Cohort Profile: Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS)

Hébert, Harry L.; Shepherd, Bridget; Milburn, Keith; Veluchamy, Abirami; Wang, Meng; Carr, Fiona; Donnelly, Louise A.; Tavendale, Roger; Leese, Graham; Colhoun, Helen M.; Dow, Ellie; Morris, Andrew D.; Doney, Alex S.F.; Lang, Chim C.; Pearson, Ewan R.; Smith, Blair H.; Palmer, Colin N.A.

doi:10.1093/ije/dyx140

Cited by 70 publications

(94 citation statements)

References 94 publications

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“…Patients with T2D were identified from the Genetics of Diabetes Audit and Research Tayside Scotland (GoDARTS) registry; the full details have been published previously . In brief, GoDARTS is an epidemiological cohort study comprising 18 306 participants including 10 149 with T2D and 8157 controls without T2D.…”

Section: Methodsmentioning

confidence: 99%

Microvascular disease and heart failure with reduced and preserved ejection fraction in type 2 diabetes

et al. 2020

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Aims Identification of patients with type 2 diabetes (T2D) at increased risk of incident heart failure (HF) beyond traditional risk factors such as prior myocardial infarction (MI) might allow selection of patients who would benefit from preventative treatment. Microvascular disease (MiVD) is thought to play a pathophysiological role in the development of HF in T2D; however, its association with new-onset HF with reduced or preserved ejection fraction has not been specifically defined. Methods and results Patients in the Genetics of Diabetes Audit and Research Tayside Scotland study were linked to echocardiography, prescriptions, and clinical outcomes. In total, 9141 patients with T2D were identified for analysis. Clinical variables and the presence of retinopathy, nephropathy, and neuropathy were assessed. Cumulative incidence was calculated for the association of both individual and the total number of MiVD states and incident HF. Median follow-up was 9.3 years. In total, there were 900 HF events. The presence of any MiVD was independently associated with both HF with reduced ejection fraction (hazard ratio 1.40; 95% confidence interval 1.11-1.76, P = 0.004) and HF with preserved ejection fraction (hazard ratio 1.38; 95% confidence interval 1.10-1.72, P = 0.005), with a stepwise association between the number of MiVD states and risk of incident HF (P for trend <0.001). Similar associations were found in sensitivity analyses limited to patients without a prior MI, and using competing risks analysis. Conclusions Individuals with T2D and with MiVD are at risk of incident HF independent of a history of prior HF or MI. Patients with MiVD could benefit from screening for HF and individualized therapy with treatments that lower HF risk.

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Section: Methodsmentioning

confidence: 99%

Microvascular disease and heart failure with reduced and preserved ejection fraction in type 2 diabetes

et al. 2020

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“…Participants in the discovery phase of this study were obtained from the two independent cohorts, the GoDARTS [45] and the ORCADES. GoDARTS comprises individuals of European-heritage from Tayside, Scotland who provided a sample of blood for genetic analysis and consent to link their genetic information to the anonymized electronic health records.…”

Section: Methodsmentioning

confidence: 99%

Novel locus influencing retinal venular tortuosity is also associated with risk of coronary artery disease

Veluchamy

Ballerini

Vitart

et al. 2017

Preprint

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Structural variation in retinal blood vessels is associated with global vascular health in humans and may provide a readily accessible indicator of several diseases of vascular origin. Increasing evidence suggests variation in retinal vasculature is highly heritable. This study aimed to identify genetic determinants of retinal vascular traits. We reported a meta-analysis of genome-wide association studies (GWAS) for quantitative retinal vascular traits derived using semi-automatic image analysis of digital retinal photographs from the Genetics of Diabetes Audit and Research in Tayside (GoDARTS) (n=1736) and the Orkney Complex Disease Study (ORCADES) (n=1358) cohorts. We identified a novel genome-wide significant locus at 19q13 (ACTN4/CAPN12) for retinal venular tortuosity (TortV), and one at 13q34 (COL4A2) for retinal arteriolar tortuosity (TortA); these two loci were subsequently confirmed in three independent cohorts (n=1413). In the combined analysis in ACTN4/CAPN12 the lead single nucleotide polymorphism (SNP) was rs1808382 (n=4507; Beta=−0.109; standard error (SE) =0.015; P=2.39×10−13) and in COL4A2 it was rs7991229 (n=4507; Beta=0.103; SE=0.015; P=4.66×10−12). Notably, the ACTN4/CAPN12 locus associated with retinal TortV is also associated with coronary artery disease and heart rate. Our findings demonstrate the contribution of genetics in retinal tortuosity traits, and provide new insights into cardiovascular diseases.Author SummaryRetinal vascular features are associated with wide range of diseases related to vascular health and provide an opportunity to understand early structural changes in vasculature which may help to predict disease risk. Emerging evidence indicates that retinal tortuosity traits are both associated with vascular health and highly heritable. However, the genetic architecture of retinal vascular tortuosity has not been investigated. We therefore performed a genome-wide association study on retinal arteriolar tortuosity (TortA) and retinal venular tortuosity trait (TortV) using data from two independent discovery cohorts of 3094 individuals of European-heritage. We found a novel associations at 19q13 (ACTN4/CAPN12) for TortV, and one at 13q34 (COL4A2) for TortA at discovery stage and validated in three independent cohorts. A significant association was subsequently found between lead SNPs at 19q13 and coronary artery disease, cardiovascular vascular risk factors and heart rate. We also performed genome-wide association studies for retinal vascular calibres and optic disc radius (ODradius) and replicated previously reported locus at 10q21.3 for ODradius. Our findings highlight genetic impacts on retinal venular tortuosity and it is association with cardiovascular disease. This may provide a molecular pathophysiological foundation for use of retinal vascular traits as biomarkers for cardiovascular diseases.

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“…We study the medical records of 6871 patients with type 2 diabetes extracted from the GoDARTS database [23][24][25][26].…”

Section: Datamentioning

confidence: 99%

“…First we choose one representative method from each of these approaches and consider how well they handle data imbalance, interpretation of the model, prediction performance, and the effects of dichotomization. We then apply the methods to a subset of the Genetics of Diabetes Audit and Research (GoDARTS) data set [23]. This data set investigates the time until a type 2 diabetes patient goes onto insulin from the day of diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Methods for Disease Progression Prediction Through a GoDARTS Study

Nielsen

Donnelly

et al. 2020

Preprint

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Background: In recent years, a variety of new machine learning methods are being employed in prediction of disease progression, e.g. random forest or neural networks, but how do they compare to and are they direct substitutes for the more traditional statistical methods like the Cox proportional hazards model? In this paper, we compare three of the most commonly used approaches to model prediction of disease progression. We consider a type 2 diabetes case from a cohort-based population in Tayside, UK. In this study, the time until a patient goes onto insulin treatment is of interest; in particular discriminating between slow and fast progression. This means that we are both interested in the results as a raw time-to-insulin prediction but also in a dichotomized outcome making the prediction a classification.Methods: Three different methods for prediction are considered: A Cox proportional hazards model, random forest for survival data and a neural network on the dichotomized outcome. The performance is evaluated using survival performance measures (concordance indices and the integrated Brier score) and using the accuracy, sensitivity, specificity, and Matthews correlation.coefficient for the corresponding classification problems.Results: We found no improvement when using the conditional inference forest over the Cox model. The neural network out performed the conditional inference forest in the classification problem. We discuss the limitations of the three approaches and where they each excel in terms of prediction performance, interpretation, and how they handle data imbalance.

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Cohort Profile: Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS)

Cited by 70 publications

References 94 publications

Microvascular disease and heart failure with reduced and preserved ejection fraction in type 2 diabetes

Microvascular disease and heart failure with reduced and preserved ejection fraction in type 2 diabetes

Novel locus influencing retinal venular tortuosity is also associated with risk of coronary artery disease

A Comparison of Methods for Disease Progression Prediction Through a GoDARTS Study

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