Nature and Science of Sleep最新文献

Sleep, an intrinsic aspect of human life, is experienced by individuals differently which may be influenced by personality traits and characteristics. Exploring how these traits influence behaviors and sleep routines could be used to inform more personalized and effective interventions to promote better sleep. Our objective was to summarize the existing literature on the relationship between personality traits and sleep patterns through a systematic review. An abstract and keyword search was conducted in PsycINFO, Cochrane and PubMed, collecting relevant literature, published between January 1980 and June 2024. A total of 1713 records were found, of which 18 studies were analyzed in the descriptive synthesis. Relevant studies covered populations in 11 different countries, Australia, China, Estonia, Finland, Germany, Italy, Japan, Poland, Turkey, the United Kingdom, and the United States, comprising a total of 58,812 subjects. All studies reported an association between a sleep pattern with at least one of the Big Five personality traits (agreeableness, conscientiousness, extraversion, neuroticism, openness to experience). Ten studies found associations between personality and sleep quality, all of which reported a link between neuroticism and sleep quality (effect sizes 0.183-0.40). Five studies found an association between conscientiousness and morningness (effect sizes 0.16-0.35). Other sleep patterns linked to personality traits included sleep duration, nightmare frequency and distress, sleep deficiency, sleep continuity, insomnia severity and sleep problems, sleep hygiene, sleep latency and daytime sleepiness. This novel systematic review confirms that sleep and personality traits are related, suggesting that those traits should be considered when trying to understand or change one's sleep behavior.

Introduction: Sleep deprivation(SD) has numerous negative effects on mental health. A growing body of research has confirmed the implication of gut microbiota in mental disorders. However, the specific modifications in mammalian gut microbiota following SD exhibit variations across different studies.

Methods: Male specific-pathogen-free Wistar rats were given a modified multiple-platform exposure for 7 days of SD. Fecal samples were obtained from the control and SD groups both at baseline and after 7 days of SD. We utilized 16S rDNA gene sequencing to investigate the gut microbial composition and functional pathways in rats.

Results: Analysis of the microbiota composition revealed a significant change in gut microbial composition after chronic SD, especially at the phylum level. The relative abundances of p_Firmicutes, g_Romboutsia, and g_Enterococcus increased, whereas those of p_Bacteroidetes, p_Verrucomicrobia, p_Fusobacteria, g_Akkermansia, and g_Cetobacterium decreased in animals after chronic SD compared with controls or animals before SD. The ratio of Firmicutes to Bacteroidetes exhibited an increase following SD. The relative abundance of gut microbiota related to the functional pathways of GABAergic and glutamatergic synapses was observed to be diminished in rats following SD compared to pre-SD.

Conclusion: Collectively, these findings suggest that chronic SD causes significant alterations in both the structural composition and functional pathways of the gut microbiome. Further researches are necessary to investigate the chronological and causal connections among SD, the gut microbiota and mental disorders.

Objective: We aimed to evaluate the effect of light-dark cycle alteration and soft drink consumption on the acceleration of type 1 diabetes mellitus (T1DM) development among non-obese diabetic (NOD) mice model.

Methods: We exposed female NOD and C57BL/6 mice from the age of 5 weeks to either adlib soft drink consumption and/or T20 light-dark cycle alteration until the development of diabetes, or the mice reached the age of 30 weeks. Each group consisted of 7-15 mice. We monitored weight, length, blood glucose level, and insulin autoantibody (IAA) levels weekly.

Results: Out of 75 NOD and 22 C57BL/6 mice, 41 NOD mice developed diabetes, and 6 mice died between 7 and 8 weeks of age. The mean time to development of T1DM among NOD control mice was 20 weeks. The time to development of T1DM was accelerated by two weeks in the NOD mice exposed to light-dark cycle alteration, hazard ratio of 2.65,95th CI (0.70, 10.04) p = 0.15). The other groups developed T1DM, similar to the control group.

Conclusion: There was a trend toward earlier development of T1DM among NOD mice exposed to light-dark cycle alteration, but this difference was not statistically significant. Further studies are needed to confirm our findings using larger sample sizes and different animal species.

Purpose: With girls typically exhibiting higher rates of myopia than boys, however, the mechanisms behind this gender difference remain unclear. This study aims to investigate the gender disparities in the relationship between myopia, sleep duration, physical activity, and BMI.

Patients and methods: A total of 3138 primary and secondary school students were included. Mplus 8.3 was used to perform the multiple mediation analysis.

Results: Sleep duration was indicated to directly affect myopia (β=0.273, 95% CI=0.184-0.356) and through physical activity, BMI, physical activity and BMI three significantly mediation pathways, respectively. In terms of gender, the mediating direct effect of sleep duration on myopia of boys was 66.96%, which is much higher than that of girls' 50.91%. And the mediating indirect effect of sleep duration on myopia through physical activity and BMI are 32.65% and 12.10% respectively among girls, both of which are significantly higher than that of boys.

Conclusion: The study found that there are significant differences in the impact of sleep duration on myopia in children and adolescents of different genders. In this regard, while paying attention to the sleep duration of children and adolescents, special attention should also be paid to the indirect impact of girls' physical activity and BMI on myopia, and targeted measures should be formulated according to children of different genders to effectively protect the eye health of children and adolescents.

Objective: Light exposure techniques have been recommended to combat sleep issues caused by disruption to circadian regularity in the athletic population, although studies are lacking.

Methods: A total of 17 professional male Australian Football athletes (age ± SD: 22 ± 3 years) wore a wrist actigraph to measure sleep parameters, and a wearable light sensor to measure melanopic equivalent daylight illuminance (mEDI, in lux) for 14 days. Participants completed three sleep questionnaires at the end of the data collection period and completed well-being surveys 6 times. The Sleep Regularity Index (SRI) for each player was also calculated from actigraphy data. Light exposure data were organised into three different timeframes: morning (wake time + 2 hours), daytime (end of morning to 6 pm), and evening (2 hours leading up to bedtime) for analysis. Repeated measures correlation was conducted for objective sleep measures and mEDI values per timeframe. Pearson's correlation was conducted on subjective sleep measures and well-being measures against mEDI values per timeframe.

Results: Higher morning light was associated with significantly (p < 0.001) greater total sleep time (r = 0.31). Higher daytime light exposure was associated with higher subjective sleep quality (r = 0.48, p < 0.05). Higher evening light exposure was associated with higher Athlete Sleep Screening Questionnaire (ASSQ) global scores (r = 0.52, p < 0.05). There were no other significant correlations between light exposure and sleep or well-being measures (p > 0.05).

Conclusion: Higher morning and daylight exposure levels were associated with various positive objective and subjective sleep measures in professional team sport athletes, supporting the need for education on optimising light exposure to improve circadian function, sleep, and health.

Background: Sleep played an important part in human health, and COVID-19 led to a continuous deterioration of sleep. However, the causal relationship between micronutrient and sleep disorder was not yet fully understood.

Methods: In this research, the genetic causal relationship between micronutrient and sleep disorder was analyzed utilizing a two-sample Mendelian randomization (MR). Single nucleotide polymorphisms (SNPs) were used as instrumental variables. The analyses were conducted using the MR-Egger, inverse variance weighted, weighted mode, weighted median, simple mode, Cochran's Q test and leave-one-out.

Results: Our results suggested that 8 genetically predicted micronutrients participated in sleep disorders, including liver iron (L-iron) and iron in sleeping too much, spleen iron (S-iron) in sleeplessness/insomnia, trouble falling or staying asleep, sleep duration (undersleepers) and nonorganic sleeping disorders, iron metabolism disorder (IMD) and vitamin B12 deficiency anaemia (VB12DA) in narcolepsy, urine sodium (uNa) in narcolepsy, sleep apnea syndrome and sleep disorder, vitamin D (VD) in sleep duration (oversleepers), 25-Hydroxyvitamin D (25(OH)D) in trouble falling or staying asleep.

Conclusion: Our study used Mendelian randomization methods at the SNP level to explore the potential causal relationship among L-iron, iron, S-iron, IMD, uNa, 25(OH)D, VD, VB12DA with certain sleep disorder subtypes. Our results uncovered a micronutrient-based strategy for alleviating sleep disorder symptoms.

Introduction: The field of automatic respiratory analysis focuses mainly on breath detection on signals such as audio recordings, or nasal flow measurement, which suffer from issues with background noise and other disturbances. Here we introduce a novel algorithm designed to isolate individual respiratory cycles on a thoracic respiratory inductance plethysmography signal using the non-invasive signal of the respiratory inductance plethysmography belts.

Purpose: The algorithm locates breaths using signal processing and statistical methods on the thoracic respiratory inductance plethysmography belt and enables the analysis of sleep data on an individual breath level.

Patients and methods: The algorithm was evaluated against a cohort of 31 participants, both healthy and diagnosed with obstructive sleep apnea. The dataset consisted of 13 female and 18 male participants between the ages of 20 and 69. The algorithm was evaluated on 7.3 hours of hand-annotated data from the cohort, or 8782 individual breaths in total. The algorithm was specifically evaluated on a dataset containing many sleep-disordered breathing events to confirm that it did not suffer in terms of accuracy when detecting breaths in the presence of sleep-disordered breathing. The algorithm was also evaluated across many participants, and we found that its accuracy was consistent across people. Source code for the algorithm was made public via an open-source Python library.

Results: The proposed algorithm achieved an estimated 94% accuracy when detecting breaths in respiratory signals while producing false positives that amount to only 5% of the total number of detections. The accuracy was not affected by the presence of respiratory related events, such as obstructive apneas or snoring.

Conclusion: This work presents an automatic respiratory cycle algorithm suitable for use as an analytical tool for research based on individual breaths in sleep recordings that include respiratory inductance plethysmography.