Risk Factor For Sleep Disorders Research Articles

Pain, obesity, adenosine salvage disruption, and smoking behavior mediate the effect of gut microbiota on sleep disorders: results from network Mendelian randomization and 16S rDNA sequencing.

The objective of this study is to investigate the indirect causalities between gut microbiota and sleep disorders. In stage 1, we utilized 196 gut microbiota as the exposure factor and conducted a two-sample univariable Mendelian randomization (MR) analysis on five sleep disorders: insomnia, excessive daytime sleepiness (EDS), sleep-wake rhythm disorders (SWRD), obstructive sleep apnea (OSA), and isolated REM sleep behavior disorder (iRBD). In stage 2, we validated the MR findings by comparing fecal microbiota abundance between patients and healthy controls through 16S rDNA sequencing. In stage 3, we explored the indirect pathways by which the microbiota affects sleep, using 205 gut microbiota metabolic pathways and 9 common risk factors for sleep disorders as candidate mediators in a network MR analysis. In stage 1, the univariable MR analysis identified 14 microbiota potentially influencing five different sleep disorders. In stage 2, the results from our observational study validated four of these associations. In stage 3, the network MR analysis revealed that the Negativicutes class and Selenomonadales order might worsen insomnia by increasing pain [mediation: 12.43% (95% CI: 0.47, 24.39%)]. Oxalobacter could raise EDS by disrupting adenosine reuptake [25.39% (1.84, 48.95%)]. Allisonella may elevate OSA risk via obesity promotion [36.88% (17.23, 56.54%)], while the Eubacterium xylanophilum group may lower OSA risk by decreasing smoking behavior [7.70% (0.66, 14.74%)]. Triangulation of evidence from the MR and observational study revealed indirect causal relationships between the microbiota and sleep disorders, offering fresh perspectives on how gut microbiota modulate sleep.

Prevalence and influencing factors of sleep disorders in medical students after the COVID-19 pandemic

BackgroundThe prevalence of sleep disorders among medical students was high during the COVID-19 pandemic. However, fewer studies have been conducted on sleep disorders among medical students after the COVID-19 pandemic. This study investigated the prevalence and factors influencing sleep disorders among Chinese medical students after COVID-19.MethodsA total of 1,194 Chinese medical students were included in this study from 9th to 12th July 2023. We used the Self-administered Chinese scale to collect the demographic characteristics. In addition, we used the Chinese versions of the Self-rating Depression Scale (SDS), the Self-rating Anxiety Scale (SAS), and the Pittsburgh Sleep Quality Index (PSQI) to assess subjects’ depression, anxiety, and sleep disorders, respectively. The chi-square test and binary logistic regression were used to identify factors influencing sleep disorders. The receiver operating characteristic (ROC) curve was utilized to assess the predictive value of relevant variables for sleep disorders.ResultsWe found the prevalence of sleep disorders among medical students after COVID-19 was 82.3%. According to logistic regression results, medical students with depression were 1.151 times more likely to have sleep disorders than those without depression (OR = 1.151, 95% CI 1.114 to 1.188). Doctoral students were 1.908 times more likely to have sleep disorders than graduate and undergraduate students (OR = 1.908, 95% CI 1.264 to 2.880).ConclusionThe prevalence of sleep disorders among medical students is high after COVID-19. In addition, high academic levels and depression are risk factors for sleep disorders. Therefore, medical colleges and administrators should pay more attention to sleep disorders in medical students after the COVID-19 pandemic. Regular assessment of sleep disorders and depression is essential.

Development and validation of a prediction nomogram for sleep disorders in hospitalized patients with acute myocardial infarction

PurposeSleep disorders are becoming more prevalent in hospitalized patients with acute myocardial infarction (AMI). We aimed to investigate the risk factors for sleep disorders in hospitalized patients with AMI, then develop and validate a prediction nomogram for the risk of sleep disorders.MethodsClinical data were collected from patients with AMI hospitalized in our hospital from January 2020 to June 2023. All patients were divided into the training group and the validation group with a ratio of 7:3 in sequential order. The LASSO regression analysis and multivariate logistic regression analysis were used to screen potential risk factors for sleep disorders. The concordance index (C-index), calibration curves, and decision curve analysis (DCA) were plotted.ResultsA total of 256 hospitalized patients with AMI were enrolled. Patients were divided into the training group (180) and the validation group (76) according to a scale of 7:3. Of the 256 patients, 90 patients (35.16%) suffered from sleep disorders, and 33 patients (12.89%) needed hypnotics. The variables screened by LASSO regression included age, smoking, NYHA class, anxiety status at admission, depression status at admission, and strangeness of environment. A nomogram model was established by incorporating the risk factors selected. The C-index, calibration curve, and DCA showed good predictive performance.ConclusionsWe identified six clinical characteristics as predictors of sleep disorders in hospitalized patients with AMI. It helps nurses make appropriate decisions in clinical practice.

Risk Factors for Sleep Disorders in Patients with Chronic Pain: A Meta-Analysis

Background: Chronic pain and sleep disorders frequently co-occur, exacerbating each other in a vicious cycle. This meta-analysis aimed to identify and quantify risk factors associated with sleep disorders in individuals experiencing chronic pain. Methods: A systematic search of PubMed, Embase, Web of Science, and Cochrane Library databases was conducted from January 2018 to June 2024. Studies assessing sleep disorders (insomnia, obstructive sleep apnea, restless legs syndrome) in adults with chronic pain (non-cancer pain lasting >3 months) were included. Data on demographics, pain characteristics, sleep measures, and potential risk factors were extracted. Meta-analyses were performed using random-effects models to estimate pooled odds ratios (ORs) and 95% confidence intervals (CIs). Results: Twenty-seven studies (n = 12,453 participants) met the inclusion criteria. Chronic pain significantly increased the odds of having any sleep disorder (OR 2.83, 95% CI 2.19-3.65). Specific risk factors identified included: Female gender: OR 1.41 (95% CI 1.18-1.67); Higher pain intensity: OR 1.15 per 1-unit increase on a 0-10 scale (95% CI 1.08-1.23); Longer pain duration: OR 1.04 per year (95% CI 1.01-1.07); Presence of depression or anxiety: OR 2.32 (95% CI 1.85-2.91); Use of opioid medications: OR 1.58 (95% CI 1.23-2.04). Conclusion: Chronic pain is a substantial risk factor for sleep disorders. Gender, pain intensity, duration, comorbid mental health conditions, and opioid use emerged as modifiable risk factors. Targeted interventions addressing these factors may improve sleep outcomes in individuals with chronic pain.

Comparison of model feature importance statistics to identify covariates that contribute most to model accuracy in prediction of insomnia.

Sleep is critical to a person's physical and mental health and there is a need to create high performing machine learning models and critically understand how models rank covariates. The study aimed to compare how different model metrics rank the importance of various covariates. A cross-sectional cohort study was conducted retrospectively using the National Health and Nutrition Examination Survey (NHANES), which is publicly available. This study employed univariate logistic models to filter out strong, independent covariates associated with sleep disorder outcome, which were then used in machine-learning models, of which, the most optimal was chosen. The machine-learning model was used to rank model covariates based on gain, cover, and frequency to identify risk factors for sleep disorder and feature importance was evaluated using both univariable and multivariable t-statistics. A correlation matrix was created to determine the similarity of the importance of variables ranked by different model metrics. The XGBoost model had the highest mean AUROC of 0.865 (SD = 0.010) with Accuracy of 0.762 (SD = 0.019), F1 of 0.875 (SD = 0.766), Sensitivity of 0.768 (SD = 0.023), Specificity of 0.782 (SD = 0.025), Positive Predictive Value of 0.806 (SD = 0.025), and Negative Predictive Value of 0.737 (SD = 0.034). The model metrics from the machine learning of gain and cover were strongly positively correlated with one another (r > 0.70). Model metrics from the multivariable model and univariable model were weakly negatively correlated with machine learning model metrics (R between -0.3 and 0). The ranking of important variables associated with sleep disorder in this cohort from the machine learning models were not related to those from regression models.

Evaluation of risk factors related to sleep disorders in patients undergoing hemodialysis using a nomogram model.

This study aimed to investigate the risk factors related to sleep disorders in patients undergoing hemodialysis using a nomogram model. A cross-sectional survey was conducted in a hospital in Zhejiang province, China from January 1, 2020, to November 31, 2022 among patients undergoing hemodialysis. Dietary intake was assessed applying a Food Frequency Questionnaire. Sleep quality was evaluated by the Pittsburgh Sleep Quality Index. Evaluation of risk factors related to sleep disorders in patients undergoing hemodialysis was using a nomogram model. This study included 201 patients and 87 individuals (43.3%, 87/201) exhibited sleep disorders. The average age of included patients was 51.1 ± 9.0 years, with males accounting for 55.7% (112/201). Results from nomogram model exhibited that potential risk factors for sleep disorders in patients undergoing hemodialysis included female, advanced age, increased creatinine and alanine aminotransferase levels, as well as higher red meat consumption. Inversely, protective factors against sleep disorders in these patients included higher consumption of poultry, fish, vegetables, and dietary fiber. The C-index demonstrated a high level of discriminative ability (0.922). This study found that age, sex, and dietary factors were associated with sleep disorders in hemodialysis patients. Hemodialysis patients with sleep disorders require urgent dietary guidance.

Associations between sleep quality and coping style among front-line nurses: a cross-sectional study in China

Abstract Aims Good sleep is crucial for healthcare professionals, especially nurses, to prevent errors and injuries that may occur during caregiving. This study investigated the sleep quality and coping styles of nurses during the post-pandemic period, as well as related factors. Subject and methods An online observational cross-sectional study involving 1022 nursing staff was conducted. Participants completed questionnaires on job characteristics, the Pittsburgh Sleep Quality Index (PSQI) and Simplified Coping Style Questionnaire (SCSQ). Pearson’s correlation and multiple logistic regression analyses confirmed interactions between these factors. Results A total of 1022 nurses completed the survey, of whom 90.90% (n = 929) were female, and 87.38% were over 25 years old. The PSQI score was 10.51(2.23), and 91.1% (n = 931) of the nurses had sleep disturbances. In this study, we found that PSQI was associated with SCSQ. Multivariate analysis showed that age (36–40 years), frequency of night shifts (9–11 times/month, ≥ 12 times/month), and less humanistic care were risk factors for sleep disorders among night shift nurses (P < 0.05), whereas frequency of exercise (1 time/week), non-attendance of training and learning after night shifts, and SCSQ positivity were protective factors for sleep disorders (P < 0.05). Conclusions Sleep quality of medical staff, especially nurses, was affected by shift work during the post-pandemic era. Designing the best strategy for employees who are more likely to have sleep disorders, such as experienced nurses, will support them through sleep hygiene education and strengthen the humanistic solicitude of nurses.

Fatigue, depression, and sleep disorders are more prevalent in patients with metabolic-associated fatty liver diseases.

To determine the prevalence and risk factors for depression, sleep disturbances, and exhaustion in MAFLD patients. Two hundred twenty-four consecutive patients with MAFLD attending the outpatient clinic from April to October 2023; were subjected to clinical evaluation, laboratory testing including non-invasive laboratory markers, fibroscan (measuring steatosis and fibrosis), and different quantitative and qualitative fatigue scores. A control group including 342 patients without MAFLD was taken. The prevalence of fatigue, depression, and sleeping disorders in the MAFLD group was 67.8%, 75%, 62.5% vs 21%, 16.4%, and 19.5% in the control group respectively ( P = <0.001, P = <0.001 and P = <0.001). MAFLD with fatigue was significantly associated with the presence and severity of steatosis and fibrosis by fibroscan ( P = <0.0001). By univariate and multivariate analysis: age, BMI, waist circumference, T2DM, hypertension, steatosis, fibrosis, and Fib-4 were considered risk factors for fatigue in the MAFLD group. The age, high social level, diabetes, hypertension, steatosis, fibrosis, and fib-4 were considered, by univariate and multivariate analysis, independent risk factors for depression in the MAFLD group. age, BMI, waist circumference, diabetes, hypertension, steatosis, fibrosis, and fib-4 were independent risk factors for sleep disorders in MAFLD. Fatigue, sleeping disorders, and depression are more prevalent in MAFLD patients than in the general population. The lower health utility scores in patients with MAFLD are associated with more advanced stages of the disease.

The Causal Association Between Blood Lead and Sleep Disorders: Evidence from National Health and Nutrition Examination Survey and Mendelian Randomization Analysis

BackgroundPoor sleep quality is a global public health concern. This study aimed to identify the risk factors for sleep disorders and clarify their causal effects.MethodsData were obtained from the National Health and Nutrition Examination Survey (NHANES) and Mendelian randomization (MR)-Base databases. Baseline characteristics of individuals with and without sleep disorders were compared. A multivariate logistic regression analysis was performed to calculate the effects of each variable on sleep disorders. Causal effects of blood lead levels and hypertension on sleep disorders were assessed using MR analysis.ResultsIn total, 3660 individuals were enrolled in the study. The prevalence of self-reported sleep disorders was 26.21%. Serum lead level, serum mercury level, serum retinol level, prevalence of hypertension, and daily vigorous work duration were significantly higher for those in the sleep disorders group than the control group. After adjusting for various covariates, the effects of serum lead and hypertension on sleep disorders were stable from logistic regression models 1–4. MR analysis showed that blood lead levels were causally related to the risk of sleep disorders (odds ratio (OR) = 1.09, 95% confidence interval (CI) 1.01–1.17, P = 0.030). There was no causal link between elevated blood pressure and sleep disorders (OR = 0.99, 95% CI 0.94–1.04, P = 0.757). Goodness-of-fit tests and sensitivity analyses were used to verify the reliability of the results.ConclusionsBlood lead is positively and causally associated with an increased risk of sleep disorders. These findings provide a novel perspective regarding sleep protection. Taking effective measures to reduce lead exposure may significantly improve sleep health.

Possible association between Toxoplasma gondii infection and sleep disorders: A systematic review.

Few studies investigated the relationship between toxoplasmosis and sleep disorders. Sleep disorders can lead to suicidal behavior and traffic accidents. Therefore, the purpose of this study is to collect information and investigate the possible relationship between Toxoplasma gondii (T. gondii) and sleep disorders. To achieve the goal of the present study, five English databases (PubMed, ScienceDirect, Web of Science, Scopus, and ProQuest) were systematically searched for related studies from their inception until October 18, 2022. The obtained articles were screened based on the title, abstract and full text. Then, the quality of the papers investigating the relationship between toxoplasmosis and sleep disorders was evaluated, and finally, the data from the relevant studies were extracted in a Microsoft Excel data sheet. Eight articles (4 case-control and 4 cross-sectional studies) were entered in this systematic review containing 926 patients with sleep disorders and 1877 people without sleep disorders in casecontrol studies, out of which 212 (22.89%) and 392 (20.88%) individuals were positive for anti-T. gondii IgG antibody using different serological methods. Also, 2885 people with sleep disorders were investigated for anti-T. gondii IgG antibody in cross-sectional studies, out of which 1559 (54.03%) cases were positive. The results of this study suggest that T. gondii infection may be a risk factor for sleep disorders. However, the number of related studies is small, and there are contradictions in the findings of these articles. Therefore, further studies are necessary to clarify the possible association between T. gondii infections and sleep disorders.

Analysis of sleep for the American population: Result from NHANES database

ObjectivesTo assess the contemporary prevalence and decade-long trends of sleep duration, sleep disorders and trouble sleeping among adults in the United States, as well as their risk factors, from 2005 to 2018. Materials and methodsWe used National Health and Nutrition Examination Survey data to calculate the sleep duration and weighted prevalence of sleep disorders and trouble sleeping in adults aged 20 years or older. Sleep duration, sleep disorders and trouble sleeping were assessed by questionnaire. ResultsA total of 27,399 people were included in the survey on sleep duration, with a weighted percentage of normal sleep (7–8 h/night) of 56.33 % (95 % CI, 53.06–59.60 %) and a weighted percentage of short sleep (5–6 h/night) of 31.73 %. In stratified descriptions, participants aged 40–49 years were more likely to sleep less than five hours, while women aged 80 years and older were more likely to sleep longer and blacks were more likely to sleep shorter. A total of 27,406 participants were included in the survey for sleep disorders. The weighted proportion of the population with sleep disorders was 8.44 % (95 % CI, 7.79–9.8 %). Independent risk factors for sleep disorders were being 40–69 years old, being white, having a high education level, smoking, having hypertension, diabetes, heart disease, and BMI ≥ 25. From 2005 to 2014, the prevalence of sleep disorders increased year by year, from 7.44 % in 2005–2006 to 10.40 % in 2013–2014 (P for Trend<0.001). A total of 38,165 participants were included in the survey on trouble sleeping. The weighted proportion of the population with troubled sleeping was 27.30 % (25.70–28.90 %). Independent risk factors for troubled sleeping were being 30–79 years old, being white, having a high education level, smoking, drinking, having hypertension, diabetes, heart disease and BMI ≥ 25. From 2005 to 2018, the prevalence of trouble sleeping increased annually, from 24.44 % in 2005–2006 to 30.58 % in 2017–2018 (P for trend<0.001). ConclusionAdults in the United States are likely to have abnormal sleep durations, and the prevalence of sleep disorders and troubled sleeping is on the rise.

Assessment of Lumbar Spinal Stenosis as a Risk Factor for Development of Sleep Disorder: The Locomotive Syndrome and Health Outcome in Aizu Cohort Study (LOHAS).

Insomnia has been reported to coexist with various musculoskeletal disorders. Although lumbar spinal stenosis (LSS) is the most frequently operated on spinal disease, the causal relationship between LSS and development of sleep disorders remains unclear due to lack of longitudinal studies. This study aimed to determine whether LSS was a risk factor for developing new sleep disorders, primarily insomnia, using a prospective cohort of community residents. This study was a prospective cohort study. Participants aged ≥65 years from the "Locomotive Syndrome and Health Outcomes in Aizu Cohort Study (LOHAS)" conducted in 2008 formed our study population. LSS was diagnosed using the self-administered, self-reported history questionnaire, a validated diagnostic support tool for LSS. Sleep disorder was investigated using a questionnaire during the 2-year follow-up. The impact of LSS on sleep disorder onset was analyzed after adjusting for potential confounders, such as age, sex, obesity, hypertension, diabetes, depression, and smoking habits, using propensity score matching. Of the 489 participants who were followed up for two years, 38 (7.8%) had newly developed a sleep disorder in 2010. After adjusting for confounding factors, a comparison of 133 participants each in the control and LSS groups showed significantly higher frequency of new-onset sleep disorders (19 [14.3%] in the LSS group versus 6 [4.5%] in the control group). LSS was found to be an independent risk factor for sleep disorders.

Prevalence of Sleep Disorders Among the General Population of the Jazan Region of Southwest Saudi Arabia.

Sleep disorders impose a substantial burden on the global population, leading to an array of health complications. Understanding their prevalence and associated risk factors is crucial to formulating effective interventions. This study aimed to determine the prevalence and associated risk factors of sleep disorders among residents of the Jazan region of Saudi Arabia. This cross-sectional study conducted an online survey from December 2022 to March 2023. The sample comprised 670 respondents aged 18 years and older residing in Jazan. Demographic data, lifestyle habits, sleep patterns, and sleep disorder symptoms were assessed. The participants were predominantly women (62.2%), with an average age of 30.99 years and a normal body mass index. The analyses revealed that 28.8% of the respondents reported experiencing sleep disorders, and 13.4% particularly had obstructive sleep apnea. Nearly half of the participants reported having primary insomnia, excessive daytime sleepiness, and restless leg syndrome. Significant associations were found between sleep disorders and older age (p = 0.012), obesity (p = 0.043), short or thin neck (p = 0.034), smoking (p = 0.003), caffeine use (p = 0.001), existing health conditions (p = 0.001), medication use (p = 0.013), lack of daytime naps (p = 0.043), and frequent nighttime awakenings to urinate (p = 0.001). The most common self-reported reasons for nightly awakenings were urination, anxiety or stress, discomfort or noise, and health conditions. The findings suggest a high prevalence of sleep disorders among adults in Jazan, Saudi Arabia. Various demographic, lifestyle, and health-related factors are linked to these disorders. Therefore, targeted sleep health education and interventions could be instrumental in tackling this significant public health issue.

1259-P: Resting Heart Rate and Sleep Disorders in Maturity-Onset Diabetes of the Young

Background: Sleep disturbances have been linked to higher resting heart rate (RHR), a marker of cardiovascular risk. Obstructive sleep apnea (OSA) and insomnia are common in other types of diabetes, but they have not been studied in Maturity Onset Diabetes of the Young (MODY), a unique subtype characterized by patients being lean and not using insulin. Objective: To evaluate sleep disorders and RHR in MODY. Methods: Participants were recruited from the Monogenic Diabetes Registry at the University of Chicago. The presence and severity of OSA was objectively assessed by a validated home screening device. Insomnia was assessed by a validated survey (Insomnia Severity Index). Resting (sleeping) heart rate was assessed by Fitbit activity tracker for 2 weeks. Results: Considering all patients (n=51; Table 1), OSA was found in 37% (31% mild, 2% moderate, 4% severe). Insomnia was found in 47% of patients (10% clinical insomnia and 37% subthreshold insomnia). Overall, the mean RHR was 66±9 bpm, with transcription factor (TF) related MODY (HNF1A, HNF4A, HNF1B) having significantly higher RHR compared to GCK-MODY (70±10 bpm vs. 63±8.7 bpm; p=0.01). Conclusion: OSA and insomnia are common in patients with MODY despite their lack of traditional risk factors for sleep disorders. TF-related MODY has a higher RHR compared to GCK-MODY. Future research should investigate the pathophysiology of sleep-diabetes relationship in MODY. Disclosure M.A.Arosemena: None. K.Wroblewski: None. E.Tasali: None. L.H.Philipson: Advisory Panel; Nevro Corp., Research Support; Novo Nordisk, Dompé, Provention Bio, Inc., Imcyse, Novo Nordisk Foundation.

Prevalence and influencing factors of sleep disorders among preschool children in Urumqi city: a cross-sectional survey

BackgroundSleep disorders refer to physiological and psychological states that cause adverse consequences due to the inability to fall asleep or poor sleep quality. The prevalence of sleep disorders varies greatly in different countries and regions due to different causes. This study aimed to investigate the prevalence and influencing factors of sleep disorders among preschool children in Urumqi city, China.MethodsA cross-sectional study was conducted with stratified random cluster sampling. Children aged 3–6 years old in one kindergarten randomly selected from each of the 8 districts of Urumqi from March to July 2022, and their parents were surveyed with a sleep quality questionnaire.ResultsThe prevalence of sleep disorders among preschool children in Urumqi was 14.29% (191/1336), and the prevalence of different symptoms was 42.81% for limb movements, 19.61% for snoring, 18.11% for bruxism, 16.39% for sleep talking, 12.57% for sweating, 11.60% for nocturnal awakening, 8.46% for nightmares, 6.89% for bed wetting, 3.74% for apnea, and 3.29% for sleepwalking. The prevalence of body movements, snoring, sweating, night-wake, nightmares, bed-wetting, apnea, and sleepwalking among different ethnicities were significantly different (P < 0.05). Multivariate analysis revealed that the major risk factors of sleep disorders were difficulty adapting to new environments, unwillingness to express emotions, inconsistent attitudes of the family toward children’s education, running before bedtime, strict family education methods, etc.ConclusionThe prevalence of sleep disorders in preschool children in Urumqi is lower than the average level reported in other studies. Many factors affect the prevalence of sleep disorders in preschool children, but it is necessary to focus on the ability to adapt to new environments, psychological problems, and the impact of family education on sleep disorders. Further studies on the prevention and treatment of sleep disorders are needed for different ethnicities.

Use of machine learning to identify risk factors for insomnia.

Sleep is critical to a person's physical and mental health, but there are few studies systematically assessing risk factors for sleep disorders. The objective of this study was to identify risk factors for a sleep disorder through machine-learning and assess this methodology. A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data. A physician diagnosis of insomnia was the outcome of this study. Univariate logistic models, with insomnia as the outcome, were used to identify covariates that were associated with insomnia. Covariates that had a p<0.0001 on univariate analysis were included within the final machine-learning model. The machine learning model XGBoost was used due to its prevalence within the literature as well as its increased predictive accuracy in healthcare prediction. Model covariates were ranked according to the cover statistic to identify risk factors for insomnia. Shapely Additive Explanations (SHAP) were utilized to visualize the relationship between these potential risk factors and insomnia. Of the 7,929 patients that met the inclusion criteria in this study, 4,055 (51% were female, 3,874 (49%) were male. The mean age was 49.2 (SD = 18.4), with 2,885 (36%) White patients, 2,144 (27%) Black patients, 1,639 (21%) Hispanic patients, and 1,261 (16%) patients of another race. The machine learning model had 64 out of a total of 684 features that were found to be significant on univariate analysis (P<0.0001 used). These were fitted into the XGBoost model and an AUROC = 0.87, Sensitivity = 0.77, Specificity = 0.77 were observed. The top four highest ranked features by cover, a measure of the percentage contribution of the covariate to the overall model prediction, were the Patient Health Questionnaire depression survey (PHQ-9) (Cover = 31.1%), age (Cover = 7.54%), physician recommendation of exercise (Cover = 3.86%), weight (Cover = 2.99%), and waist circumference (Cover = 2.70%). Machine learning models can effectively predict risk for a sleep disorder using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors.

Occupational Exposure to Pesticides as a Risk Factor for Sleep Disorders.

Inadequate sleep has been linked to a variety of impairments in bodily functions, including endocrine, metabolic, higher cortical function, and neurological disorders. For this reason, the aim of this study was to analyze the link between occupational pesticide exposure and sleep health among farmers in Almeria. A cross-sectional study was conducted among a population living on the coast of Almeria (southeastern Spain), where about 33,321 hectares of land are used for intensive agriculture in plastic greenhouses. A total of 380 individuals participated in the study: 189 greenhouse workers and 191 control subjects. The participants were contacted during their annual scheduled occupational health survey. Data on sleep disturbances were collected using the Spanish version of the Oviedo Sleep Questionnaire. Agricultural workers were found to be at a significantly higher risk of insomnia, especially among those who did not wear protective gloves (OR = 3.12; 95% C.I. = 1.93-3.85; p = 0.04) or masks (OR = 2.43; 95% C.I. = 1.19-4.96; p = 0.01). The highest risk of insomnia related to pesticide applicators was observed in those who did not wear a mask (OR = 4.19; 95% C.I. = 1.30-13.50; p = 0.01) or goggles (OR = 4.61; 95% C.I. = 1.38-10.40; p = 0.01). This study supports previous findings indicating an increased risk of sleep disorder in agricultural workers exposed to pesticides at work.

Sleep disorders in the acute phase of coronavirus disease 2019: an overview and risk factor study

BackgroundSleep disorders are common during the outbreak of pandemic diseases, and similar disorders are noted in hospitalized COVID-19 patients. It is valuable to explore the clinical manifestations and risk factors for sleep disorders in COVID-19 patients.MethodsInpatients with COVID-19 were enrolled. Detailed clinical information was collected, and sleep quality was assessed by PSQI. Patients were divided into a sleep disorder group and a normal group based on a PSQI ≥ 7, and the clinical features were compared between the groups.ResultsFifty-three patients were enrolled, and 47.2% presented sleep disorders. Sleep disorders were associated with older age (> 50), anemia and carbon dioxide retention. Furthermore, factors associated with abnormal component scores of the PSQI were: (1) patients with older age were more likely to have decreased sleep quality, prolonged sleep latency, decreased sleep efficiency, sleep disturbances, and daytime dysfunction; (2) decreased sleep quality and prolonged sleep latency were associated with dyspnea, whereas carbon dioxide retention and more lobes involved in chest CT were associated with prolonged sleep latency; (3) decreased sleep efficiency was more prevalent in patients with anemia.ConclusionsSleep disorders were prevalent in patients during the acute phase of COVID-19, and many risk factors (older age, anemia, carbon dioxide retention, the number of lobes involved in chest CT, and dyspnea) were identified. It is important to assess the presence of sleep disorders in patients to provide early intervention.

Low Levels of Adenosine and GDNF Are Potential Risk Factors for Parkinson's Disease with Sleep Disorders.

Sleep disturbances are the most prevalent non-motor symptoms in the preclinical stage of Parkinson's disease (PD). Adenosine, glial-derived neurotrophic factor (GDNF), and associated neurotransmitters are crucial in the control of sleep arousal. This study aimed to detect the serum levels of adenosine, GDNF, and associated neurotransmitters and explored their correlations with PD with sleep disorders. Demographic characteristics and clinical information of PD patients and healthy participants were assessed. Serum concentrations of adenosine, GDNF, and related neurotransmitters were detected by ELISA and LC-MS. The correlation between serum levels of adenosine, GDNF, and associated neurotransmitters and sleep disorders was explored using logistic regression. PD patients with sleep disorders had higher scores of HAMA, HAMD, ESS, UPDRS-III, and H-Y stage. Lower levels of adenosine, GDNF, and γ-GABA were observed in PD patients who had sleep problems. Logistic regression analysis showed adenosine and GDNF were protective factors for preventing sleep disorders. Adenosine combined with GDNF had a higher diagnostic efficiency in predicting PD with sleep disorders by ROC analysis. This study revealed low adenosine and GDNF levels may be risk factors for sleep disorders in PD. The decrease of serum adenosine and GDNF levels may contribute to the diagnosis of PD with sleep disturbances.

Objective and Subjective Sleep Patterns in Adults With Maturity-Onset Diabetes of the Young (MODY).

To examine sleep patterns in adults with maturity-onset diabetes of the young (MODY). Adults with glucokinase (GCK)-MODY and transcription factor (TF)-related MODY (HNF1A, HNF1B, HNF4A) were recruited (n = 24; age 46.0 years, 79% women, BMI 24.7 kg/m2) from The University of Chicago's Monogenic Diabetes Registry. Sleep patterns were assessed by 2-week wrist actigraphy (total 315 nights), one night of a home sleep apnea test, and validated surveys. Overall, compared with established criteria, 29% of participants had sleep latency ≥15 min, 38% had sleep efficiency ≤85%, 46% had wake after sleep onset >40 min, all indicating poor objective sleep quality. Among all participants, 54% had a sleep duration below the recommended minimum of 7 h, 88% reported poor sleep quality, 58% had obstructive sleep apnea, and 71% reported insomnia. Compared with GCK-MODY, participants with TF-related MODY had poorer objective sleep quality and increased night-to-night variability in sleep patterns. Sleep disturbances appear to be common in adults with MODY despite absent traditional risk factors for sleep disorders. Future research investigating the sleep-diabetes relationship is warranted in this population.