Metformin in non-diabetic hyperglycaemia: The glint feasibility RCT

Abstract

Background: The treatment of people with diabetes with metformin can reduce cardiovascular disease (CVD) and may reduce the risk of cancer. However, it is unknown whether or not metformin can reduce the risk of these outcomes in people with elevated blood glucose levels below the threshold for diabetes [i.e. non-diabetic hyperglycaemia (NDH)]. Objective: To assess the feasibility of the Glucose Lowering In Non-diabetic hyperglycaemia Trial (GLINT) and to estimate the key parameters to inform the design of the full trial. These parameters include the recruitment strategy, randomisation, electronic data capture, postal drug distribution, retention, study medication adherence, safety monitoring and remote collection of outcome data. Design: A multicentre, individually randomised, double-blind, parallel-group, pragmatic, primary prevention trial. Participants were individually randomised on a 1: 1 basis, blocked within each site. Setting: General practices and clinical research facilities in Cambridgeshire, Norfolk and Leicestershire. Participants: Males and females aged ≥ 40 years with NDH who had a high risk of CVD. Interventions: Prolonged-release metformin (500 mg) (Glucophage® SR, Merck KGaA, Bedfont Cross, Middlesex, UK) or the matched placebo, up to three tablets per day, distributed by post. Main outcome measures: Recruitment rates; adherence to study medication; laboratory results at baseline and 3 and 6 months; reliability and acceptability of study drug delivery; questionnaire return rates; and quality of life. Results: We sent 5251 invitations, with 511 individuals consenting to participate. Of these, 249 were eligible and were randomised between March and November 2015 (125 to the metformin group and 124 to the placebo group). Participants were followed up for 0.99 years [standard deviation (SD) 0.30 years]. The use of electronic medical records to identify potentially eligible individuals in individual practices was resource intensive. Participants were generally elderly [mean age 70 years (SD 6.7 years)], overweight [mean body mass index 30.1 kg/m 2 (SD 4.5 kg/m 2 )] and male (88%), and the mean modelled 10-year CVD risk was 28.8% (SD 8.5%). Randomisation, postal delivery of the study drug and outcome assessment using registers/medical records were feasible and acceptable to participants. Most participants were able to take three tablets per day, but premature discontinuation of the study drug was common (≈30% of participants by 6 months), although there were no differences between the groups. All randomised participants returned questionnaires at baseline and 67% of participants returned questionnaires by the end of the study. There was no between-group difference in Short Form questionnaire-8 items or EuroQol-5 Dimensions scores. Compared with placebo, metformin was associated with small improvements in the mean glycated haemoglobin level [-0.82 mmol/mol, 95% confidence interval (CI) -1.39 to -0.24 mmol/mol], mean estimated glomerular filtration rate (2.31 ml/minute/1.73 m 2 , 95%CI -0.2 to 4.81 ml/minute/1.73 m 2 ) and mean low-density lipoprotein cholesterol level (-0.11mmol/l, 95% CI -0.25 to 0.02mmol/l) and a reduction in mean plasma vitamin B12 level (-16.4 ng/l, 95% CI -32.9 to -0.01 ng/l). There were 35 serious adverse events (13 in the placebo group, 22 in the metformin group), with none deemed to be treatment related. Limitations: Changes to sponsorship reduced the study duration, the limited availability of information in medical records reduced recruitment efficiency and discontinuation of study medication exceeded forecasts. Conclusions: A large, pragmatic trial comparing the effects of prolonged-release metformin and placebo on the risk of CVD events is potentially feasible. However, changes to the study design and conduct are recommended to enable an efficient scaling up of the trial. Recommendations include changing the inclusion criteria to recruit people with pre-existing CVD to increase the recruitment and event rates, using large primary/secondary care databases to increase recruitment rates, conducting follow-up remotely to improve efficiency and including a run-in period prior to randomisation to optimise trial adherence.