Common variants near ATM are associated with glycemic response to metformin in type 2 diabetes

Microbial succinate promotes the response to metformin by upregulating secretory immunoglobulin a in intestinal immunity

Metformin is the first-line pharmacotherapy for type 2 diabetes mellitus; however, many patients respond poorly to this drug in clinical practice. The potential involvement of microbiota-mediated intestinal immunity and related signals in metformin responsiveness has not been previously investigated. In this study, we successfully constructed a humanized mouse model by fecal transplantation of the gut microbiota from clinical metformin-treated - responders and non-responders, and reproduced the difference in clinical phenotypes of responsiveness to metformin. The abundance of Bacteroides thetaiotaomicron, considered a representative differential bacterium of metformin responsiveness, and the level of secretory immunoglobulin A (SIgA) in intestinal immunity increased significantly in responder recipient mice following metformin treatment. In contrast, no significant alterations in B. thetaiotaomicron and SIgA were observed in non-responder recipient mice. The study of IgA-/- mice confirmed that downregulated expression or deficiency of SIgA resulted in non-response to metformin, meaning that metformin was unable to improve dysfunctional glucose metabolism and reduce intestinal and adipose tissue inflammation, ultimately leading to systemic insulin resistance. Furthermore, supplementation with succinate, a microbial product of B. thetaiotaomicron, potentially reversed the non-response to metformin by inducing the production of SIgA. In conclusion, we demonstrated that upregulated SIgA, which could be regulated by succinate, was functionally involved in metformin response through its influence on immune cell-mediated inflammation and insulin resistance. Conversely, an inability to regulate SIgA may result in a lack of response to metformin.

The direct targets of metformin in diabetes and beyond

Metformin, an oral antihyperglycemic drug that has been in use for over 60 years, remains a first-line therapy for type 2 diabetes (T2D). Numerous studies have suggested that metformin promotes health benefits beyond T2D management, including weight loss, cancer prevention and treatment, and anti-aging, through several proposed mechanistic targets. Here we discuss the established effects of metformin and the progress made in identifying its direct targets. Additionally, we emphasize the importance of elucidating the structural bases of the drug and its direct targets. Ultimately, this review aims to highlight the current state of knowledge regarding metformin and its related emerging discoveries, while also outlining critical future research directions.

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 = 932-28,880). Our discovery analyses in participants of European ancestry recover previously reported pharmacogenetic signals at genome-wide significance level (APOE, LPA and SLCO1B1) and a novel rare variant association in GIMAP5 with HbA1c response to metformin. Importantly, these associations are treatment-specific and not associated with biomarker progression in medication-naive individuals. We also found polygenic risk scores to predict drug response, though they explained less than 2% of the variance. In summary, we present an EHR-based framework to study the genetics of drug response and systematically investigated the common and rare pharmacogenetic contribution to cardiometabolic drug response phenotypes in 41,732 UK Biobank and 14,277 All of Us participants.

Genome-Wide Association Study to Identify Genetic Variants Associated With Diabetic Maculopathy

Purpose

Diabetic maculopathy (including diabetic macular edema [DME]) is the leading cause of vision loss in people with diabetes. We aimed to identify the genetic determinants of diabetic maculopathy.

Methods

We conducted a genome-wide association study (GWAS) in two cohorts with a meta-analysis. The Australian cohort comprised 551 cases of DME and 599 controls recruited from the states of South Australia and Tasmania. The Scottish cohort comprised 1951 cases of diabetic maculopathy and 6541 controls from the Genetics of Diabetes Audit and Research in Tayside Scotland study (GoDARTS). Genotyping, imputation, and association analysis using logistic regression were conducted in each cohort, before combining summary statistics in a meta-analysis using the GWAMA package.

Results

A locus on chromosome 7 reached genome-wide significance in GoDARTS but showed the opposite direction of effect in the Australian cohort. The meta-analysis identified two suggestive associations (P < 5 × 10-6) for diabetic maculopathy risk with similar effect direction; one at chromosome 1 close to the RNU5E-1 gene and one at chromosome 13 upstream of the ERICH6B gene. The two loci were evaluated in silico for potential functional links to diabetic maculopathy. Both are located in regulatory regions and have annotations indicating regulatory functions. They are also expression quantitative trait locus (eQTLs) for genes plausibly involved in diabetic maculopathy pathogenesis, with links to folate metabolism and the regulation of VEGF.

Conclusions

The study suggests several promising SNPs and genes related to diabetic maculopathy risk. Despite being the largest genetic study of diabetic maculopathy to date, larger, homogeneous cohorts will be required to identify robust genetic risk loci for the disease.

Harnessing Pharmacomultiomics for Precision Medicine in Diabetes: A Comprehensive Review

Type 2 diabetes (T2D) is the fastest-growing non-communicable disease worldwide, accounting for around 90% of all diabetes cases and imposing a significant health burden globally. Due to its phenotypic heterogeneity and composite genetic underpinnings, T2D requires a precision medicine approach personalized to individual molecular profiles, thereby shifting away from the traditional "one-size-fits-all" medical methods. This review advocates for a thorough pharmacomultiomics approach to enhance precision medicine for T2D. It emphasizes personalized treatment strategies that enhance treatment efficacy while minimizing adverse effects by integrating data from genomics, proteomics, metabolomics, transcriptomics, microbiomics, and epigenomics. We summarize key findings on candidate genes impacting diabetic medication responses and explore the potential of pharmacometabolomics in predicting drug efficacy. The role of pharmacoproteomics in prognosis and discovering new therapeutic targets is discussed, along with transcriptomics' contribution to understanding T2D pathophysiology. Additionally, pharmacomicrobiomics is explored to understand gut microbiota interactions with antidiabetic drugs. Emerging evidence on utilizing epigenomic profiles in improving drug efficacy and personalized treatment is also reviewed, illustrating their implications in personalized medicine. In this paper, we discuss the integration of these layers of omics data, examining recently developed paradigms that leverage complex data to deepen our understanding of diabetes. Such integrative approaches advance precision medicine strategies to tackle the disease by better understanding its complex biology.

Diabetes in China: epidemiology, pathophysiology and multi-omics

Although diabetes is now a global epidemic, China has the highest number of affected people, presenting profound public health and socioeconomic challenges. In China, rapid ecological and lifestyle shifts have dramatically altered diabetes epidemiology and risk factors. In this Review, we summarize the epidemiological trends and the impact of traditional and emerging risk factors on Chinese diabetes prevalence. We also explore recent genetic, metagenomic and metabolomic studies of diabetes in Chinese, highlighting their role in pathogenesis and clinical management. Although heterogeneity across these multidimensional areas poses major analytic challenges in classifying patterns or features, they have also provided an opportunity to increase the accuracy and specificity of diagnosis for personalized treatment and prevention. National strategies and ongoing research are essential for improving diabetes detection, prevention and control, and for personalizing care to alleviate societal impacts and maintain quality of life.

(Poly)phenols and diabetes: From effects to mechanisms by systematic multigenomic analysis

Diabetes is a chronic and multifactorial metabolic disease with increasing numbers of patients worldwide, characterized by loss of pancreatic β-cell mass and function with subsequent insulin deficiency. Thus, restoring functional β-cells could significantly impact disease management. The beneficial effects of natural compounds, namely (poly)phenols, in diabetes have gained increasing interest, due to their pleiotropic actions in several cellular processes, including in glucose homeostasis. These compounds are able to modulate nutri(epi)genomic mechanisms by interacting with cell signaling proteins and transcription factors (TFs). However, the underlying mechanisms of action, particularly of (poly)phenol metabolites resulting from digestion and colonic microbiota action, are yet to be elucidated. This study explored the multigenomic effects of (poly)phenols and their metabolites to uncover modulatory networks and mechanisms linked to diabetes. Published studies on gene expression alterations modulated by (poly)phenolic compounds or (poly)phenol-rich extracts were integrated, encompassing studies conducted on individuals with diabetes, animal models mimicking diabetes, and pancreatic β-cell lines. Bioinformatic analysis identified differentially expressed genes and potential regulatory factors, with roles in cell signaling pathways (FoxO, AMPK, p53), endocrine resistance, immune system pathways, apoptosis, and cellular senescence. Interestingly, in silico 3D docking analyses revealed potential interactions between key TFs (FOXO1, PPARG, SIRT1, and MAFA) and some metabolites. Apigenin, luteolin, and naringenin glucuronide forms showed the best binding capacity to SIRT1. The integrative analysis of (poly)phenol metabolites data highlights the potential of these molecules for nutraceutical/pharmaceutical development aimed at managing diabetes whose incidence increases with age.

Advancements in precision medicine: multi-omics approach for tailored metformin treatment in type 2 diabetes

Metformin has become the frontline treatment in addressing the significant global health challenge of type 2 diabetes due to its proven effectiveness in lowering blood glucose levels. However, the reality is that many patients struggle to achieve their glycemic targets with the medication and the cause behind this variability has not been investigated thoroughly. While genetic factors account for only about a third of this response variability, the potential influence of metabolomics and the gut microbiome on drug efficacy opens new avenues for investigation. This review explores the different molecular signatures to uncover how the complex interplay between genetics, metabolic profiles, and gut microbiota can shape individual responses to metformin. By highlighting the insights from recent studies and identifying knowledge gaps regarding metformin-microbiota interplay, we aim to highlight the path toward more personalized and effective diabetes management strategies and moving beyond the one-size-fits-all approach.

Effects and interaction of single nucleotide polymorphisms at the pharmacokinetic/pharmacodynamic site: insights from the Rotterdam study into metformin clinical response and dose titration

Our study investigated the impact of genetic variations on metformin glycemic response in a cohort from the Rotterdam Study, comprising 14,926 individuals followed for up to 27 years. Among 1285 metformin users of European ancestry, using linear mixed models, we analyzed the association of single nucleotide polymorphisms (SNPs) and a Polygenic Risk Score (PRS) with glycemic response, measured by changes in metformin dosage or HbA1c levels. While individual genetic variants showed no significant association, rs622342 on SLC2A1 correlated with increased glycemic response only in metformin monotherapy patients (β = -2.09, P-value < 0.001). The collective effect of variants, as represented by PRS, weakly correlated with changes in metformin dosage (β = 0.023, P-value = 0.027). Synergistic interaction was observed between rs7124355 and rs8192675. Our findings suggest that while higher PRS correlates with increased metformin dosage, its modest effect size limits clinical utility, emphasizing the need for future research in diverse populations to refine genetic risk models.

Pharmacometabolomics of sulfonylureas in patients with type 2 diabetes: a cross-sectional study

Background

Sulfonylureas have been a longstanding pharmacotherapy in the management of type 2 diabetes, with potential benefits beyond glycemic control. Although sulfonylureas are effective, interindividual variability exists in drug response. Pharmacometabolomics is a potent method for elucidating variations in individual drug response. Identifying unique metabolites associated with treatment response can improve our ability to predict outcomes and optimize treatment strategies for individual patients. Our objective is to identify metabolic signatures associated with good and poor response to sulfonylureas, which could enhance our capability to anticipate treatment outcome.

Methods

In this cross-sectional study, clinical and metabolomics data for 137 patients with type 2 diabetes who are taking sulfonylurea as a monotherapy or a combination therapy were obtained from Qatar Biobank. Patients were empirically categorized according to their glycosylated hemoglobin levels into poor and good responders to sulfonylureas. To examine variations in metabolic signatures between the two distinct groups, we have employed orthogonal partial least squares discriminant analysis and linear models while correcting for demographic confounders and metformin usage.

Results

Good responders showed increased levels of acylcholines, gamma glutamyl amino acids, sphingomyelins, methionine, and a novel metabolite 6-bromotryptophan. Conversely, poor responders showed increased levels of metabolites of glucose metabolism and branched chain amino acid metabolites.

Conclusion

The results of this study have the potential to empower our knowledge of variability in patient response to sulfonylureas, and carry significant implications for advancing precision medicine in type 2 diabetes management.

The Influence of OCT3 and MATE2 Genetic Polymorphisms in Poor Response to Metformin in Type 2 Diabetes Mellitus

BACKGROUND

The response of patients with Type 2 diabetes mellitus (T2DM) to metformin may be a variation because of genetic differences in solute carrier (SLC) transporter proteins and other effect factors, which have an important effect on how metformin is processed in the body and its efficiency for glycaemic control.

AIM

This study was conducted to investigate the impact of certain genetic variants of the organic cation transporter genes OCT3 (SLC22A3 rs12194182 and rs8187722) and MATE2 (SLC47A2 rs12943590) and their association with glycaemic parameters in patients with T2DM who respond poorly to metformin.

PATIENTS AND METHODS

This cross-sectional study involved 150 Iraqi cases with T2DM who were prescribed a daily dose of (1000 mg/day) metformin for a minimum of 3 months. Various parameters included are as follows: demographic data, glycaemic parameters and three SNPs: rs12943590 variant of SLC47A2, rs12194182 and rs8187722 variant of SLC22A3 using the standard PCR-sequencing technique.

RESULTS

Thirty-nine patients (26.17%) were responders, whereas 111 patients (73.82%) could not respond to metformin treatment. Upon analysing the genotypes of the rs12943590 variants of SLC47A2, rs12194182 and rs8187722 SNPs of SLC22A3, the present findings revealed a nonsignificant association of genetic variations in all SNPs with metformin response. SLC47A2 (rs12943590) showed nonsignificant associations of the GG, AA and AG genotyping; SLC22A3 (rs12194182) showed nonsignificant associations of the TT, TC and CC genotyping; and SLC22A3 (rs8187722) showed nonsignificant associations of the AA, CC and AC genotyping between two groups.

CONCLUSION

Variations in genes SLC22A3 and SLC47A2 did not have a significant role in the response of patients with T2DM to metformin (1000 mg/day).

Metformin and its potential influence on cell fate decision between apoptosis and senescence in cancer, with a special emphasis on glioblastoma

Despite reaching enormous achievements in therapeutic approaches worldwide, GBM still remains the most incurable malignancy among various cancers. It emphasizes the necessity of adjuvant therapies from the perspectives of both patients and healthcare providers. Therefore, most emerging studies have focused on various complementary and adjuvant therapies. Among them, metabolic therapy has received special attention, and metformin has been considered as a treatment in various types of cancer, including GBM. It is clearly evident that reaching efficient approaches without a comprehensive evaluation of the key mechanisms is not possible. Among the studied mechanisms, one of the more challenging ones is the effect of metformin on apoptosis and senescence. Moreover, metformin is well known as an insulin sensitizer. However, if insulin signaling is facilitated in the tumor microenvironment, it may result in tumor growth. Therefore, to partially resolve some paradoxical issues, we conducted a narrative review of related studies to address the following questions as comprehensively as possible: 1) Does the improvement of cellular insulin function resulting from metformin have detrimental or beneficial effects on GBM cells? 2) If these effects are detrimental to GBM cells, which is more important: apoptosis or senescence? 3) What determines the cellular decision between apoptosis and senescence?

Repurposing Metformin for the Treatment of Atrial Fibrillation: Current Insights

Metformin is an orally effective anti-hyperglycemic drug that despite being introduced over 60 years ago is still utilized by an estimated 120 to 150 million people worldwide for the treatment of type 2 diabetes (T2D). Metformin is used off-label for the treatment of polycystic ovary syndrome (PCOS) and for pre-diabetes and weight loss. Metformin is a safe, inexpensive drug with side effects mostly limited to gastrointestinal issues. Prospective clinical data from the United Kingdom Prospective Diabetes Study (UKPDS), completed in 1998, demonstrated that metformin not only has excellent therapeutic efficacy as an anti-diabetes drug but also that good glycemic control reduced the risk of micro- and macro-vascular complications, especially in obese patients and thereby reduced the risk of diabetes-associated cardiovascular disease (CVD). Based on a long history of clinical use and an excellent safety record metformin has been investigated to be repurposed for numerous other diseases including as an anti-aging agent, Alzheimer's disease and other dementias, cancer, COVID-19 and also atrial fibrillation (AF). AF is the most frequently diagnosed cardiac arrythmia and its prevalence is increasing globally as the population ages. The argument for repurposing metformin for AF is based on a combination of retrospective clinical data and in vivo and in vitro pre-clinical laboratory studies. In this review, we critically evaluate the evidence that metformin has cardioprotective actions and assess whether the clinical and pre-clinical evidence support the use of metformin to reduce the risk and treat AF.

404-error "Disease not found": Unleashing the translational potential of -omics approaches beyond traditional disease classification in heart failure research

The emergence of personalized medicine, facilitated by the progress in -omics technologies, has initiated a new era in medical diagnostics and treatment. This review examines the potential of -omics approaches in heart failure, a condition that has not yet fully capitalized on personalized strategies compared to other medical fields like cancer therapy. Here, we argue that integrating multi-omics technology with systems medicine approaches could fundamentally transform heart failure management, moving away from the traditional paradigm of 'one size fits all'. Our review examines how omics can enhance understanding of heart failure's molecular foundations and contribute to a more comprehensive disease classification. We draw attention to the current state of medical practice that only relies on clinical evidence and a number of standard laboratory tests. At the same time, we propose a shift towards a universal approach that uses quantitative data from multi-omics to unravel complex molecular interactions. The discussion centres around the potential of the transition as a means to enhance individual risk assessment and emphasizes management within clinical settings. While the use of omics in cardiovascular research is not recent, many past studies have focused only on a single omics approach. In order to achieve a better understanding of disease mechanisms, we explore more holistic approaches using genomics, transcriptomics, epigenomics, and proteomics. This review concludes with a call to action to adopt multi-omics in clinical trials and practice to pave the way for more personalized disease management and more effective heart failure interventions.

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.

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.

Mapping novel QTL and fine mapping of previously identified QTL associated with glucose tolerance using the collaborative cross mice

A chronic metabolic illness, type 2 diabetes (T2D) is a polygenic and multifactorial complicated disease. With an estimated 463 million persons aged 20 to 79 having diabetes, the number is expected to rise to 700 million by 2045, creating a significant worldwide health burden. Polygenic variants of diabetes are influenced by environmental variables. T2D is regarded as a silent illness that can advance for years before being diagnosed. Finding genetic markers for T2D and metabolic syndrome in groups with similar environmental exposure is therefore essential to understanding the mechanism of such complex characteristic illnesses. So herein, we demonstrated the exclusive use of the collaborative cross (CC) mouse reference population to identify novel quantitative trait loci (QTL) and, subsequently, suggested genes associated with host glucose tolerance in response to a high-fat diet. In this study, we used 539 mice from 60 different CC lines. The diabetogenic effect in response to high-fat dietary challenge was measured by the three-hour intraperitoneal glucose tolerance test (IPGTT) test after 12 weeks of dietary challenge. Data analysis was performed using a statistical software package IBM SPSS Statistic 23. Afterward, blood glucose concentration at the specific and between different time points during the IPGTT assay and the total area under the curve (AUC0-180) of the glucose clearance was computed and utilized as a marker for the presence and severity of diabetes. The observed AUC0-180 averages for males and females were 51,267.5 and 36,537.5 mg/dL, respectively, representing a 1.4-fold difference in favor of females with lower AUC0-180 indicating adequate glucose clearance. The AUC0-180 mean differences between the sexes within each specific CC line varied widely within the CC population. A total of 46 QTL associated with the different studied phenotypes, designated as T2DSL and its number, for Type 2 Diabetes Specific Locus and its number, were identified during our study, among which 19 QTL were not previously mapped. The genomic interval of the remaining 27 QTL previously reported, were fine mapped in our study. The genomic positions of 40 of the mapped QTL overlapped (clustered) on 11 different peaks or close genomic positions, while the remaining 6 QTL were unique. Further, our study showed a complex pattern of haplotype effects of the founders, with the wild-derived strains (mainly PWK) playing a significant role in the increase of AUC values.

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.

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.

Pharmacokinetics in Pharmacometabolomics: Towards Personalized Medication

Indiscriminate drug administration may lead to drug therapy results with varying effects on patients, and the proposal of personalized medication can help patients to receive effective drug therapy. Conventional ways of personalized medication, such as pharmacogenomics and therapeutic drug monitoring (TDM), can only be implemented from a single perspective. The development of pharmacometabolomics provides a research method for the realization of precise drug administration, which integrates the environmental and genetic factors, and applies metabolomics technology to study how to predict different drug therapeutic responses of organisms based on baseline metabolic levels. The published research on pharmacometabolomics has achieved satisfactory results in predicting the pharmacokinetics, pharmacodynamics, and the discovery of biomarkers of drugs. Among them, the pharmacokinetics related to pharmacometabolomics are used to explore individual variability in drug metabolism from the level of metabolism of the drugs in vivo and the level of endogenous metabolite changes. By searching for relevant literature with the keyword "pharmacometabolomics" on the two major literature retrieval websites, PubMed and Web of Science, from 2006 to 2023, we reviewed articles in the field of pharmacometabolomics that incorporated pharmacokinetics into their research. This review explains the therapeutic effects of drugs on the body from the perspective of endogenous metabolites and pharmacokinetic principles, and reports the latest advances in pharmacometabolomics related to pharmacokinetics to provide research ideas and methods for advancing the implementation of personalized medication.

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.

Pharmacometabolomic Approach to Investigate the Response to Metformin in Patients with Type 2 Diabetes: A Cross-Sectional Study

Metformin constitutes the foundation therapy in type 2 diabetes (T2D). Despite its multiple beneficial effects and widespread use, there is considerable inter-individual variability in response to metformin. Our objective is to identify metabolic signatures associated with poor and good responses to metformin, which may improve our ability to predict outcomes for metformin treatment. In this cross-sectional study, clinical and metabolic data for 119 patients with type 2 diabetes taking metformin were collected from the Qatar Biobank. Patients were empirically dichotomized according to their HbA1C levels into good and poor responders. Differences in the level of metabolites between these two groups were compared using orthogonal partial least square discriminate analysis (OPLS-DA) and linear models. Good responders showed increased levels of sphingomyelins, acylcholines, and glutathione metabolites. On the other hand, poor responders showed increased levels of metabolites resulting from glucose metabolism and gut microbiota metabolites. The results of this study have the potential to increase our knowledge of patient response variability to metformin and carry significant implications for enabling personalized medicine.

Ataxia-telangiectasia clinical trial landscape and the obstacles to overcome

INTRODUCTION

Ataxia telangiectasia (A-T) is a life-limiting autosomal recessive disease characterized by cerebellar degeneration, ocular telangiectasias, and sinopulmonary disease. Since there is no cure for A-T, the standard of care is primarily supportive.

AREAS COVERED

We review clinical trials available in PubMed from 1990 to 2023 focused on lessening A-T disease burden. These approaches include genetic interventions, such as antisense oligonucleotides, designed to ameliorate disease progression in patients with select mutations. These approaches also include pharmacologic treatments that target oxidative stress, inflammation, and mitochondrial exhaustion, to attenuate neurological progression in A-T. Finally, we discuss the use of biological immunotherapies for the treatment of malignancies and granulomatous disease, along with other supportive therapies being used for the treatment of pulmonary disease and metabolic syndrome.

EXPERT OPINION

Barriers to successful genetic and pharmacologic interventions in A-T include the need for personalized treatment approaches based on patient-specific ATM mutations and phenotypes, lack of an animal model for the neurologic phenotype, and extreme rarity of disease making large-scale randomized trials difficult to perform. Ongoing efforts are needed to diagnose patients earlier, discover more effective therapies, and include more individuals in clinical trials, with the goal to lessen disease burden and to find a cure for patients with A-T.

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).

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.

A publication-wide association study (PWAS), historical language models to prioritise novel therapeutic drug targets

Most biomedical knowledge is published as text, making it challenging to analyse using traditional statistical methods. In contrast, machine-interpretable data primarily comes from structured property databases, which represent only a fraction of the knowledge present in the biomedical literature. Crucial insights and inferences can be drawn from these publications by the scientific community. We trained language models on literature from different time periods to evaluate their ranking of prospective gene-disease associations and protein-protein interactions. Using 28 distinct historical text corpora of abstracts published between 1995 and 2022, we trained independent Word2Vec models to prioritise associations that were likely to be reported in future years. This study demonstrates that biomedical knowledge can be encoded as word embeddings without the need for human labelling or supervision. Language models effectively capture drug discovery concepts such as clinical tractability, disease associations, and biochemical pathways. Additionally, these models can prioritise hypotheses years before their initial reporting. Our findings underscore the potential for extracting yet-to-be-discovered relationships through data-driven approaches, leading to generalised biomedical literature mining for potential therapeutic drug targets. The Publication-Wide Association Study (PWAS) enables the prioritisation of under-explored targets and provides a scalable system for accelerating early-stage target ranking, irrespective of the specific disease of interest.

Metformin and HER2-positive breast cancer: Mechanisms and therapeutic implications

Due to the strong association between diabetes and cancer incidents, several anti-diabetic drugs, including metformin, have been examined for their anticancer activity. Metformin is a biguanide antihyperglycemic agent used as a first-line drug for type II diabetes mellitus. It exhibits anticancer activity by impacting different molecular pathways, such as AMP-inducible protein kinase (AMPK)-dependent and AMPK-independent pathways. Additionally, Metformin indirectly inhibits IGF-1R signaling, which is highly activated in breast malignancy. On the other hand, breast cancer is one of the major causes of cancer-related morbidity and mortality worldwide, where the human epidermal growth factor receptor-positive (HER2-positive) subtype is one of the most aggressive ones with a high rate of lymph node metastasis. In this review, we summarize the association between diabetes and human cancer, listing recent evidence of metformin's anticancer activity. A special focus is dedicated to HER2-positive breast cancer with regards to the interaction between HER2 and IGF-1R. Then, we discuss combination therapy strategies of metformin and other anti-diabetic drugs in HER2-positive breast cancer.

A Triphenylphosphonium-Functionalized Delivery System for an ATM Kinase Inhibitor That Ameliorates Doxorubicin Resistance in Breast Carcinoma Mammospheres

The enzyme ataxia-telangiectasia mutated (ATM) kinase is a pluripotent signaling mediator which activates cellular responses to genotoxic and metabolic stress. It has been shown that ATM enables the growth of mammalian adenocarcinoma stem cells, and therefore the potential benefits in cancer chemotherapy of a number of ATM inhibitors, such as KU-55933 (KU), are currently being investigated. We assayed the effects of utilizing a triphenylphosphonium-functionalized nanocarrier delivery system for KU on breast cancer cells grown either as a monolayer or in three-dimensional mammospheres. We observed that the encapsulated KU was effective against chemotherapy-resistant mammospheres of breast cancer cells, while having comparably lower cytotoxicity against adherent cells grown as monolayers. We also noted that the encapsulated KU sensitized the mammospheres to the anthracycline drug doxorubicin significantly, while having only a weak effect on adherent breast cancer cells. Our results suggest that triphenylphosphonium-functionalized drug delivery systems that contain encapsulated KU, or compounds with a similar impact, are a useful addition to chemotherapeutic treatment schemes that target proliferating cancers.

Neoadjuvant docetaxel, epirubicin, and cyclophosphamide with or without metformin in breast cancer patients with metabolic abnormality: results from the randomized Phase II NeoMET trial

PURPOSE

Breast cancer patients with metabolic syndrome (MetS) and its components show worse treatment responses to chemotherapy. Metformin is a widely used antidiabetic drug which also shows potential anticancer effect. This study aims to evaluate the efficacy, safety, and metabolic parameters change of metformin combined with docetaxel, epirubicin, and cyclophosphamide (TEC) in neoadjuvant treatment (NAT) for breast cancer patients with metabolic abnormality.

METHODS

Eligible breast cancer patients were randomized to receive six cycles of TEC (docetaxel 75 mg/m², epirubicin 75 mg/m², and cyclophosphamide 500 mg/m², d1, q3w) or TEC with metformin (TECM, TEC with oral metformin 850 mg once daily for the first cycle, then 850 mg twice daily for the following cycles). The primary end point was total pathological complete response (tpCR, ypTis/0N0) rate.

RESULTS

Ninety-two patients were enrolled and randomized from October 2013 to December 2019: 88 patients were available for response and safety assessment. The tpCR rates were 12.5% (5/40) and 14.6% (7/48) in the TEC and TECM groups, respectively (P = 0.777). There was no difference in Ki67 decrease after NAT between two groups (P = 0.456). Toxicity profile were similar between two groups. No grade 3 or higher diarrhea were recorded. Total cholesterol (TC) and high-density lipoprotein cholesterol worsened after NAT in the TEC arm but remained stable in the TECM arm. The absolute increase of TC and low-density lipoprotein cholesterol (LDL-C) was significantly lower in the TECM group compared with the TEC group. After a median follow-up of 40.8 (4.7-70.8) months, no survival difference was observed between TEC and TECM groups (all P > 0.05).

CONCLUSION

Adding metformin to TEC didn't increase pCR rate and disease outcome in breast cancer patients with metabolic abnormality. However, additional metformin treatment with chemotherapy would prevent TC and LDL-C increase after NAT. Trial Registration ClinicalTrials.gov Identifier: NCT01929811.

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.

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.

Association of SLC22A1, SLC22A2, SLC47A1, and SLC47A2 Polymorphisms with Metformin Efficacy in Type 2 Diabetic Patients

Response to metformin, first-line therapy for type 2 diabetes mellitus (T2DM), exists interindividual variation. Considering that transporters belonging to the solute carrier (SLC) superfamily are determinants of metformin pharmacokinetics, we evaluated the effects of promoter variants in organic cation transporter 1 (OCT1) (SLC22A1 rs628031), OCT2 (SLC22A2 rs316019), multidrug and toxin extrusion protein 1 (MATE1) (SLC47A1 rs2289669), and MATE2 (SLC47A2 rs12943590) on the variation in metformin response. The glucose-lowering effects and improvement of insulin resistance of metformin were assessed in newly diagnosed, treatment-naive type 2 diabetic patients of Han nationality in Chaoshan China (n = 93) receiving metformin. Fasting plasma glucose (FPG), fasting insulin (FINS), glycated hemoglobin A1 (HbA1_C), homeostasis model assessment-insulin sensitivity (HOMA-IS), and homeostasis model assessment-insulin resistance (HOMA-IR) were the main metformin efficacy measurements. There were significant correlations between both SLC47A1 rs2289669 and SLC47A2 rs12943590 and the efficacy of metformin in individuals with T2DM. In normal weight T2DM patients, significant associations between the AA and GG genotypes of the rs2289669 variant of SLC47A1 and a greater reduction in FINS and HOMA-IR were detected. A significant correlation was observed between the AG genotype of the rs12943590 polymorphism of SLC47A2 and a greater reduction in HOMA-IR. Gene-environment interaction analysis showed that in the FINS interaction model, the second-order of dose30_g-SLC47A2 rs12943590 was statistically significant. The variants of SLC47A1 rs2289669 and SLC47A2 rs12943590 could be predictors of insulin resistance in type 2 diabetic patients treated with metformin. The second-order interaction of dose30_g-SLC47A2 rs12943590 may have a significant effect on FINS in patients with T2DM on metformin treatment. These findings suggest that promoter variants of SLC47A1 and SLC47A2 are important determinants of metformin transport and response in type 2 diabetes mellitus.

Ethnic, gender and other sociodemographic biases in genome-wide association studies for the most burdensome non-communicable diseases: 2005-2022

INTRODUCTION

Since 2005, disease-related human genetic diversity has been intensively characterized using genome-wide association studies (GWAS). Understanding how and by whom this work was performed may yield valuable insights into the generalizability of GWAS discoveries to global populations and how high-impact genetics research can be equitably sustained in the future.

MATERIALS AND METHODS

We mined the NHGRI-EBI GWAS Catalog (2005-2022) for the most burdensome non-communicable causes of death worldwide. We then compared (i) the geographic, ethnic and socioeconomic characteristics of study populations; (ii) the geographic and socioeconomic characteristics of the regions within which researchers were located and (iii) the extent to which male and female investigators undertook and led the research.

RESULTS

The research institutions leading the work are often US-based (37%), while the origin of samples is more diverse, with the Nordic countries having contributed as much data to GWAS as the United States (~17% of data). The majority of first (60%), senior (75%) and all (66%) authors are male; although proportions vary by disease and leadership level, male co-authors are the ubiquitous majority. The vast majority (91%) of complex trait GWAS has been performed in European ancestry populations, with cohorts and scientists predominantly located in medium-to-high socioeconomically ranked countries; apart from East Asians (~5%), other ethnicities rarely feature in published GWAS. See: https://hugofitipaldi.shinyapps.io/gwas_results/ to browse all results.

CONCLUSION

Most GWAS cohorts are of European ancestry residing outside the United States, with a smaller yet meaningful proportion of East Asian ancestry. Papers describing GWAS research are predominantly authored by male scientists based in medium-to-high income countries.

Effects of single-nucleotide polymorphism on the pharmacokinetics and pharmacodynamics of metformin

INTRODUCTION

Metformin has been recognized as the first-choice drug for type 2 diabetes mellitus (T2DM). The potency of metformin in the treatment of type 2 diabetes has always been in the spotlight and shown significant individual differences. Based on previous studies, the efficacy of metformin is related to the single-nucleotide polymorphisms of transporter genes carried by patients, amongst which a variety of gene polymorphisms of transporter and target protein genes affect the effectiveness and adverse repercussion of metformin.

AREAS COVERED

Here, we reviewed the current knowledge about gene polymorphisms impacting metformin efficacy based on transporter and drug target proteins.

EXPERT OPINION

The reason for the difference in clinical drug potency of metformin can be attributed to the gene polymorphism of drug transporters and drug target proteins in the human body. Substantial evidence shows that genetic polymorphisms in transporters such as organic cation transporter 1 (OCT1) and organic cation transporter 2 (OCT2) affect the glucose-lowering effectiveness of metformin. However, optimization of individualized dosing regimens of metformin is necessary to clarify the role of several polymorphisms.

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.

Autophagy-nutrient sensing pathways in diabetic complications

The incidence of diabetes has been increasing in recent decades which is affecting the population of both, developed and developing countries. Diabetes is associated with micro and macrovascular complications which predominantly result from hyperglycemia and disrupted metabolic pathways. Persistent hyperglycemia leads to increased reactive oxygen species (ROS) generation, formation of misfolded and abnormal proteins, and disruption of normal cellular functioning. The inability to maintain metabolic homeostasis under excessive energy and nutrient input, which induces insulin resistance, is a crucial feature during the transition from obesity to diabetes. According to various study reports, redox alterations, intracellular stress and chronic inflammation responses have all been linked to dysregulated energy metabolism and insulin resistance. Autophagy has been considered a cleansing mechanism to prevent these anomalies and restore cellular homeostasis. However, disrupted autophagy has been linked to the pathogenesis of metabolic disorders such as obesity and diabetes. Recent studies have reported that the regulation of autophagy has a beneficial role against these conditions. When there is plenty of food, nutrient-sensing pathways activate anabolism and storage, but the shortage of food activates homeostatic mechanisms like autophagy, which mobilises internal stockpiles. These nutrient-sensing pathways are well conserved in eukaryotes and are involved in the regulation of autophagy which includes SIRT1, mTOR and AMPK. The current review focuses on the role of SIRT1, mTOR and AMPK in regulating autophagy and suggests autophagy along with these nutrient-sensing pathways as potential therapeutic targets in reducing the progression of various diabetic complications.

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.

Role of human organic cation transporter-1 (OCT-1/SLC22A1) in modulating the response to metformin in patients with type 2 diabetes

BACKGROUND

Organic cation transporter 1 primarily governs the action of metformin in the liver. There are considerable inter-individual variations in metformin response. In light of this, it is crucial to obtain a greater understanding of the influence of OCT1 expression or polymorphism in the context of variable responses elicited by metformin treatment.

RESULTS

We observed that the variable response to metformin in the responders and non-responders is independent of isoform variation and mRNA expression of OCT-1. We also observed an insignificant difference in the serum metformin levels of the patient groups. Further, molecular docking provided us with an insight into the hotspot regions of OCT-1 for metformin binding. Genotyping of these regions revealed SNPs 156T>C and 1222A>G in both the groups, while as 181C>T and 1201G>A were found only in non-responders. The 181T>C and 1222A>G changes were further found to alter OCT-1 structure in silico and affect metformin transport in vitro which was illustrated by their effect on the activation of AMPK, the marker for metformin activity.

CONCLUSION

Taken together, our results corroborate the role of OCT-1 in the transport of metformin and also point at OCT1 genetic variations possibly affecting the transport of metformin into the cells and hence its subsequent action in responders and non-responders.

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.

Association of SLC22A1 rs622342 and ATM rs11212617 polymorphisms with metformin efficacy in patients with type 2 diabetes

Metformin is the first-choice oral anti-hyperglycemic drug for type 2 diabetes mellitus (T2DM) patients. There are controversies about the association of SLC22A1 rs622342, which was not reported in the Chinese population, and ataxia-telangiectasia mutated (ATM) rs11212617 polymorphisms with metformin efficacy in T2DM. Our study was to investigate the effects of the two single nucleotide polymorphisms on the efficacy of metformin in T2DM of Han nationality in Chaoshan China. After enrollment, 82 newly diagnosed T2DM patients went on 2-month metformin monotherapy. According to BMI before treatment, the patients were divided into a normal weight group (≥18.5 and <25 kg/m2) and an overweight group (BMI ≥ 25 and <30 kg/m2). T-test, Pearson χ2 test, and regression analysis, which adjusted for age, BMI, sex, the dose of metformin, education, tea drink, smoking, and sweet, were used to evaluate the effects of rs622342 and rs11212617 on several variables, such as fasting plasma glucose (FPG). Compared with the AA or CC genotype, patients with AC genotype of rs622342 achieved greater reduction in Δ60FPG and Δ(60-30)FPG (P = 0.00820, 0.00089, respectively). For 11212617, the reduction in Δ30FPG and Δ60FPG was significantly different among patients with the AC genotype (P = 0.00026, 0.00820, respectively). Our results indicated that common variants of SLC22A1 rs622342 and ATM rs11212617 were associated with the efficacy of metformin in T2DM of Han nationality in Chaoshan China.

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.