By Max Bingham, PhD
“Moving From Confusion to Clarity”: Glucose Management Indicator to Personalize Diabetes Treatments
Many patients and clinicians like to have an estimate of laboratory-measured A1C derived from their continuous glucose monitoring (CGM) readings. But there has been confusion and outright frustration when the two measures do not agree. In a Perspective article for Diabetes Care, Bergenstal et al. (p. 2275) propose a solution that would make it clear that a variance in laboratory-measured A1C and a CGM-derived estimate of A1C (eA1C) is common, is not unexpected, and that this difference can have important and helpful diabetes management implications. They propose a rebranding and a recalculated measure of eA1C that they call the glucose management indicator or GMI. GMI should address a number of issues associated with both measures and result in a more personalized or individualized diabetes management plan. Based on data from 528 individuals who had CGM-derived GMI and laboratory A1C values measured concurrently, the authors carefully explain that the two measures are only likely to agree at a rate of ∼20%, and ∼30% of measures will likely be at least 0.5% A1C units apart. However, they suggest that there are sound reasons for the differences and that these differences might allow a clinician or individual with diabetes to use the more immediate CGM-derived data to help them make appropriate diabetes management decisions. They also provide examples of how to explain the clinical management implications of a GMI value to patients, focusing on values that might be lower or higher than the laboratory A1C. Author Richard M. Bergenstal said: “Our team of authors wanted to tackle an area of confusion in clinical care and bring some clarity to the value of using the newly proposed GMI metric along with other CGM measures to help personalize diabetes care.”
Declining U.S. Mortality Due to Cardiovascular Diseases, Particularly in the Context of Diabetes
Mortality in the U.S. due to cardiovascular disease (CVD) significantly declined between 1988 and 2015 with the greatest reductions seen in individuals with diabetes, according to an analysis by Cheng et al. (p. 2306). Large reductions were seen in mortality due to ischemic heart disease and stroke while heart failure and arrhythmia mortality did not decline. While adults with diabetes experienced substantial reductions in mortality due to CVD, they still (in 2015) had a higher risk than adults without diabetes. As a result, the authors suggest that while disparities in mortality rates have evidently narrowed, primary prevention of exposure to risk factors should continue and that improvements to monitoring and detection of cases are still needed. The conclusions come from an analysis of the National Health Interview Survey with additional mortality follow-up to the end of 2015. The authors then estimated national trends in mortality according to major CVD, ischemic heart disease, stroke, heart failure, and arrhythmia according to demographic factors and also diabetes status. They found that over an average of nearly 12 years of follow-up of just over 677,000 adults, there were significant decreases in mortality due to major CVD in adults older than 54 years. Indeed, relative changes over a 10-year period among adults with diabetes were -33% for major CVD, -40% for ischemic heart disease, and -29% for stroke. Meanwhile the absolute decrease for men with diabetes far exceeded the drop seen in adults without diabetes. Men also saw greater decreases in mortality due to CVD in comparison to women. According to author Yiling J. Cheng: “Despite a decrease in death rates due to heart attack or stroke among adults aged 55 years and older with diabetes, these benefits were not seen in younger adults. Interventions are still needed among all ages to manage risk factors and improve treatment of cardiovascular diseases.”
Case-Control Study: Gut Microbiota in Type 1 Diabetes Differs in Comparison to MODY2 and Healthy Control Subjects
Gut microbiota composition and functionality in type 1 diabetes fundamentally differs from healthy control subjects and also the nonimmune genetic form of diabetes, maturity-onset diabetes of the young 2 (MODY2), according to Leiva-Gea et al. (p. 2385). As a result, they suggest that gut microbiota appears to play a role in the autoimmune process of type 1 diabetes, which raises the question of whether modulation of the gut microbiota can alter the course of the disease. Using a cohort of 15 children with type 1 diabetes, 15 children with MODY2, and 13 healthy children, the authors compared microbiota composition in fecal samples and also the regulation of a series of microbial metabolic pathways. Additional analyses included serum zonulin levels to estimate intestinal permeability. They found that in comparison to the healthy control subjects, fecal samples from patients with type 1 diabetes had a lower microbial diversity but significantly altered abundance of a variety of bacterial genera. MODY2, meanwhile, was also associated with altered abundance of a number of different genera. They also found that both types of diabetes were associated with increased serum levels of zonulin, potentially indicating increased intestinal permeability. Lipopolysaccharides and proinflammatory cytokines were also raised in type 1 diabetes. Metagenomic analysis of the samples revealed a whole series of metabolic pathways that appeared to be altered according to diabetes type. Authors José Carlos Fernández-García and María Isabel Queipo-Ortuño said: “In recent years, gut microbiota analysis has become a very useful tool to identify risk factors associated with the development or onset of multiple diseases, including type 1 diabetes. Future research should aim to identify specific gut microbiota profiles and functional pathways serving as potential biomarkers for the development of type 1 diabetes. In this line, microbiota-based therapies could also potentially be designed to prevent type 1 diabetes in high-risk patients.”
Continuous Glucose Monitoring Can Predict Diabetic Retinopathy Risk
Time in range according to continuous glucose monitoring (CGM) is associated with the prevalence of all stages of diabetic retinopathy, according to Lu et al. (p. 2370). In addition, various measures of glucose variability were significantly higher in patients with more advanced retinopathy. As a result, the authors of this study suggest that the approach may offer a solution to the perennial issue of HbA1c not always being an accurate predictor of macrovascular/microvascular complications in some patients. The study involved just under 3,300 Chinese patients with type 2 diabetes and involved them using a CGM device for three days to assess glucose variability. Time in range of 3.9–10.0 mmol/L during a 24-h period was the target for the patients. They were also independently assessed for stages of diabetic retinopathy graded according fundus photography. The authors found that nearly 24% had some form of mild to advanced diabetic retinopathy. The patients that had more severe retinopathy had much lower time in range according the CGM measurements and much greater glucose variability. Reductions in quartiles of time in range also correlated with ascending prevalence and severity of retinopathy. As a result of the study, the authors suggest that time in range as captured by CGM measurements should now be more broadly accepted as a clinical measurement and may overcome some of the limitations of HbA1c measurements. Commenting more widely on the study, author Weiping Jia said: “Time in range is a simple and intuitive metric of glycemic control, but its clinical value is not fully recognized. Our data implies that time in range provides valuable information in relation to risk of diabetic retinopathy beyond HbA1c, rather than just being an efficacy measure for antidiabetes interventions. Of course, time in range is not sufficient to assess a patient’s overall glycemic status. We believe that the wider application of CGM may provide further insights into the various dimensions of glycemic control.”
