Improvements in sensor accuracy, greater convenience and ease of use, and expanding reimbursement have led to growing adoption of continuous glucose monitoring (CGM). However, successful utilization of CGM technology in routine clinical practice remains relatively low. This may be due in part to the lack of clear and agreed-upon glycemic targets that both diabetes teams and people with diabetes can work toward. Although unified recommendations for use of key CGM metrics have been established in three separate peer-reviewed articles, formal adoption by diabetes professional organizations and guidance in the practical application of these metrics in clinical practice have been lacking. In February 2019, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress convened an international panel of physicians, researchers, and individuals with diabetes who are expert in CGM technologies to address this issue. This article summarizes the ATTD consensus recommendations for relevant aspects of CGM data utilization and reporting among the various diabetes populations.

Summary: The prevalence of diabetes in China has increased rapidly from 0.67% in 1980 to 10.4% in 2013, with the aging of the population and westernization of lifestyle. Since its foundation in 1991, the Chinese Diabetes Society (CDS) has been dedicated to improving academic exchange and the academic level of diabetes research in China. From 2003 to 2014, four versions of Chinese diabetes care guidelines have been published. The guidelines have played an important role in standardizing clinical practice and improving the status quo of diabetes prevention and control in China. Since September 2016, the CDS has invited experts in cardiovascular diseases, psychiatric diseases, nutrition, and traditional Chinese medicine to work with endocrinologists from the CDS to review the new clinical research evidence related to diabetes over the previous 4 years. Over a year of careful revision, this has resulted in the present, new version of guidelines for prevention and care of type 2 diabetes in China. The main contents include epidemiology of type 2 diabetes in China; diagnosis and classification of diabetes; primary, secondary, and tertiary diabetes prevention; diabetes education and management support; blood glucose monitoring; integrated control targets for type 2 diabetes and treatments for hyperglycaemia; medical nutrition therapy; exercise therapy for type 2 diabetes; smoking cessation; pharmacologic therapy for hyperglycaemia; metabolic surgery for type 2 diabetes; prevention and treatment of cardiovascular and cerebrovascular diseases in patients with type 2 diabetes; hypoglycaemia; chronic diabetic complications; special types of diabetes; metabolic syndrome; and diabetes and traditional Chinese medicine.

Retinal screening contributes to early detection of diabetic retinopathy and timely treatment. To facilitate the screening process, we develop a deep learning system, named DeepDR, that can detect early-to-late stages of diabetic retinopathy. DeepDR is trained for real-time image quality assessment, lesion detection and grading using 466,247 fundus images from 121,342 patients with diabetes. Evaluation is performed on a local dataset with 200,136 fundus images from 52,004 patients and three external datasets with a total of 209,322 images. The area under the receiver operating characteristic curves for detecting microaneurysms, cotton-wool spots, hard exudates and hemorrhages are 0.901, 0.941, 0.954 and 0.967, respectively. The grading of diabetic retinopathy as mild, moderate, severe and proliferative achieves area under the curves of 0.943, 0.955, 0.960 and 0.972, respectively. In external validations, the area under the curves for grading range from 0.916 to 0.970, which further supports the system is efficient for diabetic retinopathy grading.

Metformin, the most prescribed antidiabetic medicine, has shown other benefits such as anti-ageing and anticancer effects^1–4. For clinical doses of metformin, AMP-activated protein kinase (AMPK) has a major role in its mechanism of action^4,5; however, the direct molecular target of metformin remains unknown. Here we show that clinically relevant concentrations of metformin inhibit the lysosomal proton pump v-ATPase, which is a central node for AMPK activation following glucose starvation⁶. We synthesize a photoactive metformin probe and identify PEN2, a subunit of γ-secretase⁷, as a binding partner of metformin with a dissociation constant at micromolar levels. Metformin-bound PEN2 forms a complex with ATP6AP1, a subunit of the v-ATPase⁸, which leads to the inhibition of v-ATPase and the activation of AMPK without effects on cellular AMP levels. Knockout of PEN2 or re-introduction of a PEN2 mutant that does not bind ATP6AP1 blunts AMPK activation. In vivo, liver-specific knockout of Pen2 abolishes metformin-mediated reduction of hepatic fat content, whereas intestine-specific knockout of Pen2 impairs its glucose-lowering effects. Furthermore, knockdown of pen-2 in Caenorhabditis elegans abrogates metformin-induced extension of lifespan. Together, these findings reveal that metformin binds PEN2 and initiates a signalling route that intersects, through ATP6AP1, the lysosomal glucose-sensing pathway for AMPK activation. This ensures that metformin exerts its therapeutic benefits in patients without substantial adverse effects.

OBJECTIVE Continuous glucose monitoring (CGM) has provided new measures of glycemic control that link to diabetes complications. This study investigated the association between the time in range (TIR) assessed by CGM and diabetic retinopathy (DR). RESEARCH DESIGN AND METHODS A total of 3,262 patients with type 2 diabetes were recruited. TIR was defined as the percentage of time spent within the glucose range of 3.9–10.0 mmol/L during a 24-h period. Measures of glycemic variability (GV) were assessed as well. DR was determined by using fundus photography and graded as 1) non-DR; 2) mild nonproliferative DR (NPDR); 3) moderate NPDR; or 4) vision-threatening DR (VTDR). RESULTS The overall prevalence of DR was 23.9% (mild NPDR 10.9%, moderate NPDR 6.1%, VTDR 6.9%). Patients with more advanced DR had significantly less TIR and higher measures of GV (all P for trend <0.01). The prevalence of DR on the basis of severity decreased with ascending TIR quartiles (all P for trend <0.001), and the severity of DR was inversely correlated with TIR quartiles (r = −0.147; P < 0.001). Multinomial logistic regression revealed significant associations between TIR and all stages of DR (mild NPDR, P = 0.018; moderate NPDR, P = 0.014; VTDR, P = 0.019) after controlling for age, sex, BMI, diabetes duration, blood pressure, lipid profile, and HbA_1c. Further adjustment of GV metrics partially attenuated these associations, although the link between TIR and the presence of any DR remained significant. CONCLUSIONS TIR assessed by CGM is associated with DR in type 2 diabetes.