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Habitual sleep duration has been associated with cardiometabolic disease, via several mechanistic pathways, but few have been thoroughly explored. One hypothesis is that short and/or long sleep duration is associated with a proinflammatory state, which could increase risk for cardiovascular and metabolic diseases. This hypothesis has been largely explored in the context of experimental sleep deprivation studies which have attempted to demonstrate changes in proinflammatory markers following acute sleep loss in the laboratory. Despite the controlled environment available in these studies, samples tend to lack generalization to the population at large and acute sleep deprivation may not be a perfect analog for short sleep. To address these limitations, population based studies have explored associations between proinflammatory markers and habitual sleep duration. This review summarizes what is known from experimental and cross-sectional studies about the association between sleep duration, cardiovascular disease, and proinflammatory biomarkers. First, the association between sleep duration with both morbidity and mortality, with a focus on cardiovascular disease, is reviewed. Then, a brief review of the potential role of proinflammatory markers in cardiovascular disease is presented. The majority of this review details specific findings related to specific molecules, including tumor necrosis factor-α, interleukins-1, -6, and -17, C-reactive protein, coagulation molecules, cellular adhesion molecules, and visfatin. Finally, a discussion of the limitations of current studies and future directions is provided.

Mild cognitive impairment (MCI) and Alzheimer's disease (AD) affect a high proportion of the elderly population with an increasing prevalence. Sleep disturbances are frequent in those with MCI and AD. This review summarizes existing research on sleep disturbances and neuroinflammation in MCI and AD. Although strong evidence supports various pathways linking sleep and AD pathology, the temporal direction of this central relationship is not yet known. Improved understanding of sleep disturbance and neuroinflammation in MCI and AD may aid in the identification of targets for their prevention.

Introduction African Americans (AA)s have worse inflammation, worse sleep, and a greater incidence of Alzheimer's disease (AD) compared to whites; however, no studies have examined associations between biomarkers, sleep, and cognition, and differences by race. Methods Seventy‐six cognitively normal, middle aged (45–65 years) adults with a parental history of AD were included in this study. Associations between biomarkers (tumor necrosis factor‐α [TNF‐α], interleukin‐10 [IL‐10], intercellular adhesion molecule‐1 [ICAM‐1],, and C‐reactive protein [CRP]) and self‐reported sleep or cognition measures, were assessed. Results Average sleep duration was significantly lower for AA versus whites (average[SD]) in hours: 6.02(1.18) versus 7.23(0.91), P = .000004). We found a statistically significant association between plasma IL‐10 and sleep duration (Spearman's ρ = 0.26, P = .04) and CSF ICAM‐1 and sleep quality (Spearman's ρ = 0.30, P = .03). Discussion Longer sleep duration is positively associated with plasma IL‐10 levels irrespective of race. Sleep quality was positively associated with CSF ICAM‐1 only in African Americans.

PurposeUS adults who report experiencing insufficient sleep are more likely to suffer from metabolic disorders such as hyperlipidemia, diabetes, and obesity than those with sufficient sleep. Less is understood about the underlying molecular mechanisms connecting these phenomena. A systematic, qualitative review of metabolomics studies exploring metabolic changes in response to sleep insufficiency, sleep deprivation, or circadian disruption was conducted in accordance with PRISMA guidelines.MethodsAn electronic literature review in the PubMed database was performed considering publications through May 2021 and screening and eligibility criteria were applied to articles retrieved. The following keywords were used: “metabolomics” and “sleep disorders” or “sleep deprivation” or “sleep disturbance” or “circadian rhythm.” After screening and addition of studies included from reference lists of retrieved studies, 16 records were identified for review.ResultsConsistent changes in metabolites were observed across studies between individuals experiencing sleep deprivation compared to non-sleep deprived controls. Significant increases in phosphatidylcholines, acylcarnitines, sphingolipids, and other lipids were consistent across studies. Increased levels of amino acids such as tryptophan and phenylalanine were also noted. However, studies were limited to small samples of young, healthy, mostly male participants conducted in short inpatient sessions, limiting generalizability.ConclusionChanges in lipid and amino acid metabolites accompanying sleep deprivation and/or circadian rhythms may indicate cellular membrane and protein breakdown underlying the connection between sleep disturbance, hyperlipidemia, and other metabolic disorders. Larger epidemiological studies examining changes in the human metabolome in response to chronic insufficient sleep would help elucidate this relationship.

PurposeAlthough the mechanism is unclear, daytime sleepiness, a common sequela of obstructive sleep apnea (OSA), has been found to be correlated with a adverse cardiovascular outcomes. Reviewing metabolomics mechanisms of sleep disturbances and cardiovascular disease may help to explain this correlation.MethodsThis review examines the current literature on the relationships between sleepiness, sleep duration, and metabolites in sleep apnea.ResultsAlthough there is a lack of comprehensive literature in this emerging area, existing studies point to a variety of metabolites in different pathways that are associated with sleepiness and sleep duration.ConclusionAdvancing metabolomics research in sleep apnea will guide symptom research and provide alternate and novel opportunities for effective treatment for patients with OSA.

There is no established reference standard for subjective measures of sleepiness in older adults. This study compares the Observation and Interview-based Diurnal Sleepiness Inventory (ODSI) with two existing instruments for measurement of sleepiness and daily functioning, the Epworth Sleepiness Scale (ESS) and Functional Outcomes of Sleep Questionnaire (FOSQ). A total of 125 study participants were included in this study and were administered the ODSI, ESS and FOSQ; subjects had a mean age of 70.9 ± 5.27 years, mean Apnea-Hypopnea Index of 31.9 ± 27.9 events/hour and normal cognitive functioning (Mini-Mental State Examination score > 24). The ODSI showed a significant association with the ESS (Spearman's ρ: 0.67, < 0.001) and with the FOSQ (Spearman's ρ: -0.52, < 0.001). The ODSI 1 item (assessing sleepiness in active situations) was borderline significantly correlated with the ESS (β = 0.14; 95% confidence interval [CI], -0.01 to 0.29; = 0.069). ODSI 2 item (sleepiness in passive situations) was correlated with the ESS (β = 1.65; 95% CI, 1.32 to 1.98; < 0.001). Both ODSI 1 (β = -0.15; 95% CI, -0.24 to -0.07; < 0.001) and ODSI 2 (β = -0.35; 95% CI, -0.55 to 0.16; < 0.001) were significantly correlated with the FOSQ. The ODSI is a suitable measure of sleepiness and is appropriate for usage in clinical care in older adults.

During the COVID-19 pandemic, genomics and bioinformatics have emerged as essential public health tools. The genomic data acquired using these methods have supported the global health response, facilitated the development of testing methods and allowed the timely tracking of novel SARS-CoV-2 variants. Yet the virtually unlimited potential for rapid generation and analysis of genomic data is also coupled with unique technical, scientific and organizational challenges. Here, we discuss the application of genomic and computational methods for efficient data-driven COVID-19 response, the advantages of the democratization of viral sequencing around the world and the challenges associated with viral genome data collection and processing.

PurposeSleep disturbances are prevalent among patients with human immunodeficiency virus (HIV), even those who are being treated on antiretroviral therapy. It is important to understand the metabolomic mechanisms underlying sleep disturbances among people living with HIV (PLWH).MethodsA review of recent literature was performed to explore the use of metabolomics in understanding sleep among PLWH.ResultsWe found only two studies that used metabolomics to explore sleep health among PLWH.ConclusionThis paper reviews common sleep disorders in HIV, the existing metabolomic studies that may explain the relationship, and implications for future research. The use of metabolomics in exploring sleep disorders among PLWH will help to elucidate mechanistic links to improve patient outcomes.

More research is needed to understand possible occupational reproductive risks for cosmetologists, specifically hairdressers and nail technicians, two occupations that often share workspace and exposure to hair dyes and nail polish. Cosmetologists are predominantly females of reproductive age; thus, they may be at higher risk for the effects of exposure to reproductive toxins. The purpose of this article is to inform nurses and public health professionals about occupational exposures for cosmetologists and discuss interventions to reduce the risks of reproductive disorders among susceptible worker populations. [Workplace Health Saf 2013;61(12):522--528.]

Study Objectives: We aimed to develop a new three-item assessment tool for daytime sleepiness in older adults, the Observation and interview-based Diurnal Sleepiness Inventory (ODSI) and determine its validity, internal consistency, test-retest reliability, and optimal cutoff score. Methods: A total of 133 elderly subjects including 73 patients with obstructive sleep apnea (OSA) (mean age, 79 y) and 60 controls (mean age, 80 y) were consecutively enrolled and answered all questionnaires. The ODSI questionnaire was validated using the Epworth Sleepiness Scale considered as a gold standard. Reliability, validity, and cut-points were tested. Results: The ODSI has acceptable validity, internal consistency, and test-retest reliability properties. The ODSI has internal consistency and a reliability coefficient (Pearson rho) of 0.70 for its three items, which suggests strong reliability. The estimated sensitivity and specificity were 0.842 with 95% confidence interval [0.624; 0.945] and 0.851 [0.761; 0.911], respectively. The consistency of summated scale scores during test and retest sessions was high (r = 0.970, 95% bootstrap confidence interval [0.898; 0.991]). Receiver operating characteristic analysis suggests that a cut-point of 6 is effective for identifying older adults with excessive levels of daytime sleepiness. Conclusions: The ODSI is a brief, valid, easy-to-administer three-item assessment that can screen for excessive daytime sleepiness among elderly patients with OSA. Citation: Onen F, Lalanne C, Pak VM, Gooneratne N, Falissard B, Onen SH. A three-item instrument for measuring daytime sleepiness: the Observation and Interview Based Diurnal Sleepiness Inventory (ODSI). J Clin Sleep Med 2016;12(4):505–512.