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Light exposure fundamentally influences human physiology and behavior, with light being the most important zeitgeber of the circadian system. Throughout the day, people are exposed to various scenes differing in light level, spectral composition and spatio-temporal properties. Personalized light exposure can be measured through wearable light loggers and dosimeters, including wrist-worn actimeters containing light sensors, yielding time series of an individual's light exposure. There is growing interest in relating light exposure patterns to health outcomes, requiring analytic techniques to summarize light exposure properties. Building on the previously published Python-based pyActigraphy module, here we introduce the module pyLight . This module allows users to extract light exposure data recordings from a wide range of devices. It also includes software tools to clean and filter the data, and to compute common metrics for quantifying and visualizing light exposure data. For this tutorial, we demonstrate the use of pyLight in one example dataset with the following processing steps: (1) loading, accessing and visual inspection of a publicly available dataset, (2) truncation, masking, filtering and binarization of the dataset, (3) calculation of summary metrics, including time above threshold (TAT) and mean light timing above threshold (MLiT). The pyLight module paves the way for open-source, large-scale automated analyses of light-exposure data., Competing Interests: Disclosure statement No potential conflict of interest was reported by the author(s).

Previous studies have reported inconsistent results about exogenous melatonin's sleep-promoting effects. A possible explanation relies on the heterogeneity in administration schedule and dose, which might be accountable for differences in treatment efficacy. In this paper, we undertook a systematic review and meta-analysis of double-blind, randomized controlled trials performed on patients with insomnia and healthy volunteers, evaluating the effect of melatonin administration on sleep-related parameters. The standardized mean difference between treatment and placebo groups in terms of sleep onset latency and total sleep time were used as outcomes. Dose-response and meta-regression models were estimated to explore how time of administration, dose, and other treatment-related parameters might affect exogenous melatonin's efficacy. We included 26 randomized controlled trials published between 1987 and 2020, for a total of 1689 observations. Dose-response meta-analysis showed that melatonin gradually reduces sleep onset latency and increases total sleep time, peaking at 4 mg/day. Meta-regression models showed that insomnia status (β = 0.50, p < 0.001) and time between treatment administration and the sleep episode (β = -0.16, p = 0.023) were significant predictors of sleep onset latency, while the time of day (β = -0.086, p < 0.01) was the only significant predictor of total sleep time. Our results suggest that advancing the timing of administration (3 h before the desired bedtime) and increasing the administered dose (4 mg/day), as compared to the exogenous melatonin schedule most used in clinical practice (2 mg 30 min before the desired bedtime), might optimize the efficacy of exogenous melatonin in promoting sleep., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Circadian misalignment due to night work has been associated with an elevated risk for chronic diseases. We investigated the effects of circadian misalignment using shotgun protein profiling of peripheral blood mononuclear cells taken from healthy humans during a constant routine protocol, which was conducted immediately after participants had been subjected to a 3-day simulated night shift schedule or a 3-day simulated day shift schedule. By comparing proteomic profiles between the simulated shift conditions, we identified proteins and pathways that are associated with the effects of circadian misalignment and observed that insulin regulation pathways and inflammation-related proteins displayed markedly different temporal patterns after simulated night shift. Further, by integrating the proteomic profiles with previously assessed metabolomic profiles in a network-based approach, we found key associations between circadian dysregulation of protein-level pathways and metabolites of interest in the context of chronic metabolic diseases. Endogenous circadian rhythms in circulating glucose and insulin differed between the simulated shift conditions. Overall, our results suggest that circadian misalignment is associated with a tug of war between central clock mechanisms controlling insulin secretion and peripheral clock mechanisms regulating insulin sensitivity, which may lead to adverse long-term outcomes such as diabetes and obesity. Our study provides a molecular-level mechanism linking circadian misalignment and adverse long-term health consequences of night work.

The objective of the present study was to test the effects of an inpatient management system (CircadianCare) aimed at limiting the negative impact of hospitalization on sleep by enhancing circadian rhythmicity. Fifty inpatients were randomized to either CircadianCare ( n  = 25; 18 males, 62.4 ± 1.9 years) or standard of care ( n  = 25; 14 males, 64.5 ± 2.3 years). On admission, all underwent a full sleep-wake evaluation; they then completed daily sleep diaries and wore an actigraph for the whole length of hospitalization. On days 1 (T0), 7 (T1), and 14 (T2, if still hospitalized), salivary melatonin for dim light melatonin onset (DLMO) and 24-h skin temperature were recorded. In addition, environmental noise, temperature, and illuminance were monitored. Patients in the CircadianCare arm followed 1 of 3 schedules for light/dark, meal, and physical activity timings, based on their diurnal preference/habits. They wore short-wavelength-enriched light-emitting glasses for 45 min after awakening and short-wavelength light filter shades from 18:00 h until sleep onset. While the first, primary registered outcome (reduced sleep-onset latency on actigraphy or diary) was not met, based on sleep diaries, there was a trend (0.05 <  p  < 0.1) toward an advance in bedtime for CircadianCare compared to standard of care patients between T0 and T1. Similarly, DLMO time significantly advanced in the small group of patients for whom it could be computed on both occasions, with untreated ones starting from earlier baseline values. Patients sleeping near the window had significantly higher sleep efficiency, regardless of treatment arm. As noise fluctuation increased, so did the number of night awakenings, regardless of treatment arm. In conclusion, the CircadianCare management system showed positive results in terms of advancing sleep timing and the circadian rhythm of melatonin. Furthermore, our study identified a combination of environmental noise and lighting indices, which could be easily modulated to prevent hospitalization-related insomnia., Competing Interests: Conflict of interest statementThe authors have no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Purpose: Time-related eating patterns have been associated with metabolic and nutritional diseases such as obesity. However, there is a lack of representative studies on this subject. This study's aim was to assess the association between the timing of eating and obesity in a large and representative sample of the Brazilian adult population (POF 2008-2009 survey)., Methods: Two days of adults' food diary (n = 21,020) were used to estimate tertiles of first and last meal intake times, eating midpoint, caloric midpoint time, and calories consumed from 18:00 h onwards. BMI was estimated and its values, as well as excess weight (BMI ≥ 25 kg/m 2 ) and obesity (BMI ≥ 30 kg/m 2 ) were used as outcomes. Multiple linear and logistic regressions were performed., Results: The first (β = 0.65, 95% CI 0.37-0.93) and last food intake time (β = 0.40, 95% CI 0.14-0.66), eating midpoint (β = 0.61, 95% CI 0.34-0.88) and calories consumed after 21:00 h (β = 0.74, 95% CI 0.32-1.16) and 22:00 h (β = 0.75, 95% CI 0.18-1.32) were positively associated with BMI. The likelihood of having excess weight or obesity was significantly higher in the third tertile of the first food intake time (OR = 1.28, 95% CI 1.13-1.45 and OR = 1.34, 95% CI 1.13-1.58, respectively), last food intake time (OR = 1.16, 95% CI 1.03-1.32; and OR = 1.18, 95% CI 1.00-1.41, respectively), eating midpoint (OR = 1.28, 95% CI 1.13-1.45; and OR = 1.35, 95% CI 1.14-1.59, respectively) and energy consumption after 21:00 h (OR = 1.33, 95% CI 1.10-1.59)., Conclusion: Chrononutrition meal patterns indicative of late meal intake were significantly associated with high BMI, excess weight and obesity in the Brazilian population., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.)

Background and Aims: Chemotherapy-induced peripheral neurotoxicity (CIPN) is one of the most common dose-limiting side effects of paclitaxel (PTX) treatment. Many age-related changes have been hypothesized to underlie susceptibility to damage or impaired regeneration/repair after nerve injury. The results of these studies, however, are inconclusive and other potential biomarkers of nerve impairment need to be investigated., Methods: Twenty-four young (2 months) and 24 adult (9 months) Wistar male rats were randomized to either PTX treatment (10 mg/kg i.v. once/week for 4 weeks) or vehicle administration. Neurophysiological and behavioral tests were performed at baseline, after 4 weeks of treatment and 2-week follow-up. Skin biopsies and nerve specimens collected from sacrificed animals were examined for intraepidermal nerve fiber (IENF) density assessment and nerve morphology/morphometry. Blood and liver samples were collected for targeted metabolomics analysis., Results: At the end of treatment, the neurophysiological studies revealed a reduction in sensory nerve action potential amplitude (p < .05) in the caudal nerve of young PTX-animals, and in both the digital and caudal nerve of adult PTX-animals (p < .05). A significant decrease in the mechanical threshold was observed only in young PTX-animals (p < .001), but not in adult PTX-ones. Nevertheless, both young and adult PTX-rats had reduced IENF density (p < .0001), which persisted at the end of follow-up period. Targeted metabolomics analysis showed significant differences in the plasma metabolite profiles between PTX-animals developing peripheral neuropathy and age-matched controls, with triglycerides, diglycerides, acylcarnitines, carnosine, long chain ceramides, sphingolipids, and bile acids playing a major role in the response to PTX administration., Interpretation: Our study identifies for the first time multiple related metabolic axes involved in PTX-induced peripheral neurotoxicity, and suggests age-related differences in CIPN manifestations and in the metabolic profile., (© 2023 Peripheral Nerve Society.)

Background: Light exposure significantly impacts human health, regulating our circadian clock, sleep-wake cycle and other physiological processes. With the emergence of wearable light loggers and dosimeters, research on real-world light exposure effects is growing. There is a critical need to standardize data collection and documentation across studies., Results: This article proposes a new metadata descriptor designed to capture crucial information within personalized light exposure datasets collected with wearable light loggers and dosimeters. The descriptor, developed collaboratively by international experts, has a modular structure for future expansion and customization. It covers four key domains: study design, participant characteristics, dataset details, and device specifications. Each domain includes specific metadata fields for comprehensive documentation. The user-friendly descriptor is available in JSON format. A web interface simplifies generating compliant JSON files for broad accessibility. Version control allows for future improvements., Conclusions: Our metadata descriptor empowers researchers to enhance the quality and value of their light dosimetry datasets by making them FAIR (findable, accessible, interoperable and reusable). Ultimately, its adoption will advance our understanding of how light exposure affects human physiology and behaviour in real-world settings., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2024.)

Background: Night shift work is associated with sleep disturbances, obesity, and cardiometabolic diseases. Disruption of the circadian clock system has been suggested to be an independent cause of type 2 diabetes and cardiovascular disease in shift workers. We aimed to improve alignment of circadian timing with social and environmental factors with administration of melatonin., Methods: In a randomized, placebo-controlled, prospective study, we analysed the effects of 2 mg of sustained-release melatonin versus placebo on glucose tolerance, insulin resistance indices, sleep quality, circadian profiles of plasma melatonin and cortisol, and diurnal blood pressure profiles in 24 rotating night shift workers during 12 weeks of treatment, followed by 12 weeks of wash-out. In a novel design, the time of melatonin administration (at night or in the morning) depended upon the shift schedule. We also compared the baseline profiles of the night shift (NS) workers with 12 healthy non-night shift (NNS)-working controls., Results: We found significantly impaired indices of insulin resistance at baseline in NS versus NNS (p < 0.05), but no differences in oral glucose tolerance tests nor in the diurnal profiles of melatonin, cortisol, or blood pressure. Twelve weeks of melatonin treatment did not significantly improve insulin resistance, nor did it significantly affect diurnal blood pressure or melatonin and cortisol profiles. Melatonin administration, however, caused a significant improvement in sleep quality which was significantly impaired in NS versus NNS at baseline (p < 0.001)., Conclusions: Rotating night shift work causes mild-to-moderate impairment of sleep quality and insulin resistance. Melatonin treatment at bedtime improves sleep quality, but does not significantly affect insulin resistance in rotating night shift workers after 12 weeks of administration., Competing Interests: Declaration of Competing Interest None., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Obesity is a major cause of type 2 diabetes. Transition from obesity to type 2 diabetes manifests in the dysregulation of hormones controlling glucose homeostasis and inflammation. As metabolism is a dynamic process that changes across 24 h, we assessed diurnal rhythmicity in a panel of 10 diabetes-related hormones. Plasma hormones were analysed every 2 h over 24 h in a controlled laboratory study with hourly isocaloric drinks during wake. To separate effects of body mass from type 2 diabetes, we recruited three groups of middle-aged men: an overweight (OW) group with type 2 diabetes and two control groups (lean and OW). Average daily concentrations of glucose, triacylglycerol and all the hormones except visfatin were significantly higher in the OW group compared to the lean group (P < 0.001). In type 2 diabetes, glucose, insulin, C-peptide, glucose-dependent insulinotropic peptide and glucagon-like peptide-1 increased further (P < 0.05), whereas triacylglycerol, ghrelin and plasminogen activator inhibitor-1 concentrations were significantly lower compared to the OW group (P < 0.001). Insulin, C-peptide, glucose-dependent insulinotropic peptide and leptin exhibited significant diurnal rhythms in all study groups (P < 0.05). Other hormones were only rhythmic in 1 or 2 groups. In every group, hormones associated with glucose regulation (insulin, C-peptide, glucose-dependent insulinotropic peptide, ghrelin and plasminogen activator inhibitor-1), triacylglycerol and glucose peaked in the afternoon, whereas glucagon and hormones associated with appetite and inflammation peaked at night. Thus being OW with or without type 2 diabetes significantly affected hormone concentrations but did not affect the timing of the hormonal rhythms.

Intrinsically driven ultradian rhythms in the hourly range are often co-expressed with circadian rhythms in various physiological processes including metabolic processes such as feeding behaviour, gene expression and cellular metabolism. Several behavioural observations show that reduced energy intake or increased energy expenditure leads to a re-balancing of ultradian and circadian timing, favouring ultradian feeding and activity patterns when energy availability is limited. This suggests a close link between ultradian rhythmicity and metabolic homeostasis, but we currently lack models to test this hypothesis at a cellular level. We therefore transduced 3T3-L1 pre-adipocyte cells with a reporter construct that drives a destabilised luciferase via the Pdcd5 promotor, a gene we previously showed to exhibit robust ultradian rhythms in vitro . Ultradian rhythmicity in Pdcd5 promotor driven bioluminescence was observed in >80% of all cultures that were synchronised with dexamethasone, whereas significantly lower numbers exhibited ultradian rhythmicity in non-synchronised cultures (∼11%). Cosine fits to ultradian bioluminescence rhythms in cells cultured and measured in low glucose concentrations (2 mM and 5 mM), exhibited significantly higher amplitudes than all other cultures, and a shorter period (6.9 h vs. 8.2 h, N = 12). Our findings show substantial ultradian rhythmicity in Pdcd5 promotor activity in cells in which the circadian clocks have been synchronised in vitro , which is in line with observations of circadian synchronisation of behavioural ultradian rhythms. Critically, we show that the amplitude of ultradian rhythms is enhanced in low glucose conditions, suggesting that low energy availability enhances ultradian rhythmicity at the cellular level in vitro ., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Ting, Psomas, Skene and Van der Veen.)

The global COVID-19 pandemic resulted in widespread harms but also rapid advances in vaccine development, diagnostic testing, and treatment. As the disease moves to endemic status, the need to identify characteristic biomarkers of the disease for diagnostics or therapeutics has lessened, but lessons can still be learned to inform biomarker research in dealing with future pathogens. In this work, we test five sets of research-derived biomarkers against an independent targeted and quantitative Liquid Chromatography-Mass Spectrometry metabolomics dataset to evaluate how robustly these proposed panels would distinguish between COVID-19-positive and negative patients in a hospital setting. We further evaluate a crowdsourced panel comprising the COVID-19 metabolomics biomarkers most commonly mentioned in the literature between 2020 and 2023. The best-performing panel in the independent dataset-measured by F1 score (0.76) and AUROC (0.77)-included nine biomarkers: lactic acid, glutamate, aspartate, phenylalanine, β-alanine, ornithine, arachidonic acid, choline, and hypoxanthine. Panels comprising fewer metabolites performed less well, showing weaker statistical significance in the independent cohort than originally reported in their respective discovery studies. Whilst the studies reviewed here were small and may be subject to confounders, it is desirable that biomarker panels be resilient across cohorts if they are to find use in the clinic, highlighting the importance of assessing the robustness and reproducibility of metabolomics analyses in independent populations.

Human food intake and its timing are a complex behavior that can be influenced by a variety of factors, some of which may vary from season to season or from region to region. In this study, our aim was to investigate the seasonal variation in food intake times, with a particular focus on how these may vary across different regions of a country. We conducted an analysis of data from 20,622 adults from the National Household Budget Survey (POF-IBGE), encompassing complete food diaries collected from individuals residing in Brazil, and thereby ensuring representation across different latitudes. Each participant's daily food intake was reported for two non-consecutive days at different times in the same week using food diaries. An ANOVA revealed a later food intake time in the evening in high-latitude regions compared to low-latitude regions. The Sidak post-hoc test showed a significant interaction effect between region and season, demonstrating a pattern of early First Intake Time and Eating Midpoint in the Northeast region during spring/summer. Additionally, we observed an independent effect of the region, as early food intake times were found in low-latitude regions. These findings offer a basis for discussing food intake times among individuals living in different regions located on distinct latitudes.

Asymmetric dimethylarginine (ADMA) and symmetric dimethylarginine (SDMA) interfere with nitric oxide (NO) formation from L-arginine via different mechanisms. ADMA is a biomarker of cardiovascular disease and mortality, whilst SDMA is a biomarker of mortality after ischemic stroke. Homoarginine, another L-arginine-derived amino acid, is associated with stroke and congestive heart failure. Acute ischemic events like myocardial infarction show a time-of-day variation in the timing of their onset, as do NO-mediated vascular function and blood pressure. We studied whether the plasma concentrations of L-arginine-related amino acid metabolites show diurnal variation in a clinical study comparing 12 non-night shift workers with 60 rotating night shift workers. The plasma concentrations of L-arginine-related biomarkers, melatonin, and cortisol were measured every 3 h during a 24-h period. In addition, 24-h blood pressure recordings were performed. In non-night shift workers, L-arginine and homoarginine plasma concentrations showed diurnal variation with a 12-h period, which were both attenuated in night shift workers. ADMA and SDMA showed a 24-h rhythmicity with no significant differences in phase between night shift and non-night shift workers. The plasma profiles of melatonin and cortisol were not significantly different between both groups, suggesting that the rotating night shift work does not have a major influence on central suprachiasmatic nuclei clock timing. In addition, systolic and diastolic blood pressure patterns were similar between both groups. Our data show diurnal variation of dimethylarginines with the timing of their acrophases corresponding to the published timing of the peak incidence of cardiac ischemic events., Competing Interests: D.J.S. and B.M. are Past Co-Directors of Stockgrand Ltd. The other authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Circadian rhythms influence physiology, metabolism, and molecular processes in the human body. Estimation of individual body time (circadian phase) is therefore highly relevant for individual optimization of behavior (sleep, meals, sports), diagnostic sampling, medical treatment, and for treatment of circadian rhythm disorders. Here, we provide a partial least squares regression (PLSR) machine learning approach that uses plasma-derived metabolomics data in one or more samples to estimate dim light melatonin onset (DLMO) as a proxy for circadian phase of the human body. For this purpose, our protocol was aimed to stay close to real-life conditions. We found that a metabolomics approach optimized for either women or men under entrained conditions performed equally well or better than existing approaches using more labor-intensive RNA sequencing-based methods. Although estimation of circadian body time using blood-targeted metabolomics requires further validation in shift work and other real-world conditions, it currently may offer a robust, feasible technique with relatively high accuracy to aid personalized optimization of behavior and clinical treatment after appropriate validation in patient populations.

Circadian rhythms, metabolism, and nutrition are closely linked. 1 Timing of a three-meal daily feeding pattern synchronizes some human circadian rhythms. 2 Despite animal data showing anticipation of food availability, linked to a food-entrainable oscillator, 3 it is unknown whether human physiology predicts mealtimes and restricted food availability. In a controlled laboratory protocol, we tested the hypothesis that the human circadian system anticipates large meals. Twenty-four male participants undertook an 8-day laboratory study, with strict sleep-wake schedules, light-dark schedules, and food intake. For 6 days, participants consumed either hourly small meals throughout the waking period or two large daily meals (7.5 and 14.5 h after wake-up). All participants then undertook a 37-h constant routine. Interstitial glucose was measured every 15 min throughout the protocol. Hunger was assessed hourly during waking periods. Saliva melatonin was measured in the constant routine. During the 6-day feeding pattern, both groups exhibited increasing glucose concentration early each morning. In the small meal group, glucose concentrations continued to increase across the day. However, in the large meal group, glucose concentrations decreased from 2 h after waking until the first meal. Average 24-h glucose concentration did not differ between groups. In the constant routine, there was no difference in melatonin onset between groups, but antiphasic glucose rhythms were observed, with low glucose at the time of previous meals in the large meal group. Moreover, in the large meal group, constant routine hunger scores increased before the predicted meal times. These data support the existence of human food anticipation., Competing Interests: Declaration of interests J.D.J. has collaborated with Nestlé and has previously undertaken consultancy work for Kellogg’s., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Metabolic rhythms include rapid, ultradian (hourly) dynamics, but it remains unclear what their relationship to circadian metabolic rhythms is, and what role meal timing plays in coordinating these ultradian rhythms in metabolism. Here, we characterized widespread ultradian rhythms under ad libitum feeding conditions in the plasma metabolome of the vole, the gold standard animal model for behavioral ultradian rhythms, naturally expressing ~2-h foraging rhythms throughout the day and night. These ultradian metabolite rhythms co-expressed with diurnal 24-h rhythms in the same metabolites and did not align with food intake patterns. Specifically, under light-dark entrained conditions we showed twice daily entrainment of phase and period of ultradian behavioral rhythms associated with phase adjustment of the ultradian cycle around the light-dark and dark-light transitions. These ultradian activity patterns also drove an ultradian feeding pattern. We used a unique approach to map this behavioral activity/feeding status to high temporal resolution (every 90 min) measures of plasma metabolite profiles across the 24-h light-dark cycle. A total of 148 known metabolites were detected in vole plasma. Supervised, discriminant analysis did not group metabolite concentration by feeding status, instead, unsupervised clustering of metabolite time courses revealed clusters of metabolites that exhibited significant ultradian rhythms with periods different from the feeding cycle. Two clusters with dissimilar ultradian dynamics, one lipid-enriched (period = 3.4 h) and one amino acid-enriched (period = 4.1 h), both showed co-expression with diurnal cycles. A third cluster solely comprised of glycerophospholipids (specifically ether-linked phosphatidylcholines) expressed an 11.9 h ultradian rhythm without co-expressed diurnal rhythmicity. Our findings show coordinated co-expression of diurnal metabolic rhythms with rapid dynamics in feeding and metabolism. These findings reveal that ultradian rhythms are integral to biological timing of metabolic regulation, and will be important in interpreting the impact of circadian desynchrony and meal timing on metabolic rhythms., (© 2023 The Authors. The FASEB Journal published by Wiley Periodicals LLC on behalf of Federation of American Societies for Experimental Biology.)

Background: Metabolic abnormalities have long been predicted in Huntington's disease (HD) but remain poorly characterized. Chronobiological dysregulation has been described in HD and may include abnormalities in circadian-driven metabolism., Objective: Here we investigated metabolite profiles in the transgenic sheep model of HD (OVT73) at presymptomatic ages. Our goal was to understand changes to the metabolome as well as potential metabolite rhythm changes associated with HD., Methods: We used targeted liquid chromatography mass spectrometry (LC-MS) metabolomics to analyze metabolites in plasma samples taken from female HD transgenic and normal (control) sheep aged 5 and 7 years. Samples were taken hourly across a 27-h period. The resulting dataset was investigated by machine learning and chronobiological analysis., Results: The metabolic profiles of HD and control sheep were separable by machine learning at both ages. We found both absolute and rhythmic differences in metabolites in HD compared to control sheep at 5 years of age. An increase in both the number of disturbed metabolites and the magnitude of change of acrophase (the time at which the rhythms peak) was seen in samples from 7-year-old HD compared to control sheep. There were striking similarities between the dysregulated metabolites identified in HD sheep and human patients (notably of phosphatidylcholines, amino acids, urea, and threonine)., Conclusion: This work provides the first integrated analysis of changes in metabolism and circadian rhythmicity of metabolites in a large animal model of presymptomatic HD.

Background: Daily rhythms are observed in humans and almost all other organisms. Most of these observed rhythms reflect both underlying endogenous circadian rhythms and evoked responses from behaviours such as sleep/wake, eating/fasting, rest/activity, posture changes and exercise. For many research and clinical purposes, it is important to understand the contribution of the endogenous circadian component to these observed rhythms., Content: The goal of this manuscript is to provide guidance on best practices in measuring metrics of endogenous circadian rhythms in humans and promote the inclusion of circadian rhythms assessments in studies of health and disease. Circadian rhythms affect all aspects of physiology. By specifying minimal experimental conditions for studies, we aim to improve the quality, reliability and interpretability of research into circadian and daily (i.e., time-of-day) rhythms and facilitate the interpretation of clinical and translational findings within the context of human circadian rhythms. We describe protocols, variables and analyses commonly used for studying human daily rhythms, including how to assess the relative contributions of the endogenous circadian system and other daily patterns in behaviours or the environment. We conclude with recommendations for protocols, variables, analyses, definitions and examples of circadian terminology., Conclusion: Although circadian rhythms and daily effects on health outcomes can be challenging to distinguish in practice, this distinction may be important in many clinical settings. Identifying and targeting the appropriate underlying (patho)physiology is a medical goal. This review provides methods for identifying circadian effects to aid in the interpretation of published work and the inclusion of circadian factors in clinical research and practice., (© 2022 The Authors. Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.)

Mechanistic studies are needed to understand how rotating shift work perturbs metabolic processing. We collected plasma samples (n = 196) from 49 males, rotating car factory shift workers at the beginning and end of a night-shift (22:00-06:00 h) and day-shift (06:00 h-14:00 h). Samples underwent targeted LC-MS/MS metabolomics and concentrations of 130 metabolites were log 2 -transformed and pareto-scaled. An elastic net selected the most influential metabolites for linear mixed models examining within-person variation in metabolite levels at night-shift end (06:00 h) compared to day-shift start (06:00 h). Quantitative enrichment analysis explored differentially enriched biological pathways between sample time points. We included 20 metabolites (amino acids, biogenic amines, acylcarnitines, glycerophospholipids) in mixed models. Night-shift was associated with changes in concentrations of arginine (geometric mean ratio [GMR] 2.30, 95%CI 1.25, 4.23), glutamine (GMR 2.22, 95%CI 1.53, 3.24), kynurenine (GMR 3.22, 95%CI 1.05, 9.87), lysoPC18:2 (GMR 1.86, 95%CI 1.11, 3.11), lysoPC20:3 (GMR 2.48, 95%CI 1.05, 5.83), PCaa34:2 (GMR 2.27, 95%CI 1.16, 4.44), and PCae38:5 (GMR 1.66, 95%CI 1.02, 2.68). Tryptophan metabolism, glutathione metabolism, alanine metabolism, glycine and serine metabolism, and urea cycle were pathways differing between shifts. Night shift work was associated with changes in metabolites and the perturbation of metabolic and biochemical pathways related to a variety of health outcomes.

Treatments for COVID-19 infections have improved dramatically since the beginning of the pandemic, and glucocorticoids have been a key tool in improving mortality rates. The UK's National Institute for Health and Care Excellence guidance is for treatment to be targeted only at those requiring oxygen supplementation, however, and the interactions between glucocorticoids and COVID-19 are not completely understood. In this work, a multi-omic analysis of 98 inpatient-recruited participants was performed by quantitative metabolomics (using targeted liquid chromatography-mass spectrometry) and data-independent acquisition proteomics. Both 'omics datasets were analysed for statistically significant features and pathways differentiating participants whose treatment regimens did or did not include glucocorticoids. Metabolomic differences in glucocorticoid-treated patients included the modulation of cortisol and bile acid concentrations in serum, but no alleviation of serum dyslipidemia or increased amino acid concentrations (including tyrosine and arginine) in the glucocorticoid-treated cohort relative to the untreated cohort. Proteomic pathway analysis indicated neutrophil and platelet degranulation as influenced by glucocorticoid treatment. These results are in keeping with the key role of platelet-associated pathways and neutrophils in COVID-19 pathogenesis and provide opportunity for further understanding of glucocorticoid action. The findings also, however, highlight that glucocorticoids are not fully effective across the wide range of 'omics dysregulation caused by COVID-19 infections.

Background: The COVID-19 pandemic is likely to represent an ongoing global health issue given the potential for new variants, vaccine escape and the low likelihood of eliminating all reservoirs of the disease. Whilst diagnostic testing has progressed at a fast pace, the metabolic drivers of outcomes-and whether markers can be found in different biofluids-are not well understood. Recent research has shown that serum metabolomics has potential for prognosis of disease progression. In a hospital setting, collection of saliva samples is more convenient for both staff and patients, and therefore offers an alternative sampling matrix to serum., Methods: Saliva samples were collected from hospitalised patients with clinical suspicion of COVID-19, alongside clinical metadata. COVID-19 diagnosis was confirmed using RT-PCR testing, and COVID-19 severity was classified using clinical descriptors (respiratory rate, peripheral oxygen saturation score and C-reactive protein levels). Metabolites were extracted and analysed using high resolution liquid chromatography-mass spectrometry, and the resulting peak area matrix was analysed using multivariate techniques., Results: Positive percent agreement of 1.00 between a partial least squares-discriminant analysis metabolomics model employing a panel of 6 features (5 of which were amino acids, one that could be identified by formula only) and the clinical diagnosis of COVID-19 severity was achieved. The negative percent agreement with the clinical severity diagnosis was also 1.00, leading to an area under receiver operating characteristics curve of 1.00 for the panel of features identified., Conclusions: In this exploratory work, we found that saliva metabolomics and in particular amino acids can be capable of separating high severity COVID-19 patients from low severity COVID-19 patients. This expands the atlas of COVID-19 metabolic dysregulation and could in future offer the basis of a quick and non-invasive means of sampling patients, intended to supplement existing clinical tests, with the goal of offering timely treatment to patients with potentially poor outcomes., Competing Interests: The authors have declared that no competing interests exist.

The effect of COVID-19 infection on the human metabolome has been widely reported, but to date all such studies have focused on a single wave of infection. COVID-19 has generated numerous waves of disease with different clinical presentations, and therefore it is pertinent to explore whether metabolic disturbance changes accordingly, to gain a better understanding of its impact on host metabolism and enable better treatments. This work used a targeted metabolomics platform (Biocrates Life Sciences) to analyze the serum of 164 hospitalized patients, 123 with confirmed positive COVID-19 RT-PCR tests and 41 providing negative tests, across two waves of infection. Seven COVID-19-positive patients also provided longitudinal samples 2-7 months after infection. Changes to metabolites and lipids between positive and negative patients were found to be dependent on collection wave. A machine learning model identified six metabolites that were robust in diagnosing positive patients across both waves of infection: TG (22:1&#95;32:5), TG (18:0&#95;36:3), glutamic acid (Glu), glycolithocholic acid (GLCA), aspartic acid (Asp) and methionine sulfoxide (Met-SO), with an accuracy of 91%. Although some metabolites (TG (18:0&#95;36:3) and Asp) returned to normal after infection, glutamic acid was still dysregulated in the longitudinal samples. This work demonstrates, for the first time, that metabolic dysregulation has partially changed over the course of the pandemic, reflecting changes in variants, clinical presentation and treatment regimes. It also shows that some metabolic changes are robust across waves, and these can differentiate COVID-19-positive individuals from controls in a hospital setting. This research also supports the hypothesis that some metabolic pathways are disrupted several months after COVID-19 infection.