Predictors of treatment response to liraglutide in type 2 diabetes in a real-world setting

Abstract

Aims: There is an unmet need among healthcare providers to identify subgroups of patients with type 2 diabetes who are most likely to respond to treatment.

Methods: Data were taken from electronic medical records of participants of an observational, retrospective study in Italy. We used logistic regression models to assess the odds of achieving glycated haemoglobin (HbA_1c) reduction ≥ 1.0% point after 12-month treatment with liraglutide (primary endpoint), according to various patient-related factors. RECursive Partitioning and AMalgamation (RECPAM) analysis was used to identify distinct homogeneous patient subgroups with different odds of achieving the primary endpoint.

Results: Data from 1325 patients were included, of which 577 (43.5%) achieved HbA_1c reduction ≥ 1.0% point (10.9 mmol/mol) after 12 months. Logistic regression showed that for each additional 1% HbA_1c at baseline, the odds of reaching this endpoint were increased 3.5 times (95% CI: 2.90-4.32). By use of RECPAM analysis, five distinct responder subgroups were identified, with baseline HbA_1c and diabetes duration as the two splitting variables. Patients in the most poorly controlled subgroup (RECPAM Class 1, mean baseline HbA_1c > 9.1% [76 mmol/mol]) had a 28-fold higher odds of reaching the endpoint versus patients in the best-controlled group (mean baseline HbA_1c ≤ 7.5% [58 mmol/mol]). Mean HbA_1c reduction from baseline was as large as - 2.2% (24 mol/mol) in the former versus - 0.1% (1.1 mmol/mol) in the latter. Mean weight reduction ranged from 2.5 to 4.3 kg across RECPAM subgroups.

Conclusions: Glycaemic response to liraglutide is largely driven by baseline HbA_1c levels and, to a lesser extent, by diabetes duration.

Keywords: GLP-1RA; Liraglutide; RECPAM analysis; Response to therapy; Type 2 diabetes.

Fig. 1

Subgroups of patients with type 2 diabetes with different odds of achieving a HbA_1c reduction ≥ 1.0% (10.9 mmo/mol) after 12 months of treatment with liraglutide, identified using RECPAM analysis. The tree-growing algorithm modelled the odds for achieving HbA_1c reduction ≥ 1.0%-point using multivariate logistic regression. Splitting variables were automatically selected by the RECPAM routine among the covariates used in the multivariate analysis and are shown between branches. Cut-offs sending patients to the left or right sibling were also automatically chosen by the RECPAM routine and are reported on the relative branches.  %, proportion of patients in subgroup achieving a reduction in HbA_1c ≥ 1.0% (10.9 mmol/mol); circles indicate subgroups of patients and squares indicate final RECPAM classes. Numbers inside circles and squares indicate number of patients achieving HbA_1c reduction ≥ 1.0% (10.9 mmol/mol). HbA_1c, glycated haemoglobin; OR, unadjusted odds ratio (95% confidence interval); RECPAM, RECursive Partitioning and AMalgamation