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

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.