scholarly journals Causal Evaluation of Laboratory Markers in Type 2 Diabetes on Cancer and Vascular Diseases Using Various Mendelian Randomization Tools

2020 ◽  
Vol 11 ◽  
Author(s):  
Heejin Jin ◽  
Sanghun Lee ◽  
Sungho Won
Keyword(s):  
Type 2 Diabetes ◽  
Large Scale ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Meta Analysis ◽  
Density Lipoprotein ◽  
Postprandial Glucose ◽  
Genome Wide Association Studies ◽  
Weighted Median

Multiple studies have demonstrated the effects of type 2 diabetes (T2D) on various human diseases; however, most of these were observational epidemiological studies that suffered from many potential biases including reported confounding and reverse causations. In this article, we investigated whether cancer and vascular disease can be affected by T2D-related traits, including fasting plasma glucose (FPG), 2-h postprandial glucose (2h-PG), and glycated hemoglobin A1c (HbA1c) levels, by using Mendelian randomization (MR). The summary statistics for FPG, 2h-PG, and HbA1c level were obtained through meta-analyses of large-scale genome-wide association studies that included data from 133,010 nondiabetic individuals from collaborating Meta-analysis of Glucose and Insulin Related Traits Consortium studies. Thereafter, based on the statistical assumptions for MR analyses, the most reliable approaches including inverse-variance-weighted (IVW), MR-Egger, MR-Egger with a simulation extrapolation (SIMEX), weighted median, and MR-pleiotropy residual sum and outlier (MR-PRESSO) methods were applied to identify traits affected by FPG, 2h-PG, and HbAlc. We found that coronary artery disease is affected by FPG, as per the IVW [log odds ratio (logOR): 0.21; P = 0.012], MR-Egger (SIMEX) (logOR: 0.22; P = 0.014), MR-PRESSO (logOR: 0.18; P = 0.045), and weighted median (logOR: 0.29; P < 0.001) methods but not as per the MR-Egger (logOR: 0.13; P = 0.426) approach. Furthermore, low-density lipoprotein cholesterol levels are affected by HbA1c, as per the IVW [beta (B): 0.23; P = 0.015), MR-Egger (B: 0.45; P = 0.046), MR-Egger (SIMEX) (B: 0.27; P = 0.007), MR-PRESSO (B; 0.14; P = 0.010), and the weighted median (B: 0.15; P = 0.012] methods. Further studies of the associated biological mechanisms are required to validate and understand the disease-specific differences identified in the TD2-related causal effects of each trait.

scholarly journals Causal Evaluation of Laboratory Markers in Type 2 Diabetes on Cancer and Vascular Diseases Using Various Mendelian Randomization Tools

2020 ◽  
Author(s):  
Heejin Jin ◽  
Sanghun Lee ◽  
Sungho Won
Keyword(s):  
Type 2 Diabetes ◽  
Plasma Glucose ◽  
Large Scale ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Density Lipoprotein ◽  
Genome Wide Association Studies ◽  
Postprandial Plasma Glucose ◽  
Weighted Median

Multiple studies have demonstrated the effects of type 2 diabetes (T2D) on various human diseases; however, most of these were observational epidemiological studies that suffered from many potential biases including reported confounding and reverse causations. In this article, we investigated whether cancer and vascular disease can be affected by T2D-related traits, including fasting plasma glucose (FPG), 2-h postprandial plasma glucose (2h-PG), and glycated hemoglobin A1c (HbA1c) levels, by using Mendelian randomization (MR). The summary statistics for FPG, 2h-PG, and HbA1c were obtained through meta-analyses of large-scale genome-wide association studies that included data from 133,010 non-diabetic individuals from collaborating Meta-Analysis of Glucose and Insulin related traits Consortium studies. Thereafter, based on the statistical assumptions for MR analyses, the most reliable approaches including inverse-variance-weighted (IVW), MR-Egger, MR-Egger with a simulation extrapolation (SIMEX), weighted median and MR-Pleiotropy RESidual Sum and Outlier (MR-PRESSO) methods were applied to identify traits affected by FPG, 2h-PG, and HbAlc. We found that coronary artery disease is affected by FPG, as per the IVW [log odds ratio (logOR): 0.21; P=0.012], MR-Egger (SIMEX) (logOR: 0.22; P=0.014), MR-PRESSO (logOR: 0.18; P=0.045), and weighted median (logOR: 0.29; P<0.001) methods, but not as per the MR-Egger (logOR: 0.13; P=0.426) approach. Furthermore, low-density lipoprotein cholesterol levels are affected by HbA1c, as per the IVW (beta (B): 0.23; P=0.015), MR-Egger (B: 0.45; P=0.046), MR-Egger (SIMEX) (B: 0.27; P=0.007), MR-PRESSO (B; 0.14; P=0.010), and the weighted median (B: 0.15; P=0.012) methods. Further studies of the associated biological mechanisms are required to validate and understand the disease-specific differences identified in the TD2-related causal effects of each trait.

scholarly journals Mendelian randomization studies of biomarkers and type 2 diabetes

2015 ◽  
Vol 4 (4) ◽  
pp. 249-260 ◽  
Author(s):  
Ali Abbasi
Keyword(s):  
Type 2 Diabetes ◽  
Genetic Variants ◽  
Large Scale ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Causal Effects ◽  
Current Evidence ◽  
Genome Wide Association Studies ◽  
Glycaemic Traits

Many biomarkers are associated with type 2 diabetes (T2D) risk in epidemiological observations. The aim of this study was to identify and summarize current evidence for causal effects of biomarkers on T2D. A systematic literature search in PubMed and EMBASE (until April 2015) was done to identify Mendelian randomization studies that examined potential causal effects of biomarkers on T2D. To replicate the findings of identified studies, data from two large-scale, genome-wide association studies (GWAS) were used: DIAbetes Genetics Replication And Meta-analysis (DIAGRAMv3) for T2D and the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) for glycaemic traits. GWAS summary statistics were extracted for the same genetic variants (or proxy variants), which were used in the original Mendelian randomization studies. Of the 21 biomarkers (from 28 studies), ten have been reported to be causally associated with T2D in Mendelian randomization. Most biomarkers were investigated in a single cohort study or population. Of the ten biomarkers that were identified, nominally significant associations with T2D or glycaemic traits were reached for those genetic variants related to bilirubin, pro-B-type natriuretic peptide, delta-6 desaturase and dimethylglycine based on the summary data from DIAGRAMv3 or MAGIC. Several Mendelian randomization studies investigated the nature of associations of biomarkers with T2D. However, there were only a few biomarkers that may have causal effects on T2D. Further research is needed to broadly evaluate the causal effects of multiple biomarkers on T2D and glycaemic traits using data from large-scale cohorts or GWAS including many different genetic variants.

scholarly journals Causal Association Between Periodontitis and Type 2 Diabetes: A Bidirectional Two-Sample Mendelian Randomization Analysis

2022 ◽  
Vol 12 ◽  
Author(s):  
Yong-Bo Wang ◽  
Si-Yu Yan ◽  
Xu-Hui Li ◽  
Qiao Huang ◽  
Li-Sha Luo ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Large Scale ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Causal Association ◽  
Bidirectional Association

Background: Previous observational studies have reported a bidirectional association between periodontitis and type 2 diabetes, but the causality of these relationships remains unestablished. We clarified the bidirectional causal association through two-sample Mendelian randomization (MR).Methods: We obtained summary-level data for periodontitis and type 2 diabetes from several published large-scale genome-wide association studies (GWAS) of individuals of European ancestry. For the casual effect of periodontitis on type 2 diabetes, we used five independent single-nucleotide polymorphisms (SNPs) specific to periodontitis from three GWAS. The summary statistics for the associations of exposure-related SNPs with type 2 diabetes were drawn from the GWAS in the Diabetes Genetics Replication and Meta-analysis (DIAGRAM) consortium and the FinnGen consortium R5 release, respectively. For the reversed causal inference, 132 and 49 SNPs associated with type 2 diabetes from the DIAGRAM consortium and the FinnGen consortium R5 release were included, and the summary-level statistics were obtained from the Gene-Lifestyle Interactions in Dental Endpoints consortium. Multiple approaches of MR were carried out.Results: Periodontitis was not causally related with the risk of type 2 diabetes (all p &gt; 0.05). No causal effect of type 2 diabetes on periodontitis was found (all p &gt; 0.05). Estimates were consistent across multiple MR analyses.Conclusion: This study based on genetic data does not support a bidirectional causal association between periodontitis and type 2 diabetes.

Genome-Wide Association Studies Identify Two Novel Loci Conferring Susceptibility to Diabetic Retinopathy in Japanese Patients with Type 2 Diabetes

2021 ◽  
Author(s):  
Minako Imamura ◽  
Atsushi Takahashi ◽  
Masatoshi Matsunami ◽  
Momoko Horikoshi ◽  
Minoru Iwata ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Diabetic Retinopathy ◽  
Association Studies ◽  
Meta Analysis ◽  
Japanese Patients ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Stage 1

Abstract Several reports have suggested that genetic susceptibility contributes to the development and progression of diabetic retinopathy. We aimed to identify genetic loci that confer susceptibility to diabetic retinopathy in Japanese patients with type 2 diabetes. We analysed 5 790 508 single nucleotide polymorphisms (SNPs) in 8880 Japanese patients with type 2 diabetes, 4839 retinopathy cases and 4041 controls, as well as 2217 independent Japanese patients with type 2 diabetes, 693 retinopathy cases, and 1524 controls. The results of these two genome-wide association studies (GWAS) were combined with an inverse variance meta-analysis (Stage-1), followed by de novo genotyping for the candidate SNP loci (p &lt; 1.0 × 10−4) in an independent case–control study (Stage-2, 2260 cases and 723 controls). After combining the association data (Stage-1 and -2) using meta-analysis, the associations of two loci reached a genome-wide significance level: rs12630354 near STT3B on chromosome 3, p = 1.62 × 10−9, odds ratio (OR) = 1.17, 95% confidence interval (CI) 1.11–1.23, and rs140508424 within PALM2 on chromosome 9, p = 4.19 × 10−8, OR = 1.61, 95% CI 1.36–1.91. However, the association of these two loci were not replicated in Korean, European, or African American populations. Gene-based analysis using Stage-1 GWAS data identified a gene-level association of EHD3 with susceptibility to diabetic retinopathy (p = 2.17 × 10−6). In conclusion, we identified two novel SNP loci, STT3B and PALM2, and a novel gene, EHD3, that confers susceptibility to diabetic retinopathy; however, further replication studies are required to validate these associations.

scholarly journals A comprehensive evaluation of methods for Mendelian randomization using realistic simulations and an analysis of 38 biomarkers for risk of type 2 diabetes

2021 ◽  
Author(s):  
Guanghao Qi ◽  
Nilanjan Chatterjee
Keyword(s):  
Type 2 Diabetes ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Real Data ◽  
Causal Effects ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Simulation Studies ◽  
Sample Sizes

Abstract Background Previous studies have often evaluated methods for Mendelian randomization (MR) analysis based on simulations that do not adequately reflect the data-generating mechanisms in genome-wide association studies (GWAS) and there are often discrepancies in the performance of MR methods in simulations and real data sets. Methods We use a simulation framework that generates data on full GWAS for two traits under a realistic model for effect-size distribution coherent with the heritability, co-heritability and polygenicity typically observed for complex traits. We further use recent data generated from GWAS of 38 biomarkers in the UK Biobank and performed down sampling to investigate trends in estimates of causal effects of these biomarkers on the risk of type 2 diabetes (T2D). Results Simulation studies show that weighted mode and MRMix are the only two methods that maintain the correct type I error rate in a diverse set of scenarios. Between the two methods, MRMix tends to be more powerful for larger GWAS whereas the opposite is true for smaller sample sizes. Among the other methods, random-effect IVW (inverse-variance weighted method), MR-Robust and MR-RAPS (robust adjust profile score) tend to perform best in maintaining a low mean-squared error when the InSIDE assumption is satisfied, but can produce large bias when InSIDE is violated. In real-data analysis, some biomarkers showed major heterogeneity in estimates of their causal effects on the risk of T2D across the different methods and estimates from many methods trended in one direction with increasing sample size with patterns similar to those observed in simulation studies. Conclusion The relative performance of different MR methods depends heavily on the sample sizes of the underlying GWAS, the proportion of valid instruments and the validity of the InSIDE assumption. Down-sampling analysis can be used in large GWAS for the possible detection of bias in the MR methods.

Life Course Adiposity and Alzheimer’s Disease: A Mendelian Randomization Study

2021 ◽  
pp. 1-10
Author(s):  
Xian Li ◽  
Yan Tian ◽  
Yu-Xiang Yang ◽  
Ya-Hui Ma ◽  
Xue-Ning Shen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Life Course ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Meta Analysis ◽  
Genome Wide Association Studies ◽  
Fat Percentage ◽  
Regression Methods ◽  
Weighted Median

Background: Several studies showed that life course adiposity was associated with Alzheimer’s disease (AD). However, the underlying causality remains unclear. Objective: We aimed to examine the causal relationship between life course adiposity and AD using Mendelian randomization (MR) analysis. Methods: Instrumental variants were obtained from large genome-wide association studies (GWAS) for life course adiposity, including birth weight (BW), childhood body mass index (BMI), adult BMI, waist circumference (WC), waist-to-hip ratio (WHR), and body fat percentage (BFP). A meta-analysis of GWAS for AD including 71,880 cases and 383,378 controls was used in this study. MR analyses were performed using inverse variance weighted (IVW), weighted median, and MR-Egger regression methods. We calculated odds ratios (ORs) per genetically predicted standard deviation (1-SD) unit increase in each trait for AD. Results: Genetically predicted 1-SD increase in adult BMI was significantly associated with higher risk of AD (IVW: OR = 1.03, 95% confidence interval [CI] = 1.01–1.05, p = 2.7×10–3) after Bonferroni correction. The weighted median method indicated a significant association between BW and AD (OR = 0.94, 95% CI = 0.90–0.98, p = 1.8×10–3). We also found suggestive associations of AD with WC (IVW: OR = 1.03, 95% CI = 1.00–1.07, p = 0.048) and WHR (weighted median: OR = 1.04, 95% CI = 1.00–1.07, p = 0.029). No association was detected of AD with childhood BMI and BFP. Conclusion: Our study demonstrated that lower BW and higher adult BMI had causal effects on increased AD risk.

scholarly journals Evaluation of glycemic traits in susceptibility to COVID-19 risk: a Mendelian randomization study

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Shiu Lun Au Yeung ◽  
Jie V Zhao ◽  
C Mary Schooling
Keyword(s):  
Type 2 Diabetes ◽  
Genetic Predisposition ◽  
Mendelian Randomization ◽  
Fasting Glucose ◽  
Association Studies ◽  
Sensitivity Analyses ◽  
Genome Wide Association Studies ◽  
Inverse Association ◽  
Wide Confidence Interval

Abstract Background Observational studies suggest poorer glycemic traits and type 2 diabetes associated with coronavirus disease 2019 (COVID-19) risk although these findings could be confounded by socioeconomic position. We conducted a two-sample Mendelian randomization to clarify their role in COVID-19 risk and specific COVID-19 phenotypes (hospitalized and severe cases). Method We identified genetic instruments for fasting glucose (n = 133,010), 2 h glucose (n = 42,854), glycated hemoglobin (n = 123,665), and type 2 diabetes (74,124 cases and 824,006 controls) from genome wide association studies and applied them to COVID-19 Host Genetics Initiative summary statistics (17,965 COVID-19 cases and 1,370,547 population controls). We used inverse variance weighting to obtain the causal estimates of glycemic traits and genetic predisposition to type 2 diabetes in COVID-19 risk. Sensitivity analyses included MR-Egger and weighted median method. Results We found genetic predisposition to type 2 diabetes was not associated with any COVID-19 phenotype (OR: 1.00 per unit increase in log odds of having diabetes, 95%CI 0.97 to 1.04 for overall COVID-19; OR: 1.02, 95%CI 0.95 to 1.09 for hospitalized COVID-19; and OR: 1.00, 95%CI 0.93 to 1.08 for severe COVID-19). There were no strong evidence for an association of glycemic traits in COVID-19 phenotypes, apart from a potential inverse association for fasting glucose albeit with wide confidence interval. Conclusion We provide some genetic evidence that poorer glycemic traits and predisposition to type 2 diabetes unlikely increase the risk of COVID-19. Although our study did not indicate glycemic traits increase severity of COVID-19, additional studies are needed to verify our findings.

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 Polygenic Prediction of Type 2 Diabetes in Africa

Diabetes Care ◽  
2022 ◽  
Author(s):  
Tinashe Chikowore ◽  
Kenneth Ekoru ◽  
Marijana Vujkovi ◽  
Dipender Gill ◽  
Fraser Pirie ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
African American ◽  
Association Studies ◽  
Meta Analysis ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Data Set ◽  
Control Subjects ◽  
Design And Methods

OBJECTIVE Polygenic prediction of type 2 diabetes (T2D) in continental Africans is adversely affected by the limited number of genome-wide association studies (GWAS) of T2D from Africa and the poor transferability of European-derived polygenic risk scores (PRSs) in diverse ethnicities. We set out to evaluate if African American–, European-, or multiethnic-derived PRSs would improve polygenic prediction in continental Africans. RESEARCH DESIGN AND METHODS Using the PRSice software, ethnic-specific PRSs were computed with weights from the T2D GWAS multiancestry meta-analysis of 228,499 case and 1,178,783 control subjects. The South African Zulu study (n = 1,602 case and 981 control subjects) was used as the target data set. Validation and assessment of the best predictive PRS association with age at diagnosis were conducted in the Africa America Diabetes Mellitus (AADM) study (n = 2,148 case and 2,161 control subjects). RESULTS The discriminatory ability of the African American and multiethnic PRSs was similar. However, the African American–derived PRS was more transferable in all the countries represented in the AADM cohort and predictive of T2D in the country combined analysis compared with the European- and multiethnic-derived scores. Notably, participants in the 10th decile of this PRS had a 3.63-fold greater risk (odds ratio 3.63; 95% CI 2.19–4.03; P = 2.79 × 10−17) per risk allele of developing diabetes and were diagnosed 2.6 years earlier than those in the first decile. CONCLUSIONS African American–derived PRS enhances polygenic prediction of T2D in continental Africans. Improved representation of non-European populations (including Africans) in GWAS promises to provide better tools for precision medicine interventions in T2D.

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