Impact of smoking on sleep macro- and microstructure

Smoking disrupts the relationship between cerebrospinal fluid IL-1β and multiple subdimensions of sleep

Objective

Inflammatory factors and cigarette smoking have been associated with sleep disorders but molecular mechanisms that regulate sleep, specifically the role of interleukin-1β (IL-1β), remain controversial. Individuals' cerebrospinal fluid (CSF) IL-1β, smoking behavior, and sleep data were collected to investigate how smoking may influence the relationship between CSF IL-1β and sleep via moderation analysis.

Methods

A total of 191 Chinese male patients, including 104 non-smokers and 87 active smokers, scheduled for anterior cruciate ligament reconstructive surgery, were recruited. Pittsburgh Sleep Quality Index (PSQI) scores were collected prior to surgery, and CSF samples were obtained during preoperative lumbar puncture.

Results

Active smokers compared to non-smokers exhibited higher scores across various subdimensions of PSQI, specifically poorer sleep quality, increased sleep latency, reduced sleep efficiency, and heightened sleep disturbance (all p < 0.05). Positive correlations were observed between CSF IL-1β levels and PSQI total scores, as well as several subdimensions of sleep (all p < 0.05) in non-smokers. In contrast, a negative correlation was observed between CSF IL-1β levels and sleep efficiency scores (p < 0.05) among active smokers. Moderation analysis revealed that smoking negatively moderated the relationship between CSF IL-1β and sleep, particularly in PSQI total scores (β = -0.95, p < 0.001), sleep latency scores (β = -1.05, p < 0.001), and sleep disturbance scores (β = -0.45, p < 0.05).

Conclusions

The current study found that smoking disrupts multiple subdimensions of sleep and moderates the effect of the neuroinflammatory factor CSF IL-1β on PSQI sleep latency and sleep disturbance scores.

Association between household sleep environment and sleep health characteristics in middle-aged adults: The CARDIA sleep study

OBJECTIVES

Household environmental factors and sleep hygiene may contribute to poor sleep health. We identified associations between household sleep environment (HHSE) and sleep health characteristics in White and Black adults.

METHODS

This study included cross-sectional data from the CARDIA sleep ancillary study at Year 35 (n=711). HHSE was assessed in two domains (sleep disruptors and sleep hygiene) using a questionnaire, and higher scores indicated more sleep disruptors or poorer sleep hygiene. Sleep outcomes included (1) self-reported sleep quality and daytime sleepiness and (2) actigraphy-measured sleep duration, sleep percentage, sleep timing (midpoint sleep time), and sleep regularity. We used robust regression to estimate differences in sleep outcomes corresponding to each 1-point increment in HHSE. Racial differences in associations of interest were examined by testing for interaction.

RESULTS

Participants' mean age was 61.5 (SD=3.6) years, 63% were women, and 36.7% were Black. After multivariable adjustment (β [95% CI]), more sleep disruptors (0.145 [0.04, 0.24]) and poor sleep hygiene (0.170 [0.10, 0.23]) were associated with self-reported poor sleep quality. Poor sleep hygiene was associated with actigraphy-measured shorter sleep duration (-1.397 [-2.73, -0.01]) and sleep irregularity (0.017 [0.01, 0.02]). In stratified analysis, more sleep disruptors were associated with poor sleep quality (0.320 [0.10, 0.53]) and greater daytime sleepiness (0.330 [0.11, 0.54]) only in Black participants.

CONCLUSIONS

Poor HHSE was related to self-reported poor sleep quality and to objective shorter sleep duration and sleep irregularity. Targeted interventions to mitigate sleep disruptors and promote good sleep hygiene may help to improve sleep health.

Objective and subjective measurement of sleep in people who use substances: Emerging evidence and recommendations from a systematic review

People who use substances commonly experience sleep disruptions, affecting the regulation of physical and mental health, and presenting a significant barrier to treatment success. Sleep impairments are noted in all phases of substance use; however, differences between subjective versus objective methods used to measure sleep quality have been reported. While polysomnography is the gold-standard for sleep measurement, recent advances in actigraphy may help address the discordance between subjective and objective sleep reports. This systematic review examined emerging evidence (2016-present) for sleep impairment in people who use substances, with the twofold goal of: (1) identifying whether sleep outcomes vary across substance type (alcohol, nicotine, cannabis, cocaine, methamphetamine and opioids); and (2) contrasting results from subjective and objective measures. While some differences between subjective and objective sleep were noted, there was overwhelming evidence of clinically relevant sleep impairment in people who use alcohol, nicotine, cocaine, methamphetamine and opioids, with less consistent results for cannabis. Gaps in the literature are identified and future recommendations are presented, including utilization of common methodological frameworks, identification of mechanisms, and closer examination of sleep across stages of substance use and the interconnection between sleep and return to use.

Abnormal power and spindle wave activity during sleep in young smokers

Introduction

Smoking is associated with significant alterations in sleep architecture. Previous studies have revealed changes in the subjective sleep of young smokers, but research on objective sleep assessment using polysomnography (PSG) is limited. This study aims to explore electroencephalography (EEG) power and sleep spindle activity during the sleep of young smokers, as well as to assess the relationship between sleep and smoking variables.

Methods

We collected overnight PSG data from 19 young smokers and 16 non-smokers and assessed nicotine dependence and cumulative effects using the Fagerstrom Nicotine Dependence Test (FTND) and pack-year. Power spectral analysis and sleep spindle detection are used to analyze EEG activity during sleep.

Results

Compared to the non-smokers, young smokers showed increased alpha power in the frontal and central regions and decreased delta power in the central region. The frontal region showed enhanced sleep spindle duration and density. Notably, both relative alpha power and sleep spindle duration in frontal showed a positive correlation with Pack-year.

Discussion

Sleep EEG power and sleep spindle activity in frontal may serve as biomarkers to assess the sleep quality of young smokers. It may improve the understanding of the relationship of sleep and smoking.

Sleep Pattern, Lifestyle Pattern, and Risks of Overall and 20 Types of Cancers: Findings From the UK Biobank Cohort

Objectives

Sleep health and other lifestyle behaviours are gaining increasing attention in public health, particularly for cancer prevention, but a comprehensive assessment is lacking.

Methods

The study included 380,042 UK Biobank participants. A healthy sleep score was constructed based on five sleep factors: chronotype, sleep duration, insomnia, snoring, and daytime dozing. A healthy lifestyle score was constructed based on four lifestyle factors: smoking, alcohol consumption, diet and physical activity. The effect of healthy sleep and lifestyle on cancer risk was examined by Cox proportional hazard models.

Results

Both healthy sleep and lifestyle patterns were significantly associated with a reduced risk of overall cancer and specific cancer sites. Participants with healthy sleep and lifestyle patterns had a lower risk of overall cancer (HR = 0.72, 95% CI = 0.68-0.77), liver cancer (HR = 0.53, 95% CI = 0.31-0.90), bladder cancer (HR = 0.61, 95% CI = 0.47-0.79), lung cancer (HR = 0.22, 95% CI = 0.19-0.27), and colorectal cancer (HR = 0.80, 95% CI = 0.66-0.96) compared to those with unhealthy patterns.

Conclusion

Our findings highlight the importance of public health education and interventions to improve sleep and other lifestyle behaviours for cancer prevention.

Smoking History and Nicotine Dependence Alter Sleep Features in Patients with Obstructive Sleep Apnea-Hypopnea Syndrome

Introduction: There are many aspects in the relationship between smoking and sleep that have not been investigated thoroughly yet, especially in regards to obstructive sleep apnea-hypopnea syndrome (OSAHS). Methods: In this cross-sectional study, 2359 participants, who have visited the sleep clinic of our hospital during a 13-year period and were former or current smokers, were included. Their smoking history, measured in packyears of smoking, and their nicotine dependence, measured with the Fagerström scale, were correlated with various epidemiological and sleep-related variables. Results: Patients with respiratory, cardiovascular and metabolic comorbidities were older, more obese and presented a significantly greater history in packyears of smoking. Packyears were positively correlated with the Epworth sleepiness scale (ESS) (r = 0.06, p = 0.007), with %REM sleep time (r = 0.19, p = 0.042), apnea-hypopnea index (AHI) (r = 0.10, p < 0.001), oxygen desaturation index (ODI) (r = 0.10, p < 0.001), mean and maximum apnea duration (r = 0.10, p < 0.001 and r = 0.11, p < 0.001, respectively), while they were negatively correlated with mean and minimum SaO2 (r = -0.18, p < 0.001 and r = -0.13, p < 0.001, respectively). Furthermore, smoking history exhibited a significantly increasing trend with increasing OSA diagnosis and severity (p < 0.001). Patients with abnormal movements during sleep and those with restless sleep showed a significantly higher nicotine dependence, measured with the Fagerström scale, compared to those without abnormal movements or restless sleep (5.4 ± 2.8 vs. 4.7 ± 2.8, p = 0.002 and 5.1 ± 2.9 vs. 4.7 ± 2.7, p = 0.043). Conclusions: Smoking history in packyears probably affects OSAHS characteristics, while nicotine dependence seems to be related more with abnormal sleep behaviors.

Network analysis of smoking-related sleep characteristics in Chinese adults

The associations between multiple sleep characteristics and smoking behavior are inconsistent, and it is unclear which sleep characteristics are most crucial for tobacco prevention. This study aimed to explore the associations between smoking status/intensity and multiple sleep characteristics and to identify the potential core domain of smoking-related sleep using network analysis. Data were obtained from a survey of cancer-related risk factors among Chinese adults. Logistic regression models were used to quantify the associations between sleep characteristics and smoking status/intensity. Network analyses were employed to identify the core sleep characteristics. A total of 5,228 participants with a median age of 44 years old were included in the study. Current smoking was significantly positively associated with long nap time, difficulty falling asleep, late bedtime, getting up after 7 am, and waking up earlier than expected. There was significant positive association between current smoking and short sleep duration in young adults under 45 years old. Late bedtime and getting up after 7 am were only associated with current heavy smoking, but not current light smoking. Network analyses showed that multiple smoking-related sleep characteristics were interconnected, with difficulty falling asleep and late bedtime as central characteristics in the network. The study found that the associations between sleep characteristics and smoking varied by age and smoking intensity and highlights the potential benefits of sleep health promotion in smoking cessation, with a particular focus on difficulty falling asleep and late bedtime.

Associations of classical HLA alleles with sleep behaviours

Immune dysregulation has been observed in individuals with sleep disturbance, with HLA molecules play a crucial role in the immune response. This study aimed to investigate the associations between HLA alleles and sleep behaviours, considering several environmental factors. Data were sourced from the UK Biobank. Logistic regression analyses were performed to explore the associations between 359 HLA alleles and 4 sleep behaviours, including chronotype (n = 204,636), insomnia (n = 227,553), snoring (n = 214,350) and daytime dozing (n = 227,197). Furthermore, gene-environmental interaction studies (GEIS) were conducted to evaluate the interactions of HLA alleles with environmental factors on sleep behaviours. This study analysed a total sample and subgroups stratified by sex to elucidate the impact of HLA alleles on sleep behaviours. Our findings revealed several associations between specific HLA alleles and sleep behaviours. Notably, HLA-A*23:01 was associated with evening chronotype in the total sample (OR = 0.918, 95%CI: 0.872-0.965), while HLA-A*32:01 was associated with evening chronotype in males (OR = 1.089, 95%CI: 1.037-1.144). Furthermore, GEIS identified multiple sets of interactions associated with sleep behaviours. For example, the interaction of HLA-DPA1*01:04 with alcohol consumption was associated with daytime dozing in the total sample (OR = 1.993, 95%CI: 1.351-2.941), while the interaction of HLA-DQB1*05:04 with ever suffered mental distress preventing usual activities was associated with insomnia in males (OR = 0.409, 95%CI: 0.254-0.658). Our findings highlight the involvement of HLA in sleep regulation and underscore the potential interactions between HLA alleles and environmental factors in modulating susceptibility to sleep behaviours.

The Interactions between Smoking and Sleep

Smoking a cigarette before bed or first thing in the morning is a common habit. In this review, the relationship between smoking and sleep is investigated based on the existing literature. Out of 6504 unique items that were identified via a PubMed search related to smoking and sleep, 151 were included in this review. Tobacco smoking disrupts sleep architecture by reducing slow wave and rapid eye movement (REM) sleep and undermining sleep quality. Furthermore, smoking affects sleep-related co-morbidities, such as obstructive sleep apnea-hypopnea syndrome (OSAHS), insomnia, parasomnias, arousals, bruxism, and restless legs, as well as non-sleep-related conditions such as cardiovascular, metabolic, respiratory, neurologic, psychiatric, inflammatory, gynecologic and pediatric issues, while poor sleep quality also seems to worsen the chances of successful smoking cessation. In conclusion, the existing literature suggests that there is a wicked relation between smoking and sleep.

The Association Between Sleep and Quality of Life Among Visitors of Lifestyle Clinics at the Ministry of National Guard-Health Affairs, Western Region, Saudi Arabia

BACKGROUND

Sleep quality significantly impacts health-related quality of life (QoL). However, the link between sleep quality and QoL needs further exploration, especially in the context of Saudi Arabia.

OBJECTIVE

To assess the effect of sleep quality on QoL among visitors of lifestyle clinics at the National Guard primary healthcare centers in the Western region of Saudi Arabia in 2023.

METHODS

A cross-sectional study was conducted among 369 participants. Data were collected using a questionnaire that addressed sociodemographic characteristics, sleep quality, and perceived QoL. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), while QoL was measured using the Short Form-20 (SF-20) QoL scale. Statistical analysis was performed using SPSS (IBM Corp., Armonk, NY).

RESULTS

The median age of participants was 38 years, and the median PSQI score was 9, indicating poor sleep quality. Significant differences in sleep quality were found relating to gender and income. Male gender and low-income status were associated with lower sleep quality. The scores for sleep quality varied between 3 and 16, with a median score of 9, while QoL scores ranged from 34 to 92, with a median score of 72. Good sleep quality was found only among 4.9% of the participants and was associated with higher QoL scores. The correlation between sleep quality and QoL was significant, with a correlation coefficient of -0.399 (p-value < 0.001).

CONCLUSION

The study identifies a significant correlation between poor sleep quality and lower QoL among visitors of lifestyle clinics in the Western region of Saudi Arabia. These findings suggest the necessity of addressing sleep quality in interventions aimed at improving overall well-being.

Associations between smoking, sex steroid hormones, trouble sleeping, and depression among U.S. adults: a cross-sectional study from NHANES (2013-2016)

BACKGROUND

Dose-response and nonlinear relationships of cigarette exposure with sleep disturbances and depression are warranted, and the potential mechanism of sex hormones in such associations remains unclear.

METHODS

Cigarette exposure, trouble sleeping, and depression were assessed by standard questionnaires, and the levels of cotinine and sex steroid hormones were determined among 9900 adults from the National Health and Nutrition Examination Survey (NHANES). Multiple linear regression, logistic regression, and mediation models were conducted to evaluate the associations between smoking, sex steroid hormones, trouble sleeping, and depression.

RESULTS

With never smokers as a reference, current smokers had a higher prevalence of trouble sleeping (OR = 1.931, 95% CI: 1.680, 2.219) and depression (OR = 2.525, 95% CI: 1.936, 3.293) as well as testosterone level (β = 0.083, 95% CI: 0.028, 0.140). Pack-years of smoking and cigarettes per day were positively associated with the prevalence of trouble sleeping and depression as well as testosterone level (Ptrend <0.05). The restricted cubic spline model showed linear relationships of cotinine with trouble sleeping, depression, and testosterone. The positive associations of cigarettes per day with trouble sleeping and depression were greater in females than that in males (Pmodification <0.05). However, the potential role of sex hormones was not observed in the association of cotinine with trouble sleeping or depression (Pmediation >0.05).

CONCLUSION

Smoking may induce sex hormone disturbance and increase the risk of sleep problems and depression symptoms, and ceasing smoking may reduce the risk of such complications.

Associations between e-cigarette use and sleep health among adults in the United States, NHANES 2015-2018

OBJECTIVE

To examine the associations between e-cigarette use or dual (e-cigarette and combustible cigarette) use and short sleep duration and trouble sleeping among U.S. adults.

METHODS

We used 2015-2018 data from the National Health and Nutrition Examination Survey (NHANES) (n = 11,659). E-cigarette use and dual use were categorized as current, former, and never use. Short sleep duration was defined as sleep duration ≤6 h. Trouble sleeping was self-reported. Weighted logistic regression analyses were performed.

RESULTS

Among those with current e-cigarette use, 53.9 % were with current dual use and 23.8 % were with former dual use. Compared to never e-cigarette use, current e-cigarette use was associated with significantly higher odds of trouble sleeping (OR = 2.16, 95 % CI: 1.49-3.13), adjusting for potential confounders. Significant associations were also observed for former e-cigarette use versus never use with trouble sleeping (OR = 1.54, 95 % CI: 1.15-2.07) after full adjustment. Current cigarette use was associated with both short sleep duration (OR = 1.65, 95 % CI: 1.28-2.14) and trouble sleeping (OR = 1.36, 95 % CI: 1.03-1.79) after full adjustment. Additionally, the fully adjusted ORs for short sleep duration and trouble sleeping were 1.64 (95 % CI: 1.06-2.54) and 2.14 (95 % CI: 1.34-3.42) among those with current dual use, and 1.46 (95 % CI: 1.17-1.81) and 2.11 (95 % CI: 1.66-2.67) among those with former dual use, compared to those without dual use.

CONCLUSIONS

Current cigarette use or dual use is associated with significantly higher odds of short sleep duration and trouble sleeping. Moreover, former e-cigarette use or dual use is associated with increased odds of trouble sleeping.

Validation of the Chinese version of the Sleep Regularity Questionnaire (SRQ) and analysis of influencing factors

BACKGROUND

Currently, there is no instrument to measure sleep regularity in China. In this study, the Sleep Regularity Questionnaire（SRQ） was translated into Chinese, tested for reliability and validity, and analyzed for factors affecting sleep regularity.

METHODS

The English version of the SRQ was translated into Chinese, and a total of 3642 individuals were included in this research. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to examine the underlying factor structure of the Chinese version of the SRQ and to measure its reliability and validity. In addition, the correlations between sleep regularity and general information, personal habits, self-control, stress, anxiety, and depression were explored.

RESULTS

The Cronbach's α of the Chinese SRQ was 0.858, supporting the two-factor structure. Sleep regularity was statistically different between gender and ethnicity (p < 0.05), and personal habits (exercise, continued eating after dinner, smoking and drinking) had an effect on sleep regularity. Sleep regularity was positively associated with individual self-control and negatively associated with stress, anxiety, and depression.

CONCLUSIONS

The Chinese version of the SRQ has excellent reliability and validity. There are two dimensions, namely circadian regularity and sleep continuity regularity, which can be used to assess the sleep regularity of Chinese adults. The results of this study showed that males and Han Chinese having better sleep regularity. And people with good lifestyle habits and greater self-control sleep more regularly, while stress, anxiety and depression can affect individuals' sleep regularity.

Effects of smoking on sleep architecture and ventilatory parameters including apneas: Results of the Tab-OSA study

Background

The interaction between smoking and sleep seems appears to be bidirectional, but few studies evaluated the impact of smoking and its cessation on objective sleep parameters. In this context, this new study aimed to assess the impact of smoking and its cessation on sleep architecture and on ventilatory sleep parameters, particularly the presence of sleep apnea syndrome (apnea-hypopnea index (AHI)≥15). Methods: Patients hospitalized for polysomnographic sleep exploration were compared according to their smoking status: active smokers (AS), former smokers (FS), non-smokers (NoNi). Psychiatric and non-psychiatric co-morbidities and treatment or substance use were taken into account in the analyses.

Results

A total of 170 participants were included (N = 37 FS, 39 AS, 86 NoNi). A significant decrease in the mean nocturnal O2 saturation was observed for FS and AS compared to NoNi. No differences were found regarding AHI. Regarding sleep architecture, we observed a significant decrease in the slow wave sleep duration for AS compared to NoNi, and interestingly not between FS and NoNi.

Conclusion

This study suggests that current smokers suffer from alterations in both sleep architecture and ventilatory parameters, the later appears to persist even after smoking cessation.

Prevalence and associated factors of poor sleep quality among Chinese retirees: A multicenter cross-sectional study

BACKGROUND

Sleep problems are common among older adults worldwide. The present study aims to determine the prevalence of poor sleep quality and its independent factors among retired people in health check-ups population in China.

METHOD

In this multicenter, cross-sectional survey in 2017, a group of retired people was invited to participate in an online survey of health status, and their data, including socio-demographic information, lifestyle, and medical characteristics, were recorded. Binary logistic regression was used to analyze the independent factors responsible for poor sleep quality.

RESULTS

Data from 17,408 responders who met the inclusion criteria were analyzed; among them, 53.04 % (95 % CI = 52 %-54 %) reported poor sleep quality. Binary logistic regression showed that poor sleep quality was associated with a number of factors, including being female (OR = 1.42, 95 % CI = 1.32-1.53), being single (OR = 1.35, 95 % CI = 1.18-1.54), non-smoker (OR = 1.12, 95 % CI = 1.03-1.22), physical inactivity (OR = 1.14, 95 % CI = 1.05-1.23), poor self-rated health status (OR = 1.69, 95 % CI = 1.43-2.00), long-term medication use (OR = 1.05, 95 % CI = 1.07-1.23), chronic pain (OR = 1.33, 95 % CI = 1.22-1.45), comorbidity (OR = 1.16, 95 % CI = 1.07-1.25), and depressive symptoms (mild depression: OR = 2.14, 95 % CI = 1.96-2.34; moderate depression: OR = 4.00, 95 % CI = 3.49-4.58, moderately severe depression: OR = 4.15, 95 % CI = 3.47-4.97, severe depression: OR = 4.27, 95 % CI = 2.93-6.22); while age (OR = 0.99, 95 % CI = 0.99-1.00) was negatively related to poor sleep quality.

CONCLUSION

The prevalence of poor sleep quality in the studied population is relatively high (53.04 %). Sleep problems are common among Chinese retirees, especially older females, and have a great impact on their quality of life. People living with depression, chronic diseases, and chronic pain were at a higher risk of developing sleep disorders. Therefore, it is critical to formulate effective management strategies for Chinese retirees with poor sleep quality in the context of healthy aging.

Emotional Regulation Activities Moderates the Risk of Depression in People With Sleep Disturbance: Evidence From a Community Health Survey

Background and Objective It is widely known that poor sleep quality is closely related to depression, but there are limited studies on the mediating factors.Methods Based on national health survey data, the relationship between sleep quality and depressive symptoms was explored, and structural equations were drawn to determine whether emotional regulation activities, such as regular breakfast, regular exercise, smoking, and drinking contribute to poor sleep quality-induced depression.Results There was a significant correlation between poor sleep quality and depressive symptoms. Regular breakfast tended to mediate between poor sleep quality and depression the most, followed by smoking, drinking, and regular exercise.Conclusions Several emotional regulation activities including regular breakfast, can play a protective role in the process leading to poor sleep quality-induced depression. A follow-up study is required to examine the clinical aspect of how regular breakfast can prevent the process of poor sleep quality to depression.