Clements, Mark A

Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Introduction We prospectively investigated the associations between diabetes-related family conflict, parent engagement in child type 1 diabetes (T1D) care, and child glycated hemoglobin (HbA1c) in 127 families of school-age children who we recruited within the first year of their T1D diagnosis. Research design and methods Parents completed the Diabetes Family Conflict Scale-Revised (DFCS-R) to assess for diabetes-related family conflict and the Diabetes Self-Management Questionnaire-Brief (DSMQ-Brief) to assess parent engagement in child T1D care at the initial study visit (T1) and at 12 (T2) and 27 (T3) months later. We also collected child HbA1c at these time points. Our analyses included Pearson correlations and repeated measures linear mixed models controlling for child age, sex, and T1D duration at T1. Results Parents’ DFCS-R scores negatively correlated with DSMQ-Brief scores (r=−0.13, p<0.05) and positively correlated with children’s HbA1c (r=0.26, p<0.001). In our linear mixed models, parents’ DSMQ-Brief scores were unchanged at T2 (β=−0.71, 95% CI −1.59 to 0.16) and higher at T3 (β=8.01, 95% CI 6.89 to 9.13) compared with T1, and there was an association between increasing DFCS-R and decreasing DSMQ-Brief scores (β=−0.14, 95% CI −0.21 to −0.06). Child HbA1c values were significantly higher at T2 (β=0.66, 95% CI 0.38 to 0.94) and T3 (β=0.95, 95% CI 0.63 to 1.27) compared with T1, and there was an association between increasing DFCS-R scores and increasing child HbA1c (β=0.04, 95% CI 0.02 to 0.06). Conclusions Increasing diabetes-specific family conflict early in T1D may associate with decreasing parent engagement in child T1D care and increasing child HbA1c, suggesting a need to assess and intervene on diabetes-specific family conflict. Trial registration number NCT03698708.