Greater sensitivity of the circadian system of women to bright light, but not dim-to-moderate light.

Women typically sleep and wake earlier than men and have been shown to have earlier circadian timing relative to the light/dark cycle that synchronizes the clock. A potential mechanism for earlier timing in women is an altered response of the circadian system to evening light. We characterized individual-level dose-response curves for light-induced melatonin suppression using a within-subjects protocol. Fifty-six participants (29 women, 27 men; aged 18-30 years) were exposed to a range of light illuminances (10, 30, 50, 100, 200, 400, and 2000 lux) using melatonin suppression relative to a dim control (<1 lux) as a marker of light sensitivity. Women were free from hormonal contraception. To examine the potential influence of sex hormones, estradiol and progesterone was examined in women and testosterone was examined in a subset of men. Menstrual phase was monitored using self-reports and estradiol and progesterone levels. Women exhibited significantly greater melatonin suppression than men under the 400-lux and 2000-lux conditions, but not under lower light conditions (10-200 lux). Light sensitivity did not differ by menstrual phase, nor was it associated with levels of estradiol, progesterone, or testosterone, suggesting the sex differences in light sensitivity were not acutely driven by circulating levels of sex hormones. These results suggest that sex differences in circadian timing are not due to differences in the response to dim/moderate light exposures typically experienced in the evening. The finding of increased bright light sensitivity in women suggests that sex differences in circadian timing could plausibly instead be driven by a greater sensitivity to phase-advancing effects of bright morning light.

A Protocol to Determine Circadian Phase by At-Home Salivary Dim Light Melatonin Onset Assessment.

Internal circadian phase assessment is increasingly acknowledged as a critical clinical tool for the diagnosis, monitoring, and treatment of circadian rhythm sleep-wake disorders and for investigating circadian timing in other medical disorders. The widespread use of in-laboratory circadian phase assessments in routine practice has been limited, most likely because circadian phase assessment is not required by formal diagnostic nosologies, and is not generally covered by insurance. At-home assessment of salivary dim light melatonin onset (DLMO, a validated circadian phase marker) is an increasingly accepted approach to assess circadian phase. This approach may help meet the increased demand for assessments and has the advantages of lower cost and greater patient convenience. We reviewed the literature describing at-home salivary DLMO assessment methods and identified factors deemed to be important to successful implementation. Here, we provide specific protocol recommendations for conducting at-home salivary DLMO assessments to facilitate a standardized approach for clinical and research purposes. Key factors include control of lighting, sampling rate, and timing, and measures of patient compliance. We include findings from implementation of an optimization algorithm to determine the most efficient number and timing of samples in patients with Delayed Sleep-Wake Phase Disorder. We also provide recommendations for assay methods and interpretation. Providing definitive criteria for each factor, along with detailed instructions for protocol implementation, will enable more widespread adoption of at-home circadian phase assessments as a standardized clinical diagnostic, monitoring, and treatment tool.

Power Analysis for Human Melatonin Suppression Experiments.

In humans, the nocturnal secretion of melatonin by the pineal gland is suppressed by ocular exposure to light. In the laboratory, melatonin suppression is a biomarker for this neuroendocrine pathway. Recent work has found that individuals differ substantially in their melatonin-suppressive response to light, with the most sensitive individuals being up to 60 times more sensitive than the least sensitive individuals. Planning experiments with melatonin suppression as an outcome needs to incorporate these individual differences, particularly in common resource-limited scenarios where running within-subjects studies at multiple light levels is costly and resource-intensive and may not be feasible with respect to participant compliance. Here, we present a novel framework for virtual laboratory melatonin suppression experiments, incorporating a Bayesian statistical model. We provide a Shiny web app for power analyses that allows users to modify various experimental parameters (sample size, individual-level heterogeneity, statistical significance threshold, light levels), and simulate a systematic shift in sensitivity (e.g., due to a pharmacological or other intervention). Our framework helps experimenters to design compelling and robust studies, offering novel insights into the underlying biological variability in melatonin suppression relevant for practical applications.

Accurate detection of acute sleep deprivation using a metabolomic biomarker-A machine learning approach.

Sleep deprivation enhances risk for serious injury and fatality on the roads and in workplaces. To facilitate future management of these risks through advanced detection, we developed and validated a metabolomic biomarker of sleep deprivation in healthy, young participants, across three experiments. Bi-hourly plasma samples from 2 × 40-hour extended wake protocols (for train/test models) and 1 × 40-hour protocol with an 8-hour overnight sleep interval were analyzed by untargeted liquid chromatography-mass spectrometry. Using a knowledge-based machine learning approach, five consistently important variables were used to build predictive models. Sleep deprivation (24 to 38 hours awake) was predicted accurately in classification models [versus well-rested (0 to 16 hours)] (accuracy = 94.7%/AUC 99.2%, 79.3%/AUC 89.1%) and to a lesser extent in regression (R2 = 86.1 and 47.8%) models for within- and between-participant models, respectively. Metabolites were identified for replicability/future deployment. This approach for detecting acute sleep deprivation offers potential to reduce accidents through "fitness for duty" or "post-accident analysis" assessments.

Higher central circadian temperature amplitude is associated with greater metabolite rhythmicity in humans.

Robust circadian rhythms are essential for optimal health. The central circadian clock controls temperature rhythms, which are known to organize the timing of peripheral circadian rhythms in rodents. In humans, however, it is unknown whether temperature rhythms relate to the organization of circadian rhythms throughout the body. We assessed core body temperature amplitude and the rhythmicity of 929 blood plasma metabolites across a 40-h constant routine protocol, controlling for behavioral and environmental factors that mask endogenous temperature rhythms, in 23 healthy individuals (mean [± SD] age = 25.4 ± 5.7 years, 5 women). Valid core body temperature data were available in 17/23 (mean [± SD] age = 25.6 ± 6.3 years, 1 woman). Individuals with higher core body temperature amplitude had a greater number of metabolites exhibiting circadian rhythms (R2 = 0.37, p = .009). Higher core body temperature amplitude was also associated with less variability in the free-fitted periods of metabolite rhythms within an individual (R2 = 0.47, p = .002). These findings indicate that a more robust central circadian clock is associated with greater organization of circadian metabolite rhythms in humans. Metabolite rhythms may therefore provide a window into the strength of the central circadian clock.

Personal light exposure patterns and incidence of type 2 diabetes: analysis of 13 million hours of light sensor data and 670,000 person-years of prospective observation.

Background: Light at night disrupts circadian rhythms, and circadian disruption is a risk factor for type 2 diabetes. Whether personal light exposure predicts diabetes risk has not been demonstrated in a large prospective cohort. We therefore assessed whether personal light exposure patterns predicted risk of incident type 2 diabetes in UK Biobank participants, using ∼13 million hours of light sensor data. Methods: Participants (N = 84,790, age (M ± SD) = 62.3 ± 7.9 years, 58% female) wore light sensors for one week, recording day and night light exposure. Circadian amplitude and phase were modeled from weekly light data. Incident type 2 diabetes was recorded (1997 cases; 7.9 ± 1.2 years follow-up; excluding diabetes cases prior to light-tracking). Risk of incident type 2 diabetes was assessed as a function of day and night light, circadian phase, and circadian amplitude, adjusting for age, sex, ethnicity, socioeconomic and lifestyle factors, and polygenic risk. Findings: Compared to people with dark nights (0-50th percentiles), diabetes risk was incrementally higher across brighter night light exposure percentiles (50-70th: multivariable-adjusted HR = 1.29 [1.14-1.46]; 70-90th: 1.39 [1.24-1.57]; and 90-100th: 1.53 [1.32-1.77]). Diabetes risk was higher in people with lower modeled circadian amplitude (aHR = 1.07 [1.03-1.10] per SD), and with early or late circadian phase (aHR range: 1.06-1.26). Night light and polygenic risk independently predicted higher diabetes risk. The difference in diabetes risk between people with bright and dark nights was similar to the difference between people with low and moderate genetic risk. Interpretation: Type 2 diabetes risk was higher in people exposed to brighter night light, and in people exposed to light patterns that may disrupt circadian rhythms. Avoidance of light at night could be a simple and cost-effective recommendation that mitigates risk of diabetes, even in those with high genetic risk. Funding: Australian Government Research Training Program.

Genome-wide association analysis of composite sleep health scores in 413,904 individuals.

Recent genome-wide association studies (GWASs) of several individual sleep traits have identified hundreds of genetic loci, suggesting diverse mechanisms. Moreover, sleep traits are moderately correlated, and together may provide a more complete picture of sleep health, while also illuminating distinct domains. Here we construct novel sleep health scores (SHSs) incorporating five core self-report measures: sleep duration, insomnia symptoms, chronotype, snoring, and daytime sleepiness, using additive (SHS-ADD) and five principal components-based (SHS-PCs) approaches. GWASs of these six SHSs identify 28 significant novel loci adjusting for multiple testing on six traits (p<8.3e-9), along with 341 previously reported loci (p<5e-08). The heritability of the first three SHS-PCs equals or exceeds that of SHS-ADD (SNP-h2=0.094), while revealing sleep-domain-specific genetic discoveries. Significant loci enrich in multiple brain tissues and in metabolic and neuronal pathways. Post GWAS analyses uncover novel genetic mechanisms underlying sleep health and reveal connections to behavioral, psychological, and cardiometabolic traits.