Sleep Pattern, Genetic Susceptibility, and Abdominal Aortic Aneurysm in UK Biobank Participants: Large-Scale Cohort Study.

Background: Abdominal aortic aneurysm (AAA) is an important cause of cardiovascular mortality. Objectives: The authors aimed to explore the associations between sleep patterns and genetic susceptibility to AAA. Methods: We included 344,855 UK Biobank study participants free of AAA at baseline. A sleep pattern was defined by chronotype, sleep duration, insomnia, snoring, and daytime sleepiness, and an overall sleep score was constructed with a range from 0 to 5, where a high score denotes a healthy sleep pattern. Polygenic risk score based on 22 single nucleotide polymorphisms was categorized into tertiles and used to evaluate the genetic risk for AAA. Cox proportional hazards regression models were used to assess the association between sleep, genetic factors, and the incidence of AAA. Results: During a median of 12.59 years of follow-up, 1,622 incident AAA cases were identified. The HR per 1-point increase in the sleep score was 0.91 (95% CI: 0.86-0.96) for AAA. Unhealthy sleep patterns, defined as a sleep score ranging from 0 to 3, were found to be associated with a higher risk of AAA for the intermediate (HR: 1.18, 95% CI: 1.06-1.31) and poor sleep patterns (HR: 1.40, 95% CI: 1.13-1.73), respectively, compared to the healthy pattern. Participants with poor sleep patterns and high genetic risks had a 2.5-fold higher risk of AAA than those with healthy sleep patterns and low genetic risk. Conclusions: In this large prospective study, healthy sleep patterns were associated with a lower risk of AAA among participants with low, intermediate, or high genetic risk.

Obesity, beverage consumption and sleep patterns in rural African women in relation to advertising of these beverages.

BACKGROUND: The burden of obesity-related, non-communicable diseases in South Africa is persistent, with poor and black South African women particularly vulnerable. The purpose of the present study was to determine relationships between obesity, physical activity, sleep patterns and beverage consumption among black South African women in a rural village in the Limpopo province. METHODS: A cross-sectional study was conducted among 200 rural-dwelling African women. Data were collected on beverage consumption, sociodemographic information, sleep patterns and anthropometry using self-reported questionnaires. RESULTS: The mean body mass index (BMI) was 28.5±7.3 kg/m2, with 40% being classified as obese (BMI ≥30 kg/m2) and the mean sleep score was 4.68±2.51. Participants with very bad habitual sleeping patterns consumed significantly more sugar-sweetened beverages and alcohol than those with very good sleeping patterns. We also observed that when total coffee with sugar, fruit juice, total sugar-sweetened beverages and weight decreased the number of hours participants slept increased. CONCLUSIONS: The study identified significant associations between body weight, sleep duration and sugar-sweetened beverage consumption among rural black South African women. This underscores a need to address unhealthy lifestyle behaviours to lower incidences of non-communicable diseases in rural-dwelling women.

Association of Sleep Pattern and Genetic Susceptibility with Obstructive Sleep Apnea: A Prospective Analysis of the UK Biobank.

Purpose: The prevalence of obstructive sleep apnea (OSA) is high worldwide. This study aimed to quantify the relationship between the incidence of OSA and sleep patterns and genetic susceptibility. Methods: A total of 355,133 white British participants enrolled in the UK Biobank between 2006 and 2010 with follow-up data until September 2021 were recruited. We evaluated sleep patterns using a customized sleep scoring method based on the low-risk sleep phenotype, defined as follows: morning chronotype, 7-8 hours of sleep per day, never/rarely experience insomnia, no snoring, no frequent daytime sleepiness, never/rarely nap, and easily getting up early. The polygenic risk score was calculated to assess genetic susceptibility to OSA. Cox proportional hazard models were used to evaluate the associations between OSA and sleep patterns and genetic susceptibility. Results: During a mean follow-up of 12.57 years, 4618 participants were diagnosed with OSA (age: 56.83 ± 7.69 years, women: 31.3%). Compared with those with a poor sleep pattern, participants with a normal (HR: 0.42, 95% CI: 0.38-0.46), ideal (HR: 0.21, 95% CI: 0.19-0.24), or optimal (HR: 0.15, 95% CI: 0.12-0.18) sleep pattern were significantly more likely to have OSA. The genetic susceptibility of 173,239 participants was calculated, and the results showed that poor (HR: 3.67, 95% CI: 2.95-4.57) and normal (HR: 1.89, 95% CI: 1.66-2.16) sleep patterns with high genetic susceptibility can increase the risk for OSA. Conclusion: This large-scale prospective study provides evidence suggesting that sleep patterns across seven low-risk sleep phenotypes may protect against OSA in individuals with varying degrees of genetic susceptibility.

Corrigendum: Association between napping and type 2 diabetes mellitus.

[This corrects the article DOI: 10.3389/fendo.2024.1294638.].

Sleep patterns in Metropolitan and Regional areas in Japan: Before and during COVID-19 social restrictions.

Sleep timing is an important output of the circadian system. The COVID-19-mandated social restrictions significantly altered commuting time and sleep duration regionally in Japan. This study aimed to elucidate sleep patterns, especially chronotype and social jetlag (SJL), due to changes in social time pressure through the social restrictions between the Metropolitan and Regional areas in Japan. As part of the Global Chrono Corona Survey 2020 (GCCS), the data were collected during social restrictions (SR), but pre-COVID-19 behaviours were also queried retrospectively. We analyzed a cohort of 729 respondents representing both the Metropolitan and the Regional areas separately for workdays and work-free days. While the areas showed no difference in SJL before SR, the differential decrease was larger in the Metropolitan area during SR, resulting in a significant difference in SJL between the areas. The outdoor light exposure before SR was 30 min longer in the Metropolitan areas than in the Regional; during SR both areas showed similarly low (below 1 h) outdoor light exposures. The variables associated with decreased SJL were the Metropolitan areas, work-from-home, a no-usage alarm clock on workdays, and chronotypes (mid-sleep time on free days corrected for sleep deficit accumulated over the workweek, MSFsc) during SR. The results suggest that relaxed social schedules, as reflected in the increased frequency of work-from-home and reduced alarm clock use, and moving towards earlier MSFsc during SR were linked to decreased SJL and were more prominent in the Metropolitan areas. This study provides insights into sleep patterns and the social time pressure markers, by comparison between residential groups in Japan.

Association between napping and type 2 diabetes mellitus.

As the incidence of type 2 diabetes mellitus (T2DM) is increasing rapidly and its consequences are severe, effective intervention and prevention, including sleep-related interventions, are urgently needed. As a component of sleep architecture, naps, alone or in combination with nocturnal sleep, may influence the onset and progression of T2DM. Overall, napping is associated with an increased risk of T2DM in women, especially in postmenopausal White women. Our study showed that napping >30 minutes (min) increased the risk of T2DM by 8-21%. In addition, non-optimal nighttime sleep increases T2DM risk, and this effect combines with the effect of napping. For nondiabetic patients, napping >30 min could increase the risks of high HbA1c levels and impaired fasting glucose (IFG), which would increase the risk of developing T2DM later on. For diabetic patients, prolonged napping may further impair glycemic control and increase the risk of developing diabetic complications (e.g., diabetic nephropathy) in the distant future. The following three mechanisms are suggested as interpretations for the association between napping and T2DM. First, napping >30 min increases the levels of important inflammatory factors, including interleukin 6 and C-reactive protein, elevating the risks of inflammation, associated adiposity and T2DM. Second, the interaction between postmenopausal hormonal changes and napping further increases insulin resistance. Third, prolonged napping may also affect melatonin secretion by interfering with nighttime sleep, leading to circadian rhythm disruption and further increasing the risk of T2DM. This review summarizes the existing evidence on the effect of napping on T2DM and provides detailed information for future T2DM intervention and prevention strategies that address napping.

Daily Activities in European Children and Adolescents During COVID-19 School Closure: A Longitudinal Study Exploring Physical Activity, Use of Screens, and Sleep Patterns.

This longitudinal study aimed to analyze the evolution of patterns of daily activities (physical activity time, screen usage time, and sleep hours) in European youth during school closure due to the COVID-19 health crisis. Participants were 624 caregivers of children and adolescents aged 3-18 from Italy, Spain, and Portugal. Evaluations were online, and four time-points were considered: retrospective measurement of daily activities before confinement (T1), and two (T2), five (T3), and eight (T4) weeks after starting the lockdown. Generally accepted international guidelines on physical activity time, screen usage time, and hours of sleep by age group were used to determine whether the pattern might increase the risk for ill health or not. To estimate the evolution of daily activities, generalized estimating equations (GEE) were used. The percentage of children who practiced less than 60 min of daily exercise increased significantly from before home confinement (47.8%) to T2 (86.4%); it slightly decreased at T3 (79.8%), and remained stable at T4 (76.1%). The percentage of children who made excessive use of screens (according to their age group) significantly increased from T1 to T2 and remained stable and high in the rest of the evaluations. The percentage of children who slept fewer or more hours than recommended for their age group remained stable between T1 and T4, although there was a significant increase at T3. In general, results found unhealthier behaviors as confinement was extended. Results are discussed in order to find strategies for promoting healthy daily activities for future pandemics.

Sleep and Ultramarathon: Exploring Patterns, Strategies, and Repercussions of 1,154 Mountain Ultramarathons Finishers.

BACKGROUND: Sleep and physical performance are strongly related and mutually influence each other. Athletes, particularly in disciplines like offshore sailing and ultra-endurance sports, often suffer from sleep deprivation due to factors like irregular training times, travel, and the extended duration of events like 100-mile mountain races. Despite growing interest in sleep's role in sports science, few studies have specifically investigated the sleep patterns of ultramarathon runners. This study aimed to investigate sleep patterns and sleep management strategies in ultramarathons, and the repercussions of sleep deprivation during and after races. METHODS: This cross-sectional study using e-survey was conducted on 1154 runners from two ultramarathons (a 165 km race with 9,576 m positive elevation; 2018 finish time [23:18:48-66:04:00], and a 111 km race with 6,433 m elevation; [15:34:56 - 41:54:16]). RESULTS: The results revealed that 58% of the runners reported implementing sleep management strategies before or during the race. Most runners began the race with some level of sleep debt (-50 min a week before the race). During the races, 77% of runners slept, with the cumulative sleep duration varying based on race duration and the number of nights spent on the race (76 min at 165 km and 27 min at 111 km). Short naps lasting less than 30 min were the most popular strategy. The prevalence of symptoms attributed to sleep deprivation during the race was high (80%), with reported falls and hallucinations. After the race, runners reported recovering a normal state of wakefulness relatively quickly (within two days); 22% believed that sleep deprivation during the race increased the risk of accidents in everyday life. CONCLUSION: This study provides valuable insights into sleep patterns and strategies in ultramarathon running and emphasizes the importance of adequate sleep management for performance and post-race recovery.

Sleep stage prediction using multimodal body network and circadian rhythm.

Quality sleep plays a vital role in living beings as it contributes extensively to the healing process and the removal of waste products from the body. Poor sleep may lead to depression, memory deficits, heart, and metabolic problems, etc. Sleep usually works in cycles and repeats itself by transitioning into different stages of sleep. This study is unique in that it uses wearable devices to collect multiple parameters from subjects and uses this information to predict sleep stages and sleep patterns. For the multivariate multiclass sleep stage prediction problem, we have experimented with both memoryless (ML) and memory-based models on seven database instances, that is, five from the collected dataset and two from the existing datasets. The Random Forest classifier outclassed the ML models that are LR, MLP, kNN, and SVM with accuracy (ACC) of 0.96 and Cohen Kappa 0.96, and the memory-based model long short-term memory (LSTM) performed well on all the datasets with the maximum attained accuracy of 0.88 and Kappa 0.82. The proposed methodology was also validated on a longitudinal dataset, the Multiethnic Study of Atherosclerosis (MESA), with ACC and Kappa of 0.75 and 0.64 for ML models and 0.86 and 0.78 for memory-based models, respectively, and from another benchmarked Apple Watch dataset available on Physio-Net with ACC and Kappa of 0.93 and 0.93 for ML and 0.92 and 0.87 for memory-based models, respectively. The given methodology showed better results than the original work and indicates that the memory-based method works better to capture the sleep pattern.

Mediterranean diet adherence and sleep pattern: a systematic review of observational studies.

BACKGROUND AND AIMS: Despite a huge body of evidence on the linkage between dietary intakes and pattern of sleeping, the findings are controversial. The current study aimed to summarize earlier findings on the association between adherence to Mediterranean diet (MD) and pattern of sleeping. METHODS: This study performed based on PRISMA guideline. Systematically search was applied in PubMed, Scopus and Google Scholar to find out relevant publications appeared up to February 2023. No restrictions on language and time of publication were applied. Duplicate citations were removed. We included observational studies which assessed MD as the main exposure and kind of sleep disorders as the main outcome. RESULTS: A total of 20 observational studies included. Out of these studies, two were cohort studies and 18 had a cross-sectional design. A total of 21,714 participants included. Usual dietary intakes were assessed using a validated Food Frequency Questionnaire, and a diet history questionnaire. Some studies did not report methods of measuring habitual dietary intakes. Adherence to MD was evaluated by KIDMED questionnaire, PREMED, alternate Mediterranean (aMed) questionnaire, MEDAS questionnaire, MedDietScore, MEDI-LITE score, modified Mediterranean Diet Score (mMDS), Mediterranean food pattern (MFP) and modified Mediterranean diet score (mMED). Pattern of sleeping was examined as sleep quality, sleep duration, sleep latency, sleep efficacy, sleepiness, sleep disturbance, taking a nap and some other sleep disorders. CONCLUSION: In conclusion, findings of published studies highlighted the importance of consumption of MD for better sleep quality.

Multi-night home assessment of sleep structure in OSA with and without insomnia.

OBJECTIVE: To explore sleep structure in participants with obstructive sleep apnea (OSA) and comorbid insomnia (COMISA) and participants with OSA without insomnia (OSA-only) using both single-night polysomnography and multi-night wrist-worn photoplethysmography/accelerometry. METHODS: Multi-night 4-class sleep-staging was performed with a validated algorithm based on actigraphy and heart rate variability, in 67 COMISA (23 women, median age: 51 years) and 50 OSA-only (15 women, median age: 51) participants. Sleep statistics were compared using linear regression models and mixed-effects models. Multi-night variability was explored using a clustering approach and between- and within-participant analysis. RESULTS: Polysomnographic parameters showed no significant group differences. Multi-night measurements, during 13.4 ± 5.2 nights per subject, demonstrated a longer sleep onset latency and lower sleep efficiency for the COMISA group. Detailed analysis of wake parameters revealed longer mean durations of awakenings in COMISA, as well as higher numbers of awakenings lasting 5 min and longer (WKN≥5min) and longer wake after sleep onset containing only awakenings of 5 min or longer. Within-participant variance was significantly larger in COMISA for sleep onset latency, sleep efficiency, mean duration of awakenings and WKN≥5min. Unsupervised clustering uncovered three clusters; participants with consistently high values for at least one of the wake parameters, participants with consistently low values, and participants displaying higher variability. CONCLUSION: Patients with COMISA more often showed extended, and more variable periods of wakefulness. These observations were not discernible using single night polysomnography, highlighting the relevance of multi-night measurements to assess characteristics indicative for insomnia.

Physical activity and sleep pattern in relation to incident Parkinson's disease: a cohort study.

BACKGROUND: How physical activity (PA) and different sleep traits and overall sleep pattern interact in the development of Parkinson's disease (PD) remain unknown. OBJECTIVE: To prospectively investigate the joint associations of PA and sleep pattern with risk of PD. METHODS: Included were 339,666 PD-free participants from the UK Biobank. Baseline PA levels were grouped into low (< 600 MET-mins/week), medium (600 to < 3000 MET-mins/week) and high (≥ 3000 MET-mins/week) according to the instructions of the UK Biobank. Healthy sleep traits (chronotype, sleep duration, insomnia, snoring, and daytime sleepiness) were scored from 0 to 5 and were categorized into "ideal sleep pattern" (≥ 3 sleep scores) and "poor sleep pattern" (0-2 sleep scores). Hazard ratios (HRs) and 95% confidence intervals (CIs) of PD were estimated by Cox proportional hazards models. RESULTS: During a median of 11.8 years of follow-up, 1,966 PD events were identified. The PD risk was lower in participants with high PA (HR = 0.73; 95% CI: 0.64, 0.84), compared to those with low PA; and participants with ideal sleep pattern also had a lower risk of PD (HR = 0.78; 95% CI: 0.69, 0.87), compared to those with poor sleep pattern. When jointly investigating the combined effect, participants with both high PA and ideal sleep pattern had the lowest risk of incident PD (HR = 0.55; 95% CI: 0.44, 0.69), compared to those with low PA and poor sleep pattern; notably, participants with high PA but poor sleep pattern also gained benefit on PD risk reduction (HR = 0.74; 95% CI: 0.55, 0.99). CONCLUSIONS: Both high PA and ideal sleep pattern were independently associated with lower risk of developing PD, and those with both high PA level and ideal sleep pattern had the lowest risk. Our results suggest that improving PA levels and sleep quality may be promising intervention targets for the prevention of PD.

Sleep Patterns of Premedical Undergraduate Students: Pilot Study and Protocol Evaluation.

BACKGROUND: Poor sleep hygiene persists in college students today, despite its heavy implications on adolescent development and academic performance. Although sleep patterns in undergraduates have been broadly investigated, no study has exclusively assessed the sleep patterns of premedical undergraduate students. A gap also exists in the knowledge of how students perceive their sleep patterns compared to their actual sleep patterns. OBJECTIVE: This study aims to address 2 research questions: What are the sleep patterns of premedical undergraduate students? Would the proposed study protocol be feasible to examine the perception of sleep quality and promote sleep behavioral changes in premedical undergraduate students? METHODS: An anonymous survey was conducted with premedical students in the Medical Science Baccalaureate program at an R1: doctoral university in the Midwest United States to investigate their sleep habits and understand their demographics. The survey consisted of both Pittsburg Sleep Quality Index (PSQI) questionnaire items (1-9) and participant demographic questions. To examine the proposed protocol feasibility, we recruited 5 students from the survey pool for addressing the perception of sleep quality and changes. These participants followed a 2-week protocol wearing Fitbit Inspire 2 watches and underwent preassessments, midassessments, and postassessments. Participants completed daily reflections and semistructured interviews along with PSQI questionnaires during assessments. RESULTS: According to 103 survey responses, premedical students slept an average of 7.1 hours per night. Only a quarter (26/103) of the participants experienced good sleep quality (PSQI<5), although there was no significant difference (P=.11) in the proportions of good (PSQI<5) versus poor sleepers (PSQI≥5) across cohorts. When students perceived no problem at all in their sleep quality, 50% (14/28) of them actually had poor sleep quality. Among the larger proportion of students who perceived sleep quality as only a slight problem, 26% (11/43) of them presented poor sleep quality. High stress levels were associated with poor sleep quality. This study reveals Fitbit as a beneficial tool in raising sleep awareness. Participants highlighted Fitbit elements that aid in comprehension such as being able to visualize their sleep stage breakdown and receive an overview of their sleep pattern by simply looking at their Fitbit sleep scores. In terms of protocol evaluation, participants believed that assessments were conducted within the expected duration, and they did not have a strong opinion about the frequency of survey administration. However, Fitbit was found to provide notable variation daily, leading to missing data. Moreover, the Fitbit app's feature description was vague and could lead to confusion. CONCLUSIONS: Poor sleep quality experienced by unaware premedical students points to a need for raising sleep awareness and developing effective interventions. Future work should refine our study protocol based on lessons learned and health behavior theories and use Fitbit as an informatics solution to promote healthy sleep behaviors.

Association between co-sleeping in the first year of life and preschoolers´ sleep patterns.

This study aimed to investigate the association between co-sleeping practiced during the first year of life and preschoolers' sleep patterns. A cross-sectional study including toddlers was designed to analyze their sleep patterns. The Brief Infant Sleep Questionnaire, validated in Spanish, was used to measure sleep quality. A latent class analysis was performed to identify qualitative subgroups in the sample and explore the effects of co-sleeping. The sleep patterns of 276 children were analyzed. A total of 181 (65%) parents reported having practiced co-sleeping with their children. The latent class analysis identified a two-class solution with two different sleep patterns. One of them showed a worse quality sleep pattern, which had a significant association with having practiced co-sleeping during the first year of life, and with the fact that they were still sleeping in the parents' room, among other characteristics related to co-sleeping and parental concerns. Breastfeeding also showed association with a worse quality sleep pattern.  Conclusion: Based on the present findings, co-sleeping during the first year of life appears to be associated with poor sleep patterns in young preschoolers. What is Known: • Co-sleeping shows benefits for infants and parents, mainly facilitating successful breastfeeding. • Literature on the effect of co-sleeping in lately sleep quality in children and their parents is very limited. What is New: • Co-sleeping practiced during the first year of life could be associated with a worse sleep pattern measured with BISQ-E tool. • A balance between the correct practice of co-sleeping and the achievement of a healthy sleep routine in preschool should probably be part of parents' health education.

Sleep patterns, genetic predisposition, and risk of chronic liver disease: A prospective study of 408,560 UK Biobank participants.

BACKGROUND: Little is known about the role that combined sleep behaviors play in the association with chronic liver disease (CLD) risk. METHODS: We included 408,560 participants initially free of CLD from the UK Biobank. A healthy sleep pattern was defined by early chronotype, sleep duration of 7-8 h/day, no insomnia, no snoring, and no excessive daytime sleepiness. Cox regression models were used to examine the association of healthy sleep pattern with incident CLD and their interaction with PNPLA3 genetic risk. RESULTS: During a median 12.5 years of follow-up, we documented 10,915 incident all-cause CLD cases, including 388 viral hepatitis, 4782 non-alcoholic fatty liver disease (NAFLD), 1356 cirrhosis, 973 alcoholic liver disease, and 725 liver cancer cases. Compared to participants with a healthy sleep score of 0-1, the hazard ratio (HR) (95 % confidence interval [CI]) for those with a sleep score of 5 was 0.54 (0.49, 0.60) for CLD, 0.52 (0.30, 0.90) for viral hepatitis, 0.47 (0.41, 0.55) for NAFLD, 0.57 (0.43, 0.75) for cirrhosis, 0.32 (0.23, 0.44) for alcoholic liver disease, and 0.53 (0.37, 0.77) for liver cancer. Healthy sleep pattern and PNPLA3 genetic risk exerted significant additive effects on CLD risk (relative excess risk due to the interaction: 0.05; attributable proportion due to the interaction: 13 %). LIMITATIONS: Measurement error was unavoidable for self-reported data on sleep behaviors. CONCLUSIONS: Our analyses provide evidence that healthy sleep pattern was inversely associated with the development of CLD, and participants with higher genetic risk were more likely to develop CLD when exposed to the unhealthy sleep pattern.

Association between sleep pattern and incidence of hypertension: A prospective cohort study of older adult participants in the Chinese longitudinal healthy longevity survey.

BACKGROUND: The relationship between sleep duration or sleep quality and the risk of hypertension has been previously examined. However, little is known regarding the association between sleep duration and quality and the risk of developing hypertension in the older adult Chinese population. METHODS: The sleep patterns of 5683 participants without hypertension at baseline from the Chinese Longitudinal Healthy Longevity Survey were analyzed. Cox proportional hazard models were used to study the associations between sleep patterns and hypertension. RESULTS: It was found that 1712 (30.12%) of the 5683 participants had an unhealthy sleep pattern. After an average follow-up of 3.31 years, 1350 of the participants had hypertension. Compared with participants with an unhealthy sleep pattern, those with a healthy sleep pattern had a 20% (hazard ratio = 0.80, 95% confidence interval = 0.67-0.94, P = = 0.008) lower risk of incident hypertension in the fully adjusted models. In addition, an approximately linear dose-response association was observed between sleep duration and the incidence of hypertension (P for non-linear =0.43). Subgroup analyses demonstrated significant interactions between age and sleep pattern concerning hypertension (P for interaction <0.05). Several sensitivity analyses were conducted, and the obtained findings were similar to the main results. CONCLUSIONS: A healthy sleep pattern, comprising an adequate sleep duration and good sleep quality, can help reduce hypertension risk. Thus, a healthy sleep pattern is crucial to decreasing hypertension in older Chinese adults in a rapidly aging society.

Sleep patterns and risks of incident cardiovascular disease and mortality among people with type 2 diabetes: a prospective study of the UK Biobank.

BACKGROUND: To explore the relationship between sleep patterns and cardiovascular disease (CVD) incidence and mortality risk in a population with type 2 diabetes through a UK Biobank sample. METHODS: A total of 6860 patients with type 2 diabetes were included in this study. Five sleep factors (including Chronotype, sleep duration, insomnia, daytime sleepiness, and snoring) were collected as a questionnaire. The calculation generates a sleep score of 0-5, and then three sleep patterns were defined based on the sleep scores: poor sleep pattern (0-2), Intermediate sleep pattern (3-4), and healthy sleep pattern (5). HRs and 95% confidence intervals were calculated by multivariate COX proportional risk model adjustment. Restricted cubic splines were used to validate linear associations between sleep scores CVD events. RESULTS: Our results found a reduced risk of CVD events in individuals with healthy sleep patterns compared to participants with poor sleep patterns. CVD Mortality (HR, 0.690; 95% CI 0.519-0.916), ASCVD (Atherosclerosis CVD) (HR, 0.784; 95% CI 0.671-0.915), CAD (Coronary Artery Disease) (HR, 0.737; 95% CI 0.618-0.879), PAD (Peripheral Arterial Disease) (HR, 0.612; 95% CI 0.418-0.896), Heart Failure (HR, 0.653; 95% CI 0.488-0.875). Restricted cubic spline responded to a negative linear correlation between sleep scores and CVD Mortality, ASCVD, CAD, PAD, and Heart Failure. CONCLUSIONS: Healthy sleep patterns are significantly associated with a reduced risk of CVD Mortality, ASCVD, CAD, PAD, and Heart Failure in the diabetes population.

Adherence to a healthy sleep pattern and risk of urologic cancers: A large prospective cohort study.

OBJECTIVE: A variety of unhealthy sleep behaviors have been shown to be associated with an increased risk of urologic cancers. However, little is known about the association between the overall sleep patterns and urologic cancers. To prospectively investigate the associations between a healthy sleep pattern and the risks of urologic cancers, including bladder cancer (BCa) and renal cell carcinoma (RCC). METHODS: In this prospective cohort study, 377,144 participants free of cancer at baseline were recruited from the UK Biobank. Data on sleep behaviors were collected through questionnaires at recruitment. The incident urologic cancer cases were determined through linkage to national cancer and death registries. We established a healthy sleep score according to five sleep traits (sleep duration, chronotype, insomnia, snoring, and daytime sleepiness). Cox proportional hazard regression models were used to calculate the adjusted hazard ratios and 95% confidence intervals to assess the relationship between the healthy sleep score and the risk of urologic cancers. RESULTS: During a median of ≥9 years of follow-up, we identified 1986 incident urologic cancer cases, including 1272 BCa cases and 706 RCC cases. Compared with the participants with a poor sleep pattern (score of 0-2), the multivariable-adjusted hazard ratio and 95% confidence interval were 0.85 (0.75 to 0.96) for urologic cancers, 0.80 (0.68 to 0.93) for BCa, and 0.91 (0.74, 1.12) for RCC, respectively, for those with the healthier sleep pattern (score of 4-5). CONCLUSION: Our results indicate that a healthy sleep pattern is associated with lower risks of urologic cancers.

Postoperative Sleep Quality of Insomnia Patients After TIVA Anesthesia: A Prospective Study.

PURPOSE: The purpose of this study is to observe the postoperative sleep quality of insomnia patients undergoing laparoscopic gynecologic oncology surgery after total intravenous anesthesia. DESIGN: Prospective study. METHODS: We conducted a prospective, observational study in our hospital. All patients underwent propofol-remifentanil anesthesia without other sedative medications before or during the operation. Pittsburgh Sleep Quality Index (PSQI) scores of the baseline value, night-1 (the first night after surgery), night-3, night-5, and night-30 were observed. FINDINGS: Sixty-nine female insomnia patients were allocated based on the results of the PSQI and the diagnostic criteria of insomnia. The PSQI global scores were respectively 6 (5-8), 5 (4-6), 5 (3-6), and 6 (5-7) on night-1, night-3, night-5, and night-30, significantly lower than the baseline 7 (6-8) (P < 0.05). The 5 components (subjective sleep quality, sleep latency, sleep duration, sleep efficiency and daytime dysfunction) had significant changes at different postoperative time points (P < 0.05). The daytime dysfunction could also be improved 1 month after the surgery (P < 0.05). In contrast, the variations of sleep disturbance and use of sleep medication had no statistical differences. CONCLUSIONS: The sleep quality of female patients with insomnia was improved on the first night after surgery in the sides of sleep latency and daytime dysfunction, and the improvement could also be obtained 1 month after propofol-remifentanil general anesthesia.