Genetic Tools for Coronary Risk Assessment in Type 2 Diabetes: A Cohort Study From the ACCORD Clinical Trial

Morieri, Mario Luca; Gao, He; Pigeyre, Marie; Shah, Hetal; Sjaarda, Jennifer; Mendonca, Christine; Hastings, Timothy; Buranasupkajorn, Patinut; Motsinger‐Reif, Alison A.; Rotroff, Daniel M.; Sigal, Ronald J.; Marcovina, Santica M.; Kraft, Peter; Buse, John B.; Wagner, Michael J.; Gerstein, Hertzel C.; Mychaleckyj, Josyf C.; Paré, Guillaume; Doria, Alessandro

doi:10.2337/dc18-0709

Cited by 35 publications

(40 citation statements)

References 43 publications

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“…For instance, in our angiographic cohort where the duration of type 2 diabetes is generally long and usage of lipid-lowering drugs is prevalent, high LDL is not associated with multivessel stenosis (OR = 0.97; 95% CI 0.59-1.61; p = 0.91). In contrast, the genetic mechanisms likely remain persistent and have been shown to be promising in identifying individuals with type 2 diabetes at a high CHD risk [36,37]. In this study, we show that a CHD PRS, capturing more genetic risk than previous genetic risk scores, maintains its association.…”

Section: A Chd Prs Could Potentially Help To Identify Individuals Witmentioning

confidence: 62%

Polygenic risk for coronary heart disease acts through atherosclerosis in type 2 diabetes

Forgetta

et al. 2020

Cardiovasc Diabetol

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Background: Type 2 diabetes increases the risk of coronary heart disease (CHD), yet the mechanisms involved remain poorly described. Polygenic risk scores (PRS) provide an opportunity to understand risk factors since they reflect etiologic pathways from the entire genome. We therefore tested whether a PRS for CHD influenced risk of CHD in individuals with type 2 diabetes and which risk factors were associated with this PRS. Methods:We tested the association of a CHD PRS with CHD and its traditional clinical risk factors amongst individuals with type 2 diabetes in UK Biobank (N = 21,102). We next tested the association of the CHD PRS with atherosclerotic burden in a cohort of 352 genome-wide genotyped participants with type 2 diabetes who had undergone coronary angiograms. Results:In the UK Biobank we found that the CHD PRS was strongly associated with CHD amongst individuals with type 2 diabetes (OR per standard deviation increase = 1.50; p = 1.5 × 10 − 59 ). But this CHD PRS was, at best, only weakly associated with traditional clinical risk factors, such as hypertension, hyperlipidemia, glycemic control, obesity and smoking. Conversely, in the angiographic cohort, the CHD PRS was strongly associated with multivessel stenosis (OR = 1.65; p = 4.9 × 10 − 4 ) and increased number of major stenotic lesions (OR = 1.35; p = 9.4 × 10 − 3 ). Conclusions:Polygenic predisposition to CHD is strongly associated with atherosclerotic burden in individuals with type 2 diabetes and this effect is largely independent of traditional clinical risk factors. This suggests that genetic risk for CHD acts through atherosclerosis with little effect on most traditional risk factors, providing the opportunity to explore new biological pathways.

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Section: A Chd Prs Could Potentially Help To Identify Individuals Witmentioning

confidence: 62%

Polygenic risk for coronary heart disease acts through atherosclerosis in type 2 diabetes

Forgetta

et al. 2020

Cardiovasc Diabetol

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“…In a large-scale study of CAD risk prediction in T2D patients, adding a weighted GRS comprised of 204 CAD candidate SNPs to a model of 13 clinical predictors such as age, sex, history of CAD, smoking habits, and SBP lead to an 8% improvement in risk classification. However, the AUCs of the models did not appear to be good enough to distinguish high- and low-risk individuals (i.e., genetic 0.567, clinical 0.675, combined 0.681), and all participants were of European ancestry [ 29 ]. Recently, the issue of limited generalizability of European derived PRS has been raised, and the importance of developing PRS specific to non-European populations is emphasized [ 30 , 31 ].…”

Section: Discussionmentioning

confidence: 99%

The Liability Threshold Model for Predicting the Risk of Cardiovascular Disease in Patients with Type 2 Diabetes: A Multi-Cohort Study of Korean Adults

2020

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Personalized risk prediction for diabetic cardiovascular disease (DCVD) is at the core of precision medicine in type 2 diabetes (T2D). We first identified three marker sets consisting of 15, 47, and 231 tagging single nucleotide polymorphisms (tSNPs) associated with DCVD using a linear mixed model in 2378 T2D patients obtained from four population-based Korean cohorts. Using the genetic variants with even modest effects on phenotypic variance, we observed improved risk stratification accuracy beyond traditional risk factors (AUC, 0.63 to 0.97). With a cutoff point of 0.21, the discrete genetic liability threshold model consisting of 231 SNPs (GLT231) correctly classified 87.7% of 2378 T2D patients as high or low risk of DCVD. For the same set of SNP markers, the GLT and polygenic risk score (PRS) models showed similar predictive performance, and we observed consistency between the GLT and PRS models in that the model based on a larger number of SNP markers showed much-improved predictability. In silico gene expression analysis, additional information was provided on the functional role of the genes identified in this study. In particular, HDAC4, CDKN2B, CELSR2, and MRAS appear to be major hubs in the functional gene network for DCVD. The proposed risk prediction approach based on the liability threshold model may help identify T2D patients at high CVD risk in East Asian populations with further external validations.

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“…Efforts based on this approach were initially disappointing due to the small number of genetic markers that were available when these studies were carried out and the rudimental way in which predictive performance was measured ( 18 ). However, more recent studies, taking full advantage of the abundant crop of CHD loci identified to date, indicate that this strategy can be effective ( 19 – 21 ).…”

Section: Leveraging Genetics To Develop Predictive Algorithmsmentioning

confidence: 99%

“…Among white subjects in this cohort ( n = 5,360), a GRS based on all 204 SNPs reported in Fig. 2 (GRS 204 ) was strongly associated with a positive CVD history at study entry (OR per GRS 204 SD 1.40, 95% CI 1.32–1.49, P = 3 × 10 −27 ) as well as with an increased risk of major CHD events during follow-up (average follow-up length 4.9 years; hazard ratio [HR] per GRS 204 SD 1.27, 95% CI 1.18–1.37, P = 4 × 10 −10 ) ( 19 ). As shown in Fig.…”

Section: Leveraging Genetics To Develop Predictive Algorithmsmentioning

confidence: 99%

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Leveraging Genetics to Improve Cardiovascular Health in Diabetes: The 2018 Edwin Bierman Award Lecture

Doria

2019

Diabetes

Self Cite

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The past decade has witnessed an exponential increase in our ability to search the genome for genetic factors predisposing to cardiovascular disease (CVD) and in particular coronary heart disease (CHD). Identifying these genes could lead to the development of innovative strategies to prevent the cardiovascular complications of diabetes by allowing us to 1 ) create predictive algorithms for the identification of patients at especially high risk of CVD so that these individuals can undergo preventive interventions early in the natural history of the disease; 2 ) discover as yet unknown disease pathways linking diabetes to atherosclerosis, which can be used as targets for the development of new CVD-preventing drugs specifically directed at subjects with diabetes; and 3 ) devise personalized programs increasing the cost-effectiveness of preventive interventions by tailoring them to the genetic background of each patient. Substantial progress has been made in each of these three areas as exemplified by the recent development of a CHD genetic risk score improving CHD prediction among subjects with type 2 diabetes, the discovery of a diabetes-specific CHD locus on 1q25 pointing to glutamine synthase ( GLUL ) and the γ-glutamyl cycle as key regulators of CHD risk in diabetes, and the identification of two genetic loci allowing the selection of patients with type 2 diabetes who may especially benefit from intensive glycemic control. Translating these discoveries into clinical practice will not be without challenges, but the potential rewards, from the perspective of public health as well as that of persons with diabetes, make this goal worth pursuing.

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Genetic Tools for Coronary Risk Assessment in Type 2 Diabetes: A Cohort Study From the ACCORD Clinical Trial

Cited by 35 publications

References 43 publications

Polygenic risk for coronary heart disease acts through atherosclerosis in type 2 diabetes

Polygenic risk for coronary heart disease acts through atherosclerosis in type 2 diabetes

The Liability Threshold Model for Predicting the Risk of Cardiovascular Disease in Patients with Type 2 Diabetes: A Multi-Cohort Study of Korean Adults

Leveraging Genetics to Improve Cardiovascular Health in Diabetes: The 2018 Edwin Bierman Award Lecture

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