Effects of Behavioral Cycle. The effects of the behavioral cycle, independent of the circadian cycle, on leptin, glucose, insulin, epinephrine, norepinephrine, and cortisol are shown in Fig. 2, Left panels. Leptin varied significantly across the behavioral cycle, with a trough around breakfast and a peak after the last meal, coinciding with the onset of the scheduled sleep episode (P 0.001, peak-to-trough 44%). Also, both glucose and insulin varied significantly across the behavioral cycle (glucose: P 0.001, peak-to-trough 26%; insulin: P 0.001, peak-to-trough 158%), presumably the result of the timing of meals. Both epinephrine and norepinephrine varied significantly across the behavioral cycle with peaks during the wake episode and troughs during the sleep episode (epinephrine: P 0.001, peak-totrough 83%; norepinephrine: P 0.001, peak-to-trough 72%). Cortisol varied significantly across the behavioral cycle, peaking after awakening and with a trough at the onset of the scheduled sleep episode (P 0.001, peak-to-trough 38%). Effect of Circadian Cycle. The effects of the circadian cycle, independent of the behavioral cycle, on leptin, glucose, insulin, epinephrine, norepinephrine, and cortisol, are shown in Fig. 2, Right panels. Glucose had a significant endogenous circadian rhythm (P 0.018, peak-to-trough 4%), with a peak during the biological night (circadian bin 300° and 0°; equivalent to22:30– 06:30 in these subjects). Epinephrine exhibited a significant endogenous circadian rhythm (P 0.001, peak-to-trough 53%), with a peak during the biological day (circadian bin 180°; equivalent to 14:30–18:30). Cortisol had a significant endogenous circadian rhythm (P 0.001, peak-to-trough 113%), with a peak at the end of the biological night (60°; close to habitual wake time). There were no significant circadian rhythms in leptin, insulin, or norepinephrine.

https://www.pnas.org/content/pnas/106/11/4453.full.pdf

Adverse metabolic and cardiovascular consequences of circadian misalignment

The Metabolic Consequences of Sleep Deprivation

Chronic sleep loss as a consequence of voluntary bedtime restriction is an endemic condition in modern society. Although sleep exerts marked modulatory effects on glucose metabolism, and molecular mechanisms for the interaction between sleeping and feeding have been documented, the potential impact of recurrent sleep curtailment on the risk for diabetes and obesity has only recently been investigated. In laboratory studies of healthy young adults submitted to recurrent partial sleep restriction, marked alterations in glucose metabolism including decreased glucose tolerance and insulin sensitivity have been demonstrated. The neuroendocrine regulation of appetite was also affected as the levels of the anorexigenic hormone leptin were decreased, whereas the levels of the orexigenic factor ghrelin were increased. Importantly, these neuroendocrine abnormalities were correlated with increased hunger and appetite, which may lead to overeating and weight gain. Consistent with these laboratory findings, a growing body of epidemiological evidence supports an association between short sleep duration and the risk for obesity and diabetes. Chronic sleep loss may also be the consequence of pathological conditions such as sleep-disordered breathing. In this increasingly prevalent syndrome, a feedforward cascade of negative events generated by sleep loss, sleep fragmentation, and hypoxia are likely to exacerbate the severity of metabolic disturbances. In conclusion, chronic sleep loss, behavioral or sleep disorder related, may represent a novel risk factor for weight gain, insulin resistance, and Type 2 diabetes.

http://sites.oxy.edu/clint/physio/article/sleeplossanovelriskfactorforinsulinresistance.pdf

Sleep loss: A novel risk factor for insulin resistance and Type 2 diabetes

In evolutionary terms, GH and intracellular STAT 5 signaling is a very old regulatory system. Whereas insulin dominates periprandially, GH may be viewed as the primary anabolic hormone during stress and fasting. GH exerts anabolic effects directly and through stimulation of IGF-I, insulin, and free fatty acids (FFA). When subjects are well nourished, the GH-induced stimulation of IGF-I and insulin is important for anabolic storage and growth of lean body mass (LBM), adipose tissue, and glycogen reserves. During fasting and other catabolic states, GH predominantly stimulates the release and oxidation of FFA, which leads to decreased glucose and protein oxidation and preservation of LBM and glycogen stores. The most prominent metabolic effect of GH is a marked increase in lipolysis and FFA levels. In the basal state, the effects of GH on protein metabolism are modest and include increased protein synthesis and decreased breakdown at the whole body level and in muscle together with decreased amino acid degradation/oxidation and decreased hepatic urea formation. During fasting and stress, the effects of GH on protein metabolism become more pronounced; lack of GH during fasting increases protein loss and urea production rates by approximately 50%, with a similar increase in muscle protein breakdown. GH is a counterregulatory hormone that antagonizes the hepatic and peripheral effects of insulin on glucose metabolism via mechanisms involving the concomitant increase in FFA flux and uptake. This ability of GH to induce insulin resistance is significant for the defense against hypoglycemia, for the development of "stress" diabetes during fasting and inflammatory illness, and perhaps for the "Dawn" phenomenon (the increase in insulin requirements in the early morning hours). Adult patients with GH deficiency are insulin resistant-probably related to increased adiposity, reduced LBM, and impaired physical performance-which temporarily worsens when GH treatment is initiated. Conversely, despite increased LBM and decreased fat mass, patients with acromegaly are consistently insulin resistant and become more sensitive after appropriate treatment.

Effects of growth hormone on glucose, lipid, and protein metabolism in human subjects.

During the past few decades, sleep curtailment has become a very common in industrialized countries. This trend for shorter sleep duration has developed over the same time period as the dramatic increase in the prevalence of obesity and diabetes. Evidence is rapidly accumulating to indicate that chronic partial sleep loss may increase the risk of obesity and diabetes. Laboratory studies in healthy volunteers have shown that experimental sleep restriction is associated with an adverse impact on glucose homeostasis. Insulin sensitivity decreases rapidly and markedly without adequate compensation in beta cell function, resulting in an elevated risk of diabetes. Prospective epidemiologic studies in both children and adults are consistent with a causative role of short sleep in the increased risk of diabetes. Sleep curtailment is also associated with a dysregulation of the neuroendocrine control of appetite, with a reduction of the satiety factor, leptin, and an increase in the hunger-promoting hormone, ghrelin. Thus, sleep loss may alter the ability of leptin and ghrelin to accurately signal caloric need, acting in concert to produce an internal misperception of insufficient energy availability. The adverse impact of sleep deprivation on appetite regulation is likely to be driven by increased activity in neuronal populations expressing the excitatory peptides orexins that promote both waking and feeding. Consistent with the laboratory evidence, multiple epidemiologic studies have shown an association between short sleep and higher body mass index after controlling for a variety of possible confounders.

/pdf/associations-between-sleep-loss-and-increased-risk-of-3hv9nde8qt.pdf

Associations between sleep loss and increased risk of obesity and diabetes

To define the roles of circadian rhythmicity (intrinsic effects of time of day independent of the sleep or wake condition) and sleep (intrinsic effects of the sleep condition, irrespective of the time of day) on the 24-h variation in glucose tolerance, eight normal men were studied during constant glucose infusion for a total of 53 h. The period of study included 8 h of nocturnal sleep, 28 h of continuous wakefulness, and 8 h of daytime sleep. Blood samples for the measurement of glucose, insulin, C-peptide, cortisol, and growth hormone were collected at 20-min intervals throughout the entire study. Insulin secretion rates were derived from C-peptide levels by deconvolution. Sleep was polygraphically monitored. During nocturnal sleep, levels of glucose and insulin secretion increased by 31 +/- 5% and 60 +/- 11%, respectively, and returned to baseline in the morning. During sleep deprivation, glucose levels and insulin secretion rose again to reach a maximum at a time corresponding to the beginning of the habitual sleep period. The magnitude of the rise above morning levels averaged 17 +/- 5% for glucose and 49 +/- 8% for calculated insulin secretion. Serum insulin levels did not parallel the circadian variation in insulin secretion, indicating the existence of an approximate 40% increase in insulin clearance during the night. Daytime sleep was associated with a 16 +/- 3% rise in glucose levels, a 55 +/- 7% rise in insulin secretion, and a 39 +/- 5% rise in serum insulin. The diurnal variation in insulin secretion was inversely related to the cortisol rhythm, with a significant correlation of the magnitudes of their morning to evening excursions. Sleep-associated rises in glucose correlated with the amount of concomitant growth hormone secreted. These studies demonstrate previously underappreciated effects of circadian rhythmicity and sleep on glucose levels, insulin secretion, and insulin clearance, and suggest that these effects could be partially mediated by cortisol and growth hormone.

/pdf/modulation-of-glucose-regulation-and-insulin-secretion-by-ari9x86rqc.pdf

Modulation of glucose regulation and insulin secretion by circadian rhythmicity and sleep.

In vivo studies of beta-cell secretory function have demonstrated the existence of rapid insulin oscillations of small amplitude recurring every 8-15 min in normal subjects. This study evaluated the effects of pancreas transplant on rapid insulin oscillations. Samples for glucose, insulin, and C-peptide were drawn during constant glucose infusion at 2-min intervals for 90 min from six successful Px patients with type I diabetes mellitus, from six normal nondiabetic control subjects, and from three Kx subjects. A computerized algorithm (ULTRA) was used for pulse detection. In the Px group, the average insulin pulse period was significantly shorter than in both the control and Kx groups (Px 8.1 +/- 0.5, control 12.5 +/- 0.7, Kx 12.4 +/- 0.5 min, P < 0.0005). By contrast, the C-peptide pulse periods (Px 16.8 +/- 2.3, control 14.7 +/- 1.2, Kx 15.3 +/- 1.5 min) were similar in the three groups. Spectral analysis confirmed that the frequency of the insulin pulses was increased in the Px group. The absolute amplitude of the insulin pulses was greater in the Px group (P < 0.001) while the amplitude of the C-peptide pulses did not differ between the groups. Cross-correlation analysis demonstrated maximal correlation coefficients at a lag of 0 min between insulin and C-peptide (control r = 0.33, P < 0.0001; Kx r = 0.17, P = 0.06) and between insulin and glucose (control r = 0.21, P < 0.001; Kx r = 0.20, P < 0.02) in the control and Kx groups, respectively, whereas no significant correlations were observed at any lag in the Px group.(ABSTRACT TRUNCATED AT 250 WORDS)

Oscillatory insulin secretion after pancreas transplant

The present study was undertaken to define the optimal experimental and analytical conditions necessary to reproducibly detect, in the systemic blood, small-amplitude rapid oscillations (period 8-15 min) of insulin and C-peptide. Samples for insulin, C-peptide, and glucose were drawn at 2-min intervals for 2 h from six normal subjects during constant glucose infusion and from five of the same subjects under basal conditions. To reduce measurement error, insulin and C-peptide levels were measured 16 times at each time point. Three algorithms for pulse analysis (ULTRA, Cluster, PulseFit) were used to identify significant pulses, whereas autocorrelation and spectral analysis were used to identify potential regular periodic components in the data. In three of the five subjects studied under basal conditions, regular rapid oscillations could be consistently detected by autocorrelation when the analysis was based on eight replicates but not on duplicate series. In the remaining two basal studies and in all studies during glucose infusion, the majority of profiles did not have a significant periodic component. However, formal pulse analysis demonstrated that the number of pulses was similar during glucose infusion and basal conditions. Reproducibility was enhanced by increasing the number of replicates used in the analysis. We conclude that in the analysis of small-amplitude rapid insulin and C-peptide oscillations, the sensitivity and specificity of the analysis is likely to be enhanced by performing multiple estimations at each time point and by using a minimum of two contrasting analytical approaches for pulse detection, which incorporate a method evaluating periodicity in conjunction with a pulse detection program designed to evaluate each individual oscillation separately.

D. C. Roland

Papers

Modulation of glucose regulation and insulin secretion by circadian rhythmicity and sleep.

Oscillatory insulin secretion after pancreas transplant

Analytical problems in detecting rapid insulin secretory pulses in normal humans.