scholarly journals Large-scale analyses provide no evidence for gene-gene interactions influencing type 2 diabetes risk

2020 ◽  
Author(s):  
Ada Admin ◽  
Abhishek Nag ◽  
Mark I McCarthy ◽  
Anubha Mahajan
Keyword(s):  
Type 2 Diabetes ◽  
Large Scale ◽  
Gene Interaction ◽  
Risk Scores ◽  
Biological Processes ◽  
Common Variants ◽  
Risk Variants ◽  
Significant Gene ◽  
Linear Interactions

A growing number of genetic loci have been shown to influence individual predisposition to type 2 diabetes (T2D). Despite longstanding interest in understanding whether non-linear interactions between these risk-variants additionally influence T2D-risk, the ability to detect significant gene-gene interaction (GGI) effects has to date been limited. To increase power to detect GGI effects, we combined recent advances in the fine-mapping of causal T2D-risk variants with the increased sample size available within UK Biobank (375,736 unrelated European participants, including 16,430 T2D cases). In addition to conventional single variant-based analysis, we employed a complementary polygenic score-based approach which included partitioned T2D-risk scores that capture biological processes relevant to T2D pathophysiology. Nevertheless, we found no evidence in support of GGI effects influencing T2D-risk. The present study was powered to detect interactions between common variants with odds ratios >1.2, so these findings place limits on the contribution of GGIs to the overall heritability of T2D.<b> </b>

scholarly journals Large-scale analyses provide no evidence for gene-gene interactions influencing type 2 diabetes risk

2020 ◽  
Author(s):  
Ada Admin ◽  
Abhishek Nag ◽  
Mark I McCarthy ◽  
Anubha Mahajan
Keyword(s):  
Type 2 Diabetes ◽  
Large Scale ◽  
Gene Interaction ◽  
Risk Scores ◽  
Biological Processes ◽  
Common Variants ◽  
Risk Variants ◽  
Significant Gene ◽  
Linear Interactions

A growing number of genetic loci have been shown to influence individual predisposition to type 2 diabetes (T2D). Despite longstanding interest in understanding whether non-linear interactions between these risk-variants additionally influence T2D-risk, the ability to detect significant gene-gene interaction (GGI) effects has to date been limited. To increase power to detect GGI effects, we combined recent advances in the fine-mapping of causal T2D-risk variants with the increased sample size available within UK Biobank (375,736 unrelated European participants, including 16,430 T2D cases). In addition to conventional single variant-based analysis, we employed a complementary polygenic score-based approach which included partitioned T2D-risk scores that capture biological processes relevant to T2D pathophysiology. Nevertheless, we found no evidence in support of GGI effects influencing T2D-risk. The present study was powered to detect interactions between common variants with odds ratios >1.2, so these findings place limits on the contribution of GGIs to the overall heritability of T2D.<b> </b>

scholarly journals Validation of a Trans-Ancestry Polygenic Risk Score for Type 2 Diabetes in Diverse Populations

2021 ◽  
Author(s):  
Tian Ge ◽  
Amit Patki ◽  
Vinodh Srinivasasainagendra ◽  
Yen-Feng Lin ◽  
Marguerite Ryan Irvin ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
African American ◽  
Large Scale ◽  
Association Studies ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Polygenic Risk

ABSTRACTType 2 diabetes (T2D) is a worldwide scourge caused by both genetic and environmental risk factors that disproportionately afflicts communities of color. Leveraging existing large-scale genome-wide association studies (GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and intervention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non-European populations is critical for an equitable deployment of PRS to clinical practice that benefits global populations. Here we integrate T2D GWAS in European, African American and East Asian populations to construct a trans-ancestry T2D PRS using a newly developed Bayesian polygenic modeling method, and evaluate the PRS in the multi-ethnic eMERGE study, four African American cohorts, and the Taiwan Biobank. The trans-ancestry PRS was significantly associated with T2D status across the ancestral groups examined, and the top 2% of the PRS distribution can identify individuals with an approximately 2.5-4.5 fold of increase in T2D risk, suggesting the potential of using the trans-ancestry PRS as a meaningful index of risk among diverse patients in clinical settings. Our efforts represent the first step towards the implementation of the T2D PRS into routine healthcare.

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.

Large-Scale Cross-Sectional Study of the Problem Areas in Diabetes Survey among Japanese Patients with Type 2 Diabetes Mellitus

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 2393-PUB
Author(s):  
KENICHIRO TAKAHASHI ◽  
MINORI SHINODA ◽  
RIKA SAKAMOTO ◽  
JUN SUZUKI ◽  
TADASHI YAMAKAWA ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Large Scale ◽  
Japanese Patients ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Diabetes Survey

scholarly journals Improving the management of type 2 diabetes through large-scale general practice: the role of a data-driven and technology-enabled education programme

2021 ◽  
Vol 10 (1) ◽  
pp. e001087
Author(s):  
Tarek F Radwan ◽  
Yvette Agyako ◽  
Alireza Ettefaghian ◽  
Tahira Kamran ◽  
Omar Din ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Large Scale ◽  
Education Programme ◽  
Educational Programme ◽  
Data Driven ◽  
Treatment Targets ◽  
Care Processes ◽  
Data Driven Approach

A quality improvement (QI) scheme was launched in 2017, covering a large group of 25 general practices working with a deprived registered population. The aim was to improve the measurable quality of care in a population where type 2 diabetes (T2D) care had previously proved challenging. A complex set of QI interventions were co-designed by a team of primary care clinicians and educationalists and managers. These interventions included organisation-wide goal setting, using a data-driven approach, ensuring staff engagement, implementing an educational programme for pharmacists, facilitating web-based QI learning at-scale and using methods which ensured sustainability. This programme was used to optimise the management of T2D through improving the eight care processes and three treatment targets which form part of the annual national diabetes audit for patients with T2D. With the implemented improvement interventions, there was significant improvement in all care processes and all treatment targets for patients with diabetes. Achievement of all the eight care processes improved by 46.0% (p<0.001) while achievement of all three treatment targets improved by 13.5% (p<0.001). The QI programme provides an example of a data-driven large-scale multicomponent intervention delivered in primary care in ethnically diverse and socially deprived areas.

scholarly journals A novel drug response score more accurately predicts renoprotective drug effects than existing renal risk scores

2021 ◽  
Vol 12 ◽  
pp. 204201882097419
Author(s):  
Nienke M. A. Idzerda ◽  
Sok Cin Tye ◽  
Dick de Zeeuw ◽  
Hiddo J. L. Heerspink
Keyword(s):  
Type 2 Diabetes ◽  
Kidney Disease ◽  
Serum Creatinine ◽  
Risk Score ◽  
Drug Response ◽  
Drug Effects ◽  
Risk Scores ◽  
Renal Outcome ◽  
Response Score

Background: Risk factor-based equations are used to predict risk of kidney disease progression in patients with type 2 diabetes order to guide treatment decisions. It is, however, unknown whether these models can also be used to predict the effects of drugs on clinical outcomes. Methods: The previously developed Parameter Response Efficacy (PRE) score, which integrates multiple short-term drug effects, was first compared with the existing risk scores, Kidney Failure Risk Equation (KFRE) and The Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation (ADVANCE) renal risk score, in its performance to predict end-stage renal disease (ESRD; KFRE) and doubling of serum creatinine or ESRD (ADVANCE). Second, changes in the risk scores were compared after 6 months’ treatment to predict the long-term effects of losartan on these renal outcomes in patients with type 2 diabetes and chronic kidney disease. Results: The KFRE, ADVANCE and PRE scores showed similarly good performance in predicting renal risk. However, for prediction of the effect of losartan, the KFRE risk score predicted a relative risk change in the occurrence of ESRD of 3.1% [95% confidence interval (CI) −5 to 12], whereas the observed risk change was −28.8% (95% CI −42.0 to −11.5). For the composite endpoint of doubling of serum creatinine or ESRD, the ADVANCE score predicted a risk change of −12.4% (95% CI −17 to −7), which underestimated the observed risk change −21.8% (95% CI −34 to −6). The PRE score predicted renal risk changes that were close to the observed risk changes with losartan treatment [−24.0% (95% CI −30 to −17) and −22.6% (95% CI −23 to −16) for ESRD and the composite renal outcome, respectively]. Conclusion: A drug response score such as the PRE score may assist in improving clinical decision making and implement precision medicine strategies.

scholarly journals Metabolic Spectrum of Liver Failure in Type 2 Diabetes and Obesity: From NAFLD to NASH to HCC

2021 ◽  
Vol 22 (9) ◽  
pp. 4495
Author(s):  
Hyunmi Kim ◽  
Da Som Lee ◽  
Tae Hyeon An ◽  
Hyun-Ju Park ◽  
Won Kon Kim ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Liver Disease ◽  
Liver Failure ◽  
Fatty Liver Disease ◽  
Liver Diseases ◽  
Biological Processes ◽  
Chronic Liver Diseases ◽  
Nonalcoholic Fatty Liver ◽  
Diabetes And Obesity

Liver disease is the spectrum of liver damage ranging from simple steatosis called as nonalcoholic fatty liver disease (NAFLD) to hepatocellular carcinoma (HCC). Clinically, NAFLD and type 2 diabetes coexist. Type 2 diabetes contributes to biological processes driving the severity of NAFLD, the primary cause for development of chronic liver diseases. In the last 20 years, the rate of non-viral NAFLD/NASH-derived HCC has been increasing rapidly. As there are currently no suitable drugs for treatment of NAFLD and NASH, a class of thiazolidinediones (TZDs) drugs for the treatment of type 2 diabetes is sometimes used to improve liver failure despite the risk of side effects. Therefore, diagnosis, prevention, and treatment of the development and progression of NAFLD and NASH are important issues. In this review, we will discuss the pathogenesis of NAFLD/NASH and NAFLD/NASH-derived HCC and the current promising pharmacological therapies of NAFLD/NASH. Further, we will provide insights into “adipose-derived adipokines” and “liver-derived hepatokines” as diagnostic and therapeutic targets from NAFLD to HCC.

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