Background: Insulin-naive subjects with type 2 diabetes (T2D) start basal insulin titration from a low initial insulin dose (IID), which is adjusted weekly or twice per week based on fasting plasma glucose (FPG) measurement as recommended by the American Diabetes Association (ADA). The procedure to reach the optimal insulin dose (OID) is time-consuming, especially in subjects with high insulin needs (HIN). The aim of this study is to provide a fast and effective, but still safe, insulin titration algorithm in insulin-naive T2D subjects with HIN.
Method: To do that, we in silico cloned 300 subjects, matching a real population of insulin-naive T2D and used a logistic regression model to classify them as subjects with HIN or subjects with low insulin needs (LIN). Then, we applied to the subjects with HIN both a more aggressive insulin dose initiation (SMART-IID) and two newly developed titration algorithms (continuous glucose monitoring [CGM]-BASED and SMART-CGM-BASED) in which CGM was used to guide the decision-making process.
Results: The new titration algorithm applied to HIN-classified individuals guaranteed a faster reaching of OID, with significant improvements in time in range (TIR) and reduction in time above range (TAR) in the first months of the trial, without any clinically significant increase in the risk of hypoglycemia.
Conclusions: Smart basal insulin titration algorithms enable insulin-naive T2D individuals to achieve OID and improve their glycemic control faster than standard guidelines, without jeopardizing patient safety.
Keywords: basal insulin titration; classification; continuous glucose monitoring; mathematical models.