Myostatin Inhibition in Muscle, but Not Adipose Tissue, Decreases Fat Mass and Improves Insulin Sensitivity

Myostatin (Mstn) is a secreted growth factor expressed in skeletal muscle and adipose tissue that negatively regulates skeletal muscle mass. Mstn−/− mice have a dramatic increase in muscle mass, reduction in fat mass, and resistance to diet-induced and genetic obesity. To determine how Mstn deletion causes reduced adiposity and resistance to obesity, we analyzed substrate utilization and insulin sensitivity in Mstn−/− mice fed a standard chow. Despite reduced lipid oxidation in skeletal muscle, Mstn−/− mice had no change in the rate of whole body lipid oxidation. In contrast, Mstn−/− mice had increased glucose utilization and insulin sensitivity as measured by indirect calorimetry, glucose and insulin tolerance tests, and hyperinsulinemic-euglycemic clamp. To determine whether these metabolic effects were due primarily to the loss of myostatin signaling in muscle or adipose tissue, we compared two transgenic mouse lines carrying a dominant negative activin IIB receptor expressed specifically in adipocytes or skeletal muscle. We found that inhibition of myostatin signaling in adipose tissue had no effect on body composition, weight gain, or glucose and insulin tolerance in mice fed a standard diet or a high-fat diet. In contrast, inhibition of myostatin signaling in skeletal muscle, like Mstn deletion, resulted in increased lean mass, decreased fat mass, improved glucose metabolism on standard and high-fat diets, and resistance to diet-induced obesity. Our results demonstrate that Mstn−/− mice have an increase in insulin sensitivity and glucose uptake, and that the reduction in adipose tissue mass in Mstn−/− mice is an indirect result of metabolic changes in skeletal muscle. These data suggest that increasing muscle mass by administration of myostatin antagonists may be a promising therapeutic target for treating patients with obesity or diabetes

Resisting Sleep Pressure:Impact on Resting State Functional Network Connectivity

In today's 24/7 society, sleep restriction is a common phenomenon which leads to increased levels of sleep pressure in daily life. However, the magnitude and extent of impairment of brain functioning due to increased sleep pressure is still not completely understood. Resting state network (RSN) analyses have become increasingly popular because they allow us to investigate brain activity patterns in the absence of a specific task and to identify changes under different levels of vigilance (e.g. due to increased sleep pressure). RSNs are commonly derived from BOLD fMRI signals but studies progressively also employ cerebral blood flow (CBF) signals. To investigate the impact of sleep pressure on RSNs, we examined RSNs of participants under high (19 h awake) and normal (10 h awake) sleep pressure with three imaging modalities (arterial spin labeling, BOLD, pseudo BOLD) while providing confirmation of vigilance states in most conditions. We demonstrated that CBF and pseudo BOLD signals (measured with arterial spin labeling) are suited to derive independent component analysis based RSNs. The spatial map differences of these RSNs were rather small, suggesting a strong biological substrate underlying these networks. Interestingly, increased sleep pressure, namely longer time awake, specifically changed the functional network connectivity (FNC) between RSNs. In summary, all FNCs of the default mode network with any other network or component showed increasing effects as a function of increased 'time awake'. All other FNCs became more anti-correlated with increased 'time awake'. The sensorimotor networks were the only ones who showed a within network change of FNC, namely decreased connectivity as function of 'time awake'. These specific changes of FNC could reflect both compensatory mechanisms aiming to fight sleep as well as a first reduction of consciousness while becoming drowsy. We think that the specific changes observed in functional network connectivity could imply an impairment of information transfer between the affected RSNs

Technologies That Assess the Location of Physical Activity and Sedentary Behavior: A Systematic Review

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.Background: The location in which physical activity and sedentary behavior are performed can provide valuable behavioral information, both in isolation and synergistically with other areas of physical activity and sedentary behavior research. Global positioning systems (GPS) have been used in physical activity research to identify outdoor location; however, while GPS can receive signals in certain indoor environments, it is not able to provide room- or subroom-level location. On average, adults spend a high proportion of their time indoors. A measure of indoor location would, therefore, provide valuable behavioral information. Objective: This systematic review sought to identify and critique technology which has been or could be used to assess the location of physical activity and sedentary behavior. Methods: To identify published research papers, four electronic databases were searched using key terms built around behavior, technology, and location. To be eligible for inclusion, papers were required to be published in English and describe a wearable or portable technology or device capable of measuring location. Searches were performed up to February 4, 2015. This was supplemented by backward and forward reference searching. In an attempt to include novel devices which may not yet have made their way into the published research, searches were also performed using three Internet search engines. Specialized software was used to download search results and thus mitigate the potential pitfalls of changing search algorithms. Results: A total of 188 research papers met the inclusion criteria. Global positioning systems were the most widely used location technology in the published research, followed by wearable cameras, and radio-frequency identification. Internet search engines identified 81 global positioning systems, 35 real-time locating systems, and 21 wearable cameras. Real-time locating systems determine the indoor location of a wearable tag via the known location of reference nodes. Although the type of reference node and location determination method varies between manufacturers, Wi-Fi appears to be the most popular method. Conclusions: The addition of location information to existing measures of physical activity and sedentary behavior will provide important behavioral information