The C allele of ATM rs11212617 does not associate with metformin response in the Diabetes Prevention Program

Identification of Genetic Variation Influencing Metformin Response in a Multiancestry Genome-Wide Association Study in the Diabetes Prevention Program (DPP)

Genome-wide significant loci for metformin response in type 2 diabetes reported elsewhere have not been replicated in the Diabetes Prevention Program (DPP). To assess pharmacogenetic interactions in prediabetes, we conducted a genome-wide association study (GWAS) in the DPP. Cox proportional hazards models tested associations with diabetes incidence in the metformin (MET; n = 876) and placebo (PBO; n = 887) arms. Multiple linear regression assessed association with 1-year change in metformin-related quantitative traits, adjusted for baseline trait, age, sex, and 10 ancestry principal components. We tested for gene-by-treatment interaction. No significant associations emerged for diabetes incidence. We identified four genome-wide significant variants after correcting for correlated traits (P < 9 × 10-9). In the MET arm, rs144322333 near ENOSF1 (minor allele frequency [MAF]AFR = 0.07; MAFEUR = 0.002) was associated with an increase in percentage of glycated hemoglobin (per minor allele, β = 0.39 [95% CI 0.28, 0.50]; P = 2.8 × 10-12). rs145591055 near OMSR (MAF = 0.10 in American Indians) was associated with weight loss (kilograms) (per G allele, β = -7.55 [95% CI -9.88, -5.22]; P = 3.2 × 10-10) in the MET arm. Neither variant was significant in PBO; gene-by-treatment interaction was significant for both variants [P(G×T) < 1.0 × 10-4]. Replication in individuals with diabetes did not yield significant findings. A GWAS for metformin response in prediabetes revealed novel ethnic-specific associations that require further investigation but may have implications for tailored therapy.

On the Verge of Precision Medicine in Diabetes

The epidemic of type 2 diabetes (T2D) is a significant global public health challenge and a major cause of morbidity and mortality. Despite the recent proliferation of pharmacological agents for the treatment of T2D, current therapies simply treat the symptom, i.e. hyperglycemia, and do not directly address the underlying disease process or modify the disease course. This article summarizes how genomic discovery has contributed to unraveling the heterogeneity in T2D, reviews relevant discoveries in the pharmacogenetics of five commonly prescribed glucose-lowering agents, presents evidence supporting how pharmacogenetics can be leveraged to advance precision medicine, and calls attention to important research gaps to its implementation to guide treatment choices.

Genetic and Phenotypic Factors Affecting Glycemic Response to Metformin Therapy in Patients with Type 2 Diabetes Mellitus

Metformin is an oral hypoglycemic agent widely used in clinical practice for treatment of patients with type 2 diabetes mellitus (T2DM). The wide interindividual variability of response to metformin therapy was shown, and recently the impact of several genetic variants was reported. To assess the independent and combined effect of the genetic polymorphism on glycemic response to metformin, we performed an association analysis of the variants in ATM, SLC22A1, SLC47A1, and SLC2A2 genes with metformin response in 299 patients with T2DM. Likewise, the distribution of allele and genotype frequencies of the studied gene variants was analyzed in an extended group of patients with T2DM (n = 464) and a population group (n = 129). According to our results, one variant, rs12208357 in the SLC22A1 gene, had a significant impact on response to metformin in T2DM patients. Carriers of TT genotype and T allele had a lower response to metformin compared to carriers of CC/CT genotypes and C allele (p-value = 0.0246, p-value = 0.0059, respectively). To identify the parameters that had the greatest importance for the prediction of the therapy response to metformin, we next built a set of machine learning models, based on the various combinations of genetic and phenotypic characteristics. The model based on a set of four parameters, including gender, rs12208357 genotype, familial T2DM background, and waist–hip ratio (WHR) showed the highest prediction accuracy for the response to metformin therapy in patients with T2DM (AUC = 0.62 in cross-validation). Further pharmacogenetic studies may aid in the discovery of the fundamental mechanisms of type 2 diabetes, the identification of new drug targets, and finally, it could advance the development of personalized treatment.

Role of human organic cation transporter-1 (OCT-1/SLC22A1) in modulating the response to metformin in patients with type 2 diabetes

BACKGROUND

Organic cation transporter 1 primarily governs the action of metformin in the liver. There are considerable inter-individual variations in metformin response. In light of this, it is crucial to obtain a greater understanding of the influence of OCT1 expression or polymorphism in the context of variable responses elicited by metformin treatment.

RESULTS

We observed that the variable response to metformin in the responders and non-responders is independent of isoform variation and mRNA expression of OCT-1. We also observed an insignificant difference in the serum metformin levels of the patient groups. Further, molecular docking provided us with an insight into the hotspot regions of OCT-1 for metformin binding. Genotyping of these regions revealed SNPs 156T>C and 1222A>G in both the groups, while as 181C>T and 1201G>A were found only in non-responders. The 181T>C and 1222A>G changes were further found to alter OCT-1 structure in silico and affect metformin transport in vitro which was illustrated by their effect on the activation of AMPK, the marker for metformin activity.

CONCLUSION

Taken together, our results corroborate the role of OCT-1 in the transport of metformin and also point at OCT1 genetic variations possibly affecting the transport of metformin into the cells and hence its subsequent action in responders and non-responders.

Metformin for diabetes prevention: update of the evidence base

We have conducted a narrative review based on a structured search strategy, focusing on the effects of metformin on the progression of non-diabetic hyperglycemia to clinical type 2 diabetes mellitus. The principal trials that demonstrated a significantly lower incidence of diabetes in at-risk populations randomized to metformin (mostly with impaired glucose tolerance [IGT]) were published mainly from 1999 to 2012. Metformin reduced the 3-year risk of diabetes by -31% in the randomized phase of the Diabetes Prevention Program (DPP), vs. -58% for intensive lifestyle intervention (ILI). Metformin was most effective in younger, heavier subjects. Diminishing but still significant reductions in diabetes risk for subjects originally randomized to these groups were present in the trial's epidemiological follow-up, the DPP Outcomes Study (DPPOS) at 10 years (-18 and -34%, respectively), 15 years (-18 and -27%), and 22 years (-18 and -25%). Long-term weight loss was also seen in both groups, with better maintenance under metformin. Subgroup analyses from the DPP/DPPOS have shed important light on the actions of metformin, including a greater effect in women with prior gestational diabetes, and a reduction in coronary artery calcium in men that might suggest a cardioprotective effect. Improvements in long-term clinical outcomes with metformin in people with non-diabetic hyperglycemia ("prediabetes") have yet to be demonstrated, but cardiovascular and microvascular benefits were seen for those in the DPPOS who did not vs. did develop diabetes. Multiple health economic analyses suggest that either metformin or ILI is cost-effective in a community setting. Long-term diabetes prevention with metformin is feasible and is supported in influential guidelines for selected groups of subjects. Future research will demonstrate whether intervention with metformin in people with non-diabetic hyperglycemia will improve long-term clinical outcomes.

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 HbA_1c, 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: β=−0.212, P=0.046; validation group: β=−0.269, P=0.028) and PRKAG2 rs1105842 (discovery group: β=0.205, P=0.048; validation group: β=0.273, P=0.025). C allele carriers of rs2727528 and C allele carriers of rs1105842 would have a larger difference of HbA_1c 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.

Effect of metformin versus placebo on metabolic factors in the MA.32 randomized breast cancer trial

Metformin may exert anticancer effects through indirect (mediated by metabolic changes) or direct mechanisms. The goal was to examine metformin impact on metabolic factors in non-diabetic subjects and determine whether this impact varies by baseline BMI, insulin, and rs11212617 SNP in CCTG MA.32, a double-blind placebo-controlled randomized adjuvant breast cancer (BC) trial. 3649 subjects with T1-3, N0-3, M0 BC were randomized; pretreatment and 6-month on-treatment fasting plasma was centrally assayed for insulin, leptin, highly sensitive C-reactive protein (hsCRP). Glucose was measured locally and homeostasis model assessment (HOMA) calculated. Genomic DNA was analyzed for the rs11212617 SNP. Absolute and relative change of metabolic factors (metformin versus placebo) were compared using Wilcoxon rank and t-tests. Regression models were adjusted for baseline differences and assessed interactions with baseline BMI, insulin, and the SNP. Mean age was 52 years. The majority had T2/3, node positive, hormone receptor positive, HER2 negative BC treated with (neo)adjuvant chemotherapy and hormone therapy. Median baseline body mass index (BMI) was 27.4 kg/m2 (metformin) and 27.3 kg/m2 (placebo). Median weight change was -1.4 kg (metformin) vs +0.5 kg (placebo). Significant improvements were seen in all metabolic factors, with 6 month standardized ratios (metformin/placebo) of 0.85 (insulin), 0.83 (HOMA), 0.80 (leptin), and 0.84 (hsCRP), with no qualitative interactions with baseline BMI or insulin. Changes did not differ by rs11212617 allele. Metformin (vs placebo) led to significant improvements in weight and metabolic factors; these changes did not differ by rs11212617 allele status.

Pharmacogenetic Evaluation of Metformin and Sulphonylurea Response in Mexican Mestizos with Type 2 Diabetes

BACKGROUND

In Mexico, approximately 25% of patients with type 2 diabetes (T2D) have adequate glycemic control. Polymorphisms in pharmacogenetic genes have been shown to have clinical consequences resulting in drug toxicity or therapeutic inefficacy.

OBJECTIVE

The study aimed to evaluate the impact of variants in genes known to be involved in response to oral hypoglycemic drugs, such as CYP2C9, OCT, MATE, ABCA1 and C11orf65, in the Mexican Mestizo population of T2D patients.

METHODS

In this study, 265 patients with T2D were enrolled from the Hospital Juárez de México, Mexico City. Genotyping was performed by TaqMan® assays. SNP-SNP interactions were analyzed using the multifactor dimensionality reduction (MDR) method.

RESULTS

Carriers of the del allele of rs72552763 could achieve better glycemic control than noncarriers. There was a significant difference in plasma glucose and HbA1c levels among rs622342 genotypes. The results suggested an SNP-SNP interaction between rs72552763 and rs622342 OCT1 and rs12943590 MATE2.

CONCLUSION

The interaction between rs72552763 and rs622342 in OCT1, and rs12943590 in MATE2 suggested an important role of these polymorphisms in metformin response in T2D Mexican Mestizo population.

The presence of two reduced function variants in CYP2C9 influences the acute response to glipizide

AIMS

To examine whether the presence of two common missense variants in the CYP2C9 gene (rs1799853, encoding Arg144Cys and denoted as *2, and rs1057910, encoding Ile359Leu and denoted as *3) influences the acute physiological response to a single glipizide dose in individuals naïve to diabetes medications.

METHODS

In the Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH), 786 individuals genotyped for rs1799853/rs41291560 (*2) and rs1057910/rs9332214 (*3) were treated with 5 mg glipizide in the fasting state. Glucose and insulin levels were measured at baseline, 30, 60, 90, 120, 180 and 240 min for calculation of phenotypic endpoints of glipizide response. The challenge was aborted as a result of hypoglycaemia, defined as glucose <2.8 mmol/l or hypoglycaemia-related symptoms.

RESULTS

Carriers with two reduced function alleles had a 50% larger insulin area under the curve than carriers with zero or one copy (P=0.037), although this finding was primarily driven by an individual with a robust insulin response. In adjusted analyses, the risk of aborting the glipizide challenge was doubled in two-copy carriers (P=0.034). No significant findings were observed in glucose-based endpoints.

CONCLUSIONS

Carriers of two reduced function alleles in CYP2C9 may experience an increased insulin response to glipizide and be predisposed to a higher risk of hypoglycaemia, although no effect of genotype was seen in glucose-based measurements. Further studies are needed to clarify the utility of CYP2C9 genotyping to guide sulfonylurea treatment.

Pharmacogenetics of Type 2 Diabetes-Progress and Prospects

Type 2 diabetes mellitus (T2D) is a chronic metabolic disease resulting from insulin resistance and progressively reduced insulin secretion, which leads to impaired glucose utilization, dyslipidemia and hyperinsulinemia and progressive pancreatic beta cell dysfunction. The incidence of type 2 diabetes mellitus is increasing worldwide and nowadays T2D already became a global epidemic. The well-known interindividual variability of T2D drug actions such as biguanides, sulfonylureas/meglitinides, DPP-4 inhibitors/GLP1R agonists and SGLT-2 inhibitors may be caused, among other things, by genetic factors. Pharmacogenetic findings may aid in identifying new drug targets and obtaining in-depth knowledge of the causes of disease and its physiological processes, thereby, providing an opportunity to elaborate an algorithm for tailor or precision treatment. The aim of this article is to summarize recent progress and discoveries for T2D pharmacogenetics and to discuss the factors which limit the furthering accumulation of genetic variability knowledge in patient response to therapy that will allow improvement the personalized treatment of T2D.

Evaluation of the suitability of 19 pharmacogenomics biomarkers for individualized metformin therapy for type 2 diabetes patients

Objectives Type 2 Diabetes mellitus is a progressive metabolic disease characterized by relative insulin insufficiency and insulin resistance resulting in hyperglycemia. Despite the widespread use of metformin, there is considerable variation in treatment response; with approximately one-third of patients failing to achieve adequate glycemic control. Studies have reported the involvement of single nucleotide polymorphisms and their interactions in genetic pathways i.e., pharmacodynamics and pharmacokinetics. This study aims to investigate the association between 19 pharmacogenetics biomarkers and response to metformin treatment. Methods MassARRAY panels were designed and optimized by Inqaba Biotechnical Industries, to genotype 19 biomarkers for 140 type 2 diabetic outpatients. Results The CT genotype of the rs12752688 polymorphism was significantly associated with increased response to metformin therapy after correction (OR=0.33, 95% CI [0.16-0.68], p-value=0.006). An association was also found between the GA genotype of SLC47A2 rs12943590 and a decreased response to metformin therapy after correction (OR=2.29, 95% CI [1.01-5.21], p-value=0.01). Conclusions This is the first study investigating the association between genetic variants and responsiveness to medication for diabetic patients from the indigenous Nguni population in South Africa. It is suggested that rs12752688 and rs12943590 be included in pharmacogenomics profiling systems to individualize metformin therapy for diabetic patients from African populations.

Evaluation of the suitability of 19 pharmacogenomics biomarkers for individualized metformin therapy for type 2 diabetes patients

Objectives Type 2 Diabetes mellitus is a progressive metabolic disease characterized by relative insulin insufficiency and insulin resistance resulting in hyperglycemia. Despite the widespread use of metformin, there is considerable variation in treatment response; with approximately one-third of patients failing to achieve adequate glycemic control. Studies have reported the involvement of single nucleotide polymorphisms and their interactions in genetic pathways i.e., pharmacodynamics and pharmacokinetics. This study aims to investigate the association between 19 pharmacogenetics biomarkers and response to metformin treatment. Methods MassARRAY panels were designed and optimized by Inqaba Biotechnical Industries, to genotype 19 biomarkers for 140 type 2 diabetic outpatients. Results The CT genotype of the rs12752688 polymorphism was significantly associated with increased response to metformin therapy after correction (OR=0.33, 95% CI [0.16-0.68], p-value=0.006). An association was also found between the GA genotype of SLC47A2 rs12943590 and a decreased response to metformin therapy after correction (OR=2.29, 95% CI [1.01-5.21], p-value=0.01). Conclusions This is the first study investigating the association between genetic variants and responsiveness to medication for diabetic patients from the indigenous Nguni population in South Africa. It is suggested that rs12752688 and rs12943590 be included in pharmacogenomics profiling systems to individualize metformin therapy for diabetic patients from African populations.

A review of clinical pharmacogenetics Studies in African populations

Effective interventions and treatments for complex diseases have been implemented globally, however, coverage in Africa has been comparatively lower due to lack of capacity, clinical applicability and knowledge on the genetic contribution to disease and treatment. Currently, there is a scarcity of genetic data on African populations, which have enormous genetic diversity. Pharmacogenomics studies have the potential to revolutionise treatment of diseases, therefore, African populations are likely to benefit from these approaches to identify likely responders, reduce adverse side effects and optimise drug dosing. This review discusses clinical pharmacogenetics studies conducted in African populations, focusing on studies that examined drug response in complex diseases relevant to healthcare. Several pharmacogenetics associations have emerged from African studies, as have gaps in knowledge.

SLC22A1 rs622342 Polymorphism Predicts Insulin Resistance Improvement in Patients with Type 2 Diabetes Mellitus Treated with Metformin: A Cross-Sectional Study

BACKGROUND

Metformin is the most widely used oral antidiabetic agent and can reduce insulin resistance (IR) effectively. Organic cation transporter 1 (encoded by SLC22A1) is responsible for the transport of metformin, and ataxia-telangiectasia-mutated (ATM) is a gene relating to the DNA repair and cell cycle control. The aim of this study was to evaluate if the genetic variants in SLC22A1 rs622342 and ATM rs11212617 could be effective predictors of islet function improvement in patients with type 2 diabetes mellitus (T2DM) on metformin treatment.

METHODS

This cross-sectional study included 111 patients with T2DM treated with metformin. Genotyping was performed by the dideoxy chain-termination method. The homeostatic indexes of IR (HOMA-IR) and beta-cell function (HOMA-BCF) were determined according to the homeostasis model assessment.

RESULTS

Fasting plasma glucose (FPG) levels, HbA_1c levels, and HOMA-IR were significantly higher in patients with the rs622342 AA genotype than in those with C allele (P < 0.05). However, these significant differences were not observed between rs11212617 genotype groups. Further data analysis revealed that the association between the rs622342 polymorphism and HOMA-IR was gender related, and so was rs11212617 polymorphism and HOMA-BCF. HOMA-IR was significantly higher in males with rs622342 AA genotype than in those with C allele (P=0.021), and HOMA-BCF value was significantly higher in females carrying rs11212617 CC genotype than in those with A allele (P=0.038). The common logarithm (Lg10) of HOMA-BCF was positively correlated with the reciprocal of HbA_1c (r = 0.629, P < 0.001) and negatively associated with Lg10 FPG (r = -0.708, P < 0.001).

CONCLUSIONS

The variant of rs622342 could be a predictor of insulin sensitivity in patients with T2DM treated with metformin. The association between the rs622342 polymorphism and HOMA-IR and the association between the rs11212617 polymorphism and HOMA-BCF were both gender related.

SLC22A1 And ATM Genes Polymorphisms Are Associated With The Risk Of Type 2 Diabetes Mellitus In Western Saudi Arabia: A Case-Control Study

Introduction

Type 2 diabetes mellitus (T2DM) is a major global health problem that is progressively affected by genetic and environmental factors. The aim of this study is to determine the influence of solute carrier family 22 member 1 (SLC22A1) rs628031 and rs461473, and ataxia telangiectasia mutated (ATM) rs11212617 polymorphisms on the risk of T2DM in Saudi Arabia by considering many parameters associated with glycemic control of T2DM, such as body mass index (BMI), fasting blood glucose, glycated hemoglobin (HbA1c), and triglyceride.

Methods

In a case-control study, genomic DNA from controls and diabetic groups was isolated and genotyped for each single-nucleotide polymorphism.

Results

There were significant correlations between T2DM and both BMI and HbA1c. Significant associations between G/G and A/G genotypes of rs628031 and rs461473 variants of SLC22A1 and high levels of HbA1c were detected. Therefore, G was predicted to be the risk allele among the assessed SLC22A1 variants. A significant correlation was observed between A/A and A/C genotypes of the rs11212617 polymorphism of ATM and elevated HbA1c. Relative risk calculation confirmed A to be the risk allele in the T2DM population.

Conclusion

Our study showed the risk of the assessed SLC22A1 and ATM variants on glycemic control parameters in diabetic patients.

Gene-lifestyle interaction on risk of type 2 diabetes: A systematic review

The pathophysiological influence of gene-lifestyle interactions on the risk to develop type 2 diabetes (T2D) is currently under intensive research. This systematic review summarizes the evidence for gene-lifestyle interactions regarding T2D incidence. MEDLINE, EMBASE, and Web of Science were systematically searched until 31 January 2019 to identify publication with (a) prospective study design; (b) T2D incidence; (c) gene-diet, gene-physical activity, and gene-weight loss intervention interaction; and (d) population who are healthy or prediabetic. Of 66 eligible publications, 28 reported significant interactions. A variety of different genetic variants and dietary factors were studied. Variants at TCF7L2 were most frequently investigated and showed interactions with fiber and whole grain on T2D incidence. Further gene-diet interactions were reported for, eg, a western dietary pattern with a T2D-GRS, fat and carbohydrate with IRS1 rs2943641, and heme iron with variants of HFE. Physical activity showed interaction with HNF1B, IRS1, PPARγ, ADRA2B, SLC2A2, and ABCC8 variants and weight loss interventions with ENPP1, PPARγ, ADIPOR2, ADRA2B, TNFα, and LIPC variants. However, most findings represent single study findings obtained in European ethnicities. Although some interactions have been reported, their conclusiveness is still low, as most findings were not yet replicated across multiple study populations.

Common Variants in 22 Genes Regulate Response to Metformin Intervention in Children with Obesity: A Pharmacogenetic Study of a Randomized Controlled Trial

Metformin is a first-line oral antidiabetic agent that has shown additional effects in treating obesity and metabolic syndrome. Inter-individual variability in metformin response could be partially explained by the genetic component. Here, we aimed to test whether common genetic variants can predict the response to metformin intervention in obese children. The study was a multicenter and double-blind randomized controlled trial that was stratified according to sex and pubertal status in 160 children with obesity. Children were randomly assigned to receive either metformin (1g/d) or placebo for six months after meeting the defined inclusion criteria. We conducted a post hoc genotyping study in 124 individuals (59 placebo, 65 treated) comprising finally 231 genetic variants in candidate genes. We provide evidence for 28 common variants as promising pharmacogenetics regulators of metformin response in terms of a wide range of anthropometric and biochemical outcomes, including body mass index (BMI) Z-score, and glucose, lipid, and inflammatory traits. Although no association remained statistically significant after multiple-test correction, our findings support previously reported variants in metformin transporters or targets as well as identify novel and promising loci, such as the ADYC3 and the BDNF genes, with plausible biological relation to the metformin's action mechanism. Trial Registration: Registered on the European Clinical Trials Database (EudraCT, ID: 2010-023061-21) on 14 November 2011 (URL: https://www.clinicaltrialsregister.eu/ctr-search/trial/2010-023061-21/ES).

Clinical and genetic predictors of diabetes drug's response

Diabetes is a major health problem worldwide. Glycemic control is the main goal in the management of type 2 diabetes. While many anti-diabetic drugs and guidelines are available, almost half of diabetic patients do not reach their treatment goal and develop complications. The glucose-lowering response to anti-diabetic drug differs significantly between individuals. Relatively little is known about the factors that might underlie this response. The identification of predictors of response to anti-diabetic drugs is essential for treatment personalization. Unfortunately, the evidence on predictors of drugs response in type 2 diabetes is scarce. Only a few trials were designed for specific groups of patients (e.g. patients with renal impairment or older patients), while subgroup analyses of larger trials are frequently unreported. Physicians need help in picking the drug which provides the maximal benefit, with minimal side effects, in the right dose, for a specific patient, using an omics-based approach besides the phenotypic characteristics.

Pharmacogenetics of Metformin for Medication-Induced Weight Gain in Autism Spectrum Disorder

Objectives: We recently found that metformin attenuated weight gain due to mixed dopamine and serotonin receptor antagonists, commonly termed atypical antipsychotics, in children and adolescents with autism spectrum disorder (ASD). Previous studies have found that genetic variation predicts response to metformin in diabetes. In this study, we aimed to assess whether response to metformin for weight gain in this population is associated with variants in five genes previously implicated in metformin response in diabetes. Methods: Youth with ASD who experienced significant weight gain while taking mixed receptor antagonist medications were randomly assigned to metformin or placebo for 16 weeks, followed by open-label metformin treatment for 16 weeks. In the 53 participants with available DNA samples, we used a linear, mixed model analysis to assess response in the first 16 weeks of metformin treatment, whether in the randomized or open-label period, based upon genotypes at polymorphisms in five genes previously associated with metformin response in diabetes: ATM, SLC2A2, MATE1, MATE2, and OCT1. Results: In the primary analysis, both ATM and OCT1 showed significant effects of genotype on change in body mass index z-scores, the primary outcome measure, during the first 16 weeks of treatment with metformin. No other polymorphism showed a significant difference. Conclusion: As has been shown for metformin treatment in diabetes, genetic variation may predict response to metformin for weight gain in youth with ASD treated with mixed receptor antagonists. Further work is needed to replicate these findings and evaluate whether they can be used prospectively to improve outcomes.

The C Allele of ATM rs11212617 Associates With Higher Pathological Complete Remission Rate in Breast Cancer Patients Treated With Neoadjuvant Metformin

Background: The minor allele (C) of the single-nucleotide polymorphism (SNP) rs11212617, located near the ataxia telangiectasia mutated (ATM) gene, has been associated with an increased likelihood of treatment success with metformin in type 2 diabetes. We herein investigated whether the same SNP would predict clinical response to neoadjuvant metformin in women with early breast cancer (BC). Methods: DNA was collected from 79 patients included in the intention-to-treat population of the METTEN study, a phase 2 clinical trial of HER2-positive BC patients randomized to receive either metformin combined with anthracycline/taxane-based chemotherapy and trastuzumab or equivalent regimen without metformin, before surgery. SNP rs11212617 genotyping was assessed using allelic discrimination by quantitative polymerase chain reaction. Results: Logistic regression analyses revealed a significant relationship between the rs11212617 genotype and the ability of treatment arms to achieve a pathological complete response (pCR) in patients (odds ratio [OR]_{genotype×arm} = 10.33, 95% confidence interval [CI]: 1.29-82.89, p = 0.028). In the metformin-containing arm, patients bearing the rs11212617 C allele had a significantly higher probability of pCR (OR _A/C,C/C = 7.94, 95%CI: 1.60-39.42, p = 0.011). Conversely, no association was found between rs11212617 and clinical response in the reference arm (OR _A/C,C/C = 0.77, 95%CI: 0.20-2.92, p = 0.700). After controlling for tumor size and hormone receptor status, the rs11212617 C allele remained a significant predictor of pCR solely in the metformin-containing arm. Conclusions: If reproducible, the rs11212617 C allele might warrant consideration as a predictive clinical biomarker to inform the personalized use of metformin in BC patients. Trial Registration: EU Clinical Trials Register, EudraCT number 2011-000490-30. Registered 28 February 2011, https://www.clinicaltrialsregister.eu/ctr-search/trial/2011-000490-30/ES.

Pharmacogenetics of type 2 diabetes mellitus, the route toward tailored medicine

Type 2 diabetes mellitus (T2DM) is a chronic disease that has reached the levels of a global epidemic. In order to achieve optimal glucose control, it is often necessary to rely on combination therapy of multiple drugs or insulin because uncontrolled glucose levels result in T2DM progression and enhanced risk of complications and mortality. Several antihyperglycemic agents have been developed over time, and T2DM pharmacotherapy should be prescribed based on suitability for the individual patient's characteristics. Pharmacogenetics is the branch of genetics that investigates how our genome influences individual responses to drugs, therapeutic outcomes, and incidence of adverse effects. In this review, we evaluated the pharmacogenetic evidences currently available in the literature, and we identified the top informative genetic variants associated with response to the most common anti-diabetic drugs: metformin, DPP-4 inhibitors/GLP1R agonists, thiazolidinediones, and sulfonylureas/meglitinides. Overall, we found 40 polymorphisms for each drug class in a total of 71 loci, and we examined the possibility of encouraging genetic screening of these variants/loci in order to critically implement decision-making about the therapeutic approach through precision medicine strategies. It is possible then to anticipate that when the clinical practice will take advantage of the genetic information of the diabetic patients, this will provide a useful resource for the prevention of T2DM progression, enabling the identification of the precise drug that is most likely to be effective and safe for each patient and the reduction of the economic impact on a global scale.

The Continuing Evolution of Precision Health in Type 2 Diabetes: Achievements and Challenges

PURPOSE OF REVIEW

The purpose of this review was to summarize recent advances in the genomics of type 2 diabetes (T2D) and to highlight current initiatives to advance precision health.

RECENT FINDINGS

Generation of multi-omic data to measure each of the "biologic layers," developments in describing genomic function and annotation in T2D relevant tissue, along with the increasing recognition that T2D is a heterogeneous disease, and large-scale collaborations have all contributed to advancing our understanding of the molecular basis of T2D. Substantial advances have been made in understanding the molecular basis of T2D pathogenesis, such that precision health diabetes is increasingly becoming a reality. For precision diabetes to become a routine in clinical and public health, additional large-scale multi-omic initiatives are needed along with better assessment of our environment to delineate an individual's diabetes subtype for improved detection and management.

Metformin transporter pharmacogenomics: insights into drug disposition-where are we now?

INTRODUCTION

Metformin is recommended as first-line treatment for type 2 diabetes (T2D) by all major diabetes guidelines. With appropriate usage it is safe and effective overall, but its efficacy and tolerability show considerable variation between individuals. It is a substrate for several drug transporters and polymorphisms in these transporter genes have shown effects on metformin pharmacokinetics and pharmacodynamics. Areas covered: This article provides a review of the current status of the influence of transporter pharmacogenomics on metformin efficacy and tolerability. The transporter variants identified to have an important influence on the absorption, distribution, and elimination of metformin, particularly those in organic cation transporter 1 (OCT1, gene SLC22A1), are reviewed. Expert opinion: Candidate gene studies have shown that genetic variations in SLC22A1 and other drug transporters influence the pharmacokinetics, glycemic responses, and gastrointestinal intolerance to metformin, although results are somewhat discordant. Conversely, genome-wide association studies of metformin response have identified signals in the pharmacodynamic pathways rather than the transporters involved in metformin disposition. Currently, pharmacogenomic testing to predict metformin response and tolerability may not have a clinical role, but with additional data from larger studies and availability of safe and effective alternative antidiabetic agents, this is likely to change.

Prevention and Treatment of Type 2 Diabetes: A Pathophysiological-Based Approach

Prediabetes affects approximately 40% of American adults. Randomized trials report that a proportion of individuals with prediabetes develop diabetes despite caloric restriction, physical activity, and/or when treated with metformin, the first-line medication for patients with type 2 diabetes mellitus (T2DM). Currently, there are no valid predictors of the effectiveness of these measures in determining who will and who will not progress to the T2DM state. Few studies have examined the clinical and phenotypic predictors of better and worse glycemic response to lifestyle interventions and metformin in prediabetes and diabetes. Further studies incorporating 'omic' approaches to discover novel markers of phenotypes and treatment effectiveness may pave the way to personalizing the treatment of prediabetes and diabetes.

Pharmacogenetics of Antidiabetic Drugs

Pharmacogenetic studies of antidiabetic drugs have so far focused largely on response to metformin, which is the first-line therapy for treatment of type 2 diabetes (T2D). The first studies of metformin pharmacogenetics were focused on candidate genes that were implicated in metformin pharmacokinetics and transport. Since 2011, genome-wide association studies have been conducted in large cohorts of individuals with T2D identifying genes that are associated with glycemic response to metformin. There have been fewer pharmacogenetic studies of other antidiabetic drugs, and those have been largely limited to candidate gene studies with small sample sizes. Understanding the pharmacogenetics of antidiabetes medications is important for the integration of genetic screening into therapeutic decision making, and to achieve the goal of "precision medicine" for patients with T2D. In this chapter, we provide a review of the pharmacogenetics investigations of metformin and other antidiabetes medications. In addition, we highlight the importance of collaborative efforts with large sample size and representation from multiple ethnic groups in pharmacogenetics studies.

Pharmacogenetics and target identification in diabetes

In diabetes, pharmacogenetics can be used both to identify patient subgroups who will have most benefit and/or least harm from a particularly treatment, and to gain insights into the molecular mechanisms of drug action and disease aetiology. There is increasing evidence that genetic variation alters response to diabetes treatments-both in terms of glycaemic response and side effects. This can be seen with dramatic impact on clinical care, in patients with genetic forms of diabetes such as Maturity Onset Diabetes of the Young caused by HNF1A mutations, and Neonatal diabetes due to activating mutations in ABCC8 or KCNJ11. Beyond monogenic diabetes, pharmacogenetic variants have yet to impact on clinical practice, yet the effect sizes (e.g. for metformin intolerance and OCT1 variants; or for metformin action and SLC2A2 variants) are potentially of clinical utility, especially if the genotype is already known at the point of prescribing. Over the next few years, increasing cohort sizes and linkage at scale to electronic medical records will provide considerable potential for stratification and novel target identification in diabetes.

The pharmacogenetics of metformin

Despite its widespread use as the first-line agent for the treatment of type 2 diabetes, it has become clear that metformin does not work optimally for everyone. Elucidating who are the likely metformin responders and non-responders is hampered by our limited knowledge of its precise molecular mechanism of action. One approach to achieve the related goals of stratifying patients into response subgroups and identifying the molecular targets of metformin involves the deployment of agnostic genome-wide approaches in cohorts of appropriate size to attain sufficient statistical power. While candidate gene studies have shed some light on the role of genetic variation in influencing metformin response, genome-wide association studies are beginning to provide additional insight that is unconstrained by prior knowledge. To fully realise their potential, much larger samples need to be assembled via international collaboration, preferably involving the academic community, government and the pharmaceutical industry.

Using Personalized Medicine in the Management of Diabetes Mellitus

Diabetes mellitus is a worldwide problem with an immense pharmacoeconomic burden. The multifactorial and complex nature of the disease lends itself to personalized pharmacotherapeutic approaches to treatment. Variability in individual risk and subsequent development of diabetes has been reported in addition to differences in response to the many oral glucose lowering therapies currently available for diabetes pharmacotherapy. Pharmacogenomic studies have attempted to uncover the heritable components of individual variability in risk susceptibility and response to pharmacotherapy. We review the current pharmacogenomics evidence as it relates to common oral glucose lowering therapies and how they can be utilized in the management of polygenic and monogenic forms of diabetes. Evidence supports the use of genetic testing and personalized approaches to the treatment of monogenic diabetes of the young. The data are not as robust for the current application of pharmacogenetic approaches to the treatment of polygenic type 2 diabetes mellitus, but there are suggestions as to future applications in this regard. We reviewed pertinent primary literature sources as well as current evidence-based guidelines on diabetes management.

Personalized medicine: Genetic risk prediction of drug response

Pharmacogenomics (PGx), a substantial component of "personalized medicine", seeks to understand each individual's genetic composition to optimize drug therapy -- maximizing beneficial drug response, while minimizing adverse drug reactions (ADRs). Drug responses are highly variable because innumerable factors contribute to ultimate phenotypic outcomes. Recent genome-wide PGx studies have provided some insight into genetic basis of variability in drug response. These can be grouped into three categories. [a] Monogenic (Mendelian) traits include early examples mostly of inherited disorders, and some severe (idiosyncratic) ADRs typically influenced by single rare coding variants. [b] Predominantly oligogenic traits represent variation largely influenced by a small number of major pharmacokinetic or pharmacodynamic genes. [c] Complex PGx traits resemble most multifactorial quantitative traits -- influenced by numerous small-effect variants, together with epigenetic effects and environmental factors. Prediction of monogenic drug responses is relatively simple, involving detection of underlying mutations; due to rarity of these events and incomplete penetrance, however, prospective tests based on genotype will have high false-positive rates, plus pharmacoeconomics will require justification. Prediction of predominantly oligogenic traits is slowly improving. Although a substantial fraction of variation can be explained by limited numbers of large-effect genetic variants, uncertainty in successful predictions and overall cost-benefit ratios will make such tests elusive for everyday clinical use. Prediction of complex PGx traits is almost impossible in the foreseeable future. Genome-wide association studies of large cohorts will continue to discover relevant genetic variants; however, these small-effect variants, combined, explain only a small fraction of phenotypic variance -- thus having limited predictive power and clinical utility.

Using genetics to inform new therapeutics for diabetes

The genetic architecture of diabetes has been extensively studied. Numerous genetic markers for diabetes have been reported. However, the translation of such knowledge into clinical interventions has been inadequate. Areas covered: We performed a literature search on various frontiers in diabetes treatment that could be improved using genetic information: (1) understanding the mechanisms of existing antidiabetic drugs, (2) repurposing existing drugs for the treatment of diabetes, (3) complementing clinical trial findings; (4) finding novel treatment approaches; (5) better estimation of the efficacy of metabolic surgery. Expert commentary: The translation of genetic information to clinical intervention requires further study, including the development of an appropriate genetic risk score algorithm for type 2 diabetes. Genomic studies provide empirical explanations for clinical trial findings. Moreover, the mechanisms of antidiabetic drugs should be thoroughly investigated to enable clinical trials and pharmacogenomics studies of these drugs. As metabolic surgery becomes more prevalent for the treatment of diabetes, genetic approaches may improve patient prioritization.

Pharmacogenetics in type 2 diabetes: precision medicine or discovery tool?

In recent years, technological and analytical advances have led to an explosion in the discovery of genetic loci associated with type 2 diabetes. However, their ability to improve prediction of disease outcomes beyond standard clinical risk factors has been limited. On the other hand, genetic effects on drug response may be stronger than those commonly seen for disease incidence. Pharmacogenetic findings may aid in identifying new drug targets, elucidate pathophysiology, unravel disease heterogeneity, help prioritise specific genes in regions of genetic association, and contribute to personalised or precision treatment. In diabetes, precedent for the successful application of pharmacogenetic concepts exists in its monogenic subtypes, such as MODY or neonatal diabetes. Whether similar insights will emerge for the much more common entity of type 2 diabetes remains to be seen. As genetic approaches advance, the progressive deployment of candidate gene, large-scale genotyping and genome-wide association studies has begun to produce suggestive results that may transform clinical practice. However, many barriers to the translation of diabetes pharmacogenetic discoveries to the clinic still remain. This perspective offers a contemporary overview of the field with a focus on sulfonylureas and metformin, identifies the major uses of pharmacogenetics, and highlights potential limitations and future directions.

A Longitudinal HbA1c Model Elucidates Genes Linked to Disease Progression on Metformin

One-third of type-2 diabetic patients respond poorly to metformin. Despite extensive research, the impact of genetic and nongenetic factors on long-term outcome is unknown. In this study we combine nonlinear mixed effect modeling with computational genetic methodologies to identify predictors of long-term response. In all, 1,056 patients contributed their genetic, demographic, and long-term HbA1c data. The top nine variants (of 12,000 variants in 267 candidate genes) accounted for approximately one-third of the variability in the disease progression parameter. Average serum creatinine level, age, and weight were determinants of symptomatic response; however, explaining negligible variability. Two single nucleotide polymorphisms (SNPs) in CSMD1 gene (rs2617102, rs2954625) and one SNP in a pharmacologically relevant SLC22A2 gene (rs316009) influenced disease progression, with minor alleles leading to less and more favorable outcomes, respectively. Overall, our study highlights the influence of genetic factors on long-term HbA1c response and provides a computational model, which when validated, may be used to individualize treatment.

Leveraging Genetics to Advance Type 2 Diabetes Prevention

In this Perspective, Jose Florez discusses how information from genetics and genomics may be able to contribute to prevention of type 2 diabetes and predicting individual responses to behavioral and other interventions.

Pharmacogenetics in type 2 diabetes: influence on response to oral hypoglycemic agents

Type 2 diabetes is one of the leading causes of morbidity and mortality, consuming a significant proportion of public health spending. Oral hypoglycemic agents (OHAs) are the frontline treatment approaches after lifestyle changes. However, huge interindividual variation in response to OHAs results in unnecessary treatment failure. In addition to nongenetic factors, genetic factors are thought to contribute to much of such variability, highlighting the importance of the potential of pharmacogenetics to improve therapeutic outcome. Despite the presence of conflicting results, significant progress has been made in an effort to identify the genetic markers associated with pharmacokinetics, pharmacodynamics, and ultimately therapeutic response and/or adverse outcomes to OHAs. As such, this article presents a comprehensive review of current knowledge on pharmacogenetics of OHAs and provides insights into knowledge gaps and future directions.

Pharmacogenetics: Implications for Modern Type 2 Diabetes Therapy

Many clinical treatment studies have reported remarkable interindividual variability in the response to pharmaceutical drugs, and uncovered the existence of inadequate treatment response, non-response, and even adverse drug reactions. Pharmacogenetics addresses the impact of genetic variants on treatment outcome including side-effects. In recent years, it has also entered the field of clinical diabetes research. In modern type 2 diabetes therapy, metformin is established as first-line drug. The latest pharmaceutical developments, including incretin mimetics, dipeptidyl peptidase 4 inhibitors (gliptins), and sodium/glucose cotransporter 2 inhibitors (gliflozins), are currently experiencing a marked increase in clinical use, while the prescriptions of α-glucosidase inhibitors, sulfonylureas, meglitinides (glinides), and thiazolidinediones (glitazones) are declining, predominantly because of reported side-effects. This review summarizes the current knowledge about gene-drug interactions observed in therapy studies with the above drugs. We report drug interactions with candidate genes involved in the pharmacokinetics (e.g., drug transporters) and pharmacodynamics (drug targets and downstream signaling steps) of the drugs, with known type 2 diabetes risk genes and previously unknown genes derived from hypothesis-free approaches such as genome-wide association studies. Moreover, some new and promising candidate genes for future pharmacogenetic assessment are highlighted. Finally, we critically appraise the current state of type 2 diabetes pharmacogenetics in the light of its impact on therapeutic decisions, and we refer to major problems, and make suggestions for future efforts in this field to help improve the clinical relevance of the results, and to establish genetically determined treatment failure.

Phenotypic and genetic characteristics of patients with type 2 diabetes with different responses to metformin therapy in Novosibirsk region

Aim: The purpose of this study was to examine the phenotypic and genetic characteristics of patients with type 2 diabetes mellitus (T2DM) with different responses to treatment with metformin (MF) in the Novosibirsk region. Materials and methods: We examined 460 patients with T2DM in the Novosibirsk region. Patients were divided into groups according to their HbA1c level: patients who achieved the target HbA1c level during MF therapy (n = 209) and those who did not reach the target HbA1c level (n=251). Genotyping of ATM (rs11212617) was performed using polymerase chain reaction by TaqMan. Results: Patients who achieved the target HbA1c level during MF treatment (good response) were older (61. 1±9. 1 years vs. 57. 4±8. 4 years, p=0. 001), had later onset of diabetes (54. 6 ± 10. 1 years vs. 49. 2±8. 5 years, p = 0. 0001) and shorter duration of diabetes (6. 5±5. 9 years vs. 8. 2±6. 1 years, p=0. 03) compared with those who did not achieve the target HbA1c level. There was no statistically significant association between ATM rs11212617 and achieving the target HbA1c level among all patients [odds ratio (OR)=0. 94, 95% confidence interval = (0. 73–1. 23), p=0. 67] or those with MF monotherapy [OR=0. 90, (0. 65–1. 25), p=0. 54] or combination therapy [OR=1. 02, (0. 72–1. 43), p=0. 92]. There was an effect of age on response to MF therapy in all three groups (all patients: p=0. 001, MF monotherapy group: p=0. 04, combination therapy group: p=0. 0009). In the MF monotherapy group, low dose MF was associated with a good response (p=0. 03), and in the combination therapy group, males were more likely to have a good response (p=0. 003). Patients with genotype C/C or A/C for ATM (rs11212617) compared with those with genotype A/A were more likely to have high levels of triglycerides [2. 33 (1. 52–4. 2) mmol/l, 2. 09 (1. 35–3. 0) mmol/l and 1. 99 (1. 49–3. 21) mmol/l, respectively, p=0. 001], coronary heart disease (CHD) (13. 4%, 13. 4% and 9. 6%, respectively, p=0. 009) and myocardial infarction (7. 8%, 3. 2% and 4. 0%, respectively, p=0. 001). Conclusion: Patients with T2DM who had a good response to MF therapy were older, more likely to be male and had a later onset of T2DM. Genotype C/C for ATM rs11212617 was associated with high triglycerides, CHD and myocardial infarction. ATM rs11212617 was not associated with response to MF therapy in the Novosibirsk region. 

Impact of ATM and SLC22A1 Polymorphisms on Therapeutic Response to Metformin in Iranian Diabetic Patients

Metabolic syndrome and its pathological sequel, type 2 diabetes are considered as important global health problems. Metformin is the most common drug prescribed for patients with this disorder. Consequently, understanding the genetic pathways involved in pharmacokinetics and pharmacodynamics of this drug can have a considerable effect on the personalized treatment of type 2 diabetes. In this study, we evaluated the association between rs11212617 polymorphism of ATM gene and rs628031 of SLC22A1 gene with response to treatment in newly diagnosed type 2 diabetes patients. We genotyped rs11212617 and rs628031 polymorphism by PCR based restriction fragment length polymorphism (RFLP) and assessed the role of this polymorphisms on response to treatment in 140 patients who have been recently diagnosed with type 2 diabetes and were under monotherapy with metformin for 6 months. Response to metformin was defined by HbA1c and fasting blood sugar (FBS) values. Based on such evaluations, patients were divided into two groups: responders (n= 63) and non-responders (n= 77). No significant association was found between these polymorphisms and response to treatment (OR= 0.86, [95% CI 0.52-1.41], P= 0.32) for rs11212617 and (OR= 0.45, [95% CI 0.64-1.76], P= 0.45) for rs 628031. The reported gene variants in ATM and SLC22A1 are not significantly associated with metformin treatment response in type 2 diabetic patients in an Iranian population.

Genome-Wide Association and Trans-ethnic Meta-Analysis for Advanced Diabetic Kidney Disease: Family Investigation of Nephropathy and Diabetes (FIND)

Diabetic kidney disease (DKD) is the most common etiology of chronic kidney disease (CKD) in the industrialized world and accounts for much of the excess mortality in patients with diabetes mellitus. Approximately 45% of U.S. patients with incident end-stage kidney disease (ESKD) have DKD. Independent of glycemic control, DKD aggregates in families and has higher incidence rates in African, Mexican, and American Indian ancestral groups relative to European populations. The Family Investigation of Nephropathy and Diabetes (FIND) performed a genome-wide association study (GWAS) contrasting 6,197 unrelated individuals with advanced DKD with healthy and diabetic individuals lacking nephropathy of European American, African American, Mexican American, or American Indian ancestry. A large-scale replication and trans-ethnic meta-analysis included 7,539 additional European American, African American and American Indian DKD cases and non-nephropathy controls. Within ethnic group meta-analysis of discovery GWAS and replication set results identified genome-wide significant evidence for association between DKD and rs12523822 on chromosome 6q25.2 in American Indians (P = 5.74x10^-9). The strongest signal of association in the trans-ethnic meta-analysis was with a SNP in strong linkage disequilibrium with rs12523822 (rs955333; P = 1.31x10^-8), with directionally consistent results across ethnic groups. These 6q25.2 SNPs are located between the SCAF8 and CNKSR3 genes, a region with DKD relevant changes in gene expression and an eQTL with IPCEF1, a gene co-translated with CNKSR3. Several other SNPs demonstrated suggestive evidence of association with DKD, within and across populations. These data identify a novel DKD susceptibility locus with consistent directions of effect across diverse ancestral groups and provide insight into the genetic architecture of DKD.

Type 2 diabetes is the most common cause of severe kidney disease worldwide and diabetic kidney disease (DKD) associates with premature death. Individuals of non-European ancestry have the highest burden of type 2 DKD; hence understanding the causes of DKD remains critical to reducing health disparities. Family studies demonstrate that genes regulate the onset and progression of DKD; however, identifying these genes has proven to be challenging. The Family Investigation of Diabetes and Nephropathy consortium (FIND) recruited a large multi-ethnic collection of individuals with type 2 diabetes with and without kidney disease in order to detect genes associated with DKD. FIND discovered and replicated a DKD-associated genetic locus on human chromosome 6q25.2 (rs955333) between the SCAF8 and CNKSR genes. Findings were supported by significantly different expression of genes in this region from kidney tissue of subjects with, versus without DKD. The present findings identify a novel kidney disease susceptibility locus in individuals with type 2 diabetes which is consistent across subjects of differing ancestries. In addition, FIND results provide a rich catalogue of genetic variation in DKD patients for future research on the genetic architecture regulating this common and devastating disease.

Genetic variants of OCT1 influence glycemic response to metformin in Han Chinese patients with type-2 diabetes mellitus in Shanghai

AIMS/HYPOTHESIS

Genetic variation in OCT1 can influence the glycemic response to metformin. We evaluated the effects of the OCT1 single-nucleotide polymorphisms (SNPs), rs1867351, rs4709400, rs628031, and rs2297374, on metformin efficacy in type-2 diabetes mellitus (DM) patients.

METHODS

We performed a single-center prospective analysis of the distributions of these SNPs in a cohort of Han Chinese subjects in Shanghai, China (HCS), and evaluated the effects of each SNP on glycemic control in HCS DM patients following 3 months of incident metformin treatment.

RESULTS

The allele frequencies of rs4709400 and rs628031 in our HCS control group differed from those previously reported for Han Chinese subjects in Beijing (HCB), as well as those previously reported for Caucasians and Africans, whereas the allele frequencies of rs1867351 and rs2297374 were more similar to those in HCB subjects. The DM patients with the rs1867351 T/T or rs4709400 G/G genotype exhibited greater reductions in postprandial plasma glucose (PPG), compared to those with different genotypes of these SNPs. The DM patients with the rs2297374 C/T, rs4709400 G/G, or rs628031 G/G genotype exhibited greater reductions in fasting plasma glucose (FPG), and those with the rs1867351 T/T, rs628031 A/A, or rs2297374 C/T genotype exhibited greater reductions in HbA1c , compared to those with different genotypes of these SNPs. Conclusions /interpretation: The rs1867351, rs4709400, rs628031, and rs2297374 SNPs of OCT1 have selective effects on FPG, PPG, and HbA1c in HCS DM patients in response to metformin treatment. Future studies of these SNPs in larger samples of HCS DM patients are warranted.

Genetics of drug response in type 2 diabetes

The introduction of several new drug groups into the treatment of type 2 diabetes in the past few decades leads to an increased requirement for an individualized treatment approach. A personalized treatment is important from the point of view of both efficacy and safety. Recent guidelines are based mainly on entirely phenotypic characteristics such as diabetes duration, presence of macrovascular complications, or risk of hypoglycemia with the use of individual drugs. So far, genetic knowledge is used to guide treatment in the monogenic forms of diabetes. With the accumulating pharmacogenetic evidence in type 2 diabetes, there are reasonable expectations that genetics might help in the adjustment of drug doses to reduce severe side effects, as well as to make better therapeutic choices among the drugs available for the treatment of diabetes.

Therapeutic Use of Metformin in Prediabetes and Diabetes Prevention

People with elevated, non-diabetic, levels of blood glucose are at risk of progressing to clinical type 2 diabetes and are commonly termed 'prediabetic'. The term prediabetes usually refers to high-normal fasting plasma glucose (impaired fasting glucose) and/or plasma glucose 2 h following a 75 g oral glucose tolerance test (impaired glucose tolerance). Current US guidelines consider high-normal HbA1c to also represent a prediabetic state. Individuals with prediabetic levels of dysglycaemia are already at elevated risk of damage to the microvasculature and macrovasculature, resembling the long-term complications of diabetes. Halting or reversing the progressive decline in insulin sensitivity and β-cell function holds the key to achieving prevention of type 2 diabetes in at-risk subjects. Lifestyle interventions aimed at inducing weight loss, pharmacologic treatments (metformin, thiazolidinediones, acarbose, basal insulin and drugs for weight loss) and bariatric surgery have all been shown to reduce the risk of progression to type 2 diabetes in prediabetic subjects. However, lifestyle interventions are difficult for patients to maintain and the weight loss achieved tends to be regained over time. Metformin enhances the action of insulin in liver and skeletal muscle, and its efficacy for delaying or preventing the onset of diabetes has been proven in large, well-designed, randomised trials, such as the Diabetes Prevention Program and other studies. Decades of clinical use have demonstrated that metformin is generally well-tolerated and safe. We have reviewed in detail the evidence base supporting the therapeutic use of metformin for diabetes prevention.

Personalized medicine in diabetes mellitus: current opportunities and future prospects

Diabetes mellitus affects approximately 382 million individuals worldwide and is a leading cause of morbidity and mortality. Over 40 and nearly 80 genetic loci influencing susceptibility to type 1 and type 2 diabetes, respectively, have been identified. In addition, there is emerging evidence that some genetic variants help to predict response to treatment. Other variants confer apparent protection from diabetes or its complications and may lead to development of novel treatment approaches. Currently, there is clear clinical utility to genetic testing to find the at least 1% of diabetic individuals who have monogenic diabetes (e.g., maturity-onset diabetes of the young and KATP channel neonatal diabetes). Diagnosing many of these currently underdiagnosed types of diabetes enables personalized treatment, resulting in improved and less invasive glucose control, better prediction of prognosis, and enhanced familial risk assessment. Efforts to enhance the rate of detection, diagnosis, and personalized treatment of individuals with monogenic diabetes should set the stage for effective clinical translation of current genetic, pharmacogenetic, and pharmacogenomic research of more complex forms of diabetes.

Stratified medicine for the use of antidiabetic medication in treatment of type II diabetes and cancer: where do we go from here?

At present, the global diabetes epidemic is affecting 347 million individuals, 90% of whom are diagnosed with type II diabetes mellitus (T2DM). T2DM is commonly treated with more than one type of therapy, including oral antidiabetic drugs (OADs) and agents used in the treatment of diabetic complications. Several pharmacological classes of OADs are currently available for the treatment of T2DM, of which insulin secretagogues (i.e. sulphonylureas and meglitinides), insulin sensitizers [thiazolidinediones (TZDs)] and biguanides are the most commonly prescribed. Although many of these OADs have been used for more than half a century in the treatment of T2DM, the pharmacogenomic characteristics of these compounds have only recently been investigated, primarily in retrospective studies. Recent advances in pharmacogenomics have led to the identification of polymorphisms that affect the expression and function of drug-metabolizing enzymes and drug transporters, as well as drug targets and receptors. These polymorphisms have been shown to affect the therapeutic response to and side effects associated with OADs. The aim of this review was to provide an up-to-date summary of some of the pharmacogenomic data obtained from studies of T2DM treatment, with a focus on polymorphisms in genes affecting pharmacokinetics, pharmacodynamics and treatment outcome of the most commonly prescribed OADs. In addition, the implications of pharmacogenomics in the use of the OAD metformin in cancer will be briefly discussed. Finally, we will focus on recent advances in novel 'omics' technologies and discuss how these might aid in the personalized management of T2DM.

RS11212617 is associated with metformin treatment response in type 2 diabetes in Shanghai local Chinese population

OBJECTIVE

This study was designed to examine the relevance of single-nucleotide polymorphism (SNP) rs11212617 with treatment success in type 2 diabetes patients from Shanghai local Chinese Han population.

METHODS

We genotyped rs11212617 in incident metformin users of type 2 diabetes patients from Shanghai local Chinese Han population. Association between rs11212617 and changes in HbA1c, fasting plasma glucose and postprandial glucose level were analysed.

RESULTS

Two hundred and seventy-four incident metformin users were included in the study sample. The SNP rs11212617 was significantly associated with metformin response in Shanghai local Chinese Han population.

CONCLUSION

The rs11212617 is associated with a reduction in HbA1c, fasting plasma glucose and postprandial glucose level. These results suggest that metformin treatment may be more efficacious in Shanghai and valuable for Chinese daily clinical practice.

Allele and genotype frequency of a genetic variant in ataxia telangiectasia mutated gene affecting glycemic response to metformin in South Indian population

Allele and genotype frequency of a genetic variant in ATM gene affecting glycemic response to metformin in South Indian population.

CONTEXT

The novel polymorphism in ATM gene (rs11212617), which is implicated to have association with metformin response, exhibits inter-ethnic variability in the allele and genotype frequency distribution.

AIMS AND DESIGN

The objective of the present study is to establish the allele and genotype frequency of rs11212617 single nucleotide polymorphism in ATM gene, in South Indian population and to find if this variant has any role in the etiology of type 2 diabetes mellitus.

MATERIALS AND METHODS

The study was performed in 2 cohorts of populations, 112 healthy volunteers and 118 type 2 diabetes mellitus patients. Genomic deoxyribonucleic acid (DNA) was extracted from peripheral blood leucocytes by phenol-chloroform method and genotyping was performed by real-time polymerase chain reaction using TaqMan assay.

RESULTS

In South Indian population, the frequency of major A allele was 0.65 and the minor C allele was 0.35. AA and CC are the homozygous genotypes with frequency of 0.39 and 0.09 respectively. The frequency of heterozygous genotype AC (0.52) was found to be higher than the homozygotes. There was no significant difference in the frequency distribution in the diabetic population, which implies that this variant does not have any causative role in the disease etiology. The frequency distributions were found to be significantly different from the distributions in other ethnic populations such as Caucasians, Chinese, Japanese and Africans. But there was no significant difference when compared with the Gujarati Indians of Houston.

CONCLUSION

The frequency distribution of this novel variant in South Indian population forms a framework for further gene disease association studies to establish the association of this variant with metformin response. Our study could not find any association of this variant with respect to the disease etiology.

Metformin pharmacogenomics: current status and future directions

The incidence of type 2 diabetes (T2D) and its costs to the health care system continue to rise. Despite the availability of at least 10 drug classes for the treatment of T2D, metformin remains the most widely used first-line pharmacotherapy for its treatment; however, marked interindividual variability in response and few clinical or biomarker predictors of response reduce its optimal use. As clinical care moves toward precision medicine, a variety of broad discovery-based "omics" approaches will be required. Technical innovation, decreasing sequencing cost, and routine sample storage and processing has made pharmacogenomics the most widely applied discovery-based approach to date. This opens up the opportunity to understand the genetics underlying the interindividual variation in metformin responses in order for clinicians to prescribe specific treatments to given individuals for better efficacy and safety: metformin for those predicted to respond and alternative therapies for those predicted to be nonresponders or who are at increased risk for adverse side effects. Furthermore, understanding of the genetic determinants of metformin response may lead to the identification of novel targets and development of more effective agents for diabetes treatment. The goals of this workshop sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases were to review the state of research on metformin pharmacogenomics, discuss the scientific and clinical hurdles to furthering our knowledge of the variability in patient responses to metformin, and consider how to effectively use this increased understanding to improve patient outcomes.