The putative causal effect of type 2 diabetes in risk of cataract: a Mendelian randomization study in East Asian

AbstractBackgroundThe epidemiological association between type 2 diabetes and cataract has been well-established. However, it remains unclear whether the two diseases share a genetic basis, and if so, whether this reflects a causal relationship.MethodsWe utilized East Asian population-based genome-wide association studies (GWAS) summary statistics of type 2 diabetes (Ncase=36,614, Ncontrol=155,150) and cataract (Ncase=24,622, Ncontrol=187,831) to comprehensively investigate the shared genetics between the two diseases. We performed 1. linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics (ρ-HESS) to estimate the genetic correlation and local genetic correlation between type 2 diabetes and cataract; 2. multiple Mendelian randomization (MR) analyses to infer the putative causality between type 2 diabetes and cataract; and 3. Summary-data-based Mendelian randomization (SMR) to identify candidate risk genes underling the causality.ResultsWe observed a strong genetic correlation (rg=0.58; p-value=5.60×10−6) between type 2 diabetes and cataract. Both ρ-HESS and multiple MR methods consistently showed a putative causal effect of type 2 diabetes on cataract, with estimated liability-scale MR odds ratios (ORs) at around 1.10 (95% confidence interval [CI] ranging from 1.06 to 1.17). In contrast, no evidence supports a causal effect of cataract on type 2 diabetes. SMR analysis identified two novel genes MIR4453HG (βSMR=−0.34, p-value=6.41×10−8) and KCNK17 (βSMR=−0.07, p-value=2.49×10−10), whose expression levels were likely involved in the putative causality of type 2 diabetes on cataract.ConclusionsOur results provided robust evidence supporting a causal effect of type 2 diabetes on the risk of cataract in East Asians, and posed new paths on guiding prevention and early-stage diagnosis of cataract in type 2 diabetes patients.Key MessagesWe utilized genome-wide association studies of type 2 diabetes and cataract in a large Japanese population-based cohort and find a strong genetic overlap underlying the two diseases.We performed multiple Mendelian randomization models and consistently disclosed a putative causal effect of type 2 diabetes on the development of cataract.We revealed two candidate genes MIR4453HG and KCNK17 whose expression levelss are likely relevant to the causality between type 2 diabetes and cataract.Our study provided theoretical fundament at the genetic level for improving early diagnosis, prevention and treatment of cataract in type 2 diabetes patients in clinical practice

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

Frontiers in Genetics ◽

10.3389/fgene.2021.792396 ◽

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 > 0.05). No causal effect of type 2 diabetes on periodontitis was found (all p > 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.

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Predicting Type 2 Diabetes Based on Polymorphisms From Genome-Wide Association Studies: A Population-Based Study

Diabetes ◽

10.2337/db08-0425 ◽

2008 ◽

Vol 57 (11) ◽

pp. 3122-3128 ◽

Cited By ~ 191

Author(s):

M. van Hoek ◽

A. Dehghan ◽

J. C.M. Witteman ◽

C. M. van Duijn ◽

A. G. Uitterlinden ◽

...

Keyword(s):

Type 2 Diabetes ◽

Association Studies ◽

Population Based ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Population Based Study ◽

Genome Wide

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Mendelian Randomization Study on Amino Acid Metabolism Suggests Tyrosine as Causal Trait for Type 2 Diabetes

Nutrients ◽

10.3390/nu12123890 ◽

2020 ◽

Vol 12 (12) ◽

pp. 3890

Author(s):

Susanne Jäger ◽

Rafael Cuadrat ◽

Clemens Wittenbecher ◽

Anna Floegel ◽

Per Hoffmann ◽

...

Keyword(s):

Type 2 Diabetes ◽

Amino Acids ◽

Amino Acid ◽

Mendelian Randomization ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Inverse Association ◽

Genome Wide

Circulating levels of branched-chain amino acids, glycine, or aromatic amino acids have been associated with risk of type 2 diabetes. However, whether those associations reflect causal relationships or are rather driven by early processes of disease development is unclear. We selected diabetes-related amino acid ratios based on metabolic network structures and investigated causal effects of these ratios and single amino acids on the risk of type 2 diabetes in two-sample Mendelian randomization studies. Selection of genetic instruments for amino acid traits relied on genome-wide association studies in a representative sub-cohort (up to 2265 participants) of the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study and public data from genome-wide association studies on single amino acids. For the selected instruments, outcome associations were drawn from the DIAGRAM (DIAbetes Genetics Replication And Meta-analysis, 74,124 cases and 824,006 controls) consortium. Mendelian randomization results indicate an inverse association for a per standard deviation increase in ln-transformed tyrosine/methionine ratio with type 2 diabetes (OR = 0.87 (0.81–0.93)). Multivariable Mendelian randomization revealed inverse association for higher log10-transformed tyrosine levels with type 2 diabetes (OR = 0.19 (0.04–0.88)), independent of other amino acids. Tyrosine might be a causal trait for type 2 diabetes independent of other diabetes-associated amino acids.

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

Human Molecular Genetics ◽

10.1093/hmg/ddab044 ◽

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 < 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.

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

International Journal of Epidemiology ◽

10.1093/ije/dyaa262 ◽

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.

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

BMC Medicine ◽

10.1186/s12916-021-01944-3 ◽

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.

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