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.

Effect of metabolites levels on type 2 diabetes mellitus and glycemic traits: a Mendelian randomization study

2021 ◽  
Author(s):  
Yue Sun ◽  
Ya-Ke Lu ◽  
Hao-Yu Gao ◽  
Yu-Xiang Yan
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Meta Analysis ◽  
Significance Level ◽  
Inverse Variance ◽  
Meta Analyses

Abstract Objective To assess the causal associations of plasma levels of metabolites with type 2 diabetes mellitus (T2DM) and glycemic traits. Methods Two-sample mendelian randomization (MR) was conducted to assess the causal associations. Genetic variants strongly associated with metabolites at genome-wide significance level (P < 5 × 10 −8) were selected from public GWAS, and SNPs of Outcomes were obtained from the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) consortium for T2DM and from the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) for the fasting glucose, insulin and HbA1c. The Wald ratio and inverse-variance weighted methods were used for analyses, and MR-Egger was used for sensitivity analysis. Results The β estimates per 1 SD increasement of arachidonic acid (AA) level was 0.16 (95% CI: 0.078, 0.242; P<0.001). Genetic predisposition to higher plasma AA levels were associated with higher FG levels (β 0.10 [95%CI: 0.064, 0.134], P<0.001), higher HbA1c levels (β 0.04 [95%CI: 0.027, 0.061]) and lower FI levels (β -0.025 [95%CI: -0.047, -0.002], P=0.033). Besides, 2-hydroxybutyric acid (2-HBA) might have positive causal effect on glycemic traits. Conclusions Our findings suggest that AA and 2-HBA may have the causal associations on T2DM and glycemic traits. It is beneficial for clarifying the pathogenesis of T2DM, which would be valuable for early identification and prevention for T2DM.

Obesity and cardiovascular outcomes: another look at a meta-analysis of Mendelian randomization studies

2019 ◽  
Vol 68 (2) ◽  
pp. 357-363 ◽  
Author(s):  
George A Kelley ◽  
Kristi S Kelley ◽  
Brian L Stauffer
Keyword(s):  
Type 2 Diabetes ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Mendelian Randomization ◽  
Meta Analysis ◽  
Positive Association ◽  
Small Study ◽  
Point Estimates ◽  
Inverse Variance

This study used the inverse variance heterogeneity (IVhet) model to conduct a reanalysis of a recent meta-analysis that reported a positive association, based on the random-effects (RE) model, between obesity and the incidence of type 2 diabetes and coronary heart disease, but not all-cause stroke, in adults. Data emanated from a recent meta-analysis of five Mendelian randomization studies representing 881,692 adults. Results were pooled using the IVhet model and reported as OR’s and 95% CI. Small-study effects were examined using the Doi plot and Luis Furuya-Kanamori (LFK) index. Influence analysis was also conducted. The association between obesity and type 2 diabetes, coronary heart disease, and all-cause stroke was, respectively, 1.38 (95% CI 1.00 to 1.90, p=0.05, I2=93%), 1.10 (95% CI 0.90 to 1.35, p=0.35, I2=87%), and 1.02 (95% CI 0.95 to 1.09, p=0.64, I2=0%). Compared with the original RE model, results were similar for all-cause stroke, but point estimates for type 2 diabetes and coronary heart disease were smaller (29.3% and 9.8%) with wider (7.0% and 14.7%), overlapping CI. Major asymmetry suggestive of small-study effects was observed (LFK=3.59). With the exception of one study for type 2 diabetes, results remained uncertain (overlapping 95% CI) when each study was deleted from the model once. A lack of certainty exists regarding the association between obesity and the incidence of type 2 diabetes, coronary heart disease, and all-cause stroke in adults.

scholarly journals Association of homocysteine with type 2 diabetes: a meta-analysis implementing Mendelian randomization approach

BMC Genomics ◽  
2013 ◽  
Vol 14 (1) ◽  
pp. 867 ◽  
Author(s):  
Tao Huang ◽  
JingJing Ren ◽  
Jinyan Huang ◽  
Duo Li
Keyword(s):  
Type 2 Diabetes ◽  
Mendelian Randomization ◽  
Meta Analysis

scholarly journals Desaturase Activity and the Risk of Type 2 Diabetes and Coronary Artery Disease: A Mendelian Randomization Study

Nutrients ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2261 ◽  
Author(s):  
Susanne Jäger ◽  
Rafael Cuadrat ◽  
Per Hoffmann ◽  
Clemens Wittenbecher ◽  
Matthias B. Schulze
Keyword(s):  
Type 2 Diabetes ◽  
Coronary Artery Disease ◽  
Fatty Acids ◽  
Coronary Artery ◽  
Cardiometabolic Risk ◽  
Mendelian Randomization ◽  
Meta Analysis ◽  
Genome Wide ◽  
Artery Disease

Estimated Δ5-desaturase (D5D) and Δ6-desaturase (D6D) are key enzymes in metabolism of polyunsaturated fatty acids (PUFA) and have been associated with cardiometabolic risk; however, causality needs to be clarified. We applied two-sample Mendelian randomization (MR) approach using a representative sub-cohort of the European Prospective Investigation into Cancer and Nutrition (EPIC)–Potsdam Study and public data from DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) and Coronary ARtery DIsease Genome wide Replication and Meta-analysis (CARDIoGRAM) genome-wide association studies (GWAS). Furthermore, we addressed confounding by linkage disequilibrium (LD) as all instruments from FADS1 (encoding D5D) are in LD with FADS2 (encoding D6D) variants. Our univariable MRs revealed risk-increasing total effects of both, D6D and D5D on type 2 diabetes (T2DM) risk; and risk-increasing total effect of D6D on risk of coronary artery disease (CAD). The multivariable MR approach could not unambiguously allocate a direct causal effect to either of the individual desaturases. Our results suggest that D6D is causally linked to cardiometabolic risk, which is likely due to downstream production of fatty acids and products resulting from high D6D activity. For D5D, we found indication for causal effects on T2DM and CAD, which could, however, still be confounded by LD.

scholarly journals SUN-016 Mendelian Randomization Reveals That Polycystic Ovary Syndrome Is Not a Causal Risk Factor for Type 2 Diabetes, Coronary Heart Disease, or Stroke

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Tiantian Zhu ◽  
Mark O Goodarzi
Keyword(s):  
Type 2 Diabetes ◽  
Risk Factor ◽  
Polycystic Ovary Syndrome ◽  
Observational Studies ◽  
Mendelian Randomization ◽  
Polycystic Ovary ◽  
Meta Analysis ◽  
Ovary Syndrome ◽  
Causal Risk Factor

Abstract Polycystic ovary syndrome (PCOS) is now recognized not only as a cosmetic and reproductive disorder, but also as a metabolic disorder with important consequences. The balance of the literature suggests that PCOS increases the risk of future type 2 diabetes (T2D); however, whether PCOS increases the risk of coronary heart disease (CHD) and/or stroke is more controversial. Despite a high burden of cardiovascular risk factors (which suggests a high risk of events), the mostly small and retrospective cohort studies have yielded conflicting results. Meta-analyses of these studies suggested at best a 1.4 to 2-fold increased risk of CHD and stroke, which attenuated to 1.2-1.6 when accounting for body mass index (BMI) (1,2). Given that observational studies may be biased by confounders between the risk factor and outcome (in this case, elevated BMI often present in PCOS), we performed Mendelian randomization (MR) analyses to examine the possible causal effect of polycystic ovary syndrome (PCOS) with T2D, CHD and stroke. MR uses genetic variants as instruments to represent exposures of interest to assess causality between exposures and outcomes (3). It is increasingly being used because it overcomes confounding and reverse causation, which often plague observational studies. The instrument variables for PCOS were constructed based on 14 SNPs derived from a published GWAS meta-analysis for PCOS conducted in European cohorts (10,074 cases and 103,164 controls) (4). The SNP to outcomes estimates were obtained from the DIAMANTE T2D GWAS (74,124 T2D cases and 824,006 controls) (5), the CHD GWAS meta-analysis of the UK Biobank plus CARDIoGRAMplusC4D (122,733 cases and 424,528 controls) (6) and the MEGASTROKE consortium GWAS (67,162 cases and 454,450 controls) (7). MR analyses were conducted using three methods: inverse variance weighted (IVW) (primary method), weighted median and MR Egger (sensitivity analyses). In our study, no significant association of genetically predicted PCOS with T2D (OR 0.97, CI 0.91-1.02), CHD (OR 0.99, CI 0.95-1.03) or stroke (OR 0.98, CI 0.93-1.02) was observed. Our findings suggest that PCOS is not a causal risk factor for T2D, CHD or stroke. The observed associations of PCOS with these three diseases from observational studies are likely due to confounding factors and small sample sizes. Given that MR has found that increasing BMI is causal for PCOS as well as T2D and CHD, overweight/obesity is the likely confounding variable. These results suggest a critical revision of how we counsel and manage women with PCOS. Reference: 1. Zhou Y, et al. Gynecol Endocrinol 2017; 33:904-910 2. De Groot PC, et al. Hum Reprod Update 2011; 17:495-500 3. Davey Smith G, et al. Hum Mol Genet. 2014; 23:R89-R98. 4. Day F, et al. PLoS Genet. 2018;14(12). 5. Mahajan A, et al. Nat Genet. 2018;50(11):1505-1513. 6. van der Harst P, et al. Circ Res. 2018;122(3):433-443. 7. Malik R, et al. Nat Genet. 2018;50(4):524-537.

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 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 &lt; 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.

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