scholarly journals Genetics Insights in the Relationship Between Type 2 Diabetes and Coronary Heart Disease

2020 ◽  
Vol 126 (11) ◽  
pp. 1526-1548 ◽  
Author(s):  
Mark O. Goodarzi ◽  
Jerome I. Rotter
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Genetic Relationship ◽  
Large Scale ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Genome Wide

Diabetes mellitus is a major risk factor for coronary heart disease (CHD). The major form of diabetes mellitus is type 2 diabetes mellitus (T2D), which is thus largely responsible for the CHD association in the general population. Recent years have seen major advances in the genetics of T2D, principally through ever-increasing large-scale genome-wide association studies. This article addresses the question of whether this expanding knowledge of the genomics of T2D provides insight into the etiologic relationship between T2D and CHD. We will investigate this relationship by reviewing the evidence for shared genetic loci between T2D and CHD; by examining the formal testing of this interaction (Mendelian randomization studies assessing whether T2D is causal for CHD); and then turn to the implications of this genetic relationship for therapies for CHD, for therapies for T2D, and for therapies that affect both. In conclusion, the growing knowledge of the genetic relationship between T2D and CHD is beginning to provide the promise for improved prevention and treatment of both disorders.

scholarly journals Sequence Variants ofADIPOQand Association with Type 2 Diabetes Mellitus in Taiwan Chinese Han Population

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ming-Kai Tsai ◽  
Hui-Min David Wang ◽  
Jeng-Chuan Shiang ◽  
I-Hung Chen ◽  
Chih-Chiang Wang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Large Scale ◽  
Association Studies ◽  
Chinese Han Population ◽  
Genome Wide Association Studies ◽  
Chinese Han ◽  
Han Population

Diabetes is a serious global health problem. Large-scale genome-wide association studies identified loci for type 2 diabetes mellitus (T2DM), including adiponectin (ADIPOQ) gene and transcription factor 7-like 2 (TCF7L2), but few studies clarified the effect of genetic polymorphisms ofADIPOQandTCF7L2on risk of T2DM. We attempted to elucidate association between T2DM and polymorphic variations of both in Taiwan’s Chinese Han population, with our retrospective case-control study genotyping single nucleotide polymorphisms (SNPs) inADIPOQandTCF7L2genes both in 149 T2DM patients and in 139 healthy controls from Taiwan. Statistical analysis gauged association of these polymorphisms with risk of T2DM to showADIPOQrs1501299 polymorphism variations strongly correlated with T2DM risk(P=0.042), with rs2241766 polymorphism being not associated with T2DM(P=0.967). However, both polymorphisms rs7903146 and rs12255372 ofTCF7L2were rarely detected in Taiwanese people. This study avers thatADIPOQrs1501299 polymorphism contributes to risk of T2DM in the Taiwanese population.

Progress in the genetics of common obesity and type 2 diabetes

2010 ◽  
Vol 12 ◽  
Author(s):  
Karani S. Vimaleswaran ◽  
Ruth J.F. Loos
Keyword(s):  
Type 2 Diabetes ◽  
Large Scale ◽  
Association Studies ◽  
Genome Wide Association ◽  
Epidemiological Methods ◽  
Genome Wide Association Studies ◽  
Obesity And Diabetes ◽  
Genome Wide ◽  
Genomics And Proteomics

The prevalence of obesity and diabetes, which are heritable traits that arise from the interactions of multiple genes and lifestyle factors, continues to rise worldwide, causing serious health problems and imposing a substantial economic burden on societies. For the past 15 years, candidate gene and genome-wide linkage studies have been the main genetic epidemiological approaches to identify genetic loci for obesity and diabetes, yet progress has been slow and success limited. The genome-wide association approach, which has become available in recent years, has dramatically changed the pace of gene discoveries. Genome-wide association is a hypothesis-generating approach that aims to identify new loci associated with the disease or trait of interest. So far, three waves of large-scale genome-wide association studies have identified 19 loci for common obesity and 18 for common type 2 diabetes. Although the combined contribution of these loci to the variation in obesity and diabetes risk is small and their predictive value is typically low, these recently identified loci are set to substantially improve our insights into the pathophysiology of obesity and diabetes. This will require integration of genetic epidemiological methods with functional genomics and proteomics. However, the use of these novel insights for genetic screening and personalised treatment lies some way off in the future.

scholarly journals Association Analysis of Genetic Variants with Type 2 Diabetes in a Mongolian Population in China

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Haihua Bai ◽  
Haiping Liu ◽  
Suyalatu Suyalatu ◽  
Xiaosen Guo ◽  
Shandan Chu ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Large Scale ◽  
Risk Allele ◽  
Association Studies ◽  
Northern China ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genome Wide

The large scale genome wide association studies (GWAS) have identified approximately 80 single nucleotide polymorphisms (SNPs) conferring susceptibility to type 2 diabetes (T2D). However, most of these loci have not been replicated in diverse populations and much genetic heterogeneity has been observed across ethnic groups. We tested 28 SNPs previously found to be associated with T2D by GWAS in a Mongolian sample of Northern China (497 diagnosed with T2D and 469 controls) for association with T2D and diabetes related quantitative traits. We replicated T2D association of 11 SNPs, namely, rs7578326 (IRS1), rs1531343 (HMGA2), rs8042680 (PRC1), rs7578597 (THADA), rs1333051 (CDKN2), rs6723108 (TMEM163), rs163182 and rs2237897 (KCNQ1), rs1387153 (MTNR1B), rs243021 (BCL11A), and rs10229583 (PAX4) in our sample. Further, we showed that risk allele of the strongest T2D associated SNP in our sample, rs757832 (IRS1), is associated with increased level of TG. We observed substantial difference of T2D risk allele frequency between the Mongolian sample and the 1000G Caucasian sample for a few SNPs, including rs6723108 (TMEM163) whose risk allele reaches near fixation in the Mongolian sample. Further study of genetic architecture of these variants in susceptibility of T2D is needed to understand the role of these variants in heterogeneous populations.

scholarly journals Could personalised risk prediction for type 2 diabetes using polygenic risk scores direct prevention, enhance diagnostics, or improve treatment?

2020 ◽  
Vol 5 ◽  
pp. 206
Author(s):  
Mathilde Boecker ◽  
Alvina G. Lai
Keyword(s):  
Type 2 Diabetes ◽  
Risk Prediction ◽  
Large Scale ◽  
Association Studies ◽  
Genome Wide Association ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Polygenic Risk ◽  
Genome Wide

Over the past three decades, the number of people globally with diabetes mellitus has more than doubled. It is estimated that by 2030, 439 million people will be suffering from the disease, 90-95% of whom will have type 2 diabetes (T2D). In 2017, 5 million deaths globally were attributable to T2D, placing it in the top 10 global causes of death. Because T2D is a result of both genetic and environmental factors, identification of individuals with high genetic risk can help direct early interventions to prevent progression to more serious complications. Genome-wide association studies have identified ~400 variants associated with T2D that can be used to calculate polygenic risk scores (PRS). Although PRSs are not currently more accurate than clinical predictors and do not yet predict risk with equal accuracy across all ethnic populations, they have several potential clinical uses. Here, we discuss potential usages of PRS for predicting T2D and for informing and optimising interventions. We also touch on possible health inequality risks of PRS and the feasibility of large-scale implementation of PRS in clinical practice. Before PRSs can be used as a therapeutic tool, it is important that further polygenic risk models are derived using non-European genome-wide association studies to ensure that risk prediction is accurate for all ethnic groups. Furthermore, it is essential that the ethical, social and legal implications of PRS are considered before their implementation in any context.

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