The Internal Circadian Clock Increases Hunger and Appetite in the Evening Independent of Food Intake and Other Behaviors

Objective: Despite the extended overnight fast, paradoxically, people are typically not ravenous in the morning and breakfast is typically the smallest meal of the day. Here we assessed whether this paradox could be explained by an endogenous circadian influence on appetite with a morning trough, while controlling for sleep/wake and fasting/feeding effects. Design and Methods We studied 12 healthy non-obese adults (6 male; age, 20–42 year) throughout a 13-day laboratory protocol that balanced all behaviors, including eucaloric meals and sleep periods, evenly across the endogenous circadian cycle. Participants rated their appetite and food preferences by visual analog scales. Results: There was a large endogenous circadian rhythm in hunger, with the trough in the biological morning (8 AM) and peak in the biological evening (8 PM; peak-to-trough amplitude=17%; P=0.004). Similarly phased significant endogenous circadian rhythms were present in appetite for sweets, salty and starchy foods, fruits, meats/poultry, food overall, and for estimates of how much food participants could eat (amplitudes 14–25%; all P < 0.05). Conclusions: In people who sleep at night, the intrinsic circadian evening peak in appetite may promote larger meals before the fasting period necessitated by sleep. Furthermore, the circadian decline in hunger across the night would theoretically counteract the fasting-induced hunger increase that could otherwise disrupt sleep

Feedback through graph motifs relates structure and function in complex networks

In physics, biology and engineering, network systems abound. How does the connectivity of a network system combine with the behavior of its individual components to determine its collective function? We approach this question for networks with linear time-invariant dynamics by relating internal network feedbacks to the statistical prevalence of connectivity motifs, a set of surprisingly simple and local statistics of connectivity. This results in a reduced order model of the network input-output dynamics in terms of motifs structures. As an example, the new formulation dramatically simplifies the classic Erdos-Renyi graph, reducing the overall network behavior to one proportional feedback wrapped around the dynamics of a single node. For general networks, higher-order motifs systematically provide further layers and types of feedback to regulate the network response. Thus, the local connectivity shapes temporal and spectral processing by the network as a whole, and we show how this enables robust, yet tunable, functionality such as extending the time constant with which networks remember past signals. The theory also extends to networks composed from heterogeneous nodes with distinct dynamics and connectivity, and patterned input to (and readout from) subsets of nodes. These statistical descriptions provide a powerful theoretical framework to understand the functionality of real-world network systems, as we illustrate with examples including the mouse brain connectome.Comment: 31 pages, 20 figure

Association of C2, a derivative of the radial artery pressure waveform, with new onset of type 2 diabetes mellitus: the MESA study.

BackgroundAlthough microvascular dysfunction is known to result from diabetes, it might also lead to diabetes. Lower values of C2, a derivative of the radial artery pressure waveform, indicate microvascular dysfunction and predict hypertension and cardiovascular disease (CVD). We studied the association of C2 with incident diabetes in subjects free of overt CVD.MethodsAmong multi-ethnic participants (n = 5214), aged 45-84&nbsp;years with no diabetes, C2 was derived from the radial artery pressure waveform. Incident diabetes (N = 651) was diagnosed as new fasting glucose ≥ 126&nbsp;mg/dL or antidiabetic medicine over ~ 10&nbsp;years. The relative incidence density (RID) for incident diabetes per standard deviation (SD) of C2 was studied during ~ 10&nbsp;years follow-up using four levels of adjustment.ResultsMean C2 at baseline was 4.58 ± 2.85&nbsp;mL/mmHg × 100. The RID for incident diabetes per SD of C2 was 0.90 (95% CI 0.82-0.99, P = 0.03). After adjustment for demographics plus body size, the corresponding RID was 0.81 (95% CI 0.73-0.89, P &lt; 0.0001); body mass index (BMI) was the dominant covariate here. After adjustment for demographics plus cardiovascular risk factors, the RID was 0.98 (95% CI 0.89, 1.07, P = 0.63). After adjustment for all the parameters in the previous models, the RID was 0.87 (95% CI 0.78, 0.96, P = 0.006).ConclusionsIn a multi-ethnic sample free of overt CVD and diabetes at baseline, C2 predicted incident diabetes after adjustment for demographics, BMI and CVD risk factors. Differences in arterial blood pressure wave morphology may indicate a long-term risk trajectory for diabetes, independently of body size and the classical risk factors

Noninvasive fractal biomarker of clock neurotransmitter disturbance in humans with dementia

Human motor activity has a robust, intrinsic fractal structure with similar patterns from minutes to hours. The fractal activity patterns appear to be physiologically important because the patterns persist under different environmental conditions but are significantly altered/reduced with aging and Alzheimer's disease (AD). Here, we report that dementia patients, known to have disrupted circadian rhythmicity, also have disrupted fractal activity patterns and that the disruption is more pronounced in patients with more amyloid plaques (a marker of AD severity). Moreover, the degree of fractal activity disruption is strongly associated with vasopressinergic and neurotensinergic neurons (two major circadian neurotransmitters) in postmortem suprachiasmatic nucleus (SCN), and can better predict changes of the two neurotransmitters than traditional circadian measures. These findings suggest that the SCN impacts human activity regulation at multiple time scales and that disrupted fractal activity may serve as a non-invasive biomarker of SCN neurodegeneration in dementia

Scale Invariance and Nonlinear Patterns of Human Activity

We investigate if known extrinsic and intrinsic factors fully account for the complex features observed in recordings of human activity as measured from forearm motion in subjects undergoing their regular daily routine. We demonstrate that the apparently random forearm motion possesses previously unrecognized dynamic patterns characterized by fractal and nonlinear dynamics. These patterns are unaffected by changes in the average activity level, and persist when the same subjects undergo time-isolation laboratory experiments designed to account for the circadian phase and to control the known extrinsic factors. We attribute these patterns to a novel intrinsic multi-scale dynamic regulation of human activity.Comment: 4 pages, three figure

Fractal Patterns of Neural Activity Exist within the Suprachiasmatic Nucleus and Require Extrinsic Network Interactions

The mammalian central circadian pacemaker (the suprachiasmatic nucleus, SCN) contains thousands of neurons that are coupled through a complex network of interactions. In addition to the established role of the SCN in generating rhythms of ∼24 hours in many physiological functions, the SCN was recently shown to be necessary for normal self-similar/fractal organization of motor activity and heart rate over a wide range of time scales—from minutes to 24 hours. To test whether the neural network within the SCN is sufficient to generate such fractal patterns, we studied multi-unit neural activity of in vivo and in vitro SCNs in rodents. In vivo SCN-neural activity exhibited fractal patterns that are virtually identical in mice and rats and are similar to those in motor activity at time scales from minutes up to 10 hours. In addition, these patterns remained unchanged when the main afferent signal to the SCN, namely light, was removed. However, the fractal patterns of SCN-neural activity are not autonomous within the SCN as these patterns completely broke down in the isolated in vitro SCN despite persistence of circadian rhythmicity. Thus, SCN-neural activity is fractal in the intact organism and these fractal patterns require network interactions between the SCN and extra-SCN nodes. Such a fractal control network could underlie the fractal regulation observed in many physiological functions that involve the SCN, including motor control and heart rate regulation