Keywords
iron - ferritin - rheumatoid arthritis - Mendelian randomization - single nucleotide
polymorphism
Introduction
Rheumatoid arthritis (RA) is a common systemic autoimmune and inflammatory disease
that is predominantly featured by synovial inflammation. It stands as the most prevalent
form of inflammatory arthritis. The incidence of RA is approximately 0.5 to 1%, with
a male-to-female ratio of 2.5/1. It typically affects individuals between the ages
of 40 and 70, with the incidence increasing with age. Tragically, approximately 40%
of patients with RA become disabled after 10 years.[1]
RA attacks the synovial cells and chondrocytes in joints, causing inflammation in
the synovium, damage to cartilage, and even bone erosion. As the disease progresses,
local changes in the synovial tissue lead to chronic, symmetrical inflammation in
multiple joints (polyarthritis) and may involve tissues beyond the joints (extra-articular
lesions).[2] RA primarily affects small joints, like those in the hands, wrists, and feet, with
recurring flare-ups and symmetrical symptoms. Common clinical symptoms include joint
pain, swelling, and stiffness, which can worsen over time and lead to joint destruction
and disability.[1] This significantly impacts a patient's functionality and quality of life.
RA currently lacks a definitive cure. Treatment focuses on managing symptoms and slowing
disease progression.[2] Further research is crucial to improve prevention, diagnosis, and treatment options
for this debilitating condition.
While the pathogenesis of RA remains not fully understood, several risk factors are
established, including gender, smoking status, obesity, Graves' disease, and several
genetic alterations.[2]
Micronutrients are essential for maintaining homeostasis and are linked to many diseases,
including RA. Iron, a key component of hemoglobin and myoglobin, is critical for transporting
oxygen throughout the body, storing it in muscles, and enabling cells to utilize it
for energy production. It also plays a vital role within mitochondria by helping transfer
electrons in the energy-generating electron transport chain. In addition, iron is
an essential trace element critical for various biological processes,[3] playing a central role in maintaining human health. Balanced iron levels are crucial,
as both deficiency and overload are associated with numerous diseases, impacting immune
function and inflammatory responses.[4]
[5] Tightly regulated iron homeostasis ensures optimal health. Recent advancements have
shed light on iron's role in modulating immune cell function and its connection to
various human diseases.[6] For instance, intracellular iron in neuroinflammatory diseases appears to drive
the differentiation of pathogenic Th cells by promoting the production of the proinflammatory
cytokine GM-CSF.[7] Conversely, iron deficiency hinders B cell proliferation and antibody responses,
highlighting its potential role in humoral immunity and vaccination efficacy.[8] Given RA is characterized as an autoimmune disease, and taking into account the
facts that the blood iron levels in patients with RA were significantly lower than
the control and negatively correlated with disease activity,[9] we hypothesize that abnormal iron status might be a causal risk factor for RA. However,
current knowledge on iron's role in RA development remains limited, with studies yielding
inconsistent findings.[1]
[2]
[10]
[11]
[12]
Mendelian randomization (MR) study offers a powerful approach to estimate the causal
effect of an exposure on a disease outcome. It leverages genetic variants as instrumental
variables (IVs) for the exposure. Under specific assumptions, MR can isolate the causal
effect by effectively bypassing confounding variables that might distort traditional
observational studies. These assumptions center around the IVs: (1) they must be robustly
associated with the exposure, (2) they should have no independent association with
factors that confound the exposure–outcome relationship, and (3) their influence on
the outcome must solely operate through the exposure (given confounders are absent).
An additional assumption, monotonicity, is often required to definitively establish
causality.
The potential for a two-way causal relationship between iron deficiency and RA necessitates
a robust approach to untangle their association. MR is ideally suited for this purpose.
This study will leverage MR to investigate whether iron deficiency causally influences
the risk of developing RA.
Methods
MR Study Design
We performed a two-sample MR study to investigate the potential causal associations
of four sets of IVs regarding iron status with the risks of RA. Three hypotheses of
our MR study are that (1) genetic IVs are strongly associated with the iron status,
including ferritin, serum iron, total iron-binding capacity (TIBC), and transferrin
saturation percentage (TSP); (2) they are not associated with any potential confounders;
and (3) they do not affect RA independent of the iron status ([Fig. 1]). There is no need to pre-register the protocol.
Fig. 1 The causal-directed acyclic graph of the two-sample Mendelian randomization study.
Three hypotheses of MR study are that (1) instrumental variables (SNPs, single nucleotide
polymorphisms) are strongly associated with the iron status, including ferritin, serum
iron, total iron-binding capacity, and transferrin saturation; (2) they are not associated
with any potential confounders; and (3) they do not affect rheumatoid arthritis independent
of the iron status.
Data Source and Software
The work presented was performed using publicly available summary-level data from
published genome-wide association study (GWAS).
Iron status data were sourced from the largest available GWAS on iron traits.[13]
[14] This GWAS is a meta-analysis of studies conducted in six European populations (DeCODE,
INTERNAL, SardiNIA, DBDS, HUNT, and MGI). It analyzed four iron status biomarkers:
serum iron (n = 236,612), TSP (n = 198,516), ferritin (n = 257,953), and TIBC (n = 208,422). The GWAS datasets are publicly available for download through NTNU Open
Research Data (https://doi.org/10.18710/S9TJEL).
RA outcome data were retrieved from the IEU open GWAS project, specifically GWAS ID
“ebi-a-GCST90038685” (https://gwas.mrcieu.ac.uk). This dataset comprises 5,427 RA cases and 479,171 controls.
All analyses were performed using R version 4.3.2 (R Foundation for Statistical Computing,
Vienna, Austria). While the MR analysis code is available upon reasonable request
from the corresponding author, institutional review board approval was not required
because this study utilized publicly available summary statistics and did not involve
any patient interaction.
Instrumental Variable Selection
To ensure the robustness of the MR analysis results, we implemented a rigorous quality
control protocol for instrument variable (IV) selection. First, strong associations
between the IVs and exposure were established. Single nucleotide polymorphisms (SNPs)
significantly associated with each iron trait at a genome-wide level (p < 5 × 10−8) were extracted from the iron GWAS meta-analysis. Second, to minimize the influence
of linkage disequilibrium (LD) on the analysis, we excluded SNPs in high LD (defined
by r
2 < 0.001 and clump distance > 10,000 kb). This ensured independent effects of the
selected IVs. Third, we excluded SNPs directly associated with the outcome (p < 5 × 10−6) to minimize potential pleiotropic effects. Finally, we controlled for confounding
factors potentially influencing the MR analysis, such as smoking and obesity, by removing
them from the PhenoScanner database (http://www.phenoscanner.medschl.cam.ac.uk). Palindromic
SNPs with intermediate allele frequencies were excluded. Proxies were used to replace
the missing SNPs in the outcome dataset.
Statistical Analysis
A two-sample MR analysis was performed using the “TwoSampleMR” package to investigate
the causal relationship between iron status and RA development. The random-effects
inverse-variance weighted (IVW) method served as the primary analysis due to its accuracy
when IV assumptions hold. Additionally, several complementary methods were employed
for robustness: MR-Egger to detect and adjust for pleiotropic effects (though with
potential for lower precision, indicated by a p-value for intercept <0.05), weighted median for accuracy assuming at least half the
IVs are valid, and simple/weighted mode for alternative estimates. The lower power
of these complementary methods compared to IVW necessitated prioritizing the IVW findings
for interpretation.
To assess potential heterogeneity, which could bias the results, we employed the MR-heterogeneity
assay. Funnel plots were also generated to visually inspect for heterogeneity. MR-PRESSO
and leave-one-out tests were used to identify and remove outliers in the MR analysis.
Additionally, the intercept term of the MR-Egger method was analyzed (p-value < 0.05 for significance) to identify pleiotropic effects, where SNPs influence
both exposure and other traits besides the outcome. The flowchart of this study is
presented in [Fig. 2].
Fig. 2 Mendelian randomization study flowchart. The boxes represent research steps and the
arrows indicate the flow direction. GWAS, genome-wide association study; MR, Mendelian
randomization; SNP, single nucleotide polymorphism.
Results
IV Selection
Following stringent quality control, we identified SNPs significantly associated with
ferritin, serum iron, TIBC, and TSP. We then excluded SNPs in LD to avoid redundancy.
This resulted in 51 ferritin-associated SNPs, 25 iron-associated SNPs, 34 TIBC-associated
SNPs, and 25 TSP-associated SNPs. Importantly, none of these SNPs showed association
with the outcome or potential confounders. Finally, we identified and removed outliers:
rs114355928 from ferritin, rs9273076 from TIBC, and rs185520326 from TSP.
MR Analysis
The MR scatter plot and random-effects IVW analysis provided strong evidence supporting
the ferritin and serum iron were significantly inversely associated with RA development.
Ferritin had an odds ratio (OR) of 0.997 (95% confidence interval [CI]: 0.995–0.997;
p = 0.010), indicating that a one-unit increase in ferritin is associated with a 0.3%
decrease in the odds of RA. Similarly, serum iron had an OR of 0.997 (95% CI: 0.995–0.999;
p = 0.014). The results of complementary analyses (MR-Egger, weighted median, simple
mode, and weighted mode) showed the same trend as IVW. On the other hand, the IVW
and other analyses found no significant causal associations between TIBC (OR = 1.0,
95% CI: 0.999–1.002; p = 0.592) or TSP (OR = 0.998, 95% CI: 0.996–1.000; p = 0.080) and risk of developing RA ([Fig. 3]).
Fig. 3 Scatter plots visualize the causal association between genetically predicted iron
status and rheumatoid arthritis using five Mendelian randomization (MR) methods. Each
data point represents an instrumental variable (IV), with its horizontal position
reflecting the SNP effect on an iron status measure: (A) ferritin, (B) serum iron, (C) TIBC, and (D) TSP. The vertical position shows the SNP effect on rheumatoid arthritis. Lines connect
the SNP effects for each MR method: inverse-variance weighted (light blue), MR-Egger
(dark blue), simple mode (light green), weighted median (dark green), and weighted
mode (pink). The line's slope represents the causal estimate. A negative slope suggests
an inverse effect of iron status on rheumatoid arthritis. MR, Mendelian randomization;
SNP, single nucleotide polymorphism; TIBC, total iron-binding capacity; TSP, transferrin
saturation percentage.
Our analyses revealed no significant evidence of heterogeneity in the causal estimates
for ferritin (p = 0.706), serum iron (p = 0.076), or TSP (p = 0.081). Additionally, symmetrical funnel ([Fig. 4]) plots for ferritin, iron, and TSP suggested minimal publication bias. Furthermore,
the leave-one-out analysis ([Fig. 5]) confirmed that no single SNP significantly influenced the overall association between
iron status and RA. Finally, MR-Egger intercept analysis detected no pleiotropic effects
for any of the investigated exposures: ferritin (p = 0.320), serum iron (p = 0.974), TIBC (p = 0.178), or TSP (p = 0.155).
Fig. 4 The funnel plots of the causality of iron status and rheumatoid arthritis were symmetrically
distributed for (A) ferritin, (B) serum iron, (C) TIBC, but not (D) TSP. TIBC, total iron-binding capacity; TSP, transferrin saturation percentage.
Fig. 5 The leave-one-out graph indicated that removal of any SNPs had no fundamental effect
on the results, suggesting that the MR results were reliable for (A) ferritin, (B) serum iron, (C) TIBC, and (D) TSP. MR, Mendelian randomization; TIBC, total iron-binding capacity; TSP, transferrin
saturation percentage.
Discussions
Emerging studies reveal a complex interplay between iron metabolism and autoimmune
diseases. In neuroinflammatory conditions, intracellular iron appears to fuel the
development of pathogenic Th cells by promoting GM-CSF production.[7] Conversely, iron deficiency hinders B cell proliferation and antibody responses,
highlighting its role in regulating humoral immunity and potentially impacting vaccination
efficacy.[8] Recent findings link the production of tetrahydrobiopterin (BH4) in activated T
cells to changes in iron metabolism and mitochondrial function. Blocking BH4 synthesis
improved outcomes in T cell-mediated autoimmunity and allergic inflammation, suggesting
a promising therapeutic target.[15] Collectively, these studies underscore the critical role of iron metabolism in T
cell function and autoimmune disease development. Furthermore, a recent study[16] suggests that iron overload can exacerbate the differentiation of Tfh and Th1/Th17
cells, ultimately promoting antibody production and autoimmune response in systemic
lupus erythematosus. The findings solidify the established importance of iron in autoimmune
disorders.
Given the chronic autoimmune nature of RA, it is reasonable to hypothesize that iron
metabolism plays a role in its pathogenesis. However, existing studies on RA present
conflicting data, necessitating further investigation. For example, in a prospective
cohort study consisting of 546 cases of incident RA among 82,063 women,[1] the multivariate models revealed no association between RA and any measure of iron
or protein intake, including red meat, poultry, and fish. While a two-sample MR study[11] suggested a protective effect of genetically determined high iron status against
RA, this finding was not replicated in subsequent studies. Two additional MR studies[10]
[12] and a meta-analysis[2] did not observe an association between iron-related SNPs and RA development.
It is generally accepted that RA often leads to iron deficiency and anemia, which
can worsen physical disability and increase mortality.[17]
[18] Iron deficiency was found in 64% of RA patients, while for women it was 76%. Thus,
iron deficiency was very common among RA patients and its prevalence was several times
higher than the prevalence of anemia. Iron deficiency can be absolute or functional.[19] Absolute iron deficiency occurs when the body's iron stores are depleted, often
due to insufficient dietary iron intake, impaired iron absorption in the gut, or chronic
blood loss, typically from the gastrointestinal tract. Functional iron deficiency,
on the other hand, arises when iron is available but not readily usable by cellular
processes.[19] When we explore the question if iron status is a causal risk of developing RA, the
above-mentioned facts constitute a reverse causation and complicate the study.
MR is a powerful technique that leverages genetic IVs as proxies to investigate causal
relationships between environmental exposures and health outcomes. Since these genetic
variants are randomly assigned at conception, they are less susceptible to confounding
factors like lifestyle, obesity, or environmental influences that might otherwise
distort the true cause-and-effect relationship. MR studies, when equipped with sufficient
sample sizes and carefully chosen SNPs, offer a significant advantage by mitigating
the influence of reverse causation, potentially leading to more definitive conclusions.
Our study identified a potential protective effect of genetically predicted higher
ferritin and serum iron levels against RA. This finding contrasts with previous MR
studies[10]
[12] that did not observe an association between iron-related SNPs and RA development.
The discrepancy likely stems from methodological differences. While prior studies
used a limited number of IVs (3–11 SNPs) in samples of less than 50,000 individuals,
our analysis leveraged a larger dataset (>250,000 individuals) and a more comprehensive
set of IVs (50 ferritin-associated SNPs and 25 iron-associated SNPs). This allowed
us to detect a more subtle causal relationship between ferritin/iron levels and RA
development.
Ferritin, a protein found throughout the body, acts as a cellular iron bank. It safely
stores iron and releases it when needed. In contrast, serum iron refers to the iron
circulating in the bloodstream, readily available for functions like red blood cell
production. Together, ferritin and serum iron represent most of the body's iron stores.
While TIBC and TSP reflect the body's capacity to transport and distribute iron, our
findings suggest that the total amount of iron rather than its distribution plays
a role in RA. This raises the possibility that iron supplementation could be a strategy
for RA prevention, but further research is needed. To strengthen our findings and
establish a better causal link, we propose conducting prospective cohort studies,
which could monitor ferritin and iron levels, track long-term iron supplementation,
and assess the incidence of RA. This approach would provide more compelling evidence
regarding the potential role of iron in RA prevention.
Conclusion
This MR study suggests that individuals with genes linked to higher iron levels may
have a lower risk of developing RA. Our findings indicate that the total amount of
iron in the body, rather than how it is distributed within tissues, might be more
important for RA. This raises the intriguing possibility that iron supplementation
could be a preventative strategy, but further research is necessary before making
any recommendations.
