Nature and Science of Sleep最新文献

Background: The 24 Solar Terms of the traditional Chinese lunisolar calendar reflect seasonal and climatic changes that may influence sleep. Few large-scale studies have examined sleep quality and stability across these seasonal markers in chronic insomnia.

Methods: This retrospective observational study analyzed anonymized data from 25,428 chronic insomnia patients using the "Good Sleep 365" platform at Zhejiang University's Affiliated Mental Health Center (2018-2023). Sleep quality and stability were assessed via the Pittsburgh Sleep Quality Index (PSQI), incorporating the total score to reflect overall sleep quality, along with score reduction and reduction rate to capture changes and stability over time. Seasonal autoregressive integrated moving average (SARIMA) models were applied to time-series data; 2023 data validated the models, and 2024 predictions were generated.

Results: Sleep quality was poorer during Grain Rain (Guyu) and Cold Dew (Hanlu), with mean PSQI scores of 9.53 and 9.48, respectively, whereas it was better during Major Snow (Daxue) and Minor Snow (Xiaoxue), with mean PSQI scores of 8.91 and 8.96, respectively. Women were more sensitive to seasonal variations than men (P<0.05), while patients aged 45-59 showed greater fluctuations (P<0.05). No significant associations were found between sleep and key solar terms such as Beginning of Spring (Lichun), Beginning of Summer (Lixia), Beginning of Autumn (Liqiu), and Beginning of Winter (Lidong), etc. SARIMA(1,0,1)(0,1,1)[24] best modeled sleep quality and fluctuations, and SARIMA(1,0,1)(1,0,1)[24] for improvements; both models demonstrated good fit and predictions are mostly contained within the confidence intervals.

Conclusion: Sleep quality and stability vary across the 24 Solar Terms, with notable gender and age differences. SARIMA models are able to reflect these patterns to a certain extent, with the majority of predictions lying within the confidence intervals, which may contribute to personalized insomnia management. Cultural context of the 24 Solar Terms adds interpretive value. Limitations of this study include reliance on self-reported PSQI scores, lack of direct meteorological data, and other factors.

Background: Heightened inflammatory state is considered a key factor linking obstructive sleep apnea (OSA) with cardiovascular disease (CVD).

Objective: This study aimed to assess the level of novel hematologic inflammatory biomarkers including neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), ratio of neutrophil count to HDL level (NHR), ratio of monocyte count to HDL level (MHR), monocyte count multiplied by neutrophil-to-lymphocyte ratio (SIRI) in OSA patients, and explored the relationships between these inflammatory biomarkers and cardiovascular risk.

Methods: This study enrolled 974 patients with OSA and all data were collected after admission. Spearman correlation was used to explore the correlations between sleep parameters and inflammatory indices. Logistic regression and receiver operating characteristic (ROC) analysis were employed to assess the association between the novel hematologic inflammatory indices and CVD in OSA patients.

Results: Correlation analysis showed that most inflammatory indices were closely related to nocturnal hypoxia in OSA patients. Multivariate logistic regression suggested that NLR (OR=1.085, 95% CI: 1.017-1.158), MLR (OR=3.708, 95% CI: 1.322-10.404), NHR (OR=1.074, 95% CI: 1.020-1.131), MHR (OR=2.116, 95% CI: 1.205-3.715), and SIRI (OR=1.148, 95% CI: 1.035-1.272) were positively correlated with CVD in OSA patients after adjusting all confounding factors. Moreover, the area under the curve (AUC) of NLR, MLR, NHR, MHR, and SIRI for discriminating OSA patients with CVD was 0.734, 0.735, 0.736, 0.734, and 0.735, respectively, after adjusting for all confounders.

Conclusion: Inflammatory indices including NLR, MLR, NHR, MHR, and SIRI were promising biomarkers for CVD in OSA patients, which might aid in the early identification of CVD risk in clinical. These easily obtainable markers may facilitate CVD risk stratification in OSA patients.

Purpose: To investigate the effect of preoperative sleep disorders (SD) on postoperative enteral nutrition intolerance (ENI) and intestinal barrier, and explore its potential mechanism.

Patients and methods: This study was a prospective cohort study that included 67 patients (26 in SD group and 41 in non-SD group) undergoing digestive tract tumor surgery. Preoperative sleep status was assessed using the Pittsburgh Sleep Quality Index. Postoperative ENI was evaluated using the Enteral Nutrition Tolerance Scale. Perioperative serum cortisol, intestinal barrier markers (D-lactate, diamine oxidase and human lipopolysaccharide binding protein), ferroptosis markers (ferrous ions, reduced glutathione and lipid peroxide malondialdehyde) and intestinal flora characteristics were measured.

Results: The incidence of ENI in SD group was 53.8%, which was significantly higher than that in non-SD group (26.8%, P=0.038). Perioperative levels of serum intestinal barrier markers in SD group were higher than those in non-SD group (P<0.05). The preoperative cortisol level was positively correlated with the increase in the intestinal barrier marker human lipopolysaccharide binding protein (r=0.3621, P=0.0170) and ferroptosis marker malondialdehyde (r=0.3660, P=0.0171). In SD group, the relative abundance of opportunistic pathogens (Enterobacteriaceae, Burkholderiaceae, etc) increased, while the relative abundance of probiotics (Bifidobacteriaceae) decreased.

Conclusion: Preoperative sleep disturbances were significantly associated with the occurrence of postoperative enteral nutrition intolerance in patients with gastrointestinal tumors. The intestinal barrier damage of these patients may be related to hypothalamic-pituitary-adrenal axis activation, oxidative stress induction and intestinal flora imbalance.

Sleep is a highly elaborate biological occurrence, necessitating the combined action and participation of diverse brain regions. The sleep-wake cycle is regulated by a multitude of factors, including various hormones produced by the hypothalamus and external stimuli. Sleep disorders can accelerate the progression of numerous diseases or directly trigger the onset of many health conditions. However, the specific regulatory mechanisms remain elusive. In recent years, the role of magnesium in sleep disorders has garnered considerable attention. Magnesium not only reduces the excitability of the nervous system and alters muscle relaxation but also regulates cellular biological clocks, energy balance, and circadian rhythms, playing a crucial role in sleep regulation. Magnesium deficiency not only shortens the effective sleep duration but also impairs sleep quality, leading to various specific sleep disorders. Additionally, magnesium supplements can improve sleep parameters in a variety of sleep-related diseases, especially those associated with the occurrence and development of sleep disorders. Therefore, a more in-depth understanding of the impact of magnesium on sleep disorders may reveal new therapeutic targets for sleep-related diseases. In this review, we comprehensively summarize the latest key findings on the mechanism of action of magnesium in sleep health and its role in initiating or exacerbating common sleep disorders, providing new insights into the diagnosis and treatment of sleep disorder-related diseases.

Purpose: Snoring individuals, particularly those with obstructive sleep apnea (OSA), exhibit a higher prevalence of nonalcoholic fatty liver disease (NAFLD) and liver fibrosis. The objectives of this study were to explore potential screening biomarkers for NAFLD and liver fibrosis in the snoring population.

Methods: The research was carried out as a cross-sectional study at the Sleep Center of the First Affiliated Hospital of Fujian Medical University. All patients underwent FibroScan and overnight polysomnography (PSG). The 96-metabolism related proteins were detected using liquid chromatography-high resolution mass spectrometry (Oink Proteomics).

Results: A total of 59 NAFLD were found among the 87 snoring patients. A total of 12 proteins with differential expression levels were identified between non-NAFLD group and NAFLD group via Oink Proteomics. The correlation analysis revealed a close correlation between Cadherin 2 (CDH2) and NAFLD, liver fibrosis (with correlation coefficients of 0.394 and 0.383, respectively, both P < 0.05). The risk of NAFLD and liver fibrosis was much higher in snorers with higher levels of CHD2 compared with snorers with lower levels of CHD2 (both P < 0.05). A combination of CHD2, age, BMI, glucose, AHI and waistline presented the acceptable AUC for the detection of NAFLD and liver fibrosis in snoring patients (0.92, 95% CI: 0.86-0.98 and 0.81, 95% CI: 0.72-0.91, respectively, both p < 0.001).

Conclusion: In snoring patients, higher level of CDH2 was identified as a risk factor for NAFLD and liver fibrosis; a combination of CDH2, age, BMI, glucose, AHI and waistline could act as a convenient and effective indicator for screening NAFLD and liver fibrosis. Future research should expand the sample size and conduct multicenter validation to further explore the value of CDH2.

Background: Sleep-related complaints are common among older adults, and recent research indicates that changes in sleep patterns may be associated with alterations in the composition of the gut microbiome (GM). However, investigations into the relationship between sleep measures and GM abundance among older adults have been limited thus far. This study represents the first large-scale effort to comprehensively explore the connection between GM composition and both subjective and objective sleep measures in older adults.

Methods: The study included 279 cognitively-normal older adults from the community who had not used sleep medication, antibiotics, or probiotics for at least one month before providing stool samples. Participants were categorized as good sleepers (GS) or poor sleepers (PS) based on the Pittsburgh Sleep Quality Index (PSQI) scores. GM diversity and relative abundance were compared between both groups, and their associations with PSQI scores and objective sleep measures were also examined.

Results: Alpha and beta diversity did not show significant differences between the GS and PS groups. However, significant differences in GM relative abundance across various taxonomic levels were found between the GS and PS groups. In the overall sample, higher PSQI scores were linked to lower abundance of the species Hungatella_hathewayi (p = 0.005, false discovery rate = 0.035). However, there were no significant associations between GM abundance and objective sleep measures after corrections for multiple comparisons.

Conclusion: These findings suggest that specific gut microbial taxa are associated with subjective sleep disturbances in older adults.

Purpose: Poor sleep quality is prevalent across the population and may significantly impact both physical and mental health. However, our understanding of the complex mechanisms underlying poor sleep quality is still incomplete, particularly regarding the various contributing factors. To address this, we utilized a data-driven causal discovery analysis (CDA) approach to explore causal pathways of sleep quality.

Patients and methods: We relied on a large sample of healthy young adults from the Human Connectome Project (HCP; n = 1206 [54% female, 56% unmarried/non-cohabiting]) to explore causal pathways of sleep quality. We first used exploratory factor analysis to cluster 122 broad phenotypic variables into 21 factors and computed the functional connectivity of 13 resting-state brain networks. Then, using Greedy Fast Causal Inference (GFCI), we simultaneously integrated the obtained phenotypic factors, brain network connectivity, and sleep quality into the causal discovery analysis and ultimately constructed a causal model.

Results: The model proposes a hierarchical structure with causal effects propagating through complex interactions across multiple domains, ultimately linked to changes in sleep quality. Our causal model identified three phenotypic factors (negative affect, somaticism, and delay discounting) as directly linked to sleep quality. In addition, we examined causal models of sleep quality across gender (male and female) and relationship status (unmarried/non-cohabiting and married/cohabiting) and found some demographic-specific pathways.

Conclusion: Our data-driven model reveals complex mechanisms by which factors from different domains influence sleep quality and highlights several key factors that influence sleep quality, which may have important implications for the development of sleep theories and the improvement of sleep quality.

Introduction: Sleep spindles and cortical coherence are key electrophysiological signatures of thalamocortical communication and large-scale neural synchrony during non-REM sleep. This study aimed to evaluate the effects of acute 12-hour sleep deprivation on spindle dynamics and cortical coherence across frequency bands in mice.

Methods: Sixteen adult male C57BL/6J (8 per group) mice were randomly assigned to either a natural sleep (NS) or sleep deprivation (SD) group. We recorded bilateral frontal EEGs continuously for 12 hours following intervention. Sleep spindles (8-16 Hz) were automatically detected, and interhemispheric coherence from delta to gamma bands was computed using Welch's method.

Results: Compared to NS mice, SD mice showed a significant reduction in frontal spindle count (61 ± 14 vs 103 ± 12 spindles; mean ± SEM; p = 0.027) and amplitude (47 ± 6 µV vs 68 ± 5 µV; p = 0.002). Delta (0.5-4 Hz) and theta (4-8 Hz) interhemispheric coherence showed non-significant decreasing trends in SD mice (p > 0.05), whereas alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-40 Hz) coherence did not differ.

Conclusion: Acute sleep deprivation significantly impairs spindle generation. It also resulted in a trend toward reduced low-frequency cortical coherence, though this change was not statistically significant.

Background: Shift work is a well-established disruptor of sleep, yet the biological mechanisms driving sleep disturbances remain poorly understood. Salivary cortisol (HPA axis), α-amylase (sympathetic-adrenomedullary output), and DHEA-S (adrenal androgen with anti-glucocorticoid/resilience properties) are candidate indicators of stress-related sleep disruption. We therefore examined whether changes in these biomarkers were associated with 6-month sleep trajectories in health professionals.

Methods: In a prospective 6-month repeated-measures design, 52 healthcare professionals (daytime vs rotating shifts; mean age 31.4 ± 9.4 years; 57% female) completed validated sleep assessments, PROMIS Sleep Disturbance, PROMIS Sleep Impairment, the Sleep-Wake Disorder Index (SWDI), and the NIH 7-day Sleep Diary, at baseline and six-month follow-up. Salivary cortisol, Dehydroepiandrosterone Sulfate (DHEA-S), and alpha-amylase were collected on the morning of Day 7 of each diary period. Change scores (Δ = follow-up - baseline) were computed. Repeated-measures ANOVA, Pearson correlations, and multivariable regressions assessed group differences and biomarker-sleep associations.

Results: Compared with daytime workers, rotating shift workers reported significantly greater increases in sleep disturbance, impairment, and reduced sleep efficiency over time (all p < 0.05). Reductions in cortisol and alpha-amylase were significantly associated with worsening PROMIS Sleep Disturbance and SWDI scores (r = -0.65 and -0.53, respectively; p < 0.05). Multivariable regression showed that decreased cortisol (β = -41.845, p = 0.0064) and increased DHEA-S (β = 0.001, p = 0.0405) associated with worsening PROMIS Sleep Impairment. A combined model including reduced cortisol, and increased DHEA-S associated with greater PROMIS Sleep Disturbance (adjusted R² = 0.698).

Conclusion: In this pilot, changes in salivary cortisol and DHEA-S were associated with longitudinal changes in sleep. These results suggest potential utility for biomarker-informed risk stratification, warranting confirmation in larger, controlled studies.