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.

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

2019 ◽  
Author(s):  
Guanghao Qi ◽  
Nilanjan Chatterjee
Keyword(s):  
Type 2 Diabetes ◽  
Complex Traits ◽  
Type I Error ◽  
Mendelian Randomization ◽  
Realistic Model ◽  
Type I ◽  
Simulation Studies ◽  
Sample Sizes ◽  
Effect Size Distribution

AbstractBackgroundMendelian randomization (MR) has provided major opportunities for understanding the causal relationship among complex traits. Previous studies have often evaluated MR methods based on simulations that do not adequately reflect the data-generating mechanism in GWAS and there are often discrepancies in performance of MR methods in simulations and real datasets.MethodsWe use a simulation framework that generates data on full GWAS for two traits under realistic model for effect-size distribution coherent with 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 to investigate their causal effects on risk of type-2 diabetes using externally available GWAS summary-statistics.ResultsSimulation studies show that weighted mode and MRMix are the only two methods which maintain correct type-I error rate in a diverse set of scenarios. Between the two methods, MRMix tends to be more powerful for larger GWAS while the opposite being true for smaller sample sizes. Among the other methods, random-effect IVW, MR-Robust and MR-RAPS tend to perform best in maintaining 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 risk of type-2 diabetes across the different methods, with patterns similar to those observed in simulation studies.ConclusionsRelative performance of different MR methods depends heavily on sample sizes of underlying GWAS, proportion of valid instruments and validity of the InSIDE assumption.Key MessagesMany previous simulations studies to evaluate Mendelian randomization methods do not adequately reflect the data-generating mechanism of genome-wide association studies (GWAS).We use a simulation framework that generates data on full GWASs under realistic model informed by recent studies on effect-size distribution. We also used very recent GWAS data available on a large number of biomarkers to evaluate their causal effect on type-2 diabetes using alternative methods.Among the 10 methods that were compared, relative performance of different methods depends heavily on sample sizes of underlying GWAS, proportion of valid instruments and validity of the InSIDE assumption.Weighted mode and MRMix are the only two methods that maintain correct type I error rate in a diverse set of scenarios.

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 Type 2 Diabetes and Glycemic Traits Are Not Causal Factors of Osteoarthritis: A Two-Sample Mendelian Randomization Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhiyong Cui ◽  
Hui Feng ◽  
Baichuan He ◽  
Yong Xing ◽  
Zhaorui Liu ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Causal Effects ◽  
Sensitivity Analyses ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Knee Oa ◽  
Increased Risk

BackgroundIt remains unclear whether an increased risk of type 2 diabetes (T2D) affects the risk of osteoarthritis (OA).MethodsHere, we used two-sample Mendelian randomization (MR) to obtain non-confounded estimates of the effect of T2D and glycemic traits on hip and knee OA. We identified single-nucleotide polymorphisms (SNPs) strongly associated with T2D, fasting glucose (FG), and 2-h postprandial glucose (2hGlu) from genome-wide association studies (GWAS). We used the MR inverse variance weighted (IVW), the MR–Egger method, the weighted median (WM), and the Robust Adjusted Profile Score (MR.RAPS) to reveal the associations of T2D, FG, and 2hGlu with hip and knee OA risks. Sensitivity analyses were also conducted to verify whether heterogeneity and pleiotropy can bias the MR results.ResultsWe did not find statistically significant causal effects of genetically increased T2D risk, FG, and 2hGlu on hip and knee OA (e.g., T2D and hip OA, MR–Egger OR = 1.1708, 95% CI 0.9469–1.4476, p = 0.1547). It was confirmed that horizontal pleiotropy was unlikely to bias the causality (e.g., T2D and hip OA, MR–Egger, intercept = −0.0105, p = 0.1367). No evidence of heterogeneity was found between the genetic variants (e.g., T2D and hip OA, MR–Egger Q = 30.1362, I2 < 0.0001, p = 0.6104).ConclusionOur MR study did not support causal effects of a genetically increased T2D risk, FG, and 2hGlu on hip and knee OA risk.

scholarly journals Type 2 diabetes and glycemic traits are not causal factors of osteoarthritis: A two-sample Mendelian randomization analysis

2020 ◽  
Author(s):  
Zhiyong Cui ◽  
Hui Feng ◽  
Baichuan He ◽  
Yong Xing ◽  
Zhaorui Liu ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Causal Effects ◽  
Sensitivity Analyses ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Knee Oa ◽  
Increased Risk

Abstract Background: It remains unclear whether an increased risk of type 2 diabetes (T2D) affects the risk of osteoarthritis (OA). Methods: Here, we used two-sample Mendelian randomization (MR) to obtain non-confounded estimates of the effect of T2D and glycemic traits on hip and knee OA. We identified single nucleotide polymorphisms (SNPs) strongly associated with T2D, fasting glucose (FG) and 2-hour postprandial glucose (2hGlu) from genome-wide association studies (GWAS) . We used MR inverse variance weighted (IVW), the MR-Egger method, the weighted median (WM) and Robust Adjusted Profile Score (MR.RAPS) to reveal the associations of T2D, FG and 2hGlu with hip and knee OA risk. Sensitivity analyses were also conducted to verify whether heterogeneity and pleiotropy can bias the MR results.Results: We did not find statistically significant causal effects of genetically increased T2D risk, FG and 2hGlu on hip and knee OA (e.g., T2D and hip OA, MR-Egger OR=0.9536, 95% CI 0.5585 to 1.6283, p=0.8629). It was confirmed that horizontal pleiotropy was unlikely to bias the causality (e.g., T2D and hip OA, MR-Egger, intercept=-0.0032, p=0.8518). No evidence of heterogeneity was found between the genetic variants (e.g., T2D and hip OA, MR-Egger Q=40.5481, I2=0.1368, p=0.2389). Conclusions: Our MR study did not support causal effects of a genetically increased T2D risk, FG and 2hGlu on hip and knee OA 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 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 The putative causal effect of type 2 diabetes in risk of cataract: a Mendelian randomization study in East Asian

2021 ◽  
Author(s):  
Haoyang Zhang ◽  
Xuehao Xiu ◽  
Angli Xue ◽  
Yuedong Yang ◽  
Yuanhao Yang ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Genetic Correlation ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Population Based ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Genome Wide

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

scholarly journals Type 2 Diabetes, Metabolic Traits, and Risk of Heart Failure: A Mendelian Randomization Study

2021 ◽  
Author(s):  
Ify R Mordi ◽  
R Thomas Lumbers ◽  
Colin NA Palmer ◽  
Ewan R Pearson ◽  
Naveed Sattar ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Glycated Haemoglobin ◽  
Sensitivity Analyses ◽  
Genome Wide Association Studies ◽  
Fasting Insulin ◽  
Genetic Liability ◽  
Level Data

<b>Objective</b> <p>The aim of this study was to use Mendelian randomization (MR) techniques to estimate the causal relationships between genetic liability to type 2 diabetes, glycaemic traits and risk of HF.</p> <p><b>Research Design and Methods</b></p> <p>Summary-level data were obtained from genome-wide association studies (GWAS) of type 2 diabetes, insulin resistance (IR), glycated haemoglobin, fasting insulin and glucose and HF. MR was conducted using the inverse variance weighted (IVW) method. Sensitivity analyses included MR-Egger, weighted median and mode methods, and multivariable MR conditioning on potential mediators.</p> <p><b>Results</b></p> <p>Genetic liability to type 2 diabetes was causally related to higher risk of HF (OR: 1.13 per 1 log-unit higher risk of type 2 diabetes; 95% CI 1.11-1.14, p<0.001), however sensitivity analysis revealed evidence of directional pleiotropy. The relationship between type 2 diabetes and HF was attenuated when adjusted for coronary disease, body mass index, LDL-cholesterol and blood pressure. Genetically-instrumented higher IR was associated with higher risk of HF (OR 1.19 per 1 log-unit higher risk of IR; 95% CI 1.00-1.41, p=0.041). There were no notable associations identified between fasting insulin, glucose or glycated haemoglobin and risk of HF. Genetic liability to HF was causally linked to higher risk of type 2 diabetes (OR 1.49; 95% CI 1.01-2.19, p=0.042) though again with evidence of pleiotropy.</p> <p><b>Conclusions</b></p> These findings suggest a causal role of type 2 diabetes and IR in HF aetiology, though both the presence of bidirectional effects and directional pleiotropy highlight potential sources of bias that need to be considered.

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.

scholarly journals Type 2 Diabetes, Metabolic Traits, and Risk of Heart Failure: A Mendelian Randomization Study

2021 ◽  
Author(s):  
Ify R Mordi ◽  
R Thomas Lumbers ◽  
Colin NA Palmer ◽  
Ewan R Pearson ◽  
Naveed Sattar ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Glycated Haemoglobin ◽  
Sensitivity Analyses ◽  
Genome Wide Association Studies ◽  
Fasting Insulin ◽  
Genetic Liability ◽  
Level Data

<b>Objective</b> <p>The aim of this study was to use Mendelian randomization (MR) techniques to estimate the causal relationships between genetic liability to type 2 diabetes, glycaemic traits and risk of HF.</p> <p><b>Research Design and Methods</b></p> <p>Summary-level data were obtained from genome-wide association studies (GWAS) of type 2 diabetes, insulin resistance (IR), glycated haemoglobin, fasting insulin and glucose and HF. MR was conducted using the inverse variance weighted (IVW) method. Sensitivity analyses included MR-Egger, weighted median and mode methods, and multivariable MR conditioning on potential mediators.</p> <p><b>Results</b></p> <p>Genetic liability to type 2 diabetes was causally related to higher risk of HF (OR: 1.13 per 1 log-unit higher risk of type 2 diabetes; 95% CI 1.11-1.14, p<0.001), however sensitivity analysis revealed evidence of directional pleiotropy. The relationship between type 2 diabetes and HF was attenuated when adjusted for coronary disease, body mass index, LDL-cholesterol and blood pressure. Genetically-instrumented higher IR was associated with higher risk of HF (OR 1.19 per 1 log-unit higher risk of IR; 95% CI 1.00-1.41, p=0.041). There were no notable associations identified between fasting insulin, glucose or glycated haemoglobin and risk of HF. Genetic liability to HF was causally linked to higher risk of type 2 diabetes (OR 1.49; 95% CI 1.01-2.19, p=0.042) though again with evidence of pleiotropy.</p> <p><b>Conclusions</b></p> These findings suggest a causal role of type 2 diabetes and IR in HF aetiology, though both the presence of bidirectional effects and directional pleiotropy highlight potential sources of bias that need to be considered.

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