Genetic Support of A Causal Relationship Between Iron Status and Type 2 Diabetes: A Mendelian Randomization Study.
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ABSTRACT: Context
Iron overload is a known risk factor for type 2 diabetes (T2D); however, iron overload and iron deficiency have both been associated with metabolic disorders in observational studies.Objective
Using mendelian randomization (MR), we assessed how genetically predicted systemic iron status affected T2D risk.Methods
A 2-sample MR analysis was used to obtain a causal estimate. We selected genetic variants strongly associated (P < 5 × 10-8) with 4 biomarkers of systemic iron status from a study involving 48 972 individuals 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 a Bayesian model averaging approaches were used for the sensitivity analysis.Results
Genetically instrumented serum iron (odds ratio [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).Conclusion
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.
SUBMITTER: Wang X
PROVIDER: S-EPMC8530720 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundAtrial fibrillation is the most common arrhythmia disease. Animal and observational studies have found a link between iron status and atrial fibrillation. However, the causal relationship between iron status and AF remains unclear. The purpose of this investigation was to use Mendelian randomization (MR) analysis, which has been widely applied to estimate the causal effect, to reveal whether systemic iron status was causally related to atrial fibrillation.MethodsSingle nucleotide polymorphisms (SNPs) strongly associated (P < 5 × 10-8) with four biomarkers of systemic iron status were obtained from a genome-wide association study involving 48,972 subjects conducted by the Genetics of Iron Status consortium. Summary-level data for the genetic associations with atrial fibrillation were acquired from the AFGen (Atrial Fibrillation Genetics) consortium study (including 65,446 atrial fibrillation cases and 522,744 controls). We used a two-sample MR analysis to obtain a causal estimate and further verified credibility through sensitivity analysis.ResultsGenetically instrumented serum iron [OR 1.09; 95% confidence interval (CI) 1.02-1.16; p = 0.01], ferritin [OR 1.16; 95% CI 1.02-1.33; p = 0.02], and transferrin saturation [OR 1.05; 95% CI 1.01-1.11; p = 0.01] had positive effects on atrial fibrillation. Genetically instrumented transferrin levels [OR 0.90; 95% CI 0.86-0.97; p = 0.006] were inversely correlated with atrial fibrillation.ConclusionIn conclusion, our results strongly elucidated a causal link between genetically determined higher iron status and increased risk of atrial fibrillation. This provided new ideas for the clinical prevention and treatment of atrial fibrillation.
| S-EPMC9153204 | biostudies-literature
Project description:ObjectiveTo explore the genetic causal association between osteoporosis (OP) and iron status through Mendelian randomization (MR).MethodsPublicly available genome-wide association study (GWAS) summary data were used for MR analysis with four iron status-related indicators (ferritin, iron, total iron binding capacity, and transferrin saturation) as exposures and three different types of OP (OP, OP with pathological fracture, and postmenopausal OP with pathological fracture) as outcomes. The inverse-variance weighted (IVW) method was used to analyze the genetic causal association between the four indicators of iron status and OP. The heterogeneity of MR results was determined using IVW and MR-Egger methods. The pleiotropy of MR results was determined using MR-Egger regression. A leave-one-SNP-out test was performed to determine whether the MR results were affected by a single nucleotide polymorphism (SNP). The weighted median method was conducted to further validate our results.ResultsBased on IVW, MR-Egger and weighted median models, we found no causal association between iron status (ferritin, iron, total iron binding capacity, or transferrin saturation) and OP (Pbeta > 0.05 in all models). IVW and MR-Egger analysis of OP with pathological fracture and iron status indicators showed no potential genetic causal association (Pbeta> 0.05 in the two analyses). The results of the weighted median were consistent with those of IVW (Pbeta> 0.05 in all analyses). There was no potential genetic causal association between iron status and postmenopausal OP with pathological fracture based on serum iron (Pbeta>0.05 in all models). No heterogeneity or horizontal pleiotropy was found in any of the analyses. None of the leave-one-out tests in the analyses found any SNP that could affect the results of MR.ConclusionOur results demonstrate that there is no genetic causal association between OP and iron status, but the effects of other factors were not excluded.
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Project description:BackgroundThe association between systemic iron status and lung function was conflicting in observational studies. We aim to explore the potential causal relationships between iron status and the levels of lung function using the two-sample Mendelian randomization (MR) design.MethodsGenetic instruments associated with iron status biomarkers were retrieved from the Genetics of Iron Status (GIS) consortium (N = 48,972). Summary statistics of these genetic instruments with lung function were extracted from a meta-analysis of UK Biobank and SpiroMeta consortium (N = 400,102). The main analyses were performed using the inverse-variance weighted method, and complemented by multiple sensitivity analyses.ResultsBased on conservative genetic instruments, MR analyses showed that genetically predicted higher iron (beta: 0.036 per 1 SD increase, 95% confidence interval (CI): 0.016 to 0.056, P = 3.51 × 10-4), log10-transformed ferritin (beta: 0.081, 95% CI: 0.047 to 0.116, P = 4.11 × 10-6), and transferrin saturation (beta: 0.027, 95% CI: 0.015 to 0.038, P = 1.09 × 10-5) were associated with increased forced expiratory volume in 1 s (FEV1), whereas higher transferrin was associated with decreased FEV1 (beta: -0.036, 95% CI: -0.064 to -0.008, P = 0.01). A significant positive association between iron status and forced vital capacity (FVC) was also observed. However, there is no causal association between iron status and FEV1-to-FVC ratio (P = 0.10). Similar results were obtained from the liberal instruments analyses and multiple sensitivity analyses.ConclusionOur study provided strong evidence to support that higher iron status is causally associated with higher levels of FEV1 and FVC, but has no impact on airway obstruction, confirming iron status as an important target for lung function management.
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Project description:BackgroundType 2 diabetes (T2D) is a prevalent chronic disease associated with several comorbidities.ObjectivesThis study investigated whether the risk of T2D varied with genetically predicted insulin (INS), insulin receptor (INS-R), or insulin-like growth factor 1 receptor (IGF-1R) using genetic variants in a Mendelian randomization (MR) study.MethodsA 2-sample MR study was conducted using summary statistics from 2 genome-wide association studies (GWASs). Genetic predictors of the exposures (INS, INS-R, and IGF-1R) were obtained from a publicly available proteomics GWAS of the INTERVAL randomized controlled trial of blood donation in the United Kingdom. For T2D, the study leveraged the DIAbetes Meta-ANalysis of Trans-Ethnic association studies (DIAMANTE) consortium. The estimated associations of INS, INS-R, and IGF-1R proteins with T2D were based on independent single nucleotide polymorphisms (SNPs) strongly (P < 5 × 10-6) predicting each exposure. These SNPs were applied to publicly available genetic associations with T2D from the DIAMANTE case (n = 74,124) and control (n = 824,006) study of people of European descent. SNP-specific Wald estimates were meta-analyzed using inverse variance weighting with multiplicative random effects. Sensitivity analysis was conducted using the weighted median (WM) and MR-Egger.ResultsINS-R (based on 13 SNPs) was associated with a lower risk of T2D (OR: 0.95 per effect size; 95% CI: 0.92, 0.98; P = 0.001), with similar estimates from the WM and MR-Egger. Insulin (8 SNPs) and IGF-1R (10 SNPs) were not associated with T2D. However, 1 of the SNPs for INS-R was from the ABO blood group gene.ConclusionsThis study is consistent with a causally protective association of the INS-R with T2D. INS-R in RBCs regulates glycolysis and thus may affect their functionality and integrity. However, a pleiotropic effect via the blood group ABO gene cannot be excluded. The INS-R may be a target for intervention by repurposing existing therapeutics or otherwise to reduce the risk of T2D.
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Project description:BackgroundsMany studies have shown particulate matter has emerged as one of the major environmental risk factors for diabetes; however, studies on the causal relationship between particulate matter 2.5 (PM2.5) and diabetes based on genetic approaches are scarce. The study estimated the causal relationship between diabetes and PM2.5 using two sample mendelian randomization (TSMR).MethodsWe collected genetic data from European ancestry publicly available genome wide association studies (GWAS) summary data through the MR-BASE repository. The IEU GWAS information output PM2.5 from the Single nucleotide polymorphisms (SNPs) GWAS pipeline using pheasant-derived variables (Consortium = MRC-IEU, sample size: 423,796). The annual relationship of PM2.5 (2010) were modeled for each address using a Land Use Regression model developed as part of the European Study of Cohorts for Air Pollution Effects. Diabetes GWAS information (Consortium = MRC-IEU, sample size: 461,578) were used, and the genetic variants were used as the instrumental variables (IVs). We performed three representative Mendelian Randomization (MR) methods: Inverse Variance Weighted regression (IVW), Egger, and weighted median for causal relationship using genetic variants. Furthermore, we used a novel method called MR Mixture to identify outlier SNPs.ResultsFrom the IVW method, we revealed the causal relationship between PM2.5 and diabetes (Odds ratio [OR]: 1.041, 95% CI: 1.008-1.076, P = 0.016), and the finding was substantiated by the absence of any directional horizontal pleiotropy through MR-Egger regression (β = 0.016, P = 0.687). From the IVW fixed-effect method (i.e., one of the MR machine learning mixture methods), we excluded outlier SNP (rs1537371) and showed the best predictive model (AUC = 0.72) with a causal relationship between PM2.5 and diabetes (OR: 1.028, 95% CI: 1.006-1.049, P = 0.012).ConclusionWe identified the hypothesis that there is a causal relationship between PM2.5 and diabetes in the European population, using MR methods.
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Project description:BACKGROUND:Several observational studies have shown an association between increased circulating homocysteine and risk of type 2 diabetes (T2D). We aimed to assess whether this relation is causal using genetic data from large populations of individuals of European descent. METHODS:We investigated the association between homocysteine concentrations and blood glucose, plasma insulin, T2D in the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) cohort (n?=?1,016). A score of five previously published single nucleotide polymorphisms (SNPs) from genes involved in homocysteine metabolism were utilized as genetic instrument for homocysteine concentrations. The effect estimate of this genetic score with T2D was determined using results from the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) consortium (including 34,840 cases and 114,981 controls). Further, the effects of the genetic score with fasting glucose and insulin were determined using results from the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) (up to 38,238 non-diabetic participants). RESULTS:The genetic score provided a strong instrument for homocysteine concentrations (P?=?2.7?×?10(-143), F?=?650). In the PIVUS cohort, we found an association of homocysteine with fasting insulin [??=?0.056 (95% CI 0.021, 0.090), P?=?0.001], but not with incident diabetes. We did not find any evidence of a causal effect of homocysteine on fasting glucose, fasting insulin, or T2D (P?>?0.05 for all analyses) when using data from DIAGRAM or MAGIC studies. CONCLUSION:No evidence of a causal relationship of levels of plasma homocysteine with fasting glucose, fasting insulin, or T2D was observed.
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Project description:ObjectiveTo investigate the causal relationships between linoleic acid and type 2 diabetes, and between linoleic acid and glycemic traits in European populations.MethodsThis study employed a two-sample Mendelian randomization approach to infer causality between linoleic acid and type 2 diabetes, as well as between linoleic acid and glycemic traits, leveraging genetic variations. Data were sourced from genome-wide association study summary datasets. Random-effects inverse-variance weighted, weighted median, and MR-Egger methods were used for the two-sample Mendelian randomization analyses. Results were presented as odds ratios with a 95% confidence interval. Multiple sensitivity analyses were conducted to assess result robustness.ResultsMR findings indicated a correlation between linoleic acid levels and the risk of type 2 diabetes, fasting blood glucose, and glycated hemoglobin (HbA1c), but not with fasting insulin. Specifically: type 2 diabetes (OR: 0.811, 95% CI: 0.688-0.956, P=0.013<0.05),fasting blood glucose (β_IVW): -0.056, 95% CI: (-0.091,-0.021), P=0.002< 0.0125), glycated hemoglobin (β_IVW: -0.032, 95% CI: (-0.048,-0.015), P=0.0002< 0.0125) and Fasting insulin (β_IVW: -0.024, 95% CI: (-0.056,-0.008), P=0.136 >0.05).Reverse MR analyses showed a correlation between type 2 diabetes and reduced levels of linoleic acid (β_IVW: -0.033, 95% CI: (-0.059,-0.006), P=0.014<0.05). Multiple sensitivity analyses also detected study heterogeneity but found no evidence of horizontal pleiotropy.ConclusionHigh levels linoleic acid can reduce the risk of type 2 diabetes, fasting blood glucose, and glycated hemoglobin, but has no significant relation with fasting insulin. Type 2 diabetes can lower linoleic acid levels; however, no significant causal relationship was observed between the three glycemic traits and reduced levels of linoleic acid.
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Project description:Type 2 diabetes (T2D) frequently co-occurs with respiratory system diseases such as chronic obstructive pulmonary disease (COPD), bronchial asthma, lung cancer, interstitial lung disease, and pulmonary tuberculosis. Although a potential association is noted between these conditions, the available research is limited. To investigate the causal relationship between patients with T2D and respiratory system diseases using two-sample Mendelian randomization analysis. Causal relationships were inferred using a two-sample Mendelian randomization (MR) analysis based on publicly available genome-wide association studies. We employed the variance inverse-weighted method as the primary analytical approach based on three key assumptions underlying MR analysis. To bolster the robustness and reliability of our results, we utilized MR Egger's intercept test to detect potential pleiotropy, Cochran's Q test to assess heterogeneity, funnel plots to visualize potential bias, and "leave-one-out" sensitivity analysis to ensure that our findings were not unduly influenced by any single genetic variant. The inverse variance weighted (IVW) analysis indicated a causal relationship between T2D and COPD [Odds Ratio (OR) = 0.87; 95% Confidence Interval (CI) = 0.82-0.96; p < 0.05]. No significant heterogeneity or pleiotropy were observed through their respective tests (p > 0.05), and the statistical power calculations indicated that the results were reliable. The IVW analysis showed a negative causal relationship between T2D and bronchial asthma [OR = 0.85; 95% CI = 0.81-0.89; p < 0.05]. However, the IVW under the random-effects model indicated heterogeneity (p < 0.05), suggesting instability in the results and requiring cautious interpretation. The study found a positive causal relationship between T2D and pulmonary tuberculosis (OR = 1.24, 95% CI = 1.05-1.45, p < 0.05). However, they exhibited pleiotropy (p < 0.05), indicating their instability. No correlation between T2D and interstitial lung disease or lung cancer was observed. T2D is negatively associated with COPD, suggesting that T2D may reduce the risk of developing COPD. A negative causal relationship between T2D and bronchial asthma has been observed, but the results exhibit heterogeneity. There is a positive causal relationship between T2D and pulmonary tuberculosis, yet the findings suggest the presence of pleiotropy. No significant causal relationship between T2D and lung cancer or interstitial lung disease was observed.
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