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

The Effect of Genetic Variants of SLC22A2 (rs662301 and rs315978) on the response to Metformin in type 2 Saudi diabetic patients

PURPOSE

To investigate the allelic and genotypic frequencies of the two genetic variations, NC_000006.12: g.160275887C > T (rs662301) and NC_000006.12:g.160231826 T > C (rs315978), in the SLC22A2 gene among the Saudi population. The primary goal is to elucidate potential associations with these genetic variations and the response to metformin therapy over 6 months to enhance our knowledge of the genetic basis of Type 2 Diabetes Mellitus (T2DM) and its clinical management in the Saudi population.

MATERIALS/METHODS

76 newly diagnosed T2DM patients, aged 30 to 60, of both sexes and Saudi origin, were treated with metformin monotherapy. Blood samples were collected before and after 6 months of therapy,80 healthy individuals were included as controls. Genomic DNA was extracted. Genotyping of the SLC22A2 genetic variations was performed using TaqMan® SNP Genotyping Assays. Binary logistic regression was utilized to evaluate how certain clinical parameters influence T2DM concerning the presence of SLC22A2 gene variants.

RESULTS

Among these patients, 73.3 % were responders, and 26.7 % were non-responders. For these variants, no statistically significant differences in genotype or allele frequencies were observed between responders and non-responders (p = 0.375 and p = 0.384 for rs662301; p = 0.473 and p = 0.481 for rs315978, respectively). For the SLC22A2 variant rs662301, the C/C genotype was significantly associated with increased T2DM risk with age and elevated HbA1c levels. Similarly, rs315978 revealed higher T2DM susceptibility and HbA1c elevation in C/C genotype carriers, specifically with advancing age compared to individuals with C/T and T/T genotypes.

CONCLUSION

The study offers insights into the genetic landscape of T2DM in Saudi Arabia. Despite the absence of significant associations with treatment response, the study suggests potential age-specific associations, this highlights the complexity of the disease. This research underscores the necessity for expanded research, considering diverse populations and genetic factors, to develop personalized treatment approaches. This study serves as a foundation for future investigations into the Saudi population, recognizing the need for a larger sample size.

Genotype-Guided Model for Prediction of Tacrolimus Initial Dosing After Lung Transplantation

The determination of the appropriate initial dose for tacrolimus is crucial in achieving the target concentration promptly and avoiding adverse effects and poor prognosis. However, the trial-and-error approach is still common practice. This study aimed to establish a prediction model for an initial dosing algorithm of tacrolimus in patients receiving a lung transplant. A total of 210 lung transplant recipients were enrolled, and 26 single nucleotide polymorphisms (SNP) from 18 genes that could potentially affect tacrolimus pharmacokinetics were genotyped. Associations between SNPs and tacrolimus concentration/dose ratio were analyzed. SNPs that remained significant in pharmacogenomic analysis were further combined with clinical factors to construct a prediction model for tacrolimus initial dose. The dose needed to reach steady state tacrolimus concentrations and achieve the target range was used to validate model prediction efficiency. Our final model consisted of 7 predictors-CYP3A5 rs776746, SLCO1B3 rs4149117, SLC2A2 rs1499821, NFATc4 rs1955915, alanine aminotransferase, direct bilirubin, and hematocrit-and explained 41.4% variance in the tacrolimus concentration/dose ratio. It achieved an area under the receiver operating characteristic curve of 0.804 (95% confidence interval, 0.746-0.861). The Hosmer-Lemeshow test yielded a nonsignificant P value of .790, suggesting good fit of the model. The predicted dose exhibited good correlation with the observed dose in the early postoperative period (r = 0.748, P less than .001). Our study provided a genotype-guided prediction model for tacrolimus initial dose, which may help to guide individualized dosing of tacrolimus in the lung transplant population in clinical practice.

Leveraging large-scale biobank EHRs to enhance pharmacogenetics of cardiometabolic disease medications

Electronic health records (EHRs) coupled with large-scale biobanks offer great promises to unravel the genetic underpinnings of treatment efficacy. However, medication-induced biomarker trajectories stemming from such records remain poorly studied. Here, we extract clinical and medication prescription data from EHRs and conduct GWAS and rare variant burden tests in the UK Biobank (discovery) and the All of Us program (replication) on ten cardiometabolic drug response outcomes including lipid response to statins, HbA1c response to metformin and blood pressure response to antihypertensives (N = 740-26,669). Our findings at genome-wide significance level recover previously reported pharmacogenetic signals and also include novel associations for lipid response to statins (N = 26,669) near LDLR and ZNF800. Importantly, these associations are treatment-specific and not associated with biomarker progression in medication-naive individuals. Furthermore, we demonstrate that individuals with higher genetically determined low-density and total cholesterol baseline levels experience increased absolute, albeit lower relative biomarker reduction following statin treatment. In summary, we systematically investigated the common and rare pharmacogenetic contribution to cardiometabolic drug response phenotypes in over 50,000 UK Biobank and All of Us participants with EHR and identified clinically relevant genetic predictors for improved personalized treatment strategies.

Understanding the action mechanisms of metformin in the gastrointestinal tract

Metformin is the initial medication recommended for the treatment of type 2 diabetes mellitus (T2DM). In addition to diabetes treatment, the function of metformin also can be anti-aging, antiviral, and anti-inflammatory. Nevertheless, further exploration is required to fully understand its mode of operation. Historically, the liver has been acknowledged as the main location where metformin reduces glucose levels, however, there is increasing evidence suggesting that the gastrointestinal tract also plays a significant role in its action. In the gastrointestinal tract, metformin effects glucose uptake and absorption, increases glucagon-like peptide-1 (GLP-1) secretion, alters the composition and structure of the gut microbiota, and modulates the immune response. However, the side effects of it cannot be ignored such as gastrointestinal distress in patients. This review outlines the impact of metformin on the digestive system and explores potential explanations for variations in metformin effectiveness and adverse effects like gastrointestinal discomfort.

The effects of transcription factor 7-like 2 rs7903146 and paired box 4 rs2233580 variants associated with type 2 diabetes on the therapeutic efficacy of hypoglycemic agents

Aim

This study aims to investigate the effects of the TCF7L2 rs7903146 and PAX4 rs2233580 (R192H) variants associated with T2D on the therapeutic efficacies of various HAs in patients with T2D after follow-up for 3 years.

Methods

A total of 526 patients who were followed up at the Diabetic Clinic of Siriraj Hospital during 2016-2019 were enrolled. The variants TCF7L2 rs7903146 and PAX4 rs2233580 (R192H) were genotyped using the RNase H2 enzyme-based amplification (rhAmp) technique and the associations between genotypes and glycemic control after treatments with different combinations HA were evaluated using Generalized Estimating Equations (GEE) analysis.

Results

Patients who carried TCF7L2 rs7903146C/T + T/T genotypes when they were treated with biguanide alone had significantly lower fasting plasma glucose (FPG) than those of the patients who carried the C/C genotype (p = 0.01). Patients who carried the PAX4 rs2233580 G/G genotype when they were treated with sulfonylurea alone had significantly lower FPG than those of the patients who carried G/A + A/A genotypes (p = 0.04).

Conclusion

Genotypes of TCF7L2 rs7903146 and PAX4 rs2233580 (R192H) variants associated with T2D influence the therapeutic responses to biguanide and sulfonylurea. Different genotypes of these two variants might distinctively affect the therapeutic effects of HAs. This finding provides evidence of pharmacogenetics in the treatment of diabetes.

Genome-Wide Association Study Identifies Pharmacogenomic Variants Associated With Metformin Glycemic Response in African American Patients With Type 2 Diabetes

OBJECTIVE

Metformin is the most common treatment for type 2 diabetes (T2D). However, there have been no pharmacogenomic studies for T2D in which a population of color was used in the discovery analysis. This study sought to identify genomic variants associated with metformin response in African American patients with diabetes.

RESEARCH DESIGN AND METHODS

Patients in the discovery set were adult, African American participants from the Diabetes Multi-omic Investigation of Drug Response (DIAMOND), a cohort study of patients with T2D from a health system serving southeast Michigan. DIAMOND participants had genome-wide genotype data and longitudinal electronic records of laboratory results and medication fills. The genome-wide discovery analysis identified polymorphisms correlated to changes in glycated hemoglobin (HbA1c) levels among individuals on metformin monotherapy. Lead associations were assessed for replication in an independent cohort of African American participants from Kaiser Permanente Northern California (KPNC) and in European American participants from DIAMOND.

RESULTS

The discovery set consisted of 447 African American participants, whereas the replication sets included 353 African American KPNC participants and 466 European American DIAMOND participants. The primary analysis identified a variant, rs143276236, in the gene ARFGEF3, which met the threshold for genome-wide significance, replicated in KPNC African Americans, and was still significant in the meta-analysis (P = 1.17 × 10-9). None of the significant discovery variants replicated in European Americans DIAMOND participants.

CONCLUSIONS

We identified a novel and biologically plausible genetic variant associated with a change in HbA1c levels among African American patients on metformin monotherapy. These results highlight the importance of diversity in pharmacogenomic studies.

FTO is a major genetic link between breast cancer, obesity, and diabetes

PURPOSE

Breast cancer (BC), obesity, and type 2 diabetes mellitus (T2DM) are three complex diseases and health problems that are prevalent worldwide. The aim of this study was to investigate the common genetic associations between these diseases by referring back to the previous genome-wide association studies (GWAS).

METHODS

To this end, significant GWAS variants and common variants associated with BC, obesity, or diabetes were identified from the GWAS catalog. To perform candidate variants, the 1000-Genomes Project was used to find variants with linkage disequilibrium. Common variants between each category were identified (common candidate haplotypic variants). Finally, these variants and their associated genes were examined for SNP function analysis, gene expression, gene-gene correlation, and pathway analysis.

RESULTS

The results identified 7 variants associated with both T2DM and BC, 8 variants associated with both obesity and BC, and 167 variants associating obesity with T2DM. 91 variants and 4 haplotypic blocks such as CTC were identified on the FTO gene associated with obesity, BC, and T2DM. The results of TCGA data showed that FTO in gene expression was correlated with 6 other genes in the DNA repair pathway in BC subjects.

CONCLUSIONS

This study suggests that the FTO gene is one of the major genes shared by BC, T2DM, and obesity based on two DNA repair and inflammatory mechanisms. These results may provide a new perspective on the important role of the FTO gene and repair mechanism in the relationship between BC, obesity, and T2DM for future studies.

Tailoring Type II Diabetes Treatment: Investigating the Effect of 5-HTT Polymorphisms on HbA1c Levels after Metformin Initiation

Aims

To investigate the effect of serotonin transporter (5-HTT) polymorphisms on change in HbA1c levels six months after metformin initiation in type 2 diabetes patients.

Materials and Methods

Participants of PROVALID (PROspective cohort study in patients with type 2 diabetes mellitus for VALidation of biomarkers) within the GIANTT (Groningen Initiative to ANalyse Type 2 Diabetes Treatment) cohort who initiated metformin were genotyped for combined 5-HTTLPR/rs25531 (L∗L∗, L∗S∗, and S∗S∗) and 5-HTT VNTR (STin 2.12, 12/-, and 10/-) polymorphisms, respectively. Multiple linear regression was applied to determine the change in HbA1c level from baseline date to six months across 5-HTTLPR/VNTR genotype groups, adjusted for baseline HbA1c, age, gender, triglyceride level, low-density lipoprotein level, and serum creatinine.

Results

157 participants were included, of which 56.2% were male. The average age was 59.3 ± 9.23 years, and the mean baseline HbA1c was 7.49% ± 1.21%. 5-HTTLPR was characterized in 46 patients as L∗L∗, 70 patients as L∗S∗, and 41 patients as S∗S∗ genotypes. No significant association was found between 5-HTTLPR and 5-HTT VNTR genotypes and change in HbA1c after adjustments.

Conclusions

5-HTT polymorphisms did not affect HbA1c levels six months after the start of metformin. Further long-term studies in large samples would be relevant to determine which polymorphisms can explain the variation in response to metformin treatment.

Pharmacogenetics of sodium-glucose co-transporter-2 inhibitors: Validation of a sex-agnostic pharmacodynamic biomarker

AIM

To validate pharmacodynamic responses to sodium-glucose co-transporter-2 (SGLT2) inhibitors and test for association with genetic variants in SLC5A4, SLC5A9, and SLC2A9.

METHODS

Canagliflozin (300 mg), a SGLT2 inhibitor, was administered to 30 healthy volunteers. Several endpoints were measured to assess clinically relevant responses, including drug-induced increases in urinary excretion of glucose, sodium and uric acid.

RESULTS

This pilot study confirmed that canagliflozin (300 mg) triggered acute changes in mean levels of several biomarkers: fasting plasma glucose (-4.1 mg/dL; P = 6 × 10-5 ), serum creatinine (+0.05 mg/dL; P = 8 × 10-4 ) and serum uric acid (-0.90 mg/dL; P = 5 × 10-10 ). The effects of sex on glucosuria depended upon how data were normalized. Whereas males' responses were ~60% greater when data were normalized to body surface area, males and females exhibited similar responses when glucosuria was expressed as grams of urinary glucose per gram-creatinine. The magnitude of glucosuria was not significantly correlated with fasting plasma glucose, estimated glomerular filtration rate or age in those healthy individuals without diabetes with an estimated glomerular filtration rate of more than 60 mL/min/1.73m2 .

CONCLUSIONS

Normalizing data relative to creatinine excretion will facilitate including data from males and females in a single analysis. Furthermore, because our ongoing pharmacogenomic study (NCT02891954) is conducted in healthy individuals, this will facilitate detection of genetic associations with limited confounding by other factors such as HbA1c and renal function.

Metformin increases the uptake of glucose into the gut from the circulation in high-fat diet-fed male mice, which is enhanced by a reduction in whole-body Slc2a2 expression

OBJECTIVES

Metformin is the first line therapy recommended for type 2 diabetes. However, the precise mechanism of action remains unclear and up to a quarter of patients show some degree of intolerance to the drug, with a similar number showing poor response to treatment, limiting its effectiveness. A better understanding of the mechanism of action of metformin may improve its clinical use. SLC2A2 (GLUT2) is a transmembrane facilitated glucose transporter, with important roles in the liver, gut and pancreas. Our group previously identified single nucleotide polymorphisms in the human SLC2A2 gene, which were associated with reduced transporter expression and an improved response to metformin treatment. The aims of this study were to model Slc2a2 deficiency and measure the impact on glucose homoeostasis and metformin response in mice.

METHODS

We performed extensive metabolic phenotyping and 2-deoxy-2-[18F]fluoro-d-glucose ([18F]FDG)-positron emission tomography (PET) analysis of gut glucose uptake in high-fat diet-fed (HFD) mice with whole-body reduced Slc2a2 (Slc2a2+/-) and intestinal Slc2a2 KO, to assess the impact of metformin treatment.

RESULTS

Slc2a2 partial deficiency had no major impact on body weight and insulin sensitivity, however mice with whole-body reduced Slc2a2 expression (Slc2a2+/-) developed an age-related decline in glucose homoeostasis (as measured by glucose tolerance test) compared to wild-type (Slc2a2+/+) littermates. Glucose uptake into the gut from the circulation was enhanced by metformin exposure in Slc2a2+/+ animals fed HFD and this action of the drug was significantly higher in Slc2a2+/- animals. However, there was no effect of specifically knocking-out Slc2a2 in the mouse intestinal epithelial cells.

CONCLUSIONS

Overall, this work identifies a differential metformin response, dependent on expression of the SLC2A2 glucose transporter, and also adds to the growing evidence that metformin efficacy includes modifying glucose transport in the gut. We also describe a novel and important role for this transporter in maintaining efficient glucose homoeostasis during ageing.

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.

Acute pharmacodynamic responses to exenatide: Drug-induced increases in insulin secretion and glucose effectiveness

AIM

Glucagon-like peptide-1 receptor agonists provide multiple benefits to patients with type 2 diabetes, including improved glycaemic control, weight loss and decreased risk of major adverse cardiovascular events. Because drug responses vary among individuals, we initiated investigations to identify genetic variants associated with the magnitude of drug responses.

METHODS

Exenatide (5 μg, subcutaneously) or saline (0.2 ml, subcutaneously) was administered to 62 healthy volunteers. Frequently sampled intravenous glucose tolerance tests were conducted to assess the impact of exenatide on insulin secretion and insulin action. This pilot study was a crossover design in which participants received exenatide and saline in random order.

RESULTS

Exenatide increased first phase insulin secretion 1.9-fold (p = 1.9 × 10-9 ) and accelerated the rate of glucose disappearance 2.4-fold (p = 2 × 10-10 ). Minimal model analysis showed that exenatide increased glucose effectiveness (Sg ) by 32% (p = .0008) but did not significantly affect insulin sensitivity (Si ). The exenatide-induced increase in insulin secretion made the largest contribution to interindividual variation in exenatide-induced acceleration of glucose disappearance while interindividual variation in the drug effect on Sg contributed to a lesser extent (β = 0.58 or 0.27, respectively).

CONCLUSIONS

This pilot study provides validation for the value of a frequently sampled intravenous glucose tolerance test (including minimal model analysis) to provide primary data for our ongoing pharmacogenomic study of pharmacodynamic effects of semaglutide (NCT05071898). Three endpoints provide quantitative assessments of the effects of glucagon-like peptide-1 receptor agonists on glucose metabolism: first phase insulin secretion, glucose disappearance rates and glucose effectiveness.

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.

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

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

Genome-wide association analysis identifies ancestry-specific genetic variation associated with acute response to metformin and glipizide in SUGAR-MGH

AIMS/HYPOTHESIS

Characterisation of genetic variation that influences the response to glucose-lowering medications is instrumental to precision medicine for treatment of type 2 diabetes. The Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH) examined the acute response to metformin and glipizide in order to identify new pharmacogenetic associations for the response to common glucose-lowering medications in individuals at risk of type 2 diabetes.

METHODS

One thousand participants at risk for type 2 diabetes from diverse ancestries underwent sequential glipizide and metformin challenges. A genome-wide association study was performed using the Illumina Multi-Ethnic Genotyping Array. Imputation was performed with the TOPMed reference panel. Multiple linear regression using an additive model tested for association between genetic variants and primary endpoints of drug response. In a more focused analysis, we evaluated the influence of 804 unique type 2 diabetes- and glycaemic trait-associated variants on SUGAR-MGH outcomes and performed colocalisation analyses to identify shared genetic signals.

RESULTS

Five genome-wide significant variants were associated with metformin or glipizide response. The strongest association was between an African ancestry-specific variant (minor allele frequency [MAFAfr]=0.0283) at rs149403252 and lower fasting glucose at Visit 2 following metformin (p=1.9×10-9); carriers were found to have a 0.94 mmol/l larger decrease in fasting glucose. rs111770298, another African ancestry-specific variant (MAFAfr=0.0536), was associated with a reduced response to metformin (p=2.4×10-8), where carriers had a 0.29 mmol/l increase in fasting glucose compared with non-carriers, who experienced a 0.15 mmol/l decrease. This finding was validated in the Diabetes Prevention Program, where rs111770298 was associated with a worse glycaemic response to metformin: heterozygous carriers had an increase in HbA1c of 0.08% and non-carriers had an HbA1c increase of 0.01% after 1 year of treatment (p=3.3×10-3). We also identified associations between type 2 diabetes-associated variants and glycaemic response, including the type 2 diabetes-protective C allele of rs703972 near ZMIZ1 and increased levels of active glucagon-like peptide 1 (GLP-1) (p=1.6×10-5), supporting the role of alterations in incretin levels in type 2 diabetes pathophysiology.

CONCLUSIONS/INTERPRETATION

We present a well-phenotyped, densely genotyped, multi-ancestry resource to study gene-drug interactions, uncover novel variation associated with response to common glucose-lowering medications and provide insight into mechanisms of action of type 2 diabetes-related variation.

DATA AVAILABILITY

The complete summary statistics from this study are available at the Common Metabolic Diseases Knowledge Portal ( https://hugeamp.org ) and the GWAS Catalog ( www.ebi.ac.uk/gwas/ , accession IDs: GCST90269867 to GCST90269899).

Pharmacogenetics of SGLT2 Inhibitors: Validation of a sex-agnostic pharmacodynamic biomarker

Aim

SGLT2 inhibitors provide multiple benefits to patients with type 2 diabetes - including improved glycemic control and decreased risks of cardiorenal disease. Because drug responses vary among individuals, we initiated investigations to identify genetic variants associated with the magnitude of drug responses.

Methods

Canagliflozin (300 mg) was administered to 30 healthy volunteers. Several endpoints were measured to assess clinically relevant responses - including drug-induced increases in urinary excretion of glucose, sodium, and uric acid.

Results

This pilot study confirmed that canagliflozin (300 mg) triggered acute changes in mean levels of several biomarkers: fasting plasma glucose (-4.1 mg/dL; p=6x10), serum creatinine (+0.05 mg/dL; p=8×10 -4 ), and serum uric acid (-0.90 mg/dL; p=5×10 -10 ). The effects of sex on glucosuria depended upon how data were normalized. Whereas males' responses were ∼60% greater when data were normalized to body surface area, males and females exhibited similar responses when glucosuria was expressed as grams of urinary glucose per gram-creatinine. The magnitude of glucosuria was not significantly correlated with fasting plasma glucose, estimated GFR, or age in these healthy non-diabetic individuals with estimated GFR>60 mL/min/1.73m 2 .

Conclusions

Normalizing data relative to creatinine excretion will facilitate including data from males and females in a single analysis. Furthermore, because our ongoing pharmacogenomic study ( NCT02891954 ) is conducted in healthy individuals, this will facilitate detection of genetic associations with limited confounding by other factors such as age and renal function.

Registration

NCT02462421 ( clinicaltrials.gov ).

Funding

Research grants from the National Institute of Diabetes and Digestive and Kidney Diseases: R21DK105401, R01DK108942, T32DK098107, and P30DK072488.

Initial Insights into the Genetic Variation Associated with Metformin Treatment Failure in Youth with Type 2 Diabetes

Metformin is the first-line treatment for type 2 diabetes (T2D) in youth but with limited sustained glycemic response. To identify common variants associated with metformin response, we used a genome-wide approach in 506 youth from the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) study and examined the relationship between T2D partitioned polygenic scores (pPS), glycemic traits, and metformin response in these youth. Several variants met a suggestive threshold (P < 1 × 10-6), though none including published adult variants reached genome-wide significance. We pursued replication of top nine variants in three cohorts, and rs76195229 in ATRNL1 was associated with worse metformin response in the Metformin Genetics Consortium (n = 7,812), though statistically not being significant after Bonferroni correction (P = 0.06). A higher β-cell pPS was associated with a lower insulinogenic index (P = 0.02) and C-peptide (P = 0.047) at baseline and higher pPS related to two insulin resistance processes were associated with increased C-peptide at baseline (P = 0.04,0.02). Although pPS were not associated with changes in glycemic traits or metformin response, our results indicate a trend in the association of the β-cell pPS with reduced β-cell function over time. Our data show initial evidence for genetic variation associated with metformin response in youth with T2D.

Acute pharmacodynamic responses to exenatide: Drug-induced increases in insulin secretion and glucose effectiveness

Background

GLP1R agonists provide multiple benefits to patients with type 2 diabetes - including improved glycemic control, weight loss, and decreased risk of major adverse cardiovascular events. Because drug responses vary among individuals, we initiated investigations to identify genetic variants associated with the magnitude of drug responses.

Methods

Exenatide (5 µg, sc) or saline (0.2 mL, sc) was administered to 62 healthy volunteers. Frequently sampled intravenous glucose tolerance tests were conducted to assess the impact of exenatide on insulin secretion and insulin action. This pilot study was designed as a crossover study in which participants received exenatide and saline in random order.

Results

Exenatide increased first phase insulin secretion 1.9-fold (p=1.9×10 -9 ) and accelerated the rate of glucose disappearance 2.4-fold (p=2×10 -10 ). Minimal model analysis demonstrated that exenatide increased glucose effectiveness (S g ) by 32% (p=0.0008) but did not significantly affect insulin sensitivity (S i ). The exenatide-induced increase in insulin secretion made the largest contribution to inter-individual variation in exenatide-induced acceleration of glucose disappearance while inter-individual variation in the drug effect on S g contributed to a lesser extent (β=0.58 or 0.27, respectively).

Conclusions

This pilot study provides validation for the value of an FSIGT (including minimal model analysis) to provide primary data for our ongoing pharmacogenomic study of pharmacodynamic effects of semaglutide ( NCT05071898 ). Three endpoints provide quantitative assessments of GLP1R agonists' effects on glucose metabolism: first phase insulin secretion, glucose disappearance rates, and glucose effectiveness.

Registration

NCT02462421 (clinicaltrials.gov).

Funding

American Diabetes Association (1-16-ICTS-112); National Institute of Diabetes and Digestive and Kidney Disease (R01DK130238, T32DK098107, P30DK072488).

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.

Pharmacogenomics: current status and future perspectives

Inter-individual variability in drug response, be it efficacy or safety, is common and likely to become an increasing problem globally given the growing elderly population requiring treatment. Reasons for this inter-individual variability include genomic factors, an area of study called pharmacogenomics. With genotyping technologies now widely available and decreasing in cost, implementing pharmacogenomics into clinical practice - widely regarded as one of the initial steps in mainstreaming genomic medicine - is currently a focus in many countries worldwide. However, major challenges of implementation lie at the point of delivery into health-care systems, including the modification of current clinical pathways coupled with a massive knowledge gap in pharmacogenomics in the health-care workforce. Pharmacogenomics can also be used in a broader sense for drug discovery and development, with increasing evidence suggesting that genomically defined targets have an increased success rate during clinical development.

Real world evidence of clinical predictors of glycaemic response to glucose-lowering drugs among Chinese with type 2 diabetes

AIMS

To examine whether simple clinical features can predict the 1-year glycaemic response to glucose-lowering drugs (GLDs) among Chinese with type 2 diabetes.

MATERIALS AND METHODS

We used data from a diabetes risk assessment and complication screening programme and electronic medical records. We used linear regression models to examine the association between clinical features and 1-year glycaemic response to GLDs.

RESULTS

Use of metformin (n = 15,433), sulphonylureas (SU) (n = 15,190), dipeptidyl peptidase-4 inhibitor (DPP-4i) (n = 7947), thiazolidinedione (TZD) (n = 4107), and sodium-glucose cotransporter 2 inhibitors (SGLT-2i) (n = 1883) were associated with a mean reduction of HbA1c ranging from 0.7% to 1.3% at one year. Men had a greater response to SU but a poorer response to metformin and TZD. Older age predicted a better response to all GLDs but not SGLT-2i, whereas increasing diabetes duration was associated with a poorer response to all GLDs except for DPP-4i. Obese patients responded greater to TZD and SGLT-2i but poorer to SU than those with normal weight. Patients with a higher level of triglycerides to high-density lipoprotein cholesterol ratio had a greater glycaemic response to TZD but a smaller response to SU and DPP-4i.

CONCLUSIONS

Glycaemic response to GLDs differed considerably by clinical features among Chinese patients with type 2 diabetes.

Precision Medicine in Diabetes

Tailoring treatment or management to groups of individuals based on specific clinical, molecular, and genomic features is the concept of precision medicine. Diabetes is highly heterogenous with respect to clinical manifestations, disease progression, development of complications, and drug response. The current practice for drug treatment is largely based on evidence from clinical trials that report average effects. However, around half of patients with type 2 diabetes do not achieve glycaemic targets despite having a high level of adherence and there are substantial differences in the incidence of adverse outcomes. Therefore, there is a need to identify predictive markers that can inform differential drug responses at the point of prescribing. Recent advances in molecular genetics and increased availability of real-world and randomised trial data have started to increase our understanding of disease heterogeneity and its impact on potential treatments for specific groups. Leveraging information from simple clinical features (age, sex, BMI, ethnicity, and co-prescribed medications) and genomic markers has a potential to identify sub-groups who are likely to benefit from a given drug with minimal adverse effects. In this chapter, we will discuss the state of current evidence in the discovery of clinical and genetic markers that have the potential to optimise drug treatment in type 2 diabetes.

A Review of the Impact of Pharmacogenetics and Metabolomics on the Efficacy of Metformin in Type 2 Diabetes

Metformin is the most often prescribed drug for people with type 2 diabetes (T2D). More than 120 million patients with T2D use metformin worldwide. However, monotherapy fails to achieve glycemic control in a third of the treated patients. Genetics contribute to some of the inter-individual variations in glycemic response to metformin. Numerous pharmacogenetic studies have demonstrated that variations in genes related to pharmacokinetics and pharmacodynamics of metformin's encoding transporters are mainly associated with metformin response. The goal of this review is to evaluate the current state of metformin pharmacogenetics and metabolomics research, discuss the clinical and scientific issues that need to be resolved in order to increase our knowledge of patient response variability to metformin, and how to improve patient outcomes. Metformin's hydrophilic nature and absorption as well as its action mechanism and effectiveness on T2D initiation are discussed. The impacts of variations associated with various genes are analysed to identify and evaluate the effect of genetic polymorphisms on the therapeutic activity of metformin. The metabolic pattern of T2D and metformin is also indicated. This is to emphasise that studies of pharmacogenetics and metabolomics could expand our knowledge of metformin response in T2D.

Pharmacogenomics of GLP-1 receptor agonists: a genome-wide analysis of observational data and large randomised controlled trials

BACKGROUND

In the treatment of type 2 diabetes, GLP-1 receptor agonists lower blood glucose concentrations, body weight, and have cardiovascular benefits. The efficacy and side effects of GLP-1 receptor agonists vary between people. Human pharmacogenomic studies of this inter-individual variation can provide both biological insight into drug action and provide biomarkers to inform clinical decision making. We therefore aimed to identify genetic variants associated with glycaemic response to GLP-1 receptor agonist treatment.

METHODS

In this genome-wide analysis we included adults (aged ≥18 years) with type 2 diabetes treated with GLP-1 receptor agonists with baseline HbA_1c of 7% or more (53 mmol/mol) from four prospective observational cohorts (DIRECT, PRIBA, PROMASTER, and GoDARTS) and two randomised clinical trials (HARMONY phase 3 and AWARD). The primary endpoint was HbA_1c reduction at 6 months after starting GLP-1 receptor agonists. We evaluated variants in GLP1R, then did a genome-wide association study and gene-based burden tests.

FINDINGS

4571 adults were included in our analysis, of these, 3339 (73%) were White European, 449 (10%) Hispanic, 312 (7%) American Indian or Alaskan Native, and 471 (10%) were other, and around 2140 (47%) of the participants were women. Variation in HbA_1c reduction with GLP-1 receptor agonists treatment was associated with rs6923761G→A (Gly168Ser) in the GLP1R (0·08% [95% CI 0·04-0·12] or 0·9 mmol/mol lower reduction in HbA_1c per serine, p=6·0 × 10^-5) and low frequency variants in ARRB1 (optimal sequence kernel association test p=6·7 × 10^-8), largely driven by rs140226575G→A (Thr370Met; 0·25% [SE 0·06] or 2·7 mmol/mol  [SE 0·7] greater HbA_1c reduction per methionine, p=5·2 × 10^-6). A similar effect size for the ARRB1 Thr370Met was seen in Hispanic and American Indian or Alaska Native populations who have a higher frequency of this variant (6-11%) than in White European populations. Combining these two genes identified 4% of the population who had a 30% greater reduction in HbA_1c than the 9% of the population with the worse response.

INTERPRETATION

This genome-wide pharmacogenomic study of GLP-1 receptor agonists provides novel biological and clinical insights. Clinically, when genotype is routinely available at the point of prescribing, individuals with ARRB1 variants might benefit from earlier initiation of GLP-1 receptor agonists.

FUNDING

Innovative Medicines Initiative and the Wellcome Trust.

Using Data to Improve the Management of Diabetes: The Tayside Experience

Tayside is a region in the East of Scotland and forms one of nine local government regions in the country. It is home to approximately 416,000 individuals who fall under the National Health Service (NHS) Tayside health board, which provides health care services to the population. In Tayside, Scotland, a comprehensive informatics network for diabetes care and research has been established for over 25 years. This has expanded more recently to a comprehensive Scotland-wide clinical care system, Scottish Care Information - Diabetes (SCI-Diabetes). This has enabled improved diabetes screening and integrated management of diabetic retinopathy, neuropathy, nephropathy, cardiovascular health, and other comorbidities. The regional health informatics network links all of these specialized services with comprehensive laboratory testing, prescribing records, general practitioner records, and hospitalization records. Not only do patients benefit from the seamless interconnectedness of these data, but also the Tayside bioresource has enabled considerable research opportunities and the creation of biobanks. In this article we describe how health informatics has been used to improve care of people with diabetes in Tayside and Scotland and, through anonymized data linkage, our understanding of the phenotypic and genotypic etiology of diabetes and associated complications and comorbidities.

PAR2 promotes impaired glucose uptake and insulin resistance in NAFLD through GLUT2 and Akt interference

BACKGROUND AND AIMS

Insulin resistance and poor glycemic control are key drivers of the development of NAFLD and have recently been shown to be associated with fibrosis progression in NASH. However, the underlying mechanisms involving dysfunctional glucose metabolism and relationship with NAFLD/NASH progression remain poorly understood. We set out to determine whether protease-activated receptor 2 (PAR2), a sensor of extracellular inflammatory and coagulation proteases, links NAFLD and NASH with liver glucose metabolism.

APPROACH AND RESULTS

Here, we demonstrate that hepatic expression of PAR2 increases in patients and mice with diabetes and NAFLD/NASH. Mechanistic studies using whole-body and liver-specific PAR2-knockout mice reveal that hepatic PAR2 plays an unexpected role in suppressing glucose internalization, glycogen storage, and insulin signaling through a bifurcating Gq -dependent mechanism. PAR2 activation downregulates the major glucose transporter of liver, GLUT2, through Gq -MAPK-FoxA3 and inhibits insulin-Akt signaling through Gq -calcium-CaMKK2 pathways. Therapeutic dosing with a liver-homing pepducin, PZ-235, blocked PAR2-Gq signaling and afforded significant improvements in glycemic indices and HbA1c levels in severely diabetic mice.

CONCLUSIONS

This work provides evidence that PAR2 is a major regulator of liver glucose homeostasis and a potential target for the treatment of diabetes and NASH.

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.

Pharmacoepigenetics in type 2 diabetes: is it clinically relevant?

Data generated over nearly two decades clearly demonstrate the importance of epigenetic modifications and mechanisms in the pathogenesis of type 2 diabetes. However, the role of pharmacoepigenetics in type 2 diabetes is less well established. The field of pharmacoepigenetics covers epigenetic biomarkers that predict response to therapy, therapy-induced epigenetic alterations as well as epigenetic therapies including inhibitors of epigenetic enzymes. Not all individuals with type 2 diabetes respond to glucose-lowering therapies in the same way, and there is therefore a need for clinically useful biomarkers that discriminate responders from non-responders. Blood-based epigenetic biomarkers may be useful for this purpose. There is also a need for a better understanding of whether existing glucose-lowering therapies exert their function partly through therapy-induced epigenetic alterations. Finally, epigenetic enzymes may be drug targets for type 2 diabetes. Here, I discuss whether pharmacoepigenetics is clinically relevant for type 2 diabetes based on studies addressing this topic.

A roadmap to achieve pharmacological precision medicine in diabetes

Current pharmacological treatment of diabetes is largely algorithmic. Other than for cardiovascular disease or renal disease, where sodium-glucose cotransporter 2 inhibitors and/or glucagon-like peptide-1 receptor agonists are indicated, the choice of treatment is based upon overall risks of harm or side effect and cost, and not on probable benefit. Here we argue that a more precise approach to treatment choice is necessary to maximise benefit and minimise harm from existing diabetes therapies. We propose a roadmap to achieve precision medicine as standard of care, to discuss current progress in relation to monogenic diabetes and type 2 diabetes, and to determine what additional work is required. The first step is to identify robust and reliable genetic predictors of response, recognising that genotype is static over time and provides the skeleton upon which modifiers such as clinical phenotype and metabolic biomarkers can be overlaid. The second step is to identify these metabolic biomarkers (e.g. beta cell function, insulin sensitivity, BMI, liver fat, metabolite profile), which capture the metabolic state at the point of prescribing and may have a large impact on drug response. Third, we need to show that predictions that utilise these genetic and metabolic biomarkers improve therapeutic outcomes for patients, and fourth, that this is cost-effective. Finally, these biomarkers and prediction models need to be embedded in clinical care systems to enable effective and equitable clinical implementation. Whilst this roadmap is largely complete for monogenic diabetes, we still have considerable work to do to implement this for type 2 diabetes. Increasing collaborations, including with industry, and access to clinical trial data should enable progress to implementation of precision treatment in type 2 diabetes in the near future.

A resource for integrated genomic analysis of the human liver

In this study, we generated whole-transcriptome RNA-Seq from n = 192 genotyped liver samples and used these data with existing data from the GTEx Project (RNA-Seq) and previous liver eQTL (microarray) studies to create an enhanced transcriptomic sequence resource in the human liver. Analyses of genotype-expression associations show pronounced enrichment of associations with genes of drug response. The associations are primarily consistent across the two RNA-Seq datasets, with some modest variation, indicating the importance of obtaining multiple datasets to produce a robust resource. We further used an empirical Bayesian model to compare eQTL patterns in liver and an additional 20 GTEx tissues, finding that MHC genes, and especially class II genes, are enriched for liver-specific eQTL patterns. To illustrate the utility of the resource to augment GWAS analysis with small sample sizes, we developed a novel meta-analysis technique to combine several liver eQTL data sources. We also illustrate its application using a transcriptome-enhanced re-analysis of a study of neutropenia in pancreatic cancer patients. The associations of genotype with liver expression, including splice variation and its genetic associations, are made available in a searchable genome browser.

On the Verge of Precision Medicine in Diabetes

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

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

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

New and Emerging Research on Solute Carrier and ATP Binding Cassette Transporters in Drug Discovery and Development: Outlook from the International Transporter Consortium

Enabled by a plethora of new technologies, research in membrane transporters has exploded in the past decade. The goal of this state‐of‐the‐art article is to describe recent advances in research on membrane transporters that are particularly relevant to drug discovery and development. This review covers advances in basic, translational, and clinical research that has led to an increased understanding of membrane transporters at all levels. At the basic level, we describe the available crystal structures of membrane transporters in both the solute carrier (SLC) and ATP binding cassette superfamilies, which has been enabled by the development of cryogenic electron microscopy methods. Next, we describe new research on lysosomal and mitochondrial transporters as well as recently deorphaned transporters in the SLC superfamily. The translational section includes a summary of proteomic research, which has led to a quantitative understanding of transporter levels in various cell types and tissues and new methods to modulate transporter function, such as allosteric modulators and targeted protein degraders of transporters. The section ends with a review of the effect of the gut microbiome on modulation of transporter function followed by a presentation of 3D cell cultures, which may enable in vivo predictions of transporter function. In the clinical section, we describe new genomic and pharmacogenomic research, highlighting important polymorphisms in transporters that are clinically relevant to many drugs. Finally, we describe new clinical tools, which are becoming increasingly available to enable precision medicine, with the application of tissue‐derived small extracellular vesicles and real‐world biomarkers.

GWAS of longitudinal trajectories at biobank scale

Biobanks linked to massive, longitudinal electronic health record (EHR) data make numerous new genetic research questions feasible. One among these is the study of biomarker trajectories. For example, high blood pressure measurements over visits strongly predict stroke onset, and consistently high fasting glucose and Hb1Ac levels define diabetes. Recent research reveals that not only the mean level of biomarker trajectories but also their fluctuations, or within-subject (WS) variability, are risk factors for many diseases. Glycemic variation, for instance, is recently considered an important clinical metric in diabetes management. It is crucial to identify the genetic factors that shift the mean or alter the WS variability of a biomarker trajectory. Compared to traditional cross-sectional studies, trajectory analysis utilizes more data points and captures a complete picture of the impact of time-varying factors, including medication history and lifestyle. Currently, there are no efficient tools for genome-wide association studies (GWASs) of biomarker trajectories at the biobank scale, even for just mean effects. We propose TrajGWAS, a linear mixed effect model-based method for testing genetic effects that shift the mean or alter the WS variability of a biomarker trajectory. It is scalable to biobank data with 100,000 to 1,000,000 individuals and many longitudinal measurements and robust to distributional assumptions. Simulation studies corroborate that TrajGWAS controls the type I error rate and is powerful. Analysis of eleven biomarkers measured longitudinally and extracted from UK Biobank primary care data for more than 150,000 participants with 1,800,000 observations reveals loci that significantly alter the mean or WS variability.

From Pharmacogenetics to Pharmaco-Omics:Milestones and Future Directions

The origins of pharmacogenetics date back to the 1950s, when it was established that inter-individual differences in drug response are partially determined by genetic factors. Since then, pharmacogenetics has grown into its own field, motivated by the translation of identified gene-drug interactions into therapeutic applications. Despite numerous challenges ahead, our understanding of the human pharmacogenetic landscape has greatly improved thanks to the integration of tools originating from disciplines as diverse as biochemistry, molecular biology, statistics, and computer sciences. In this review, we discuss past, present, and future developments of pharmacogenetics methodology, focusing on three milestones: how early research established the genetic basis of drug responses, how technological progress made it possible to assess the full extent of pharmacological variants, and how multi-dimensional omics datasets can improve the identification, functional validation, and mechanistic understanding of the interplay between genes and drugs. We outline novel strategies to repurpose and integrate molecular and clinical data originating from biobanks to gain insights analogous to those obtained from randomized controlled trials. Emphasizing the importance of increased diversity, we envision future directions for the field that should pave the way to the clinical implementation of pharmacogenetics.

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.

Review of pharmaceutical and therapeutic approaches for type 2 diabetes and related disorders

One of the essential diseases that are increasing in the world is type 2 diabetes (T2D), which many people around the world live with this disease. Various studies have revealed that insulin resistance, lessened insulin production has been associated with T2D, and they also show that this disease can have a genetic origin and is associated with different genes such as KCNQ1, PPAR-γ, calpain-10, ADIPOR2, TCF7L2 that can be utilized as a therapeutic target. Different therapeutic approaches and strategies such as exercise and diet, pharmacological approaches, and utilization of nanoparticles in drug delivery and gene therapy can be effective in the treatment and control of T2D. Glucagon-like peptide 1 (GLP-1) and sodium glucose cotransporter-2 (SGLT2) have both been considered as drug classes in the treatment of T2D and T2D-related diseases such as cardiovascular disease and renal disease, and have considerable influences such as diminished cardiovascular mortality in individuals with T2D, ameliorate postprandial glycaemia, ameliorate fasting glycaemia, and diminish body weight on disease treatment and improvement process. In the present review article, we have made an attempt to explore the risk factors, Genes, and diseases associated with T2D, therapeutic approaches in T2D, the influences of drugs such as Dapagliflozin, Metformin, Acarbose, Januvia (Sitagliptin), and Ertugliflozin on T2D in clinical trials and animal model studies. Research in clinical trials has promising results that support the role of these drug approaches in T2D prophylaxis and ameliorate safety even though additional clinical research is still obligatory.

Analysis of rare coding variants in 200,000 exome-sequenced subjects reveals novel genetic risk factors for type 2 diabetes

AIMS

The study aimed to elucidate the effects of rare genetic variants on the risk of type 2 diabetes (T2D).

MATERIALS AND METHODS

Weighted burden analysis of rare variants was applied to a sample of 200,000 exome-sequenced participants in the UK Biobank project, of whom over 13,000 were identified as having T2D. Variant weights were allocated based on allele frequency and predicted effect, as informed by a previous analysis of hyperlipidaemia.

RESULTS

There was an exome-wide significant increased burden of rare, functional variants in three genes, GCK, HNF4A and GIGYF1. GIGYF1 has not previously been identified as a diabetes risk gene and its product appears to be involved in the modification of insulin signalling. A number of other genes did not attain exome-wide significance but were highly ranked and potentially of interest, including ALAD, PPARG, GYG1 and GHRL. Loss of function (LOF) variants were associated with T2D in GCK and GIGYF1 whereas nonsynonymous variants annotated as probably damaging were associated in GCK and HNF4A. Overall, fewer than 1% of T2D cases carried one of these variants. In HNF1A and HNF1B there was an excess of LOF variants among cases but the small numbers of these fell short of statistical significance.

CONCLUSIONS

Rare genetic variants make an identifiable contribution to T2D in a small number of cases but these may provide valuable insights into disease mechanisms. As larger samples become available it is likely that additional genetic factors will be identified.

Integrated or Independent Actions of Metformin in Target Tissues Underlying Its Current Use and New Possible Applications in the Endocrine and Metabolic Disorder Area

Metformin is considered the first-choice drug for type 2 diabetes treatment. Actually, pleiotropic effects of metformin have been recognized, and there is evidence that this drug may have a favorable impact on health beyond its glucose-lowering activity. In summary, despite its long history, metformin is still an attractive research opportunity in the field of endocrine and metabolic diseases, age-related diseases, and cancer. To this end, its mode of action in distinct cell types is still in dispute. The aim of this work was to review the current knowledge and recent findings on the molecular mechanisms underlying the pharmacological effects of metformin in the field of metabolic and endocrine pathologies, including some endocrine tumors. Metformin is believed to act through multiple pathways that can be interconnected or work independently. Moreover, metformin effects on target tissues may be either direct or indirect, which means secondary to the actions on other tissues and consequent alterations at systemic level. Finally, as to the direct actions of metformin at cellular level, the intracellular milieu cooperates to cause differential responses to the drug between distinct cell types, despite the primary molecular targets may be the same within cells. Cellular bioenergetics can be regarded as the primary target of metformin action. Metformin can perturb the cytosolic and mitochondrial NAD/NADH ratio and the ATP/AMP ratio within cells, thus affecting enzymatic activities and metabolic and signaling pathways which depend on redox- and energy balance. In this context, the possible link between pyruvate metabolism and metformin actions is extensively discussed.

Dorothy Hodgkin Lecture 2021: Drugs, genes and diabetes

Glycaemic response to metformin and sulphonylureas is heritable - with ~34%-37% of variation explainable by common genetic variation. The premise of this review is that by understanding how genetic variation contributes to drug response we can gain insights into the mechanisms of action of diabetes drugs. Here, I focus on two old drugs, metformin and sulphonylureas, where I would suggest we still have a lot to learn about their mechanism of action or their optimal use in clinical care. The fact that reduced function variants of the key transporter that takes metformin into the liver (OCT1) do not alter glycaemic response to metformin suggests that metformin does not need to get into the liver to work. A subsequent GWAS of metformin response identifies a robust variant that alters GLUT2 expression - which may support increasing evidence that metformin works primarily in the gut. For sulphonylureas, observation from patients with neonatal diabetes due to activating K_ATP channel mutations treated with sulphonylureas identified a novel role for sulphonylureas to enable β-cell incretin response. This work led to recent studies of low-dose sulphonylurea (20 mg gliclazide) in T2DM, which identified that at this dose sulphonylureas augment the incretin effect and increase β-cell glucose sensitivity, without increasing hypoglycaemia risk. This work, prompted by studies in monogenic diabetes, suggests that we have historically been using sulphonylureas at too high a dose. With increasing availability of genetic data pharmacogenomic studies in patients with diabetes should reveal mechanistic insights into old and new diabetes drugs, with the potential for optimized use and novel therapies.

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.