Aims: We describe a new approach to estimate the risks of hypo- and hyperglycemia based on the mean and SD of the glucose distribution using optional transformations of the glucose scale to achieve a more nearly symmetrical and Gaussian distribution, if necessary. We examine the correlation of risks of hypo- and hyperglycemia calculated using different glucose thresholds and the relationships of these risks to the mean glucose, SD, and percentage coefficient of variation (%CV).
Materials and methods: Using representative continuous glucose monitoring datasets, one can predict the risk of glucose values above or below any arbitrary threshold if the glucose distribution is Gaussian or can be transformed to be Gaussian. Symmetry and gaussianness can be tested objectively and used to optimize the transformation.
Results: The method performs well with excellent correlation of predicted and observed risks of hypo- or hyperglycemia for individual subjects by time of day or for a specified range of dates. One can compare observed and calculated risks of hypo- and hyperglycemia for a series of thresholds considering their uncertainties. Thresholds such as 80 mg/dL can be used as surrogates for thresholds such as 50 mg/dL. We observe a high correlation of risk of hypoglycemia with %CV and illustrate the theoretical basis for that relationship.
Conclusions: One can estimate the historical risks of hypo- and hyperglycemia by time of day, date, day of the week, or range of dates, using any specified thresholds. Risks of hypoglycemia with one threshold (e.g., 80 mg/dL) can be used as an effective surrogate marker for hypoglycemia at other thresholds (e.g., 50 mg/dL). These estimates of risk can be useful in research studies and in the clinical care of patients with diabetes.