Publications
The aims of the present study were to obtain sleep quality and sleep timing information in a group of university students and to evaluate the effects of a circadian hygiene education initiative. All students of the University of Padova (approximately 64,000) were contacted by e-mail (major campaigns in October 2019 and October 2020) and directed to an ad hoc website for collection of demographics and sleep quality/timing information. Participants (= 5,740) received one of two sets of circadian hygiene advice ("A regular life" or "Bright days and dark nights"). Every month, they were then asked how easy it had been to comply and provided with the advice again. At any even month from joining, they completed the sleep quality/timing questionnaires again. Information on academic performance was obtained, together with representative samples of lecture (= 5,972) and examination (= 1,800) timings, plus lecture attendances (= 25,302). Fifty-two percent of students had poor sleep quality, and 82% showed signs of social jetlag. Those who joined in October 2020, after several months of lockdown and distance learning, had better sleep quality, less social jetlag, and later sleep habits. Over approximately a year, the "Bright days and dark nights" advice resulted in significantly earlier get-up times compared with the "A regular life" advice. Similarly, it also resulted in a trend toward earlier midsleep (i.e., the midpoint, expressed as clock time, between sleep onset and sleep offset) and toward a decrease in the latency between wake-up and get-up time, with no impact on sleep duration. Significant changes in most sleep quality and sleep timing variables (i.e., fewer night awakenings, less social jetlag, and delayed sleep timing during lock-down) were observed in both advice groups over approximately a year, mostly in association with pandemic-related events characterizing 2020. Early chronotype students had better academic performances compared with their later chronotype counterparts. In a multivariate model, sleep quality, chronotype and study subject (science and technology, health and medical, or social and humanities) were independent predictors of academic performance. Taken together, these results underlie the importance of designing circadian-friendly university timetables.
It has been proposed that a high sugar intake was associated with cardiovascular disease (CVD) risk and metabolic syndrome depending on the amount of carbohydrate (CHO), other nutrients in foods, and underlying metabolic disturbances. This study aimed to investigate the effects of high (HS) and low sugar (LS) diets on metabolic profiles in 25 middle-aged men at increased CVD risk in a 12-week randomised cross-over intervention study. An isocaloric dietary exchanged model consisted of HS (24% energy from sugar) and LS (6% energy from sugar) with comparable total CHO, fat and fibre composition in normal foods was used. Anthropometric, blood pressure and plasma lipid profile were measured pre- and post-intervention. Body weight, waist circumference and fat mass increased and decreased significantly after HS (by 0.7±0.3 kg, 1.4±1.0 cm and 0.5±0.3 kg) and LS (by 2.1±0.5 kg, 2.0±0.8 cm and 1.4±0.3 kg) (p
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.
Circadian misalignment remains a distinct challenge for night shift workers. Variability in individual sleep-wake/light-dark patterns might contribute to individual differences in circadian alignment in night shift workers. In this simulation study, we compared the predicted phase shift from a mathematical model of the effect of light on the human circadian pacemaker to the observed melatonin phase shift among individuals who completed one of four interventions during simulated night shift work. Two inputs to the model were used to simulate circadian phase: sleep-wake/light-dark patterns measured from a wrist monitor (Simulation 1) and sleep-wake/light-dark patterns measured from a wrist monitor enhanced by known light levels measured at the level of the eye during simulated night shifts (Simulation 2). The estimated phase shift from the model was within 2 hours of the observed phase shift in similar to 80% of night shift workers for both simulations; none of the model-predicted phase shifts was more than similar to 3 hours from the observed phase shift. Overall, the root-mean-square error between observed and predicted phase shifts was better for Simulation 1. The light input from the wrist monitor informed by actual light level measured at the eye performed better in the sub-group exposed to bright light during their night shifts. The findings from this simulation study suggest that using a mathematical model combined with sleep-wake and light exposure data from a wrist monitor can facilitate the design of shift work schedules to enhance circadian alignment, which is expected to improve sleep, alertness, and performance.
Studying circadian rhythms in most human tissues is hampered by difficulty in collecting serial samples. Here we reveal circadian rhythms in the transcriptome and metabolic pathways of human white adipose tissue. Subcutaneous adipose tissue was taken from seven healthy males under highly controlled ‘constant routine’ conditions. Five biopsies per participant were taken at six-hourly intervals for microarray analysis and in silico integrative metabolic modelling. We identified 837 transcripts exhibiting circadian expression profiles (2% of 41619 transcript targeting probes on the array), with clear separation of transcripts peaking in the morning (258 probes) and evening (579 probes). There was only partial overlap of our rhythmic transcripts with published animal adipose and human blood transcriptome data. Morning-peaking transcripts associated with regulation of gene expression, nitrogen compound metabolism, and nucleic acid biology; evening-peaking transcripts associated with organic acid metabolism, cofactor metabolism and redox activity. In silico pathway analysis further indicated circadian regulation of lipid and nucleic acid metabolism; it also predicted circadian variation in key metabolic pathways such as the citric acid cycle and branched chain amino acid degradation. In summary, in vivo circadian rhythms exist in multiple adipose metabolic pathways, including those involved in lipid metabolism, and core aspects of cellular biochemistry.
Metabolic profiling of individuals with type 2 diabetes mellitus (T2DM) has previously been limited to single-time-point samples, ignoring time-of-day variation. Here, we tested our hypothesis that body mass and T2DM affect daily rhythmicity and concentrations of circulating metabolites across a 24-h day in 3 age-matched, male groups—lean, overweight/obese (OW/OB), and OW/OB with T2DM—in controlled laboratory conditions, which were not confounded by large meals. By using targeted liquid chromatography/mass spectrometry metabolomics, we quantified 130 plasma metabolites every 2 h over 24 h, and we show that average metabolite concentrations were significantly altered by increased body mass (90 of 130) and T2DM (56 of 130). Thirty-eight percent of metabolites exhibited daily rhythms in at least 1 study group, and where a metabolite was rhythmic in >1 group, its peak time was comparable. The optimal time of day was assessed to provide discriminating biomarkers. This differed between metabolite classes and study groups—for example, phospholipids showed maximal difference at 5:00 AM (lean vs. OW/OB) and at 5:00 PM (OW/OB vs. T2DM). Metabolites that were identified with both robust 24-h rhythms and significant concentration differences between study groups emphasize the importance of controlling the time of day for diagnosis and biomarker discovery, offering a significant improvement over current single sampling.—Isherwood, C. M., Van der Veen, D. R., Johnston, J. D., Skene, D. J. Twenty-four-hour rhythmicity of circulating metabolites: effect of body mass and type 2 diabetes. It is widely accepted that obesity is the main risk factor for type 2 diabetes mellitus (T2DM) (1). The progression from obesity to T2DM is largely a result of comorbidities, such as systemic inflammation and insulin resistance. Metabolic profiling by using targeted metabolomics, which enables the quantification of more than 100 low-MW intermediates of metabolism, is increasingly used to characterize (pre)diabetic phenotypes and has identified differences in metabolite profiles between those individuals who are obese and those with T2DM (2–5). Recent work by our group and others has shown a 24-h variation in the human metabolome in healthy individuals, analyzed by using a range of analytical platforms (6–12), which has demonstrated that an estimated 15–20% of the metabolome is rhythmic in blood (6, 7). Transgenic mice that carry targeted genetic manipulation of circadian clock genes also exhibit a phenotype that involves defective metabolism, and associations between the circadian timing system and metabolic responses have been reported in humans (13). Reviews of these studies, including the higher incidence of obesity, T2DM, and related disorders in shift workers, have recently been published (14, 15). Existing metabolomics studies in T2DM have been restricted to the analysis of single-time-point, mostly fasting, samples, which cannot characterize the effect of increased body mass and T2DM on rhythmic metabolites. Characterizing 24-h metabolite rhythms in T2DM compared with age- and body mass–matched controls may therefore provide novel insights into the etiology and progression of T2DM. Identification of the optimal time of day for blood sampling—when metabolite levels show the biggest difference between T2DM and controls—would also provide more discriminating diagnostic biomarkers, rather than taking a single morning fasting sample. We thus assessed the effect of increased body mass [overweight/obese (OW/OB)] and T2DM on 24-h rhythms of circulating metabolites in men by using a quantitative targeted liquid chromatography/mass spectrometry (LC/MS) metabolomics approach. As T2DM is often accompanied by obesity, we set out to distinguish the effects of T2DM from those of increased body mass by incorporating both a lean and an OW/OB control group into the current study design.
Dietary sugars are linked to the development of non-alcoholic fatty liver disease (NAFLD) and dyslipidaemia, but it is unknown if NAFLD itself influences the effects of sugars on plasma lipoproteins. To study this further, men with NAFLD (n=11) and low liver fat ‘controls’ (n= 14) were fed two iso-energetic diets, high or low in sugars (26% or 6% total energy) for 12 weeks, in a randomised, cross-over design. Fasting plasma lipid and lipoprotein kinetics were measured after each diet by stable isotope trace-labelling. There were significant differences in the production and catabolic rates of VLDL subclasses between men with NAFLD and controls, in response to the high and low sugar diets. Men with NAFLD had higher plasma concentrations of VLDL1-triacylglycerol (TAG) after the high (P
Circadian rhythms, metabolism and nutrition are intimately linked [1, 2], although effects of meal timing on the human circadian system are poorly understood. We investigated the effect of a 5-hour delay in meals on markers of the human master clock and multiple peripheral circadian rhythms. Ten healthy young men undertook a 13-day laboratory protocol. Three meals (breakfast, lunch, dinner) were given at 5-hour intervals, beginning either 0.5 (early) or 5.5 (late) hours after wake. Participants were acclimated to early meals and then switched to late meals for 6 days. After each meal schedule, participants' circadian rhythms were measured in a 37-hour constant routine that removes sleep and environmental rhythms while replacing meals with hourly isocaloric snacks. Meal timing did not alter actigraphic sleep parameters before circadian rhythm measurement. In constant routines, meal timing did not affect rhythms of subjective hunger and sleepiness, master clock markers (plasma melatonin and cortisol), plasma triglycerides, or clock gene expression in whole blood. Following late meals, however, plasma glucose rhythms were delayed by 5.69 ± 1.29 hours (p < 0.001) and average glucose concentration decreased by 0.27 ± 0.05 mM (p < 0.001). In adipose tissue, PER2 mRNA rhythms were delayed by 0.97 ± 0.29 hours (p < 0.01), indicating that human molecular clocks may be regulated by feeding time and could underpin plasma glucose changes. Timed meals therefore play a role in synchronising peripheral circadian rhythms in humans, and may have particular relevance for patients with circadian rhythm disorders, shift workers, and transmeridian travellers.
Studying communities with different levels of urbanization may further the understanding of risk factors underlying metabolic diseases. The present study is unique by comprising detailed assessment of sleep and activity, biological rhythms, and metabolic factors of men from the same geographical location and place of birth that reside in different, rural vs. town, stages of urbanization. Sleep patterns, activity, and metabolic indicators in two groups (rural, n = 22 and town/urban, n = 20) of men residing in an Amazonian community (Xapuri, Acre, Brazil) were compared. Sociodemographic, anthropometric, and metabolic variables – fasting glucose, insulin resistance, triglycerides, total HDL cholesterol, LDL cholesterol, and VLDL cholesterol – were assessed. Sleep patterns, light exposure, and physical activity levels were additionally assessed by actigraphy, plus daily activities were recorded in diaries for 10 days. Town/urban dwellers were found to have significantly higher body weight, fasting glucose, insulin levels, and insulin resistance than rural dwellers, whereas triglycerides levels were similar. Town/Urban dwellers had shorter sleep duration (p < .01) and later sleep onset and offset times (p = .01). Our findings show an association between stage of urbanization and presence of risk factors for metabolic disorders, such as overweight, insulin resistance, increased glucose levels, short sleep duration, and less natural light exposure during work times.
Urbanization has contributed to extended wakefulness, which may in turn be associated with eating over a longer period. Here, we present a field study conducted in four groups with different work hours and places of living in order to investigate eating behavior (duration, content, and timing). Anthropometric measures were taken from the participants (rural (n = 22); town (n = 19); city-day workers (n = 11); city-night workers (n = 14)). In addition, a sociodemographic questionnaire was self-answered and 24-h food recalls were applied for three days. The 24-h food recalls revealed that fat intake varied according to the groups, with the highest consumption by the city-day workers. By contrast, city-day workers had the lowest intake of carbohydrate, whereas the rural group had the highest. In general, all groups had some degree of inadequacy in food consumption. Eating duration was negatively correlated with total energy intake, fat, and protein consumption in the rural and town groups. There was a positive correlation between body mass index and eating duration in both city groups. The rural group had the earliest start time of eating, and this was associated with a lower body mass index. This study suggested that food content and timing, as well as eating duration, differed according to place of living, which in turn may be linked to lifestyle.
Intrinsically photosensitive retinal ganglion cells (ipRGCs), whose photopigment melanopsin has a peak of sensitivity in the short wavelength range of the spectrum, constitute a common light input pathway to the olivary pretectal nucleus (OPN), the pupillary light reflex (PLR) regulatory centre, and to the suprachiasmatic nuclei (SCN), the major pacemaker of the circadian system. Thus, evaluating PLR under short wavelength light (λmax 500 nm) and creating an integrated PLR parameter, as a possible tool to indirectly assess the status of the circadian system, becomes of interest. Nine monochromatic, photon-matched light stimuli (300 s), in 10 nm increments from λmax 420 to 500 nm were administered to 15 healthy young participants (8 females), analyzing: i) the PLR; ii) wrist temperature (WT) and motor activity rhythms (WA), iii) light exposure (L) pattern and iv) diurnal preference (Horne- Östberg), sleep quality (Pittsburgh) and daytime sleepiness (Epworth). Linear correlations between the different PLR parameters and circadian status index obtained from WT, WA and L recordings and scores from questionnaires were calculated. In summary, we found markers of robust circadian rhythms, namely high stability, reduced fragmentation, high amplitude, phase advance and low internal desynchronization, were correlated with a reduced PLR to 460–490 nm wavelengths. Integrated circadian (CSI) and PLR (cp-PLR) parameters are proposed, that also showed an inverse correlation. These results demonstrate, for the first time, the existence of a close relationship between the circadian system robustness and the pupillary reflex response, two non-visual functions primarily under melanopsin-ipRGC input.
The pupillary light reflex (PLR) is a neurological reflex driven by rods, cones, and melanopsin-containing retinal ganglion cells. Our aim was to achieve a more precise picture of the effects of 5-min duration monochromatic light stimuli, alone or in combination, on the human PLR, to determine its spectral sensitivity and to assess the importance of photon flux. Using pupillometry, the PLR was assessed in 13 participants (6 women) aged 27.2 ± 5.41 years (mean ± SD) during 5-min light stimuli of purple (437 nm), blue (479 nm), red (627 nm), and combinations of red+purple or red+blue light. In addition, nine 5-min, photon-matched light stimuli, ranging in 10 nm increments peaking between 420 and 500 nm were tested in 15 participants (8 women) aged 25.7 ± 8.90 years. Maximum pupil constriction, time to achieve this, constriction velocity, area under the curve (AUC) at short (0–60 s), and longer duration (240–300 s) light exposures, and 6-s post-illumination pupillary response (6-s PIPR) were assessed. Photoreceptor activation was estimated by mathematical modeling. The velocity of constriction was significantly faster with blue monochromatic light than with red or purple light. Within the blue light spectrum (between 420 and 500 nm), the velocity of constriction was significantly faster with the 480 nm light stimulus, while the slowest pupil constriction was observed with 430 nm light. Maximum pupil constriction was achieved with 470 nm light, and the greatest AUC0−60 and AUC240−300 was observed with 490 and 460 nm light, respectively. The 6-s PIPR was maximum after 490 nm light stimulus. Both the transient (AUC0−60) and sustained (AUC240−300) response was significantly correlated with melanopic activation. Higher photon fluxes for both purple and blue light produced greater amplitude sustained pupillary constriction. The findings confirm human PLR dependence on wavelength, monochromatic or bichromatic light and photon flux under 5-min duration light stimuli. Since the most rapid and high amplitude PLR occurred within the 460–490 nm light range (alone or combined), our results suggest that color discrimination should be studied under total or partial substitution of this blue light range (460–490 nm) by shorter wavelengths (~440 nm). Thus for nocturnal lighting, replacement of blue light with purple light might be a plausible solution to preserve color discrimination while minimizing melanopic activation.
Circadian rhythms, metabolism, and nutrition are closely linked.1 Timing of a 3-meal daily feeding pattern synchronises some human circadian rhythms.2 Despite animal data showing anticipation of food availability, linked to a Food Entrainable Oscillator3, 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 six 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-hour constant routine. Interstitial glucose was measured every 15 minutes 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.