11 research outputs found

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

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    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

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

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    \ua9 The Author(s) 2024. Published by Oxford University Press on behalf of the Endocrine Society.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

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

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    This is the final version. Available from Nature Research via the DOI in this record. Data availability: Template data collection forms and the data extracted from included studies are available upon request. All studies identified by our search protocol are detailed in Supplementary Tables 1–8.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.Medical Research CouncilBHF-Turing Cardiovascular Data Science AwardNational Institutes of HealthNational Institute for Health and Care ResearchResearch Englan

    Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine

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    Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits

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    Effective interventions in preventing gestational diabetes mellitus: A systematic review and meta-analysis

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    \ua9 The Author(s) 2024.Background: Lifestyle choices, metformin, and dietary supplements may prevent GDM, but the effect of intervention characteristics has not been identified. This review evaluated intervention characteristics to inform the implementation of GDM prevention interventions. Methods: Ovid, MEDLINE/PubMed, and EMBASE databases were searched. The Template for Intervention Description and Replication (TIDieR) framework was used to examine intervention characteristics (who, what, when, where, and how). Subgroup analysis was performed by intervention characteristics. Results: 116 studies involving 40,940 participants are included. Group-based physical activity interventions (RR 0.66; 95% CI 0.46, 0.95) reduce the incidence of GDM compared with individual or mixed (individual and group) delivery format (subgroup p-value = 0.04). Physical activity interventions delivered at healthcare facilities reduce the risk of GDM (RR 0.59; 95% CI 0.49, 0.72) compared with home-based interventions (subgroup p-value = 0.03). No other intervention characteristics impact the effectiveness of all other interventions. Conclusions: Dietary, physical activity, diet plus physical activity, metformin, and myoinositol interventions reduce the incidence of GDM compared with control interventions. Group and healthcare facility-based physical activity interventions show better effectiveness in preventing GDM than individual and community-based interventions. Other intervention characteristics (e.g. utilization of e-health) don’t impact the effectiveness of lifestyle interventions, and thus, interventions may require consideration of the local context
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