Nonlinear relationship between sleep midpoint and depression symptoms: a cross-sectional study of US adults

The role of dietary intake of live microbes in the association between leisure-time physical activity and depressive symptoms: a population-based study

Current research has shown promising associations between factors such as diet, total physical activity, and mental health outcomes, acknowledging the intricate interplay between these variables. However, the role of dietary intake of live microbes, coupled with leisure-time physical activity (LTPA), in their relationship to depressive symptoms necessitates further exploration. The present study examined a cohort of 25 747 individuals who participated in the National Health and Nutrition Examination Survey between the years 2007 and 2018. Patient's Health Questionnaire (PHQ-9) was employed, whereby individuals scoring ≥ 10 were classified as exhibiting symptoms of depression. LTPA status was reported by the Global Physical Activity Questionnaire and calculated by metabolic equivalent-minutes/week. Foods consumed by participants were evaluated by live microbes per gram, which were categorized into three groups: low, medium, and high. After controlling for all covariates, findings indicated that LTPA was negatively associated with depressive symptoms (OR (95% confidence interval (CI): 0.983 (0.976, 0.990), p < 0.001). Participating in more LTPA was positively correlated with consuming all three levels of dietary live microbes (low, β (95% CI): 0.086 (0.063, 0.109); medium, β (95% CI): 0.009 (0.007, 0.012); high, β (95% CI): 0.002 (0.001, 0.002)). Moreover, taking more foods with medium live microbes was associated with lower depressive likelihood (OR (95% CI): 0.931(0.882, 0.982), p = 0.010). Intake of medium and high levels of live microbes mediated the association between LTPA and depressive symptoms by 4.15% and 0.83%, respectively. Dietary intake of foods containing medium and high levels of live microbes may be a mediator of LTPA's negative association with depressive symptoms.

Sleep-wake changes and incident depressive symptoms in midlife women

Our study aimed to investigate the relationship between sleep-wake changes and depressive symptoms events among midlife women. We enrolled 1579 women aged 44-56 years who had no clinically relevant depressive symptoms at baseline. Depressive symptoms were assessed at each visit using the Center for Epidemiologic Studies Depression scale. At the third and fourth follow-up visits, women reported their sleep habits. The sleep midpoint was defined as the time to fall asleep plus one-half of the sleep duration. Sleep-wake changes were determined by the difference in the midpoint of sleep between the third and fourth visits, which were 1 year apart. The median follow-up time was 7 years (range 1-7 years). Cox proportional hazard models were fitted to calculate hazard ratios and 95% confidence intervals for the incidence of depressive symptoms associated with sleep-wake changes. After adjusting for potential confounding factors, the hazard ratio (95% confidence interval) of depressive symptoms for severe sleep midpoint changes was 1.51 (1.12, 2.05) compared with mild sleep midpoint changes. This relationship remained statistically significant and changed little when additionally controlling for sleep duration, sleep quality, insomnia symptoms, use of sleep medications, use of nervous medications, glucose, insulin, lipids, dietary energy intake, and C-reactive protein. Our findings indicate that exposure to long-term severe sleep-wake changes increases the risk of depressive symptoms in midlife women.

Exploring the potential relationship between short sleep risks and cognitive function from the perspective of inflammatory biomarkers and cellular pathways: Insights from population-based and mice studies

AIMS

The molecular mechanism of short-sleep conditions on cognition remains largely unknown. This research aimed to investigate associations between short sleep, inflammatory biomarkers and cognitive function in the US population (NHANES data 2011-2014) and explore cellular mechanisms in mice.

METHODS

Systemic immune-inflammation index (SII) was calculated using blood-cell based biomarkers. Further, we employed integrated bioinformatics and single-cell transcriptomics (GSE137665) to examine how short sleep exposure influenced the molecular pathways associated with inflammation in the brain. To explore the signaling pathways and biological processes of sleep deprivation, we carried out enrichment analyses utilizing the GO and KEGG databases.

RESULTS

Population results showed that, compared with normal sleep group, severe short sleep was associated with lower cognitive ability in all the four tests. Moreover, a higher SII level was correlated with lower scores of cognitive tests. In mice study, elevated activation of the inflammatory pathway was observed in cell subgroups of neurons within the sleep deprivation and recovery sleep cohorts. Additionally, heightened expression of oxidative stress and integrated stress response pathways was noted in GABAergic neurons during sleep deprivation.

CONCLUSION

This study contributed to the understanding of the influence of short sleep on cognitive function and its cellular mechanisms.

Inverted U-shaped relationship between sleep duration and phenotypic age in US adults: a population-based study

Sleep is a modifiable behavior that can be targeted in interventions aimed at promoting healthy aging. This study aims to (i) identify the sleep duration trend in US adults; (ii) investigate the relationship between sleep duration and phenotypic age; and (iii) explore the role of exercise in this relationship. Phenotypic age as a novel index was calculated according to biomarkers collected from US adults based on the National Health and Nutrition Examination Survey (NHANES). Sleep information was self-reported by participants and discerned through individual interviews. The principal analytical method employed was weighted multivariable linear regression modeling, which accommodated for the complex multi-stage sampling design. The potential non-linear relationship was explored using a restricted cubic spline (RCS) model. Furthermore, subgroup analyses evaluated the potential effects of sociodemographic and lifestyle factors on the primary study outcomes. A total of 13,569 participants were finally included in, thereby resulting in a weighted population of 78,880,615. An examination of the temporal trends in sleep duration revealed a declining proportion of individuals with insufficient and markedly deficient sleep time since the 2015-2016 cycle. Taken normal sleep group as a reference, participants with extreme short sleep [β (95% CI) 0.582 (0.018, 1.146), p = 0.044] and long sleep [β (95% CI) 0.694 (0.186, 1.203), p = 0.010] were both positively associated with phenotypic age using the fully adjusted model. According to the dose-response relationship between sleep duration and phenotypic age, long sleep duration can benefit from regular exercise activity, whereas short sleep duration with more exercise tended to have higher phenotypic age. There is an inverted U-shaped relationship between short and long sleep durations and phenotypic age. This study represents an important step forward in our understanding of the complex relationship between sleep and healthy aging. By shedding light on this topic and providing practical exercise recommendations for promoting healthy sleep habits, researchers can help individuals live longer, healthier, and more fulfilling lives.

The role of education attainment on 24-hour movement behavior in emerging adults: evidence from a population-based study

Purpose

The purpose of this study was to explore the relationship between education level and health behavior including sleep, work activity, exercise activity, and sedentary behavior among emerging adults.

Methods

This study utilized data from the National Health and Nutrition Examination Survey (NHANES) collected between 2007 and 2018. The study sample included 4,484 emerging adults aged 18-25 years and the weighted participants were 30,057,813. Weighted multivariable regression analysis was performed to investigate the association between education level and the aforementioned health behavior, adjusting for age, gender, race/ethnicity, marital status, poverty-income ratio, BMI, smoking, and alcohol drinking status.

Results

This study revealed that higher education level was associated with shorter sleep duration [Fully adjusted model, β (95% CI): -0.588 (-0.929, -0.246), p < 0.001]. Additionally, those with higher education levels were more likely to allocate time in sedentary behavior [β (95% CI): 90.162 (41.087, 139.238), p < 0.001]. Moreover, higher education level was related to less work activity [β (95% CI): -806.991 (-1,500.280, -113.703), p = 0.023] and more exercise activity time [β (95% CI): 118.196 (-21.992, 258.385), p = 0.097]. Subgroup analysis further verified this trend and detected that males with higher education level tended to participate in less work activity [β (95% CI): -1,139.972 (-2,136.707, -143.237), p = 0.026] while females with higher education level tended to engage in more exercise activity [Fully adjusted model, β (95% CI): 141.709 (45.468, 237.950), p = 0.004].

Conclusion

This study highlighted the importance of education level as a significant factor in promoting healthy behavior among emerging adults. The findings underscored the need for the Ministry of Education to prioritize educating this demographic about the significance of maintaining adequate sleep patterns and reducing sedentary habits. Encouraging them to allocate more time for work and physical activities can significantly contribute to their overall wellbeing and success, ultimately fostering a healthier next generation.

Opposing brain signatures of sleep in task-based and resting-state conditions

Sleep and depression have a complex, bidirectional relationship, with sleep-associated alterations in brain dynamics and structure impacting a range of symptoms and cognitive abilities. Previous work describing these relationships has provided an incomplete picture by investigating only one or two types of sleep measures, depression, or neuroimaging modalities in parallel. We analyze the correlations between brainwide neural signatures of sleep, cognition, and depression in task and resting-state data from over 30,000 individuals from the UK Biobank and Human Connectome Project. Neural signatures of insomnia and depression are negatively correlated with those of sleep duration measured by accelerometer in the task condition but positively correlated in the resting-state condition. Our results show that resting-state neural signatures of insomnia and depression resemble that of rested wakefulness. This is further supported by our finding of hypoconnectivity in task but hyperconnectivity in resting-state data in association with insomnia and depression. These observations dispute conventional assumptions about the neurofunctional manifestations of hyper- and hypo-somnia, and may explain inconsistent findings in the literature.