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All species organize behaviors to optimally match daily changes in the environment, leading to pronounced activity/rest cycles that track the light/dark cycle. Endogenous, approximately 24-hour circadian rhythms in the brain, autonomic nervous system, heart, and vasculature prepare the cardiovascular system for optimal function during these anticipated behavioral cycles. Cardiovascular circadian rhythms, however, may be a double-edged sword. The normal amplified responses in the morning may aid the transition from sleep to activity, but such exaggerated responses are potentially perilous in individuals susceptible to adverse cardiovascular events. Indeed, the occurrence of stroke, myocardial infarction, and sudden cardiac death all have daily patterns, striking most frequently in the morning. Furthermore, chronic disruptions of the circadian clock, as with night-shift work, contribute to increased cardiovascular risk. Here we highlight the importance of the circadian system to normal cardiovascular function and to cardiovascular disease, and identify opportunities for optimizing timing of medications in cardiovascular disease.

Significance It is established that glucose tolerance decreases from the morning to the evening, and that shift work is a risk factor for diabetes. However, the relative importance of the endogenous circadian system, the behavioral cycle (including the sleep/wake and fasting/feeding cycles), and circadian misalignment on glucose tolerance is unclear. We show that the magnitude of the effect of the endogenous circadian system on glucose tolerance and on pancreatic β-cell function was much larger than that of the behavioral cycle in causing the decrease in glucose tolerance from morning to evening. Also, independent from circadian phase and the behavioral cycle, circadian misalignment resulting from simulated night work lowered glucose tolerance—without diminishing effects upon repeated exposure—with direct relevance for shift workers. Glucose tolerance is lower in the evening and at night than in the morning. However, the relative contribution of the circadian system vs. the behavioral cycle (including the sleep/wake and fasting/feeding cycles) is unclear. Furthermore, although shift work is a diabetes risk factor, the separate impact on glucose tolerance of the behavioral cycle, circadian phase, and circadian disruption (i.e., misalignment between the central circadian pacemaker and the behavioral cycle) has not been systematically studied. Here we show—by using two 8-d laboratory protocols—in healthy adults that the circadian system and circadian misalignment have distinct influences on glucose tolerance, both separate from the behavioral cycle. First, postprandial glucose was 17% higher (i.e., lower glucose tolerance) in the biological evening (8:00 PM) than morning (8:00 AM; i.e., a circadian phase effect), independent of the behavioral cycle effect. Second, circadian misalignment itself (12-h behavioral cycle inversion) increased postprandial glucose by 6%. Third, these variations in glucose tolerance appeared to be explained, at least in part, by different mechanisms: during the biological evening by decreased pancreatic β-cell function (27% lower early-phase insulin) and during circadian misalignment presumably by decreased insulin sensitivity (elevated postprandial glucose despite 14% higher late-phase insulin) without change in early-phase insulin. We explored possible contributing factors, including changes in polysomnographic sleep and 24-h hormonal profiles. We demonstrate that the circadian system importantly contributes to the reduced glucose tolerance observed in the evening compared with the morning. Separately, circadian misalignment reduces glucose tolerance, providing a mechanism to help explain the increased diabetes risk in shift workers.

Obstructive sleep apnea is a risk factor for mortality, but its diagnostic metric-the apnea-hypopnea index-is a poor risk predictor. The apnea-hypopnea index does not capture the range of physiological variability within and between patients, such as degree of hypoxemia and sleep fragmentation, that reflect differences in pathophysiological contributions of airway collapsibility, chemoreceptive negative feedback loop gain, and arousal threshold. To test whether respiratory event duration, a heritable sleep apnea trait reflective of arousal threshold, predicts all-cause mortality. Mortality risk as a function of event duration was estimated by Cox proportional hazards in the Sleep Heart Health Study, a prospective community-based cohort. Gender-specific hazard ratios were also calculated. Among 5,712 participants, 1,290 deaths occurred over 11 years of follow-up. After adjusting for demographic factors (mean age, 63 yr; 52% female), apnea-hypopnea index (mean, 13.8; SD, 15.0), smoking, and prevalent cardiometabolic disease, individuals with the shortest-duration events had a significant hazard ratio for all-cause mortality of 1.31 (95% confidence interval, 1.11-1.54). This relationship was observed in both men and women and was strongest in those with moderate sleep apnea (hazard ratio, 1.59; 95% confidence interval, 1.11-2.28). Short respiratory event duration, a marker for low arousal threshold, predicts mortality in men and women. Individuals with shorter respiratory events may be predisposed to increased ventilatory instability and/or have augmented autonomic nervous system responses that increase the likelihood of adverse health outcomes, underscoring the importance of assessing physiological variation in obstructive sleep apnea.

There is considerable epidemiological evidence that shift work is associated with increased risk for obesity, diabetes, and cardiovascular disease, perhaps the result of physiologic maladaptation to chronically sleeping and eating at abnormal circadian times. To begin to understand underlying mechanisms, we determined the effects of such misalignment between behavioral cycles (fasting/feeding and sleep/wake cycles) and endogenous circadian cycles on metabolic, autonomic, and endocrine predictors of obesity, diabetes, and cardiovascular risk. Ten adults (5 female) underwent a 10-day laboratory protocol, wherein subjects ate and slept at all phases of the circadian cycle--achieved by scheduling a recurring 28-h \"day.\" Subjects ate 4 isocaloric meals each 28-h \"day.\" For 8 days, plasma leptin, insulin, glucose, and cortisol were measured hourly, urinary catecholamines 2 hourly (totaling [almost equal to]1,000 assays/subject), and blood pressure, heart rate, cardiac vagal modulation, oxygen consumption, respiratory exchange ratio, and polysomnographic sleep daily. Core body temperature was recorded continuously for 10 days to assess circadian phase. Circadian misalignment, when subjects ate and slept [almost equal to]12 h out of phase from their habitual times, systematically decreased leptin (-17%, P < 0.001), increased glucose (+6%, P < 0.001) despite increased insulin (+22%, P = 0.006), completely reversed the daily cortisol rhythm (P < 0.001), increased mean arterial pressure (+3%, P = 0.001), and reduced sleep efficiency (-20%, P < 0.002). Notably, circadian misalignment caused 3 of 8 subjects (with sufficient available data) to exhibit postprandial glucose responses in the range typical of a prediabetic state. These findings demonstrate the adverse cardiometabolic implications of circadian misalignment, as occurs acutely with jet lag and chronically with shift work.

Asthma often worsens at night. To determine if the endogenous circadian system contributes to the nocturnal worsening of asthma, independent of sleep and other behavioral and environmental day/night cycles, we studied patients with asthma (without steroid use) over 3 wk in an ambulatory setting (with combined circadian, environmental, and behavioral effects) and across the circadian cycle in two complementary laboratory protocols performed in dim light, which separated circadian from environmental and behavioral effects: 1) a 38-h “constant routine,” with continuous wakefulness, constant posture, 2-hourly isocaloric snacks, and 2) a 196-h “forced desynchrony” incorporating seven identical recurring 28-h sleep/wake cycles with all behaviors evenly scheduled across the circadian cycle. Indices of pulmonary function varied across the day in the ambulatory setting, and both laboratory protocols revealed significant circadian rhythms, with lowest function during the biological night, around 4:00 AM, uncovering a nocturnal exacerbation of asthma usually unnoticed or hidden by the presence of sleep. We also discovered a circadian rhythm in symptom-based rescue bronchodilator use (β2-adrenergic agonist inhaler) whereby inhaler use was four times more likely during the circadian night than day. There were additive influences on asthma from the circadian system plus sleep and other behavioral or environmental effects. Individuals with the lowest average pulmonary function tended to have the largest daily circadian variations and the largest behavioral cycle effects on asthma. When sleep was modeled to occur at night, the summed circadian, behavioral/environmental cycle effects almost perfectly matched the ambulatory data. Thus, the circadian system contributes to the common nocturnal worsening of asthma, implying that internal biological time should be considered for optimal therapy.

Objectives To evaluate the effect of a deep learning–based computer-aided diagnosis (DL-CAD) system on experienced and less-experienced radiologists in reading prostate mpMRI. Methods In this retrospective, multi-reader multi-case study, a consecutive set of 184 patients examined between 01/2018 and 08/2019 were enrolled. Ground truth was combined targeted and 12-core systematic transrectal ultrasound-guided biopsy. Four radiologists, two experienced and two less-experienced, evaluated each case twice, once without (DL-CAD-) and once assisted by DL-CAD (DL-CAD+). ROC analysis, sensitivities, specificities, PPV and NPV were calculated to compare the diagnostic accuracy for the diagnosis of prostate cancer (PCa) between the two groups (DL-CAD- vs. DL-CAD+). Spearman’s correlation coefficients were evaluated to assess the relationship between PI-RADS category and Gleason score (GS). Also, the median reading times were compared for the two reading groups. Results In total, 172 patients were included in the final analysis. With DL-CAD assistance, the overall AUC of the less-experienced radiologists increased significantly from 0.66 to 0.80 ( p = 0.001; cutoff ISUP GG ≥ 1) and from 0.68 to 0.80 ( p = 0.002; cutoff ISUP GG ≥ 2). Experienced radiologists showed an AUC increase from 0.81 to 0.86 ( p = 0.146; cutoff ISUP GG ≥ 1) and from 0.81 to 0.84 ( p = 0.433; cutoff ISUP GG ≥ 2). Furthermore, the correlation between PI-RADS category and GS improved significantly in the DL-CAD + group (0.45 vs. 0.57; p = 0.03), while the median reading time was reduced from 157 to 150 s ( p = 0.023). Conclusions DL-CAD assistance increased the mean detection performance, with the most significant benefit for the less-experienced radiologist; with the help of DL-CAD less-experienced radiologists reached performances comparable to that of experienced radiologists. Key Points • DL-CAD used as a concurrent reading aid helps radiologists to distinguish between benign and cancerous lesions in prostate MRI. • With the help of DL-CAD, less-experienced radiologists may achieve detection performances comparable to that of experienced radiologists. • DL-CAD assistance increases the correlation between PI-RADS category and cancer grade.

Abstract Risk for adverse cardiovascular events increases when blood pressure does not decrease at night (“non-dipping,” <10% decrease from daytime blood pressure). Shiftwork alters relationships between behaviors and endogenous circadian rhythms (i.e., circadian disruption along with variable sleep timing), and chronic shiftwork increases cardiovascular disease risk. To determine whether transitioning into shiftwork changes the overnight blood pressure dipping pattern, we leveraged a natural experiment that occurs when newly-hired bus operators transition from a daytime training schedule into an early-morning shiftwork or daywork schedule. Twenty participants were studied in a 90-day protocol upon new employment and underwent cardio-metabolic health assessments, including ambulatory blood pressure monitoring, and weekly sleep-wake diaries. Measurements were repeated after ~30 and 90 days after transitioning to a day or an early-morning shiftwork schedule. Newly-hired shiftworkers displayed dramatic changes in overnight blood pressure, with 62% converting from a healthy dipping blood pressure to the nondipping pattern, resulting in 93% of shiftworkers displaying a nondipping phenotype at 90-days. In contrast, 50% of dayworkers had a nondipping profile at baseline and this decreased to 0% at 90-days, a significant difference from shiftworkers (p = .001). At 90-days, overnight blood pressure dipping was ~7% less in shiftworkers than dayworkers (–6.3% [95%CI –3.7 to –8.8%] vs –13.1% [–10.3 to –15.9%]: p < .01), with changes in dipping associated with changes in sleep timing variability (r2 = .28, p = .03). The observed changes in overnight blood pressure dipping in newly-hired early-morning shiftworkers, which were associated with sleep timing variability, may be an early warning sign of increased cardiovascular risk among shiftworkers.

The risk of adverse cardiovascular events peaks in the morning (≈9:00 AM) with a secondary peak in the evening (≈8:00 PM) and a trough at night. This pattern is generally believed to be caused by the day/night distribution of behavioral triggers, but it is unknown whether the endogenous circadian system contributes to these daily fluctuations. Thus, we tested the hypotheses that the circadian system modulates autonomic, hemodynamic, and hemostatic risk markers at rest, and that behavioral stressors have different effects when they occur at different internal circadian phases. Twelve healthy adults were each studied in a 240-h forced desynchrony protocol in dim light while standardized rest and exercise periods were uniformly distributed across the circadian cycle. At rest, there were large circadian variations in plasma cortisol (peak-to-trough ≈85% of mean, peaking at a circadian phase corresponding to ≈9:00 AM) and in circulating catecholamines (epinephrine, ≈70%; norepinephrine, ≈35%, peaking during the biological day). At ≈8:00 PM, there was a circadian peak in blood pressure and a trough in cardiac vagal modulation. Sympathetic variables were consistently lowest and vagal markers highest during the biological night. We detected no simple circadian effect on hemostasis, although platelet aggregability had two peaks: at ≈noon and ≈11:00 PM. There was circadian modulation of the cardiovascular reactivity to exercise, with greatest vagal withdrawal at ≈9:00 AM and peaks in catecholamine reactivity at ≈9:00 AM and ≈9:00 PM. Thus, the circadian system modulates numerous cardiovascular risk markers at rest as well as their reactivity to exercise, with resultant profiles that could potentially contribute to the day/night pattern of adverse cardiovascular events.

Spontaneous synchronization over large networks is ubiquitous in nature, ranging from inanimate to biological systems. In the human brain, neuronal synchronization and de-synchronization occur during sleep, with the greatest degree of neuronal synchronization during slow wave sleep (SWS). The current sleep classification schema is based on electroencephalography and provides common criteria for clinicians and researchers to describe stages of non-rapid eye movement (NREM) sleep as well as rapid eye movement (REM) sleep. These sleep stage classifications have been based on convenient heuristic criteria, with little consideration of the accompanying normal physiological changes across those same sleep stages. To begin to resolve those inconsistencies, first focusing only on NREM sleep, we propose a simple cluster synchronization model to explain the emergence of SWS in healthy people without sleep disorders. We apply the empirical mode decomposition (EMD) analysis to quantify slow wave activity in electroencephalograms, and provide quantitative evidence to support our model. Based on this synchronization model, NREM sleep can be classified as SWS and non-SWS, such that NREM sleep can be considered as an intrinsically bistable process. Finally, we develop an automated algorithm for SWS classification. We show that this new approach can unify brain wave dynamics and their corresponding physiologic changes.