Emerging evidence suggests that inflammation provides a link between obesity and insulin resistance. The noncanonical IκB kinases IKKε and TANK-binding kinase 1 (TBK1) are induced in liver and fat after high fat diet by NF-κB activation, and in turn initiate a program of counter-inflammation that preserves energy storage. Here, we report the discovery of a small molecule inhibitor of these kinases called amlexanox. Treatment of obese mice with amlexanox elevates energy expenditure through increased thermogenesis, producing weight loss, improved insulin sensitivity and decreased steatosis in obese mice. Because of its record of safety in patients, amlexanox may be an interesting candidate for clinical evaluation in the treatment of obesity and related disorders.

This study suggests that determination of haptoglobin phenotype may contribute to the algorithm used in CVD risk stratification, and in evaluation of new therapies to prevent CVD in the diabetic patient.

BackgroundRegular physical activity is known to be beneficial for people with type 2 diabetes. Nevertheless, most of the people who have diabetes lead a sedentary lifestyle. Smartphones create new possibilities for helping people to adhere to their physical activity goals through continuous monitoring and communication, coupled with personalized feedback.ObjectiveThe aim of this study was to help type 2 diabetes patients increase the level of their physical activity.MethodsWe provided 27 sedentary type 2 diabetes patients with a smartphone-based pedometer and a personal plan for physical activity. Patients were sent short message service messages to encourage physical activity between once a day and once per week. Messages were personalized through a Reinforcement Learning algorithm so as to improve each participant’s compliance with the activity regimen. The algorithm was compared with a static policy for sending messages and weekly reminders.ResultsOur results show that participants who received messages generated by the learning algorithm increased the amount of activity and pace of walking, whereas the control group patients did not. Patients assigned to the learning algorithm group experienced a superior reduction in blood glucose levels (glycated hemoglobin [HbA1c]) compared with control policies, and longer participation caused greater reductions in blood glucose levels. The learning algorithm improved gradually in predicting which messages would lead participants to exercise.ConclusionsMobile phone apps coupled with a learning algorithm can improve adherence to exercise in diabetic patients. This algorithm can be used in large populations of diabetic patients to improve health and glycemic control. Our results can be expanded to other areas where computer-led health coaching of humans may have a positive impact. Summary of a part of this manuscript has been previously published as a letter in Diabetes Care, 2016.