The time of dim light melatonin onset (DLMO) is the gold standard for circadian phase assessment in humans, but collection of samples for DLMO is time and resourceintensive. Numerous studies have attempted to estimate circadian phase from actigraphy data, but most of these studies have involved individuals on controlled and stable sleep-wake schedules, with mean errors reported between 0.5 and 1 hour. We found that such algorithms are less successful in estimating DLMO in a population of college students with more irregular schedules: Mean errors in estimating the time of DLMO are approximately 1.5-1.6 hours. We reframed the problem as a classification problem and estimated whether an individual's current phase was before or after DLMO. Using a neural network, we found high classification accuracy of about 90%, which decreased the mean error in DLMO estimation-identifying the time at which the switch in classification occurs-to approximately 1.3 hours. To test whether this classification approach was valid when activity and circadian rhythms are decoupled, we applied the same neural network to data from inpatient forced desynchrony studies in which participants are scheduled to sleep and wake at all circadian phases (rather than their habitual schedules). In participants on forced desynchrony protocols, overall classification accuracy dropped to 55%-65% with a range of 20%-80% for a given day; this accuracy was highly dependent upon the phase angle (ie, time) between DLMO and sleep onset, with the highest accuracy at phase angles associated with nighttime sleep. Circadian patterns in activity, therefore, should be included 2 of 13 | BROWN et al.

The molecular circadian clock is driven by interlocked transcriptional-translational feedback loops, producing oscillations in the expressions of genes and proteins to coordinate the timing of biological processes throughout the body. Modeling this system gives insight into the underlying processes driving oscillations in an activator-repressor architecture and allows us to make predictions about how to manipulate these oscillations. The knockdown or upregulation of different cellular components using small molecules can disrupt these rhythms, causing a phase shift, and we aim to determine the dosing of such molecules with a model-based control strategy. Mathematical models allow us to predict the phase response of the circadian clock to these interventions and time them appropriately but only if the model has enough physiological detail to describe these responses while maintaining enough simplicity for online optimization. We build a control-relevant, physiologically-based model of the two main feedback loops of the mammalian molecular clock, which provides sufficient detail to consider multi-input control. Our model captures experimentally observed peak to trough ratios, relative abundances, and phase differences in the model species, and we independently validate this model by showing that the in silico model reproduces much of the behavior that is observed in vitro under genetic knockout conditions. Because our model produces valid phase responses, it can be used in a model predictive control algorithm to determine inputs to shift phase. Our model allows us to consider multi-input control through small molecules that act on both feedback loops, and we find that changes to the parameters of the negative feedback loop are much stronger inputs for shifting phase. The strongest inputs predicted by this model provide targets for new experimental small molecules and suggest that the function of the positive feedback loop is to stabilize the oscillations while linking the circadian system to other clock-controlled processes.

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Objective: Later circadian timing of energy intake is associated with higher body fat percentage. Current methods for obtaining accurate circadian timing are labor-and cost-intensive, limiting practical application of this relationship. This study investigated whether the timing of energy intake relative to a mathematically modeled circadian time, derived from easily collected ambulatory data, would differ between participants with a lean or overweight/obesity body fat percentage.Methods: Participants (N = 87) wore a light-and activity-measuring device (actigraph) throughout a cross-sectional 30-day study. For 7 consecutive days within these 30 days, participants used a time-stamped-picture phone application to record energy intake. Body fat percentage was recorded. Circadian time was defined using melatonin onset from in-laboratory collected repeat saliva sampling or using light and activity or activity data alone entered into a mathematical model.Results: Participants with overweight/obesity body fat percentages ate 50% of their daily calories significantly closer to model-predicted melatonin onset from light and activity data (0.61 hours closer) or activity data alone (0.86 hours closer; both log-rank p < 0.05).Conclusions: Use of mathematically modeled circadian timing resulted in similar relationships between the timing of energy intake and body composition as that observed using in-laboratory collected metrics. These findings may facilitate use of circadian timing in time-based interventions.

Introduction Actigraphy is a non-invasive method that allows long-term recordings of activity, light, and other variables in diverse environments. In real-world settings, activity usually has a 24-hour rhythm that may arise from sleep/wake-associated behavior and/or circadian rhythmicity. We tested for an independent circadian component using data from people living on non-24 hours “days” in the laboratory. Methods Data are from five inpatient studies with tightly-controlled forced desynchrony (FD) conditions. Participants (19–34 yo) were healthy by history, physical exam, laboratory tests of blood and urine, and clinical polysomnography, and did not report using prescription medicines. Caffeine-containing substances were prohibited during the study. Protocol 1: 7 participants (3 F) T-cycle (i.e., FD sleep-wake cycle duration) = 42.85h; Rest:Activity ratio 1:3.3. Protocol 2: 8 participants (3 F) T cycle =42.85h; Rest:Activity 1:2. Protocol 3: 9 participants (3 F) T cycle =28.0h; Rest:Activity 1:2. Protocol 4: 7 participants (3 F) T cycle =20.0h; Rest:Activity ratio 1:3.3. Protocol 5: 7 participants (5 F) T cycle =20.0h; Rest:Activity 1:2. At all times except during showers, participants wore an actiwatch that measured activity levels and light. Melatonin period and phase 0 (i.e., fit maximum) were computed using non-orthogonal spectral analyses. Data were analyzed relative to 3-hr Circadian Phase bins (1/8 of computed circadian period for each individual) and 3-hr Wake Duration bins. Activity data were summarized using Zero-Inflated-Poison-based statistics for each Circadian*Wake Duration bin for each individual and then across individuals within each study. Repeated measures ANOVA were conducted. Statistics were performed using SAS. Results For all protocols, there were significant differences (all p&lt;0.007) by individual participant, by Circadian Phase, and by Wake Duration bin, but not by the interaction term (Circadian Phase* Wake Duration). Highest levels of activity were at Circadian Phase 7.5–10.5 (~10am–1pm) and lowest values at Circadian Phase -1.5–1.5 (~midnight–3 am). Activity values were lowest at scheduled sleep times. Conclusion Circadian rhythms independent of sleep/wake behaviors influence activity levels and may be an important component of analyses. In individuals living on non-24-hr days (e.g., some blind people and some sighted people with Non-24-hr Sleep Disorder), it may be possible to derive circadian-based metrics. Support (if any) NIH K24-HL105664, P01-AG009975, T32-HL007901, K01-HL146992.