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

Estimating heritability of glycaemic response to metformin using nationwide electronic health records and population-sized pedigree

Background

Variability of response to medication is a well-known phenomenon, determined by both environmental and genetic factors. Understanding the heritable component of the response to medication is of great interest but challenging due to several reasons, including small study cohorts and computational limitations.

Methods

Here, we study the heritability of variation in the glycaemic response to metformin, first-line therapeutic agent for type 2 diabetes (T2D), by leveraging 18 years of electronic health records (EHR) data from Israel’s largest healthcare service provider, consisting of over five million patients of diverse ethnicities and socio-economic background. Our cohort consists of 80,788 T2D patients treated with metformin, with an accumulated number of 1,611,591 HbA1C measurements and 4,581,097 metformin prescriptions. We estimate the explained variance of glycated hemoglobin (HbA1c%) reduction due to inheritance by constructing a six-generation population-size pedigree from national registries and linking it to medical health records.

Results

Using Linear Mixed Model-based framework, a common-practice method for heritability estimation, we calculate a heritability measure of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}^{2}=12.6 \%$$\end{document}h2=12.6% (95% CI, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$6.1 \%\! -\!19.1 \%$$\end{document}6.1%−19.1%) for absolute reduction of HbA1c% after metformin treatment in the entire cohort, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}^{2}=21.0 \%$$\end{document}h2=21.0% (95% CI, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$7.8 \%\! -\!34.4 \%$$\end{document}7.8%−34.4%) for males and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}^{2}=22.9 \%$$\end{document}h2=22.9% (95% CI, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10.0 \%\! -\!35.7 \%$$\end{document}10.0%−35.7%) in females. Results remain unchanged after adjusting for pre-treatment HbA1c%, and in proportional reduction of HbA1c%.

Conclusions

To the best of our knowledge, our work is the first to estimate heritability of drug response using solely EHR data combining a pedigree-based kinship matrix. We demonstrate that while response to metformin treatment has a heritable component, most of the variation is likely due to other factors, further motivating non-genetic analyses aimed at unraveling metformin’s action mechanism.

Individuals in a population might respond differently to the same medication and this phenomenon is commonly attributed to either genes or the environment. Here, we studied the familial aspects of the response to metformin, a medication used in the treatment of type 2 diabetes. We combined information from 18 years of medical records identifying newly treated patients with type 2 diabetes with information about how the trait was inherited within their families. We calculated a metric that tells us how well differences in people’s genes account for differences in their traits, and demonstrate that although the difference in response to metformin is in part explained by the genes people with type 2 diabetes inherit, most of it is not explained by genes. This finding contributes to a better understanding of differences in metformin response and might help inform treatment in future.

Kalka and Gavrieli et al. assessed the heritability of variation in the glycaemic response to metformin by leveraging electronic health records data gathered from a large cohort of patients with diabetes and combining it with pedigree information. The authors show that although the variability in this response has a heritable component, most of it is likely non-genetic.

Genome-Wide Meta-analysis Identifies Genetic Variants Associated With Glycemic Response to Sulfonylureas

OBJECTIVE

Sulfonylureas, the first available drugs for the management of type 2 diabetes, remain widely prescribed today. However, there exists significant variability in glycemic response to treatment. We aimed to establish heritability of sulfonylurea response and identify genetic variants and interacting treatments associated with HbA1c reduction.

RESEARCH DESIGN AND METHODS

As an initiative of the Metformin Genetics Plus Consortium (MetGen Plus) and the DIabetes REsearCh on patient straTification (DIRECT) consortium, 5,485 White Europeans with type 2 diabetes treated with sulfonylureas were recruited from six referral centers in Europe and North America. We first estimated heritability using the generalized restricted maximum likelihood approach and then undertook genome-wide association studies of glycemic response to sulfonylureas measured as HbA1c reduction after 12 months of therapy followed by meta-analysis. These results were supported by acute glipizide challenge in humans who were naïve to type 2 diabetes medications, cis expression quantitative trait loci (eQTL), and functional validation in cellular models. Finally, we examined for possible drug-drug-gene interactions.

RESULTS

After establishing that sulfonylurea response is heritable (mean ± SEM 37 ± 11%), we identified two independent loci near the GXYLT1 and SLCO1B1 genes associated with HbA1c reduction at a genome-wide scale (P < 5 × 10-8). The C allele at rs1234032, near GXYLT1, was associated with 0.14% (1.5 mmol/mol), P = 2.39 × 10-8), lower reduction in HbA1c. Similarly, the C allele was associated with higher glucose trough levels (β = 1.61, P = 0.005) in healthy volunteers in the SUGAR-MGH given glipizide (N = 857). In 3,029 human whole blood samples, the C allele is a cis eQTL for increased expression of GXYLT1 (β = 0.21, P = 2.04 × 10-58). The C allele of rs10770791, in an intronic region of SLCO1B1, was associated with 0.11% (1.2 mmol/mol) greater reduction in HbA1c (P = 4.80 × 10-8). In 1,183 human liver samples, the C allele at rs10770791 is a cis eQTL for reduced SLCO1B1 expression (P = 1.61 × 10-7), which, together with functional studies in cells expressing SLCO1B1, supports a key role for hepatic SLCO1B1 (encoding OATP1B1) in regulation of sulfonylurea transport. Further, a significant interaction between statin use and SLCO1B1 genotype was observed (P = 0.001). In statin nonusers, C allele homozygotes at rs10770791 had a large absolute reduction in HbA1c (0.48 ± 0.12% [5.2 ± 1.26 mmol/mol]), equivalent to that associated with initiation of a dipeptidyl peptidase 4 inhibitor.

CONCLUSIONS

We have identified clinically important genetic effects at genome-wide levels of significance, and important drug-drug-gene interactions, which include commonly prescribed statins. With increasing availability of genetic data embedded in clinical records these findings will be important in prescribing glucose-lowering drugs.

Genomic editing of metformin efficacy-associated genetic variants in SLC47A1 does not alter SLC47A1 expression

Several pharmacogenetics studies have identified an association between a greater metformin-dependent reduction in HbA1c levels and the minor A allele at rs2289669 in intron 10 of SLC47A1, encoding multidrug and toxin extrusion 1 (MATE1), a presumed metformin transporter. It is currently unknown if the rs2289669 locus is a cis-eQTL, which would validate its role as predictor of metformin efficacy. We looked at association between common genetic variants in the SLC47A1 gene region and HbA1c reduction after metformin treatment using locus-wise meta-analysis from the MetGen consortium. CRISPR-Cas9 was applied to perform allele editing of, or genomic deletion around, rs2289669 and of the closely linked rs8065082 in HepG2 cells. The genome-edited cells were evaluated for SLC47A1 expression and splicing. None of the common variants including rs2289669 showed significant association with metformin response. Genomic editing of either rs2289669 or rs8065082 did not alter SLC47A1 expression or splicing. Experimental and in silico analyses show that the rs2289669-containing haploblock does not appear to carry genetic variants that could explain its previously reported association with metformin efficacy.

Personalized approach for type 2 diabetes pharmacotherapy: where are we and where do we need to be?

INTRODUCTION

Cluster analysis has identified distinct groups of type 2 diabetes (T2D) subjects with distinct metabolic characteristics. Thus, personalizing pharmacologic therapy to individual phenotypic and pathophysiologic characteristics has potential to improve metabolic control and reduce risk of microvascular and macrovascular complications.

AREAS COVERED

The authors review the classification of T2D, genetic markers, pathophysiology and natural history of T2D, the ABCDE approach to therapy, the ADA/EASD stepwise approach to therapy, available antidiabetic agents, and provide a more rational therapeutic approach based upon pathophysiology and cardiovascular and renal outcome trials.

EXPERT OPINION

Although insulin resistance is the earliest detectable abnormality, overt T2D does not occur in the absence of progressive beta cell failure. Because of the complex etiology of T2D (Ominous Octet), initiation of therapy with combined agents that (i) target both insulin resistance and beta cell dysfunction and (ii) prevent macrovascular, as well as microvascular, complications will be required. The ratio of C-peptide at 120 minutes (OGTT) to baseline C-peptide predicts with high sensitivity who will respond to metformin, the response to glucose-lowering agents and provides a useful tool to guide optimal glucose lowering therapy.

Multi-omics profiling: the way towards precision medicine in metabolic diseases

Metabolic diseases including type 2 diabetes mellitus (T2DM), non-alcoholic fatty liver disease (NAFLD), and metabolic syndrome (MetS) are alarming health burdens around the world, while therapies for these diseases are far from satisfying as their etiologies are not completely clear yet. T2DM, NAFLD, and MetS are all complex and multifactorial metabolic disorders based on the interactions between genetics and environment. Omics studies such as genetics, transcriptomics, epigenetics, proteomics, and metabolomics are all promising approaches in accurately characterizing these diseases. And the most effective treatments for individuals can be achieved via omics pathways, which is the theme of precision medicine. In this review, we summarized the multi-omics studies of T2DM, NAFLD, and MetS in recent years, provided a theoretical basis for their pathogenesis and the effective prevention and treatment, and highlighted the biomarkers and future strategies for precision medicine.

Multi-scale inference of genetic trait architecture using biologically annotated neural networks

In this article, we present Biologically Annotated Neural Networks (BANNs), a nonlinear probabilistic framework for association mapping in genome-wide association (GWA) studies. BANNs are feedforward models with partially connected architectures that are based on biological annotations. This setup yields a fully interpretable neural network where the input layer encodes SNP-level effects, and the hidden layer models the aggregated effects among SNP-sets. We treat the weights and connections of the network as random variables with prior distributions that reflect how genetic effects manifest at different genomic scales. The BANNs software uses variational inference to provide posterior summaries which allow researchers to simultaneously perform (i) mapping with SNPs and (ii) enrichment analyses with SNP-sets on complex traits. Through simulations, we show that our method improves upon state-of-the-art association mapping and enrichment approaches across a wide range of genetic architectures. We then further illustrate the benefits of BANNs by analyzing real GWA data assayed in approximately 2,000 heterogenous stock of mice from the Wellcome Trust Centre for Human Genetics and approximately 7,000 individuals from the Framingham Heart Study. Lastly, using a random subset of individuals of European ancestry from the UK Biobank, we show that BANNs is able to replicate known associations in high and low-density lipoprotein cholesterol content.

Genomewide Association Studies in Pharmacogenomics

The increasing availability of genotype data linked with information about drug-response phenotypes has enabled genomewide association studies (GWAS) that uncover genetic determinants of drug response. GWAS have discovered associations between genetic variants and both drug efficacy and adverse drug reactions. Despite these successes, the design of GWAS in pharmacogenomics (PGx) faces unique challenges. In this review, we analyze the last decade of GWAS in PGx. We review trends in publications over time, including the drugs and drug classes studied and the clinical phenotypes used. Several data sharing consortia have contributed substantially to the PGx GWAS literature. We anticipate increased focus on biobanks and highlight phenotypes that would best enable future PGx discoveries.

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.

Novel therapies with precision mechanisms for type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is one of the greatest health crises of our time and its prevalence is projected to increase by >50% globally by 2045. Currently, 10 classes of drugs are approved by the US Food and Drug Administration for the treatment of T2DM. Drugs in development for T2DM must show meaningful reductions in glycaemic parameters as well as cardiovascular safety. Results from an increasing number of cardiovascular outcome trials using modern T2DM therapeutics have shown a reduced risk of atherosclerotic cardiovascular disease, congestive heart failure and chronic kidney disease. Hence, guidelines have become increasingly evidence based and more patient centred, focusing on reaching individualized glycaemic goals while optimizing safety, non-glycaemic benefits and the prevention of complications. The bar has been raised for novel therapies under development for T2DM as they are now expected to achieve these aims and possibly even treat concurrent comorbidities. Indeed, the pharmaceutical pipeline for T2DM is fertile. Drugs that augment insulin sensitivity, stimulate insulin secretion or the incretin axis, or suppress hepatic glucose production are active in more than 7,000 global trials using new mechanisms of action. Our collective goal of being able to truly personalize medicine for T2DM has never been closer at hand.

Pharmacogenetics of novel glucose-lowering drugs

The aim of this work was to review studies in which genetic variants were assessed with respect to metabolic response to treatment with novel glucose-lowering drugs: dipeptidyl peptidase-4 inhibitors (DPP-4i), glucagon-like peptide-1 receptor agonists (GLP-1 RA) and sodium-glucose cotransporter 2 inhibitors (SGLT2i). In total, 22 studies were retrieved from the literature (MEDLINE). Variants of the GLP-1 receptor gene (GLP1R) were associated with a smaller reduction in HbA1c in response to DPP-4i. Variants of a number of other genes (KCNQ1, KCNJ11, CTRB1/2, PRKD1, CDKAL1, IL6 promoter region, TCF7L2, DPP4, PNPLA3) have also been related to DPP-4i response, although replication studies are lacking. The GLP1R gene was also reported to play a role in the response to GLP-1 RA, with larger weight reductions being reported in carriers of GLP1R variant alleles. There were variants of a few other genes (CNR1, TCF7L2, SORCS1) described to be related to GLP-1 RA. For SGLT2i, studies have focused on genes affecting renal glucose reabsorption (e.g. SLC5A2) but no relationship between SLC5A2 variants and response to empagliflozin has been found. The relevance of the included studies is limited due to small genetic effects, low sample sizes, limited statistical power, inadequate statistics (lack of gene-drug interactions), inadequate accounting for confounders and effects modifiers, and a lack of replication studies. Most studies have been based on candidate genes. Genome-wide association studies, in that respect, may be a more promising approach to providing novel insights. However, the identification of distinct subgroups of type 2 diabetes might also be necessary before pharmacogenetic studies can be successfully used for a stratified prescription of novel glucose-lowering drugs.

Expression and Function of Organic Cation Transporter 2 in Pancreas

Organic cation transporters (OCT) play an important role in mediating cellular uptake of several pharmaceuticals, such as the antidiabetic drug metformin and the platinum-derived chemotherapeutics. Since these drugs can also affect the pancreas, here it was investigated whether these transporters are expressed in this organ. An interaction between OCT2 and the glucose transporter 2 (GLUT2), which is expressed with important functional consequences in the kidneys and in the pancreas, has already been demonstrated elsewhere. Therefore, here it was further investigated whether the two proteins have a functional relationship. It was demonstrated that OCT2 is expressed in pancreas, probably in β cells of Langerhans islets, together with GLUT2. However, a co-localization was only evident in a cell-line model of rat pancreatic β cells under incubation with high glucose concentration. High glucose stimulated OCT2 expression and activity. On the other side, studies conducted in human embryonic kidney cells stably expressing OCT2, showed that overexpression of GLUT2 decreased OCT2 activity. Unfortunately, pull-down experiments aimed to confirm a physical OCT2/GLUT2 interaction were not successful. Renal glucose excretion was reduced in mice with genetic deletion of OCT2. Nonetheless, in these mice no regulation of known kidney glucose transporters was measured. Therefore, it may be speculated that OCT2 may influence cellular trafficking of GLUT2, without changing its amount. OCT2 may play a role in drug uptake of the pancreas, and its activity may be regulated by glucose and GLUT2. Vice versa, GLUT2 activity may be regulated through an interaction with OCT2.

Therapeutic Dilemma in personalized medicine

The practice of medicine depends over a long time on identifying therapies that target an entire population. The increase in scientific knowledge over the years has led to the gradual change towards individualization and personalization of drug therapy. The hope of this change is to achieve a better clinical response to given medications and reduction of their adverse effects. Tailoring of medicine on the road of personalized medicine considers molecular and genetic mapping of the individual. However, many factors still impede the smooth application of personalized medicine and represent challenges or limitations in its achievement. In this article, we put some clinical examples that show dilemmas in the application of personalized medicine such as opioids in pain control, fluoropyrimidines in malignancy, clopidogrel as antiplatelet therapy and oral hypoglycemic drugs in Type2 diabetes in adults. Shaping the future of medicine through the application of personalized medicine for a particular patient needs to put into consideration many factors such as patient's genetic makeup and life style, pathology of the disease and dynamic changes in its course as well as interactions between administered drugs and their effects on metabolizing enzymes. We hope in the coming years, the personalized medicine will foster changes in health care system in the way not only to treat patients but also to prevent diseases.

Advantages of the use of metformin in patients with impaired uric acid metabolism

Metformin is one of the oldest and at the same time relevant and effective drugs for the treatment of type 2 diabetes. At the same time, the mechanism of the hypoglycemic effect was not completely clear until recently. Current data suggest that the mechanism of action of metformin contributes to the development of an anti-inflammatory effect, as well as a decrease in the level of uric acid, and its use can be potentially useful in patients with hyperuricemia and gout.

Dose-dependent accumulation of glucose in the intestinal wall and lumen induced by metformin as revealed by 18 F-labelled fluorodeoxyglucose positron emission tomography-MRI

AIM

To investigate the relationships between various clinical variables and the metformin-induced accumulation of fluorodeoxyglucose (FDG) in the intestine, with distinction between the intestinal wall and lumen, in individuals with type 2 diabetes who were receiving metformin treatment and underwent ¹⁸ F-labelled FDG ([¹⁸ F]FDG) positron emission tomography (PET)-MRI.

MATERIALS AND METHODS

We evaluated intestinal accumulation of [¹⁸ F]FDG with both subjective (a five-point visual scale determined by two experienced radiologists) and objective analyses (measurement of the maximum standardized uptake value [SUV_max ]) in 26 individuals with type 2 diabetes who were receiving metformin and underwent [¹⁸ F]FDG PET-MRI. [¹⁸ F]FDG accumulation within the intestinal wall was discriminated from that in the lumen on the basis of SUV_max .

RESULTS

SUV_max for the large intestine was correlated with blood glucose level (BG) and metformin dose, but not with age, body mass index, HbA1c level or estimated glomerular filtration rate (eGFR). SUV_max for the small intestine was not correlated with any of these variables. Visual scale analysis yielded essentially similar results. Metformin dose and eGFR were correlated with SUV_max for the wall and lumen of the large intestine, whereas BG was correlated with that for the wall. Multivariable analysis identified metformin dose as an explanatory factor for SUV_max in the wall and lumen of the large intestine after adjustment for potential confounders including BG and eGFR.

CONCLUSIONS

Metformin dose is an independent determinant of [¹⁸ F]FDG accumulation in the wall and lumen of the large intestine in individuals treated with this drug.

Pharmacological treatment of hyperglycemia in type 2 diabetes

Diabetes mellitus is a major public health problem, affecting about 10% of the population. Pharmacotherapy aims to protect against microvascular complications, including blindness, end-stage kidney disease, and amputations. Landmark clinical trials have demonstrated that intensive glycemic control slows progression of microvascular complications (retinopathy, nephropathy, and neuropathy). Long-term follow-up has demonstrated that intensive glycemic control also decreases risk of macrovascular disease, albeit rigorous evidence of macrovascular benefit did not emerge for over a decade. The US FDA's recent requirement for dedicated cardiovascular outcome trials ushered in a golden age for understanding the clinical profiles of new type 2 diabetes drugs. Some clinical trials with sodium-glucose cotransporter-2 (SGLT2) inhibitors and glucagon-like peptide 1 (GLP1) receptor agonists reported data demonstrating cardiovascular benefit (decreased risk of major adverse cardiovascular events and hospitalization for heart failure) and slower progression of diabetic kidney disease. This Review discusses current guidelines for use of the 12 classes of drugs approved to promote glycemic control in patients with type 2 diabetes. The Review also anticipates future developments with potential to improve the standard of care: availability of generic dipeptidylpeptidase-4 (DPP4) inhibitors and SGLT2 inhibitors; precision medicine to identify the best drugs for individual patients; and new therapies to protect against chronic complications of diabetes.

A Polygenic Score for Type 2 Diabetes Risk Is Associated With Both the Acute and Sustained Response to Sulfonylureas

There is a limited understanding of how genetic loci associated with glycemic traits and type 2 diabetes (T2D) influence the response to antidiabetic medications. Polygenic scores provide increasing power to detect patterns of disease predisposition that might benefit from a targeted pharmacologic intervention. In the Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH), we constructed weighted polygenic scores using known genome-wide significant associations for T2D, fasting glucose, and fasting insulin, comprising 65, 43, and 13 single nucleotide polymorphisms, respectively. Multiple linear regression tested for associations between scores and glycemic traits as well as pharmacodynamic end points, adjusting for age, sex, race, and BMI. A higher T2D score was nominally associated with a shorter time to insulin peak, greater glucose area over the curve, shorter time to glucose trough, and steeper slope to glucose trough after glipizide. In replication, a higher T2D score was associated with a greater 1-year hemoglobin A1c reduction to sulfonylureas in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) study (P = 0.02). Our findings suggest that individuals with a higher genetic burden for T2D experience a greater acute and sustained response to sulfonylureas.

Genetic Loci and Physiologic Pathways Involved in Gestational Diabetes Mellitus Implicated Through Clustering

Hundreds of common genetic variants acting through distinguishable physiologic pathways influence the risk of type 2 diabetes (T2D). It is unknown to what extent the physiology underlying gestational diabetes mellitus (GDM) is distinct from that underlying T2D. In this study of >5,000 pregnant women from three cohorts, we aimed to identify physiologically related groups of maternal variants associated with GDM using two complementary approaches that were based on Bayesian nonnegative matrix factorization (bNMF) clustering. First, we tested five bNMF clusters of maternal T2D-associated variants grouped on the basis of physiology outside of pregnancy for association with GDM. We found that cluster polygenic scores representing genetic determinants of reduced β-cell function and abnormal hepatic lipid metabolism were associated with GDM; these clusters were not associated with infant birth weight. Second, we derived bNMF clusters of maternal variants on the basis of pregnancy physiology and tested these clusters for association with GDM. We identified a cluster that was strongly associated with GDM as well as associated with higher infant birth weight. The effect size for this cluster's association with GDM appeared greater than that for T2D. Our findings imply that the genetic and physiologic pathways that lead to GDM differ, at least in part, from those that lead to T2D.

Pharmacogenomic Studies of Current Antidiabetic Agents and Potential New Drug Targets for Precision Medicine of Diabetes

Diabetes is a major threat to people's health and has become a burden worldwide. Current drugs for diabetes have limitations, such as different drug responses among individuals, failure to achieve glycemic control, and adverse effects. Exploring more effective therapeutic strategies for patients with diabetes is crucial. Currently pharmacogenomics has provided potential for individualized drug therapy based on genetic and genomic information of patients, and has made precision medicine possible. Responses and adverse effects to antidiabetic drugs are significantly associated with gene polymorphisms in patients. Many new targets for diabetes also have been discovered and developed, and even entered clinical trial phases. This review summarizes pharmacogenomic evidence of some current antidiabetic agents applied in clinical settings, and highlights potential drugs with new targets for diabetes, which represent a more effective treatment in the future.

Revealing Alteration in the Hepatic Glucose Metabolism of Genetically Improved Carp, Jayanti Rohu Labeo rohita Fed a High Carbohydrate Diet Using Transcriptome Sequencing

Although feed cost is the greatest concern in aquaculture, the inclusion of carbohydrates in the fish diet, and their assimilation, are still not well understood in aquaculture species. We identified molecular events that occur due to the inclusion of high carbohydrate levels in the diets of genetically improved 'Jayanti rohu' Labeo rohita. To reveal transcriptional changes in the liver of rohu, a feeding experiment was conducted with three doses of gelatinized starch (20% (control), 40%, and 60%). Transcriptome sequencing revealed totals of 15,232 (4464 up- and 4343 down-regulated) and 15,360 (4478 up- and 4171 down-regulated) differentially expressed genes. Up-regulated transcripts associated with glucose metabolisms, such as hexokinase, PHK, glycogen synthase and PGK, were found in fish fed diets with high starch levels. Interestingly, a de novo lipogenesis mechanism was found to be enriched in the livers of treated fish due to up-regulated transcripts such as FAS, ACCα, and PPARγ. The insulin signaling pathways with enriched PPAR and mTOR were identified by Kyoto Encyclopedia of Genes and Genome (KEGG) as a result of high carbohydrates. This work revealed for the first time the atypical regulation transcripts associated with glucose metabolism and lipogenesis in the livers of Jayanti rohu due to the inclusion of high carbohydrate levels in the diet. This study also encourages the exploration of early nutritional programming for enhancing glucose efficiency in carp species, for sustainable and cost-effective aquaculture production.

Oncology and Pharmacogenomics Insights in Polycystic Ovary Syndrome: An Integrative Analysis

Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovaries. Epidemiological findings revealed that women with PCOS are prone to develop certain cancer types due to their shared metabolic and endocrine abnormalities. However, the mechanism that relates PCOS and oncogenesis has not been addressed. Herein, in this review article the genomic status, transcriptional and protein profiles of 264 strongly PCOS related genes (PRG) were evaluated in endometrial cancer (EC), ovarian cancer (OV) and breast cancer (BC) exploring oncogenic databases. The genomic alterations of PRG were significantly higher when compared with a set of non-diseases genes in all cancer types. PTEN had the highest number of mutations in EC, TP53, in OC, and FSHR, in BC. Based on clinical data, women older than 50 years and Black or African American females carried the highest ratio of genomic alterations among all cancer types. The most altered signaling pathways were p53 in EC and OC, while Fc epsilon RI in BC. After evaluating PRG in normal and cancer tissue, downregulation of the differentially expressed genes was a common feature. Less than 30 proteins were up and downregulated in all cancer contexts. We identified 36 highly altered genes, among them 10 were shared between the three cancer types analyzed, which are involved in the cell proliferation regulation, response to hormone and to endogenous stimulus. Despite limited PCOS pharmacogenomics studies, 10 SNPs are reported to be associated with drug response. All were missense mutations, except for rs8111699, an intronic variant characterized as a regulatory element and presumably binding site for transcription factors. In conclusion, in silico analysis revealed key genes that might participate in PCOS and oncogenesis, which could aid in early cancer diagnosis. Pharmacogenomics efforts have implicated SNPs in drug response, yet still remain to be found.

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.

Epigenetic markers associated with metformin response and intolerance in drug-naïve patients with type 2 diabetes

Metformin is the first-line pharmacotherapy for managing type 2 diabetes (T2D). However, many patients with T2D do not respond to or tolerate metformin well. Currently, there are no phenotypes that successfully predict glycemic response to, or tolerance of, metformin. We explored whether blood-based epigenetic markers could discriminate metformin response and tolerance by analyzing genome-wide DNA methylation in drug-naïve patients with T2D at the time of their diagnosis. DNA methylation of 11 and 4 sites differed between glycemic responders/nonresponders and metformin-tolerant/intolerant patients, respectively, in discovery and replication cohorts. Greater methylation at these sites associated with a higher risk of not responding to or not tolerating metformin with odds ratios between 1.43 and 3.09 per 1-SD methylation increase. Methylation risk scores (MRSs) of the 11 identified sites differed between glycemic responders and nonresponders with areas under the curve (AUCs) of 0.80 to 0.98. MRSs of the 4 sites associated with future metformin intolerance generated AUCs of 0.85 to 0.93. Some of these blood-based methylation markers mirrored the epigenetic pattern in adipose tissue, a key tissue in diabetes pathogenesis, and genes to which these markers were annotated to had biological functions in hepatocytes that altered metformin-related phenotypes. Overall, we could discriminate between glycemic responders/nonresponders and participants tolerant/intolerant to metformin at diagnosis by measuring blood-based epigenetic markers in drug-naïve patients with T2D. This epigenetics-based tool may be further developed to help patients with T2D receive optimal therapy.

Reducing Glut2 throughout the body does not result in cognitive behaviour differences in aged male mice

Objectives

GLUT2 is a major facilitative glucose transporter, expressed from the SLC2A2 gene, with essential roles in the liver. Recent work in mice has shown that preventing Glut2 production in specific neuronal populations increases sugar-seeking behaviour, highlighting the importance of Slc2a2 gene expression in the brain. It implies that reduced GLUT2 in the brain, due to genetic polymorphisms or disease, impacts health through behaviour change. Defects in glucose transport in the brain are observed in conditions including type-2 diabetes and dementia. Few studies have directly examined the effect of modulating neuronal glucose transporter expression on cognitive function. The aim of this study was to investigate whether inactivating one Slc2a2 allele throughout the body had major effects on cognition. Cognitive tests to assess recognition memory, spatial working memory and anxiety were performed in Slc2a2 whole-body heterozygous mice (i.e. reduced Glut2 mRNA and protein), alongside littermates expressing normal levels of the transporter.

Results

No significant effects on neurological functions and cognitive capabilities were observed in mice lacking one Slc2a2 allele when fed a chow diet. This suggests that the minor variations in GLUT2 levels that occur in the human population are unlikely to influence behaviour and basic cognition.

The Impact of Genetic Polymorphisms in Organic Cation Transporters on Renal Drug Disposition

A considerable number of drugs and/or their metabolites are excreted by the kidneys through glomerular filtration and active renal tubule secretion via transporter proteins. Uptake transporters in the proximal tubule are part of the solute carrier (SLC) superfamily, and include the organic cation transporters (OCTs). Several studies have shown that specific genetic polymorphisms in OCTs alter drug disposition and may lead to nephrotoxicity. Multiple single nucleotide polymorphisms (SNPs) have been reported for the OCT genes (SLC22A1, SLC22A2 and SLC22A3), which can influence the proteins’ structure and expression levels and affect their transport function. A gain-in-function mutation may lead to accumulation of drugs in renal proximal tubule cells, eventually leading to nephrotoxicity. This review illustrates the impact of genetic polymorphisms in OCTs on renal drug disposition and kidney injury, the clinical significances and how to personalize therapies to minimize the risk of drug toxicity.

Precision medicine in diabetes: a Consensus Report from the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD)

The convergence of advances in medical science, human biology, data science and technology has enabled the generation of new insights into the phenotype known as 'diabetes'. Increased knowledge of this condition has emerged from populations around the world, illuminating the differences in how diabetes presents, its variable prevalence and how best practice in treatment varies between populations. In parallel, focus has been placed on the development of tools for the application of precision medicine to numerous conditions. This Consensus Report presents the American Diabetes Association (ADA) Precision Medicine in Diabetes Initiative in partnership with the European Association for the Study of Diabetes (EASD), including its mission, the current state of the field and prospects for the future. Expert opinions are presented on areas of precision diagnostics and precision therapeutics (including prevention and treatment) and key barriers to and opportunities for implementation of precision diabetes medicine, with better care and outcomes around the globe, are highlighted. Cases where precision diagnosis is already feasible and effective (i.e. monogenic forms of diabetes) are presented, while the major hurdles to the global implementation of precision diagnosis of complex forms of diabetes are discussed. The situation is similar for precision therapeutics, in which the appropriate therapy will often change over time owing to the manner in which diabetes evolves within individual patients. This Consensus Report describes a foundation for precision diabetes medicine, while highlighting what remains to be done to realise its potential. This, combined with a subsequent, detailed evidence-based review (due 2022), will provide a roadmap for precision medicine in diabetes that helps improve the quality of life for all those with diabetes.

Precision Medicine in Diabetes: A Consensus Report From the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD)

The convergence of advances in medical science, human biology, data science, and technology has enabled the generation of new insights into the phenotype known as "diabetes." Increased knowledge of this condition has emerged from populations around the world, illuminating the differences in how diabetes presents, its variable prevalence, and how best practice in treatment varies between populations. In parallel, focus has been placed on the development of tools for the application of precision medicine to numerous conditions. This Consensus Report presents the American Diabetes Association (ADA) Precision Medicine in Diabetes Initiative in partnership with the European Association for the Study of Diabetes (EASD), including its mission, the current state of the field, and prospects for the future. Expert opinions are presented on areas of precision diagnostics and precision therapeutics (including prevention and treatment), and key barriers to and opportunities for implementation of precision diabetes medicine, with better care and outcomes around the globe, are highlighted. Cases where precision diagnosis is already feasible and effective (i.e., monogenic forms of diabetes) are presented, while the major hurdles to the global implementation of precision diagnosis of complex forms of diabetes are discussed. The situation is similar for precision therapeutics, in which the appropriate therapy will often change over time owing to the manner in which diabetes evolves within individual patients. This Consensus Report describes a foundation for precision diabetes medicine, while highlighting what remains to be done to realize its potential. This, combined with a subsequent, detailed evidence-based review (due 2022), will provide a roadmap for precision medicine in diabetes that helps improve the quality of life for all those with diabetes.

Obesity, clinical, and genetic predictors for glycemic progression in Chinese patients with type 2 diabetes: A cohort study using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank

BACKGROUND

Type 2 diabetes (T2D) is a progressive disease whereby there is often deterioration in glucose control despite escalation in treatment. There is significant heterogeneity to this progression of glycemia after onset of diabetes, yet the factors that influence glycemic progression are not well understood. Given the tremendous burden of diabetes in the Chinese population, and limited knowledge on factors that influence glycemia, we aim to identify the clinical and genetic predictors for glycemic progression in Chinese patients with T2D.

METHODS AND FINDINGS

In 1995-2007, 7,091 insulin-naïve Chinese patients (mean age 56.8 ± 13.3 [SD] years; mean age of T2D onset 51.1 ± 12.7 years; 47% men; 28.4% current or ex-smokers; median duration of diabetes 4 [IQR: 1-9] years; mean HbA1c 7.4% ± 1.7%; mean body mass index [BMI] 25.3 ± 4.0 kg/m2) were followed prospectively in the Hong Kong Diabetes Register. We examined associations of BMI and other clinical and genetic factors with glycemic progression defined as requirement of continuous insulin treatment, or 2 consecutive HbA1c ≥8.5% while on ≥2 oral glucose-lowering drugs (OGLDs), with validation in another multicenter cohort of Hong Kong Diabetes Biobank. During a median follow-up period of 8.8 (IQR: 4.8-13.3) years, incidence of glycemic progression was 48.0 (95% confidence interval [CI] 46.3-49.8) per 1,000 person-years with 2,519 patients started on insulin. Among the latter, 33.2% had a lag period of 1.3 years before insulin was initiated. Risk of progression was associated with extremes of BMI and high HbA1c. On multivariate Cox analysis, early age at diagnosis, microvascular complications, high triglyceride levels, and tobacco use were additional independent predictors for glycemic progression. A polygenic risk score (PRS) including 123 known risk variants for T2D also predicted rapid progression to insulin therapy (hazard ratio [HR]: 1.07 [95% CI 1.03-1.12] per SD; P = 0.001), with validation in the replication cohort (HR: 1.24 [95% CI 1.06-1.46] per SD; P = 0.008). A PRS using 63 BMI-related variants predicted BMI (beta [SE] = 0.312 [0.057] per SD; P = 5.84 × 10-8) but not glycemic progression (HR: 1.01 [95% CI 0.96-1.05] per SD; P = 0.747). Limitations of this study include potential misdiagnosis of T2D and lack of detailed data of drug use during follow-up in the replication cohort.

CONCLUSIONS

Our results show that approximately 5% of patients with T2D failed OGLDs annually in this clinic-based cohort. The independent associations of modifiable and genetic risk factors allow more precise identification of high-risk patients for early intensive control of multiple risk factors to prevent glycemic progression.

Noncoding SNPs associated with increased GDF15 levels located in a metformin-activated enhancer region upstream of GDF15

Aim: GDF15 levels are a biomarker for metformin use. We performed the functional annotation of noncoding genome-wide association study (GWAS) SNPs for GDF15 levels and the Genotype-Tissue Expression (GTEx)-expression quantitative trait loci (eQTLs) for GDF15 expression within metformin-activated enhancers around GDF15. Materials & methods: These enhancers were identified using chromatin immunoprecipitation followed by sequencing data for active (H3K27ac) and silenced (H3K27me3) histone marks on human hepatocytes treated with metformin, Encyclopedia of DNA Elements data and cis-regulatory elements assignment tools. Results: The GWAS lead SNP rs888663, the SNP rs62122429 associated with GDF15 levels in the Outcome Reduction with Initial Glargine Intervention trial, and the GTEx-expression quantitative trait locus rs4808791 for GDF15 expression in whole blood are located in a metformin-activated enhancer upstream of GDF15 and tightly linked in Europeans and East Asians. Conclusion: Noncoding variation within a metformin-activated enhancer may increase GDF15 expression and help to predict GDF15 levels.

Objectives, design and main findings until 2020 from the Rotterdam Study

The Rotterdam Study is an ongoing prospective cohort study that started in 1990 in the city of Rotterdam, The Netherlands. The study aims to unravel etiology, preclinical course, natural history and potential targets for intervention for chronic diseases in mid-life and late-life. The study focuses on 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 1700 research articles and reports. This article provides an update on the rationale and design of the study. It also presents a summary of the major findings from the preceding 3 years and outlines developments for the coming period.

Diabetes: Is There a Future for Pharmacogenomics Guided Treatment?

Diabetes is a disease defined on the basis of hyperglycemia. There are monogenic forms of diabetes where defining the genetic cause has a dramatic impact on treatment—with patients being able to transition from insulin to sulfonylureas. However, the majority of diabetes is type 2 diabetes. This review outlines the robust evidence accrued to date for pharmacogenetics of metformin, sulfonylureas, thiazolidinediones, and dipeptidyl peptidase‐4 inhibitors but highlights that these variants will only be of clinical utility when the genotype is already known at the point of prescribing. The future of pharmacogenetics in diabetes and other common complex disease relies on a paradigm shift—that of preemptive panel genotyping and use of clinical decision support tools to assimilate this genetic information with other clinical phenotypic data and to present this information simply to the prescriber. Given the recent dramatic fall in genotyping costs, this future is not far off.

Genetic variants associated with glycemic response to treatment with dipeptidylpeptidase 4 inhibitors

Aim: We examined associations of eight SNPs in/near seven candidate genes with glycemic response to 6 month treatment with DPP4 inhibitors. Patients & methods: 206 patients with type 2 diabetes (116 men and 90 women) were treated with sitagliptin or vildagliptin (both 100 mg/day) in combination with metformin or metformin/sulphonylurea over 6 months, and the reduction in glycated hemoglobin (HbA1c) was measured. Results: Rs6923761 in GLP1R was significantly associated with a reduction in HbA1c (adjusted p = 0.006). Homozygotes for the minor A allele had smaller reduction in HbA1c by 0.4% (4 mmol/mol) than the G allele carriers (p = 0.016). Conclusion: The missense variant rs6923761 in the GLP1R gene was associated with a smaller glycemic response to 6 month gliptin therapy in diabetic patients of central European origin.

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.

A New Liver Expression Quantitative Trait Locus Map From 1,183 Individuals Provides Evidence for Novel Expression Quantitative Trait Loci of Drug Response, Metabolic, and Sex-Biased Phenotypes

Expression quantitative trait locus (eQTL) studies in human liver are crucial for elucidating how genetic variation influences variability in disease risk and therapeutic outcomes and may help guide strategies to obtain maximal efficacy and safety of clinical interventions. Associations between expression microarray and genome-wide genotype data from four human liver eQTL studies (n = 1,183) were analyzed. More than 2.3 million cis-eQTLs for 15,668 genes were identified. When eQTLs were filtered against a list of 1,496 drug response genes, 187,829 cis-eQTLs for 1,191 genes were identified. Additionally, 1,683 sex-biased cis-eQTLs were identified, as well as 49 and 73 cis-eQTLs that colocalized with genome-wide association study signals for blood metabolite or lipid levels, respectively. Translational relevance of these results is evidenced by linking DPYD eQTLs to differences in safety of chemotherapy, linking the sex-biased regulation of PCSK9 expression to anti-lipid therapy, and identifying the G-protein coupled receptor GPR180 as a novel drug target for hypertriglyceridemia.

Ficus deltoidea extract down-regulates protein tyrosine phosphatase 1B expression in a rat model of type 2 diabetes mellitus: a new insight into its antidiabetic mechanism

Ficus deltoidea var. deltoidea Jack (FD) is a well-known plant used in Malay folklore medicine to lower blood glucose in diabetic patients. For further research of the antihyperglycemic mechanisms, the protein tyrosine phosphatase 1B (PTP1B)-inhibitory effect of FD was analysed both in vitro and in vivo. To optimise a method for FD extraction, water, 50, 70, 80, 90 and 95 % ethanol extracts were prepared and determined for their total phenolic and triterpene contents, and PTP1B-inhibition capacity. Among the tested extracts, 70 % ethanol FD extract showed a significant PTP1B inhibition (92·0 % inhibition at 200 µg/ml) and high phenolic and triterpene contents. A bioassay-guided fractionation of the 70 % ethanol extract led to the isolation of a new triterpene (3β,11β-dihydroxyolean-12-en-23-oic acid; F3) along with six known compounds. In vivo, 4 weeks' administration of 70 % ethanol FD extract (125, 250 and 500 mg/kg/d) to streptozotocin-nicotinamide-induced type 2 diabetic rats reversed the abnormal changes of blood glucose, insulin, total Hb, GLUT2, lipid profile, and oxidative stress in liver and pancreas. Moreover, FD reduced the mRNA expression of the key gluconeogenic enzymes (phosphoenolpyruvate carboxykinase and glucose 6-phosphatase) and restored insulin receptor and GLUT2 encoding gene (Slc2a2) expression. In addition, FD significantly down-regulated the hepatic PTP1B gene expression. These results revealed that FD could potentially improve insulin sensitivity, suppress hepatic glucose output and enhance glucose uptake in type 2 diabetes mellitus through down-regulation of PTP1B. Together, our findings give scientific evidence for the traditional use of FD as an antidiabetic agent.

Homogeneity in the association of body mass index with type 2 diabetes across the UK Biobank: A Mendelian randomization study

BACKGROUND

Lifestyle interventions to reduce body mass index (BMI) are critical public health strategies for type 2 diabetes prevention. While weight loss interventions have shown demonstrable benefit for high-risk and prediabetic individuals, we aimed to determine whether the same benefits apply to those at lower risk.

METHODS AND FINDINGS

We performed a multi-stratum Mendelian randomization study of the effect size of BMI on diabetes odds in 287,394 unrelated individuals of self-reported white British ancestry in the UK Biobank, who were recruited from across the United Kingdom from 2006 to 2010 when they were between the ages of 40 and 69 years. Individuals were stratified on the following diabetes risk factors: BMI, diabetes family history, and genome-wide diabetes polygenic risk score. The main outcome measure was the odds ratio of diabetes per 1-kg/m2 BMI reduction, in the full cohort and in each stratum. Diabetes prevalence increased sharply with BMI, family history of diabetes, and genetic risk. Conversely, predicted risk reduction from weight loss was strikingly similar across BMI and genetic risk categories. Weight loss was predicted to substantially reduce diabetes odds even among lower-risk individuals: for instance, a 1-kg/m2 BMI reduction was associated with a 1.37-fold reduction (95% CI 1.12-1.68) in diabetes odds among non-overweight individuals (BMI < 25 kg/m2) without a family history of diabetes, similar to that in obese individuals (BMI ≥ 30 kg/m2) with a family history (1.21-fold reduction, 95% CI 1.13-1.29). A key limitation of this analysis is that the BMI-altering DNA sequence polymorphisms it studies represent cumulative predisposition over an individual's entire lifetime, and may consequently incorrectly estimate the risk modification potential of weight loss interventions later in life.

CONCLUSIONS

In a population-scale cohort, lower BMI was consistently associated with reduced diabetes risk across BMI, family history, and genetic risk categories, suggesting all individuals can substantially reduce their diabetes risk through weight loss. Our results support the broad deployment of weight loss interventions to individuals at all levels of diabetes risk.

Effects of TCF7L2 rs7903146 variant on metformin response in patients with type 2 diabetes

The response to metformin, the most commonly used drug for the treatment of type 2 diabetes (T2D), is highly variable. The common variant rs7903146 C>T within the transcription factor 7-like 2 gene (TCF7L2) is the strongest genetic risk factor associated with T2D to date. In this study, we explored the effects of the TCF7L2 rs7903146 genotype on metformin response in T2D. The study included 86 newly diagnosed patients with T2D, incident users of metformin. Levels of fasting glucose, insulin, HbA1c, total cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides, and anthropometric parameters were measured prior to metformin therapy, and 6 and 12 months after the treatment. Genotyping of the TCF7L2 rs7903146 was performed by the Sequenom MassARRAY® iPLEX® platform. At baseline, the diabetes risk allele (T) showed an association with lower triglyceride levels (p = 0.037). After 12 months of metformin treatment, the T allele was associated with 25.9% lower fasting insulin levels (95% CI 10.9-38.3%, p = 0.002) and 29.1% lower HOMA-IR index (95% CI 10.1-44.1%, p = 0.005), after adjustment for baseline values. Moreover, the T allele was associated with 6.7% lower fasting glucose levels (95% CI 1.1-12.0%, p = 0.021), adjusted for baseline glucose and baseline HOMA-%B levels, after 6 months of metformin treatment. This effect was more pronounced in the TT carriers who had 16.8% lower fasting glucose levels (95% CI 7.0-25.6%, p = 0.002) compared to the patients with CC genotype. Our results suggest that the TCF7L2 rs7903146 variant affects markers of insulin resistance and glycemic response to metformin in newly diagnosed patients with T2D within the first year of metformin treatment.

Understanding the glucoregulatory mechanisms of metformin in type 2 diabetes mellitus

Despite its position as the first-line drug for treatment of type 2 diabetes mellitus, the mechanisms underlying the plasma glucose level-lowering effects of metformin (1,1-dimethylbiguanide) still remain incompletely understood. Metformin is thought to exert its primary antidiabetic action through the suppression of hepatic glucose production. In addition, the discovery that metformin inhibits the mitochondrial respiratory chain complex 1 has placed energy metabolism and activation of AMP-activated protein kinase (AMPK) at the centre of its proposed mechanism of action. However, the role of AMPK has been challenged and might only account for indirect changes in hepatic insulin sensitivity. Various mechanisms involving alterations in cellular energy charge, AMP-mediated inhibition of adenylate cyclase or fructose-1,6-bisphosphatase 1 and modulation of the cellular redox state through direct inhibition of mitochondrial glycerol-3-phosphate dehydrogenase have been proposed for the acute inhibition of gluconeogenesis by metformin. Emerging evidence suggests that metformin could improve obesity-induced meta-inflammation via direct and indirect effects on tissue-resident immune cells in metabolic organs (that is, adipose tissue, the gastrointestinal tract and the liver). Furthermore, the gastrointestinal tract also has a major role in metformin action through modulation of glucose-lowering hormone glucagon-like peptide 1 and the intestinal bile acid pool and alterations in gut microbiota composition.

Translational genomics and precision medicine: Moving from the lab to the clinic

Translational genomics aims to improve human health by building on discoveries made through genetics research and applying them in the clinical setting. This progress has been made possible by technological advances in genomics and analytics and by the digital revolution. Such advances should enable the development of prognostic markers, tailored interventions, and the design of prophylactic preventive approaches. We are at the cusp of predicting disease risk for some disorders by means of polygenic risk scores integrated with classical epidemiological risk factors. This should lead to better risk stratification and clinical decision-making. A deeper understanding of the link between genome-wide sequence and association with well-characterized phenotypes will empower the development of biomarkers to aid diagnosis, inform disease progression trajectories, and allow better targeting of treatments to those patients most likely to respond.

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.

Human Genetics of Obesity and Type 2 Diabetes Mellitus: Past, Present, and Future

Type 2 diabetes mellitus (T2D) and obesity already represent 2 of the most prominent risk factors for cardiovascular disease, and are destined to increase in importance given the global changes in lifestyle. Ten years have passed since the first round of genome-wide association studies for T2D and obesity. During this decade, we have witnessed remarkable developments in human genetics. We have graduated from the despair of candidate gene-based studies that generated few consistently replicated genotype-phenotype associations, to the excitement of an exponential harvest of loci robustly associated with medical outcomes through ever larger genome-wide association study meta-analyses. As well as discovering hundreds of loci, genome-wide association studies have provided transformative insights into the genetic architecture of T2D and other complex traits, highlighting the extent of polygenicity and the tiny effect sizes of many common risk alleles. Genome-wide association studies have also provided a critical starting point for discovering new biology relevant to these traits. Expectations are high that these discoveries will foster development of more effective strategies for intervention, through optimization of precision medicine approaches. In this article, we review current knowledge and provide suggestions for the next steps in genetic research for T2D and obesity. We focus on four areas relevant to precision medicine: genetic architecture, pharmacogenetics and other gene-environment interactions, mechanistic inference, and drug development. As we describe, the genetic architecture of complex traits has major implications for the prospects of precision medicine, rendering some anticipated approaches decidedly unrealistic. We highlight obstacles to the translation of human genetic findings into mechanism inference but are optimistic that, as these are overcome, there is untapped potential for novel drugs and more effective strategies for treating and preventing T2D and obesity.

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.

Molecular and biochemical monitoring of the possible herb-drug interaction between Momordica charantia extract and glibenclamide in diabetic rats

Momordica charantia is used in folk medicine to manage diabetes mellitus. In this study, we investigated the possible herb-drug interaction between M. charantia fruit extract (MCFE) and glibenclamide (GLB) in streptozotocin-diabetic rats. Rats were divided into 7 groups. The 1st group received 3% Tween 80. The 2nd–5th groups were diabetic rats received vehicle, GLB (5 mg/kg), MCFE (250 and 500 mg/kg), respectively. The 6th–7th groups administered GLB plus MCFE (250 and 500 mg/kg), respectively. After 8 weeks, fasting blood glucose (FBG), insulin and glycosylated hemoglobin (HbA1c) levels were assessed. Histopathological and immunohistochemical examinations of the pancreases were done. Quantitative RT-PCR was used to analyze hepatic mRNA expression of insulin receptor (INR), glucose transporter 2 (Slc2a2) and peroxisome proliferator-activated receptor α (PPAR-α) genes. All medicaments greatly reduced FBG in diabetic rats when compared with diabetic control group. GLB plus MCFE combination was better than GLB alone in improving levels of insulin and HbA1c. All medicaments restored insulin content of pancreatic β-cells and reduced glucagon and somatostatin of alpha and delta endocrine cells. Moreover, GLB plus MCFE-500 was the most efficient in restoring INR, Slc2a2 and PPAR-α mRNA expression to their normal levels. In conclusion, MCFE in combination with GLB gives greater glycemic improvement than GLB monotherapy.

Functional and structural analysis of rare SLC2A2 variants associated with Fanconi-Bickel syndrome and metabolic traits

Deleterious variants in SLC2A2 cause Fanconi-Bickel Syndrome (FBS), a glycogen storage disorder, whereas less common variants in SLC2A2 associate with numerous metabolic diseases. Phenotypic heterogeneity in FBS has been observed, but its causes remain unknown. Our goal was to functionally characterize rare SLC2A2 variants found in FBS and metabolic disease-associated variants to understand the impact of these variants on GLUT2 activity and expression and establish genotype-phenotype correlations. Complementary RNA-injected Xenopus laevis oocytes were used to study mutant transporter activity and membrane expression. GLUT2 homology models were constructed for mutation analysis using GLUT1, GLUT3, and XylE as templates. Seventeen FBS variants were characterized. Only c.457_462delCTTATA (p.Leu153_Ile154del) exhibited residual glucose uptake. Functional characterization revealed that only half of the variants were expressed on the plasma membrane. Most less common variants (except c.593 C>A (p.Thr198Lys) and c.1087 G>T (p.Ala363Ser)) exhibited similar GLUT2 transport activity as the wild type. Structural analysis of GLUT2 revealed that variants affect substrate-binding, steric hindrance, or overall transporter structure. The mutant transporter that is associated with a milder FBS phenotype, p.Leu153_Ile154del, retained transport activity. These results improve our overall understanding of the underlying causes of FBS and impact of GLUT2 function on various clinical phenotypes ranging from rare to common disease.

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.