This study evaluated the association of time in range (TIR) of 70-180 mg/dL (3.9-10 mmol/L) with the development or progression of retinopathy and development of microalbuminuria using the Diabetes Control and Complications Trial (DCCT) data set in order to validate the use of TIR as an outcome measure for clinical trials. RESEARCH DESIGN AND METHODS In the DCCT, blood glucose concentrations were measured at a central laboratory from seven fingerstick samples (seven-point testing: pre-and 90-min postmeals and at bedtime) collected during 1 day every 3 months. Retinopathy progression was assessed every 6 months and urinary microalbuminuria development every 12 months. Proportional hazards models were used to assess the association of TIR and other glycemic metrics, computed from the seven-point fingerstick data, with the rate of development of microvascular complications. RESULTS Mean TIR of seven-point profiles for the 1,440 participants was 41 6 16%. The hazard rate of development of retinopathy progression was increased by 64% (95% CI 51-78), and development of the microalbuminuria outcome was increased by 40% (95% CI 25-56), for each 10 percentage points lower TIR (P < 0.001 for each). Results were similar for mean glucose and hyperglycemia metrics. CONCLUSIONS Based on these results, a compelling case can be made that TIR is strongly associated with the risk of microvascular complications and should be an acceptable end point for clinical trials. Although hemoglobin A 1c remains a valuable outcome metric in clinical trials, TIR and other glycemic metricsdespecially when measured with continuous glucose monitoringdadd value as outcome measures in many studies. Hemoglobin A 1c (A1C) became the gold standard for assessing glycemic management after the landmark Diabetes Control and Complications Trial (DCCT) demonstrated the strong association between A1C levels and the risk of chronic diabetic vascular complications, and laboratory methods were developed so that A1C levels could be readily measured with a high degree of precision. Although its important role in diabetes management as a clinical trials outcome and as a predictor of long-term diabetic complications cannot be overstated, A1C does have certain limitations. A1C is a measure of hyperglycemia, but it provides no indication of hypoglycemia, glycemic variability, or daily patterns of glycemia. Notably, considerable

While A1C is well established as an important risk marker for diabetes complications, with the increasing use of continuous glucose monitoring (CGM) to help facilitate safe and effective diabetes management, it is important to understand how CGM metrics, such as mean glucose, and A1C correlate. Estimated A1C (eA1C) is a measure converting the mean glucose from CGM or self-monitored blood glucose readings, using a formula derived from glucose readings from a population of individuals, into an estimate of a simultaneously measured laboratory A1C. Many patients and clinicians find the eA1C to be a helpful educational tool, but others are often confused or even frustrated if the eA1C and laboratory-measured A1C do not agree. In the U.S., the Food and Drug Administration determined that the nomenclature of eA1C needed to change. This led the authors to work toward a multipart solution to facilitate the retention of such a metric, which includes renaming the eA1C the glucose management indicator (GMI) and generating a new formula for converting CGM-derived mean glucose to GMI based on recent clinical trials using the most accurate CGM systems available. The final aspect of ensuring a smooth transition from the old eA1C to the new GMI is providing new CGM analyses and explanations to further understand how to interpret GMI and use it most effectively in clinical practice. This Perspective will address why a new name for eA1C was needed, why GMI was selected as the new name, how GMI is calculated, and how to understand and explain GMI if one chooses to use GMI as a tool in diabetes education or management.

IN BRIEF This study quantitatively measures diabetes stigma and its associated psychosocial impact in a large population of U.S. patients with type 1 or type 2 diabetes using an online survey sent to 12,000 people with diabetes. A majority of respondents with type 1 (76%) or type 2 (52%) diabetes reported that diabetes comes with stigma. Perceptions of stigma were significantly higher among respondents with type 1 diabetes than among those with type 2 diabetes, with the highest rate in parents of children with type 1 diabetes (83%) and the lowest rate in people with type 2 diabetes who did not use insulin (49%). Our results suggest that a disturbingly high percentage of people with diabetes experience stigma, particularly those with type 1 or type 2 diabetes who are on intensive insulin therapy. The experience of stigma disproportionately affects those with a higher BMI, higher A1C, and poorer self-reported blood glucose control, suggesting that those who need the most help are also the most affected by stigma.

Drug repositioning, the process of discovering, validating, and marketing previously approved drugs for new indications, is of growing interest to academia and industry due to reduced time and costs associated with repositioned drugs. Computational methods for repositioning are appealing because they putatively nominate the most promising candidate drugs for a given indication. Comparing the wide array of computational repositioning methods, however, is a challenge due to inconsistencies in method validation in the field. Furthermore, a common simplifying assumption, that all novel predictions are false, is intellectually unsatisfying and hinders reproducibility. We address this assumption by providing a gold standard database, repoDB, that consists of both true positives (approved drugs), and true negatives (failed drugs). We have made the full database and all code used to prepare it publicly available, and have developed a web application that allows users to browse subsets of the data (http://apps.chiragjpgroup.org/repoDB/).