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

Decoding the nexus: branched-chain amino acids and their connection with sleep, circadian rhythms, and cardiometabolic health

The sleep-wake cycle stands as an integrative process essential for sustaining optimal brain function and, either directly or indirectly, overall body health, encompassing metabolic and cardiovascular well-being. Given the heightened metabolic activity of the brain, there exists a considerable demand for nutrients in comparison to other organs. Among these, the branched-chain amino acids, comprising leucine, isoleucine, and valine, display distinctive significance, from their contribution to protein structure to their involvement in overall metabolism, especially in cerebral processes. Among the first amino acids that are released into circulation post-food intake, branched-chain amino acids assume a pivotal role in the regulation of protein synthesis, modulating insulin secretion and the amino acid sensing pathway of target of rapamycin. Branched-chain amino acids are key players in influencing the brain's uptake of monoamine precursors, competing for a shared transporter. Beyond their involvement in protein synthesis, these amino acids contribute to the metabolic cycles of γ-aminobutyric acid and glutamate, as well as energy metabolism. Notably, they impact GABAergic neurons and the excitation/inhibition balance. The rhythmicity of branched-chain amino acids in plasma concentrations, observed over a 24-hour cycle and conserved in rodent models, is under circadian clock control. The mechanisms underlying those rhythms and the physiological consequences of their disruption are not fully understood. Disturbed sleep, obesity, diabetes, and cardiovascular diseases can elevate branched-chain amino acid concentrations or modify their oscillatory dynamics. The mechanisms driving these effects are currently the focal point of ongoing research efforts, since normalizing branched-chain amino acid levels has the ability to alleviate the severity of these pathologies. In this context, the Drosophila model, though underutilized, holds promise in shedding new light on these mechanisms. Initial findings indicate its potential to introduce novel concepts, particularly in elucidating the intricate connections between the circadian clock, sleep/wake, and metabolism. Consequently, the use and transport of branched-chain amino acids emerge as critical components and orchestrators in the web of interactions across multiple organs throughout the sleep/wake cycle. They could represent one of the so far elusive mechanisms connecting sleep patterns to metabolic and cardiovascular health, paving the way for potential therapeutic interventions.

Untargeted Metabolome Atlas for Sleep Phenotypes in the Hispanic Community Health Study/Study of Latinos

Sleep is essential to maintaining health and wellbeing of individuals, influencing a variety of outcomes from mental health to cardiometabolic disease. This study aims to assess the relationships between various sleep phenotypes and blood metabolites. Utilizing data from the Hispanic Community Health Study/Study of Latinos, we performed association analyses between 40 sleep phenotypes, grouped in several domains (i.e., sleep disordered breathing (SDB), sleep duration, timing, insomnia symptoms, and heart rate during sleep), and 768 metabolites measured via untargeted metabolomics profiling. Network analysis was employed to visualize and interpret the associations between sleep phenotypes and metabolites. The patterns of statistically significant associations between sleep phenotypes and metabolites differed by superpathways, and highlighted subpathways of interest for future studies. For example, some xenobiotic metabolites were associated with sleep duration and heart rate phenotypes (e.g. 1H-indole-7-acetic acid, 4-allylphenol sulfate), while ketone bodies and fatty acid metabolism metabolites were associated with sleep timing measures (e.g. 3-hydroxybutyrate (BHBA), 3-hydroxyhexanoylcarnitine (1)). Heart rate phenotypes had the overall largest number of detected metabolite associations. Many of these associations were shared with both SDB and with sleep timing phenotypes, while SDB phenotypes shared relatively few metabolite associations with sleep duration measures. A number of metabolites were associated with multiple sleep phenotypes, from a few domains. The amino acids vanillylmandelate (VMA) and 1-carboxyethylisoleucine were associated with the greatest number of sleep phenotypes, from all domains other than insomnia. This atlas of sleep-metabolite associations will facilitate hypothesis generation and further study of the metabolic underpinnings of sleep health.

Targeted metabolomics-based understanding of the sleep disturbances in drug-naïve patients with schizophrenia

BACKGROUND

Sleep disturbances are a common occurrence in patients with schizophrenia, yet the underlying pathogenesis remain poorly understood. Here, we performed a targeted metabolomics-based approach to explore the potential biological mechanisms contributing to sleep disturbances in schizophrenia.

METHODS

Plasma samples from 59 drug-naïve patients with schizophrenia and 36 healthy controls were subjected to liquid chromatography-mass spectrometry (LC-MS) targeted metabolomics analysis, allowing for the quantification and profiling of 271 metabolites. Sleep quality and clinical symptoms were assessed using the Pittsburgh Sleep Quality Index (PSQI) and the Positive and Negative Symptom Scale (PANSS), respectively. Partial correlation analysis and orthogonal partial least squares discriminant analysis (OPLS-DA) model were used to identify metabolites specifically associated with sleep disturbances in drug-naïve schizophrenia.

RESULTS

16 characteristic metabolites were observed significantly associated with sleep disturbances in drug-naïve patients with schizophrenia. Furthermore, the glycerophospholipid metabolism (Impact: 0.138, p<0.001), the butanoate metabolism (Impact: 0.032, p=0.008), and the sphingolipid metabolism (Impact: 0.270, p=0.104) were identified as metabolic pathways associated with sleep disturbances in drug-naïve patients with schizophrenia.

CONCLUSIONS

Our study identified 16 characteristic metabolites (mainly lipids) and 3 metabolic pathways related to sleep disturbances in drug-naïve schizophrenia. The detection of these distinct metabolites provide valuable insights into the underlying biological mechanisms associated with sleep disturbances in schizophrenia.

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.

Sleep extension and cardiometabolic health: what it is, possible mechanisms and real-world applications

Short sleep duration is associated with heightened cardiometabolic disease risk and has reached epidemic proportions among children, adolescents and adults. Potential mechanisms underlying this association are complex and multifaceted, including disturbances in circadian timing, food intake and appetitive hormones, brain regions linked to control of hedonic eating, physical activity, an altered microbiome and impaired insulin sensitivity. Sleep extension, or increasing total sleep duration, is an emerging and ecologically relevant intervention with significant potential to advance our understanding of the mechanisms underlying the association between short sleep duration and the risk of cardiometabolic disease. If effective, sleep extension interventions have potential to improve cardiometabolic health across the lifespan. Existing data show that sleep extension is feasible and might have potential cardiometabolic health benefits, although there are limitations that the field must overcome. Notably, most existing studies are short term (2-8 weeks), use different sleep extension strategies, analyse a wide array of cardiometabolic health outcomes in different populations and, frequently, lack adequate statistical power, thus limiting robust scientific conclusions. Overcoming these limitations will require fully powered, randomized studies conducted in people with habitual short sleep duration and existing cardiometabolic risk factors. Additionally, randomized controlled trials comparing different sleep extension strategies are essential to determine the most effective interventions. Ongoing and future research should focus on elucidating the potential cardiometabolic health benefits of sleep extension. Such studies have high potential to generate crucial knowledge with potential to improve health and quality of life for those struggling with short sleep duration.

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.

Sleep insufficiency, circadian rhythms, and metabolomics: the connection between metabolic and sleep disorders

PURPOSE

US 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.

METHODS

An 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.

RESULTS

Consistent 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.

CONCLUSION

Changes 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.

Insufficient sleep and weekend recovery sleep: classification by a metabolomics-based machine learning ensemble

Although weekend recovery sleep is common, the physiological responses to weekend recovery sleep are not fully elucidated. Identifying molecular biomarkers that represent adequate versus insufficient sleep could help advance our understanding of weekend recovery sleep. Here, we identified potential molecular biomarkers of insufficient sleep and defined the impact of weekend recovery sleep on these biomarkers using metabolomics in a randomized controlled trial. Healthy adults (n = 34) were randomized into three groups: control (CON: 9-h sleep opportunities); sleep restriction (SR: 5-h sleep opportunities); or weekend recovery (WR: simulated workweek of 5-h sleep opportunities followed by ad libitum weekend recovery sleep and then 2 days with 5-h sleep opportunities). Blood for metabolomics was collected on the simulated Monday immediately following the weekend. Nine machine learning models, including a machine learning ensemble, were built to classify samples from SR versus CON. Notably, SR showed decreased glycerophospholipids and sphingolipids versus CON. The machine learning ensemble showed the highest G-mean performance and classified 50% of the WR samples as insufficient sleep. Our findings show insufficient sleep and recovery sleep influence the plasma metabolome and suggest more than one weekend of recovery sleep may be necessary for the identified biomarkers to return to healthy adequate sleep levels.

Persistent Biofluid Small-Molecule Alterations Induced by Trypanosoma cruzi Infection Are Not Restored by Parasite Elimination

Chagas disease (CD), caused by Trypanosoma cruzi (T. cruzi) protozoa, is a complicated parasitic illness with inadequate medical measures for diagnosing infection and monitoring treatment success. To address this gap, we analyzed changes in the metabolome of T. cruzi-infected mice via liquid chromatography tandem mass spectrometry of clinically accessible biofluids: saliva, urine, and plasma. Urine was the most indicative of infection status across mouse and parasite genotypes. Metabolites perturbed by infection in urine include kynurenate, acylcarnitines, and threonylcarbamoyladenosine. Based on these results, we sought to implement urine as a tool for the assessment of CD treatment success. Strikingly, it was found that mice with parasite clearance following benznidazole antiparasitic treatment had an overall urine metabolome comparable to that of mice that failed to clear parasites. These results provide a complementary hypothesis to explain clinical trial data in which benznidazole treatment did not improve patient outcomes in late-stage disease, even in patients with successful parasite clearance. Overall, this study provides insights into new small-molecule-based CD diagnostic methods and a new approach to assess functional responses to treatment.

Sleep deprivation and aging are metabolically linked across tissues

STUDY OBJECTIVES

Insufficient sleep is a concerning hallmark of modern society because sleep deprivation (SD) is a risk factor for neurodegenerative and cardiometabolic disorders. SD imparts an aging-like effect on learning and memory, although little is known about possible common molecular underpinnings of SD and aging. Here, we examine this question by profiling metabolic features across different tissues after acute SD in young adult and aged mice.

METHODS

Young adult and aged mice were subjected to acute SD for 5 hours. Blood plasma, hippocampus, and liver samples were subjected to UPLC-MS/MS-based metabolic profiling.

RESULTS

SD preferentially impacts peripheral plasma and liver profiles (e.g. ketone body metabolism) whereas the hippocampus is more impacted by aging. We further demonstrate that aged animals exhibit SD-like metabolic features at baseline. Hepatic alterations include parallel changes in nicotinamide metabolism between aging and SD in young animals. Overall, metabolism in young adult animals is more impacted by SD, which in turn induces aging-like features. A set of nine metabolites was classified (79% correct) based on age and sleep status across all four groups.

CONCLUSIONS

Our metabolic observations demonstrate striking parallels to previous observations in studies of learning and memory and define a molecular metabolic signature of sleep loss and aging.

Modulatory effect of n-3 polyunsaturated fatty acids on depressive-like behaviors in rats with chronic sleep deprivation: potential involvement of melatonin receptor pathway and brain lipidome

Clinical evidence suggests that a bidirectional relationship is present between sleep loss and psychiatric disorders. Both melatonin receptor agonist ramelteon (RMT) and n-3 polyunsaturated fatty acids (n-3 PUFAs) exhibit antidepressant effects, while their underlying molecular mechanisms might be different. Thus, the present study aims to investigate the add-on effects and possible mechanisms of how RMT and different n-3 PUFAs modulate the melatonin receptor pathway as well as brain lipidome to ameliorate the neuropsychiatric behaviors displayed in rats under chronic sleep deprivation. Thirty-one 6-week-old male Wistar rats were divided into five groups: control (C), sleep deprivation (S), sleep deprivation treated with RMT (SR), sleep deprivation treated with RMT and eicosapentaenoic acid (C20:5n-3, EPA) (SRE), and sleep deprivation treated with RMT and docosahexaenoic acid (C22:6n-3, DHA) (SRD) groups. The results reveal that RMT plus EPA alleviated depressive-like behavior when the rats were subjected to the forced swimming test, whereas RMT plus DHA alleviated anxiety-like behavior when the rats were subjected to the elevated plus maze test. The results of a western blot analysis further revealed that compared with the rats in the S group, those in the SRE and SRD groups exhibited a significantly increased expression of MT₂ in the prefrontal cortex, with greater benefits observed in the SRE group. In addition, decreased BDNF and TrkB expression levels were upregulated only in the SRE group. Lipidomic analysis further revealed possible involvement of aberrant lipid metabolism and neuropsychiatric behaviors. RMT plus EPA demonstrated promise as having the effects of reversing the levels of the potential biomarkers of depressive-like behaviors. RMT plus EPA or DHA could ameliorate depressive- and anxiety-like behaviors in sleep-deprived rats through the alteration of the lipidome and MT₂ receptor pathway in the brain, whereas EPA and DHA exerted a differential effect.

Proteostasis failure exacerbates neuronal circuit dysfunction and sleep impairments in Alzheimer's disease

Failed proteostasis is a well-documented feature of Alzheimer's disease, particularly, reduced protein degradation and clearance. However, the contribution of failed proteostasis to neuronal circuit dysfunction is an emerging concept in neurodegenerative research and will prove critical in understanding cognitive decline. Our objective is to convey Alzheimer's disease progression with the growing evidence for a bidirectional relationship of sleep disruption and proteostasis failure. Proteostasis dysfunction and tauopathy in Alzheimer's disease disrupts neurons that regulate the sleep-wake cycle, which presents behavior as impaired slow wave and rapid eye movement sleep patterns. Subsequent sleep loss further impairs protein clearance. Sleep loss is a defined feature seen early in many neurodegenerative disorders and contributes to memory impairments in Alzheimer's disease. Canonical pathological hallmarks, β-amyloid, and tau, directly disrupt sleep, and neurodegeneration of locus coeruleus, hippocampal and hypothalamic neurons from tau proteinopathy causes disruption of the neuronal circuitry of sleep. Acting in a positive-feedback-loop, sleep loss and circadian rhythm disruption then increase spread of β-amyloid and tau, through impairments of proteasome, autophagy, unfolded protein response and glymphatic clearance. This phenomenon extends beyond β-amyloid and tau, with interactions of sleep impairment with the homeostasis of TDP-43, α-synuclein, FUS, and huntingtin proteins, implicating sleep loss as an important consideration in an array of neurodegenerative diseases and in cases of mixed neuropathology. Critically, the dynamics of this interaction in the neurodegenerative environment are not fully elucidated and are deserving of further discussion and research. Finally, we propose sleep-enhancing therapeutics as potential interventions for promoting healthy proteostasis, including β-amyloid and tau clearance, mechanistically linking these processes. With further clinical and preclinical research, we propose this dynamic interaction as a diagnostic and therapeutic framework, informing precise single- and combinatorial-treatments for Alzheimer's disease and other brain disorders.

Multi-Omics Analysis of Lung Tissue Demonstrates Changes to Lipid Metabolism during Allergic Sensitization in Mice

Allergy and asthma pathogenesis are associated with the dysregulation of metabolic pathways. To understand the effects of allergen sensitization on metabolic pathways, we conducted a multi-omics study using BALB/cJ mice sensitized to house dust mite (HDM) extract or saline. Lung tissue was used to perform untargeted metabolomics and transcriptomics while both lung tissue and plasma were used for targeted lipidomics. Following statistical comparisons, an integrated pathway analysis was conducted. Histopathological changes demonstrated an allergic response in HDM-sensitized mice. Untargeted metabolomics showed 391 lung tissue compounds were significantly different between HDM and control mice (adjusted p < 0.05); with most compounds mapping to glycerophospholipid and sphingolipid pathways. Several lung oxylipins, including 14-HDHA, 8-HETE, 15-HETE, 6-keto-PGF1α, and PGE2 were significantly elevated in HDM-sensitized mice (p < 0.05). Global gene expression analysis showed upregulated calcium channel, G protein–signaling, and mTORC1 signaling pathways. Genes related to oxylipin metabolism such as Cox, Cyp450s, and cPla2 trended upwards. Joint analysis of metabolomics and transcriptomics supported a role for glycerophospholipid and sphingolipid metabolism following HDM sensitization. Collectively, our multi-omics results linked decreased glycerophospholipid and sphingolipid compounds and increased oxylipins with allergic sensitization; concurrent upregulation of associated gene pathways supports a role for bioactive lipids in the pathogenesis of allergy and asthma.

Metabolomics-based Sleepiness Markers for Risk Prevention and Traffic Safety (ME-SMART): a monocentric, controlled, randomized, crossover trial

BACKGROUND

Too little sleep and the consequences thereof are a heavy burden in modern societies. In contrast to alcohol or illicit drug use, there are no quick roadside or workplace tests for objective biomarkers for sleepiness. We hypothesize that changes in physiological functions (such as sleep-wake regulation) are reflected in changes of endogenous metabolism and should therefore be detectable as a change in metabolic profiles. This study will allow for creating a reliable and objective panel of candidate biomarkers being indicative for sleepiness and its behavioral outcomes.

METHODS

This is a monocentric, controlled, randomized, crossover, clinical study to detect potential biomarkers. Each of the anticipated 24 participants will be allocated in randomized order to each of the three study arms (control, sleep restriction, and sleep deprivation). These only differ in the amount of hours slept per night. In the control condition, participants will adhere to a 16/8 h wake/sleep regime. In both sleep restriction and sleep deprivation conditions, participants will accumulate a total sleep deficit of 8 h, achieved by different wake/sleep regimes that simulate real-life scenarios. The primary outcome is changes in the metabolic profile (i.e., metabolome) in oral fluid. Secondary outcome measures will include driving performance, psychomotor vigilance test, d2 Test of Attention, visual attention test, subjective (situational) sleepiness, electroencephalographic changes, behavioral markers of sleepiness, changes in metabolite concentrations in exhaled breath and finger sweat, and correlation of metabolic changes among biological matrices.

DISCUSSION

This is the first trial of its kind that investigates complete metabolic profiles combined with performance monitoring in humans over a multi-day period involving different sleep-wake schedules. Hereby, we aim to establish a candidate biomarker panel being indicative for sleepiness and its behavioral outcomes. To date, there are no robust and easily accessible biomarkers for the detection of sleepiness, even though the vast damage on society is well known. Thus, our findings will be of high value for many related disciplines.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier NCT05585515, released on 18.10.2022; Swiss National Clinical Trial Portal SNCTP000005089, registered on 12 August 2022.

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.

Integrated Proteomics and Metabolomics Analysis in Pregnant Rat Hippocampus After Circadian Rhythm Inversion

To investigate the changes in proteins, metabolites, and related mechanisms in the hypothalamus of pregnant rats after circadian rhythm inversion during the whole pregnancy cycle. A total of 12 Wistar female rats aged 7 weeks were randomly divided into control (six rats) and experimental (six rats) groups at the beginning of pregnancy. The control group followed a 12-h light and dark cycle (6 a.m. to 6 p.m. light, 6 p.m. to 6 a.m. dark the next day), and the experimental group followed a completely inverted circadian rhythm (6 p.m. to 6 a.m. light the next day, 6 a.m. to 6 p.m. dark). Postpartum data were collected until 7–24 h after delivery, and hypothalamus samples were collected in two groups for quantitative proteomic and metabolism analyses. The differential proteins and metabolites of the two groups were screened by univariate combined with multivariate statistical analyses, and the differential proteins and metabolites enriched pathways were annotated with relevant databases to analyze the potential mechanisms after circadian rhythm inversion. A comparison of postpartum data showed that circadian rhythm inversion can affect the number of offspring and the average weight of offspring in pregnant rats. Compared with the control group, the expression of 20 proteins and 37 metabolites was significantly changed in the experimental group. The integrated analysis between proteins and metabolites found that RGD1562758 and lysophosphatidylcholine acyltransferase 1 (LPCAT1) proteins were closely associated with carbon metabolism (choline, NAD+, L-glutamine, theobromine, D-fructose, and pyruvate) and glycerophospholipid metabolism (choline, NAD+, L-glutamine, phosphatidylcholine, theobromine, D-fructose, pyruvate, and arachidonate). Moreover, the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the differential metabolites enriched in adenosine triphosphate (ATP)–binding cassette (ABC) transporters. Our study suggested that circadian rhythm inversion in pregnant rats may affect the numbers, the average weight of offspring, and the expressions of proteins and metabolism in the hypothalamus, which may provide a comprehensive overview of the molecular profile of circadian rhythm inversion in pregnant groups.

A New Approach to Understanding Cancer-Related Fatigue: Leveraging the 3P Model to Facilitate Risk Prediction and Clinical Care

Simple Summary

For the growing number of cancer survivors worldwide, fatigue presents a major hurdle to function and quality of life. Treatment options for cancer-related fatigue are still emerging, and our current understanding of its etiology is limited. In this paper, we describe a new application of a comprehensive model for cancer-related fatigue: the predisposing, precipitating, and perpetuating (3P) factors model. We propose that the 3P model may be leveraged—particularly using metabolomics, the microbiome, and inflammation in conjunction with behavioral science—to better understand the pathophysiology of cancer-related fatigue.

Abstract

A major gap impeding development of new treatments for cancer-related fatigue is an inadequate understanding of the complex biological, clinical, demographic, and lifestyle mechanisms underlying fatigue. In this paper, we describe a new application of a comprehensive model for cancer-related fatigue: the predisposing, precipitating, and perpetuating (3P) factors model. This model framework outlined herein, which incorporates the emerging field of metabolomics, may help to frame a more in-depth analysis of the etiology of cancer-related fatigue as well as a broader and more personalized set of approaches to the clinical treatment of fatigue in oncology care. Included within this review paper is an in-depth description of the proposed biological mechanisms of cancer-related fatigue, as well as a presentation of the 3P model’s application to this phenomenon. We conclude that a clinical focus on organization risk stratification and treatment around the 3P model may be warranted, and future research may benefit from expanding the 3P model to understand fatigue not only in oncology, but also across a variety of chronic conditions.

The Study of Variation of Metabolites by Sleep Deficiency, and Intervention Possibility of Aerobic Exercise

The purpose of this study is to determine the difference in sleep-related factors and metabolites between normal sleep (NS) and sleep deficiency (SD) and to analyze the variations in metabolites according to the intensity of aerobic exercise under SD conditions. This study was conducted on 32 healthy male university students. Participants experienced both NS (8 h of sleep per night for 3 consecutive days) and SD (4 h of sleep per night for 3 consecutive days). After the SD period, the participants underwent treatment for 30 min by the assigned group [sleep supplement after SD (SSD), low-intensity aerobic exercise after SD (LES), moderate-intensity aerobic exercise after SD (MES), high-intensity aerobic exercise after SD (HES)]. For analysis, sleep-related factors were measured, and metabolites were analyzed by untargeted metabolite analysis using gas chromatography-time-of-flight mass spectrometry. As a result, SD showed that total sleep time (TST), duration of rapid eye movement (REM), duration of light sleep, and duration of deep sleep were significantly decreased compared to NS, whereas the Pittsburgh sleep quality index (PSQI), Epworth sleepiness scale (ESS), and visual analogue scale (VAS) were significantly increased compared to NS. The difference in metabolites between NS and SD showed that there were significant changes in the seven metabolites. There were 18 metabolites that changed according to the treatment groups in SD conditions. In summary, SD can exacerbate sleep quality, induce daytime sleepiness, increase fatigue, and increase metabolites that cause insulin resistance. Aerobic exercise under SD conditions can reduce metabolites that induce insulin resistance and increase the metabolites that help relieve depression caused by SD. However, HES has a negative effect, which increases fatigue, whereas LES has no negative effect. Thus, this study suggests that LES is the most appropriate exercise method under SD conditions.

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.

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.

Metabolomics in Sleep, Insomnia and Sleep Apnea

Sleep-wake disorders are highly prevalent disorders, which can lead to negative effects on cognitive, emotional and interpersonal functioning, and can cause maladaptive metabolic changes. Recent studies support the notion that metabolic processes correlate with sleep. The study of metabolite biomarkers (metabolomics) in a large-scale manner offers unique opportunities to provide insights into the pathology of diseases by revealing alterations in metabolic pathways. This review aims to summarize the status of metabolomic analyses-based knowledge on sleep disorders and to present knowledge in understanding the metabolic role of sleep in psychiatric disorders. Overall, findings suggest that sleep-wake disorders lead to pronounced alterations in specific metabolic pathways, which might contribute to the association of sleep disorders with other psychiatric disorders and medical conditions. These alterations are mainly related to changes in the metabolism of branched-chain amino acids, as well as glucose and lipid metabolism. In insomnia, alterations in branched-chain amino acid and glucose metabolism were shown among studies. In obstructive sleep apnea, biomarkers related to lipid metabolism seem to be of special importance. Future studies are needed to examine severity, subtypes and treatment of sleep-wake disorders in the context of metabolite levels.