92 research outputs found
Precision Medicine in Diabetes
No full textTailoring 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 in diabetes mellitus:insights into drug action and drug discovery
No full textGenomic studies have greatly advanced our understanding of the multifactorial aetiology of type 2 diabetes mellitus (T2DM) as well as the multiple subtypes of monogenic diabetes mellitus. In this Review, we discuss the existing pharmacogenetic evidence in both monogenic diabetes mellitus and T2DM. We highlight mechanistic insights from the study of adverse effects and the efficacy of antidiabetic drugs. The identification of extreme sulfonylurea sensitivity in patients with diabetes mellitus owing to heterozygous mutations in HNF1A represents a clear example of how pharmacogenetics can direct patient care. However, pharmacogenomic studies of response to antidiabetic drugs in T2DM has yet to be translated into clinical practice, although some moderate genetic effects have now been described that merit follow-up in trials in which patients are selected according to genotype. We also discuss how future pharmacogenomic findings could provide insights into treatment response in diabetes mellitus that, in addition to other areas of human genetics, facilitates drug discovery and drug development for T2DM.</p
Utilizing Large Electronic Medical Record Data Sets to Identify Novel Drug–Gene Interactions for Commonly Used Drugs
No full textReal-world prescribing of drugs differs from the experimental systems, physiological-pharmacokinetic models, and clinical trials used in drug development and licensing, with drugs often used in patients with multiple comorbidities with resultant polypharmacy. The increasing availability of large biobanks linked to electronic healthcare records enables the potential to identify novel drug–gene interactions in large populations of patients. In this study we used three Scottish cohorts and UK Biobank to identify drug–gene interactions for the 50 most commonly used drugs and 162 variants in genes involved in drug pharmacokinetics. We defined two phenotypes based upon prescribing behavior—drug-stop or dose-decrease. Using this approach, we replicate 11 known drug–gene interactions including, for example, CYP2C9/CYP2C8 variants and sulfonylurea/thiazolidinedione prescribing and ABCB1/ABCG2 variants and statin prescribing. We identify eight novel associations after Bonferroni correction, three of which are replicated or validated in the UK Biobank or have other supporting results: The C-allele at rs4918758 in CYP2C9 was associated with a 25% (15–44%) lower odds of dose reduction of quinine, P = 1.6 × 10 −5; the A-allele at rs9895420 in ABCC3 was associated with a 46% (24–62%) reduction in odds of dose reduction with doxazosin, P = 1.2 × 10 −4, and altered blood pressure response in the UK Biobank; the CYP2D6*2 variant was associated with a 30% (18–40%) reduction in odds of stopping ramipril treatment, P = 1.01 × 10 −5, with similar results seen for enalapril and lisinopril and with other CYP2D6 variants. This study highlights the scope of using large population bioresources linked to medical record data to explore drug–gene interactions at scale. </p
Weight variability and cardiovascular outcomes: a systematic review and meta-analysis
No full textThe association between body weight variability and the risk of cardiovascular disease (CVD) has been investigated previously with mixed findings. However, there has been no extensive study which systematically evaluates the current evidence. Furthermore, the impact of ethnicity and type 2 diabetes on this phenomena has not yet been investigated. Therefore, the aim of this study was to comprehensively evaluate the effect of weight variability on risk of CVD (any cardiovascular (CV) event, composite CV outcome, CV death, Stroke, Myocardial Infarction) and the influence of ethnicity and type 2 diabetes status on the observed association. A systematic review and meta-analysis was performed according to the meta-analyses of observational studies in epidemiology (MOOSE) guidelines. The electronic databases PubMed, Web of Science, and the Cochrane Library were searched for studies that investigated the relationship between body weight or BMI variability and CV diseases using Medical Subject Headings (MeSH) terms and keywords. The relative risks (RRs) for the outcomes were collected from studies, pooled, and analysed using a random-effects model to estimate the overall relative risk. Of 5645 articles screened, 23 studies with a total population of 15,382,537 fulfilled the prespecified criteria and were included. Individuals in the highest strata of body weight variability were found to have significantly increased risk of any CV event (RR = 1.27; 95% Confidence Interval (CI) 1.17–1.38; P < 0.0001; I(2) = 97.28%), cardiovascular death (RR = 1.29; 95% CI 1.03–1.60; P < 0.0001; I(2) = 55.16%), myocardial infarction (RR = 1.32; 95% CI 1.09–1.59; P = 0.0037; I(2) = 97.14%), stroke (RR = 1.21; 95% CI 1.19–1.24; P < 0.0001; I(2) = 0.06%), and compound CVD outcomes (RR = 1.36; 95% CI 1.08–1.73; P = 0.01; I(2) = 92.41%). Similar RRs were observed regarding BMI variability and per unit standard deviation (SD) increase in body weight variability. Comparable effects were seen in people with and without diabetes, in White Europeans and Asians. In conclusion, body weight variability is associated with increased risk of CV diseases regardless of ethnicity or diabetes status. Future research is needed to prove a causative link between weight variability and CVD risk, as appropriate interventions to maintain stable weight could positively influence CVD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12933-022-01735-x
The Genetics of Adverse Drug Outcomes in Type 2 Diabetes:A Systematic Review
No full textBackground: Adverse drug reactions (ADR) are a major clinical problem accounting for significant hospital admission rates, morbidity, mortality, and health care costs. One-third of people with diabetes experience at least one ADR. However, there is notable interindividual heterogeneity resulting in patient harm and unnecessary medical costs. Genomics is at the forefront of research to understand interindividual variability, and there are many genotype-drug response associations in diabetes with inconsistent findings. Here, we conducted a systematic review to comprehensively examine and synthesize the effect of genetic polymorphisms on the incidence of ADRs of oral glucose-lowering drugs in people with type 2 diabetes. Methods: A literature search was made to identify articles that included specific results of research on genetic polymorphism and adverse effects associated with oral glucose-lowering drugs. The electronic search was carried out on 3rd October 2020, through Cochrane Library, PubMed, and Web of Science using keywords and MeSH terms. Result: Eighteen articles consisting of 10, 383 subjects were included in this review. Carriers of reduced-function alleles of organic cation transporter 1 (OCT 1, encoded by SLC22A1) or reduced expression alleles of plasma membrane monoamine transporter (PMAT, encoded by SLC29A4) or serotonin transporter (SERT, encoded by SLC6A4) were associated with increased incidence of metformin-related gastrointestinal (GI) adverse effects. These effects were shown to exacerbate by concomitant treatment with gut transporter inhibiting drugs. The CYP2C9 alleles, * 2 (rs1799853C>T) and * 3 (rs1057910A>C) that are predictive of low enzyme activity were more common in subjects who experienced hypoglycemia after treatment with sulfonylureas. However, there was no significant association between sulfonylurea-related hypoglycemia and genetic variants in the ATP-binding cassette transporter sub-family C member 8 (ABCC8)/Potassium Inwardly Rectifying Channel Subfamily J Member 11 (KCNJ11). Compared to the wild type, the low enzyme activity C allele at CYP2C8* 3 (rs1057910A>C) was associated with less weight gain whereas the C allele at rs6123045 in the NFATC2 gene was significantly associated with edema from rosiglitazone treatment. Conclusion: In spite of limited studies investigating genetics and ADR in diabetes, some convincing results are emerging. Genetic variants in genes encoding drug transporters and metabolizing enzymes are implicated in metformin-related GI adverse effects, and sulfonylurea-induced hypoglycemia, respectively. Further studies to investigate newer antidiabetic drugs such as DPP-4i, GLP-1RA, and SGLT2i are warranted. In addition, pharmacogenetic studies that account for race and ethnic differences are required.</p
Evidence-based prioritisation and enrichment of genes interacting with metformin in type 2 diabetes [Elektronisk resurs]
No full textAims/hypothesis: There is an extensive body of literature suggesting the involvement of multiple loci in regulating the action of metformin; most findings lack replication, without which distinguishing true-positive from false-positive findings is difficult. To address this, we undertook evidence-based, multiple data integration to determine the validity of published evidence. Methods: We (1) built a database of published data on gene–metformin interactions using an automated text-mining approach (n = 5963 publications), (2) generated evidence scores for each reported locus, (3) from which a rank-ordered gene set was generated, and (4) determined the extent to which this gene set was enriched for glycaemic response through replication analyses in a well-powered independent genome-wide association study (GWAS) dataset from the Genetics of Diabetes and Audit Research Tayside Study (GoDARTS). Results: From the literature search, seven genes were identified that are related to the clinical outcomes of metformin. Fifteen genes were linked with either metformin pharmacokinetics or pharmacodynamics, and the expression profiles of a further 51 genes were found to be responsive to metformin. Gene-set enrichment analysis consisting of the three sets and two more composite sets derived from the above three showed no significant enrichment in four of the gene sets. However, we detected significant enrichment of genes in the least prioritised category (a gene set in which their expression is affected by metformin) with glycaemic response to metformin (p = 0.03). This gene set includes novel candidate genes such as SLC2A4 (p = 3.24 × 10−04) and G6PC (p = 4.77 × 10−04). Conclusions/interpretation: We have described a semi-automated text-mining and evidence-scoring algorithm that facilitates the organisation and extraction of useful information about gene–drug interactions. We further validated the output of this algorithm in a drug-response GWAS dataset, providing novel candidate loci for gene–metformin interactions
Additional file 1 of Weight variability and cardiovascular outcomes: a systematic review and meta-analysis
No full textAdditional file 1: Appendix S1. Search Strategies. Appendix S2. Description of Emails. Appendix S3. List of Inclusion and Exclusion Criteria. Table S1. Descriptions and Definitions of Weight Variability Metrics. Table S2. Table of Study Characteristics. Table S3. Additional Table of Study Characteristics. Figure S1. Results of Per +1 SD in Weight Variability Analysis. Figure S2. Results of Degree of BMI Variability Analysis. Figure S3. Results of Ethnicity Stratification. Figure S4. Results of Diabetes Status Stratification. Figure S5. Results of Metric of Variability Stratification. Figure S6. Results of Quantile Stratification. Figure S7. Results of Previous Cardiovascular Disease Stratification. Figure S8. Results of adjustment for change in BMI or average BMI stratification. Figure S9. Results of Univariate Meta-Regression by Age. Figure S10. Egger’s Regression and Funnel Plots. Figure S11. Results of Newcastle-Ottawa Bias Analysis. Appendix S4. MOOSE Checklist. Table S4. Newcastle – Ottawa Scale Quality Assessment Results. Appendix S5. Newcastle-Ottawa Quality Assessment Scale
Genomic editing of metformin efficacy-associated genetic variants in<i> SLC47A1</i> does not alter <i>SLC47A1</i> expression
No full textSeveral pharmacogenetics studies have identified an association between a greater metformindependent reduction in HbA1c levels and the minor A allele at rs2289669 in intron 10 of SLC47A1, encoding multidrug and toxin extrusion 1 (MATE1), a presumed metformin transporter. It is currently unknown if the rs2289669 locus is a cis-eQTL, which would validate its role as predictor of metformin efficacy. We looked at association between common genetic variants in the SLC47A1 gene region and HbA1c reduction after metformin treatment using locus-wise meta-analysis from the MetGen consortium. CRISPR-Cas9 was applied to perform allele editing of, or genomic deletion around, rs2289669 and of the closely linked rs8065082 in HepG2 cells. The genome-edited cells were evaluated for SLC47A1 expression and splicing. None of the common variants including rs2289669 showed significant association with metformin response. Genomic editing of either rs2289669 or rs8065082 did not alter SLC47A1 expression or splicing. Experimental and in silico analyses show that the rs2289669-containing haploblock does not appear to carry genetic variants that could explain its previously reported association with metformin efficacy
Evidence-based prioritisation and enrichment of genes interacting with metformin in type 2 diabetes
No full textAims/hypothesis: There is an extensive body of literature suggesting the involvement of multiple loci in regulating the action of metformin; most findings lack replication, without which distinguishing true-positive from false-positive findings is difficult. To address this, we undertook evidence-based, multiple data integration to determine the validity of published evidence. Methods: We (1) built a database of published data on gene–metformin interactions using an automated text-mining approach (n = 5963 publications), (2) generated evidence scores for each reported locus, (3) from which a rank-ordered gene set was generated, and (4) determined the extent to which this gene set was enriched for glycaemic response through replication analyses in a well-powered independent genome-wide association study (GWAS) dataset from the Genetics of Diabetes and Audit Research Tayside Study (GoDARTS). Results: From the literature search, seven genes were identified that are related to the clinical outcomes of metformin. Fifteen genes were linked with either metformin pharmacokinetics or pharmacodynamics, and the expression profiles of a further 51 genes were found to be responsive to metformin. Gene-set enrichment analysis consisting of the three sets and two more composite sets derived from the above three showed no significant enrichment in four of the gene sets. However, we detected significant enrichment of genes in the least prioritised category (a gene set in which their expression is affected by metformin) with glycaemic response to metformin (p = 0.03). This gene set includes novel candidate genes such as SLC2A4 (p = 3.24 × 10−04) and G6PC (p = 4.77 × 10−04). Conclusions/interpretation: We have described a semi-automated text-mining and evidence-scoring algorithm that facilitates the organisation and extraction of useful information about gene–drug interactions. We further validated the output of this algorithm in a drug-response GWAS dataset, providing novel candidate loci for gene–metformin interactions
CYP2C8 and SLCO1B1 variants and therapeutic response to thiazolidinediones in patients with type 2 diabetes.
No full textObjective: Thiazolidinediones (TZDs) are putatively transported into the liver by OATP1B1 (encoded by SLCO1B1) and metabolized by CYP450 2C8 enzyme (encoded by CYP2C8). Whilst CYP2C8*3 has been shown to alter TZD pharmacokinetics, it has not been shown to alter efficacy.Design: We genotyped 833 Scottish Type 2 diabetes patients treated with pioglitazone or rosiglitazone and jointly investigated association of variants in these two genes with therapeutic outcome.Result: The CYP2C8*3 variant was associated with reduced glycaemic response to rosiglitazone (P = 0.01) and less weight gain (P = 0.02). The SLCO1B1 521T>C variant was associated with enhanced glycaemic response to rosiglitazone (P = 0.04). The super responders defined by combined genotypes at CYP2C8 and SLCO1B1 had a 0.39% (4 mmol/mol) greater HbA1c reduction (P = 0.006) than the poor responders. Neither of the variants had a significant impact on pioglitazone response.Conclusion: These results show that variants in CYP2C8 and SLCO1B1 have a large clinical impact on the therapeutic response to rosiglitazone, and highlight the importance of studying transporter and metabolising genes together in pharmacogenetics
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