A variant of the glucose transporter gene SLC2A2 modifies the glycaemic response to metformin therapy in recently diagnosed type 2 diabetes

Expression of the C-allele of intronic rs8192675 in SLC2A2 is associated with improved glucose response to metformin

Glucose is a critical nutrient for energy metabolism. The SLC2A2 gene is essential for glucose sensing and homeostasis, as it encodes the facilitated glucose transporter GLUT2. During diabetes treatment, the C-allele of rs8192675 in SLC2A2 has been found to regulate the action of metformin and reduce the absolute level of HbA1c more effectively than the T-allele. In this study, stable HEK293T cell lines carrying the CC, CT, and TT genotypes of rs8192675 in SLC2A2 were generated using CRISPR/Cas9-mediated genome editing. GLUT2 mRNA and protein levels were elevated in cell clones with the TC genotype compared to those with the CC genotype but were reduced relative to the TT genotype. Additionally, high concentrations of glucose or fructose induced more GLUT2 protein production in CT-genotype cells than that induced in CC-genotype cells, yet less than that induced in TT-genotype cells. Metformin induced a greater increase in GLUT2 expression and a smaller increase in activated AMPK protein expression in CC-genotype cells than those induced in TT-genotype cells, resulting in a remarkable reduction in activated mTOR and S6 levels. This study directly supports the biological mechanism linking the C-allele of rs8192675 with improved treatment outcomes in metformin therapy for diabetes.

Examining the Genetic Role of rs8192675 Variant in Saudi Women Diagnosed with Polycystic Ovary Syndrome

Polycystic ovary syndrome is a complex disorder defined by the Rotterdam criteria. Insulin resistance is a common factor for the development of type 2 diabetes mellitus among women with PCOS. The SLC2A2 gene has been identified as a T2DM gene by genome-wide association studies in the rs8192675 SNP. This study aimed to investigate the rs8192675 SNP in women diagnosed with PCOS on a molecular level and further for T2DM development in the Saudi women. In this case-control study, 100 PCOS women and 100 healthy controls were selected. Among 100 PCOS women, 28 women showed T2DM development. Genotyping for rs8192675 SNP was performed by PCR-RFLP analysis. Additionally, Sanger sequencing was performed to validate the RFLP analysis. The obtained data were used for a statistical analysis for the genotype and allele frequencies, logistic regression, and ANOVA analysis. The clinical data confirmed the positive association between FBG, FI, FSH, TT, TC, HDLc, LDLc, and family histories (p < 0.05). HWE analysis was associated in both the PCOS cases and the control individuals. Genotype and allele frequencies were associated in PCOS women and strongly associated with women with PCOS who developed T2DM (p < 0.05). No association was found in the logistic regression model or ANOVA analysis studied in women with PCOS (p > 0.05). A strong association was observed between the rs8192675 SNP and women with PCOS who developed T2DM using ANOVA analysis (p < 0.05). This study confirms that the rs8192675 SNP is associated with women with PCOS and strongly associated with women with PCOS with developed T2DM in Saudi Arabia.

Identification of Novel Intronic SNPs in Transporter Genes Associated with Metformin Side Effects

Metformin is a widely used and effective medication in type 2 diabetes (T2DM) as well as in polycystic ovary syndrome (PCOS). Single nucleotide polymorphisms (SNPs) contribute to the occurrence of metformin side effects. The aim of the present study was to identify intronic genetic variants modifying the occurrence of metformin side effects and to replicate them in individuals with T2DM and in women with PCOS. We performed Next Generation Sequencing (Illumina Next Seq) of 115 SNPs in a discovery cohort of 120 metformin users and conducted a systematic literature review. Selected SNPs were analysed in two independent cohorts of individuals with either T2DM or PCOS, using 5'-3'exonucleaseassay. A total of 14 SNPs in the organic cation transporters (OCTs) showed associations with side effects in an unadjusted binary logistic regression model, with eight SNPs remaining significantly associated after appropriate adjustment in the discovery cohort. Five SNPs were confirmed in a combined analysis of both replication cohorts but showed different association patterns in subgroup analyses. In an unweighted polygenic risk score (PRS), the risk for metformin side effects increased with the number of risk alleles. Intronic SNPs in the OCT cluster contribute to the development of metformin side effects in individuals with T2DM and in women with PCOS and are therefore of interest for personalized therapy options.

Metformin: update on mechanisms of action and repurposing potential

Currently, metformin is the first-line medication to treat type 2 diabetes mellitus (T2DM) in most guidelines and is used daily by >200 million patients. Surprisingly, the mechanisms underlying its therapeutic action are complex and are still not fully understood. Early evidence highlighted the liver as the major organ involved in the effect of metformin on reducing blood levels of glucose. However, increasing evidence points towards other sites of action that might also have an important role, including the gastrointestinal tract, the gut microbial communities and the tissue-resident immune cells. At the molecular level, it seems that the mechanisms of action vary depending on the dose of metformin used and duration of treatment. Initial studies have shown that metformin targets hepatic mitochondria; however, the identification of a novel target at low concentrations of metformin at the lysosome surface might reveal a new mechanism of action. Based on the efficacy and safety records in T2DM, attention has been given to the repurposing of metformin as part of adjunct therapy for the treatment of cancer, age-related diseases, inflammatory diseases and COVID-19. In this Review, we highlight the latest advances in our understanding of the mechanisms of action of metformin and discuss potential emerging novel therapeutic uses.

Identification of Transporter Polymorphisms Influencing Metformin Pharmacokinetics in Healthy Volunteers

For patients with type 2 diabetes, metformin is the most often recommended drug. However, there are substantial individual differences in the pharmacological response to metformin. To investigate the effect of transporter polymorphisms on metformin pharmacokinetics in an environment free of confounding variables, we conducted our study on healthy participants. This is the first investigation to consider demographic characteristics alongside all transporters involved in metformin distribution. Pharmacokinetic parameters of metformin were found to be affected by age, sex, ethnicity, and several polymorphisms. Age and SLC22A4 and SLC47A2 polymorphisms affected the area under the concentration-time curve (AUC). However, after adjusting for dose-to-weight ratio (dW), sex, age, and ethnicity, along with SLC22A3 and SLC22A4, influenced AUC. The maximum concentration was affected by age and SLC22A1, but after adjusting for dW, it was affected by sex, age, ethnicity, ABCG2, and SLC22A4. The time to reach the maximum concentration was influenced by sex, like half-life, which was also affected by SLC22A3. The volume of distribution and clearance was affected by sex, age, ethnicity and SLC22A3. Alternatively, the pharmacokinetics of metformin was unaffected by polymorphisms in ABCB1, SLC2A2, SLC22A2, or SLC47A1. Therefore, our study demonstrates that a multifactorial approach to all patient characteristics is necessary for better individualization.

A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes

Obesity and type 2 diabetes are causally related, yet there is considerable heterogeneity in the consequences of both conditions and the mechanisms of action are poorly defined. Here we show a genetic-driven approach defining two obesity profiles that convey highly concordant and discordant diabetogenic effects. We annotate and then compare association signals for these profiles across clinical and molecular phenotypic layers. Key differences are identified in a wide range of traits, including cardiovascular mortality, fat distribution, liver metabolism, blood pressure, specific lipid fractions and blood levels of proteins involved in extracellular matrix remodelling. We find marginal differences in abundance of Bacteroidetes and Firmicutes bacteria in the gut. Instrumental analyses reveal prominent causal roles for waist-to-hip ratio, blood pressure and cholesterol content of high-density lipoprotein particles in the development of diabetes in obesity. We prioritize 17 genes from the discordant signature that convey protection against type 2 diabetes in obesity, which may represent logical targets for precision medicine approaches.

Polymorphisms in glucose homeostasis genes are associated with cardiovascular and renal parameters in patients with diabetic nephropathy

BACKGROUND

Diabetic nephropathy (DN) has become the major cause of end-stage kidney disease and is associated to an extremely high cardiovascular (CV) risk.

METHODS

We screened 318 DN patients for 23 SNPs in four glucose transporters (SLC2A1, SLC2A2, SLC5A1 and SLC5A2) and in KCNJ11 and ABCC8, which participate in insulin secretion. Regression models were utilised to identify associations with renal parameters, atherosclerosis measurements and CV events. In addition, 506 individuals with normal renal function were also genotyped as a control group.

RESULTS

In the patient's cohort, common carotid intima media thickness values were higher in carriers of ABCC8 rs3758953 and rs2188966 vs. non-carriers [0.78(0.25) vs. 0.72(0.22) mm, p < 0.05 and 0.79(0.26) vs. 0.72(0.22) mm, p < 0.05], respectively. Furthermore, ABCC8 rs1799859 was linked to presence of plaque in these patients [1.89(1.03-3.46), p < 0.05]. Two variants, SLC2A2 rs8192675 and SLC5A2 rs9924771, were associated with better [OR = 0.49 (0.30-0.81), p < 0.01] and worse [OR = 1.92 (1.15-3.21), p < 0.05] CV event-free survival, respectively. With regard to renal variables, rs841848 and rs710218 in SLC2A1, as well as rs3813008 in SLC5A2, significantly altered estimated glomerular filtration rate values [carriers vs. non-carriers: 30.41(22.57) vs. 28.25(20.10), p < 0.05; 28.95(21.11) vs. 29.52(21.66), p < 0.05 and 32.03(18.06) vs. 28.14(23.06) ml/min/1.73 m2, p < 0.05]. In addition, ABCC8 rs3758947 was associated with higher albumin-to-creatinine ratios [193.5(1139.91) vs. 160(652.90) mg/g, p < 0.05]. The epistasis analysis of SNP-pairs interactions showed that ABCC8 rs3758947 interacted with several SNPs in SLC2A2 to significantly affect CV events (p < 0.01). No SNPs were associated with DN risk.

CONCLUSIONS

Polymorphisms in genes determining glucose homeostasis may play a relevant role in renal parameters and CV-related outcomes of DN patients.

Islet Gene View-a tool to facilitate islet research

Characterization of gene expression in pancreatic islets and its alteration in type 2 diabetes (T2D) are vital in understanding islet function and T2D pathogenesis. We leveraged RNA sequencing and genome-wide genotyping in islets from 188 donors to create the Islet Gene View (IGW) platform to make this information easily accessible to the scientific community. Expression data were related to islet phenotypes, diabetes status, other islet-expressed genes, islet hormone-encoding genes and for expression in insulin target tissues. The IGW web application produces output graphs for a particular gene of interest. In IGW, 284 differentially expressed genes (DEGs) were identified in T2D donor islets compared with controls. Forty percent of DEGs showed cell-type enrichment and a large proportion significantly co-expressed with islet hormone-encoding genes; glucagon (<i>GCG</i>, 56%), amylin (<i>IAPP</i>, 52%), insulin (<i>INS</i>, 44%), and somatostatin (<i>SST</i>, 24%). Inhibition of two DEGs, <i>UNC5D</i> and <i>SERPINE2</i>, impaired glucose-stimulated insulin secretion and impacted cell survival in a human β-cell model. The exploratory use of IGW could help designing more comprehensive functional follow-up studies and serve to identify therapeutic targets in T2D.

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

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

Clinical Study on the Relationship between the SNP rs8192675 (C/C) Site of SLC2A2 Gene and the Hypoglycemic Effect of Metformin in Type 2 Diabetes

This study investigates the correlation between the gene polymorphism of rs8192675 (C/C) locus of SLC2A2 in patients with type 2 diabetes (T2DM) and the efficacy of metformin. For this purpose, we have selected 110 T2DM patients (T2DM group) and 110 healthy people (control group) who were treated in our hospital from January 2019 to January 2020 as the research subjects. PCR-restriction fragment length polymorphism (PCR-RFLP) method detects the distribution frequency of gene polymorphism. The patients in the T2DM group were treated with metformin and followed up for 90 days to analyze the relationship between the efficacy of metformin and the SLC2A2 gene polymorphism. The genotypes of SLC2A2 rs8192675 in the control group and in the T2DM group conformed to the Hardy–Weinberg equilibrium law. Compared with the control group, the CT type and the CC type at rs8192675 in the T2DM group were significantly higher (P < 0.05). For rs8192675, there was no significant difference in TT, CT, CC FPG, 2hPBG, and HbA1c levels before treatment (P > 0.05); after metformin treatment, the reduction in FPG, 2hPBG, and HbA1c in CC patients was lower than that of TT and CT patients (P < 0.05). SLC2A2 gene polymorphism site rs8192675 CC type T2DM patients are sensitive to metformin and have a better hypoglycemic effect.

Evidence for Shared Genetic Aetiology Between Schizophrenia, Cardiometabolic, and Inflammation-Related Traits: Genetic Correlation and Colocalization Analyses

BACKGROUND

Schizophrenia commonly co-occurs with cardiometabolic and inflammation-related traits. It is unclear to what extent the comorbidity could be explained by shared genetic aetiology.

METHODS

We used GWAS data to estimate shared genetic aetiology between schizophrenia, cardiometabolic, and inflammation-related traits: fasting insulin (FI), fasting glucose, glycated haemoglobin, glucose tolerance, type 2 diabetes (T2D), lipids, body mass index (BMI), coronary artery disease (CAD), and C-reactive protein (CRP). We examined genome-wide correlation using linkage disequilibrium score regression (LDSC); stratified by minor-allele frequency using genetic covariance analyzer (GNOVA); then refined to locus-level using heritability estimation from summary statistics (ρ-HESS). Regions with local correlation were used in hypothesis prioritization multi-trait colocalization to examine for colocalisation, implying common genetic aetiology.

RESULTS

We found evidence for weak genome-wide negative correlation of schizophrenia with T2D (rg = -0.07; 95% C.I., -0.03,0.12; P = .002) and BMI (rg = -0.09; 95% C.I., -0.06, -0.12; P = 1.83 × 10-5). We found a trend of evidence for positive genetic correlation between schizophrenia and cardiometabolic traits confined to lower-frequency variants. This was underpinned by 85 regions of locus-level correlation with evidence of opposing mechanisms. Ten loci showed strong evidence of colocalization. Four of those (rs6265 (BDNF); rs8192675 (SLC2A2); rs3800229 (FOXO3); rs17514846 (FURIN)) are implicated in brain-derived neurotrophic factor (BDNF)-related pathways.

CONCLUSIONS

LDSC may lead to downwardly-biased genetic correlation estimates between schizophrenia, cardiometabolic, and inflammation-related traits. Common genetic aetiology for these traits could be confined to lower-frequency common variants and involve opposing mechanisms. Genes related to BDNF and glucose transport amongst others may partly explain the comorbidity between schizophrenia and cardiometabolic disorders.

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.

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.

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.

The Need for Precision Medicine to be Applied to Diabetes

Precision medicine refers to the tailoring of medical treatment for an individual based on large amounts of biologic and extrinsic data. The fast advancing fields of molecular biology, gene sequencing, machine learning, and other technologies enable precision medicine to utilize this detailed information to enhance clinical management decision-making for an individual in the real time of the disease course. Traditional clinical decision making is based on reacting to a relatively limited number of phenotypes that are determined by history, physical examination, and conventional lab tests. Precision medicine depends on highly detailed profiling of the patient's genetic, morphologic, and metabolic makeup. The precision medicine approach can be applied to individuals with diabetes to select treatments most likely to offer benefit and least likely to cause side effects, offering prospects of improved clinical outcomes and economic costs savings over current empiric practices. As genetic, metabolomic, immunologic, and other sophisticated testing becomes less expensive and more widespread in the medical record, it is expected that precision medicine will become increasingly applied to diabetes care.

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.

Monounsaturated fat rapidly induces hepatic gluconeogenesis and whole-body insulin resistance

BACKGROUNDWhile saturated fat intake leads to insulin resistance and nonalcoholic fatty liver, Mediterranean-like diets enriched in monounsaturated fatty acids (MUFA) may have beneficial effects. This study examined effects of MUFA on tissue-specific insulin sensitivity and energy metabolism.METHODSA randomized placebo-controlled cross-over study enrolled 16 glucose-tolerant volunteers to receive either oil (OIL, ~1.18 g/kg), rich in MUFA, or vehicle (VCL, water) on 2 occasions. Insulin sensitivity was assessed during preclamp and hyperinsulinemic-euglycemic clamp conditions. Ingestion of 2H2O/acetaminophen was combined with [6,6-2H2]glucose infusion and in vivo 13C/31P/1H/ex vivo 2H-magnet resonance spectroscopy to quantify hepatic glucose and energy fluxes.RESULTSOIL increased plasma triglycerides and oleic acid concentrations by 44% and 66% compared with VCL. Upon OIL intervention, preclamp hepatic and whole-body insulin sensitivity markedly decreased by 28% and 27%, respectively, along with 61% higher rates of hepatic gluconeogenesis and 32% lower rates of net glycogenolysis, while hepatic triglyceride and ATP concentrations did not differ from VCL. During insulin stimulation hepatic and whole-body insulin sensitivity were reduced by 21% and 25%, respectively, after OIL ingestion compared with that in controls.CONCLUSIONA single MUFA-load suffices to induce insulin resistance but affects neither hepatic triglycerides nor energy-rich phosphates. These data indicate that amount of ingested fat, rather than its composition, primarily determines the development of acute insulin resistance.TRIAL REGISTRATIONClinicalTrials.gov NCT01736202.FUNDINGGerman Diabetes Center, German Federal Ministry of Health, Ministry of Culture and Science of the state of North Rhine-Westphalia, German Federal Ministry of Education and Research, German Diabetes Association, German Center for Diabetes Research, Portugal Foundation for Science and Technology, European Regional Development Fund, and Rede Nacional de Ressonancia Magnética Nuclear.