Melissa A. St. Hilaire

Human responses to bright light of different durations

•  Light is the strongest time cue for entrainment and phase resetting of the circadian clock. •  In humans, exposure to long‐duration light (6.5 h) in the late evening/early night causes phase delays, suppresses melatonin and increases alertness. •  Here we studied the effects of different durations of exposure to a single high‐intensity (∼10,000 lux) light pulse (0.2 h, 1 h, 2.5 h and 4.0 h) on phase shifting, suppression of melatonin and self‐reported sleepiness in young men and women. •  Phase‐resetting and melatonin‐suppression responses were dose dependent and non‐linear; shorter light exposures more efficiently phase‐shift the clock, suppress melatonin and induce alertness.

Human phase response curve to a 1 h pulse of bright white light

•  The human circadian pacemaker generates near‐24‐h rhythms that set the timing of many physiological, metabolic and behavioural body rhythms, and is synchronized to environmental time primarily by the 24 h light–dark cycle. •  The magnitude and direction of the resetting response of the pacemaker to light depends on the time of day of exposure, and the change in responses over the day is summarized in a phase response curve (PRC). •  A previous PRC showed that a 6.7 h bright white light exposure maximally shifted the circadian pacemaker by over 3 h. •  We show that a PRC to a 1 h bright white light pulse maximally shifted the circadian pacemaker by ∼2 h, despite representing only ∼15% of the exposure duration. •  This study demonstrates that the circadian pacemaker is sensitive to short‐duration light pulses with a non‐linear relationship between light duration and the amount of resetting.

Melanopsin and rod-cone photoreceptors play different roles in mediating pupillary light responses during exposure to continuous light in humans.

In mammals, the pupillary light reflex is mediated by intrinsically photosensitive melanopsin-containing retinal ganglion cells that also receive input from rod–cone photoreceptors. To assess the relative contribution of melanopsin and rod–cone photoreceptors to the pupillary light reflex in humans, we compared pupillary light responses in normally sighted individuals (n = 24) with a blind individual lacking rod–cone function. Here, we show that visual photoreceptors are required for normal pupillary responses to continuous light exposure at low irradiance levels, and for sustained pupillary constriction during exposure to light in the long-wavelength portion of the visual spectrum. In the absence of rod–cone function, pupillomotor responses are slow and sustained, and cannot track intermittent light stimuli, suggesting that rods/cones are required for encoding fast modulations in light intensity. In sighted individuals, pupillary constriction decreased monotonically for at least 30 min during exposure to continuous low-irradiance light, indicating that steady-state pupillary responses are an order of magnitude slower than previously reported. Exposure to low-irradiance intermittent green light (543 nm; 0.1–4 Hz) for 30 min, which was given to activate cone photoreceptors repeatedly, elicited sustained pupillary constriction responses that were more than twice as great compared with exposure to continuous green light. Our findings demonstrate nonredundant roles for rod–cone photoreceptors and melanopsin in mediating pupillary responses to continuous light. Moreover, our results suggest that it might be possible to enhance nonvisual light responses to low-irradiance exposures by using intermittent light to activate cone photoreceptors repeatedly in humans.

Human phase response curve to a single 6.5 h pulse of short‐wavelength light

•  The human ∼24 h circadian pacemaker ensures appropriate timing of physiological, behavioural and metabolic events and is synchronized to the 24 h day primarily by the 24 h light–dark cycle. •  The direction and magnitude of photic resetting depend on the timing of light exposure, and are described by a phase response curve (PRC). •  The human circadian photoreception system is functionally and anatomically distinct from the visual system and employs a novel photoreceptor, melanopsin, which is maximally sensitive to short‐wavelength (blue) visible light. •  We constructed a PRC to 6.5 h of blue (480 nm) light and compared it with a prior 6.7 h white light PRC; the blue light PRC achieved ∼75% of the resetting response of the white light PRC. •  This study suggests that short‐wavelength visible light exposures may be more efficient than traditional high‐intensity white light exposures for treatment of circadian rhythm sleep disorders.

Review: On Mathematical Modeling of Circadian Rhythms, Performance, and Alertness

Mathematical models of neurobehavioral performance and alertness have both basic science and practical applications. These models can be especially useful in predicting the effect of different sleep-wake schedules on human neurobehavioral objective performance and subjective alertness under many conditions. Several relevant models currently exist in the literature. In principle, the development and refinement of any mathematical model should be based on an explicit modeling methodology, such as the Box modeling paradigm, that formally defines the model structure and calculates the set of parameters. While most mathematical models of neurobehavioral performance and alertness include homeostatic, circadian, and sleep inertia components and their interactions, there may be fundamental differences in the equations included in these models. In part, these may be due to differences in the assumptions of the underlying physiology. Because the choice of model equations can have a dramatic influence on the results, it is necessary to consider these differences in assumptions when examining the results from a model and when comparing results across models. This article presents principles of mathematical modeling and examples of how such procedures can be applied to the development and refinement of mathematical models of neurobehavioral performance and alertness. This article also presents several methods of testing and comparing these models, suggests different uses of the models, and discusses problems with current models.

A physiologically based mathematical model of melatonin including ocular light suppression and interactions with the circadian pacemaker

Abstract:  The rhythm of plasma melatonin concentration is currently the most accurate marker of the endogenous human circadian pacemaker. A number of methods exist to estimate circadian phase and amplitude from the observed melatonin rhythm. However, almost all these methods are limited because they depend on the shape and amplitude of the melatonin pulse, which vary among individuals and can be affected by environmental influences, especially light. Furthermore, these methods are not based on the underlying known physiology of melatonin secretion and clearance, and therefore cannot accurately quantify changes in secretion and clearance observed under different experimental conditions. A published physiologically‐based mathematical model of plasma melatonin can estimate synthesis onset and offset of melatonin under dim light conditions. We amended this model to include the known effect of melatonin suppression by ocular light exposure and to include a new compartment to model salivary melatonin concentration, which is widely used in clinical settings to determine circadian phase. This updated model has been incorporated into an existing mathematical model of the human circadian pacemaker and can be used to simulate experimental protocols under a number of conditions.

Impact of common diabetes risk variant in MTNR1B on sleep, circadian and melatonin physiology

The risk of type 2 diabetes (T2D) is increased by abnormalities in sleep quantity and quality, circadian alignment, and melatonin regulation. A common genetic variant in a receptor for the circadian-regulated hormone melatonin (MTNR1B) is associated with increased fasting blood glucose and risk of T2D, but whether sleep or circadian disruption mediates this risk is unknown. We aimed to test if MTNR1B diabetes risk variant rs10830963 associates with measures of sleep or circadian physiology in intensive in-laboratory protocols (n = 58–96) or cross-sectional studies with sleep quantity and quality and timing measures from self-report (n = 4,307–10,332), actigraphy (n = 1,513), or polysomnography (n = 3,021). In the in-laboratory studies, we found a significant association with a substantially longer duration of elevated melatonin levels (41 min) and delayed circadian phase of dim-light melatonin offset (1.37 h), partially mediated through delayed offset of melatonin synthesis. Furthermore, increased T2D risk in MTNR1B risk allele carriers was more pronounced in early risers versus late risers as determined by 7 days of actigraphy. Our results provide the surprising insight that the MTNR1B risk allele influences dynamics of melatonin secretion, generating a novel hypothesis that the MTNR1B risk allele may extend the duration of endogenous melatonin production later into the morning and that early waking may magnify the diabetes risk conferred by the risk allele.

Circadian Melatonin Rhythm Following Traumatic Brain Injury.

Background: Sleep-wake disturbances are highly prevalent following traumatic brain injury (TBI), impeding rehabilitaion and quality of life. However, the mechanisms underlying these sleep disturnbances are unclear, and efficacious treatments are lacking. To investigate possible mechanisms underlying sleep disturbance in TBI, we examined characteristics of the circadian rhythm of melatonin, a hormone involved in sleep-wake regulation. We compared TBI patients reporting sleep disturbance with age- and gender-matched healthy volunteers. Methods: We conducted an overnight observational study with salivary melatonin samples collected hourly in 9 patients with severe TBI and 9 controls. Salivary dim light melatonin onset (DLMO) as well as melatonin synthesis onset (SynOn) and offset (SynOff) were used to determine circadian timing. Total overnight salivary melatonin production was calculated as the area under the curve from melatonin synthesis onset to offset. Results: Compared with healthy individuals, TBI patients showed 42% less melatonin production overnight (d = 0.87; P = .034). The timing of DLMO was delayed by approximately 1.5 hours in patients with TBI compared with controls (d = 1.23; P = .003). Conclusions: In patients with TBI, melatonin production was attenuated overnight, and the timing of melatonin secretion was delayed. We suggest that disruption to the circadian regulation of melatonin synthesis is a feature of severe TBI, possibly contributing to the sleep difficulties that are commonly reported in this population.

Comparison of Amplitude Recovery Dynamics of Two Limit Cycle Oscillator Models of the Human Circadian Pacemaker

At an organism level, the mammalian circadian pacemaker is a two‐dimensional system. For these two dimensions, phase (relative timing) and amplitude of the circadian pacemaker are commonly used. Both the phase and the amplitude (A) of the human circadian pacemaker can be observed within multiple physiological measures—including plasma cortisol, plasma melatonin, and core body temperature (CBT)—all of which are also used as markers of the circadian system. Although most previous work has concentrated on changes in phase of the circadian system, critically timed light exposure can significantly reduce the amplitude of the pacemaker. The rate at which the amplitude recovers to its equilibrium level after reduction can have physiological significance. Two mathematical models that describe the phase and amplitude dynamics of the pacemaker have been reported. These models are essentially equivalent in predictions of phase and in predictions of amplitude recovery for small changes from an equilibrium value (A=1), but are markedly different in the prediction of recovery rates when A<0.6. To determine which dynamic model best describes the amplitude recovery observed in experimental data; both models were fit to CBT data using a maximum likelihood procedure and compared using Akaikes Information Criterion (AIC). For all subjects, the model with the lower recovery rate provided a better fit to data in terms of AIC, supporting evidence that the amplitude recovery of the endogenous pacemaker is slow at low amplitudes. Experiments derived from model predictions are proposed to test the influence of low amplitude recovery on the physiological and neurobehavioral functions.

Analysis method and experimental conditions affect computed circadian phase from melatonin data.

Accurate determination of circadian phase is necessary for research and clinical purposes because of the influence of the master circadian pacemaker on multiple physiologic functions. Melatonin is presently the most accurate marker of the activity of the human circadian pacemaker. Current methods of analyzing the plasma melatonin rhythm can be grouped into three categories: curve-fitting, threshold-based and physiologically-based linear differential equations. To determine which method provides the most accurate assessment of circadian phase, we compared the ability to fit the data and the variability of phase estimates for seventeen different markers of melatonin phase derived from these methodological categories. We used data from three experimental conditions under which circadian rhythms - and therefore calculated melatonin phase - were expected to remain constant or progress uniformly. Melatonin profiles from older subjects and subjects with lower melatonin amplitude were less likely to be fit by all analysis methods. When circadian drift over multiple study days was algebraically removed, there were no significant differences between analysis methods of melatonin onsets (P = 0.57), but there were significant differences between those of melatonin offsets (P<0.0001). For a subset of phase assessment methods, we also examined the effects of data loss on variability of phase estimates by systematically removing data in 2-hour segments. Data loss near onset of melatonin secretion differentially affected phase estimates from the methods, with some methods incorrectly assigning phases too early while other methods assigning phases too late; missing data at other times did not affect analyses of the melatonin profile. We conclude that melatonin data set characteristics, including amplitude and completeness of data collection, differentially affect the results depending on the melatonin analysis method used.