Association between night sleep latency and hypertension: A cross-sectional study

Association of sleep quality with cognitive dysfunction in middle-aged and elderly adults: a cross-sectional study in China

Objectives

Sleep is an indispensable part of human health, which can help us to restore physical strength, enhance immunity and maintain nervous system stability. The relationship between sleep quality and cognitive dysfunction is unclear, especially at the community population level. This study aims to explore the association between sleep quality and cognitive dysfunction.

Methods

A total of 5,224 community residents were enrolled in this cross-sectional study. Cognitive function was assessed by the Mini-Mental State Examination (MMSE). Sleep quality was assessed by the multidimensional sleep questionnaire. Multivariate logistic regression was used to analyze the association between sleep quality and cognitive dysfunction. The adjusted models took into account relevant demographic, clinical, and sleep variables.

Results

A total of 3,106 participants were enrolled in this study, of whom 463 (15%) had cognitive dysfunction. Total sleep duration, staying up, sleep latency, number of awakenings, and history of sleep medications were associated with cognitive dysfunction in unadjusted models, and these effects were consistent after adjustment. First, those who slept 6-7.9 h per day (OR = 0.57, 95% CI 0.40 to 0.80, p = 0.001) had a lower risk for cognitive dysfunction compared to those who slept less than 6 h per day. Second, participants who stayed up more than 10 times over the 3 months (OR = 1.90, 95% CI 1.20 to 3.00, p = 0.006) were more likely to suffer cognitive dysfunction than those who never stayed up. Third, we also found that participants with sleep latencies of 16-30 min were less likely to experience cognitive dysfunction than those with sleep latencies of less than 16 min after adjusting confounders (OR = 0.33, 95% CI 0.23 to 0.47, p < 0.001). Fourth, participants who woke up once (OR = 1.65, 95% CI 1.19 to 2.30, p = 0.003) and three or more times (OR = 2.34, 95% CI 1.25 to 4.36, p = 0.008) after falling asleep had a higher risk than those who did not wake up at night. Last, participants taking sleep medication (OR = 2.97, 95% CI 1.19 to 7.45, p = 0.020) were more vulnerable to cognitive dysfunction, relative to participants without taking any medications.

Conclusion

Our results suggest that after adjustment for potential confounding variables, poor sleep quality is associated with cognitive dysfunction.

Towards a personalized prediction, prevention and therapy of insomnia: gut microbiota profile can discriminate between paradoxical and objective insomnia in post-menopausal women

Background

Insomnia persists as a prevalent sleep disorder among middle-aged and older adults, significantly impacting quality of life and increasing susceptibility to age-related diseases. It is classified into objective insomnia (O-IN) and paradoxical insomnia (P-IN), where subjective and objective sleep assessments diverge. Current treatment regimens for both patient groups yield unsatisfactory outcomes. Consequently, investigating the neurophysiological distinctions between P-IN and O-IN is imperative for devising novel precision interventions aligned with primary prediction, targeted prevention, and personalized medicine (PPPM) principles.Working hypothesis and methodology.Given the emerging influence of gut microbiota (GM) on sleep physiology via the gut-brain axis, our study focused on characterizing the GM profiles of a well-characterized cohort of 96 Italian postmenopausal women, comprising 54 insomniac patients (18 O-IN and 36 P-IN) and 42 controls, through 16S rRNA amplicon sequencing. Associations were explored with general and clinical history, sleep patterns, stress, hematobiochemical parameters, and nutritional patterns.

Results

Distinctive GM profiles were unveiled between O-IN and P-IN patients. O-IN patients exhibited prominence in the Coriobacteriaceae family, including Collinsella and Adlercreutzia, along with Erysipelotrichaceae, Clostridium, and Pediococcus. Conversely, P-IN patients were mainly discriminated by Bacteroides, Staphylococcus, Carnobacterium, Pseudomonas, and respective families, along with Odoribacter.

Conclusions

These findings provide valuable insights into the microbiota-mediated mechanism of O-IN versus P-IN onset. GM profiling may thus serve as a tailored stratification criterion, enabling the identification of women at risk for specific insomnia subtypes and facilitating the development of integrated microbiota-based predictive diagnostics, targeted prevention, and personalized therapies, ultimately enhancing clinical effectiveness.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00369-1.

Interaction effects between sleep-related disorders and depression on hypertension among adults: a cross-sectional study

BACKGROUND

Hypertension, sleep disorders, and depression represent notable public health issues, and their interconnected nature has long been acknowledged. The objective of this study is to explore the interplay between sleep disorders and depression in the context of hypertension.

METHODS

This cross-sectional study involved 42,143 participants aged 18 and above from the NHANES database across seven survey cycles between 2005 and 2018. After excluding those with missing data on depression, sleep disorders, and hypertension, as well as incomplete main variables, 33,383 participants remained. We used weighted logistic regression to examine the relationship between sleep disorders, depression, and hypertension. Additionally, we assessed the interaction between sleep disorders and depression on hypertension using both multiplicative and additive approaches to quantify their combined effect.

RESULTS

Compared to individuals without sleep disorders, those with sleep disorders have an increased risk of hypertension (OR = 1.51, 95% CI: 1.37-1.67). Furthermore, individuals with depression experience a significantly higher risk of hypertension compared to those with sleep disorders alone (OR = 2.34, 95% CI: 1.95-2.80). Our study reveals a positive interaction between sleep disorders and depression in relation to hypertension risk (OR = 1.07, 95% CI: 1.02-1.13). In addition, we observed the quantitative additive interaction indicators (RERI = 0.73, 95% CI: 0.56 ~ 0.92; API = 0.31, 95% CI: 0.11 ~ 0.46; SI = 2.19, 95% CI: 1.08-3.46) influencing hypertension risk. Furthermore, our research also identified that individuals with less than 7 h of sleep, a sleep latency period between 5 and 30 min, or a latency period exceeding 30 min experience a significantly increased risk of hypertension.

CONCLUSIONS

Our research uncovered separate links between sleep disorders, depression, and hypertension prevalence. Moreover, we identified an interaction between depression and sleep disorders in hypertension prevalence. Enhancing mental well-being and tackling sleep disorders could help prevent and manage hypertension. Yet, more investigation is required to establish causation and clarify mechanisms.

The mediating effect of sleep duration on metabolic syndrome severity in adults: a structural equation modeling approach

BACKGROUND

Metabolic syndrome (MetS) is associated to sleep duration. It is crucial to identify factors that disrupt sleep regulation. The study aimed to assess the indirect effect of risk factors related to MetS severity through sleep duration by utilizing a structural equation model (SEM).

METHODS

The study involving 3,935 adults from the baseline data of the Ravansar Non-Communicable Disease (RaNCD) cohort study. MetS severity scores were the outcome variables. SEM was employed to explore the relationships, utilizing IBM SPSS and AMOS version 23.

RESULTS

The mean MetS severity score was higher in women compared to men (0.25 vs. 0.16, P = 0.003). In men, socioeconomic status (SES) has a positive direct effect (β = 0.048) and a negative indirect effect (β=-0.006) on MetS severity. Increased physical activity is directly (β=-0.036) and indirectly (β=-0.093) associated with reducing MetS severity. Nap duration is directly linked to an increase (β = 0.072) but has an indirect effect (β=-0.008) in decreasing MetS severity. In women, SES has a direct (β=-0.020) and indirect (β=-0.001) inverse relationship with MetS severity. Increased physical activity is directly (β=-0.048) and indirectly (β=-0.036) associated with decreasing MetS severity in women. Nap duration is directly associated with an increase in MetS severity (β=-0.018) but indirectly contributes to its reduction (β=-0.002). Sleep duration not only directly affects MetS severity but is also influenced by age, SES, physical activity, obesity and nap duration.

CONCLUSION

Physical activity, SES, and nap duration directly and indirectly effect the MetS severity. Sleep duration was recognized as a mediating variable that supports the indirect effects.

Insight into sleep quality and its relationship with emotional intelligence: results of a cross-sectional study among Italian university students

Background

This study aimed to investigate sleep habits and examine the relationship between sleep quality and its potential predictors, namely Emotional Intelligence (EI) and perceived health status.

Methods

The present cross-sectional study was conducted between February 13 and February 28, 2023, at the "Magna Græcia" University of Catanzaro, in the Southern part of Italy. The study involved undergraduate students who were 18 years or older, proficient in the Italian language, and with no restrictions on the major attended. They completed a self-administered survey on socio-demographic information, health status, sleep quality (Pittsburgh Sleep Quality Index-PSQI), EI, and perceived health status.

Results

The majority of the sample (59.6%) was enrolled in medical or life science majors. The results showed a high prevalence of poor sleep quality and sleep latency was identified as the most affected aspect of it. The female gender and the self-perceived health status were the strongest predictors of poor sleep quality. Poor sleepers showed lower emotional clarity, emotional repair, and total EI scores. Moreover, as age increased, the odds of being classified as a poor sleeper increased by 7%.

Conclusion

The survey highlights that poor sleep health is still a significant issue and empowering individuals to make proactive decisions to adopt healthy lifestyles in the early phase of life is of paramount importance. The study exhibited the interesting role of EI in influencing sleep quality, highlighting that when emotional events are insufficiently regulated, it may result in sleep disturbances. Therefore, the promotion of sleep quality requires an integrated yet innovative approach including emotion regulation.

The effect of the interaction of sleep onset latency and age on ischemic stroke severity via inflammatory chemokines

Objective

Prolonged sleep onset latency (PSOL) and age have been linked to ischemic stroke (IS) severity and the production of chemokines and inflammation, both of which contribute to IS development. This study aimed to explore the relationship between chemokines, inflammation, and the interplay between sleep onset latency (SOL) and age in influencing stroke severity.

Methods

A cohort of 281 participants with mild to moderate IS was enrolled. Stroke severity was assessed using the National Institutes of Health Stroke Scale (NIHSS), and SOL was recorded. Serum levels of macrophage inflammatory protein-1alpha (MIP-1α), macrophage inflammatory protein-1beta (MIP-1β), monocyte chemoattractant protein-1 (MCP-1), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) were measured.

Results

NIHSS scores of middle-aged participants with PSOL were significantly higher than those with normal sleep onset latency (NSOL) (p = 0.046). This difference was also observed when compared to both the elderly with NSOL (p = 0.022), and PSOL (p < 0.001). Among middle-aged adults with PSOL, MIP-1β exhibited a protective effect on NIHSS scores (β = -0.01, t = -2.11, p = 0.039, R2 = 0.13). MIP-1α demonstrated a protective effect on NIHSS scores in the elderly with NSOL (β = -0.03, t = -2.27, p = 0.027, R2 = 0.12).

Conclusion

This study reveals a hitherto undocumented association between PSOL and IS severity, along with the potential protective effects of MIP-1β in mitigating stroke severity, especially among middle-aged patients.