Clinical and Population Studies

The Effect of Iron Status on Risk of Coronary Artery Disease

A Mendelian Randomization Study—Brief Report

Dipender Gill, Fabiola Del Greco M., Ann P. Walker, Surjit K.S. Srai, Michael A. Laffan, Cosetta Minelli
https://doi.org/10.1161/ATVBAHA.117.309757
Arteriosclerosis, Thrombosis, and Vascular Biology. 2017;37:1788-1792
Originally published July 6, 2017

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Abstract

Objective—Iron status is a modifiable trait that has been implicated in cardiovascular disease. This study uses the Mendelian randomization technique to investigate whether there is any causal effect of iron status on risk of coronary artery disease (CAD).

Approach and Results—A 2-sample Mendelian randomization approach is used to estimate the effect of iron status on CAD risk. Three loci (rs1800562 and rs1799945 in the HFE gene and rs855791 in TMPRSS6) that are each associated with serum iron, transferrin saturation, ferritin, and transferrin in a pattern suggestive of an association with systemic iron status are used as instruments. SNP (single-nucleotide polymorphism)-iron status association estimates are based on a genome-wide association study meta-analysis of 48 972 individuals. SNP-CAD estimates are derived by combining the results of a genome-wide association study meta-analysis of 60 801 CAD cases and 123 504 controls with those of a meta-analysis of 63 746 CAD cases and 130 681 controls obtained from Metabochip and genome-wide association studies. Combined Mendelian randomization estimates are obtained for each marker by pooling results across the 3 instruments. We find evidence of a protective effect of higher iron status on CAD risk (iron odds ratio, 0.94 per SD unit increase; 95% confidence interval, 0.88–1.00; P=0.039; transferrin saturation odds ratio, 0.95 per SD unit increase; 95% confidence interval, 0.91–0.99; P=0.027; log-transformed ferritin odds ratio, 0.85 per SD unit increase; 95% confidence interval, 0.73–0.98; P=0.024; and transferrin odds ratio, 1.08 per SD unit increase; 95% confidence interval, 1.01–1.16; P=0.034).

Conclusions—This Mendelian randomization study supports the hypothesis that higher iron status reduces CAD risk. These findings may highlight a therapeutic target.

Introduction

Highlights

  • Systemic iron status is a modifiable trait that has been implicated in cardiovascular disease.

  • Serum iron, transferrin saturation, ferritin, and transferrin are all markers of systemic iron status.

  • By using genetic variants associated with these 4 markers as surrogates for systemic iron status, this study implements the MR approach to demonstrate a causal effect of systemic iron status on risk of CAD.

  • These findings may highlight a possible therapeutic target for the prevention and treatment of CAD.

Iron serves in several fundamental processes, including erythropoiesis and cellular metabolism.1 Although iron status has been implicated in cardiovascular disease,1 the evidence for this is mixed. In support of a detrimental effect of higher iron status on cardiovascular risk, a reduced incidence of heart disease in premenopausal women as compared with men and postmenopausal women has been attributed to lower levels of stored iron.2 Higher iron stores have also been positively associated with risk factors for cardiovascular disease, such as type 2 diabetes mellitus.3 Furthermore, genetic mutations resulting in hereditary haemochromatosis are associated with an increased incidence of cardiovascular morbidity,4 and chelation of heavy metals using disodium EDTA in patients that have experienced a recent myocardial infarction reduced adverse cardiovascular outcomes.5 However, these findings contrast with the results of a meta-analysis of observational studies that suggests a protective effect of higher iron status on the risk of coronary heart disease (CHD).6 In addition, iron deficiency has been associated with increased mortality in patients with heart failure.7

It can be difficult to disentangle causal effects from spurious associations attributable to confounding and reverse causation in observational study. The Mendelian randomization (MR) approach can overcome these issues by using genetic variants, such as SNPs (single-nucleotide polymorphisms) as proxies or instruments for a phenotype or exposure of interest.8 It is because genetic variants are allocated randomly at the time of conception that this approach is not typically confounded by environmental factors, lifestyle factors, or reverse causation. If the underlying assumptions of MR analysis are met,8 SNPs associated with iron status can be used as instruments in an investigation of the causal effect of iron status on risk of coronary artery disease (CAD). This principle has previously been adopted to explore the causal effect of iron status on atherosclerosis,9 and a similar approach has also been taken to show that red blood cell (RBC) traits are associated with risk of CHD.10

The instruments used in an MR study must influence the intermediate phenotype of interest,8,11 which in this case is systemic iron status. Various correlated markers of iron status are available, including serum iron, transferrin saturation, ferritin, and transferrin.1,12–15 Genetic instruments for iron status that are used in an MR study should have a concordant association with each of these markers, and specifically SNPs that are deemed to increase systemic iron status should be associated with increased levels of serum iron, transferrin saturation and ferritin, and decreased levels of transferrin.9,11,16 Another potential limitation of the MR approach concerns pleiotropy, where genetic variants affect the outcome (CAD risk) through pathways that are independent of the intermediate phenotype of interest (iron status), thus, violating a fundamental assumption of MR to bias the causal effect estimates generated.8,17 In this study, we select instruments for systemic iron status and perform an MR study investigating its causal effect on CAD risk. Furthermore, we explore the possibility that any pleiotropic effects of the instruments may be biasing the estimates generated.

Materials and Methods

Materials and Methods are available in the online-only Data Supplement. The overall study design is demonstrated graphically in Figure 1.

Figure 1.
Figure 1.

Graphical representation of the 2-sample MR study design. Three SNPs (single-nucleotide polymorphisms) that each have genome-wide significant associations with increased serum iron, transferrin saturation and ferritin, and decreased transferrin levels are used as instruments for systemic iron status. By using genetic variants associated with the 4 iron status markers as surrogates, the Mendelian randomization (MR) approach is used to estimate the causal effect of systemic iron status on risk of coronary artery disease (CAD).

Results

The 3 instruments have F statistics for the 4 iron status markers ranging from 47 to 2127 (Table 1). Individual SNP-iron marker estimates are given in Table 1, whereas Table 2 reports the SNP-CAD estimates from the meta-analysis of CARDIoGRAMplusC4D 1000G and CARDIoGRAMplusC4D Metabochip.

View this table:
Table 1.

Results for the SNP-Iron Status Associations12

View this table:
Table 2.

SNP-CAD Associations Obtained by Meta-Analysis of CARDIoGRAMplusC4D 1000G (60 801 CAD Cases and 123 504 Controls) and CARDIoGRAMplusC4D Metabochip (63 746 CAD Cases and 130 681 Controls) Using a Summary Data Method That Accounts for Participant Overlap Between the 2 Studies (34 997 Cases and 49 512 Controls)40–42

Individual and pooled MR estimates for the effect of the 4 markers of iron status on risk of CAD are reported in Figure 2. The results, expressed as odds ratios (ORs) for CAD per SD unit increase in the iron status marker, demonstrate a protective effect on CAD risk for iron (OR, 0.94; 95% confidence interval [CI], 0.88–1.00; P=0.039), transferrin saturation (OR, 0.95; 95% CI, 0.91–0.99; P=0.027), and (log-transformed) ferritin (OR, 0.85; 95% CI, 0.73–0.98; P=0.024). The effect estimate for transferrin (OR, 1.08; 95% CI, 1.01–1.16; P=0.034) is also in keeping with the other results to suggest that higher iron status is protective of CAD because higher transferrin levels reflect lower iron status.

Figure 2.
Figure 2.

Forest plot of the SNP (single-nucleotide polymorphism)-specific and pooled Mendelian randomization (MR) estimates for the causal effect of each iron status marker on coronary artery disease (CAD) risk (odds ratio [OR]). The size of the black squares reflects the precision of the MR estimates and the horizontal lines indicate their 95% confidence intervals (95% CI). The pooled MR estimate is depicted by the center of the diamond, with the corner edges on either side indicating the 95% CI.

Search of an online database of SNP-phenotype associations demonstrated that all 3 instruments are also associated with RBC traits.18,19 Furthermore, the iron status raising allele at rs1800562 in the HFE gene is associated with lower low-density lipoprotein levels, and the iron status raising allele at rs1799945 in the HFE gene is associated with higher systolic and diastolic blood pressures.20,21

Discussion

This work suggests a protective effect of higher iron status on the risk of CAD. The pooled MR estimates for serum iron, transferrin saturation, ferritin, and transferrin all suggest that higher iron status lowers the risk of CAD. The objective of this study is to explore whether CAD risk is affected by iron status, and instruments were selected to reflect this. The finding that all the considered iron status makers give similar causal estimates is consistent with the effect of CAD risk being mediated by iron status rather than any individual marker. The small differences in estimates and CI widths for the causal effects of the 4 markers might be explained by chance and possibly differential measurement error across markers, rather than indicating distinct causal pathways. In addition, the variation in magnitude of the MR estimate CIs across the 3 SNPs for each iron status marker might also reflect the strengths of the SNP-iron status marker associations (as evaluated by the F statistics given in Table 1). In our current MR study, we demonstrate a causal effect of iron status on CAD risk using only the 3 SNPs associated with all 4 iron status markers at genome-wide significance. Given our interest in systemic iron status, we only include genetic variants that have shown genome-wide significant association with all 4 iron status markers in a pattern concordant with an effect on systemic iron status (ie, increased levels of serum iron, transferrin saturation and ferritin, and decreased levels of transferrin) to minimize the risk of including invalid instruments. For example, rs8177240 in the TF gene has genome-wide significant associations with serum iron and transferrin saturation but in opposite directions (Table I in the online-only Data Supplement). Because any effect on systemic iron status should have a concordant direction of effect on both serum iron and transferrin saturation, this genetic variant is unlikely to be a valid instrument. Whereas our approach has the advantage of minimizing risk of incorporating invalid instruments, it pays the price of sacrificing the additional power that might be afforded by considering as instruments all genetic variants associated with any iron status marker at genome-wide significance.22

A potential source of bias with the MR approach relates to the issue of pleiotropy.8,17 Whereas the availability of many instruments allows for implementation of statistical methods to detect and adjust for pleiotropy in sensitivity analyses, such techniques are not applicable when few instruments are available, such as in our study.23–27 Despite this, we have investigated the possibility of pleiotropy by searching for secondary phenotypes, which have shown association with the 3 instruments. The association of the 3 iron status instruments with RBC traits may be expected given the well-established relationship between iron status and anemia,12 but this would not bias the MR analysis if any effect of RBC traits on CAD risk was acting downstream of iron status, rather than independently of it.8,17 The association of the iron status raising allele at rs1800562 (HFE gene) with lower low-density lipoprotein levels and the iron status raising allele at rs1799945 (HFE gene) with higher systolic and diastolic blood pressures are likely to be affecting CAD risk independently of iron status and would, therefore, be expected to bias the MR estimates.20,21 Lower low-density lipoprotein levels and higher blood pressure are known to reduce and increase CAD risk, respectively.20,21 Consistent with the hypothesis of some bias attributable to pleiotropy, rs1800562 and rs1799945 give MR estimates for all markers that tend to, respectively, overestimate and underestimate the effect of iron status on CAD risk as compared with rs855791 (TMPRSS6 gene), although the CIs for the 3 SNPs largely overlap for all markers (Figure 2). Moreover, the pooled MR estimate across the 3 SNPs is comparable with that of rs855791 alone, which has no known pleiotropic associations. Thus, the overall conclusions of this work are unlikely to be severely affected by these pleiotropic effects.

Early work attributed the observed association of heart disease with disorders of iron storage, older age in men, and postmenopausal status in women to the effect of higher systemic iron status.2 However, consequent observational studies did not support this.28 A randomized controlled trial has demonstrated a protective effect of heavy metal chelation induced by disodium EDTA on heart disease, but it is unclear how generalizable this finding is, and the observed effect might be specific to patients that have suffered a recent myocardial infarction or attributable to effects independent of systemic iron status and overall body iron stores.5 By contrast, the conclusions of our MR study are in keeping with a systematic review and meta-analysis of prospective observational studies investigating the association of body iron status and CHD risk.6 All except 1 of the 17 studies included in this meta-analysis adjusted for smoking and major cardiovascular risk factors, such as blood pressure and lipid profile, with some studies also adjusting for social class and chronic disease.6 The risk ratio of CHD for individuals with levels of the iron status marker in the top third compared with individuals in the bottom third was 0.80 (95% CI, 0.73–0.87) for iron, 0.82 (95% CI, 0.75–0.89) for transferrin saturation, 1.03 (95% CI, 0.87–1.23) for ferritin, and 0.99 (95% CI, 0.86–1.13) for transferrin.6 The nonsignificant results for ferritin and transferrin might be attributable to confounding caused by inflammation, which would act to increase serum levels of ferritin and decrease those of transferrin,29 whereas increasing the risk of CHD,30 thus, potentially biasing the ferritin-CHD and transferrin-CHD associations to mask a true protective effect of higher iron status on CHD. The authors concluded that whereas their overall results may suggest a protective effect of higher body iron stores on risk CHD, it is difficult to infer causality because of the possibility of residual confounding and reverse causality bias.6 For example, increased iron status has also been associated with risk of diabetes mellitus, which is an established risk factor for cardiovascular disease.3,31 In our MR study, we have used genetic variants as instrumental variables for iron status to overcome these limitations of observational research and strengthen the evidence for a protective effect of iron status on CAD risk.

Iron deficiency is a treatable condition that affects ≤2 billion people worldwide.1 The suggestion here that low iron status may have a causal effect on cardiovascular disease, therefore, has potentially significant clinical and public health implications. However, it is important to interpret the findings of our MR study in context. Whereas it is unlikely that pleiotropy is wholly responsible for the pattern of our results, we cannot completely exclude this. Compensatory developmental processes, referred to as canalization, can buffer the effects of genetic variation and may have impacted on our MR estimates, although this would be expected to bias results toward the null,32 We note that the Wald-type estimator has been shown to induce bias in the MR analysis of binary outcomes, although of small magnitude (eg, <10%) in typical MR analyses.33 Use of the same combined discovery and replication results from the genome-wide association study meta-analysis to both identify the instruments and estimate their associations with iron status markers may also result in overestimation of the SNP-iron status associations (the Beavis effect or winner’s curse),11,34,35 in turn leading to underestimation of the true causal effect of iron status on CAD risk (bias toward the null), which in our 2-sample MR analysis is estimated as the SNP-CAD association divided by the SNP-iron status association. Our use of instruments that have strong associations with all 4 markers of iron status should, however, minimize any effect of such bias.36 Finally, the conclusions of our work relate to patterns of iron status observed in the population-based studies contributing to the GIS consortium (Genetics of Iron Status) and, therefore, reflect effects in the general population. Further research is needed to investigate the causal effects of iron status on CAD risk in subjects with severe iron overload or deficiency. Similarly, our study does not offer insight into whether the estimates are equally applicable to both men and women. Despite these limitations, the results of this work show consistent and biologically plausible effects. Iron status may be affecting CAD risk via effects on RBCs.10,12 Iron deficiency is also known to impact cellular metabolism37 and may increase CAD risk by this mechanism.

In conclusion, this work is suggestive of a protective effect of higher iron status on risk of cardiovascular disease. This warrants further investigation because these findings may highlight the possible therapeutic targets and risk-reduction strategies.

Disclosures

None.

Footnotes

  • Received April 9, 2017.
  • Accepted June 23, 2017.

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  • The Effect of Iron Status on Risk of Coronary Artery Disease
    Dipender Gill, Fabiola Del Greco M., Ann P. Walker, Surjit K.S. Srai, Michael A. Laffan and Cosetta Minelli
    Arteriosclerosis, Thrombosis, and Vascular Biology. 2017;37:1788-1792, originally published July 6, 2017
    https://doi.org/10.1161/ATVBAHA.117.309757
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