The human circadian metabolome

The circadian clock orchestrates many aspects of human physiology, and disruption of this clock has been implicated in various pathologies, ranging from cancer to metabolic syndrome and diabetes. Although there is evidence that metabolism and the circadian clockwork are intimately linked on a transcriptional level, whether these effects are directly under clock control or are mediated by the rest-activity cycle and the timing of food intake is unclear. To answer this question, we conducted an unbiased screen in human subjects of the metabolome of blood plasma and saliva at different times of day. To minimize indirect effects, subjects were kept in a 40-h constant routine of enforced posture, constant dim light, hourly isocaloric meals, and sleep deprivation. Under these conditions, we found that ~15% of all identified metabolites in plasma and saliva were under circadian control, most notably fatty acids in plasma and amino acids in saliva. Our data suggest that there is a strong direct effect of the endogenous circadian clock on multiple human metabolic pathways that is independent of sleep or feeding. In addition, they identify multiple potential small-molecule biomarkers of human circadian phase and sleep pressure.

Control of ventricular ciliary beating by the melanin concentrating hormone-expressing neurons of the lateral hypothalamus: a functional imaging survey

The cyclic peptide Melanin Concentrating Hormone (MCH) is known to control a large number of brain functions in mammals such as food intake and metabolism, stress response, anxiety, sleep/wake cycle, memory, and reward. Based on neuro-anatomical and electrophysiological studies these functions were attributed to neuronal circuits expressing MCHR1, the single MCH receptor in rodents. In complement to our recently published work (1) we provided here new data regarding the action of MCH on ependymocytes in the mouse brain. First, we establish that MCHR1 mRNA is expressed in the ependymal cells of the third ventricle epithelium. Second, we demonstrated a tonic control of MCH-expressing neurons on ependymal cilia beat frequency using in vitro optogenics. Finally, we performed in vivo measurements of CSF flow using fluorescent micro-beads in wild-type and MCHR1-knockout mice. Collectively, our results demonstrated that MCH-expressing neurons modulate ciliary beating of ependymal cells at the third ventricle and could contribute to maintain cerebro-spinal fluid homeostasis.

[Sausage or carrot--spoilt for choice].

As far as healthy food is concerned, it cannot be categorized simply as "good" or "bad" since its effect on health depends mainly on the amount and method consumed. Today's recommendations include a diversified diet, a diet which targets energy-balance and provides all nutrients necessary. Living in an affluent society aggravates healthy choices because of a constantly available, large assortment of food items. In general, the way food is prepared these days has changed a lot: mainly, the energy content has constantly increased, while the fiber and natural micronutrient concentrations decreased. Food items with a high energy yield, containing a lot of fat and sugar, affects our energy balance, which may lead to diseases of affluence such as type 2 diabetes, cardiovascular diseases, and some kinds of tumors.

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference. Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires. Methods: The study was conducted at the Bern University Hospital, “Inselspital” (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital’s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user’s experience with GoCARB. Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use. Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.