Variation in the Glucose Transporter gene <i>SLC2A2 </i>is associated with glycaemic response to metformin

Metformin is the first-line antidiabetic drug with over 100 million users worldwide, yet its mechanism of action remains unclear1. Here the Metformin Genetics (MetGen) Consortium reports a three-stage genome-wide association study (GWAS), consisting of 13,123 participants of different ancestries. The C allele of rs8192675 in the intron of SLC2A2, which encodes the facilitated glucose transporter GLUT2, was associated with a 0.17% (P = 6.6 × 10−14) greater metformin-induced reduction in hemoglobin A1c (HbA1c) in 10,577 participants of European ancestry. rs8192675 was the top cis expression quantitative trait locus (cis-eQTL) for SLC2A2 in 1,226 human liver samples, suggesting a key role for hepatic GLUT2 in regulation of metformin action. Among obese individuals, C-allele homozygotes at rs8192675 had a 0.33% (3.6 mmol/mol) greater absolute HbA1c reduction than T-allele homozygotes. This was about half the effect seen with the addition of a DPP-4 inhibitor, and equated to a dose difference of 550 mg of metformin, suggesting rs8192675 as a potential biomarker for stratified medicine

Long-term treatment with metformin in type 2 diabetes and methylmalonic acid: Post hoc analysis of a randomized controlled 4.3 year trial

Aims: Metformin treatment is associated with a decrease of serum vitamin B12, but whether this reflects tissue B12 deficiency is controversial. We studied the effects of metformin on serum levels of methylmalonic acid (MMA), a biomarker for tissue B12 deficiency, and on onset or progression of neuropathy. Methods: In the HOME trial, 390 insulin-treated patients with type 2 diabetes were treated with metformin or placebo for 52 months. In a post hoc analysis, we analyzed the association between metformin, MMA and a validated Neuropathy Score (NPS). Results: Metformin vs placebo increased MMA at the end of the study (95%CI: 0.019 to 0.055, p = 0.001). Mediation analysis showed that the effect of metformin on the NPS consisted of a beneficial effect through lowering HbAlc (-0.020 per gram year) and an adverse effect through increasing MMA (0.042 per gram year), resulting in a non-significant net effect (0.032 per gram year, 95% CI: -0.121 to 0.182, p = 034). Conclusion: Metformin not only reduces serum levels of B12, but also progressively increases serum MMA. The increase of MMA in metformin users was associated with significant worsening of the NPS. These results provide further support that metformin-related B12 deficiency is clinically relevant. Monitoring of B12 in users of metformin should be considered. (C) 2017 Elsevier Inc. All rights reserved

A gene variant near ATM affects the response to metformin and metformin plasma levels: a post hoc analysis of an RCT

Aim: To determine the influence of polymorphisms on the effects of metformin on HbA1c, daily dose of insulin and metformin plasma concentration. Methods: In a post hoc analysis of a 4.3 year placebo-controlled randomized trial with 390 patients with Type 2 diabetes already on insulin, we analyzed the influence of polymorphisms in genes coding for ATM and the transporters OCT1 and MATE1. Outcome measures were a combined HbA1c + daily dose of insulin Z score and metformin plasma concentrations. Results: rs11212617 (ATM) was associated with an improved Z score and a lower metformin plasma concentration. In addition, the major allele of rs2289669 (MATE1) was also associated with an improved Z score. Conclusion: The ATM SNR rs11212617 significantly affected the effect of metformin and metformin plasma concentration. Further research is needed to determine the clinical importance of these findings, in particular the effects on metformin plasma concentration

Metformin-associated prevention of weight gain in insulin-treated type 2 diabetic patients cannot be explained by decreased energy intake:A post hoc analysis of a randomized placebo-controlled 4.3-year trial

Metformin prevents weight gain in patients with type 2 diabetes (T2D). However, the mechanisms involved are still unknown. In this post hoc analysis of the HOME trial, we aimed to determine whether metformin affects energy intake. Patients with T2D were treated with 850mg metformin or received placebo added to insulin (1-3 times daily) for 4.3years. Dietary intake was assessed at baseline, after 1year and after 4.3years, according to the dietary history method. Among the 310 included participants, 179 (93 placebo, 86 metformin) completed all 3 dietary assessments. We found no significant difference in energy intake after 4.3years between the groups (metformin vs placebo: -31.0kcal/d; 95% CI, -107.4 to 45.4; F-value, 1.3; df=415; P=.27). Body weight in placebo users increased significantly more than in metformin-users during 4.3years (4.9 +/- 4.9 vs 1.1 +/- 5.2kg; t test: P.001). Linear mixed models did not show a significant effect of energy intake as explanation for the difference in weight gain between the groups (F-value, 0.1; df=1; P=.82). In conclusion, the prevention of weight gain by metformin cannot be explained by reduced energy intake

Improved viscosity modeling in patients with type 2 diabetes mellitus by accounting for enhanced red blood cell aggregation tendency

Aims: Distorted wall shear stress (WSS) in patients with type 2 diabetes mellitus (T2DM) may be partly explained by an altered red blood cell aggregation tendency (RAT) on viscosity at low shear rate (SR). The present study evaluates viscosity modeling by implementation of hematocrit and RAT in patients with and without T2DM (non-T2DM). Methods: A Couette viscometer and LORCA aggregometer provided viscosity and RAT on 6 shear rates in 55 patients (46-78 yrs, 66% male, T2DM: n = 28), following informed consent. Using a K-fold cross-validation, two linear mixed models predicted by SR and Hct and by SR, Hct and RAT were compared. Results: In non-T2DM modeling was improved in relatively low RATs (48%, p = 1.0 x 10(-11)) and became worse in relatively high RATs (-18%, p = 0.019). In T2DM the opposite was observed, as modeling became worse in relatively low RATs (-16%, p = 0.001) but was improved in relatively high RATs (22%, p = 0.022). Conclusions: In addition to confirming previous research, major differences in modeling improvement between T2DM and non-T2DM were found. Especially patients with T2DM, a high RAT and often high viscosity at low SR benefit from a more accurate viscosity modeling. Further studies should evaluate how these findings affect WSS in these patient