Baseline factors associated with better response to insulin lispro low mixture or insulin glargine: A post hoc analysis of the DURABLE study. (January 2018) - Jia, Nan; Kindracki, Zbigniew; Rodriguez, Angel
Highlights: Gradient boosting models applied post hoc to randomized clinical trial data. Baseline factors predicted unique response to insulin lispro low mix or glargine. Subgroups responded differently to these insulins than overall study population. Findings may help to tailor these insulins based on individual characteristics. Abstract: Aims: To identify baseline characteristics associated with better efficacy and safety responses to twice-daily insulin lispro low mixture (LM) or once-daily insulin glargine (IG) in insulin-naïve patients with type 2 diabetes (T2D). Methods: This post hoc analysis of the DURABLE study used the gradient-boosting method to generate hypothetical outcomes with the alternative treatment to assigned study drug to evaluate the potential additional benefit of one insulin over the other in association with influential baseline covariates in the same patient. The magnitude of additional benefit was further quantified by the generalized linear model and recursive partitioning regression tree method. Results: Baseline characteristics with the highest relative influence on 24-week outcomes in the overall population (LM, n = 1045; IG, n = 1046) were: for HbA1c change: fasting plasma glucose (FPG) (29.31%), age (28.57%); for reaching target HbA1c <7% (<53 mmol/mol): weight (22.41%); for weight change: weight (18.54%); for FPG: FPG (43.66%), age (20.8%); for 30-day hypoglycemia: FPG (57.09%), weight (10.1%). LM showed superiority over IG for HbA1c reduction and reaching HbA1c <7% overall, with clinically significant differences in HbA1c reduction (>0.4%) in some subpopulations. IG was superior over LM in most patients for less weight gain and hypoglycemia and lower FPG. Conclusions: Differences in magnitude of response to twice-daily LM and once-daily IG in association with baseline characteristics of insulin-naïve patients with T2D were found. Future real-world studies using these statistical methods could help identify patients who respond better to certain insulin regimens to help guide clinicians in treatment decisions.