Developing Biomarker Arrays Predicting Sleep and Circadian-Coupled Risks to Health

Competitive enzyme linked aptamer based assay for salivary melatonin detection

Melatonin is a key hormone that regulates the sleep-wake cycle and plays an important role in maintaining circadian rhythm and sleep onset. The daily rise in melatonin secretion is associated with an increased tendency to sleep, occurring approximately 2 h before bedtime. This correlation between melatonin levels and sleep onset makes it a reliable biomarker for circadian rhythm sleep-wake disorders. An accurate assessment of dim light melatonin onset (DLMO) is vital for understanding circadian timing and diagnosing sleep-wake cycle disruptions. However, the traditional methods for detecting melatonin in saliva are either complex or lack the sensitivity required for the accurate assessment of DLMO, especially at low concentrations. Here, we present a novel competitive enzyme-linked aptamer-based assay developed to detect melatonin in saliva. Unlike conventional assays, this technique utilizes chemically synthesized single-stranded DNA or RNA aptamers, which bind to melatonin with high specificity and sensitivity. The assay measures melatonin, attaining a linear dynamic range from 8.62 × 10‒6 M to 3.9 × 10‒11 M, with a detection limit of 2.5 × 10‒12 M (~ 0.57 pg/mL). Additionally, the aptamer showed small binding to its counter targets and acceptable recovery of melatonin when spiked in four times diluted saliva in assay buffer. Overall, the assay portrayed the potential of aptamers to detect low melatonin levels in saliva that could be beneficial in accurately determining DLMO, particularly in individuals with very low melatonin levels, such as the elderly or those with neurodegenerative conditions. Determining precise measurement of DLMO will facilitate the accurate diagnosis of circadian rhythm disruption, enabling healthcare providers to optimize the timing and selection of therapeutic and behavioural interventions tailored to an individual's unique circadian rhythm.

Potential non-invasive biomarkers of chronic sleep disorders identified by salivary metabolomic profiling among middle-aged Japanese men

Sleep disorders have become a global social problem that increases the risk of developing mental illnesses and metabolic diseases. We aimed to identify biomarkers with which to non-invasively and objectively evaluate chronic sleep disorders. We used capillary electrophoresis-Fourier transform mass spectrometry (CE-FTMS) to analyze metabolomes in saliva collected from 50 persons each with good (≤ 2) and poor (≥ 6) sleep quality scored according to the Japanese version of the Pittsburgh Sleep Quality Index (PSQI-J) self-report questionnaire. The levels of five metabolites including glycerol and hippuric acid and eight including 2-hydroxybutyric acid (2HB), were respectively decreased and increased in participants with poor sleep quality. We established a random forest model consisting of six metabolites, including glycerol and hippuric acid, with a prediction accuracy of 0.866. Correlations between metabolites and sleep satisfaction were assessed using the Oguri-Shirakawa-Azumi sleep inventory, middle-age and aged version (OSA-MA) questionnaire. The results showed that 2'-deoxyguanosine, N1-acetylspermine, and 2,4-dihydroxybenzoic acid correlated positively, whereas glucosamine 6-phosphate and trimethylamine N-oxide correlated negatively with sleep quality. These findings suggested that changes in salivary metabolites reflect pathophysiological mechanisms of chronic sleep disorders, and that saliva samples could serve as non-invasive and objective diagnostic targets for predicting habitual sleep quality.

Developing a Personalized Approach to Obstructive Sleep Apnea

All areas of medicine are focused on developing a personalized approach to diagnosis and treatment of specific conditions. This is based on the fundamental concept that all subjects with apparently the same disorder are different. There are multiple reasons for these differences. These include differences in the sequence of DNA, differences in the environment, differences in epigenetics, some of which may be driven by environmental differences and differences in the microbiome. These different factors will result in variations in multiple aspects of the phenotype. This includes different pathways to disease, different symptoms, different pattern of comorbidities and risk for adverse outcomes, and different physiological changes during sleep as a result of breathing episodes.

Personalization of Automatic Sleep Scoring: How Best to Adapt Models to Personal Domains in Wearable EEG

Wearable EEG enables us to capture large amounts of high-quality sleep data for diagnostic purposes. To make full use of this capacity we need high-performance automatic sleep scoring models. To this end, it has been noted that domain mismatch between recording equipment can be considerable, e.g. PSG to wearable EEG, but a previously observed benefit from personalizing models to individual subjects further indicates a personal domain in sleep EEG. In this work, we have investigated the extent of such a personal domain in wearable EEG, and review supervised and unsupervised approaches to personalization as found in the literature. We investigated the personalization effect of the unsupervised Adversarial Domain Adaptation and implemented an unsupervised method based on statistics alignment. No beneficial personalization effect was observed using these unsupervised methods. We find that supervised personalization leads to a substantial performance improvement on the target subject ranging from 15% Cohen's Kappa for subjects with poor performance ( ) to roughly 2% on subjects with high performance ( ). This improvement was present for models trained on both small and large data sets, indicating that even high-performance models benefit from supervised personalization. We found that this personalization can be beneficially regularized using Kullback-Leibler regularization, leading to lower variance with negligible cost to improvement. Based on the experiments, we recommend model personalization using Kullback-Leibler regularization.

Relationship between Circadian System Status, Child-Pugh Score, and Clinical Outcome in Cirrhotic Patients on Waiting Lists for Liver Transplantation

Background/Objectives: Many patients suffering from liver cirrhosis are eventually added to waiting lists for liver transplantation whose priority is established based on scales such as the Child-Pugh score. However, two marker rhythms of the circadian system, motor activity and distal temperature, are not evaluated. Methods: To determine the relationship between the functional status of the circadian system and the Child-Pugh scale in patients awaiting liver transplantation, distal temperature, motor activity, and light exposure rhythms were monitored for a full week using a wrist device (Kronowise 6.0) in 63 patients (17 women, 46 men) aged between 20 and 76 years. Results: Circadian parameters (amplitude, regularity, and fragmentation) of motor activity rhythms, distal temperature, and light exposure worsen in close association with liver disease severity as assessed by using the Child-Pugh score. Likewise, the worsening of rhythmic parameters and liver disease is associated with a deterioration in the markers of the red series: count, hemoglobin, and hematocrit. Conclusions: These results indicate the utility of ambulatory monitoring of marker rhythms to complement the clinical information provided by the Child-Pugh scale and to help establish nutrition, physical exercise, and sleep guidelines that promote better survival and quality of life in these patients.

Exploring the Role of Circadian Rhythms in Sleep and Recovery: A Review Article

Sleep is essential for every living organism. Humans spend about one-third of their lives sleeping. Sleep has been studied extensively, and the role of sleep in psychological, mental, and physical well-being is established to be the best. The rhythm of the brain between wakefulness and sleep is called the circadian rhythm, which is mainly controlled by melatonin and the pineal gland. The imbalance of this rhythm can lead to devastating effects on health. Vigorous workouts close to bedtime can interfere with falling asleep. Meal timing and composition can significantly affect sleep quality. It is advised to avoid large meals, caffeine, and alcohol before bedtime. Heavy meals close to bedtime can lead to poor sleep and hormone disruption. By following these guidelines enumerated in the article, individuals can improve sleep quality and overall health. Sleep cycles, especially rapid eye movement sleep, have a profound influence on mental and physical health. Adhering to recommended sleep practices enhances bodily restoration, fortifies the immune system, and upholds metabolic equilibrium. Sleep hygiene aligned with circadian rhythms is crucial for disease prevention and well-being. Healthcare professionals should prioritize sleep optimization strategies for patient care and public health.

Elevated oxidative stress biomarkers in adults with segmented sleep patterns

STUDY OBJECTIVES

We investigated the association between different sleep patterns and inflammatory and oxidative stress biomarkers in adults.

METHODS

A total of 321 consented adults who fulfilled the inclusion criteria were recruited in this cross-sectional study. The inclusion criteria were mainly based on apparently healthy adults aged 18-59 years. To identify sleep patterns, participants were requested to wear the actigraph for 1 week for 24 hours a day. Fasting blood was collected from each participant at day 8. The blood serum was analyzed for inflammatory and oxidative stress biomarkers. Sleep patterns were defined as monophasic (1 episode of night sleep) biphasic (2 episodes of sleep; night and aternoon siesta), and polyphasic sleep pattern (3 or more sleep episodes).

RESULTS

There was no correlation between night sleep duration, total sleep in 24 hours, and napping among inflammatory and oxidative stress biomarkers: high-sensitivity C-reactive protein, malondialdehyde, total glutathione, and basal oxidizability status. Actigraphy reports showed 3 sleep patterns in this cohort, monophasic (24.3%), biphasic-napping (45.2%) and polyphasic (30.5%). Individuals with segmented sleep patterns were significantly associated with oxidative stress biomarkers. A polyphasic sleep pattern was significantly associated with higher basal oxidizability status (P = .023), whereas a biphasic sleep pattern showed higher malondialdehyde (P = .036) as compared to a monophasic sleep pattern. Total glutathione was significantly higher in monophasic sleepers (P = .046). There was no difference in serum high-sensitivity C-reactive protein among all sleep patterns.

CONCLUSIONS

Segmented sleep in polyphasic and biphasic sleep patterns is associated with higher serum malondialdehyde and basal oxidizability status in particular. Further studies are recommended on the cardiometabolic impact of oxidative stress biomarkers in individuals with segmented sleep.

CITATION

Al Lawati I, Zadjali F, Al-Abri MA. Elevated oxidative stress biomarkers in adults with segmented sleep patterns. J Clin Sleep Med. 2024;20(6):959-966.

Accurate detection of acute sleep deprivation using a metabolomic biomarker-A machine learning approach

Sleep deprivation enhances risk for serious injury and fatality on the roads and in workplaces. To facilitate future management of these risks through advanced detection, we developed and validated a metabolomic biomarker of sleep deprivation in healthy, young participants, across three experiments. Bi-hourly plasma samples from 2 × 40-hour extended wake protocols (for train/test models) and 1 × 40-hour protocol with an 8-hour overnight sleep interval were analyzed by untargeted liquid chromatography-mass spectrometry. Using a knowledge-based machine learning approach, five consistently important variables were used to build predictive models. Sleep deprivation (24 to 38 hours awake) was predicted accurately in classification models [versus well-rested (0 to 16 hours)] (accuracy = 94.7%/AUC 99.2%, 79.3%/AUC 89.1%) and to a lesser extent in regression (R2 = 86.1 and 47.8%) models for within- and between-participant models, respectively. Metabolites were identified for replicability/future deployment. This approach for detecting acute sleep deprivation offers potential to reduce accidents through "fitness for duty" or "post-accident analysis" assessments.

Circadian rhythmicity in schizophrenia male patients with and without substance use disorder comorbidity

Circadian rhythmicity is associated to clinical variables that play an important role in both schizophrenia (SZ) and substance use disorders (SUD), although the characteristics of the coexistence of these two diagnoses (SZ +) remain mostly unknown. Hence, we studied a sample of 165 male patients divided in three groups each of 55, according to their diagnoses (SZ + , SZ, and SUD), as well as a healthy control (HC; n = 90) group. Alongside with sociodemographic and clinical variables, circadian rhythms were registered through a sleep-wake data structured interview, a circadian typology questionnaire, and distal skin temperature (DST) using the Thermochron iButton every 2 min during 48 h. Analyses showed that SZ + and SZ patients presented a longer sleep (delay in wake-up time) and mostly an intermediate circadian typology, while SUD patients slept less hours, displaying a morning typology. The DST showed the highest daily activation and stability for the SUD group, even when compared with the HC group. The presence of schizophrenia (SZ + and SZ) was related to a DST pattern with a reduced amplitude determined by a wakefulness impairment, which was more pronounced for SZ patients whose sleep period was adequate. The assessment of circadian rhythms in under treatment male patients with SZ should be focused on the diurnal period as a possible marker of either treatment adherence or patient's recovery, irrespective of the presence of a comorbid SUD. Further research with additional objective measures may provide knowledge transferable to therapeutic strategies and could be useful to establish possible endophenotypes in the future.

Distribution of dim light melatonin offset (DLMOff) and phase relationship to waketime in healthy adults and associations with chronotype

OBJECTIVES

Dim light melatonin onset, or the rise in melatonin levels representing the beginning of the biological night, is the gold standard indicator of circadian phase. Considerably less is known about dim light melatonin offset, or the decrease in melatonin to low daytime levels representing the end of the biological night. In the context of insufficient sleep, morning circadian misalignment, or energy intake after waketime but before dim light melatonin offset, is linked to impaired insulin sensitivity, suggesting the need to characterize dim light melatonin offset and identify risk for morning circadian misalignment.

METHODS

We examined the distributions of dim light melatonin offset clock hour and the phase relationship between dim light melatonin offset and waketime, and associations between dim light melatonin offset, phase relationship, and chronotype in healthy adults (N = 62) who completed baseline protocols measuring components of the circadian melatonin rhythm and chronotype.

RESULTS

74.4% demonstrated dim light melatonin offset after waketime, indicating most healthy adults wake up before the end of biological night. Later chronotype (morningness-eveningness, mid-sleep on free days corrected, and average mid-sleep) was associated with later dim light melatonin offset clock hour. Later chronotype was also associated with a larger, positive phase relationship between dim light melatonin offset and waketime, except for morningness-eveningness.

CONCLUSIONS

These findings suggest morning circadian misalignment risk among healthy adults, which would not be detected if only dim light melatonin onset were assessed. Chronotype measured by sleep timing may better predict this risk in healthy adults keeping a consistent sleep schedule than morningness-eveningness preferences. Additional research is needed to develop circadian biomarkers to predict dim light melatonin offset and evaluate appropriate dim light melatonin offset timing to promote health.

Biomarkers linking habitual short sleep duration with risk of cardiometabolic disease: current progress and future directions

Approximately one in three adults in the United States sleeps less than the recommended 7 h per night. Decades of epidemiological data and data from experimental sleep restriction studies demonstrate short sleep duration is associated with adverse cardiometabolic risk, including risk of type 2 diabetes and cardiovascular disease. However, the precise mechanisms underlying this risk are not fully elucidated and there is a lack of sleep-based interventions designed to mitigate such risk. One strategy to overcome these limitations is to develop biomarkers that link habitual short sleep duration with adverse cardiometabolic risk. Such biomarkers could inform biochemical mechanisms, identify new targets for interventions, support precision medicine by identifying individuals most likely to benefit from sleep-based interventions, and ultimately lead to improved cardiometabolic health in people with habitual short sleep durations. Early progress demonstrates proof-of-principle that omics-based technologies are a viable approach to create biochemical signatures (biomarkers) of short sleep duration, primarily derived from acute studies of experimental sleep restriction. Yet, much work remains. Notably, studies that translate early findings from experimental sleep restriction to free-living adults with habitual short sleep duration have high potential to advance the field. Such studies also create an exciting opportunity for larger randomized controlled trials that simultaneously identify biomarkers of habitual short sleep duration and evaluate the efficacy of sleep-based interventions. Ultimately, early progress in developing molecular biomarkers of short sleep duration combined with the prior decades of progress in the sleep and metabolism fields provide the foundation for exciting progress in the biomarker development space.

Colorimetric sensing assay based on aptamer-gold nanoparticles for rapid detection of salivary melatonin to monitor circadian rhythm sleep disorders

Salivary melatonin is a clinically used biomarker for diagnosing circadian rhythm sleep disorders. Current melatonin detection assays are complex, expensive, and in many cases do not adequately measure low levels of salivary melatonin. Precisely measuring melatonin levels at multiple time points is crucial for determining dim light melatonin onset to evaluate its circadian fluctuation as well as the extent of circadian disruption and consequently adapt treatment regimens. Moreover, melatonin low levels in saliva challenges the reliability of routine clinical testing. This paper presents the development of a novel, highly sensitive, yet cost-effective, colorimetric assay for the rapid detection of salivary melatonin utilizing aptamer-AuNPs. Among several types of the aptamer tested, the 36-mer MLT-A-2 aptamer-AuNP probe showed the highest sensitivity with a melatonin limit of detection of 0.0011 nM along with a limit of quantification of 0.0021 nM in saliva. Moreover, our assay showed preferential interaction with melatonin when tested in presence of other structurally similar counter-targets. Taken together, this study provides new parameters for a melatonin assay that meets adequate levels of sensitivity and selectivity. The developed colorimetric assay could be adapted in a point-of-care system for profiling salivary melatonin levels at multiple time points during 24 h, crucial for accurately diagnosing and monitoring circadian rhythm sleep disorders and beyond.

Exploring physical and biological manifestations of burnout and post-traumatic stress disorder symptoms in healthcare workers: a scoping review protocol

INTRODUCTION

The COVID-19 pandemic has strained the mental and physical well-being of healthcare workers (HCW). Increased work-related stress and limited resources have increased symptoms of anxiety, depression, insomnia and post-traumatic stress disorder (PTSD) in this population. Stress-related disorders have been strongly associated with long-term consequences, including cardiometabolic disorders, endocrine disorders and premature mortality. This scoping review aims to explore available literature on burnout, PTSD, and other mental health-associated symptoms in HCW to synthesise relationships with physiological and biological biomarkers that may be associated with increased risk of disease, creating an opportunity to summarise current biomarker knowledge and identify gaps in this literature.

METHODS AND ANALYSIS

This scoping review uses the Arksey and O'Malley six-step scoping review methodology framework. The research team will select appropriate primary sources using a search strategy developed in collaboration with a health sciences librarian. Three reviewers will initially screen the title and abstracts obtained from the literature searches, and two reviewers will conduct independent reviews of full-text studies for inclusion. The research team will be reviewing literature focusing on which burnout and/or PTSD-associated physiological and biological biomarkers have been studied, the methodologies used to study them and the correlations between the biomarkers and HCW experiencing burnout/PTSD. Data extraction forms will be completed by two reviewers for included studies and will guide literature synthesis and analysis to determine common themes.

ETHICS AND DISSEMINATION

This review does not require ethical approval. We expect results from this scoping review to identify gaps in the literature and encourage future research regarding improving biological and physiological biomarker research in HCW. Preliminary results and general themes will be communicated back to stakeholders. Results will be disseminated through peer-reviewed publications, policy briefs and conferences as well as presented to stakeholders to an effort to invest in HCW mental and physical health.

Structural properties and binding mechanism of DNA aptamers sensing saliva melatonin for diagnosis and monitoring of circadian clock and sleep disorders

Circadian desynchrony with the external light-dark cycle influences the rhythmic secretion of melatonin which is among the first signs of circadian rhythm sleep disorders. An accurate dim light melatonin onset (established indicator of circadian rhythm sleep disorders) measurement requires lengthy assays, and antibody affinities alterations, especially in patients with circadian rhythm disorders whose melatonin salivary levels vary significantly, making antibodies detection mostly inadequate. In contrast, aptamers with their numerous advantages (e.g., target selectivity, structural flexibility in tuning binding affinities, small size, etc.) can become preferable biorecognition molecules for salivary melatonin detection with high sensitivity and specificity. This study thoroughly characterizes the structural property and binding mechanism of a single-stranded DNA aptamer full sequence (MLT-C-1) and its truncated versions (MLT-A-2, MLT-A-4) to decipher its optimal characteristics for saliva melatonin detection. We use circular dichroism spectroscopy to determine aptamers' conformational changes under different ionic strengths and showed that aptamers display a hairpin loop structure where few base pairs in the stem play a significant role in melatonin binding and formation of aptamer stabilized structure. Through microscale thermophoresis, aptamers demonstrated a high binding affinity in saliva samples (MLT-C-1F K_d = 12.5 ± 1.7 nM; MLT-A-4F K_d = 11.2 ± 1.6 nM; MLT-A-2F K_d = 2.4 ± 2.8 nM; limit-of-detection achieved in pM, highest sensitivity attained for MLT-A-2F aptamer with the lowest detection limit of 1.35 pM). Our data suggest that aptamers are promising as biorecognition molecules and provide the baseline parameters for the development of an aptamer-based point-of-care diagnostic system for melatonin detection and accurate profiling of its fluctuations in saliva.

Understanding the Need for Sleep to Improve Cognition

The restorative function of sleep is shaped by its duration, timing, continuity, subjective quality, and efficiency. Current sleep recommendations specify only nocturnal duration and have been largely derived from sleep self-reports that can be imprecise and miss relevant details. Sleep duration, preferred timing, and ability to withstand sleep deprivation are heritable traits whose expression may change with age and affect the optimal sleep prescription for an individual. Prevailing societal norms and circumstances related to work and relationships interact to influence sleep opportunity and quality. The value of allocating time for sleep is revealed by the impact of its restriction on behavior, functional brain imaging, sleep macrostructure, and late-life cognition. Augmentation of sleep slow oscillations and spindles have been proposed for enhancing sleep quality, but they inconsistently achieve their goal. Crafting bespoke sleep recommendations could benefit from large-scale, longitudinal collection of objective sleep data integrated with behavioral and self-reported data.

Sleep duration, plasma metabolites, and obesity and diabetes: a metabolome-wide association study in US women

Short and long sleep duration are associated with adverse metabolic outcomes, such as obesity and diabetes. We evaluated cross-sectional differences in metabolite levels between women with self-reported habitual short (<7 h), medium (7-8 h), and long (≥9 h) sleep duration to delineate potential underlying biological mechanisms. In total, 210 metabolites were measured via liquid chromatography-mass spectrometry in 9207 women from the Nurses' Health Study (NHS; N = 5027), the NHSII (N = 2368), and the Women's Health Initiative (WHI; N = 2287). Twenty metabolites were consistently (i.e. praw < .05 in ≥2 cohorts) and/or strongly (pFDR < .05 in at least one cohort) associated with short sleep duration after multi-variable adjustment. Specifically, levels of two lysophosphatidylethanolamines, four lysophosphatidylcholines, hydroxyproline and phenylacetylglutamine were higher compared to medium sleep duration, while levels of one diacylglycerol and eleven triacylglycerols (TAGs; all with ≥3 double bonds) were lower. Moreover, enrichment analysis assessing associations of metabolites with short sleep based on biological categories demonstrated significantly increased acylcarnitine levels for short sleep. A metabolite score for short sleep duration based on 12 LASSO-regression selected metabolites was not significantly associated with prevalent and incident obesity and diabetes. Associations of single metabolites with long sleep duration were less robust. However, enrichment analysis demonstrated significant enrichment scores for four lipid classes, all of which (most markedly TAGs) were of opposite sign than the scores for short sleep. Habitual short sleep exhibits a signature on the human plasma metabolome which is different from medium and long sleep. However, we could not detect a direct link of this signature with obesity and diabetes risk.

Multiplatform-Integrated Identification of Melatonin Targets for a Triad of Psychosocial-Sleep/Circadian-Cardiometabolic Disorders

Several psychosocial, sleep/circadian, and cardiometabolic disorders have intricately interconnected pathologies involving melatonin disruption. Therefore, we hypothesize that melatonin could be a therapeutic target for treating potential comorbid diseases associated with this triad of psychosocial-sleep/circadian-cardiometabolic disorders. We investigated melatonin's target prediction and tractability for this triad of disorders. The melatonin's target prediction for the proposed psychosocial-sleep/circadian-cardiometabolic disorder triad was investigated using databases from Europe PMC, ChEMBL, Open Targets Genetics, Phenodigm, and PheWAS. The association scores for melatonin receptors MT1 and MT2 with this disorder triad were explored for evidence of target-disease predictions. The potential of melatonin as a tractable target in managing the disorder triad was investigated using supervised machine learning to identify melatonin activities in cardiovascular, neuronal, and metabolic assays at the cell, tissue, and organism levels in a curated ChEMBL database. Target-disease visualization was done by graphs created using "igraph" library-based scripts and displayed using the Gephi ForceAtlas algorithm. The combined Europe PMC (data type: text mining), ChEMBL (data type: drugs), Open Targets Genetics Portal (data type: genetic associations), PhenoDigm (data type: animal models), and PheWAS (data type: genetic associations) databases yielded types and varying levels of evidence for melatonin-disease triad correlations. Of the investigated databases, 235 association scores of melatonin receptors with the targeted diseases were greater than 0.2; to classify the evidence per disease class: 37% listed psychosocial disorders, 9% sleep/circadian disorders, and 54% cardiometabolic disorders. Using supervised machine learning, 546 cardiovascular, neuronal, or metabolic experimental assays with predicted or measured melatonin activity scores were identified in the ChEMBL curated database. Of 248 registered trials, 144 phase I to IV trials for melatonin or agonists have been completed, of which 33.3% were for psychosocial disorders, 59.7% were for sleep/circadian disorders, and 6.9% were for cardiometabolic disorders. Melatonin's druggability was evidenced by evaluating target prediction and tractability for the triad of psychosocial-sleep/circadian-cardiometabolic disorders. While melatonin research and development in sleep/circadian and psychosocial disorders is more advanced, as evidenced by melatonin association scores, substantial evidence on melatonin discovery in cardiovascular and metabolic disorders supports continued R&D in cardiometabolic disorders, as evidenced by melatonin activity scores. A multiplatform analysis provided an integrative assessment of the target-disease investigations that may justify further translational research.

Acupuncture ameliorates Mobile Phone Addiction with sleep disorders and restores salivary metabolites rhythm

OBJECTIVES

Mobile Phone Addiction (MPA) is a novel behavioral addiction resulting in circadian rhythm disorders that severely affect mental and physical health. The purpose of this study is to detect rhythmic salivary metabolites in MPA with sleep disorder (MPASD) subjects and investigate the effects of acupuncture.

METHODS

Six MPASD patients and six healthy controls among the volunteers were enrolled by MPA Tendency Scale (MPATS) and Pittsburgh Sleep Quality Index (PSQI), then the salivary samples of MPASD and healthy controls were collected every 4-h for three consecutive days. Acupuncture was administered for 7 days to MPASD subjects, then saliva samples were collected again. Salivary metabolomes were analyzed with the method of LC-MS.

RESULT

According to our investigation, 70 (57.85%) MPA patients and 56 (46.28%) MPASD patients were identified among 121 volunteers. The symptoms of the 6 MPASD subjects were significantly alleviated after acupuncture intervention. The number of rhythmic saliva metabolites dropped sharply in MPASD subjects and restored after acupuncture. Representative rhythmic saliva metabolites including melatonin, 2'-deoxyuridine, thymidine, thymidine 3',5'-cyclic monophosphate lost rhythm and restored after acupuncture, which may attribute to promising MPASD treatment and diagnosis biomarkers. The rhythmic saliva metabolites of healthy controls were mainly enriched in neuroactive ligand-receptor interaction, whereas polyketide sugar unit biosynthesis was mainly enriched in MPASD patients.

CONCLUSION

This study revealed circadian rhythm characteristics of salivary metabolites in MPASD and that acupuncture could ameliorate MPASD by restoring part of the dysrhythmia salivary metabolites.

Human circadian rhythm studies: Practical guidelines for inclusion/exclusion criteria and protocol

As interest in circadian rhythms and their effects continues to grow, there is an increasing need to perform circadian studies in humans. Although the constant routine is the gold standard for these studies, there are advantages to performing more naturalistic studies. Here, a review of protocols for such studies is provided along with sample inclusion and exclusion criteria. Sleep routines, drug use, shift work, and menstrual cycle are addressed as screening considerations. Regarding protocol, best practices for measuring melatonin, including light settings, posture, exercise, and dietary habits are described. The inclusion/exclusion recommendations and protocol guidelines are intended to reduce confounding variables in studies that do not involve the constant routine. Given practical limitations, a range of recommendations is provided from stringent to lenient. The scientific rationale behind these recommendations is discussed. However, where the science is equivocal, recommendations are based on empirical decisions made in previous studies. While not all of the recommendations listed may be practical in all research settings and with limited potential participants, the goal is to allow investigators to make well informed decisions about their screening procedures and protocol techniques and to improve rigor and reproducibility, in line with the objectives of the National Institutes of Health.

Repeated automatic sleep scoring based on ear-EEG is a valuable alternative to manually scored polysomnography

While polysomnography (PSG) is the gold standard to quantify sleep, modern technology allows for new alternatives. PSG is obtrusive, affects the sleep it is set out to measure and requires technical assistance for mounting. A number of less obtrusive solutions based on alternative methods have been introduced, but few have been clinically validated. Here we validate one of these solutions, the ear-EEG method, against concurrently recorded PSG in twenty healthy subjects each measured for four nights. Two trained technicians scored the 80 nights of PSG independently, while an automatic algorithm scored the ear-EEG. The sleep stages and eight sleep metrics (Total Sleep Time (TST), Sleep Onset Latency, Sleep Efficiency, Wake After Sleep Onset, REM latency, REM fraction of TST, N2 fraction of TST, and N3 fraction of TST) were used in the further analysis. We found the sleep metrics: Total Sleep Time, Sleep Onset Latency, Sleep Efficiency, Wake After Sleep Onset were estimated with high accuracy and precision between automatic sleep scoring and manual sleep scoring. However, the REM latency and REM fraction of sleep showed high accuracy but low precision. Further, the automatic sleep scoring systematically overestimated the N2 fraction of sleep and slightly underestimated the N3 fraction of sleep. We demonstrate that sleep metrics estimated from automatic sleep scoring based on repeated ear-EEG in some cases are more reliably estimated with repeated nights of automatically scored ear-EEG than with a single night of manually scored PSG. Thus, given the obtrusiveness and cost of PSG, ear-EEG seems to be a useful alternative for sleep staging for the single night recording and an advantageous choice for several nights of sleep monitoring.

EVs-miRNA: The New Molecular Markers for Chronic Respiratory Diseases

Idiopathic pulmonary fibrosis (IPF), chronic obstructive pulmonary disease (COPD), asthma and sleep disorders are chronic respiratory diseases that affect the airways, compromising lung function over time. These diseases affect hundreds of millions of people around the world and their frequency seems to be increasing every year. Extracellular vesicles (EVs) are small-sized vesicles released by every cell in the body. They are present in most body fluids and contain various biomolecules including proteins, lipids, mRNA and non-coding RNA (micro-RNA). The EVs can release their cargo, specifically micro-RNAs (miRNAs), to both neighboring and/or distal cells, playing a fundamental role in cell-cell communication. Recent studies have shown their possible role in the pathogenesis of various chronic respiratory diseases. The expression of miRNAs and, in particular, of miRNAs contained within the extracellular vesicles seems to be a good starting point in order to identify new potential biomarkers of disease, allowing a non-invasive clinical diagnosis. In this review we summarize some studies, present in the literature, about the functions of extracellular vesicles and miRNAs contained in extracellular vesicles in chronic respiratory diseases and we discuss the potential clinical applications of EVs and EVs-miRNAs for their possible use such as future biomarkers.

Living at the Wrong Time: Effects of Unmatching Official Time in Portugal and Western Spain

Human circadian rhythmicity is subjected to the internal circadian clock, the sun and social clocks (official time, social/work schedules). The discrepancy among these clocks, as occurs when official time does not match its geographical time zone, may produce circadian disruption. Western Spain (GMT+1/+2) and Portugal (GMT0/+1) share similar longitudes (sun time) but have different official times. This provides a unique opportunity to evaluate the effects of official time on circadian rhythmicity and sleep in elderly and retired populations (with no remunerated duties presumed, although other social commitments may be present) at both locations. Although both populations slept enough for their age (7-8 h), circadian robustness (e.g., interdaily stability, relative amplitude) was greater in Portugal, especially during weekdays, while greater desynchronization (both body temperature vs. motor activity and body temperature vs. light exposure) tended to occur in the Spaniards. Once corrected by GMT0, meals took place later in Spain than in Portugal, especially as the day progresses, and a possible interplay between bed/meal timings and internal desynchronization was found. Our results point to the possible deleterious effect on circadian system robustness when official time is misaligned with its geographical time zone.

Empirical assessment of allele frequencies of genome wide association study variants associated with obstructive sleep apnea

OBJECTIVE

Obstructive Sleep Apnea (OSA) is a heterogeneous disorder with a complex interplay of genetic and environmental factors. Over the years, with advancement in genotyping and sequencing techniques, various loci have shown an association with OSA. It is pertinent to understand the status of these associated variants in different ethnic groups. The aim of the study was to assess the genetic affinity among different population groups by evaluating the risk allele frequencies of variants associated with OSA.

METHOD

The variants associated with OSA were obtained from the GWAS catalog with a significant p value of <5 × 10-7; 95 variants were obtained (www.ebi.ac.uk/gwas). Further, the variants were narrowed down on the basis of risk allele frequencies (>5%). The fst was calculated to assess the genetic affinity between super population groups and among the sub-population groups present in the 1000 genome project.

RESULT

The fst values observed indicated all super populations were genetically related (SAS, AMR, EAS and EUR) except in the African (AFR) population group. Further, the closely related super population i.e., SAS, AMR, EAS and EUR when bifurcated on the basis of sub-population groups shows population stratification and SAS population groups form separate clusters on the MDS plot.

CONCLUSION

The study highlights genetic heterogeneity among different population groups that gets diluted and results are biased when the samples are pooled irrespective of their endogamous groups. Our results provide insight to researchers to target specific endogamous groups for future studies on OSA.

Chronodisruption and Ambulatory Circadian Monitoring in Cancer Patients: Beyond the Body Clock

Purpose of Review

Circadian rhythms impose daily rhythms a remarkable variety of metabolic and physiological functions, such as cell proliferation, inflammation, and DNA damage response. Accumulating epidemiological and genetic evidence indicates that circadian rhythms’ disruption may be linked to cancer. The integration of circadian biology into cancer research may offer new options for increasing cancer treatment effectiveness and would encompass the prevention, diagnosis, and treatment of this disease.

Recent Findings

In recent years, there has been a significant development and use of multi-modal sensors to monitor physical activity, sleep, and circadian rhythms, allowing, for the very first time, scaling accurate sleep monitoring to epidemiological research linking sleep patterns to disease, and wellness applications providing new potential applications.

Summary

This review highlights the role of circadian clock in tumorigenesis, cancer hallmarks and introduces the state-of-the-art in sleep-monitoring technologies, discussing the eventual application of insights in clinical settings and cancer research.

Challenges and Opportunities for Applying Wearable Technology to Sleep

Wearable technology has a history in sleep research dating back to the 1970s. Because modern wearable technology is relatively cheap and widely used by the general population, this represents an opportunity to leverage wearable devices to advance sleep medicine and research. However, there is a lack of published validation studies designed to quantify device performance against accepted gold standards, especially across different populations. Recommendations for conducting performance assessments and using wearable devices are now published with the goal of standardizing wearable device implementation and advancing the field.

Towards Validating the Effectiveness of Obstructive Sleep Apnea Classification from Electronic Health Records Using Machine Learning

Obstructive sleep apnea (OSA) is a common, chronic, sleep-related breathing disorder characterized by partial or complete airway obstruction in sleep. The gold standard diagnosis method is polysomnography, which estimates disease severity through the Apnea-Hypopnea Index (AHI). However, this is expensive and not widely accessible to the public. For effective screening, this work implements machine learning algorithms for classification of OSA. The model is trained with routinely acquired clinical data of 1479 records from the Wisconsin Sleep Cohort dataset. Extracted features from the electronic health records include patient demographics, laboratory blood reports, physical measurements, habitual sleep history, comorbidities, and general health questionnaire scores. For distinguishing between OSA and non-OSA patients, feature selection methods reveal the primary important predictors as waist-to-height ratio, waist circumference, neck circumference, body-mass index, lipid accumulation product, excessive daytime sleepiness, daily snoring frequency and snoring volume. Optimal hyperparameters were selected using a hybrid tuning method consisting of Bayesian Optimization and Genetic Algorithms through a five-fold cross-validation strategy. Support vector machines achieved the highest evaluation scores with accuracy: 68.06%, sensitivity: 88.76%, specificity: 40.74%, F1-score: 75.96%, PPV: 66.36% and NPV: 73.33%. We conclude that routine clinical data can be useful in prioritization of patient referral for further sleep studies.

Genetic Markers of Differential Vulnerability to Sleep Loss in Adults

In this review, we discuss reports of genotype-dependent interindividual differences in phenotypic neurobehavioral responses to total sleep deprivation or sleep restriction. We highlight the importance of using the candidate gene approach to further elucidate differential resilience and vulnerability to sleep deprivation in humans, although we acknowledge that other omics techniques and genome-wide association studies can also offer insights into biomarkers of such vulnerability. Specifically, we discuss polymorphisms in adenosinergic genes (ADA and ADORA2A), core circadian clock genes (BHLHE41/DEC2 and PER3), genes related to cognitive development and functioning (BDNF and COMT), dopaminergic genes (DRD2 and DAT), and immune and clearance genes (AQP4, DQB1*0602, and TNFα) as potential genetic indicators of differential vulnerability to deficits induced by sleep loss. Additionally, we review the efficacy of several countermeasures for the neurobehavioral impairments induced by sleep loss, including banking sleep, recovery sleep, caffeine, and naps. The discovery of reliable, novel genetic markers of differential vulnerability to sleep loss has critical implications for future research involving predictors, countermeasures, and treatments in the field of sleep and circadian science.

Identification of a Preliminary Plasma Metabolome-based Biomarker for Circadian Phase in Humans

Measuring individual circadian phase is important to diagnose and treat circadian rhythm sleep-wake disorders and circadian misalignment, inform chronotherapy, and advance circadian science. Initial findings using blood transcriptomics to predict the circadian phase marker dim-light melatonin onset (DLMO) show promise. Alternatively, there are limited attempts using metabolomics to predict DLMO and no known omics-based biomarkers predict dim-light melatonin offset (DLMOff). We analyzed the human plasma metabolome during adequate and insufficient sleep to predict DLMO and DLMOff using one blood sample. Sixteen (8 male/8 female) healthy participants aged 22.4 ± 4.8 years (mean ± SD) completed an in-laboratory study with 3 baseline days (9 h sleep opportunity/night), followed by a randomized cross-over protocol with 9-h adequate sleep and 5-h insufficient sleep conditions, each lasting 5 days. Blood was collected hourly during the final 24 h of each condition to independently determine DLMO and DLMOff. Blood samples collected every 4 h were analyzed by untargeted metabolomics and were randomly split into training (68%) and test (32%) sets for biomarker analyses. DLMO and DLMOff biomarker models were developed using partial least squares regression in the training set followed by performance assessments using the test set. At baseline, the DLMOff model showed the highest performance (0.91 R2 and 1.1 ± 1.1 h median absolute error ± interquartile range [MdAE ± IQR]), with significantly (p < 0.01) lower prediction error versus the DLMO model. When all conditions (baseline, 9 h, and 5 h) were included in performance analyses, the DLMO (0.60 R2; 2.2 ± 2.8 h MdAE; 44% of the samples with an error under 2 h) and DLMOff (0.62 R2; 1.8 ± 2.6 h MdAE; 51% of the samples with an error under 2 h) models were not statistically different. These findings show promise for metabolomics-based biomarkers of circadian phase and highlight the need to test biomarkers that predict multiple circadian phase markers under different physiological conditions.

A review of the "OMICS" for management of patients with obstructive sleep apnoea

Obstructive sleep apnaea (OSA) syndrome is a condition characterised by the presence of complete or partial collapse of the upper airways during sleep, resulting in fragmentation of sleep associated with rapid episodes of intermittent hypoxia (IH), activation of the sympathetic nervous system and oxidative stress. OSA is associated with a broad spectrum of cardiovascular, metabolic and neurocognitive comorbidities that appear to be particularly evident in obese patients, while affecting both sexes in a different manner and varying in severity according to gender and age. In recent years, studies on OSA have increased considerably, but in clinical practice, it is still a highly underdiagnosed disease. To date, the gold standard for the diagnosis of OSA is nocturnal polysomnography (PSG). However, since it is not well suited for a large number of patients, the Home Sleep Test (HST) is also an accepted diagnostic method. Currently, the major aim of research is to identify non-invasive methods to achieve a highly predictive, non-invasive screening system for these subjects. The most recent reports indicate that research in this field has made significant progress in identifying possible biomarkers in OSA, using -OMIC approaches, particularly in the fields of proteomics and metabolomics. In this review, we analyse these OMIC biomarkers found in the literature.

The influence of sleep health on dietary intake: a systematic review and meta-analysis of intervention studies

BACKGROUND

Poor dietary intake increases disease risk, and poor sleep influences diet. This systematic review and meta-analysis of intervention studies aimed to evaluate the effect of sleep health on dietary intake in adults.

METHODS

Five online databases were used to identify studies published between 1970 and 2019. Included studies were interventions that modified sleep and reported dietary outcomes.

RESULTS

Fifty four full texts were assessed and 24 publications were included. Following risk of bias appraisal, data were narratively summarised and a sub-group of studies (n = 15) was meta-analysed to determine the effect of sleep on dietary intake. One intervention modified sleep timing and 23 modified duration. Sleep duration was partially restricted (≤5.5 h night-1 ) (n = 16), totally restricted (n = 4), partially and totally restricted (n = 1), and extended (n = 2). Dietary outcomes were energy intake (n = 24), carbohydrate, fat, protein intake (n = 20), single nutrient intake (n = 5), diet quality (n = 1) and food types (n = 1). Meta-analysis indicated partial sleep restriction results in higher energy intake in intervention compared with control [standardised mean difference (SMD) = 0.37; 95% confidence interval (CI) = 0.21-0.52; P < 0.001], with a mean difference of 204 kcal (95% CI = 112-295; P < 0.001) in daily energy intake, and a higher percentage of energy from fat, protein, carbohydrate (fat: SMD = 0.33; 95% CI = 0.16-0.51; P < 0.001; protein: SMD = 0.30, 95% CI = 0.12-0.47, P = 0.001; carbohydrate: SMD = 0.22, 95% CI = 0.04-0.39, P = 0.014).

CONCLUSIONS

Partial sleep restriction with duration of ≤5.5 h day-1 increases daily energy intake, as well as fat, protein and carbohydrate intake. Further research is needed to determine the relationship between other dimensions of sleep health and dietary intake.

Sweating Out the Circadian Rhythm: A Technical Review

Circadian dysfunction or dysregulation is associated with many chronic morbidities. Current state-of-art technologies do not provide an accurate estimation of the extent of disease affliction. Recent advances call for using wearables for improving management and diagnosis of circadian related disorders. Sweat contains an abundance of relevant biomarkers like cortisol, DHEA, and so forth, which could be leveraged toward tracking the user's chronobiology. In this article, we provide a review of the key developments in the field of wearable sensors for circadian technologies. We highlight the value of using sweat along with portable electronics toward developing state-of-the-art platforms for efficient diagnosis and management of chronic conditions. Finally, we discuss challenges and opportunities for using wearable sweat sensors for circadian diagnosis and disease management.

Molecular determinants of obstructive sleep apnea

Obstructive sleep apnea (OSA) is characterized as recurrent episodes of obstruction in the upper airway during the period of sleep. The condition occurs in approximately 11% and 4% of middle-aged men and middle-aged women, respectively. Polysomnography is a diagnostic procedure that involves the constant observation of oxygen saturation and unsaturation during sleep. Usually, positive airway pressure is considered a benchmark treatment for OSA. This review summarizes the recent developments and emerging evidence from molecular biology-based research studies that show that genetic factors have an influence on OSA. The genetic aspects of OSA that have been identified include heritability and other phenotypic co-factors such as anatomical morphology. It also draws attention to the results of a polymorphic-based study that was conducted to determine the causative single nucleotide mutations associated with obesity and adverse cardiovascular risk in OSA. However, the role of such mutations and their linkage to OSA can not yet be established. Nonetheless, a large body of evidence supports a strong association between inflammatory cytokine polymorphism and obesity in the development of OSA. There are also probable intermediate factors with several gene-gene interactions. Therefore, advanced applications and modern techniques should be applied to facilitate new findings and to minimize the risk of developing OSA.

MicroRNAs to guide medical decision-making in obstructive sleep apnea: A review

Obstructive sleep apnea (OSA) is a common and frequently underdiagnosed sleep disorder tightly associated with a wide range of morbidities and an elevated risk of the main causes of mortality. This condition represents a major public health concern due to its increasing worldwide prevalence and its serious pathological consequences. Current clinical guidelines support the importance of effective diagnosis and treatment of OSA and emphasize the unmet need for biomarkers to guide medical decision-making. In recent years, the noncoding transcriptome has emerged as a new opportunity for biomarker discovery. In this review, we provide a brief overview of the current understanding of noncoding RNAs, specifically microRNAs (miRNAs). Then, we carefully address the potential role of miRNAs as novel indicators for the management of both pediatric and adult OSA, highlighting their translational applicability, particularly for diagnosis and therapy allocation. Finally, we identify the gaps in the research state-of-art, discuss current methodological and conceptual limitations and propose future key steps and perspectives for the incorporation of miRNAs into routine clinical practice.

Validity and Cost-Effectiveness of Pediatric Home Respiratory Polygraphy for the Diagnosis of Obstructive Sleep Apnea in Children: Rationale, Study Design, and Methodology

Obstructive sleep apnea (OSA) in children is a prevalent, albeit largely undiagnosed disease associated with a large spectrum of morbidities. Overnight in-lab polysomnography remains the gold standard diagnostic approach, but is time-consuming, inconvenient, and expensive, and not readily available in many places. Simplified Home Respiratory Polygraphy (HRP) approaches have been proposed to reduce costs and facilitate the diagnostic process. However, evidence supporting the validity of HRP is still scarce, hampering its implementation in routine clinical use. The objectives were: Primary; to establish the diagnostic and therapeutic decision validity of a simplified HRP approach compared to PSG among children at risk of OSA. Secondary: (a) Analyze the cost-effectiveness of the HRP versus in-lab PSG in evaluation and treatment of pediatric OSA; (b) Evaluate the impact of therapeutic interventions based on HRP versus PSG findings six months after treatment using sleep and health parameters and quality of life instruments; (c) Discovery and validity of the urine biomarkers to establish the diagnosis of OSA and changes after treatment.

An evidence map of actigraphy studies exploring longitudinal associations between rest-activity rhythms and course and outcome of bipolar disorders

BACKGROUND

Evidence mapping is a structured approach used to synthesize the state-of-the-art in an emerging field of research when systematic reviews or meta-analyses are deemed inappropriate. We employed this strategy to summarise knowledge regarding longitudinal ecological monitoring of rest-activity rhythms (RAR) and disease modifiers, course of illness, treatment response or outcome in bipolar disorders (BD).

STRUCTURE

We had two key aims: (1) to determine the number and type of actigraphy studies of in BD that explored data regarding: outcome over time (e.g. relapse/recurrence according to polarity, or recovery/remission), treatment response or illness trajectories and (2) to examine the range of actigraphy metrics that can be used to estimate disruptions of RAR and describe which individual circadian rhythm or sleep-wake cycle parameters are most consistently associated with outcome over time in BD. The mapping process incorporated four steps: clarifying the project focus, describing boundaries and 'coordinates' for mapping, searching the literature and producing a brief synopsis with summary charts of the key outputs. Twenty-seven independent studies (reported in 29 publications) were eligible for inclusion in the map. Most were small-scale, with the median sample size being 15 per study and median duration of actigraphy being about 7 days (range 1-210). Interestingly, 17 studies comprised wholly or partly of inpatients (63%). The available evidence indicated that a discrete number of RAR metrics are more consistently associated with transition between different phases of BD and/or may be predictive of longitudinal course of illness or treatment response. The metrics that show the most frequent associations represent markers of the amount, timing, or variability of RAR rather than the sleep quality metrics that are frequently targeted in contemporary studies of BD.

CONCLUSIONS

Despite 50 years of research, use of actigraphy to assess RAR in longitudinal studies and examination of these metrics and treatment response, course and outcome of BD is under-investigated. This is in marked contrast to the extensive literature on case-control or cross-sectional studies of actigraphy, especially typical sleep analysis metrics in BD. However, given the encouraging findings on putative RAR markers, we recommend increased study of putative circadian phenotypes of BD.

Evening types have social jet lag and metabolic alterations in school-age children

Chronotype has been mostly assessed with subjective scales. Objective assessment has been undertaken with actigraphy, although problems may occur in classifying chronotype. The aims of the study were to assess chronotype in school-age children using a novel integrative measurement (TAP) derived from non-invasive assessments of wrist temperature (T) physical activity (A) and body position (P) and to explore associations between chronotype, sleep disturbances, and metabolic components. Four-hundred-thirty-two children of 8-12 years were recruited from a Mediterranean area of Spain. Measurements were: (a) Chronotype objectively (7-day-rhythms of TAP) and subjectively measured (Munich-chronotype-self-reported questionnaire); (b) sleep rhythms and light exposition; (c) 7-day-diaries of food intake; (d) anthropometry and metabolic parameters; (e) academic scores. TAP acrophase was able to assess eveningness. As compared to more morning-types, more evening-types displayed lower amplitude in temperature rhythms, increased physical activity in the evening, delayed sleep and midpoint of intake and had more frequent social jet lag (P < 0.05). More evening-types had higher light intensity at 2 h before sleep and lower melatonin values (01:00 h). Eveningness associated with higher BMI and metabolic risk (higher values of insulin, glucose, triglycerides and cholesterol). Evening-types presented better grades in art. In conclusion, more evening-types, as objectively assessed, presented sleep alterations, social jet lag, obesity and higher metabolic risk.

Bioinformatic analysis of proteomic data for iron, inflammation, and hypoxic pathways in restless legs syndrome

OBJECTIVE/BACKGROUND

We performed bioinformatic analysis of proteomic data to identify the biomarkers of restless legs syndrome (RLS) and provide insights into the putative pathomechanisms, including iron deficiency, inflammation, and hypoxic pathways.

PATIENTS/METHODS

Patients with drug-naïve idiopathic RLS were recruited at a university hospital from June 2017 to February 2018. Serum samples from patients with RLS (n = 7) and healthy sex- and age-matched controls (n = 6) were evaluated by proteomic analysis. For differentially expressed proteins (DEPs) in patients with RLS, compared to those in controls, the expression profiles and protein-protein interaction (PPI) network were characterized between dysregulated proteins and extracted proteins involved in iron deficiency, hypoxia, and inflammation responses using the String database (http://string-DB.org). The PPI network was visualized by Cytoscape ver. 3. 7. 1. Statistical analyses of the validation Western blot assays were performed using a Student's t-test.

RESULTS

Interactome network analysis revealed a relationship among the eight proteins, their associated genes, and 150, 47, and 11 proteins related to iron deficiency, inflammation, and hypoxic pathways, respectively. All DEPs were well associated with inflammation, and complement 3, complement C4A, alpha-2 HS glycoprotein, and alpha-2 macroglobulin precursor were found to be in hub positions of networks involved in PPIs including iron deficiency, hypoxia pathway, and inflammation. C3 and C4A were verified using western blotting.

CONCLUSIONS

We identified key molecules that represent the selected cellular pathways as protein biomarkers by PPI network analysis. Changes in inflammation can mediate or affect the pathomechanism of RLS and can thus act as systemic biomarkers.

Novel Approaches for Assessing Circadian Rhythmicity in Humans: A Review

The temporal organization of molecular and physiological processes is driven by environmental and behavioral cycles as well as by self-sustained molecular circadian oscillators. Quantification of phase, amplitude, period, and disruption of circadian oscillators is essential for understanding their contribution to sleep-wake disorders, social jet lag, interindividual differences in entrainment, and the development of chrono-therapeutics. Traditionally, assessment of the human circadian system, and the output of the SCN in particular, has required collection of long time series of univariate markers such as melatonin or core body temperature. Data were collected in specialized laboratory protocols designed to control for environmental and behavioral influences on rhythmicity. These protocols are time-consuming, expensive, and not practical for assessing circadian status in patients or in participants in epidemiologic studies. Novel approaches for assessment of circadian parameters of the SCN or peripheral oscillators have been developed. They are based on machine learning or mathematical model-informed analyses of features extracted from 1 or a few samples of high-dimensional data, such as transcriptomes, metabolomes, long-term simultaneous recording of activity, light exposure, skin temperature, and heart rate or in vitro approaches. Here, we review whether these approaches successfully quantify parameters of central and peripheral circadian oscillators as indexed by gold standard markers. Although several approaches perform well under entrained conditions when sleep occurs at night, the methods either perform worse in other conditions such as shift work or they have not been assessed under any conditions other than entrainment and thus we do not yet know how robust they are. Novel approaches for the assessment of circadian parameters hold promise for circadian medicine, chrono-therapeutics, and chrono-epidemiology. There remains a need to validate these approaches against gold standard markers, in individuals of all sexes and ages, in patient populations, and, in particular, under conditions in which behavioral cycles are displaced.

Developing preliminary blood metabolomics-based biomarkers of insufficient sleep in humans

STUDY OBJECTIVE

Identify small molecule biomarkers of insufficient sleep using untargeted plasma metabolomics in humans undergoing experimental insufficient sleep.

METHODS

We conducted a crossover laboratory study where 16 normal-weight participants (eight men; age 22 ± 5 years; body mass index < 25 kg/m2) completed three baseline days (9 hours sleep opportunity per night) followed by 5-day insufficient (5 hours sleep opportunity per night) and adequate (9 hours sleep opportunity per night) sleep conditions. Energy balanced diets were provided during baseline, with ad libitum energy intake provided during the insufficient and adequate sleep conditions. Untargeted plasma metabolomics analyses were performed using blood samples collected every 4 hours across the final 24 hours of each condition. Biomarker models were developed using logistic regression and linear support vector machine (SVM) algorithms.

RESULTS

The top-performing biomarker model was developed by linear SVM modeling, consisted of 65 compounds, and discriminated insufficient versus adequate sleep with 74% overall accuracy and a Matthew's Correlation Coefficient of 0.39. The compounds in the top-performing biomarker model were associated with ATP Binding Cassette Transporters in Lipid Homeostasis, Phospholipid Metabolic Process, Plasma Lipoprotein Remodeling, and sphingolipid metabolism.

CONCLUSION

We identified potential metabolomics-based biomarkers of insufficient sleep in humans. Although our current biomarkers require further development and validation using independent cohorts, they have potential to advance our understanding of the negative consequences of insufficient sleep, improve diagnosis of poor sleep health, and could eventually help identify targets for countermeasures designed to mitigate the negative health consequences of insufficient sleep.

Sleep debt induces skeletal muscle injuries in athletes: A promising hypothesis

Sleep is a physiological state and it is fundamental for physical and cognitive recovery of athletes. Due to strenuous training and competitions, athletes may present sleep complaints compromising good quality and quantity of sleep. Studies have related sleep debt to the occurrence of musculoskeletal injuries in athletes, but the mechanisms that can lead to this are not entirely clear. Studies involving animals and humans have shown that poor sleep quality can cause significant changes in hormones and cytokines. Demonstrating that this hormones changes lead to a decrease of testosterone and growth hormone levels and increased cortisol levels, important hormones in the process of protein synthesis and degradation. In athletes, the sport itself is a risk factor of injuries, and sleep debt may result in overtraining syndrome associated with inflammatory markers and ultimately to immune system dysfunction. Thus, we hypothesize that athletes who have sleep debt are more susceptible to musculoskeletal injuries due to increased catabolic pathway signaling, i.e. protein degradation and decreased anabolic pathway signaling, compromising muscle integrity. In this sense, we indicate the relationship between musculoskeletal injuries and sleep debt involving new targets for immunological signaling pathways that start the reduction of the muscle recovery process.

Sleep Disturbances in MCI and AD: Neuroinflammation as a Possible Mediating Pathway

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.

Wearable technologies for developing sleep and circadian biomarkers: a summary of workshop discussions

The "International Biomarkers Workshop on Wearables in Sleep and Circadian Science" was held at the 2018 SLEEP Meeting of the Associated Professional Sleep Societies. The workshop brought together experts in consumer sleep technologies and medical devices, sleep and circadian physiology, clinical translational research, and clinical practice. The goals of the workshop were: (1) characterize the term "wearable" for use in sleep and circadian science and identify relevant sleep and circadian metrics for wearables to measure; (2) assess the current use of wearables in sleep and circadian science; (3) identify current barriers for applying wearables to sleep and circadian science; and (4) identify goals and opportunities for wearables to advance sleep and circadian science. For the purposes of biomarker development in the sleep and circadian fields, the workshop included the terms "wearables," "nearables," and "ingestibles." Given the state of the current science and technology, the limited validation of wearable devices against gold standard measurements is the primary factor limiting large-scale use of wearable technologies for sleep and circadian research. As such, the workshop committee proposed a set of best practices for validation studies and guidelines regarding how to choose a wearable device for research and clinical use. To complement validation studies, the workshop committee recommends the development of a public data repository for wearable data. Finally, sleep and circadian scientists must actively engage in the development and use of wearable devices to maintain the rigor of scientific findings and public health messages based on wearable technology.

Circadian disruption: What do we actually mean?

The circadian system regulates physiology and behavior. Acute challenges to the system, such as those experienced when traveling across time zones, will eventually result in re-synchronization to local environmental time cues, but this re-synchronization is oftentimes accompanied by adverse short-term consequences. When such challenges are experienced chronically, adaptation may not be achieved, as for example in the case of rotating night shift workers. The transient and chronic disturbance of the circadian system is most frequently referred to as "circadian disruption", but many other terms have been proposed and used to refer to similar situations. It is now beyond doubt that the circadian system contributes to health and disease, emphasizing the need for clear terminology when describing challenges to the circadian system and their consequences. The goal of this review is to provide an overview of the terms used to describe disruption of the circadian system, discuss proposed quantifications of disruption in experimental and observational settings with a focus on human research, and highlight limitations and challenges of currently available tools. For circadian research to advance as a translational science, clear, operationalizable, and scalable quantifications of circadian disruption are key, as they will enable improved assessment and reproducibility of results, ideally ranging from mechanistic settings, including animal research, to large-scale randomized clinical trials.