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Aim: To assess the potential of supervised machine‐learning techniques to identify clinical variables for predicting short‐term and long‐term glycated haemoglobin (HbA1c) response after insulin treatment initiation in patients with type 2 diabetes mellitus (T2DM). Materials and methods: We included patients with T2DM from the Groningen Initiative to Analyse Type 2 diabetes Treatment (GIANTT) database who started insulin treatment between 2007 and 2013 and had a minimum follow‐up of 2 years. Short‐ and long‐term responses at 6 (±2) and 24 (±2) months after insulin initiation, respectively, were assessed. Patients were defined as good responders if they had a decrease in HbA1c ≥ 5 mmol/mol or reached the recommended level of HbA1c ≤ 53 mmol/mol. Twenty‐four baseline clinical variables were used for the analysis and an elastic net regularization technique was used for variable selection. The performance of three traditional machine‐learning algorithms was compared for the prediction of short‐ and long‐term responses and the area under the receiver‐operating characteristic curve (AUC) was used to assess the performance of the prediction models. Results: The elastic net regularization‐based generalized linear model, which included baseline HbA1c and estimated glomerular filtration rate, correctly classified short‐ and long‐term HbA1c response after treatment initiation, with AUCs of 0.80 (95% CI 0.78–0.83) and 0.81 (95% CI 0.79–0.84), respectively, and outperformed the other machine‐learning algorithms. Using baseline HbA1c alone, an AUC = 0.71 (95% CI 0.65–0.73) and 0.72 (95% CI 0.66–0.75) was obtained for predicting short‐term and long‐term response, respectively. Conclusions: Machine‐learning algorithm performed well in the prediction of an individual's short‐term and long‐term HbA1c response using baseline clinical variables. [ABSTRACT FROM AUTHOR]

Aim: To explore the added value of diabetes‐related genetic risk scores (GRSs) to readily available clinical variables in the prediction of glycated haemoglobin (HbA1c) levels after initiation of glucose‐regulating drugs. Materials and methods: We conducted a cohort study in people with type 2 diabetes (T2DM) from the Groningen Initiative to Analyse Type 2 Diabetes Treatment (GIANTT) database who initiated metformin (MET) or sulphonylurea derivatives (SUs) and for whom blood samples were genotyped. The primary outcome was HbA1c level at 6 months, adjusted for baseline HbA1c. GRSs were based on single nucleotide polymorphisms linked to insulin sensitivity, β‐cell activity, and T2DM risk in general. Associations were analysed using multiple linear regression to assess whether adding the GRSs increased the explained variance in a prediction model that included age, gender, diabetes duration and cardio‐metabolic biomarkers. Results: We included 282 patients initiating MET and 89 patients initiating SUs. In the MET prediction model, diabetes duration of >3 months when starting MET was associated with 2.7‐mmol/mol higher HbA1c level. For SUs, no significant clinical predictors were identified. Addition of the GRS linked to insulin sensitivity (for MET), β‐cell activity (for SUs) and T2DM risk (for both) to the models did not improve the explained variance significantly (22% without vs. 22% with GRS) for the MET and (14% without vs. 14% with GRS) for the SUs model, respectively. Conclusion: This study did not indicate a significant effect of GRS related to T2DM in general or to the drugs' mechanism of action for prediction of inter‐individual HbA1c variability in the short term after initiation of MET or SU therapy. [ABSTRACT FROM AUTHOR]

Aims: To identify subgroups of patients with type 2 diabetes mellitus (T2DM) following distinct trajectories of HbA1c after insulin initiation and explore underlying differences in clinical characteristics. Materials and methods: A cohort study was conducted in patients with T2DM initiating insulin in 2007–2013 with a follow‐up of 2 to 4 years. Data were collected from the Groningen Initiative to Analyze Type 2 Diabetes Treatment (GIANTT) database. The primary outcome was subgroups with different trajectories of HbA1c patterns after insulin initiation, as identified by latent class growth modeling. Differences between subgroups were tested using one‐way ANOVA, Kruskal‐Wallis or chi‐square tests, where appropriate. Results: From 1459 patients, three subgroups with distinct HbA1c patterns were identified. Group 1 (8%) initially showed a moderate decrease followed by an increase in HbA1c 2 years later, despite receiving more comedication. Group 2 (84%) showed a stable decrease. Group 3 (8%) had a high initial level of HbA1c and a rapid decline within the first year, followed by a slow increase thereafter. Group 1 patients were on average 6–7 years younger than patients in groups 2 and 3 and were more likely to receive sulfonylureas than Group 3 patients. Group 3 patients had a shorter diabetes duration and were less well‐controlled for HbA1c, systolic blood pressure and LDL‐cholesterol at insulin initiation. Conclusions: Most patients showed a stable HbA1c response, but one out of six patients showed either a poor response, or a rapid initial response only after insulin initiation. Response patterns were associated with age, diabetes duration and risk‐factor controls at the time of insulin initiation. [ABSTRACT FROM AUTHOR]

Aims: To assess variability in systolic blood pressure (SBP) and albuminuria (urinary albumin creatinine ratio [UACR]) responses in patients with type 2 diabetes mellitus initiating renin angiotensin aldosterone system (RAAS) inhibition, and to assess the association of response variability with cardiovascular outcomes. Material and Methods: We performed an observational cohort study in patients with type 2 diabetes who started RAAS inhibition between 2007 and 2013 (n = 1600). Patients were identified from general practices in the Netherlands. Individual response in SBP and UACR was assessed during 15 months’ follow‐up. Patients were categorized as: good responders (∆SBP <0 mm Hg and ∆UACR <0%); intermediate responders (∆SBP <0 mm Hg and ∆UACR >0% or ∆SBP >0 mm Hg and ∆UACR <0%); or poor responders (∆SBP >0 mm Hg and ∆UACR >0%). Multivariable Cox regression was performed to test the association between initial RAAS inhibition response and subsequent cardiovascular outcomes. Results: After starting RAAS inhibition, the mean SBP change was −13.2 mm Hg and the median UACR was −36.6%, with large between‐individual variability, both in SBP [5th to 95th percentile: 48.5‐20] and UACR [5th to 95th percentile: −87.6 to 171.4]. In all, 812 patients (51%) were good responders, 353 (22%) had a good SBP but poor UACR response, 268 (17%) had a good UACR but poor SBP response, and 167 patients (10%) were poor responders. Good responders had a lower risk of cardiovascular events than poor responders (hazard ratio 0.51, 95% confidence interval 0.30‐0.86; P = .012). Conclusions: SBP and UACR response after RAAS inhibition initiation varied between and within individual patients with type 2 diabetes treated in primary care. Poor responders had the highest risk of cardiovascular events, therefore, more efforts are needed to develop personalized treatment plans for these patients. [ABSTRACT FROM AUTHOR]