Derivation and validation of a type 2 diabetes treatment selection algorithm for SGLT2-inhibitor and DPP4-inhibitor therapies based on glucose-lowering efficacy: cohort study using trial and routine clinical data

Objective: To establish whether clinical patient characteristics routinely measured in primary care can identify people with differing short-term benefits and risks for SGLT2-inhibitor and DPP4-inhibitor therapies, and to derive and validate a treatment selection algorithm to identify the likely optimal therapy for individual patients. Design: Prospective cohort study. Setting: Routine clinical data from United Kingdom general practice (Clinical Practice Research Datalink [CPRD]), and individual-level clinical trial data from 14 multi-country trials of SGLT2-inhibitor and DPP4-inhibitor therapies. Participants: 26,877 new users of SGLT2-inhibitor and DPP4-inhibitor therapy in CPRD over 2013-2019, and 10,414 participants randomised to SGLT2-inhibitor or DPP4-inhibitor therapy in 14 clinical trials, including 3 head-to-head trials of the two therapies (n=2,499). Main outcome measures: The primary outcome was achieved HbA1c 6 months after initiating therapy. Clinical features associated with differential HbA1c outcomes with SGLT2-inhibitor and DPP4-inhibitor therapies were identified in routine clinical data, with associations then tested in trial data. A multivariable treatment selection algorithm to predict differential HbA1c outcomes was developed in a CPRD derivation cohort (n=14,069), with validation in a CPRD validation cohort (n=9,376) and the head-to-head trials. In CPRD, we further explored the relationship between model predictions and secondary outcomes of weight loss and treatment discontinuation. Results: The final treatment selection algorithm included HbA1c, eGFR, ALT, age, and BMI, which were identified as predictors of differential HbA1c outcomes with SGLT2-inhibitor and DPP4-inhibitor therapies using both routine and trial data. In validation cohorts, patient strata predicted to have a ≥5 mmol/mol HbA1c reduction with SGLT2-inhibitor therapy compared with DPP4-inhibitor therapy (38.8% of CPRD validation sample) had an observed greater reduction of 8.8 mmol/mol [95%CI 7.8-9.8] in the CPRD validation sample, a 5.8 mmol/mol (95%CI 3.9-7.7) greater reduction in the Cantata D/D2 trials, and a 6.6 mmol/mol [95%CI 2.2-11.0]) greater reduction in the BI1245.20 trial. In CPRD, there was a greater weight reduction with SGLT2-inhibitor therapy regardless of predicted glycaemic benefit. Strata predicted to have greater reduction in HbA1c on SGLT2-inhibitor therapy had a similar risk of discontinuation as on DPP4-inhibitor therapy. In contrast, strata predicted to have greater reduction in HbA1c with DPP4-inhibitor therapy were half as likely to discontinue DPP4-inhibitor therapy than SGLT2-inhibitor therapy. Conclusions: Routinely measured clinical features are robustly associated with differential glycaemic responses to SGLT2-inhibitor and DPP4-inhibitor therapies. Combining features into a treatment selection algorithm can inform clinical decisions concerning optimal type 2 diabetes treatment choices.

Adjunctive Therapies to Optimize Closed-loop Glucose Control

Closed-loop insulin delivery systems are fast becoming the standard of care in the management of type 1 diabetes and have led to significant improvements in diabetes management. Nevertheless, there is still room for improvement for the closed-loop systems to optimize treatment and meet target glycemic control. Adjunct treatments have been introduced as an alternative method to insulin-only treatment methods to overcome diabetes treatment challenges and improve clinical and patient reported outcomes during closed-loop treatment. The adjunct treatment agents mostly consist of medications that are already approved for type 2 diabetes treatment and aim to complete the missing physiologic factors, such as the entero-endocrine system, that regulate glycemia in addition to insulin. This paper will review many of these adjunct therapies, including the basic mechanisms of action, potential benefits, side effects, and the evidence supporting their use during closed-loop treatment.

Discovery of New α-Glucosidase Inhibitors: Structure-Based Virtual Screening and Biological Evaluation

α-Glycosidase inhibitors could inhibit the digestion of carbohydrates into glucose and promote glucose conversion, which have been used for the treatment of type 2 diabetes. In the present study, 52 candidates of α-glycosidase inhibitors were selected from commercial Specs compound library based on molecular docking–based virtual screening. Four different scaffold compounds (7, 22, 37, and 44) were identified as α-glycosidase inhibitors with IC50 values ranging from 9.99 to 35.19 μM. All these four compounds exerted better inhibitory activities than the positive control (1-deoxynojirimycin, IC50 = 52.02 μM). The fluorescence quenching study and kinetic analysis revealed that all these compounds directly bind to α-glycosidase and belonged to the noncompetitive α-glycosidase inhibitors. Then, the binding modes of these four compounds were carefully investigated. Significantly, these four compounds showed nontoxicity (IC50 > 100 μM) toward the human normal hepatocyte cell line (LO2), which indicated the potential of developing into novel candidates for type 2 diabetes treatment.

Effective Dose of Rhizoma Coptidis Extract Granules for Type 2 Diabetes Treatment: A Hospital-Based Retrospective Cohort Study

Rhizoma Coptidis is a popular phytomedicine for the treatment of type 2 diabetes in Asia, but its effective dose for diabetes treatment remains confused because of diverse origins. This study aimed to investigate the dose-response effects of Rhizoma Coptidis extract granules (RCEG), produced with standardized quality control, on hypoglycemic effects in patients with type 2 diabetes. We conducted a retrospective analysis of Chang Gung Research Database from January 01, 2008 to November 30, 2017. Outpatients visiting traditional Chinese medicine clinics and receiving RCEG for type 2 diabetes treatment were included. Plasma glucose, lipid, and other parameters were analyzed from 93 patients with a total of 737 visits within 60 weeks. Scatter plots with the LOESS analysis were used to explore the association between RCEG dose and hypoglycemic effect. The minimal effective dose was chosen to divide the study population into the high-dose and low-dose RCEG groups. Non-parametric tests were used for between-group and within-group comparisons. The multivariate nonlinear mixed-effects model was applied to access the effect of treatment length and groups simultaneously on the change of HbA1c and fasting plasma glucose. The “arule” package in R was used to present the network diagram of RCEG and other co-prescriptions. We first discovered a significant relationship between RCEG dose and HbA1c reduction when the dose reached 0.08 g/kg/day or higher. We thus defined 0.08 g/kg/day of RCEG as the minimum effective dose, and a threshold to separate patients into the high-dose (≥0.08 g/kg/d) and low-dose (<0.08 g/kg/d) RCEG groups. In the high-dose RCEG group, a significant decrease in total cholesterol and a trend toward triglyceride reduction were also noted. Patients more effectively responded to RCEG treatment if they had a higher initial HbA1c level, higher heart rates, better liver function tests, and better tolerance to the higher dose and treatment duration of RCEG. In addition, digestive/tonic/dampness draining formulas and blood regulation recipes were two of the most frequent co-prescriptions with RCEG. This study concluded that RCEG at a dose exceeding 0.08 g/kg/d had beneficial effects on glycemic and lipid control, without showing nephro- or hepatotoxicity, in patients with type 2 diabetes.