Tanja Dujic

Association of Organic Cation Transporter 1 With Intolerance to Metformin in Type 2 Diabetes: A GoDARTS Study

Metformin is the most widely prescribed medication for the treatment of type 2 diabetes (T2D). However, gastrointestinal (GI) side effects develop in ~25% of patients treated with metformin, leading to the discontinuation of therapy in ~5% of cases. We hypothesized that reduced transport of metformin via organic cation transporter 1 (OCT1) could increase metformin concentration in the intestine, leading to increased risk of severe GI side effects and drug discontinuation. We compared the phenotype, carriage of reduced-function OCT1 variants, and concomitant prescribing of drugs known to inhibit OCT1 transport in 251 intolerant and 1,915 fully metformin-tolerant T2D patients. We showed that women and older people were more likely to be intolerant to metformin. Concomitant use of medications, known to inhibit OCT1 activity, was associated with intolerance (odds ratio [OR] 1.63 [95% CI 1.22–2.17], P = 0.001) as was carriage of two reduced-function OCT1 alleles compared with carriage of one or no deficient allele (OR 2.41 [95% CI 1.48–3.93], P < 0.001). Intolerance was over four times more likely to develop (OR 4.13 [95% CI 2.09–8.16], P < 0.001) in individuals with two reduced-function OCT1 alleles who were treated with OCT1 inhibitors. Our results suggest that reduced OCT1 transport is an important determinant of metformin intolerance.

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

Metformin is the first-line antidiabetic drug with over 100 million users worldwide, yet its mechanism of action remains unclear. Here the Metformin Genetics (MetGen) Consortium reports a three-stage genome-wide association study (GWAS), consisting of 13,123 participants of different ancestries. The C allele of rs8192675 in the intron of SLC2A2, which encodes the facilitated glucose transporter GLUT2, was associated with a 0.17% (P = 6.6 × 10−14) greater metformin-induced reduction in hemoglobin A1c (HbA1c) in 10,577 participants of European ancestry. rs8192675 was the top cis expression quantitative trait locus (cis-eQTL) for SLC2A2 in 1,226 human liver samples, suggesting a key role for hepatic GLUT2 in regulation of metformin action. Among obese individuals, C-allele homozygotes at rs8192675 had a 0.33% (3.6 mmol/mol) greater absolute HbA1c reduction than T-allele homozygotes. This was about half the effect seen with the addition of a DPP-4 inhibitor, and equated to a dose difference of 550 mg of metformin, suggesting rs8192675 as a potential biomarker for stratified medicine.

Pharmacogenetics and personalized treatment of type 2 diabetes.

Type 2 diabetes mellitus (T2DM) is a worldwide epidemic with considerable health and economic consequences. T2DM patients are often treated with more than one drug, including oral antidiabetic drugs (OAD) and drugs used to treat diabetic complications, such as dyslipidemia and hypertension. If genetic testing could be employed to predict treatment outcome, appropriate measures could be taken to treat T2DM more efficiently. Here we provide a review of pharmacogenetic studies focused on OAD and a role of common drug-metabolizing enzymes (DME) and drug-transporters (DT) variants in therapy outcomes. For example, genetic variations of several membrane transporters, including SLC22A1/2 and SLC47A1/2 genes, are implicated in the highly variable glycemic response to metformin, a first-line drug used to treat newly diagnosed T2DM. Furthermore, cytochrome P450 (CYP) enzymes are implicated in variation of sulphonylurea and meglitinide metabolism. Additional variants related to drug target and diabetes risk genes have been also linked to interindividual differences in the efficacy and toxicity of OAD. Thus, in addition to promoting safe and cost-effective individualized diabetes treatment, pharmacogenomics has a great potential to complement current efforts to optimize treatment of diabetes and lead towards its effective and personalized care.

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

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

Organic cation transporter 1 variants and gastrointestinal side effects of metformin in patients with Type 2 diabetes

Metformin is the most widely used oral anti‐diabetes agent and has considerable benefits over other therapies, yet 20–30% of people develop gastrointestinal side effects, and 5% are unable to tolerate metformin due to the severity of these side effects. The mechanism for gastrointestinal side effects and their considerable inter‐individual variability is unclear. We have recently shown the association between organic cation transporter 1 (OCT1) variants and severe intolerance to metformin in people with Type 2 diabetes. The aim of this study was to explore the association of OCT1 reduced‐function polymorphisms with common metformin‐induced gastrointestinal side effects in Type 2 diabetes.

Association between 11β-hydroxysteroid dehydrogenase type 1 gene polymorphisms and metabolic syndrome

Introduction: The enzyme 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) catalyzes the conversion of the hormonally inactive cortisone to active cortisol, thus facilitating glucocorticoid receptor activation in target tissues. Increased expression of 11β-HSD1 in adipose tissue has been associated with obesity and insulin resistance. In this study, we investigated the association of two 11β-HSD1 gene (HSD11B1) polymorphisms with the metabolic syndrome (MetS) and its characteristics in the Bosnian population. Materials and methods: The study included 86 participants: 43 patients diagnosed with MetS and 43 healthy controls. Subjects were genotyped for two HSD11B1 gene polymorphisms: rs846910: G>A and rs45487298: insA, by the high resolution melting curve analysis. Genotype distribution and an influence of genotypes on clinical and biochemical parameters were assessed. Results: There was no significant difference in the mutated allele frequencies for the two HSD11B1 gene polymorphisms between MetS patients and controls. In MetS patients, no significant associations between disease-associated traits and rs45487298: insA were found. Regarding rs846910: G>A variant, heterozygous patients (G/A) had significantly lower systolic (P = 0.017) and diastolic blood pressure (P = 0.015), lower HOMA-IR index (P = 0.011) and higher LDL-cholesterol levels (P = 0.049), compared to the wild-type homozygotes. In the control group, rs45487298: insA polymorphism was associated with lower fasting plasma insulin levels (P = 0.041), lower homeostasis model assessment insulin resistance (HOMA-IR) index (P = 0.041) and lower diastolic blood pressure (P = 0.048). Significant differences between rs846910: G>A genotypes in controls were not detected. Haplotype analysis confirmed the association of rs45487298: insA with markers of insulin resistance in the control subjects. Conclusions: Our results indicate that a common rs45487298: insA polymorphism in HSD11B1 gene may have a protective effect against insulin resistance.

Effect of Serotonin Transporter 5-HTTLPR Polymorphism on Gastrointestinal Intolerance to Metformin: A GoDARTS Study.

OBJECTIVE The mechanism causing gastrointestinal intolerance to metformin treatment is unknown. We have previously shown that reduced-function alleles of organic cation transporter 1 (OCT1) are associated with increased intolerance to metformin. Considering recent findings that serotonin reuptake transporter (SERT) might also be involved in metformin intestinal absorption, and the role of serotonin in gastrointestinal physiology, in this study we investigated the association between a common polymorphism in the SERT gene and metformin gastrointestinal intolerance. RESEARCH DESIGN AND METHODS We explored the effect of composite SERT 5-HTTLPR/rs25531 genotypes, L*L* (LALA), L*S* (LALG, LAS), and S*S* (SS, SLG, LGLG), in 1,356 fully tolerant and 164 extreme metformin-intolerant patients by using a logistic regression model, adjusted for age, sex, weight, OCT1 genotype, and concomitant use of medications known to inhibit OCT1 activity. RESULTS The number of low-expressing SERT S* alleles increased the odds of metformin intolerance (odds ratio [OR] 1.31 [95% CI 1.02–1.67], P = 0.031). Moreover, a multiplicative interaction between the OCT1 and SERT genotypes was observed (P = 0.003). In the analyses stratified by SERT genotype, the presence of two deficient OCT1 alleles was associated with more than a ninefold higher odds of metformin intolerance in patients carrying the L*L* genotype (OR 9.25 [95% CI 3.18–27.0], P < 10−4); however, it showed a much smaller effect in L*S* carriers and no effect in S*S* carriers. CONCLUSIONS Our results indicate that the interaction between OCT1 and SERT genes might play an important role in metformin intolerance. Further studies are needed to replicate these findings and to substantiate the hypothesis that metformin gastrointestinal side effects could be related to the reduced intestinal serotonin uptake.

Incidence of prediabetes and risk of developing cardiovascular disease in women with polycystic ovary syndrome.

Our aim was to determine the incidence of prediabetes and risk of developing cardiovascular disease (CVD) in women with polycystic ovary syndrome (PCOS). This prospective, observational study included 148 women with PCOS, without Type 2 diabetes mellitus (T2DM) and CVD present at baseline. In the fasting blood samples, we measured lipids, glucose, and insulin levels during oral glucose tolerance test, levels of C-reactive protein (CRP), steroids, 25-hydroxyvitamin D (25-OHD), prolactin, thyroid-stimulating hormone, and parathyroid hormone. The follow-up period was 3 years. At baseline, prevalent prediabetes was present in 18 (12%) of PCOS cases and it progressed to T2DM in 5 (3%) of the cases. Incident prediabetes during the follow-up was noted in 47 (32%) women or 4.7 per 1000 persons/year. Prediabetes was associated with elevated body mass index (BMI) (odds ratio [OR] = 1.089, confidence interval [CI]: 1.010; 1.174, p = 0.026), high baseline levels of CRP (OR = 3.286, CI: 1.299; 8.312, p = 0.012), homeostatic model assessment - insulin resistance (IR) (OR = 2.628, CI: 1.535; 4.498, p < 0.001), and high lipid accumulation product (LAP) (OR = 1.009, CI: 1.003; 1.016, p = 0.005). Furthermore, prediabetes was associated with low 25-OHD (OR = 0.795, CI: 0.724; 0.880, p ≤ 0.05). In addition, cardiovascular risk in PCOS women with prediabetes was high (hazard ratio = 1.092, CI: 1.036; 1.128, p < 0.001). We showed association of prediabetes with high BMI, IR, markers of inflammation, LAP, and low serum 25-OHD concentration. IR appears to be more relevant than the other predictors of prediabetes risk in this study. PCOS women are considered as a high-risk population for prediabetes.

CLASSIFICATION OF METABOLIC SYNDROME PATIENTS USINGIMPLEMENTED EXPERT SYSTEM

This paper presents the development of an Expert System for the classification of metabolic syndrome (MetS). Two-layer feedforward Artificial Neural Network (ANN) with sigmoid transfer function is used for MetS classification. In accordance with international guidelines NHBL/AHA, classification is performed based on following input parameters: waist circumference, blood pressure, glucose level, HDL cholesterol and triglycerides. Samples for training of developed Expert System are obtained from 1083 patients at hospitals in Bosnia and Herzegovina.

CLASSIFICATION OF PREDIABETES AND TYPE 2 DIABETES USINGARTIFICIAL NEURAL NETWORK

In this paper development of Artificial Neural Network for classification of prediabetes and type 2 diabetes (T2D) is presented. For development of this system 310 samples consisting of information about Fasting Plasma Glucose (FPG) and blood test called HbA1c were used. All samples were obtained from several healthcare institutions in Bosnia and Herzegovina, and diagnosis of prediabetes, T2D and healthy patients in this dataset were established by medical professionals. Two-layer feedforward backpropagation network with 15 neurons in hidden layer and sigmoid transfer function, used for classification of prediabetes and T2D in this paper, was trained with 190 samples.