 0.54–0.67). Sensitivity across pairs of scorers proved lowest for stages N1 (8.2%–63.4%) and N3 (44.8%–99.3%). Significant differences between experts and/or algorithm were found for total sleep time, sleep efficiency, time spent in N1/N2/N3 and wake after sleep onset (p ≤ 0.005), but not for sleep onset latency, rapid eye movement (REM) and slow-wave sleep (SWS) duration (N2 + N3). Our results confirm high inter-expert variability in healthy aging. Consensus appears good for REM and SWS, considered as a whole. It seems more difficult for N3, potentially because human raters adapt their interpretation according to overall changes in sleep characteristics. Although the algorithm does not substantially reduce variability, it would favour time-efficient standardization

Abstracts from the 8th International Conference on cGMP Generators, Effectors and Therapeutic Implications

This work was supported by a restricted research grant of Bayer AG

Short sleep duration is associated with teacher-reported inattention and cognitive problems in healthy school-aged children

PURPOSE: Pediatric, clinical, and research data suggest that insufficient sleep causes tiredness and daytime difficulties in terms of attention-focusing, learning, and impulse modulation in children with attention deficit hyperactivity disorder (ADHD) or in those with ADHD and primary sleep disorders. The aim of the present study was to examine whether sleep duration was associated with ADHD-like symptoms in healthy, well-developing school-aged children. PATIENTS AND METHODS: Thirty-five healthy children (20 boys, 15 girls), aged 7–11 years participated in the present study. Each child wore an actigraphic device on their nondominant wrist for two nights prior to use of polysomnography to assess their typical sleep periods. On the third night, sleep was recorded via ambulatory assessment of sleep architecture in the child’s natural sleep environment employing portable polysomnography equipment. Teachers were asked to report symptoms of inattention and hyperactivity/impulsivity on the revised Conners Teacher Rating Scale. RESULTS: Shorter sleep duration was associated with higher levels of teacher-reported ADHD-like symptoms in the domains of cognitive problems and inattention. No significant association between sleep duration and hyperactivity symptoms was evident. CONCLUSION: Short sleep duration was found to be related to teacher-derived reports of ADHD-like symptoms of inattention and cognitive functioning in healthy children

Contributions of circadian tendencies and behavioral problems to sleep onset problems of children with ADHD

Abstract Background Children with attention-deficit/hyperactivity disorder (ADHD) are two to three times more likely to experience sleep problems. The purpose of this study is to determine the relative contributions of circadian preferences and behavioral problems to sleep onset problems experienced by children with ADHD and to test for a moderation effect of ADHD diagnosis on the impact of circadian preferences and externalizing problems on sleep onset problems. Methods After initial screening, parents of children meeting inclusion criteria documented child bedtime over 4 nights, using a sleep log, and completed questionnaires regarding sleep, ADHD and demographics to assess bedtime routine prior to PSG. On the fifth night of the study, sleep was recorded via ambulatory assessment of sleep architecture in the child’s natural sleep environment employing portable polysomnography equipment. Seventy-five children (26 with ADHD and 49 controls) aged 7–11 years (mean age 8.61 years, SD 1.27 years) participated in the present study. Results In both groups of children, externalizing problems yielded significant independent contributions to the explained variance in parental reports of bedtime resistance, whereas an evening circadian tendency contributed both to parental reports of sleep onset delay and to PSG-measured sleep-onset latency. No significant interaction effect of behavioral/circadian tendency with ADHD status was evident. Conclusions Sleep onset problems in ADHD are related to different etiologies that might require different interventional strategies and can be distinguished using the parental reports on the CSHQ.</p

Variability of visual and automated sleep stage scoring in the elderly

Visual scoring of sleep recordings is characterized by inter-scorer variability. This methodological issue can be amplified in older individual recordings because sleep changes markedly in aging. Here, we aimed to investigate sleep scoring variability in aged participants through a visual-automatic sleep scoring comparison. Sleep recordings of 20 subjects (10 women, 61±5 years) were included. Automatic sleep scoring (AS) was performed by Aseega algorithm, previously validated on young healthy participants. Visual scoring (VS) was performed by two experts (VS1, VS2) from different centers according to AASM rules. Epoch-by-epoch agreements (concordance and Conger’s kappa coefficient, ) were computed. Generalized linear mixed models assessed potential scorer effects on sleep parameters (time spent in N1/N2/N3/REM, tN1/tN2/tN3/tREM; wake after sleep onset, WASO; total sleep time, TST; sleep efficiency, SE). Overall agreement between the 3 scorings was = 0.60 (moderate). Pairwise agreements were as follows: VS1 vs. VS2, 76% (=0.67); AS vs. VS1, 67% (0.54), AS vs. VS2, 74% (0.60). Agreement between AS and consensual VS was 78% (0.60). GLMMs showed disparate pairs of agreeing scorers depending on the sleep parameter considered. For tN1, AS showed differences with both VS (p < .0001) who did not differ between themselves. Differences were found between both VS for tN2 and tN3 (p < .0001) and WASO (p = .006), while AS showed no significant difference with VS2. All three scorers differed for TST (p = .05) and SE (p = .04). No differences across scorers were found for tREM. Agreement between scorers, whether between VS or AS and VS proved lower than what is usually reported in the literature for the general population. This is likely due to the fact that with ageing, sleep undergoes a series of changes with, at the macrostructural level, lower sleep stability and, at the microstructural level, lower EEG voltage dynamics. This certainly renders sleep more difficult to score, which might lead to increased inter-rater variability and rises methodological questions relative to sleep scoring in the aged population

Clinical, Genetic, and Urinary Factors Associated with Uromodulin Excretion.

BACKGROUND AND OBJECTIVES: The urinary excretion of uromodulin is influenced by common variants in the UMOD gene, and it may be related to NaCl retention and hypertension. Levels of uromodulin are also dependent of the renal function, but other determinants remain unknown. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We tested associations between the urinary excretion of uromodulin; medical history and medication; serum and urinary levels of electrolytes, glucose, and uric acid; and the genotype at the UMOD/Protein Disulfide Isomerase-Like, Testis Expressed locus (rs4293393 and rs12446492); 943 participants from the CARTaGENE Cohort, a random sample from the Canadian population of 20,004 individuals, were analyzed. Participants with available genotyping were obtained from a substudy addressing associations between common variants and cardiovascular disease in paired participants with high and low Framingham risk scores and vascular rigidity indexes. RESULTS: The population studied was 54±9 years old, with 51% women and eGFR of 9±14 ml/min per 1.73 m(2). Uromodulin excretion was 25 (11-42) mg/g creatinine. Using linear regression, it was independently higher among patients with higher eGFR, the TT genotype of rs4293393, and the TT genotype of rs12446492. The fractional excretions of urate and sodium showed a strong positive correlation with uromodulin, likely linked to the extracellular volume status. The presence of glycosuria and the use of uricosuric drugs, which both increased the fraction excretion of urate, were independently associated with a lower uromodulin excretion, suggesting novel interactions between uric acid and uromodulin excretion. CONCLUSIONS: In this large cohort, the excretion of uromodulin correlates with clinical, genetic, and urinary factors. The strongest associations were between uric acid, sodium, and uromodulin excretions and are likely linked to the extracellular volume status

Meet Spinky: An Open-Source Spindle and K-Complex Detection Toolbox Validated on the Open-Access Montreal Archive of Sleep Studies (MASS)

Sleep spindles and K-complexes are among the most prominent micro-events observed in electroencephalographic (EEG) recordings during sleep. These EEG microstructures are thought to be hallmarks of sleep-related cognitive processes. Although tedious and time-consuming, their identification and quantification is important for sleep studies in both healthy subjects and patients with sleep disorders. Therefore, procedures for automatic detection of spindles and K-complexes could provide valuable assistance to researchers and clinicians in the field. Recently, we proposed a framework for joint spindle and K-complex detection (Lajnef et al., 2015a) based on a Tunable Q-factor Wavelet Transform (TQWT; Selesnick, 2011a) and morphological component analysis (MCA). Using a wide range of performance metrics, the present article provides critical validation and benchmarking of the proposed approach by applying it to open-access EEG data from the Montreal Archive of Sleep Studies (MASS; O’Reilly et al., 2014). Importantly, the obtained scores were compared to alternative methods that were previously tested on the same database. With respect to spindle detection, our method achieved higher performance than most of the alternative methods. This was corroborated with statistic tests that took into account both sensitivity and precision (i.e., Matthew’s coefficient of correlation (MCC), F1, Cohen κ). Our proposed method has been made available to the community via an open-source tool named Spinky (for spindle and K-complex detection). Thanks to a GUI implementation and access to Matlab and Python resources, Spinky is expected to contribute to an open-science approach that will enhance replicability and reliable comparisons of classifier performances for the detection of sleep EEG microstructure in both healthy and patient populations

REM sleep is associated with the volume of the cholinergic basal forebrain in aMCI individuals

Abstract Background Rapid-eye movement (REM) sleep highly depends on the activity of cholinergic basal forebrain (BF) neurons and is reduced in Alzheimer’s disease. Here, we investigated the associations between the volume of BF nuclei and REM sleep characteristics, and the impact of cognitive status on these links, in late middle-aged and older participants. Methods Thirty-one cognitively healthy controls (66.8 ± 7.2 years old, 13 women) and 31 participants with amnestic Mild Cognitive Impairment (aMCI) (68.3 ± 8.8 years old, 7 women) were included in this cross-sectional study. All participants underwent polysomnography, a comprehensive neuropsychological assessment and Magnetic Resonance Imaging examination. REM sleep characteristics (i.e., percentage, latency and efficiency) were derived from polysomnographic recordings. T1-weighted images were preprocessed using CAT12 and the DARTEL algorithm, and we extracted the gray matter volume of BF regions of interest using a probabilistic atlas implemented in the JuBrain Anatomy Toolbox. Multiple linear regressions were performed between the volume of BF nuclei and REM sleep characteristics controlling for age, sex and total intracranial volume, in the whole cohort and in subgroups stratified by cognitive status. Results In the whole sample, lower REM sleep percentage was significantly associated to lower nucleus basalis of Meynert (Ch4) volume (β = 0.32, p = 0.009). When stratifying the cohort according to cognitive status, lower REM sleep percentage was significantly associated to both lower Ch4 (β = 0.48, p = 0.012) and total BF volumes (β = 0.44, p = 0.014) in aMCI individuals, but not in cognitively unimpaired participants. No significant associations were observed between the volume of the BF and wake after sleep onset or non-REM sleep variables. Discussion These results suggest that REM sleep disturbances may be an early manifestation of the degeneration of the BF cholinergic system before the onset of dementia, especially in participants with mild memory deficits