Heritability of variation in glycaemic response to metformin: a genome-wide complex trait analysis

Metformin treatment of juvenile mice alters aging-related developmental and metabolic phenotypes in sex-dependent and sex-independent manners

Metformin has attracted increasing interest for its potential benefits in extending healthspan and longevity. This study examined the effects of early-life metformin treatment on the development and metabolism of C57BL/6 J (B6) mice, with metformin administered to juvenile mice from 15 to 56 days of age. Metformin treatment led to decreased body weight in both sexes (P < 0.05, t-test). At 9 weeks of age, mice were euthanized and organ weights were recorded. The relative weight of retroperitoneal fat was decreased in females, while relative weights of perigonadal and retroperitoneal fat were decreased, and relative liver weight was increased in males (P < 0.05, t-test). Glucose and insulin tolerance tests (GTT and ITT) were conducted at the age of 7 weeks. ANOVA revealed a significant impairment in insulin sensitivity by the treatment, and a significantly interactive effect on glucose tolerance between sex and treatment, underscoring a disparity in GTT between sexes in response to the treatment. Metformin treatment reduced circulating insulin levels in fasting and non-fasting conditions for male mice, with no significant alterations observed in female mice. qRT-PCR analysis of glucose metabolism-related genes (Akt2, Glut2, Glut4, Irs1, Nrip1, Pi3k, Pi3kca, Pkca) in the liver and skeletal muscle reveals metformin-induced sex- and organ-specific effects on gene expression. Comparison with previous studies in heterogeneous UM-HET3 mice receiving the same treatment suggests that genetic differences may contribute to variability in the effects of metformin treatment on development and metabolism. These findings indicate that early-life metformin treatment affects development and metabolism in both sex- and genetics-dependent manners.

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.

Glucagon-Like Peptide 1 (GLP-1) Receptor Variants and Glycemic Response to Liraglutide: A Pharmacogenetics Study in Iranian People with Type 2 Diabetes Mellitus

INTRODUCTION

Pharmacogenetics studies suggest that genetic variants have a possible influence on the inter-individual differences in therapeutic response to glucagon-like peptide 1 receptor agonists (GLP-1 RAs). We aimed to examine the potential role of genetic variability of glucagon-like peptide 1 receptor (GLP-1R) on glycemic response to GLP-1 RAs in a population of Iranian people with type 2 diabetes mellitus (T2DM).

METHODS

In this study, we analyzed the data from participants in a non-inferiority randomized clinical trial between 2019 and 2020. Patients received liraglutide 1.8 mg/day subcutaneously for 24 weeks. They were stratified by the baseline hemoglobin A1c (HbA1c) into four categories: 7-7.99, 8-8.99, 9-9.99, and ≥ 10%. In each category, subjects with HbA1c reduction greater than the median ΔHbA1c value for that group were defined as optimal responders. The pooled number of optimal/suboptimal responders in the four groups was used for the comparison. We evaluated two genetic variants of GLP-1R, rs6923761 and rs10305420, using Sanger sequencing. Logistic regression analyses were performed to examine the associations of the GLP-1R variants with the glycemic response in different genetic models.

RESULTS

Out of 233 participants, 120 individuals were optimal responders. Median HbA1c reduction was - 2.5% in the optimal responder group compared with - 1.0% in the suboptimal responder group (P < 0.001). In genetic models, rs10305420 T allele homozygosity was associated with optimal glycemic response to liraglutide compared with heterozygous and wild-type homozygous states (recessive model: OR 3.28, 95% CI 1.41-7.65, P = 0.006; codominant model: OR 2.52, 95% CI 1.03-6.13, P = 0.04). No significant association was found between rs6923761 variant and HbA1c reduction.

CONCLUSION

GLP-1R rs10305420 polymorphism can explain some of the inter-individual differences in glycemic response to liraglutide in a population of Iranian people with T2DM.

Precision Medicine in Type 2 Diabetes Mellitus: Utility and Limitations

Type 2 diabetes mellitus (T2DM) is one of the most widespread diseases in Western countries, and its incidence is constantly increasing. Epidemiological studies have shown that in the next 20 years. The number of subjects affected by T2DM will double. In recent years, owing to the development and improvement in methods for studying the genome, several authors have evaluated the association between monogenic or polygenic genetic alterations and the development of metabolic diseases and complications. In addition, sedentary lifestyle and socio-economic and pandemic factors have a great impact on the habits of the population and have significantly contributed to the increase in the incidence of metabolic disorders, obesity, T2DM, metabolic syndrome, and liver steatosis. Moreover, patients with type 2 diabetes appear to respond to antihyperglycemic drugs. Only a minority of patients could be considered true non-responders. Thus, it appears clear that the main aim of precision medicine in T2DM is to identify patients who can benefit most from a specific drug class more than from the others. Precision medicine is a discipline that evaluates the applicability of genetic, lifestyle, and environmental factors to disease development. In particular, it evaluated whether these factors could affect the development of diseases and their complications, response to diet, lifestyle, and use of drugs. Thus, the objective is to find prevention models aimed at reducing the incidence of pathology and mortality and therapeutic personalized approaches, to obtain a greater probability of response and efficacy. This review aims to evaluate the applicability of precision medicine for T2DM, a healthcare burden in many countries.

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.

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.

The importance of reaction norms in dietary restriction and ageing research

Ageing research has progressed rapidly through our ability to modulate the ageing process. Pharmacological and dietary treatments can increase lifespan and have been instrumental in our understanding of the mechanisms of ageing. Recently, several studies have reported genetic variance in response to these anti-ageing interventions, questioning their universal application and making a case for personalised medicine in our field. As an extension of these findings the response to dietary restriction was found to not be repeatable when the same genetic mouse lines were retested. We show here that this effect is more widespread with the response to dietary restriction also showing low repeatability across genetic lines in the fly (Drosophila melanogaster). We further argue that variation in reaction norms, the relationship between dose and response, can explain such conflicting findings in our field. We simulate genetic variance in reaction norms and show that such variation can: 1) lead to over- or under-estimation of treatment responses, 2) dampen the response measured if a genetically heterogeneous population is studied, and 3) illustrate that genotype-by-dose-by-environment interactions can lead to low repeatability of DR and potentially other anti-ageing interventions. We suggest that putting experimental biology and personalised geroscience in a reaction norm framework will aid progress in ageing research.

Mitochondrial DNA Copy Number Is a Potential Biomarker for Treatment Choice Between Metformin and Acarbose

Metformin is the first-line drug for type 2 diabetes (T2D) while acarbose is suggested as a viable alternative in Chinese patients with newly diagnosed T2D. However, few biomarkers have been established to guide the choice between these two agents. Mitochondrial DNA (mtDNA) copy number (mtDNA-CN) is a biomarker of mitochondrial function, which is associated with various metabolic outcomes. Using data from the trial of Metformin and Acarbose in Chinese as the Initial Hypoglycaemic Treatment (MARCH) (metformin n = 214; acarbose n = 198), we examined whether mtDNA-CN was associated with response to the drugs in terms of glycemic response and β-cell function protection response. The glycemic response is defined as the maximum glucose reduction of glycated hemoglobin A_1c , fasting plasma glucose, or postprandial blood glucose during 48 weeks. β-cell function protection response is defined as the maximum increment of insulinogenic index (IGI) or disposition index (DI). For all three glycemic responses, mtDNA-CN was not significantly associated with either metformin or acarbose. Importantly, for β-cell function protection response, we found the increased mtDNA-CN was significantly associated with more IGI increment (beta: 0.84; 95% confidence interval (CI), 0.02 to 1.66) in the metformin group, but less IGI increment (beta: -1.38; 95% CI, -2.52 to -0.23) in the acarbose group. A significant interaction (P = 0.008) between mtDNA-CN and the treatment group was observed. Consistent results were also obtained when DI increment was used as a measure of β-cell function response. This study demonstrated the potential application of mtDNA-CN in guiding the treatment choice between metformin and acarbose based on β-cell protection.

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.

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.

Implications of genetic variations, differential gene expression, and allele-specific expression on metformin response in drug-naïve type 2 diabetes

PURPOSE

Metformin is widely used to treat type 2 diabetes mellitus (T2DM) individuals. Clinically, inter-individual variability of metformin response is of significant concern and is under interrogation. In this study, a targeted exome and whole transcriptome analysis were performed to identify predictive biomarkers of metformin response in drug-naïve T2DM individuals.

METHODS

The study followed a prospective study design. Drug-naïve T2DM individuals (n = 192) and controls (n = 223) were enrolled. T2DM individuals were administered with metformin monotherapy and defined as responders and non-responders based on their glycated haemoglobin change over three months. 146 T2DM individuals were used for the final analysis and remaining samples were lost during the follow-up. Target exome sequencing and RNA-seq was performed to analyze genetic and transcriptome profile. The selected SNPs were validated by genotyping and allele specific gene expression using the TaqMan assay. The gene prioritization, enrichment analysis, drug-gene interactions, disease-gene association, and correlation analysis were performed using various tools and databases.

RESULTS

rs1050152 and rs272893 in SLC22A4 were associated with improved response to metformin. The copy number loss was observed in PPARGC1A in the non-responders. The expression analysis highlighted potential differentially expressed targets for predicting metformin response (n = 35) and T2DM (n = 14). The expression of GDF15, TWISTNB, and RPL36A genes showed a maximum correlation with the change in HbA1c levels. The disease-gene association analysis highlighted MAGI2 rs113805659 to be linked with T2DM.

CONCLUSION

The results provide evidence for the genetic variations, perturbed transcriptome, allele-specific gene expression, and pathways associated with metformin drug response in T2DM.

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.

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.

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.

Single Nucleotide Polymorphism in the 3’ Untranslated Region of PRKAA2 on Cardiometabolic Parameters in Type 2 Diabetes Mellitus Patients Who Received Metformin

Purpose

This study aimed to explore the association of rs857148 A>C as 3ʹUTR variants with blood pressure, HbA1c profile, and lipid profiles as cardiometabolic parameters among patients with T2DM receiving metformin.

Patients and Methods

This cross-sectional analytic research was conducted with 114 consecutively selected patients with T2DM. Polymerase chain reaction-restriction fragment length polymorphism was conducted to determine rs857148. A total of 108 patients fulfilled inclusion and exclusion criteria.

Results

Genotype distribution agreed with the Hardy Weinberg Equation for Equilibrium (p>0.05) but wildtype allele was found as the minor allele. Subjects with CC genotype and C allele had enhanced HbA1c levels (OR=7.12; 95% CI=1.05–48.26; p=0.04; OR=1.66; 95% CI=1.06–2.60; p=0.03, respectively). It was confirmed by dominant model whereas subjects with AA tended to have reduced HbA1c compared to AC+CC genotype (OR=0.15; 95% CI=0.02–0.97; p=0.047). AC genotype had significant correlation to total cholesterol (OR=1.05; 95% CI=1.01–1.10; p=0.03) compared to AA genotype.

Conclusion

We conclude that polymorphism of rs87148, specifically CC genotype and C allele, has a significant association with HbA1c and total cholesterol after considering oral hypoglycemia agent dose, age, gender, and combination therapy, compared to AA genotype. Future studies that involve a larger sample population and more rigorous selection criteria are required.

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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.

The management of diabetes mellitus by mangiferin: advances and prospects

Diabetes mellitus has become one of the most challenging public health problems today. There are still various deficiencies that remain in existing therapeutic drugs. With increasing prevalence and mortality rates, more effective therapeutic agents are required for treatment clinically. As a kind of polyphenol and as a natural product, mangiferin has numerous pharmacological and excellent effects. In this review, the underlying mechanisms of mangiferin on diabetes mellitus and complications will be summarized. Moreover, mangiferin belongs to the BSC IV class and the clinical application and development of mangiferin are limited due to its poor aqueous solubility and fat solubility as well as low bioavailability. Our review also elaborated on improving the solubility of mangiferin by changing the dosage form and introduced the existing results, which hope to provide useful reference for mangiferin for further treating diabetes. In conclusion, mangiferin might be a potential adjuvant therapy for the treatment of diabetes mellitus and complications in the future.

Quantile-Dependent Heritability of Glucose, Insulin, Proinsulin, Insulin Resistance, and Glycated Hemoglobin

BACKGROUND

"Quantile-dependent expressivity" is a dependence of genetic effects on whether the phenotype (e.g., insulin resistance) is high or low relative to its distribution.

METHODS

Quantile-specific offspring-parent regression slopes (βOP) were estimated by quantile regression for fasting glucose concentrations in 6,453 offspring-parent pairs from the Framingham Heart Study.

RESULTS

Quantile-specific heritability (h2), estimated by 2βOP/(1 + rspouse), increased 0.0045 ± 0.0007 (p = 8.8 × 10-14) for each 1% increment in the fasting glucose distribution, that is, h2 ± SE were 0.057 ± 0.021, 0.095 ± 0.024, 0.146 ± 0.019, 0.293 ± 0.038, and 0.456 ± 0.061 at the 10th, 25th, 50th, 75th, and 90th percentiles of the fasting glucose distribution, respectively. Significant increases in quantile-specific heritability were also suggested for fasting insulin (p = 1.2 × 10-6), homeostatic model assessment of insulin resistance (HOMA-IR, p = 5.3 × 10-5), insulin/glucose ratio (p = 3.9 × 10-5), proinsulin (p = 1.4 × 10-6), proinsulin/insulin ratio (p = 2.7 × 10-5), and glucose concentrations during a glucose tolerance test (p = 0.001), and their logarithmically transformed values.

DISCUSSION/CONCLUSION

These findings suggest alternative interpretations to precision medicine and gene-environment interactions, including alternative interpretation of reported synergisms between ACE, ADRB3, PPAR-γ2, and TNF-α polymorphisms and being born small for gestational age on adult insulin resistance (fetal origin theory), and gene-adiposity (APOE, ENPP1, GCKR, IGF2BP2, IL-6, IRS-1, KIAA0280, LEPR, MFHAS1, RETN, TCF7L2), gene-exercise (INS), gene-diet (ACSL1, ELOVL6, IRS-1, PLIN, S100A9), and gene-socioeconomic interactions.

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.

The Effects of Accelerated Temperature-Controlled Stability Systems on the Release Profile of Primary Bile Acid-Based Delivery Microcapsules

Introduction: Bile acid-based drug encapsulation for oral delivery has been recently explored in our laboratory and has shown to be beneficial in terms of drug-targeted delivery and release profile, but stability at various temperatures has not previously been examined; hence, this is the aim of this study. Methods: Various types of bile acid-based microcapsules containing the drug metformin were produced and tested for accelerated temperature-controlled profiles, as well as morphology, elemental composition, drug content, resilience, floatability, wettability and release profiles at various pH values. Results: Accelerated temperature-controlled analysis showed negligible effects on morphology, size, or shape at very low temperatures (below 0 °C), while higher temperatures (above 25 °C) caused alterations. Drug contents, morphology and elemental composition remained similar, while wettability and the release profiles showed formulation-dependent effects. Discussion and Conclusion: Results suggest that bile acid-based microcapsules containing metformin are affected by temperature; hence, their shelf life is likely to be affected by storage temperature, all of which have a direct impact on drug release and stability profiles.

Scientific Advances in Diabetes: The Impact of the Innovative Medicines Initiative

Diabetes Mellitus is one of the World Health Organization's priority diseases under research by the first and second programmes of Innovative Medicines Initiative, with the acronyms IMI1 and IMI2, respectively. Up to October of 2019, 13 projects were funded by IMI for Diabetes & Metabolic disorders, namely SUMMIT, IMIDIA, DIRECT, StemBANCC, EMIF, EBiSC, INNODIA, RHAPSODY, BEAT-DKD, LITMUS, Hypo-RESOLVE, IM2PACT, and CARDIATEAM. In general, a total of €447 249 438 was spent by IMI in the area of Diabetes. In order to prompt a better integration of achievements between the different projects, we perform a literature review and used three data sources, namely the official project's websites, the contact with the project's coordinators and co-coordinator, and the CORDIS database. From the 662 citations identified, 185 were included. The data collected were integrated into the objectives proposed for the four IMI2 program research axes: (1) target and biomarker identification, (2) innovative clinical trials paradigms, (3) innovative medicines, and (4) patient-tailored adherence programmes. The IMI funded projects identified new biomarkers, medical and research tools, determinants of inter-individual variability, relevant pathways, clinical trial designs, clinical endpoints, therapeutic targets and concepts, pharmacologic agents, large-scale production strategies, and patient-centered predictive models for diabetes and its complications. Taking into account the scientific data produced, we provided a joint vision with strategies for integrating personalized medicine into healthcare practice. The major limitations of this article were the large gap of data in the libraries on the official project websites and even the Cordis database was not complete and up to date.

Therapeutic Dilemma in personalized medicine

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

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

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

Model of genetic and environmental factors associated with type 2 diabetes mellitus in a Chinese Han population

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a metabolic disorder which accounts for high morbidity and mortality due to complications like renal failure, amputations, cardiovascular disease, and cerebrovascular events.

METHODS

We collected medical reports, lifestyle details, and blood samples of individuals and used the polymerase chain reaction-ligase detection reaction method to genotype the SNPs, and a visit was conducted in August 2016 to obtain the incidence of Type 2 diabetes in the 2113 eligible people. To explore which genes and environmental factors are associated with type 2 diabetes mellitus in a Chinese Han population, we used elastic net to build a model, which is to explain which variables are strongly associated with T2DM, rather than predict the occurrence of T2DM.

RESULT

The genotype of the additive of rs964184, together with the history of hypertension, regular intake of meat and waist circumference, increased the risk of T2DM (adjusted OR = 2.38, p = 0.042; adjusted OR = 3.31, p < 0.001; adjusted OR = 1.05, p < 0.001). The TT genotype of the additive and recessive models of rs12654264, the CC genotype of the additive and dominant models of rs2065412, the TT genotype of the additive and dominant models of rs4149336, together with the degree of education, regular exercise, reduced the risk of T2DM (adjusted OR = 0.46, p = 0.017; adjusted OR = 0.53, p = 0.021; adjusted OR = 0.59, p = 0.021; adjusted OR = 0.57, p = 0.01; adjusted OR = 0.59, p = 0.021; adjusted OR = 0.57, p = 0.01; adjusted OR = 0.50, p = 0.007; adjusted OR = 0.80, p = 0.032) .

CONCLUSION

Eventually we identified a set of SNPs and environmental factors: rs5805 in the SLC12A3, rs12654264 in the HMGCR, rs2065412 and rs414936 in the ABCA1, rs96418 in the ZPR1 gene, waistline, degree of education, exercise frequency, hypertension, and the intake of meat. Although there was no interaction between these variables, people with two risk factors had a higher risk of T2DM than those only having one factor. These results provide the theoretical basis for gene and other risk factors screening to prevent T2DM.

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

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

Diabetes: Is There a Future for Pharmacogenomics Guided Treatment?

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

Metformin strongly affects transcriptome of peripheral blood cells in healthy individuals

Metformin is a commonly used antihyperglycaemic agent for the treatment of type 2 diabetes mellitus. Nevertheless, the exact mechanisms of action, underlying the various therapeutic effects of metformin, remain elusive. The goal of this study was to evaluate the alterations in longitudinal whole-blood transcriptome profiles of healthy individuals after a one-week metformin intervention in order to identify the novel molecular targets and further prompt the discovery of predictive biomarkers of metformin response. Next generation sequencing-based transcriptome analysis revealed metformin-induced differential expression of genes involved in intestinal immune network for IgA production and cytokine-cytokine receptor interaction pathways. Significantly elevated faecal sIgA levels during administration of metformin, and its correlation with the expression of genes associated with immune response (CXCR4, HLA-DQA1, MAP3K14, TNFRSF21, CCL4, ACVR1B, PF4, EPOR, CXCL8) supports a novel hypothesis of strong association between metformin and intestinal immune system, and for the first time provide evidence for altered RNA expression as a contributing mechanism of metformin’s action. In addition to universal effects, 4 clusters of functionally related genes with a subject-specific differential expression were distinguished, including genes relevant to insulin production (HNF1B, HNF1A, HNF4A, GCK, INS, NEUROD1, PAX4, PDX1, ABCC8, KCNJ11) and cholesterol homeostasis (APOB, LDLR, PCSK9). This inter-individual variation of the metformin effect on the transcriptional regulation goes in line with well-known variability of the therapeutic response to the drug.

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

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

No significant association of type 2 diabetes-related genetic risk scores with glycated haemoglobin levels after initiating metformin or sulphonylurea derivatives

AIM

To explore the added value of diabetes-related genetic risk scores (GRSs) to readily available clinical variables in the prediction of glycated haemoglobin (HbA1c) levels after initiation of glucose-regulating drugs.

MATERIALS AND METHODS

We conducted a cohort study in people with type 2 diabetes (T2DM) from the Groningen Initiative to Analyse Type 2 Diabetes Treatment (GIANTT) database who initiated metformin (MET) or sulphonylurea derivatives (SUs) and for whom blood samples were genotyped. The primary outcome was HbA1c level at 6 months, adjusted for baseline HbA1c. GRSs were based on single nucleotide polymorphisms linked to insulin sensitivity, β-cell activity, and T2DM risk in general. Associations were analysed using multiple linear regression to assess whether adding the GRSs increased the explained variance in a prediction model that included age, gender, diabetes duration and cardio-metabolic biomarkers.

RESULTS

We included 282 patients initiating MET and 89 patients initiating SUs. In the MET prediction model, diabetes duration of >3 months when starting MET was associated with 2.7-mmol/mol higher HbA1c level. For SUs, no significant clinical predictors were identified. Addition of the GRS linked to insulin sensitivity (for MET), β-cell activity (for SUs) and T2DM risk (for both) to the models did not improve the explained variance significantly (22% without vs. 22% with GRS) for the MET and (14% without vs. 14% with GRS) for the SUs model, respectively.

CONCLUSION

This study did not indicate a significant effect of GRS related to T2DM in general or to the drugs' mechanism of action for prediction of inter-individual HbA1c variability in the short term after initiation of MET or SU therapy.

Personalised nutrition - phenotypic and genetic variation in response to dietary intervention

Personalised nutrition allows individual differences in dietary, lifestyle, anthropometry, phenotype and/or genomic profile to be used to direct specific dietary advice. For personalised nutrition advice to be effective both sides need to be considered; firstly, that factors influencing variation in response to dietary intervention are identified and appropriate advice can be derived and secondly; that these are then used effectively in the provision of nutrition advice, resulting in a positive dietary and/or lifestyle behaviour change. There is considerable evidence demonstrating genetic and phenotypic influence on the biological response to the consumption of nutrients and bioactives. However, findings are often mixed, with studies often investigating at the level of a single nutrient/bioactive and/or a single genetic/phenotypic variation, meaning the derivation of specific advice at a dietary level in an individual/group of individuals can be complex. Similarly, the impact of using this information to derive personalised advice is also mixed, with some studies demonstrating no effectiveness and others showing a significant impact. The present paper will outline examples of phenotypic and genetic variation influencing response to nutritional interventions, and will consider how they could be used in the provision of personalised nutrition.

Common Variants in 22 Genes Regulate Response to Metformin Intervention in Children with Obesity: A Pharmacogenetic Study of a Randomized Controlled Trial

Metformin is a first-line oral antidiabetic agent that has shown additional effects in treating obesity and metabolic syndrome. Inter-individual variability in metformin response could be partially explained by the genetic component. Here, we aimed to test whether common genetic variants can predict the response to metformin intervention in obese children. The study was a multicenter and double-blind randomized controlled trial that was stratified according to sex and pubertal status in 160 children with obesity. Children were randomly assigned to receive either metformin (1g/d) or placebo for six months after meeting the defined inclusion criteria. We conducted a post hoc genotyping study in 124 individuals (59 placebo, 65 treated) comprising finally 231 genetic variants in candidate genes. We provide evidence for 28 common variants as promising pharmacogenetics regulators of metformin response in terms of a wide range of anthropometric and biochemical outcomes, including body mass index (BMI) Z-score, and glucose, lipid, and inflammatory traits. Although no association remained statistically significant after multiple-test correction, our findings support previously reported variants in metformin transporters or targets as well as identify novel and promising loci, such as the ADYC3 and the BDNF genes, with plausible biological relation to the metformin's action mechanism. Trial Registration: Registered on the European Clinical Trials Database (EudraCT, ID: 2010-023061-21) on 14 November 2011 (URL: https://www.clinicaltrialsregister.eu/ctr-search/trial/2010-023061-21/ES).

Clinical and genetic predictors of diabetes drug's response

Diabetes is a major health problem worldwide. Glycemic control is the main goal in the management of type 2 diabetes. While many anti-diabetic drugs and guidelines are available, almost half of diabetic patients do not reach their treatment goal and develop complications. The glucose-lowering response to anti-diabetic drug differs significantly between individuals. Relatively little is known about the factors that might underlie this response. The identification of predictors of response to anti-diabetic drugs is essential for treatment personalization. Unfortunately, the evidence on predictors of drugs response in type 2 diabetes is scarce. Only a few trials were designed for specific groups of patients (e.g. patients with renal impairment or older patients), while subgroup analyses of larger trials are frequently unreported. Physicians need help in picking the drug which provides the maximal benefit, with minimal side effects, in the right dose, for a specific patient, using an omics-based approach besides the phenotypic characteristics.

Pharmacogenetics of Metformin for Medication-Induced Weight Gain in Autism Spectrum Disorder

Objectives: We recently found that metformin attenuated weight gain due to mixed dopamine and serotonin receptor antagonists, commonly termed atypical antipsychotics, in children and adolescents with autism spectrum disorder (ASD). Previous studies have found that genetic variation predicts response to metformin in diabetes. In this study, we aimed to assess whether response to metformin for weight gain in this population is associated with variants in five genes previously implicated in metformin response in diabetes. Methods: Youth with ASD who experienced significant weight gain while taking mixed receptor antagonist medications were randomly assigned to metformin or placebo for 16 weeks, followed by open-label metformin treatment for 16 weeks. In the 53 participants with available DNA samples, we used a linear, mixed model analysis to assess response in the first 16 weeks of metformin treatment, whether in the randomized or open-label period, based upon genotypes at polymorphisms in five genes previously associated with metformin response in diabetes: ATM, SLC2A2, MATE1, MATE2, and OCT1. Results: In the primary analysis, both ATM and OCT1 showed significant effects of genotype on change in body mass index z-scores, the primary outcome measure, during the first 16 weeks of treatment with metformin. No other polymorphism showed a significant difference. Conclusion: As has been shown for metformin treatment in diabetes, genetic variation may predict response to metformin for weight gain in youth with ASD treated with mixed receptor antagonists. Further work is needed to replicate these findings and evaluate whether they can be used prospectively to improve outcomes.

Germline variants and somatic mutation signatures of breast cancer across populations of African and European ancestry in the US and Nigeria

Somatic mutation signatures may represent footprints of genetic and environmental exposures that cause different cancer. Few studies have comprehensively examined their association with germline variants, and none in an indigenous African population. SomaticSignatures was employed to extract mutation signatures based on whole-genome or whole-exome sequencing data from female patients with breast cancer (TCGA, training set, n = 1,011; Nigerian samples, validation set, n = 170), and to estimate contributions of signatures in each sample. Association between somatic signatures and common single nucleotide polymorphisms (SNPs) or rare deleterious variants were examined using linear regression. Nine stable signatures were inferred, and four signatures (APOBEC C>T, APOBEC C>G, aging and homologous recombination deficiency) were highly similar to known COSMIC signatures and explained the majority (60-85%) of signature contributions. There were significant heritable components associated with APOBEC C>T signature (h² = 0.575, p = 0.010) and the combined APOBEC signatures (h² = 0.432, p = 0.042). In TCGA dataset, seven common SNPs within or near GNB5 were significantly associated with an increased proportion (beta = 0.33, 95% CI = 0.21-0.45) of APOBEC signature contribution at genome-wide significance, while rare germline mutations in MTCL1 was also significantly associated with a higher contribution of this signature (p = 6.1 × 10^-6 ). This is the first study to identify associations between germline variants and mutational patterns in breast cancer across diverse populations and geography. The findings provide evidence to substantiate causal links between germline genetic risk variants and carcinogenesis.

Lack of effect of the SLC47A1 and SLC47A2 gene polymorphisms on the glycemic response to metformin in type 2 diabetes mellitus patients

Background This work aimed to evaluate the influence of single nucleotide polymorphisms (SNPs) in the SLC47A1 (922-158G>A; rs2289669) and SLC47A2 (-130G>A; rs12943590) genes on the relative change in HbA1c in type 2 diabetes mellitus (T2DM) patients of South India who are taking metformin as monotherapy. It also aims to study the effects of these SNPs on the dose requirement of metformin for glycemic control and the adverse effects of metformin. Methods Diabetes patients on metformin monotherapy were recruited based on the eligibility criteria (n=105). DNA was extracted and genotyping was performed with a real-time PCR system using TaqMan® SNP genotyping assay method. The HbA1c levels were measured using Bio-Rad D-10™ Hemoglobin Analyzer. Results After adjusting for multiple comparisons (Bonferroni correction) the difference found in the glycemic response between the "GG" genotype and "AG/AA" genotype groups of the SLC47A2 gene was not significant (p=0.027; which was greater than the critical value of 0.025). Patients with "GG" genotype showed a 5.5% decrease in HbA1c from baseline compared to those with the "AG/AA" genotype (0.1% increase). The SNP in the SLC47A1 gene also did not influence the glycemic response to metformin (p=0.079). The median dose requirements based on the genotypes of the rs12943590 variant (p=0.357) or rs2289669 variant (p=0.580) were not significantly different. Similarly, there was no significant difference in the occurrence of adverse effects across the genotypes in both the SLC47A1 (p=0.615) and SLC47A2 (p=0.309) genes. Conclusions The clinical response to metformin was not associated with the SNPs in the SLC47A1 and SLC47A2 genes coding for the multidrug and toxin extrusion protein (MATE) transporters. Furthermore, the studied SNPs had no influence on the dose requirement or adverse effects of metformin.

Precision medicine in diabetes: an opportunity for clinical translation

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

Metformin modifies disparity in hepatocellular carcinoma incidence in men with type 2 diabetes but without chronic liver diseases

Background

We assessed racial/ethnic disparity in hepatocellular carcinoma (HCC) incidence among men with type 2 diabetes (T2D) but without chronic liver diseases (CLD), and whether metformin use modified the disparity.

Methods

Study cohort: the nationwide Veterans Administration Health Care System electronic medical records among 40‐89 years old men with T2D; without CLD, cancer, cardiovascular or renal diseases previously; insulin and thiazolidinedione naive. Logistic regression analyses compared HCC incidence between race/ethnicity groups under no metformin use adjusted for covariates and inverse propensity score weights (IPSW) for race/ethnicity. The generalizability technique integrated with IPSW was incorporated to compare covariates adjusted odds ratios (aOR) of HCC associated with metformin use among race/ethnicity groups.

Results

Study cohort: N = 84 433; 79.47% non‐Hispanic white (NHW), 15.5% non‐Hispanic African American (NHAA), 5.03% Hispanics; 36.76% metformin users; follow‐up 6.10 ± 2.87 years; age 67.8 ± 9.8 years, HbA1c 6.57 ± 0.98%; 0.14% HCC cases. Under no metformin use, HCC incidence was lower for NHAA vs NHW (aOR = 0.60 [0.40‐0.92]), similar between NHW and Hispanics. Metformin was associated with reduced HCC risk: aOR = 0.57 (0.40‐0.81) for NHW; aOR = 0.35 (0.25‐0.47) for NHAA; aOR = 0.31 (0.22‐0.43) for Hispanics. Metformin dose >1000 mg/d was neutral for NHW; less effective for NHAA (P = 0.02); more effective for Hispanics (P = 0.002).

Conclusions

In men with T2D but without CLD nor metformin use, HCC incidence was lower for NHAA compared to NHW or Hispanics; similar between NHW and Hispanics. Metformin use reduced HCC risk and modified the race/ethnicity disparity.

Impact

Metformin's heterogeneous HCC prevention effect elucidates potential interventions to modify HCC disparity in patients with T2D.

Pharmacogenetics of type 2 diabetes mellitus, the route toward tailored medicine

Type 2 diabetes mellitus (T2DM) is a chronic disease that has reached the levels of a global epidemic. In order to achieve optimal glucose control, it is often necessary to rely on combination therapy of multiple drugs or insulin because uncontrolled glucose levels result in T2DM progression and enhanced risk of complications and mortality. Several antihyperglycemic agents have been developed over time, and T2DM pharmacotherapy should be prescribed based on suitability for the individual patient's characteristics. Pharmacogenetics is the branch of genetics that investigates how our genome influences individual responses to drugs, therapeutic outcomes, and incidence of adverse effects. In this review, we evaluated the pharmacogenetic evidences currently available in the literature, and we identified the top informative genetic variants associated with response to the most common anti-diabetic drugs: metformin, DPP-4 inhibitors/GLP1R agonists, thiazolidinediones, and sulfonylureas/meglitinides. Overall, we found 40 polymorphisms for each drug class in a total of 71 loci, and we examined the possibility of encouraging genetic screening of these variants/loci in order to critically implement decision-making about the therapeutic approach through precision medicine strategies. It is possible then to anticipate that when the clinical practice will take advantage of the genetic information of the diabetic patients, this will provide a useful resource for the prevention of T2DM progression, enabling the identification of the precise drug that is most likely to be effective and safe for each patient and the reduction of the economic impact on a global scale.

The Continuing Evolution of Precision Health in Type 2 Diabetes: Achievements and Challenges

PURPOSE OF REVIEW

The purpose of this review was to summarize recent advances in the genomics of type 2 diabetes (T2D) and to highlight current initiatives to advance precision health.

RECENT FINDINGS

Generation of multi-omic data to measure each of the "biologic layers," developments in describing genomic function and annotation in T2D relevant tissue, along with the increasing recognition that T2D is a heterogeneous disease, and large-scale collaborations have all contributed to advancing our understanding of the molecular basis of T2D. Substantial advances have been made in understanding the molecular basis of T2D pathogenesis, such that precision health diabetes is increasingly becoming a reality. For precision diabetes to become a routine in clinical and public health, additional large-scale multi-omic initiatives are needed along with better assessment of our environment to delineate an individual's diabetes subtype for improved detection and management.

Taming expectations of metformin as a treatment to extend healthspan

The anti-hyperglycemic medication metformin has potential to be the first drug tested to slow aging in humans. While the Targeting Aging with Metformin (TAME) proposal and other small-scale clinical trials have the potential to support aging as a treatment indication, we propose that the goals of the TAME trial might not be entirely consistent with the Geroscience goal of extending healthspan. There is expanding epidemiological support for the health benefits of metformin in individuals already diagnosed with overt chronic disease. However, it remains to be understood if these protective effects extend to those free of chronic disease. Within this editorial, we seek to highlight critical gaps in knowledge that should be considered when testing metformin as a treatment to target aging.

The association of metformin use with prostate cancer aggressiveness among Black Americans and White Americans in a population-based study

PURPOSE

Metformin has been associated with a reduced incidence of prostate cancer and improved prostate cancer outcomes. However, whether race modifies the association between metformin use and prostate cancer aggressiveness remains uncertain. The association between metformin use and prostate cancer aggressiveness was examined separately in Black Americans (Blacks) and White Americans (Whites).

METHODS

The study population consisted of 305 Black and 195 White research participants with incident prostate cancer and self-reported diabetes from the North Carolina-Louisiana Prostate Cancer Project. High-aggressive prostate cancer was defined using a composite measure of Gleason sum, prostate-specific antigen, and clinical stage. Multivariable logistic regression was used to assess the association between metformin use and high-aggressive prostate cancer at diagnosis, separately among Whites and Blacks, with adjustment for age, screening history, site, education, insurance, and body mass index.

RESULTS

Metformin use was associated positively with high-aggressive prostate cancer in Blacks (OR 2.01; 95% CI 1.05, 3.83). By contrast, a weak inverse association between metformin use and high-aggressive prostate cancer was found in Whites (OR 0.80, 95% CI 0.34, 1.85).

CONCLUSIONS

The association between metformin use and prostate cancer aggressiveness may be modified by race.