Later sleep timing and social jetlag are related to increased inflammation in a population with a high proportion of OSA: findings from the Cleveland Family Study

Identification and Mendelian randomization validation of pathogenic gene biomarkers in obstructive sleep apnea

Background

By 2020, obstructive sleep apnea (OSA), a prevalent respiratory disorder, had affected 26.6-43.2% of males and 8.7-27.8% of females worldwide. OSA is associated with conditions such as hypertension, diabetes, and tumor progression; however, the precise underlying pathways remain elusive. This study aims to identify genetic markers and molecular mechanisms of OSA to improve understanding and treatment strategies.

Methods

The GSE135917 dataset related to OSA was obtained from the GEO database. Differentially expressed genes (DEGs) were subsequently identified. Weighted gene co-expression network analysis (WGCNA) was conducted to pinpoint disease-associated genes. The intersection of these data enabled the identification of potential diagnostic DEGs. Further analyses included Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment studies, exploration of protein-protein interactions based on these genes, and an examination of immune infiltration. Mendelian randomization was employed to validate core genes against the Genome-Wide Association Study database.

Results

A total of 194 DEGs were identified in this study. WGCNA network analysis highlighted 2,502 DEGs associated with OSA. By intersecting these datasets, 53 diagnostic DEGs primarily involved in metabolic pathways were identified. Significant alterations were observed in immune cell populations, including memory B cells, plasma cells, naive CD4 T cells, M0 macrophages, and activated dendritic cells. CETN3, EEF1E1, PMM2, GTF2A2, and RRM2 emerged as hub genes implicated in the pathogenesis. A line graph model provides diagnostic insights. Mendelian randomization analysis confirmed a causal link between CETN3 and GTF2A2 with OSA.

Conclusion

Through WGCNA, this analysis uncovered significant genetic foundations of OSA, identifying 2,502 DEGs and 194 genes associated with the disorder. Among these, CETN3 and GTF2A2 were found to have causal relationships with OSA.

Multiplex influences on vigilance and biochemical variables induced by sleep deprivation

Introduction

Sleep loss and sleep deprivation (SD) cause deleterious influences on health, cognition, mood and behaviour. Nevertheless, insufficient sleep and SD are prevalent across many industries and occur in various emergencies. The deleterious consequences of SD have yet to be fully elucidated. This study aimed to assess the extensive influences of SD on physiology, vigilance, and plasma biochemical variables.

Methods

Seventeen volunteers were recruited to participate in a 32.5-h SD experiment. Multiple physiological and cognitive variables, including tympanic temperature, blood oxygen saturation (SaO2), and vigilance were recorded. Urinal/salivary samples were collected and subjected to cortisol or cortisone analysis, and plasma samples were subjected to transcriptomic analysis of circular RNA (circRNA) expression using microarray. Plasma neurotransmitters were measured by targeted metabolic analysis, and the levels of inflammatory factors were assessed by antibody microarray.

Results

The volunteers showed significantly increased sleepiness and decreased vigilance during SD, and the changes in circadian rhythm and plasma biochemistry were observed. The plasma calcium (p = 0.0007) was induced by SD, while ischaemia-modified albumin (IMA, p = 0.0030) and total bile acid (TBA, p = 0.0157) decreased. Differentially expressed circRNAs in plasma were identified, which are involved in multiple signaling pathways including neuronal regulation and immunity. Accordingly, SD induced a decrease in 3-hydroxybutyric acid (3OBH, p = 0.0002) and an increase in thyroxine (T4, p < 0.0001) in plasma. The plasma anti-inflammatory cytokine IL-10 was downregulated while other ten inflammatory factors were upregulated.

Conclusion

This study demonstrates that SD influences biochemical, physiological, cognitive variables, and the significantly changed variables may serve as candidates of SD markers. These findings may further our understanding of the detrimental consequence of sleep disturbance at multiple levels.

Day-to-day deviations in sleep parameters and biological aging: Findings from the NHANES 2011-2014

OBJECTIVES

The majority of the previous research has focused on the impact of average sleep parameters on longevity. In this study, we aimed to investigate the associations of day-to-day deviations in sleep parameters with biological ages among 6052 adults participating in the 2011-2014 waves of the US National Health and Nutrition Examination Survey.

METHODS

Sleep parameters, including sleep duration, efficiency, midpoint, and day-to-day deviations in sleep parameters, including standard deviation of sleep duration (sleep variability), standard deviation of sleep midpoint (sleep irregularity), catch-up sleep, and social jetlag, were obtained from 4 to 7 days of 24-h accelerometer recording. We used physiological data to compute measurements of biological aging according to 3 published algorithms: PhenoAge, Klemera-Doubal method Biological Age, and homeostatic dysregulation.

RESULTS

After adjustment of multiple covariates, we observed that all parameters of day-to-day deviations in sleep were significantly associated with biological aging with larger sleep variability, larger sleep irregularity, more catch-up sleep, and more social jetlag linked with more advanced biological aging. The significant associations of sleep irregularity, catch-up sleep, and social jetlag with biological aging indices remained even after adjustment for sleep duration, efficiency, and midpoint.

CONCLUSION

In this study, we found that day-to-day deviations in sleep parameters are independently associated with biological aging in US general population. Since day-to-day deviation in sleep is a modifiable behavioral factor, our finding suggests that intervention aiming at increasing regularity in sleep patterns may be a novel approach for extending a healthy life span.

Timing of Meals and Sleep in the Mediterranean Population: The Effect of Taste, Genetics, Environmental Determinants, and Interactions on Obesity Phenotypes

Circadian rhythms regulate the sleep-wake and feeding-fasting cycles. Sleep and feeding constitute a complex cycle that is determined by several factors. Despite the importance of sleep duration and mealtimes for many obesity phenotypes, most studies on dietary patterns have not investigated the contribution of these variables to the phenotypes analyzed. Likewise, they have not investigated the factors related to sleep or mealtimes. Thus, our aims were to investigate the link between taste perception and eating/sleep patterns and to analyze the effect of the interactions between sleep/meal patterns and genetic factors on obesity phenotypes. We conducted a cross-sectional analysis on 412 adults from the Mediterranean population. We measured taste perception (bitter, sweet, salty, sour, and umami) and assessed sleep duration and waketime. The midpoint of sleep and social jetlag was computed. From the self-reported timing of meals, we estimated the eating window, eating midpoint, and eating jetlag. Adherence to the Mediterranean diet was measured with a validated score. Selected polymorphisms in the TAS2R38, CLOCK, and FTO genes were determined, and their associations and interactions with relevant phenotypes were analyzed. We found various associations between temporal eating, sleep patterns, and taste perception. A higher bitter taste perception was associated with an earlier eating midpoint (p = 0.001), breakfast time (p = 0.043), dinner time (p = 0.009), waketime (p < 0.001), and midpoint of sleep (p = 0.009). Similar results were observed for the bitter taste polymorphism TAS2R38-rs713598, a genetic instrumental variable for bitter perception, increasing the causality of the associations. Moreover, significant gene-sleep interactions were detected between the midpoint of sleep and the TAS2R38-rs713598 (p = 0.032), FTO-rs9939609 (p = 0.037), and CLOCK-rs4580704 (p = 0.004) polymorphisms which played a role in determining obesity phenotypes. In conclusion, our study provided more information on the sleep and mealtime patterns of the general Spanish Mediterranean population than on their main relationships. Moreover, we were able to show significant associations between taste perception, specifically bitter taste; sleep time; and mealtimes as well as an interaction between sleep time and several genetic variants linked to obesity phenotypes. However, additional research is needed to better characterize the causality and mechanisms behind these associations.

Irregular sleep and cardiometabolic risk: Clinical evidence and mechanisms

Sleep regularity is an essential part of the multidimensional sleep health framework. The phenomenon of irregular sleep patterns is widespread in contemporary lifestyles. This review synthesizes clinical evidence to summarize the measures of sleep regularity and discusses the role of different sleep regularity indicators in developing cardiometabolic diseases (coronary heart disease, hypertension, obesity, and diabetes). Existing literature has proposed several measurements to assess sleep regularity, mainly including the standard deviation (SD) of sleep duration and timing, sleep regularity index (SRI), interdaily stability (IS), and social jetlag (SJL). Evidence on associations between sleep variability and cardiometabolic diseases varies depending on the measure used to characterize variability in sleep. Current studies have identified a robust association between SRI and cardiometabolic diseases. In comparison, the association between other metrics of sleep regularity and cardiometabolic diseases was mixed. Meanwhile, the associations of sleep variability with cardiometabolic diseases differ across the population. SD of sleep characteristics or IS may be more consistently associated with HbA1c in patients with diabetes compared with the general population. The association between SJL and hypertension for patients with diabetes was more accordant than in the general population. Interestingly, the age-stratified association between SJL and metabolic factors was observed in the present studies. Furthermore, the relevant literature was reviewed to generalize the potential mechanisms through which irregular sleep increases cardiometabolic risk, including circadian dysfunction, inflammation, autonomic dysfunction, hypothalamic-pituitary-adrenal (HPA) axis disorder, and gut dysbiosis. Health-related practitioners should give more attention to the role of sleep regularity on human cardiometabolic in the future.