We recently showed that interleukin (IL)-6-type cytokine signaling in adipocytes induces free fatty acid release from visceral adipocytes, thereby promoting obesity-induced hepatic insulin resistance and steatosis. In addition, IL-6-type cytokines may increase the release of leptin from adipocytes and by those means induce glucagon-like peptide 1 (GLP-1) secretion. We thus hypothesized that IL-6-type cytokine signaling in adipocytes may regulate insulin secretion. To this end, mice with adipocyte-specific knockout of gp130, the signal transducer protein of IL-6, were fed a high-fat diet for 12 weeks. Compared with control littermates, knockout mice showed impaired glucose tolerance and circulating leptin, GLP-1, and insulin levels were reduced. In line, leptin release from isolated adipocytes was reduced, and intestinal proprotein convertase subtilisin/kexin type 1 () expression, the gene encoding PC1/3, which controls GLP-1 production, was decreased in knockout mice. Importantly, treatment with the GLP-1 receptor antagonist exendin 9-39 abolished the observed difference in glucose tolerance between control and knockout mice. Ex vivo, supernatant collected from isolated adipocytes of gp130 knockout mice blunted expression and GLP-1 release from GLUTag cells. In contrast, glucose- and GLP-1-stimulated insulin secretion was not affected in islets of knockout mice. In conclusion, adipocyte-specific IL-6 signaling induces intestinal GLP-1 release to enhance insulin secretion, thereby counteracting insulin resistance in obesity.

Hyperglycemia is a common occurrence in hospitalized patients receiving parenteral and/or enteral nutrition. Although there are several approaches to manage hyperglycemia, there is no consensus on the best practice. We systematically searched PubMed, Embase, Cochrane Central, and ClinicalTrials.gov to identify records (published or registered between April 1999 and April 2019) investigating strategies to manage glucose control in adults receiving parenteral and/or enteral nutrition whilst hospitalized in noncritical care units. A total of 15 completed studies comprising 1170 patients were identified, of which 11 were clinical trials and four observational studies. Diabetes management strategies entailed adaptations of nutritional regimens in four studies, while the remainder assessed different insulin regimens and administration routes. Diabetes-specific nutritional regimens that reduced glycemic excursions, as well as algorithm-driven insulin delivery approaches that allowed for flexible glucose-responsive insulin dosing, were both effective in improving glycemic control. However, the assessed studies were, in general, of limited quality, and we see a clear need for future rigorous studies to establish standards of care for patients with hyperglycemia receiving nutrition support.

Background Quantification of dietary intake is key to the prevention and management of numerous metabolic disorders. Conventional approaches are challenging, laborious, and lack accuracy. The recent advent of depth-sensing smartphones in conjunction with computer vision could facilitate reliable quantification of food intake. Objective The objective of this study was to evaluate the accuracy of a novel smartphone app combining depth-sensing hardware with computer vision to quantify meal macronutrient content using volumetry. Methods The app ran on a smartphone with a built-in depth sensor applying structured light (iPhone X). The app estimated weight, macronutrient (carbohydrate, protein, fat), and energy content of 48 randomly chosen meals (breakfasts, cooked meals, snacks) encompassing 128 food items. The reference weight was generated by weighing individual food items using a precision scale. The study endpoints were (1) error of estimated meal weight, (2) error of estimated meal macronutrient content and energy content, (3) segmentation performance, and (4) processing time. Results In both absolute and relative terms, the mean (SD) absolute errors of the app’s estimates were 35.1 g (42.8 g; relative absolute error: 14.0% [12.2%]) for weight; 5.5 g (5.1 g; relative absolute error: 14.8% [10.9%]) for carbohydrate content; 1.3 g (1.7 g; relative absolute error: 12.3% [12.8%]) for fat content; 2.4 g (5.6 g; relative absolute error: 13.0% [13.8%]) for protein content; and 41.2 kcal (42.5 kcal; relative absolute error: 12.7% [10.8%]) for energy content. Although estimation accuracy was not affected by the viewing angle, the type of meal mattered, with slightly worse performance for cooked meals than for breakfasts and snacks. Segmentation adjustment was required for 7 of the 128 items. Mean (SD) processing time across all meals was 22.9 seconds (8.6 seconds). Conclusions This study evaluated the accuracy of a novel smartphone app with an integrated depth-sensing camera and found highly accurate volume estimation across a broad range of food items. In addition, the system demonstrated high segmentation performance and low processing time, highlighting its usability.

While the adjustment of insulin is an established strategy to reduce the risk of exercise-associated hypoglycemia for individuals with type 1 diabetes, it is not easily feasible for those treated with ultra-long-acting basal insulin. The current study determined whether pre-exercise intake of fructose attenuates the risk of exerciseinduced hypoglycemia in individuals with type 1 diabetes using insulin degludec. RESEARCH DESIGN AND METHODSFourteen male adults with type 1 diabetes completed two 60-min aerobic cycling sessions with or without prior intake (30 min) of 20 g of fructose, in a randomized two-period crossover design. Exercise was performed in the morning in a fasted state without prior insulin reduction and after 48 h of standardized diet. The primary outcome was time to hypoglycemia (plasma glucose £3.9 mmol/L) during exercise. RESULTSIntake of fructose resulted in one hypoglycemic event at 60 min compared with six hypoglycemic events at 27.5 6 9.4 min of exercise in the control condition, translating into a risk reduction of 87.8% (hazard ratio 0.12 [95% CI 0.02, 0.66]; P 5 0.015). Mean plasma glucose during exercise was 7.3 6 1.4 mmol/L with fructose and 5.5 6 1.1 mmol/L in the control group (P < 0.001). Lactate levels were higher at rest in the 30 min following fructose intake (P < 0.001) but were not significantly different from the control group during exercise (P 5 0.32). Substrate oxidation during exercise did not significantly differ between the conditions (P 5 0.73 for carbohydrate and P 5 0.48 for fat oxidation). Fructose was well tolerated. CONCLUSIONSPre-exercise intake of fructose is an easily feasible, effective, and well-tolerated strategy to alleviate the risk of exercise-induced hypoglycemia while avoiding hyperglycemia in individuals with type 1 diabetes on ultra-long-acting insulin.