Diet control and BMI impact on Metformin response in type 2 Diabetes mellitus patients.

OBJECTIVE: Aim: To assess the impact of BMI and diet control on variation in response to metformin monotherapy in Iraqi people with type 2 DM. PATIENTS AND METHODS: Materials and Methods: a cross-sectional study included 150 patients who met specific criteria, such as being between 30 and 70 years old, diagnosed with type 2 diabetes, and on a daily dose of 1000 mg metformin as a monotherapy for at least three months. Data collected included body mass index (BMI) and glycemic control parameters such as: glycated hemoglobin (HbA1c) levels, fasting blood glucose levels, fasting serum insulin levels, HOMA-IR, and insulin sensitivity. The patients according to their metformin response classified into two groups based on HbA1c as following: poor (HbA1c≥6.5% and good (HbA1c≤6.5%) responder's patients. RESULTS: Results: The statistical analysis suggests that there is no meaningful distinction in glycemic control parameters when comparing good and poor responders within specific BMI subgroups and among individuals practicing diet control. However, in a broader context, it is evident that glycemic control parameters tend to be lower in patients with lower BMI and those who are following a controlled diet. CONCLUSION: Conclusions: The correlation between diet control and BMI with glycemic control in diabetic patients, underscoring the significance of lifestyle adjustments in the management of diabetes.

The Influence of OCT3 and MATE2 Genetic Polymorphisms in Poor Response to Metformin in Type 2 Diabetes Mellitus.

BACKGROUND: The response of patients with Type 2 diabetes mellitus (T2DM) to metformin may be a variation because of genetic differences in solute carrier (SLC) transporter proteins and other effect factors, which have an important effect on how metformin is processed in the body and its efficiency for glycaemic control. AIM: This study was conducted to investigate the impact of certain genetic variants of the organic cation transporter genes OCT3 (SLC22A3 rs12194182 and rs8187722) and MATE2 (SLC47A2 rs12943590) and their association with glycaemic parameters in patients with T2DM who respond poorly to metformin. PATIENTS AND METHODS: This cross-sectional study involved 150 Iraqi cases with T2DM who were prescribed a daily dose of (1000 mg/day) metformin for a minimum of 3 months. Various parameters included are as follows: demographic data, glycaemic parameters and three SNPs: rs12943590 variant of SLC47A2, rs12194182 and rs8187722 variant of SLC22A3 using the standard PCR-sequencing technique. RESULTS: Thirty-nine patients (26.17%) were responders, whereas 111 patients (73.82%) could not respond to metformin treatment. Upon analysing the genotypes of the rs12943590 variants of SLC47A2, rs12194182 and rs8187722 SNPs of SLC22A3, the present findings revealed a nonsignificant association of genetic variations in all SNPs with metformin response. SLC47A2 (rs12943590) showed nonsignificant associations of the GG, AA and AG genotyping; SLC22A3 (rs12194182) showed nonsignificant associations of the TT, TC and CC genotyping; and SLC22A3 (rs8187722) showed nonsignificant associations of the AA, CC and AC genotyping between two groups. CONCLUSION: Variations in genes SLC22A3 and SLC47A2 did not have a significant role in the response of patients with T2DM to metformin (1000 mg/day).

Association between organic cation transporter genetic polymorphisms and metformin response and intolerance in T2DM individuals: a systematic review and meta-analysis.

Background: Variants in organic cation transporter (OCT) genes play a crucial role in metformin pharmacokinetics and are critical for diabetes treatment. However, studies investigating the effect of OCT genetic polymorphisms on metformin response have reported inconsistent results. This review and meta-analysis aimed to evaluate the associations between OCT genetic polymorphisms and metformin response and intolerance in individuals with type 2 diabetes mellitus (T2DM). Method: A systematic search was conducted on PubMed, EMBASE, CNKI, WANFANG DATA, and VIP database for identifying potential studies up to 10 November 2022. The Q-Genie tool was used to evaluate the quality of included studies. Pooled odds ratios (OR) or standardized mean differences (SMD) and 95% confidence intervals (95% CI) were calculated to determine the associations between OCT genetic polymorphisms and metformin response and intolerance that were reflected by glycemic response indexes, such as glycated hemoglobin level (HbA1c%) or change in glycated hemoglobin level (ΔHbA1c%), fasting plasma level (FPG) or change in fasting plasma glucose level (ΔFPG), the effectiveness rate of metformin treatment, and the rate of metformin intolerance. A qualitative review was performed for the variants identified just in one study and those that could not undergo pooling analysis. Results: A total of 30 related eligible studies about OCT genes (SLC22A1, SLC22A2, and SLC22A3) and metformin pharmacogenetics were identified, and 14, 3, and 6 single nucleotide polymorphisms (SNPs) in SLC22A1, SLC22A2, and SLC22A3, respectively, were investigated. Meta-analysis showed that the SLC22A1 rs622342 polymorphism was associated with a reduction in HbA1c level (AA vs. AC: SMD [95% CI] = -0.45 [-0.73--0.18]; p = 0.001). The GG genotype of the SLC22A1 rs628031 polymorphism was associated with a reduction in FPG level (GG vs. AA: SMD [95 %CI] = -0.60 [-1.04-0.16], p = 0.007; GG vs. AG: -0.45 [-0.67-0.20], p < 0.001). No statistical association was found between the remaining variants and metformin response and intolerance. Conclusion: SLC22A1 rs622342 and rs628031 polymorphisms were potentially associated with glycemic response to metformin. This evidence may provide novel insight into gene-oriented personalized medicine for diabetes.

Variants of SLC2A10 may be Linked to Poor Response to Metformin.

Purpose: A study among Filipinos revealed that only 15% of patients with diabetes achieved glycemic control, and poor response to metformin could be one of the possible reasons. Recent studies demonstrate how genetic variations influence response to metformin. Hence, the present study aimed to determine genetic variants associated with poor response to metformin. Methods: Using a candidate variant approach, 195 adult Filipino participants with newly diagnosed type 2 diabetes mellitus (T2DM) were enrolled in a case-control study. Genomic DNA from blood samples were collected. Allelic and genotypic associations of variants with poor response to metformin were determined using exact statistical methods. Results: Several polymorphisms were nominally associated with poor response to metformin (P uncorr < 0.05). The most notable is the association of multiple variants in the SLC2A10 gene-rs2425911, rs3092412, and rs2425904-with common additive genetic mode of inheritance. Other variants that have possible associations with poor drug response include rs340874 (PROX-AS1), rs815815 (CALM2), rs1333049 (CDKN2B-AS1), rs2010963 (VEGFA), rs1535435 and rs9494266 (AHI1), rs11128347 (PDZRN3), rs1805081 (NPC1), and rs13266634 (SLC30A8). Conclusion: In Filipinos, a trend for the association for several variants was noted, with further observation that several mechanisms may be involved. The results may serve as pilot data for further validation of candidate variants for T2DM pharmacotherapy.

A Two-Stage Study Identifies Two Novel Polymorphisms in PRKAG2 Affecting Metformin Response in Chinese Type 2 Diabetes Patients.

OBJECTIVE: Individual differences in glycemic response to metformin in antidiabetic treatment exist widely. Although some associated genetic variations have been discovered, they still cannot accurately predict metformin response. In the current study, we set out to investigate novel genetic variants affecting metformin response in Chinese type 2 diabetes (T2D) patients. METHODS: A two-stage study enrolled 500 T2D patients who received metformin, glibenclamide or a combination of both were recruited from 2009 to 2012 in China. Change of HbA1c, adjusted by clinical covariates, was used to evaluate glycemic response to metformin. Selected single nucleotide polymorphisms (SNPs) were genotyped using the Infinium iSelect and/or Illumina GoldenGate genotyping platform. A linear regression model was used to evaluate the association between SNPs and response. RESULTS: A total of 3739 SNPs were screened in Stage 1, of which 50 were associated with drug response. Except for one genetic variant preferred to affect glibenclamide, the remaining SNPs were subsequently verified in Stage 2, and two SNPs were successfully validated. These were PRKAG2 rs2727528 (discovery group: ß=-0.212, P=0.046; validation group: ß=-0.269, P=0.028) and PRKAG2 rs1105842 (discovery group: ß=0.205, P=0.048; validation group: ß=0.273, P=0.025). C allele carriers of rs2727528 and C allele carriers of rs1105842 would have a larger difference of HbA1c level when using metformin. CONCLUSION: Two variants rs2727528 and rs1105842 in PRKAG2, encoding γ2 subunit of AMP-activated protein kinase (AMPK), were found to be associated with metformin response in Chinese T2D patients. These findings may provide some novel information for personalized pharmacotherapy of metformin in China.