The effect of high-dose, short-term caffeine intake on the renal clearance of calcium, sodium and creatinine in healthy adults.

The consumption of caffeine has been linked to osteoporosis, believed to be due to enhanced bone resorption as a result of increased calcium excretion in the urine. However, the amount of calcium in the urine may not necessarily reflect the true effect of caffeine on calcium clearance. This study therefore examined the impact of high-dose, short-term caffeine intake on renal clearance of calcium, sodium and creatinine in healthy adults. In a double-blind clinical study, participants chewed caffeine (n = 12) or placebo (n = 12) gum for 5 minutes at 2-hour intervals over a 6-hour treatment period (800 mg total caffeine). Caffeine increased renal calcium clearance by 77%. Furthermore, the effect was positively correlated with sodium clearance and urine volume, suggesting that caffeine may act through inhibition of sodium reabsorption in the proximal convoluted tubule. This study confirmed that caffeine does increase renal calcium clearance and fosters further investigation into safe consumption of caffeine.

The impact of caffeine consumption during 50 hr of extended wakefulness on glucose metabolism, self-reported hunger and mood state.

Caffeine is known for its capacity to mitigate performance decrements. The metabolic side-effects are less well understood. This study examined the impact of cumulative caffeine doses on glucose metabolism, self-reported hunger and mood state during 50 hr of wakefulness. In a double-blind laboratory study, participants were assigned to caffeine (n = 9, 6M, age 21.3 ± 2.1 years; body mass index 21.9 ± 1.6 kg/m2 ) or placebo conditions (n = 8, 4M, age 23.0 ± 2.8 years; body mass index 21.8 ± 1.6 kg/m2 ). Following a baseline sleep (22:00 hours-08:00 hours), participants commenced 50 hr of sleep deprivation. Meal timing and composition were controlled throughout the study. Caffeine (200 mg) or placebo gum was chewed for 5 min at 01:00 hours, 03:00 hours, 05:00 hours and 07:00 hours during each night of sleep deprivation. Continual glucose monitors captured interstitial glucose 2 hr post-breakfast, at 5-min intervals. Hunger and mood state were assessed at 10:00 hours, 16:30 hours, 22:30 hours and 04:30 hours. Caffeine did not affect glucose area under the curve (p = 0.680); however, glucose response to breakfast significantly increased after 2 nights of extended wakefulness compared with baseline (p = 0.001). There was a significant main effect of day, with increased tiredness (p < 0.001), mental exhaustion (p < 0.001), irritability (p = 0.002) and stress (p < 0.001) on the second day of extended wake compared with day 1. Caffeine attenuated the rise in tiredness (p < 0.001), mental exhaustion (p = 0.044) and irritability (p = 0.018) on day 1 but not day 2. Self-reported hunger was not affected by sleep deprivation or caffeine. These data confirm the effectiveness of caffeine in improving performance under conditions of sleep deprivation by reducing feelings of tiredness, mental exhaustion and irritability without exacerbating glucose metabolism and feelings of hunger.

Functional genomics analysis identifies T and NK cell activation as a driver of epigenetic clock progression.

BACKGROUND: Epigenetic clocks use DNA methylation (DNAm) levels of specific sets of CpG dinucleotides to accurately predict individual chronological age. A popular application of these clocks is to explore whether the deviation of predicted age from chronological age is associated with disease phenotypes, where this deviation is interpreted as a potential biomarker of biological age. This wide application, however, contrasts with the limited insight in the processes that may drive the running of epigenetic clocks. RESULTS: We perform a functional genomics analysis on four epigenetic clocks, including Hannum's blood predictor and Horvath's multi-tissue predictor, using blood DNA methylome and transcriptome data from 3132 individuals. The four clocks result in similar predictions of individual chronological age, and their constituting CpGs are correlated in DNAm level and are enriched for similar histone modifications and chromatin states. Interestingly, DNAm levels of CpGs from the clocks are commonly associated with gene expression in trans. The gene sets involved are highly overlapping and enriched for T cell processes. Further analysis of the transcriptome and methylome of sorted blood cell types identifies differences in DNAm between naive and activated T and NK cells as a probable contributor to the clocks. Indeed, within the same donor, the four epigenetic clocks predict naive cells to be up to 40 years younger than activated cells. CONCLUSIONS: The ability of epigenetic clocks to predict chronological age involves their ability to detect changes in proportions of naive and activated immune blood cells, an established feature of immuno-senescence. This finding may contribute to the interpretation of associations between clock-derived measures and age-related health outcomes.

Age-related DNA hydroxymethylation is enriched for gene expression and immune system processes in human peripheral blood.

DNA methylation (DNAm) has a well-established association with age in many tissues, including peripheral blood mononuclear cells (PBMCs). Compared to DNAm, the closely related epigenetic modification known as DNA hydroxymethylation (DNAhm) was much more recently discovered in mammals. Preliminary investigations have observed a positive correlation between gene body DNAhm and cis-gene expression. While some of these studies have observed an association between age and global DNAhm, none have investigated region-specific age-related DNAhm in human blood samples. In this study, we investigated DNAhm and gene expression in PBMCs of 10 young and 10 old, healthy female volunteers. Thousands of regions were differentially hydroxymethylated in the old vs. young individuals in gene bodies, exonic regions, enhancers, and promoters. Consistent with previous work, we observed directional consistency between age-related differences in DNAhm and gene expression. Further, age-related DNAhm and genes with high levels of DNAhm were enriched for immune system processes which may support a role of age-related DNAhm in immunosenescence.

How much is left in your "sleep tank"? Proof of concept for a simple model for sleep history feedback.

Technology-supported methods for sleep recording are becoming increasingly affordable. Sleep history feedback may help with fatigue-related decision making - Should I drive? Am I fit for work? This study examines a "sleep tank" model (SleepTank™), which is analogous to the fuel tank in a car, refilled by sleep, and depleted during wake. Required inputs are sleep period time and sleep efficiency (provided by many consumer-grade actigraphs). Outputs include suggested hours remaining to "get sleep" and percentage remaining in tank (Tank%). Initial proof of concept analyses were conducted using data from a laboratory-based simulated nightshift study. Ten, healthy males (18-35y) undertook an 8h baseline sleep opportunity and daytime performance testing (BL), followed by four simulated nightshifts (2000 h-0600 h), with daytime sleep opportunities (1000 h-1600 h), then an 8 h night-time sleep opportunity to return to daytime schedule (RTDS), followed by daytime performance testing. Psychomotor Vigilance Task (PVT) and Karolinska Sleepiness Scale were performed at 1200 h on BL and RTDS, and at 1830 h, 2130 h 0000 h and 0400 h each nightshift. A 40-minute York Driving Simulation was performed at 1730 h, 2030 h and 0300 h on each nightshift. Model outputs were calculated using sleep period timing and sleep efficiency (from polysomnography) for each participant. Tank% was a significant predictor of PVT lapses (p < 0.001), and KSS (p < 0.001), such that every 5% reduction resulted in an increase of two lapses, or one point on the KSS. Tank% was also a significant predictor of %time in the Safe Zone from the driving simulator (p = 0.001), such that every 1% increase in the tank resulted in a 0.75% increase in time spent in the Safe Zone. Initial examination of the correspondence between model predictions and performance and sleepiness measures indicated relatively good predictive value. Results provide tentative evidence that this "sleep tank" model may be an informative tool to aid in individual decision-making based on sleep history.

Effects of strategic early-morning caffeine gum administration on association between salivary alpha-amylase and neurobehavioural performance during 50 h of sleep deprivation.

Self-assessment is the most common method for monitoring performance and safety in the workplace. However, discrepancies between subjective and objective measures have increased interest in physiological assessment of performance. In a double-blind placebo-controlled study, 23 healthy adults were randomly assigned to either a placebo (n = 11; 5 F, 6 M) or caffeine condition (n = 12; 4 F, 8 M) while undergoing 50 h (i.e. two days) of total sleep deprivation. In previous work, higher salivary alpha-amylase (sAA) levels were associated with improved psychomotor vigilance and simulated driving performance in the placebo condition. In this follow-up article, the effects of strategic caffeine administration on the previously reported diurnal profiles of sAA and performance, and the association between sAA and neurobehavioural performance were investigated. Participants were given a 10 h baseline sleep opportunity (monitored via standard polysomnography techniques) prior to undergoing sleep deprivation (total sleep time: placebo = 8.83 ±â€¯0.48 h; caffeine = 9.01 ±â€¯0.48 h). During sleep deprivation, caffeine gum (200 mg) was administered at 01:00 h, 03:00 h, 05:00 h, and 07:00 h to participants in the caffeine condition (n = 12). This strategic administration of caffeine gum (200 mg) has been shown to be effective at maintaining cognitive performance during extended wakefulness. Saliva samples were collected, and psychomotor vigilance and simulated driving performance assessed at three-hour intervals throughout wakefulness. Caffeine effects on diurnal variability were compared with previously reported findings in the placebo condition (n = 11). The impact of caffeine on the circadian profile of sAA coincided with changes in neurobehavioural performance. Higher sAA levels were associated with improved performance on the psychomotor vigilance test during the first 24 h of wakefulness in the caffeine condition. However, only the association between sAA and response speed (i.e. reciprocal-transform of mean reaction time) was consistent across both days of sleep deprivation. The association between sAA and driving performance was not consistent across both days of sleep deprivation. Results show that the relationship between sAA and reciprocal-transform of mean reaction time on the psychomotor vigilance test persisted in the presence of caffeine, however the association was relatively weaker as compared with the placebo condition.

Eating on nightshift: A big vs small snack impairs glucose response to breakfast.

Shift work is a risk factor for chronic diseases such as Type 2 diabetes. Food choice may play a role, however simply eating at night when the body is primed for sleep may have implications for health. This study examined the impact of consuming a big versus small snack at night on glucose metabolism. N = 31 healthy subjects (21-35 y; 18 F) participated in a simulated nightshift laboratory study that included one baseline night of sleep (22:00 h-07:00 h) and one night awake with allocation to either a big snack (2100 kJ) or small snack (840 kJ) group. The snack was consumed between 00:00-00:30 h and consisted of low fat milk, a sandwich, chips and fruit (big snack) or half sandwich and fruit (small snack). Subjects ate an identical mixed meal breakfast (2100 kJ) at 08:30 h after one full night of sleep and a simulated nightshift. Interstitial glucose was measured continuously during the entire study using Medtronic Continual Glucose Monitors. Only subjects with identical breakfast consumption and complete datasets were analysed (N = 20). Glucose data were averaged into 5-minute bins and area under the curve (AUC) was calculated for 90 min post-breakfast. Pre-breakfast, glucose levels were not significantly different between Day1 and Day2, nor were they different between snack groups (p > 0.05). A snack group by day interaction effect was found (F1,16 = 5.36, p = 0.034) and post-hocs revealed that in the big snack group, AUC response to breakfast was significantly higher following nightshift (Day2) compared to Day1 (p = 0.001). This translated to a 20.8% (SEM 5.6) increase. AUC was not significantly different between days in the small snack group. Consuming a big snack at 00:00 h impaired the glucose response to breakfast at 08:30 h, compared to a smaller snack. Further research in this area will inform dietary advice for shift workers, which could include recommendations on how much to eat as well as content.

An industry case study of 'stand-up' and 'sleepover' night shifts in disability support: Residential support worker perspectives.

PURPOSE: Residential support workers (RSW) engage in overnight "sleepover" shifts as well as more traditional "standup" night shifts. While research has investigated the consequences of night and on-call work for sleep in other industries, the sleep of RSW has not been evaluated. METHOD: In a single-provider case study, six employees completed the Pittsburgh Sleep Quality Index (PSQI), the Depression Anxiety Stress Scale (DASS), a 2-week sleep diary, and a 30 min interview, and four also completed the Shirom-Melamed Burnout Measure (SMBM). RESULTS: Participants reported sleep of poor quality, low-mild DASS scores, and evidence of SMBM scores that were elevated relative to norms. Sleep was significantly lower (p < 0.01) following "standup" shifts (mean = 4.1, SD = 1.8 h) and during "sleepover" shifts (mean = 5.6, SD = 2.0 h) compared to non-shift nights (mean = 7.3, SD = 2.3 h). Interviews suggested that sleep fluctuates with level of patient care, colleague assistance, stress, and the quality of the sleeping environment (including bed comfort, light, noise and perceived safety). CONCLUSIONS: Findings suggest that this group have sleep that is insufficient and of poor quality and that they may be at risk of burnout. Consideration of ways to optimise sleeping conditions at work (e.g. through noise or stress reduction) would be beneficial. Research in this area has the potential to facilitate improvements in health and safety in this growing industry.

It's not just what you eat but when: The impact of eating a meal during simulated shift work on driving performance.

Shiftworkers have impaired performance when driving at night and they also alter their eating patterns during nightshifts. However, it is unknown whether driving at night is influenced by the timing of eating. This study aims to explore the effects of timing of eating on simulated driving performance across four simulated nightshifts. Healthy, non-shiftworking males aged 18-35 years (n = 10) were allocated to either an eating at night (n = 5) or no eating at night (n = 5) condition. During the simulated nightshifts at 1730, 2030 and 0300 h, participants performed a 40-min driving simulation, 3-min Psychomotor Vigilance Task (PVT-B), and recorded their ratings of sleepiness on a subjective scale. Participants had a 6-h sleep opportunity during the day (1000-1600 h). Total 24-h food intake was consistent across groups; however, those in the eating at night condition ate a large meal (30% of 24-h intake) during the nightshift at 0130 h. It was found that participants in both conditions experienced increased sleepiness and PVT-B impairments at 0300 h compared to 1730 and 2030 h (p < 0.001). Further, at 0300 h, those in the eating condition displayed a significant decrease in time spent in the safe zone (p < 0.05; percentage of time within 10 km/h of the speed limit and 0.8 m of the centre of the lane) and significant increases in speed variability (p < 0.001), subjective sleepiness (p < 0.01) and number of crashes (p < 0.01) compared to those in the no eating condition. Results suggest that, for optimal performance, shiftworkers should consider restricting food intake during the night.

The impact of meal timing on performance, sleepiness, gastric upset, and hunger during simulated night shift.

This study examined the impact of eating during simulated night shift on performance and subjective complaints. Subjects were randomized to eating at night (n=5; 23.2 ± 5.5 y) or not eating at night (n=5; 26.2 ± 6.4 y). All participants were given one sleep opportunity of 8 h (22:00 h-06:00 h) before transitioning to the night shift protocol. During the four days of simulated night shift participants were awake from 16:00 h-10:00 h with a daytime sleep of 6 h (10:00 h-16:00 h). In the simulated night shift protocol, meals were provided at ≈0700 h, 1900 h and 0130 h (eating at night); or ≈0700 h, 0930 h, 1410 h and 1900 h (not eating at night). Subjects completed sleepiness, hunger and gastric complaint scales, a Digit Symbol Substitution Task and a 10-min Psychomotor Vigilance Task. Increased sleepiness and performance impairment was evident in both conditions at 0400 h (p<0.05). Performance impairment at 0400 h was exacerbated when eating at night. Not eating at night was associated with elevated hunger and a small but significant elevation in stomach upset across the night (p<0.026). Eating at night was associated with elevated bloating on night one, which decreased across the protocol. Restricting food intake may limit performance impairments at night. Dietary recommendations to improve night-shift performance must also consider worker comfort.

Timing of food intake during simulated night shift impacts glucose metabolism: A controlled study.

Eating during the night may increase the risk for obesity and type 2 diabetes in shift workers. This study examined the impact of either eating or not eating a meal at night on glucose metabolism. Participants underwent four nights of simulated night work (SW1-4, 16:00-10:00 h, <50 lux) with a daytime sleep opportunity each day (10:00-16:00 h, <3 lux). Healthy males were assigned to an eating at night (NE; n = 4, meals; 07:00, 19:00 and 01:30 h) or not eating at night (NEN; n = 7, meals; 07:00 h, 09:30, 16:10 and 19:00 h) condition. Meal tolerance tests were conducted post breakfast on pre-night shift (PRE), SW4 and following return to day shift (RTDS), and glucose and insulin area under the curve (AUC) were calculated. Mixed-effects ANOVAs were used with fixed effects of condition and day, and their interactions, and a random effect of subject identifier on the intercept. Fasting glucose and insulin were not altered by day or condition. There were significant effects of day and condition × day (both p < 0.001) for glucose AUC, with increased glucose AUC observed solely in the NE condition from PRE to SW4 (p = 0.05) and PRE to RTDS (p < 0.001). There was also a significant effect of day (p = 0.007) but not condition × day (p = 0.825) for insulin AUC, with increased insulin from PRE to RTDS in both eating at night (p = 0.040) and not eating at night (p = 0.006) conditions. Results in this small, healthy sample suggest that not eating at night may limit the metabolic consequences of simulated night work. Further study is needed to explore whether matching food intake to the biological clock could reduce the burden of type 2 diabetes in shift workers.

Decreased salivary alpha-amylase levels are associated with performance deficits during sleep loss.

During sleep deprivation, neurobehavioral functions requiring sustained levels of attention and alertness are significantly impaired. Discrepancies between subjective measures of sleepiness and objective performance during sustained operations have led to interest in physiological monitoring of operator performance. Alertness, vigilance, and arousal are modulated by the wake-promoting actions of the central noradrenergic system. Salivary alpha-amylase (sAA) has been proposed as a sensitive peripheral measure of noradrenergic activity, but limited research has investigated the relationship between sAA and performance. In a laboratory-controlled environment, we investigated the relationship between sAA levels, subjective sleepiness, and performance during two days (50h) of total sleep deprivation. Beginning at 09:00, twelve healthy participants (5 females) aged 22.5±2.5years (mean±SD) provided saliva samples, recorded ratings of subjective sleepiness, completed a brief 3-min psychomotor vigilance task (PVT-B) and performed a 40-min simulated driving task, at regular 3h intervals during wakefulness. Ratings of subjective sleepiness exhibited a constant linear increase (p<0.001) during sleep deprivation. In contrast, sAA levels showed a marked diurnal profile, with levels increasing during the day (p<0.001) and steadily declining in the evening and early-morning (p<0.001). PVT-B (mean reaction time and mean slowest 10% reaction time) and simulated driving performance (speed deviation and lane deviation) also exhibited diurnal profiles across the two days of sleep deprivation. Performance peaked in the afternoon (p<0.001) and then steadily worsened as wakefulness continued into the evening and early-morning (p<0.001). Further analysis revealed that higher sAA levels in the hour preceding each performance assessment were associated with better PVT-B and driving performance (p<0.001). These findings suggest that sAA measures may be suitable indicators of performance deficits during sustained wakefulness and highlight the potential for sAA to be considered for physiological monitoring of performance. In operational environments sAA levels, as part of a panel of physiological measures, may be useful for assessing fitness-for-duty prior to safety being compromised or when performance deficits are unknown.

A longitudinal study of DNA methylation as a potential mediator of age-related diabetes risk.

DNA methylation (DNAm) has been found to show robust and widespread age-related changes across the genome. DNAm profiles from whole blood can be used to predict human aging rates with great accuracy. We sought to test whether DNAm-based predictions of age are related to phenotypes associated with type 2 diabetes (T2D), with the goal of identifying risk factors potentially mediated by DNAm. Our participants were 43 women enrolled in the Women's Health Initiative. We obtained methylation data via the Illumina 450K Methylation array on whole blood samples from participants at three timepoints, covering on average 16 years per participant. We employed the method and software of Horvath, which uses DNAm at 353 CpGs to form a DNAm-based estimate of chronological age. We then calculated the epigenetic age acceleration, or Δage, at each timepoint. We fit linear mixed models to characterize how Δage contributed to a longitudinal model of aging and diabetes-related phenotypes and risk factors. For most participants, Δage remained constant, indicating that age acceleration is generally stable over time. We found that Δage associated with body mass index (p = 0.0012), waist circumference (p = 0.033), and fasting glucose (p = 0.0073), with the relationship with BMI maintaining significance after correction for multiple testing. Replication in a larger cohort of 157 WHI participants spanning 3 years was unsuccessful, possibly due to the shorter time frame covered. Our results suggest that DNAm has the potential to act as a mediator between aging and diabetes-related phenotypes, or alternatively, may serve as a biomarker of these phenotypes.

Caffeine administration at night during extended wakefulness effectively mitigates performance impairment but not subjective assessments of fatigue and sleepiness.

The current study investigated the effects of repeated caffeine administration on performance and subjective reports of sleepiness and fatigue during 50h extended wakefulness. Twenty-four, non-smokers aged 22.5±2.9y (mean±SD) remained awake for two nights (50h) in a controlled laboratory environment. During this period, 200mg of caffeine or placebo gum was administered at 01:00, 03:00, 05:00 and 07:00 on both nights (total of 800mg/night). Neurobehavioral performance and subjective reports were assessed throughout the wake period. Caffeine improved performance compared to placebo, but did not affect overall ratings of subjective sleepiness and fatigue. Performance and sleepiness worsened with increasing time awake for both conditions. However, caffeine slowed performance impairments such that after 50h of wakefulness performance was better following caffeine administration compared to placebo. Caffeine also slowed the increase in subjective sleepiness and performance ratings, but only during the first night of wakefulness. After two nights of sleep deprivation, there was no difference in sleepiness ratings between the two conditions. These results demonstrate that strategic administration of caffeine effectively mitigates performance impairments associated with 50h wakefulness but does not improve overall subjective assessments of sleepiness, fatigue and performance. Results indicate that while performance impairment is alleviated, individuals may continue to report feelings of sleepiness. Individuals who use caffeine as a countermeasure in sustained operations may feel as though caffeine is not effective despite impairments in objective performance being largely mitigated.

Accuracy and reliability of the Nova StatStrip® glucose meter for real-time blood glucose determinations during glucose clamp studies.

AIMS/HYPOTHESIS: The Andres clamp technique, which requires accurate and timely determination of glucose, utilizes the Beckman or Yellow Springs Instruments (YSI) glucose analyzers. Both instruments require maintenance, a dedicated operator, preparation of a plasma sample, and a duplicate measurement that takes ≥2 minutes. The Nova StatStrip glucose meter was evaluated for accuracy, reliability, and near-real-time availability of glucose. METHODS: Blood samples from 24 patients who underwent 6-hour clamp studies and 12 patients who had a standardized meal tolerance test (SMT) were measured. Specimens were analyzed simultaneously and immediately upon collection by Beckman, YSI, and Nova. RESULTS: Of 1004 data pairs for the Nova device versus Beckman, the Nova data points ranged from 32 to 444, while Beckman ranged from 42 to 412. The coefficient for the slope of Beckman versus Nova was 1.009 (r = 0.978). Using error grid analysis, the number and percentage of values for Nova were 976 (97.2%) in the A zone and 28 (2.8%) in the B zone. Of 399 data pairs for the Nova device versus YSI, the Nova data points ranged from 46 to 255, whereas YSI ranged from 47 to 231. The coefficient for the slope of YSI versus Nova was 1.023 (r = 0.989). All Nova readings fell in the A zone. Time required for final reading, in duplicate, was 15 seconds for Nova and 120-180 seconds for Beckman and YSI. CONCLUSIONS: The simplicity of Nova and its reliability, accuracy, and speed make it an acceptable replacement device for Beckman and YSI in the conduct of clamps, especially when perturbations require rapid glucose determination.