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.

Measurement of glycated hemoglobin (HbA 1c ) has been the traditional method for assessing glycemic control. However, it does not reflect intra-and interday glycemic excursions that may lead to acute events (such as hypoglycemia) or postprandial hyperglycemia, which have been linked to both microvascular and macrovascular complications. Continuous glucose monitoring (CGM), either from real-time use (rtCGM) or intermittently viewed (iCGM), addresses many of the limitations inherent in HbA 1c testing and self-monitoring of blood glucose. Although both provide the means to move beyond the HbA 1c measurement as the sole marker of glycemic control, standardized metrics for analyzing CGM data are lacking. Moreover, clear criteria for matching people with diabetes to the most appropriate glucose monitoring methodologies, as well as standardized advice about how best to use the new information they provide, have yet to be established. In February 2017, 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 these issues. This article summarizes the ATTD consensus recommendations and represents the current understanding of how CGM results can affect outcomes.Glucose measurements are critical to effective diabetes management. Although measurement of glycated hemoglobin (HbA 1c ) has been the traditional method for assessing glycemic control, it does not reflect intra-and interday glycemic excursions that may lead to acute events (such as hypoglycemia) or postprandial hyperglycemia, which have been linked to both microvascular and macrovascular complications. Moreover, although self-monitoring of blood glucose (SMBG) has been shown to improve glycemic control and quality of life in both insulin-treated and noninsulin-treated diabetes when used within a structured testing regimen (1-4) [C,C,C,C], it cannot predict impending hypoglycemia or alert for hypoglycemia (5,6) [C,C] (7).Real-time continuous glucose monitoring (rtCGM) and intermittently viewed CGM (iCGM) address many of the limitations inherent in HbA 1c testing and SMBG. rtCGM uniformly tracks the glucose concentrations in the body's interstitial fluid, providing near real-time glucose data; iCGM uses similar methodology to show continuous glucose measurements retrospectively at the time of checking. Both rtCGM and iCGM facilitate monitoring of time spent in the target glucose range ("time in range"). However, only rtCGM can warn users if glucose is trending toward hypoglycemia or hyperglycemia. With iCGM, these trends can only be viewed after physically scanning the sensor. It is often difficult to distinguish between technologies regarding issues such as calibrations, alarms/alerts, human factors of applying and wearing sensors, and the cost, which are device specific. As these technological details are subject to constant change, the term CGM is used for all issues related to the device class unless indicated otherwis...

The aim of this randomized double-blind study was to compare the within-subject variability of the glucoselowering effect of a novel insulin analog, insulin detemir, with that of insulin glargine and NPH insulin in people with type 1 diabetes. Fifty-four subjects (32 males and 22 females, age 38 ؎ 10 years [ D aily clinical experience indicates that subcutaneous administration of insulin often does not result in a reproducible metabolic effect even when injected at the same dose under comparable conditions. Nonetheless, only few studies have assessed the variability of insulin absorption after subcutaneous administration (1-7), and even fewer have assessed the variability in the glucose-lowering effect of insulin. Thus, even though variability of the glucoselowering effect is regarded as a major obstacle to achieving optimal metabolic control (8 -10), our knowledge of the variability of insulin preparations is surprisingly scarce (11,12). This is particularly true for basal insulin preparations. The few studies available report coefficients of variation (CVs) for within-and between-subject variability in the pharmacodynamic action of long-acting zinc insulin preparations to be between 35 and 55% (9) and even greater for NPH insulin (13). Compared with these findings, the variability (CV) of short-acting insulin preparations, which are reported in the range of "only" 20 -30% (10,11), are less of a concern. The development of the new long-acting insulin analogs such as insulin detemir and insulin glargine has raised the hope of concurrent lower within-subject variability. However, insulin glargine does not appear to provide any improvement in the within-subject variability compared with NPH insulin (8).The aim of this study was to compare the within-subject variability in the glucose-lowering effect of the novel long-acting insulin analog insulin detemir with that of NPH insulin and insulin glargine. Insulin detemir [Lys B29 (N ε -tetradecanoyl) des(B30) human insulin] is the first of a new class of long-acting soluble insulin analogs. Its prolonged duration of action is attributable to a combination of increased self-association (hexamer stabilization and hexamer-hexamer interaction) and albumin binding due to acylation of the amino acid lysine in position B29 with a 14 C fatty acid (myristic acid). Insulin detemir is highly albumin bound in the interstitial fluid and in plasma (14) and has been shown to elicit a protracted metabolic action, with a slow onset of action and a less pronounced peak of action compared with that observed for NPH insulin (15,16).

Among patients with type 1 diabetes, 12-week use of a closed-loop system, as compared with sensor-augmented pump therapy, improved glucose control, reduced hypoglycemia, and, in adults, resulted in a lower glycated hemoglobin level. (Funded by the JDRF and others; AP@home04 and APCam08 ClinicalTrials.gov numbers, NCT01961622 and NCT01778348.).

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.