Faculty Opinions recommendation of Genetic support of a causal relationship between iron status and type 2 diabetes: A mendelian randomization study.

2021 ◽  
Author(s):  
Om P Ganda
Keyword(s):  
Type 2 Diabetes ◽  
Causal Relationship ◽  
Mendelian Randomization ◽  
Iron Status

A Mendelian Randomization Study on Infant Length and Type 2 Diabetes Mellitus Risk

2019 ◽  
Vol 19 (4) ◽  
pp. 224-231 ◽  
Author(s):  
He Zhuang ◽  
Ying Zhang ◽  
Shuo Yang ◽  
Liang Cheng ◽  
Shu-Lin Liu
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Causal Relationship ◽  
Odds Ratio ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Pleiotropic Effect ◽  
Individual Snps

Objective: Infant length (IL) is a positively associated phenotype of type 2 diabetes mellitus (T2DM), but the causal relationship of which is still unclear. Here, we applied a Mendelian randomization (MR) study to explore the causal relationship between IL and T2DM, which has the potential to provide guidance for assessing T2DM activity and T2DM- prevention in young at-risk populations. Materials and Methods: To classify the study, a two-sample MR, using genetic instrumental variables (IVs) to explore the causal effect was applied to test the influence of IL on the risk of T2DM. In this study, MR was carried out on GWAS data using 8 independent IL SNPs as IVs. The pooled odds ratio (OR) of these SNPs was calculated by the inverse-variance weighted method for the assessment of the risk the shorter IL brings to T2DM. Sensitivity validation was conducted to identify the effect of individual SNPs. MR-Egger regression was used to detect pleiotropic bias of IVs. Results: The pooled odds ratio from the IVW method was 1.03 (95% CI 0.89-1.18, P = 0.0785), low intercept was -0.477, P = 0.252, and small fluctuation of ORs ranged from -0.062 ((0.966 - 1.03) / 1.03) to 0.05 ((1.081 - 1.03) / 1.03) in leave-one-out validation. Conclusion: We validated that the shorter IL causes no additional risk to T2DM. The sensitivity analysis and the MR-Egger regression analysis also provided adequate evidence that the above result was not due to any heterogeneity or pleiotropic effect of IVs.

scholarly journals Genetic support of a causal relationship between iron status and type 2 diabetes: a Mendelian randomization study

2021 ◽  
Author(s):  
Xinhui Wang ◽  
Xuexian Fang ◽  
Wanru Zheng ◽  
Jiahui Zhou ◽  
Zijun Song ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Iron Overload ◽  
Bayesian Model Averaging ◽  
Mendelian Randomization ◽  
Iron Status ◽  
Meta Analysis ◽  
Transferrin Saturation ◽  
Model Averaging ◽  
Underlying Mechanism

Abstract Context Iron overload is a known risk factor for type 2 diabetes (T2D); however, both iron overload and iron deficiency have been associated with metabolic disorders in observational studies. Objective Using Mendelian randomization (MR), we assessed how genetically predicted systemic iron status affected T2D risk. Design and Methods A two-sample MR analysis was used to obtain a causal estimate. We selected genetic variants strongly associated (P < 5×10 −8) with four biomarkers of systemic iron status from a study involving 48,972 subjects performed by the Genetics of Iron Status consortium and applied these biomarkers to the T2D case-control study (74,124 cases and 824,006 controls) performed by the Diabetes Genetics Replication and Meta-analysis consortium. The simple median, weighted median, MR-Egger, MR analysis using mixture-model, weighted allele scores, and MR based on Bayesian model averaging approaches were used for the sensitivity analysis. Results Genetically instrumented serum iron (OR: 1.07; 95% CI: 1.02–1.12), ferritin (OR: 1.19; 95% CI: 1.08–1.32), and transferrin saturation (OR: 1.06; 95% CI: 1.02–1.09) were positively associated with T2D. In contrast, genetically instrumented transferrin, a marker of reduced iron status, was inversely associated with T2D (OR: 0.91; 95% CI: 0.87–0.96). Conclusions Genetic evidence supports a causal link between increased systemic iron status and increased T2D risk. Further studies involving various ethnic backgrounds based on individual-level data and studies regarding the underlying mechanism are warranted for reducing the risk of T2D.

scholarly journals A causal relationship between cigarette smoking and type 2 diabetes mellitus: A Mendelian randomization study

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Shuai Yuan ◽  
Susanna C. Larsson
Keyword(s):  
Type 2 Diabetes ◽  
Cigarette Smoking ◽  
Causal Relationship ◽  
Mendelian Randomization ◽  
Meta Analysis ◽  
Smoking Initiation ◽  
Genome Wide Association ◽  
European Ancestry ◽  
Genome Wide

AbstractThe causality between smoking and type 2 diabetes is unclear. We conducted a two-sample Mendelian randomization study to explore the causal relationship between smoking initiation and type 2 diabetes. Summary-level data for type 2 diabetes were obtained from a meta-analysis of 32 genome-wide association studies (DIAbetes Genetics Replication And Meta-analysis consortium), which included 898 130 individuals of European ancestry. Totally, 377 single-nucleotide polymorphisms associated with smoking initiation at genome wide significance threshold (p < 5 × 10−8) were identified from the hitherto largest genome-wide association study on smoking. The inverse-variance weighted, weighted median, MR-Egger regression, and MR-PRESSO approaches were used to analyze the data. Genetically predicted smoking initiation was associated with type 2 diabetes with an odds ratio of 1.28 (95% confidence interval, 1.20, 1.37; p = 2.35 × 10−12). Results were consistent across sensitivity analyses and there was no evidence of horizontal pleiotropy. This study provides genetic evidence supporting a causal association between the smoking initiation and type 2 diabetes. Reducing cigarette smoking initiation can now be even more strongly recommended for type 2 diabetes prevention.

scholarly journals Birthweight, Type 2 Diabetes and Cardiovascular Disease: Addressing the Barker Hypothesis with Mendelian randomization

2017 ◽  
Author(s):  
Daniela Zanetti ◽  
Emmi Tikkanen ◽  
Stefan Gustafsson ◽  
James Rush Priest ◽  
Stephen Burgess ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Type 2 Diabetes ◽  
Cardiovascular Disease ◽  
Causal Relationship ◽  
Mendelian Randomization ◽  
Large Population ◽  
Sensitivity Analyses ◽  
Lipid Lowering ◽  
Barker Hypothesis

AbstractBackgroundLow birthweight (BW) has been associated with a higher risk of hypertension, type 2 diabetes (T2D) and cardiovascular disease (CVD) in epidemiological studies. The Barker hypothesis posits that intrauterine growth restriction resulting in lower BW is causal for these diseases, but causality and mechanisms are difficult to infer from observational studies. Mendelian randomization (MR) is a new tool to address this important question.MethodsWe performed regression analyses to assess associations of self-reported BW with CVD and T2D in 237,631 individuals from the UK Biobank, a large population-based cohort study aged 40-69 years recruited across UK in 2006-2010. Further, we assessed the causal relationship of such associations using the two- sample MR approach, estimating the causal effect by contrasting the SNP effects on the exposure with the SNP effects on the outcome using independent publicly available genome-wide association datasets.ResultsIn the observational analyses, BW showed strong inverse associations with systolic and diastolic blood pressure (β, −0.83 and −0.26; per raw unit in outcomes and SD change in BW; 95% CI, −0.90, −0.75 and −0.31, −0.22, respectively), T2D (odds ratio [OR], 0.83; 95% CI, 0.79, 0.87), lipid-lowering treatment (OR, 0.84; 95% CI, 0.81, 0.86) and CAD (hazard ratio [HR] 0.85; 95% CI, 0.78, 0.94); while the associations with adult body mass index (BMI) and body fat (β, 0.04 and 0.02; per SD change in outcomes and BW; 95% CI, 0.03, 0.04 and 0.01, 0.02, respectively) were positive. The MR analyses indicated inverse causal associations of BW with low density lipoprotein cholesterol, 2-hour glucose, CAD and T2D, and positive causal association with BMI; but no associations with blood pressure. Sensitivity analyses and robust MR methods provided consistent results and indicated no horizontal pleiotropy.ConclusionOur study indicates that lower BW is causally and directly related with increased susceptibility to CAD and T2D in adulthood. This causal relationship is not mediated by adult obesity or hypertension.

scholarly journals Sex differences in the risk of cataract associated with type 2 diabetes: a Mendelian randomization study

2021 ◽  
Author(s):  
Haoyang Zhang ◽  
Xuehao Xiu ◽  
Yuedong Yang ◽  
Yuanhao Yang ◽  
Huiying Zhao
Keyword(s):  
Type 2 Diabetes ◽  
Genetic Correlation ◽  
Causal Relationship ◽  
Mendelian Randomization ◽  
East Asian ◽  
P Value ◽  
East Asians ◽  
Confident Interval ◽  
Significant Genetic Correlation

Type 2 diabetes (T2D) is a recognized risk factor for developing cataract. However, it is unclear if the shared genetic variance and potential genetic causal relationship between T2D and cataract are different for males and females. We evaluated sex-specific genetic correlation (rg) and putative genetic causality between the two diseases by using linkage disequilibrium score regression (LDSC) and six Mendelian randomization (MR) approaches after lever-aging large-scale population-based genome-wide association studies (GWAS) summary of T2D and cataract. Application of LDSC found a significant genetic correlation between T2D and cataract in East Asian males (rg=0.68, 95% confident interval [CI]=0.17 to 1, p-value=8.60e-3) but a non-significant genetic correlation in East Asian females (rg=0.25, CI= -0.02 to 0.52, p-value=8.38e-2). MR analyses indicated a consistently stronger (paired t-test |t|=5.87, p-value=2.04e-3) causal effect of T2D on cataract in East Asian males (liability OR=1.20 to 1.41, p-value=5.86e-27 to 6.60e-6) than in females (liability OR=1.12 to 1.21, p-value=2.02e-14 to 1.82e-2). In Europeans, the LDSC analysis suggested a close significant genetic correlation between the two diseases in males (rg=0.20, 95% confident interval [CI]=0.08 to 0.32, p-value=7.00e-4) and females (rg=0.17, CI= 0.05 to 0.29, p-value=4.90e-3); but the MR analyses provided weak evidences on a causal relationship between the two diseases in both sexes. These results presented the first evidence on sex difference of the casual relationship between cataract and T2D in East Asians, and supported a potential genetic heterogeneity of the shared genetics underlying T2D and cataract between East Asians and Europeans in both sexes.

scholarly journals The Causal Relationship between Type 2 Diabetes Mellitus and Ovarian Cancer: Two-Sample Mendelian Randomization

2021 ◽  
pp. 1-12
Author(s):  
Langat Kipkirui Victor ◽  
Reuben Cheruiyot Lang’at ◽  
Ayubu Anapapa Okango
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Ovarian Cancer ◽  
Type 2 Diabetes Mellitus ◽  
Causal Relationship ◽  
Mendelian Randomization ◽  
P Value ◽  
Confounding Factors ◽  
Reverse Causation

Aims: This research aimed at determining the causal relationship between type 2 diabetes mellitus (T2DM) and ovarian cancer using two-sample Mendelian randomization technique. This is because there is an assumption that type 2 diabetes mellitus (T2DM) has a causal relationship with ovarian cancer due to the alarming rising incidence statistics. Study design: This study used a two-sample Mendelian Randomization (MR) design to undertake the causal relationship investigation. Mendelian randomization technique uses genetic variants as instrumental variables, which undergo random allocation at conception and are non-modifiable. This makes it not to be affected by confounding factors and reverse causation. The MR techniques employed are MR-Egger and Inverse Variance Weighted (IVW.) Data sources: The outcome (ovarian cancer) summary statistics was retrieved from Ovarian Cancer Association Consortium (OCAC), which has 66,450 samples (number of cases=25,509, number of controls=40,941) of European population. The exposure (T2DM) summary genetic data came from DIAGRAM plus Metabochip consortium which involved approximately 149,821 samples (number of cases=34,840, number of controls=114,981) of mixed population. Results: The study indicated that there was no evidence of causal relationship between T2DM and ovarian cancer (MR-Egger: b= -0.0476, se = 0.0619, p-value = 0.4479, IVW: b = -0.0165, se = 0.0257, p-value = 0.5217). The odds ratios indicated that the two-sample Mendelian randomization had the power to detect 0.0464 and 0.0164 decrease in variability per 1 SD for MR-Egger and IVW respectively (MR-Egger: OR = 0.9536, CI: 0.8447, 1.0765, IVW: OR = 0.9836, CI: 0.9352, 1.0345). Conclusion: This approach alleviated the usual problem of reverse causation and confounding factors hence depicting clearly that there is no causal relationship between T2DM and Ovarian cancer.

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.

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