Continuous glucose monitoring classification algorithm to identify glucotypes at diabetes risk

Stanford researchers have developed a method of assigning a “glucotype” to patients based on their temporal glycemic patterns. This algorithm classifies people with glycemic dysregulation through constant monitoring. This comprehensive metric of variability should encompass the magnitude of fluctuation, but also the rate of change, frequency of fluctuations and relative glucose concentration. This is a two-step approach; a training step and then a prediction/classification step used to categorize patients. This can be used as a pre-diagnostic for diabetes-prone patients with glycemic fluctuations and furthermore is expected to provide a more comprehensive, dynamic and granular understanding of diabetes etiology and aid in tailoring a diet for the most optimal glucose response.