Variation in the glucose transporter gene SLC2A2 is associated with glycemic response to metformin

Metabolic Effects of Metformin in Humans

BACKGROUND

Both insulin deficiency and insulin resistance due to glucagon secretion cause fasting and postprandial hyperglycemia in patients with diabetes.

INTRODUCTION

Metformin enhances insulin sensitivity, being used to prevent and treat diabetes, although its mechanism of action remains elusive.

RESULTS

Patients with diabetes fail to store glucose as hepatic glycogen via the direct pathway (glycogen synthesis from dietary glucose during the post-prandial period) and via the indirect pathway (glycogen synthesis from "de novo" synthesized glucose) owing to insulin deficiency and glucagoninduced insulin resistance. Depletion of the hepatic glycogen deposit activates gluconeogenesis to replenish the storage via the indirect pathway. Unlike healthy subjects, patients with diabetes experience glycogen cycling due to enhanced gluconeogenesis and failure to store glucose as glycogen. These defects raise hepatic glucose output causing both fasting and post-prandial hyperglycemia. Metformin reduces post-prandial plasma glucose, suggesting that the drug facilitates glucose storage as hepatic glycogen after meals. Replenishment of glycogen store attenuates the accelerated rate of gluconeogenesis and reduces both glycogen cycling and hepatic glucose output. Metformin also reduces fasting hyperglycemia due to declining hepatic glucose production. In addition, metformin reduces plasma insulin concentration in subjects with impaired glucose tolerance and diabetes and decreases the amount of insulin required for metabolic control in patients with diabetes, reflecting improvement of insulin activity. Accordingly, metformin preserves β-cell function in patients with type 2 diabetes.

CONCLUSION

Several mechanisms have been proposed to explain the metabolic effects of metformin, but evidence is not conclusive and the molecular basis of metformin action remains unknown.

Precision medicine in type 2 diabetes

The Precision Medicine Initiative defines precision medicine as 'an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment and lifestyle for each person'. This approach will facilitate more accurate treatment and prevention strategies in contrast to a one-size-fits-all approach, in which disease treatment and prevention strategies are developed for generalized usage. Diabetes is clearly more heterogeneous than the conventional subclassification into type 1 and type 2 diabetes. Monogenic forms of diabetes like MODY and neonatal diabetes have paved the way for precision medicine in diabetes, as carriers of unique mutations require unique treatment. Diagnosis of diabetes in the past has been dependent upon measuring one metabolite, glucose. By instead including six variables in a clustering analysis, we could break down diabetes into five distinct subgroups, with better prediction of disease progression and outcome. The severe insulin-resistant diabetes (SIRD) cluster showed the highest risk of kidney disease and highest prevalence of nonalcoholic fatty liver disease, whereas patients in the insulin-deficient cluster 2 (SIDD) had the highest risk of retinopathy. In the future, this will certainly be improved and expanded by including genetic, epigenetic and other biomarker to allow better prediction of outcome and choice of more precise treatment.

The Impact of Pharmacogenomics in Personalized Medicine

Recent advances in Pharmacogenomics have made it possible to understand the reasons behind the different response of a drug. Discovery of genetic variants and its association with the varying response of drug provide the basis for recommending a drug and its dose to an individual patient. Genetic makeup-based prescription, design, and implementation of therapy not only improve the outcome of treatments but also reduce the risk of toxicity and other adverse effects. A better understanding of individual variations and their effect on drug response, metabolism excretion, and toxicity will replace the trial-and-error approach of treatment. Evidence of the clinical utility of pharmacogenetics testing is only available for a few medications, and FDA labels only require pharmacogenetics testing for a small number of drugs. Although there is a great promise, there are not many examples where Pharmacogenomics impacts clinical utility. Some genetic variants related to different diseases have been reported, and many have not been studied yet. The information related to the outcome of treatment with a particular drug and a genetic variant can be used to release a warning/label for the use of that drug. There are many limitations in the way of implementing the goal of personalized medicine. Future advances in the field of genomics, diagnosis approaches, data analysis, clinical decision-making, and sustainable business model for personalization of therapy can speed up the individualization of therapy based on genetic makeup.

Perceptions of students in health and molecular life sciences regarding pharmacogenomics and personalized medicine

BACKGROUND

Increasing evidence is demonstrating that a patient's unique genetic profile can be used to detect the disease's onset, prevent its progression, and optimize its treatment. This led to the increased global efforts to implement personalized medicine (PM) and pharmacogenomics (PG) in clinical practice. Here we investigated the perceptions of students from different universities in Bosnia and Herzegovina (BH) towards PG/PM as well as related ethical, legal, and social implications (ELSI). This descriptive, cross-sectional study is based on the survey of 559 students from the Faculties of Medicine, Pharmacy, Health Studies, Genetics, and Bioengineering and other study programs.

RESULTS

Our results showed that 50% of students heard about personal genome testing companies and 69% consider having a genetic test done. A majority of students (57%) agreed that PM represents a promising healthcare model, and 40% of students agreed that their study program is well designed for understanding PG/PM. This latter opinion seems to be particularly influenced by the field of study (7.23, CI 1.99-26.2, p = 0.003). Students with this opinion are also more willing to continue their postgraduate education in the PM (OR = 4.68, CI 2.59-8.47, p < 0.001). Furthermore, 45% of students are aware of different ethical aspects of genetic testing, with most of them (46%) being concerned about the patient's privacy.

CONCLUSIONS

Our results indicate a positive attitude of biomedical students in Bosnia and Herzegovina towards genetic testing and personalized medicine. Importantly, our results emphasize the key importance of pharmacogenomic education for more efficient translation of precision medicine into clinical practice.

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.

A Global Overview of Precision Medicine in Type 2 Diabetes

The detailed characterization of human biology and behaviors is now possible at scale owing to innovations in biomarkers, bioimaging, and wearable technologies; "big data" from electronic medical records, health insurance databases, and other platforms becoming increasingly accessible; and rapidly evolving computational power and bioinformatics methods. Collectively, these advances are creating unprecedented opportunities to better understand diabetes and many other complex traits. Identifying hidden structures within these complex data sets and linking these structures to outcome data may yield unique insights into the risk factors and natural history of diabetes, which in turn may help optimize the prevention and management of the disease. This emerging area is broadly termed "precision medicine." In this Perspective, we give an overview of the evidence and barriers to the development and implementation of precision medicine in type 2 diabetes. We also discuss recently presented paradigms through which complex data might enhance our understanding of diabetes and ultimately our ability to tackle the disease more effectively than ever before.

Pleiotropic Effects of Metformin on Cancer

Metformin (MTF) is a natural compound derived from the legume Galega officinalis. It is the first line antidiabetic drug for type 2 diabetes (T2D) treatment. One of its main antidiabetic effects results from the reduction of hepatic glucose release. First scientific evidence for the anticancer effects of MTF was found in animal research, published in 2001, and some years later a retrospective observational study provided evidence that linked MTF to reduced cancer risk in T2D patients. Its pleiotropic anticancer effects were studied in numerous in vitro and in vivo studies at the molecular and cellular level. Although the majority of these studies demonstrated that MTF is associated with certain anticancer properties, clinical studies and trials provided a mixed view on its beneficial anticancer effects. This review emphasizes the pleiotropic effects of MTF and recent progress made in MTF applications in basic, preclinical, and clinical cancer research.

Genetic Variants in CPA6 and PRPF31 Are Associated With Variation in Response to Metformin in Individuals With Type 2 Diabetes

Metformin is the first-line treatment for type 2 diabetes (T2D). Although widely prescribed, the glucose-lowering mechanism for metformin is incompletely understood. Here, we used a genome-wide association approach in a diverse group of individuals with T2D from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial to identify common and rare variants associated with HbA_1c response to metformin treatment and followed up these findings in four replication cohorts. Common variants in PRPF31 and CPA6 were associated with worse and better metformin response, respectively (P < 5 × 10^-6), and meta-analysis in independent cohorts displayed similar associations with metformin response (P = 1.2 × 10^-8 and P = 0.005, respectively). Previous studies have shown that PRPF31(+/-) knockout mice have increased total body fat (P = 1.78 × 10^-6) and increased fasted circulating glucose (P = 5.73 × 10^-6). Furthermore, rare variants in STAT3 associated with worse metformin response (q <0.1). STAT3 is a ubiquitously expressed pleiotropic transcriptional activator that participates in the regulation of metabolism and feeding behavior. Here, we provide novel evidence for associations of common and rare variants in PRPF31, CPA6, and STAT3 with metformin response that may provide insight into mechanisms important for metformin efficacy in T2D.

Genomic insights into the causes of type 2 diabetes

Genome-wide association studies have implicated around 250 genomic regions in predisposition to type 2 diabetes, with evidence for causal variants and genes emerging for several of these regions. Understanding of the underlying mechanisms, including the interplay between β-cell failure, insulin sensitivity, appetite regulation, and adipose storage has been facilitated by the integration of multidimensional data for diabetes-related intermediate phenotypes, detailed genomic annotations, functional experiments, and now multiomic molecular features. Studies in diverse ethnic groups and examples from population isolates have shown the value and need for a broad genomic approach to this global disease. Transethnic discovery efforts and large-scale biobanks in diverse populations and ancestries could help to address some of the Eurocentric bias. Despite rapid progress in the discovery of the highly polygenic architecture of type 2 diabetes, dominated by common alleles with small, cumulative effects on disease risk, these insights have been of little clinical use in terms of disease prediction or prevention, and have made only small contributions to subtype classification or stratified approaches to treatment. Successful development of academia-industry partnerships for exome or genome sequencing in large biobanks could help to deliver economies of scale, with implications for the future of genomics-focused research.

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.

How American Nurses Association Code of Ethics informs genetic/genomic nursing

Members of the Ethics and Public Policy Committee of the International Society of Nurses in Genetics prepared this article to assist nurses in interpreting the American Nurses Association (2015) Code of Ethics for Nurses with Interpretive Statements (Code) within the context of genetics/genomics. The Code explicates the nursing profession's norms and responsibilities in managing ethical issues. The nearly ubiquitous application of genetic/genomic technologies in healthcare poses unique ethical challenges for nursing. Therefore, authors conducted literature searches that drew from various professional resources to elucidate implications of the code in genetic/genomic nursing practice, education, research, and public policy. We contend that the revised Code coupled with the application of genomic technologies to healthcare creates moral obligations for nurses to continually refresh their knowledge and capacities to translate genetic/genomic research into evidence-based practice, assure the ethical conduct of scientific inquiry, and continually develop or revise national/international guidelines that protect the rights of individuals and populations within the context of genetics/genomics. Thus, nurses have an ethical responsibility to remain knowledgeable about advances in genetics/genomics and incorporate emergent evidence into their work.

Pharmacogenetics of oral antidiabetic therapy

Type 2 diabetes prevalence is still on the rise worldwide. Antidiabetic drugs are widely prescribed to patients with Type 2 diabetes. Most patients start with metformin which is mostly well tolerated. However, a high percentage of patients fail to achieve glycemic control. The effectiveness of metformin as well as most other antidiabetic drugs depends among other factors on interindividual genetic differences that are up to now ignored in the treatment of Type 2 diabetes. Interestingly, many genes influencing the effectiveness of antidiabetic drugs are Type 2 diabetes risk genes making matters worse. Here, we shed light on these interindividual genetic differences.

TCF7L2 Genetic Variation Augments Incretin Resistance and Influences Response to a Sulfonylurea and Metformin: The Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH)

OBJECTIVE

The rs7903146 T allele in transcription factor 7 like 2 (TCF7L2) is strongly associated with type 2 diabetes (T2D), but the mechanisms for increased risk remain unclear. We evaluated the physiologic and hormonal effects of TCF7L2 genotype before and after interventions that influence glucose physiology.

RESEARCH DESIGN AND METHODS

We genotyped rs7903146 in 608 individuals without diabetes and recorded biochemical data before and after 1) one dose of glipizide (5 mg) on visit 1 and 2) a 75-g oral glucose tolerance test (OGTT) performed after administration of metformin 500 mg twice daily over 2 days. Incretin levels were measured in 150 of the 608 participants.

RESULTS

TT risk-allele homozygotes had 1.6 mg/dL higher baseline fasting glucose levels and 2.5 pg/mL lower glucagon levels per T allele than carriers of other genotypes at baseline. In a subset of participants, the T allele was associated with higher basal glucagon-like peptide 1 (GLP-1) levels at visit 1 (β = 1.52, P = 0.02 and β = 0.96, P = 0.002 for total and active GLP-1, respectively), and across all points of the OGTT after metformin administration. Regarding drug response, the T allele was associated with a shorter time (β = -7.00, P = 0.03) and a steeper slope (β = 0.23, P = 0.04) to trough glucose levels after glipizide administration, and lower visit 2 fasting glucose level adjusted for visit 1 fasting glucose level (β = -1.02, P = 0.04) and a greater decline in glucose level between visits (β = -1.61, P = 0.047) after metformin administration.

CONCLUSIONS

Our findings demonstrate that common variation at TCF7L2 influences acute responses to both glipizide and metformin in people without diabetes and highlight altered incretin signaling as a potential mechanism by which TCF7L2 variation increases T2D risk.

Influence of pharmacogenetic polymorphisms and demographic variables on metformin pharmacokinetics in an admixed Brazilian cohort

AIMS

To identify pharmacogenetic and demographic variables that influence the systemic exposure to metformin in an admixed Brazilian cohort.

METHODS

The extreme discordant phenotype was used to select 106 data sets from nine metformin bioequivalence trials, comprising 256 healthy adults. Eleven single-nucleotide polymorphisms in SLC22A1, SLC22A2, SLC47A1 SLC47A2 and in transcription factor SP1 were genotyped and a validated panel of ancestry informative markers was used to estimate the individual proportions of biogeographical ancestry. Two-step (univariate followed by multivariate) regression modelling was developed to identify covariates associated with systemic exposure to metformin, accessed by the area under the plasma concentration-time curve, between 0 and 48 h (AUC_0-48h ), after single oral doses of metformin (500 or 1000 mg).

RESULTS

The individual proportions of African, Amerindian and European ancestry varied widely, as anticipated from the structure of the Brazilian population The dose-adjusted, log-transformed AUC_0-48h 's (ng h ml^-1  mg^-1 ) differed largely in the two groups at the opposite ends of the distribution histogram, namely 0.82, 0.79-0.85 and 1.08, 1.06-1.11 (mean, 95% confidence interval; P = 6.10^-26 , t test). Multivariate modelling revealed that metformin AUC_0-48h increased with age, food and carriage of rs12208357 in SLC22A1 but was inversely associated with body surface area and individual proportions of African ancestry.

CONCLUSIONS

A pharmacogenetic marker in OCT1 (SLC22A1 rs12208357), combined with demographic covariates (age, body surface area and individual proportion of African ancestry) and a food effect explained 29.7% of the variability in metformin AUC_0-48h .

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.

Interaction between variants in the CYP2C9 and POR genes and the risk of sulfonylurea-induced hypoglycaemia: A GoDARTS Study

Data on the association of CYP2C9 genetic polymorphisms with sulfonylurea (SU)-induced hypoglycaemia (SH) are inconsistent. Recent studies showed that variants in the P450 oxidoreductase (POR) gene could affect CYP2C9 activity. In this study, we explored the effects of POR*28 and combined CYP2C9*2 and CYP2C9*3 genotypes on SH and the efficacy of SU treatment in type 2 diabetes. A total of 1770 patients were included in the analysis of SU efficacy, assessed as the combined outcome of the HbA1c reduction and the prescribed SU daily dose. Sixty-nine patients with severe SH were compared with 311 control patients. The number of CYP2C9 deficient alleles was associated with nearly three-fold higher odds of hypoglycaemia (OR, 2.81; 95% CI, 1.30-6.09; P = .009) and better response to SU treatment (β, -0.218; SE, 0.074; P = .003) only in patients carrying the POR*1/*1 genotype. Our results indicate that interaction between CYP2C9 and POR genes may be an important determinant of efficacy and severe adverse effects of SU treatment.

Precision medicine in diabetes: an opportunity for clinical translation

Metabolic disorders present a public health challenge of staggering proportions. In diabetes, there is an urgent need to better understand disease heterogeneity, clinical trajectories, and related comorbidities. A pressing and timely question is whether we are ready for precision medicine in diabetes. Some biological insights that have emerged during the last decade have already been used to direct clinical decision making, especially in monogenic forms of diabetes. However, much work is necessary to integrate high-dimensional explorations into complex disease architectures, less penetrant biological alterations, and broader phenotypes, such as type 2 diabetes. In addition, for precision medicine to take hold in diabetes, reproducibility, interpretability, and actionability remain key guiding objectives. In this review, we examine how mounting data sets generated during the last decade to understand biological variability are now inspiring new venues to clarify diabetes nosology and ultimately translate findings into more effective prevention and treatment strategies.

Evidence-based prioritisation and enrichment of genes interacting with metformin in type 2 diabetes

AIMS/HYPOTHESIS

There is an extensive body of literature suggesting the involvement of multiple loci in regulating the action of metformin; most findings lack replication, without which distinguishing true-positive from false-positive findings is difficult. To address this, we undertook evidence-based, multiple data integration to determine the validity of published evidence.

METHODS

We (1) built a database of published data on gene-metformin interactions using an automated text-mining approach (n = 5963 publications), (2) generated evidence scores for each reported locus, (3) from which a rank-ordered gene set was generated, and (4) determined the extent to which this gene set was enriched for glycaemic response through replication analyses in a well-powered independent genome-wide association study (GWAS) dataset from the Genetics of Diabetes and Audit Research Tayside Study (GoDARTS).

RESULTS

From the literature search, seven genes were identified that are related to the clinical outcomes of metformin. Fifteen genes were linked with either metformin pharmacokinetics or pharmacodynamics, and the expression profiles of a further 51 genes were found to be responsive to metformin. Gene-set enrichment analysis consisting of the three sets and two more composite sets derived from the above three showed no significant enrichment in four of the gene sets. However, we detected significant enrichment of genes in the least prioritised category (a gene set in which their expression is affected by metformin) with glycaemic response to metformin (p = 0.03). This gene set includes novel candidate genes such as SLC2A4 (p = 3.24 × 10^-04) and G6PC (p = 4.77 × 10^-04).

CONCLUSIONS/INTERPRETATION

We have described a semi-automated text-mining and evidence-scoring algorithm that facilitates the organisation and extraction of useful information about gene-drug interactions. We further validated the output of this algorithm in a drug-response GWAS dataset, providing novel candidate loci for gene-metformin interactions.

Genetic aetiology of glycaemic traits: approaches and insights

Glycaemic traits such as fasting and post-challenge glucose and insulin measures, as well as glycated haemoglobin (HbA1c), are used to diagnose and monitor diabetes. These traits are risk factors for cardiovascular disease even below the diabetic threshold, and their study can additionally yield insights into the pathophysiology of type 2 diabetes. To date, a diverse set of genetic approaches have led to the discovery of over 97 loci influencing glycaemic traits. In this review, we will focus on recent advances in the genetic aetiology of glycaemic traits, and the resulting biological insights. We will provide a brief overview of results ranging from common, to low- and rare-frequency variant-trait association studies, studies leveraging the diversity across populations, and studies harnessing the power of genetic and genomic approaches to gain insights into the biological underpinnings of these traits.

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.

The Rotterdam Study: 2018 update on objectives, design and main results

The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in The Netherlands. The study targets cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. Since 2016, the cohort is being expanded by persons aged 40 years and over. The findings of the Rotterdam Study have been presented in over 1500 research articles and reports (see www.erasmus-epidemiology.nl/rotterdamstudy ). This article gives the rationale of the study and its design. It also presents a summary of the major findings and an update of the objectives and methods.

The mechanisms of action of metformin

Metformin is a widely-used drug that results in clear benefits in relation to glucose metabolism and diabetes-related complications. The mechanisms underlying these benefits are complex and still not fully understood. Physiologically, metformin has been shown to reduce hepatic glucose production, yet not all of its effects can be explained by this mechanism and there is increasing evidence of a key role for the gut. At the molecular level the findings vary depending on the doses of metformin used and duration of treatment, with clear differences between acute and chronic administration. Metformin has been shown to act via both AMP-activated protein kinase (AMPK)-dependent and AMPK-independent mechanisms; by inhibition of mitochondrial respiration but also perhaps by inhibition of mitochondrial glycerophosphate dehydrogenase, and a mechanism involving the lysosome. In the last 10 years, we have moved from a simple picture, that metformin improves glycaemia by acting on the liver via AMPK activation, to a much more complex picture reflecting its multiple modes of action. More work is required to truly understand how this drug works in its target population: individuals with type 2 diabetes.

Metformin therapy for the reproductive and metabolic consequences of polycystic ovary syndrome

Polycystic ovary syndrome (PCOS), the most common hormonal disorder among women of reproductive age, has various metabolic and reproductive consequences. Metformin was originally shown to lower testosterone levels in women with PCOS in the 1990s, an effect presumably related to its insulin sensitising actions. However, the precise mechanisms of metformin action in PCOS remain unclear and there is considerable heterogeneity in the clinical response to this therapy in women with PCOS. Recent evidence indicates that genetic factors may play a significant role in predicting response to metformin therapy in PCOS and future studies are needed to further identify women who are most likely to benefit from this therapy. At present, there is no clear evidence to support broad metformin use in PCOS. Well-designed prospective trials are needed to establish clear benefit for metformin use in the treatment of the reproductive and metabolic consequences associated with PCOS.

10 Years of GWAS Discovery: Biology, Function, and Translation

Application of the experimental design of genome-wide association studies (GWASs) is now 10 years old (young), and here we review the remarkable range of discoveries it has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics. We predict the likely discoveries in the next 10 years, when GWASs will be based on millions of samples with array data imputed to a large fully sequenced reference panel and on hundreds of thousands of samples with whole-genome sequencing data.

Mining the Genome for Therapeutic Targets

Current pharmacological options for type 2 diabetes do not cure the disease. Despite the availability of multiple drug classes that modulate glycemia effectively and minimize long-term complications, these agents do not reverse pathogenesis, and in practice they are not selected to correct the molecular profile specific to the patient. Pharmaceutical companies find drug development programs increasingly costly and burdensome, and many promising compounds fail before launch to market. Human genetics can help advance the therapeutic enterprise. Genomic discovery that is agnostic to preexisting knowledge has uncovered dozens of loci that influence glycemic dysregulation. Physiological investigation has begun to define disease subtypes, clarifying heterogeneity and suggesting molecular pathways for intervention. Convincing genetic associations have paved the way for the identification of effector transcripts that underlie the phenotype, and genetic or experimental proof of gain or loss of function in select cases has clarified the direction of effect to guide therapeutic development. Genetic studies can also examine off-target effects and furnish causal inference. As this information is curated and made widely available to all stakeholders, it is hoped that it will enhance therapeutic development pipelines by accelerating efficiency, maximizing cost-effectiveness, and raising ultimate success rates.

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.

Transporters Involved in Metformin Pharmacokinetics and Treatment Response

Metformin, widely used as first-line treatment for type 2 diabetes, exists primarily as a hydrophilic cation at physiological pHs. As such, membrane transporters play a substantial role in its absorption, tissues distribution, and renal elimination. Multiple organic cation transporters are determinants of the pharmacokinetics of metformin, and many of them are important in its pharmacological action, as mediators of metformin entry into target tissues. Furthermore, a recent genome-wide association study in a large multi-ethnic population implicated polymorphisms in SLC2A2, encoding the glucose transporter, GLUT2, as important determinants of response to metformin. Here, we describe the key transporters associated with metformin pharmacokinetics and response.

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.

Variants in Pharmacokinetic Transporters and Glycemic Response to Metformin: A Metgen Meta-Analysis

Therapeutic response to metformin, a first-line drug for type 2 diabetes (T2D), is highly variable, in part likely due to genetic factors. To date, metformin pharmacogenetic studies have mainly focused on the impact of variants in metformin transporter genes, with inconsistent results. To clarify the significance of these variants in glycemic response to metformin in T2D, we performed a large-scale meta-analysis across the cohorts of the Metformin Genetics Consortium (MetGen). Nine candidate polymorphisms in five transporter genes (organic cation transporter [OCT]1, OCT2, multidrug and toxin extrusion transporter [MATE]1, MATE2-K, and OCTN1) were analyzed in up to 7,968 individuals. None of the variants showed a significant effect on metformin response in the primary analysis, or in the exploratory secondary analyses, when patients were stratified according to possible confounding genotypes or prescribed a daily dose of metformin. Our results suggest that candidate transporter gene variants have little contribution to variability in glycemic response to metformin in T2D.

Genomic Characterization of Metformin Hepatic Response

Metformin is used as a first-line therapy for type 2 diabetes (T2D) and prescribed for numerous other diseases. However, its mechanism of action in the liver has yet to be characterized in a systematic manner. To comprehensively identify genes and regulatory elements associated with metformin treatment, we carried out RNA-seq and ChIP-seq (H3K27ac, H3K27me3) on primary human hepatocytes from the same donor treated with vehicle control, metformin or metformin and compound C, an AMP-activated protein kinase (AMPK) inhibitor (allowing to identify AMPK-independent pathways). We identified thousands of metformin responsive AMPK-dependent and AMPK-independent differentially expressed genes and regulatory elements. We functionally validated several elements for metformin-induced promoter and enhancer activity. These include an enhancer in an ataxia telangiectasia mutated (ATM) intron that has SNPs in linkage disequilibrium with a metformin treatment response GWAS lead SNP (rs11212617) that showed increased enhancer activity for the associated haplotype. Expression quantitative trait locus (eQTL) liver analysis and CRISPR activation suggest that this enhancer could be regulating ATM, which has a known role in AMPK activation, and potentially also EXPH5 and DDX10, its neighboring genes. Using ChIP-seq and siRNA knockdown, we further show that activating transcription factor 3 (ATF3), our top metformin upregulated AMPK-dependent gene, could have an important role in gluconeogenesis repression. Our findings provide a genome-wide representation of metformin hepatic response, highlight important sequences that could be associated with interindividual variability in glycemic response to metformin and identify novel T2D treatment candidates.

Metformin is among the most widely prescribed drugs. It is used as a first line therapy for type 2 diabetes (T2D), and for additional diseases including cancer. The variability in response to metformin is substantial and can be caused by genetic factors. However, the molecular mechanisms of metformin action are not fully known. Here, we used various genomic assays to analyze human liver cells treated with or without metformin and identified in a genome-wide manner thousands of differentially expressed genes and gene regulatory elements affected by metformin. Follow up functional assays identified several novel genes and regulatory elements to be associated with metformin response. These include ATF3, a gene that showed gluconeogenesis repression upon metformin response and a potential regulatory element of the ATM gene that is associated with metformin treatment differences through genome-wide association studies. Combined, this work identifies several novel genes and gene regulatory elements that can be activated due to metformin treatment and thus provides candidate sequences in the human genome where nucleotide variation can lead to differences in metformin response. It also enables the identification and prioritization of novel candidates for T2D treatment.