Predictors of HbA(1c) treatment response to add-on medication following metformin monotherapy: a population-based cohort study

Evidence on the influence of patient characteristics on HbA(1c) treatment response for add-on medications in patients with type 2 diabetes (T2D) is unclear. This study aims to investigate the predictors of HbA(1c) treatment response for three add-on medications (sulfonylureas (SU), dipeptidyl peptidase-4 (DPP-4) and sodium–glucose cotransporter-2 (SGLT-2) inhibitor) in metformin monotherapy treated patients with T2D. This retrospective cohort study was conducted using the electronic health record data from six primary care clinics in Singapore. A total of 9748 adult patients with T2D on metformin monotherapy receiving SU, DPP-4 or SGLT-2 add-on were 1:1 propensity score matched to patients receiving other add-on medications. Patient demographics, laboratory results, diabetes related complications, comedications, and treatment response at two endpoints (HbA(1c) reduction ≥ 1% at 6th month, HbA(1c) goal attainment < 7% at 12th month) were examined. Multiple logistic regression analyses were used to identify patient characteristics associated with the treatment responses. After matching, there were 1073, 517, and 290 paired cohorts of SU, DPP-4 and SGLT-2 respectively. Besides baseline HbA(1c), patients with longer hypertension disease duration and higher cholesterol HDL were associated with better treatment response to SU medication add-on. Lower estimated glomerular filtration rate (eGFR), and angiotensin-II receptor medications were associated with better treatment response to DPP-4 add-on. Lower cholesterol HDL, higher creatinine serum, absence of renal complications and beta-blockers medications were associated with better treatment response to SGLT-2 add-on. The cholesterol HDL, creatinine serum, eGFR, hypertension disease duration, angiotensin-II receptors and beta-blockers class of medications can influence the HbA(1c) treatment response for SU, DPP-4 and SGLT-2 add-on medications. Knowing the patients’ characteristics that influence treatment response can assist in guiding clinical decisions when selecting the appropriate add-on medication, ultimately helping to prevent the development of diabetes-related complications.