Tetrahydrocannabinidiol (THC; one of the main psychoactive components of cannabis) has been shown to suppress REM sleep1-4 and affect sleep latency,4 5 although these findings are not consistently replicated.3 4 6 Cannabis use is also reported to affect dreaming frequency.7 Most studies investigating cannabis use and sleep have been laboratory-based, while only a limited body of research exists on dream occurrence and cannabis use. This study aimed to pilot the measurement of participants sleeping at home in their usual surroundings, in order to assess effects of cannabis use compared to non-use, on objective sleep measures, dream reports, and self-reported anxiety, memory, and sleep quality. Eleven frequent cannabis users versus 8 non-users proceeded in their usual routines, and wore Hypnodyne Zmax portable sleep acquisition headbands (recording EEG, EOG and EMG) while sleeping at home over two consecutive nights. Participants gave dream reports in three awakenings, set at two-hourly intervals on each night, and once upon morning awakening, reporting dream content and subjective ratings of the dream’s bizarreness, emotionality, and sensory experience. In addition, participants completed problem cannabis use, lifetime and nightly cannabis use, PSQI, everyday memory and trait anxiety measures. No differences were reported by participants in sleep quality, anxiety or memory between the two groups; predictably, cannabis users reported significantly more problems in relation to use of the drug. Cannabis users demonstrated significantly longer sleep latency and less REM sleep overall; no other differences occurred in objective sleep measures between groups. Cannabis users reported higher bizarreness in their dreams, but no differences were reported in dream recall or other dream measures. It is noted that small sample sizes limit the generality of findings in this study. The procedure provides a useful paradigm and encouraging initial results, however, for contemporary research related to cannabis use and sleep in naturalistic conditions, in this ongoing project. References Babson K A, Sottile J, Morabito D. Cannabis, cannabinoids, and sleep: a review of the literature. Current psychiatry reports 2017;19(4):23. Bonn-Miller MO, Babson KA, Vandrey R. Using cannabis to help you sleep: heightened frequency of medical cannabis use among those with PTSD. Drug and alcohol dependence 2014;136:162–165. Bowles N P, Herzig M X, Shea S A. Recent legalisation of cannabis use: effects on sleep, health, and workplace safety. Nature and Science of Sleep 2017;19(9):249–51 Feinberg I, Jones R, Walker J M, Cavness C, March J. Effects of high dosage delta-9-tetrahydrocannabinol on sleep patterns in man. Clinical Pharmacology Therapy 1975;17(4):458–66. Karacan I, Fernandez-Salas A, Coggins W J, Carter W E, Williams R L, Thornby J I, Villaume J P. Sleep electroencephalographic-electrooculographic characteristics of chronic marijuana users: Part I. Annals of the New York Academy of Sciences 1976;282(1):348–74. Schierenbeck T, Riemann D, Berger M, Hornyak M. Effect of illicit recreational drugs upon sleep: cocaine, ecstasy and marijuana. Sleep Medicine Reviews 2008;12(5):381–389. Tassinari CA, Ambrosetto G, Peraita-Adrado MR, Gastaut H. The neuropsychiatric syndrome of δ9-tetrahydrocannabinol and cannabis intoxication in naive subjects. In: Nahas G G, Sutin K M, Harvey D, Agurell S, Pace N, Cancro R. (Eds), Marihuana and Medicine 1999 (1st edition 649–664).

https://bmjopenrespres.bmj.com/content/bmjresp/6/Suppl_1/A23.1.full.pdf

P039 REM sleep and dream reports in frequent cannabis versus non-cannabis users

OBJECTIVES To assess the correlation of cognitive function with sleep stability and depressive-anxious symptoms in insomnia patients. METHODS Twenty-two insomnia patients with cognitive impairment (insomnia-CI), 21 insomnia patients with normal cognition (insomnia-CN), and 15 matched healthy control subjects (HCs) were enrolled and completed neuropsychological tests, the Hamilton Depression and Anxiety Scales (HAMD and HAMA), the Epworth Sleepiness Scale, the Pittsburgh Sleep Quality Index (PSQI),the Insomnia Severity Index (ISI), and the cardiopulmonary coupling (CPC) examination. Ratios of high-frequency coupling (HFC), low-frequency coupling (LFC), and very low-frequency coupling (VLFC) measured by CPC analysis represent stable sleep, unstable sleep, and wake/rapid eye movement (REM) sleep, respectively. RESULTS The HAMD, HAMA, PSQI, and ISI scores were higher in the insomnia-CN patients than in the HCs (all p < .01). However, no differences were found in the HFC, LFC, and VLFC ratio between the HCs and insomnia-CN groups. Compared with the insomnia-CN patients, insomnia-CI patients exhibited higher scores on the HAMD, HAMA (all p < .01), and PSQI (p < .05), performed worse on the Auditory Verbal Learning Test, Trial Making Test B, and Stroop Test B (all p < .01), had a lower HFC ratio, and had a higher LFC ratio in the CPC analysis (all p < .01). Furthermore, in the insomnia patients, poorer cognition was correlated with a decreased HFC ratio and an increased VLFC ratio (r = .356, p = .019; r = -.339, p =.026, respectively) and increased HAMD and HAMA scores (r = -.507, p < .001; r = -.561, p < .001, respectively); a higher VLFC ratio was correlated with an increased ISI score (r = .346, p = .023). CONCLUSIONS Cognitive deterioration in insomnia patients was associated with a decreased stable sleep ratio, an increased wake/REM sleep ratio and more severe symptoms of depression and anxiety. CPC analysis can reflect the severity of insomnia.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/brb3.2068

Cognitive deficit is correlated with sleep stability in insomnia: A cardiopulmonary coupling study.

Introduction Circadian rhythmicity is tightly regulated by clock genes. Polymorphic variations of the human PER3 gene have been shown to influence diurnal preference, sleep homeostasis and the development of mood disorders. However, it is unclear whether polymorphisms in this gene are associated with sleep duration and depression symptoms in real-life scenarios. We have investigated the relationship between sleep duration and mood traits among PER3 genotype groups in a Brazilian family-based cohort. Methods Baependi is a small rural town in Brazil that provides an opportunity to study the influence of sleep circadian patterns in a highly admixed rural population. We studied 1,100 subjects (mean age±SD 47.6±15.3, 42% male) and evaluated the effects of the coding-region variable number tandem repeat polymorphism in PER3 (rs57875989) on mood (Hospital Anxiety and Depression Scale) and self-reported sleep habits (Pittsburgh Sleep Quality Index), controlling for age and sex, using a general linear model. Results Our genotyping data showed that 33.5% of the subjects were homozygous for the shorter PER3 allele (4/4), 55.3% of participants were heterozygous (4/5) and 11.2% of subjects were homozygous for the longer allele (5/5). A main effect of PER3 genotype, sex and age was observed (p≤0.01) on depression symptoms, as well as a significant interaction between sex and sleep duration ≤7 or >7 hours. The results revealed an increase in depression symptoms in the PER3(4/4) genotype compared to PER3(4/5). It was observed also an increase in depression symptoms according to the increase of age. The depression traits were also increased in women that have reported reduced sleep duration, despite controlling for age. Discussion We observed associations between PER3 genotype, sleep duration and depression symptoms, suggesting that PER3 gene may be behaviourally relevant for mental health, especially for women under chronic sleep loss conditions. CNPq – PVE; Academy of Medical Sciences/Newton International Fellowship

https://bmjopenrespres.bmj.com/content/bmjresp/6/Suppl_1/A24.full.pdf

P041 PER3 polymorphism, sleep duration and depression symptoms in a brazilian family-based cohort, the baependi heart study

Introduction Poor sleep1 as well as white matter hyperintensities (WMH), which are macroscale markers of cerebrovascular health indicating white matter lesion,2 have been shown to increase the risk of dementia. However, the relationship between these two putative risk factors of dementia is unclear. Method Here we use data from the UK Biobank (N=5505, aged from 45 to 73) to elucidate the effect of poor sleep (insomnia, snoring, daytime sleepiness and short sleep duration) on WMH load. The sleep variables were obtained using a digital questionnaire, whereas the WMH load was derived from automated segmentation of T2 FLAIR magnetic resonance images using the BIANCA tool in FSL. Results We show that age, snoring and daytime sleepiness significantly predict a higher WMH load (linear model, adjusted R²=0.13, p Finally, a sleep burden score summing poor sleep markers significantly predicted the WMH load, when controlling for cardiovascular factors (table 1). Removing the sleep burden score leads to a significant decrease in the power of the model (ANOVA, p=0.027). Discussion This exploratory analysis confirms the impact of measures of poor sleep on cerebrovascular health, proposing a complex relationship between sleep and WMH loads involving cardiovascular features 3 4 in a large ageing population. Further work will examine the wider implications of measures of poor sleep on cognition and brain function. References Debette S, Markus HS. The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis. BMJ 2010;341:c3666. Ju YS, Lucey BP, Holtzman DM. Sleepand Alzheimer disease pathology—a bidirectional relationship. Nature reviews Neurology 2014;10(2):115. Kocevska D, Cremers LG, Lysen TS, Luik AI, Ikram MA, Vernooij MW, et al. Sleep complaints and cerebral white matter: A prospective bidirectional study. J Psychiatr Res 2019;112:77–82. Lu S, Song L, Wang D, Zhang X, Lv X, Yin H, et al. White matter hyperintensity and cognitive impairments in chronic insomniacs. Neuroreport 2019;30(9):612–618.

https://bmjopenrespres.bmj.com/content/bmjresp/6/Suppl_1/A22.full.pdf

P038 Investigating the impact of poor sleep on cardiovascular health and cerebrovascular burden in healthy ageing using the UK biobank data

Mounting evidence indicated the correlation between sleep and cerebral small vessel disease (CSVD). However, little is known about the exact causality between poor sleep and white matter injury, a typical signature of CSVD, as well as the underlying mechanisms.Spontaneously hypertensive rats (SHR) and control Wistar Kyoto rats were subjected to sleep fragmentation (SF) for 16 weeks. The effects of chronic sleep disruption on the deep white matter and cognitive performance were observed.SHR were validated as a rat model for CSVD. Fragmented sleep induced strain-dependent white matter abnormalities, characterized by reduced myelin integrity, impaired oligodendrocytes precursor cells (OPC) maturation and pro-inflammatory microglial polarization. Partially reversible phenotypes of OPC and microglia were observed in parallel following sleep recovery.Long-term SF-induced pathological effects on the deep white matter in a rat model of CSVD. The pro-inflammatory microglial activation and the block of OPC maturation may be involved in the mechanisms linking sleep to white matter injury. 

Effects of Sleep Fragmentation on White Matter Pathology in a Rat Model of Cerebral Small Vessel Disease.

Abnormal sleep architecture and white matter hyperintensities (WMHs) may be involved in cognitive impairment. There is little evidence on the connections between WMHs and chronic insomnia disorder (CID). Therefore, we evaluated the severity of WMHs and polysomnography parameters as well as cognitive tests to explore the underlying connections. There was no significant difference in sex, age, or educational attainment (years) between the two groups. The data showed significant decreases in total sleep time, sleep efficiency, and percentage of time spent in stage nonrapid eye movement sleep 3 in the CID group, but this group also showed prolonged sleep latency and increased percentages of time spent in stages nonrapid eye movement sleep 1 and nonrapid eye movement sleep 2 and wake after sleep onset. Moreover, chronic insomniacs showed poor performance on the attention, intelligence, and memory tests, as well as visual recognition and visual regeneration. Importantly, WMHs were observed to be associated with sleep latency, percentage of time spent in stage nonrapid eye movement sleep 1, and percentage of time spent in stage nonrapid eye movement sleep 3. In conclusion, our findings suggest that there are certain underlying correlations between WMHs and sleep architecture. Abnormal sleep architecture and WMHs could be involved in the impairment of cognitive function in CID. However, it remains unclear whether WMHs are a pre-existing abnormality or a consequence of CID.

White matter hyperintensity and cognitive impairments in chronic insomniacs.

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White matter hyperintensity and cognitive impairments in chronic insomniacs.