Genomic editing of metformin efficacy-associated genetic variants in SLC47A1 does not alter SLC47A1 expression.

Several pharmacogenetics studies have identified an association between a greater metformin-dependent reduction in HbA1c levels and the minor A allele at rs2289669 in intron 10 of SLC47A1, encoding multidrug and toxin extrusion 1 (MATE1), a presumed metformin transporter. It is currently unknown if the rs2289669 locus is a cis-eQTL, which would validate its role as predictor of metformin efficacy. We looked at association between common genetic variants in the SLC47A1 gene region and HbA1c reduction after metformin treatment using locus-wise meta-analysis from the MetGen consortium. CRISPR-Cas9 was applied to perform allele editing of, or genomic deletion around, rs2289669 and of the closely linked rs8065082 in HepG2 cells. The genome-edited cells were evaluated for SLC47A1 expression and splicing. None of the common variants including rs2289669 showed significant association with metformin response. Genomic editing of either rs2289669 or rs8065082 did not alter SLC47A1 expression or splicing. Experimental and in silico analyses show that the rs2289669-containing haploblock does not appear to carry genetic variants that could explain its previously reported association with metformin efficacy.

BMI variability and cardiovascular outcomes within clinical trial and real-world environments in type 2 diabetes: an IMI2 SOPHIA study.

BACKGROUND: BMI variability has been associated with increased cardiovascular disease risk in individuals with type 2 diabetes, however comparison between clinical studies and real-world observational evidence has been lacking. Furthermore, it is not known whether BMI variability has an effect independent of HbA1c variability. METHODS: We investigated the association between BMI variability and 3P-MACE risk in the Harmony Outcomes trial (n = 9198), and further analysed placebo arms of REWIND (n = 4440) and EMPA-REG OUTCOME (n = 2333) trials, followed by real-world data from the Tayside Bioresource (n = 6980) using Cox regression modelling. BMI variability was determined using average successive variability (ASV), with first major adverse cardiovascular event of non-fatal stroke, non-fatal myocardial infarction, and cardiovascular death (3P-MACE) as the primary outcome. RESULTS: After adjusting for cardiovascular risk factors, a + 1 SD increase in BMI variability was associated with increased 3P-MACE risk in Harmony Outcomes (HR 1.12, 95% CI 1.08-1.17, P < 0.001). The most variable quartile of participants experienced an 87% higher risk of 3P-MACE (P < 0.001) relative to the least variable. Similar associations were found in REWIND and Tayside Bioresource. Further analyses in the EMPA-REG OUTCOME trial did not replicate this association. BMI variability's impact on 3P-MACE risk was independent of HbA1c variability. CONCLUSIONS: In individuals with type 2 diabetes, increased BMI variability was found to be an independent risk factor for 3P-MACE across cardiovascular outcome trials and real-world datasets. Future research should attempt to establish a causal relationship between BMI variability and cardiovascular outcomes.

Weight variability and cardiovascular outcomes: a systematic review and meta-analysis.

The 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; I2 = 97.28%), cardiovascular death (RR = 1.29; 95% CI 1.03-1.60; P < 0.0001; I2 = 55.16%), myocardial infarction (RR = 1.32; 95% CI 1.09-1.59; P = 0.0037; I2 = 97.14%), stroke (RR = 1.21; 95% CI 1.19-1.24; P < 0.0001; I2 = 0.06%), and compound CVD outcomes (RR = 1.36; 95% CI 1.08-1.73; P = 0.01; I2 = 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.

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 ß-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 ß-cell pPS with reduced ß-cell function over time. Our data show initial evidence for genetic variation associated with metformin response in youth with T2D.

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.

Diabetes status modifies the long-term effect of lipoprotein-associated phospholipase A2 on major coronary events.

AIMS/HYPOTHESIS: Lipoprotein-associated phospholipase A2 (Lp-PLA2) activity has an independent prognostic association with major coronary events (MCE). However, no study has investigated whether type 2 diabetes status modifies the effect of Lp-PLA2 activity or inhibition on the risk of MCE. We investigate the interaction between diabetes status and Lp-PLA2 activity with risk of MCE. Subsequently, we test the resulting hypothesis that diabetes status will play a role in modifying the efficacy of an Lp-PLA2 inhibitor. METHODS: A retrospective cohort study design was utilised in two study populations. Discovery analyses were performed in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) cohort based in Scotland, UK. Participants were categorised by type 2 diabetes control status: poorly controlled (HbA1c ≥ 48 mmol/mol or ≥6.5%) and well-controlled (HbA1c < 48 mmol/mol or <6.5%) diabetes (n = 7420). In a secondary analysis of the Stabilization of Atherosclerotic Plaque by Initiation of Darapladib Therapy (STABILITY) trial of Lp-PLA2 inhibitor (darapladib) efficacy, 15,828 participants were stratified post hoc by type 2 diabetes diagnosis status (diabetes or no diabetes) at time of recruitment. Lp-PLA2 activity was then divided into population-specific quartiles. MCE were determined from linked medical records in GoDARTS and trial records in STABILITY. First, the interaction between diabetes control status and Lp-PLA2 activity on the outcome of MCE was explored in GoDARTS. The effect was replicated in the placebo arm of STABILITY. The effect of Lp-PLA2 on MCE was then examined in models stratified by diabetes status. This helped determine participants at higher risk. Finally, the effect of Lp-PLA2 inhibition was assessed in STABILITY in the higher risk group. Cox proportional hazards models adjusted for confounders were used to assess associations. RESULTS: In GoDARTS, a significant interaction between increased Lp-PLA2 activity (continuous and quartile divided) and diabetes control status was observed in the prediction of MCE (p < 0.0001). These effects were replicated in the placebo arm of STABILITY (p < 0.0001). In GoDARTS, stratified analyses showed that, among individuals with poorly controlled diabetes, the hazards of MCE for those with high (Q4) Lp-PLA2 activity was 1.19 compared with individuals with lower (Q1-3) Lp-PLA2 activity (95% CI 1.11, 1.38; p < 0.0001) and 1.35 (95% CI 1.16, 1.57; p < 0.0001) when compared with those with the lowest activity (Q1). Those in the higher risk group were identified as individuals with the highest Lp-PLA2 activity (Q4) and poorly controlled diabetes or diabetes. Based on these observations in untreated populations, we hypothesised that the Lp-PLA2 inhibitor would have more benefit in this higher risk group. In this risk group, Lp-PLA2 inhibitor use was associated with a 33% reduction in MCE compared with placebo (HR 0.67 [95% CI 0.50, 0.90]; p = 0.008). In contrast, Lp-PLA2 inhibitor showed no efficacy in individuals with low activity, regardless of diabetes status, or among those with no baseline diabetes and high Lp-PLA2 activity. CONCLUSIONS/INTERPRETATION: These results support the hypothesis that diabetes status modifies the association between Lp-PLA2 activity and MCE. These results suggest that cardiovascular morbidity and mortality associated with Lp-PLA2 activity is especially important in patients with type 2 diabetes, particularly those with worse glycaemic control. Further investigation of the effects of Lp-PLA2 inhibition in diabetes appears warranted. DATA AVAILABILITY: STABILITY trial data are available from clinicaltrials.gov repository through the GlaxoSmithKline clinical study register https://clinicaltrials.gov/ct2/show/NCT00799903 . GoDARTS datasets generated during and/or analysed during the current study are available following request to the GoDARTS Access Managements Group https://godarts.org/scientific-community/ .

Young-onset diabetes in Asian Indians is associated with lower measured and genetically determined beta cell function.

AIMS/HYPOTHESIS: South Asians in general, and Asian Indians in particular, have higher risk of type 2 diabetes compared with white Europeans, and a younger age of onset. The reasons for the younger age of onset in relation to obesity, beta cell function and insulin sensitivity are under-explored. METHODS: Two cohorts of Asian Indians, the ICMR-INDIAB cohort (Indian Council of Medical Research-India Diabetes Study) and the DMDSC cohort (Dr Mohan's Diabetes Specialties Centre), and one of white Europeans, the ESDC (East Scotland Diabetes Cohort), were used. Using a cross-sectional design, we examined the comparative prevalence of healthy, overweight and obese participants with young-onset diabetes, classified according to their BMI. We explored the role of clinically measured beta cell function in diabetes onset in Asian Indians. Finally, the comparative distribution of a partitioned polygenic score (pPS) for risk of diabetes due to poor beta cell function was examined. Replication of the genetic findings was sought using data from the UK Biobank. RESULTS: The prevalence of young-onset diabetes with normal BMI was 9.3% amongst white Europeans and 24-39% amongst Asian Indians. In Asian Indians with young-onset diabetes, after adjustment for family history of type 2 diabetes, sex, insulin sensitivity and HDL-cholesterol, stimulated C-peptide was 492 pmol/ml (IQR 353-616, p<0.0001) lower in lean compared with obese individuals. Asian Indians in our study, and South Asians from the UK Biobank, had a higher number of risk alleles than white Europeans. After weighting the pPS for beta cell function, Asian Indians have lower genetically determined beta cell function than white Europeans (p<0.0001). The pPS was associated with age of diagnosis in Asian Indians but not in white Europeans. The pPS explained 2% of the variation in clinically measured beta cell function, and 1.2%, 0.97%, and 0.36% of variance in age of diabetes amongst Asian Indians with normal BMI, or classified as overweight and obese BMI, respectively. CONCLUSIONS/INTERPRETATION: The prevalence of lean BMI in young-onset diabetes is over two times higher in Asian Indians compared with white Europeans. This phenotype of lean, young-onset diabetes appears driven in part by lower beta cell function. We demonstrate that Asian Indians with diabetes also have lower genetically determined beta cell function.

Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. METHODS AND FINDINGS: We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (<5% or ≥5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86; p < 0.001), which compared with a ROCAUC of 0.82 (95% CI 0.81, 0.83; p < 0.001) for a model including 9 clinically accessible variables. The IMI DIRECT prediction models outperformed existing noninvasive NAFLD prediction tools. One limitation is that these analyses were performed in adults of European ancestry residing in northern Europe, and it is unknown how well these findings will translate to people of other ancestries and exposed to environmental risk factors that differ from those of the present cohort. Another key limitation of this study is that the prediction was done on a binary outcome of liver fat quantity (<5% or ≥5%) rather than a continuous one. CONCLUSIONS: In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community. TRIAL REGISTRATION: ClinicalTrials.gov NCT03814915.

Discovery of biomarkers for glycaemic deterioration before and after the onset of type 2 diabetes: descriptive characteristics of the epidemiological studies within the IMI DIRECT Consortium.

AIMS/HYPOTHESIS: Here, we describe the characteristics of the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) epidemiological cohorts at baseline and follow-up examinations (18, 36 and 48 months of follow-up). METHODS: From a sampling frame of 24,682 adults of European ancestry enrolled in population-based cohorts across Europe, participants at varying risk of glycaemic deterioration were identified using a risk prediction algorithm (based on age, BMI, waist circumference, use of antihypertensive medication, smoking status and parental history of type 2 diabetes) and enrolled into a prospective cohort study (n = 2127) (cohort 1, prediabetes risk). We also recruited people from clinical registries with type 2 diabetes diagnosed 6-24 months previously (n = 789) into a second cohort study (cohort 2, diabetes). Follow-up examinations took place at ~18 months (both cohorts) and at ~48 months (cohort 1) or ~36 months (cohort 2) after baseline examinations. The cohorts were studied in parallel using matched protocols across seven clinical centres in northern Europe. RESULTS: Using ADA 2011 glycaemic categories, 33% (n = 693) of cohort 1 (prediabetes risk) had normal glucose regulation and 67% (n = 1419) had impaired glucose regulation. Seventy-six per cent of participants in cohort 1 was male. Cohort 1 participants had the following characteristics (mean ± SD) at baseline: age 62 (6.2) years; BMI 27.9 (4.0) kg/m2; fasting glucose 5.7 (0.6) mmol/l; 2 h glucose 5.9 (1.6) mmol/l. At the final follow-up examination the participants' clinical characteristics were as follows: fasting glucose 6.0 (0.6) mmol/l; 2 h OGTT glucose 6.5 (2.0) mmol/l. In cohort 2 (diabetes), 66% (n = 517) were treated by lifestyle modification and 34% (n = 272) were treated with metformin plus lifestyle modification at enrolment. Fifty-eight per cent of participants in cohort 2 was male. Cohort 2 participants had the following characteristics at baseline: age 62 (8.1) years; BMI 30.5 (5.0) kg/m2; fasting glucose 7.2 (1.4) mmol/l; 2 h glucose 8.6 (2.8) mmol/l. At the final follow-up examination, the participants' clinical characteristics were as follows: fasting glucose 7.9 (2.0) mmol/l; 2 h mixed-meal tolerance test glucose 9.9 (3.4) mmol/l. CONCLUSIONS/INTERPRETATION: The IMI DIRECT cohorts are intensely characterised, with a wide-variety of metabolically relevant measures assessed prospectively. We anticipate that the cohorts, made available through managed access, will provide a powerful resource for biomarker discovery, multivariate aetiological analyses and reclassification of patients for the prevention and treatment of type 2 diabetes.

Evidence-based prioritisation and enrichment of genes interacting with metformin in type 2 diabetes.

AIMS/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.

Sustained influence of metformin therapy on circulating glucagon-like peptide-1 levels in individuals with and without type 2 diabetes.

AIMS: To investigate, in the Carotid Atherosclerosis: Metformin for Insulin Resistance (CAMERA) trial (NCT00723307), whether the influence of metformin on the glucagon-like peptide (GLP)-1 axis in individuals with and without type 2 diabetes (T2DM) is sustained and related to changes in glycaemia or weight, and to investigate basal and post-meal GLP-1 levels in patients with T2DM in the cross-sectional Diabetes Research on Patient Stratification (DIRECT) study. MATERIALS AND METHODS: CAMERA was a double-blind randomized placebo-controlled trial of metformin in 173 participants without diabetes. Using 6-monthly fasted total GLP-1 levels over 18 months, we evaluated metformin's effect on total GLP-1 with repeated-measures analysis and analysis of covariance. In the DIRECT study, we examined active and total fasting and 60-minute post-meal GLP-1 levels in 775 people recently diagnosed with T2DM treated with metformin or diet, using Student's t-tests and linear regression. RESULTS: In CAMERA, metformin increased total GLP-1 at 6 (+20.7%, 95% confidence interval [CI] 4.7-39.0), 12 (+26.7%, 95% CI 10.3-45.6) and 18 months (+18.7%, 95% CI 3.8-35.7), an overall increase of 23.4% (95% CI 11.2-36.9; P < .0001) vs placebo. Adjustment for changes in glycaemia and adiposity, individually or combined, did not attenuate this effect. In the DIRECT study, metformin was associated with higher fasting active (39.1%, 95% CI 21.3-56.4) and total GLP-1 (14.1%, 95% CI 1.2-25.9) but not post-meal incremental GLP-1. These changes were independent of potential confounders including age, sex, adiposity and glycated haemoglobin. CONCLUSIONS: In people without diabetes, metformin increases total GLP-1 in a sustained manner and independently of changes in weight or glycaemia. Metformin-treated patients with T2DM also have higher fasted GLP-1 levels, independently of weight and glycaemia.

The Association of Cardiometabolic, Diet and Lifestyle Parameters With Plasma Glucagon-like Peptide-1: An IMI DIRECT Study.

CONTEXT: The role of glucagon-like peptide-1 (GLP-1) in type 2 diabetes (T2D) and obesity is not fully understood. OBJECTIVE: We investigate the association of cardiometabolic, diet, and lifestyle parameters on fasting and postprandial GLP-1 in people at risk of, or living with, T2D. METHODS: We analyzed cross-sectional data from the two Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohorts, cohort 1 (n = 2127) individuals at risk of diabetes; cohort 2 (n = 789) individuals with new-onset T2D. RESULTS: Our multiple regression analysis reveals that fasting total GLP-1 is associated with an insulin-resistant phenotype and observe a strong independent relationship with male sex, increased adiposity, and liver fat, particularly in the prediabetes population. In contrast, we showed that incremental GLP-1 decreases with worsening glycemia, higher adiposity, liver fat, male sex, and reduced insulin sensitivity in the prediabetes cohort. Higher fasting total GLP-1 was associated with a low intake of wholegrain, fruit, and vegetables in people with prediabetes, and with a high intake of red meat and alcohol in people with diabetes. CONCLUSION: These studies provide novel insights into the association between fasting and incremental GLP-1, metabolic traits of diabetes and obesity, and dietary intake, and raise intriguing questions regarding the relevance of fasting GLP-1 in the pathophysiology T2D.

A gene risk score using missense variants in SLCO1B1 is associated with earlier onset statin intolerance.

BACKGROUND AND AIMS: The efficacy of statin therapy is hindered by intolerance to the therapy, leading to discontinuation. Variants in SLCO1B1, which encodes the hepatic transporter OATB1B1, influence statin pharmacokinetics, resulting in altered plasma concentrations of the drug and its metabolites. Current pharmacogenetic guidelines require sequencing of the SLCO1B1 gene, which is more expensive and less accessible than genotyping. In this study, we aimed to develop an easy, clinically implementable functional gene risk score (GRS) of common variants in SLCO1B1 to identify patients at risk of statin intolerance. METHODS AND RESULTS: A GRS was developed from four common variants in SLCO1B1. In statin users from Tayside, Scotland, UK, those with a high-risk GRS had increased odds across three phenotypes of statin intolerance [general statin intolerance (GSI): ORGSI 2.42; 95% confidence interval (CI): 1.29-4.31, P = 0.003; statin-related myopathy: ORSRM 2.51; 95% CI: 1.28-4.53, P = 0.004; statin-related suspected rhabdomyolysis: ORSRSR 2.85; 95% CI: 1.03-6.65, P = 0.02]. In contrast, using the Val174Ala genotype alone or the recommended OATP1B1 functional phenotypes produced weaker and less reliable results. A meta-analysis with results from adjudicated cases of statin-induced myopathy in the PREDICTION-ADR Consortium confirmed these findings (ORVal174Ala 1.99; 95% CI: 1.01-3.95, P = 0.048; ORGRS 1.76; 95% CI: 1.16-2.69, P = 0.008). For those requiring high-dose statin therapy, the high-risk GRS was more consistently associated with the time to onset of statin intolerance amongst the three phenotypes compared with Val174Ala (GSI: HRVal174Ala 2.49; 95% CI: 1.09-5.68, P = 0.03; HRGRS 2.44; 95% CI: 1.46-4.08, P < 0.001). Finally, sequence kernel association testing confirmed that rare variants in SLCO1B1 are associated with the risk of intolerance (P = 0.02). CONCLUSION: We provide evidence that a GRS based on four common SLCO1B1 variants provides an easily implemented genetic tool that is more reliable than the current recommended practice in estimating the risk and predicting early-onset statin intolerance.

Treatment effect heterogeneity following type 2 diabetes treatment with GLP1-receptor agonists and SGLT2-inhibitors: a systematic review.

BACKGROUND: A precision medicine approach in type 2 diabetes requires the identification of clinical and biological features that are reproducibly associated with differences in clinical outcomes with specific anti-hyperglycaemic therapies. Robust evidence of such treatment effect heterogeneity could support more individualized clinical decisions on optimal type 2 diabetes therapy. METHODS: We performed a pre-registered systematic review of meta-analysis studies, randomized control trials, and observational studies evaluating clinical and biological features associated with heterogenous treatment effects for SGLT2-inhibitor and GLP1-receptor agonist therapies, considering glycaemic, cardiovascular, and renal outcomes. After screening 5,686 studies, we included 101 studies of SGLT2-inhibitors and 75 studies of GLP1-receptor agonists in the final systematic review. RESULTS: Here we show that the majority of included papers have methodological limitations precluding robust assessment of treatment effect heterogeneity. For SGLT2-inhibitors, multiple observational studies suggest lower renal function as a predictor of lesser glycaemic response, while markers of reduced insulin secretion predict lesser glycaemic response with GLP1-receptor agonists. For both therapies, multiple post-hoc analyses of randomized control trials (including trial meta-analysis) identify minimal clinically relevant treatment effect heterogeneity for cardiovascular and renal outcomes. CONCLUSIONS: Current evidence on treatment effect heterogeneity for SGLT2-inhibitor and GLP1-receptor agonist therapies is limited, likely reflecting the methodological limitations of published studies. Robust and appropriately powered studies are required to understand type 2 diabetes treatment effect heterogeneity and evaluate the potential for precision medicine to inform future clinical care.

This study reviews the available evidence on which patient features (such as age, sex, and blood test results) are associated with different outcomes for two recently introduced type 2 diabetes medications: SGLT2-inhibitors and GLP1-receptor agonists. Understanding what individual characteristics are associated with different response patterns may help clinical providers and people living with diabetes make more informed decisions about which type 2 diabetes treatments will work best for an individual. We focus on three outcomes: blood glucose levels (raised blood glucose is the primary symptom of diabetes and a primary aim of diabetes treatment is to lower this), heart disease, and kidney disease. We identified some potential factors that reduce effects on blood glucose levels, including poorer kidney function for SGLT2-inhibitors and lower production of the glucose-lowering hormone insulin for GLP1-receptor agonists. We did not identify clear factors that alter heart and kidney disease outcomes for either medication. Most of the studies had limitations, meaning more research is needed to fully understand the factors that influence treatment outcomes in type 2 diabetes.

Precision medicine in type 2 diabetes: A systematic review of treatment effect heterogeneity for GLP1-receptor agonists and SGLT2-inhibitors.

Background: A precision medicine approach in type 2 diabetes requires identification of clinical and biological features that are reproducibly associated with differences in clinical outcomes with specific anti-hyperglycaemic therapies. Robust evidence of such treatment effect heterogeneity could support more individualized clinical decisions on optimal type 2 diabetes therapy. Methods: We performed a pre-registered systematic review of meta-analysis studies, randomized control trials, and observational studies evaluating clinical and biological features associated with heterogenous treatment effects for SGLT2-inhibitor and GLP1-receptor agonist therapies, considering glycaemic, cardiovascular, and renal outcomes. Results: After screening 5,686 studies, we included 101 studies of SGLT2-inhibitors and 75 studies of GLP1-receptor agonists in the final systematic review. The majority of papers had methodological limitations precluding robust assessment of treatment effect heterogeneity. For glycaemic outcomes, most cohorts were observational, with multiple analyses identifying lower renal function as a predictor of lesser glycaemic response with SGLT2-inhibitors and markers of reduced insulin secretion as predictors of lesser response with GLP1-receptor agonists. For cardiovascular and renal outcomes, the majority of included studies were post-hoc analyses of randomized control trials (including meta-analysis studies) which identified limited clinically relevant treatment effect heterogeneity. Conclusions: Current evidence on treatment effect heterogeneity for SGLT2-inhibitor and GLP1-receptor agonist therapies is limited, likely reflecting the methodological limitations of published studies. Robust and appropriately powered studies are required to understand type 2 diabetes treatment effect heterogeneity and evaluate the potential for precision medicine to inform future clinical care. Plain language summary: This review identifies research that helps understand which clinical and biological factors that are associated with different outcomes for specific type 2 diabetes treatments. This information could help clinical providers and patients make better informed personalized decisions about type 2 diabetes treatments. We focused on two common type 2 diabetes treatments: SGLT2-inhibitors and GLP1-receptor agonists, and three outcomes: blood glucose control, heart disease, and kidney disease. We identified some potential factors that are likely to lessen blood glucose control including lower kidney function for SGLT2-inhibitors and lower insulin secretion for GLP1-receptor agonists. We did not identify clear factors that alter heart and renal disease outcomes for either treatment. Most of the studies had limitations, meaning more research is needed to fully understand the factors that influence treatment outcomes 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 HbA1c 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 HbA1c 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 HbA1c 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 HbA1c 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 HbA1c 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 HbA1c 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.

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, ß = 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, ß = -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.

Type 2 Diabetes risk alleles in Peptidyl-glycine Alpha-amidating Monooxygenase influence GLP-1 levels and response to GLP-1 Receptor Agonists.

Patients with type 2 diabetes vary in their response to currently available therapeutic agents (including GLP-1 receptor agonists) leading to suboptimal glycemic control and increased risk of complications. We show that human carriers of hypomorphic T2D-risk alleles in the gene encoding peptidyl-glycine alpha-amidating monooxygenase (PAM), as well as Pam-knockout mice, display increased resistance to GLP-1 in vivo. Pam inactivation in mice leads to reduced gastric GLP-1R expression and faster gastric emptying: this persists during GLP-1R agonist treatment and is rescued when GLP-1R activity is antagonized, indicating resistance to GLP-1's gastric slowing properties. Meta-analysis of human data from studies examining GLP-1R agonist response (including RCTs) reveals a relative loss of 44% and 20% of glucose lowering (measured by glycated hemoglobin) in individuals with hypomorphic PAM alleles p.S539W and p.D536G treated with GLP-1R agonist. Genetic variation in PAM has effects on incretin signaling that alters response to medication used commonly for treatment of T2D.

Heterogeneity in phenotype, disease progression and drug response in type 2 diabetes.

Type 2 diabetes (T2D) is a complex chronic disease characterized by considerable phenotypic heterogeneity. In this study, we applied a reverse graph embedding method to routinely collected data from 23,137 Scottish patients with newly diagnosed diabetes to visualize this heterogeneity and used partitioned diabetes polygenic risk scores to gain insight into the underlying biological processes. Overlaying risk of progression to outcomes of insulin requirement, chronic kidney disease, referable diabetic retinopathy and major adverse cardiovascular events, we show how these risks differ by patient phenotype. For example, patients at risk of retinopathy are phenotypically different from those at risk of cardiovascular events. We replicated our findings in the UK Biobank and the ADOPT clinical trial, also showing that the pattern of diabetes drug monotherapy response differs for different drugs. Overall, our analysis highlights how, in a European population, underlying phenotypic variation drives T2D onset and affects subsequent diabetes outcomes and drug response, demonstrating the need to incorporate these factors into personalized treatment approaches for the management of T2D.