Impact of the MIF -173G/C variant on cardiovascular disease risk: a meta-analysis of 9,047 participants

1 Introduction

Macrophage migration inhibitory factor (MIF) is an immune cytokine with pro-inflammatory, enzymatic, and hormonal functions implicated the pathogenesis of inflammatory and neoplastic diseases. MIF has various functions including in leukocyte recruitment, regulation of immune responses, inflammation, counter-regulation of glucocorticoid activity, and cellular proliferation (1). It is expressed in several immune cell types including T cells, neutrophils, monocytes/macrophages, and eosinophils and also in pituitary cells, epithelial cells, smooth muscle cells, and cardiomyocytes (2, 3), suggesting that it can have diverse roles in various pathophysiological processes (4, 5). MIF is known to play a critical role in both innate and acquired immune responses and it upregulates the expression of pro-inflammatory cytokines (6, 7). In addition, MIF is implicated in cardiovascular diseases (CVD), acting as a reliable biomarker of disease severity and being readily detectable in the blood and at sites of inflammation (8). MIF may therefore have significant impact on the prognosis of CVD patients through its ability to modulate the disease phenotype.

CVD is a common and leading cause of mortality and morbidity worldwide (9, 10). Recognizing CVD as a serious concern for global health, the WHO launched the 25 × 25 Action Plan in 2013 to reduce premature mortality due to non-communicable diseases by 25% by 2025 (11). The 2015 Global Burden of Diseases study estimated that there are 422.7 million CVD cases and 17.59 million CVD deaths worldwide (10). Furthermore, its prevalence is increasing, mostly due to population growth and aging populations, with especially high prevalences in South and East Asia due to their large and rapidly growing populations. Conversely, CVD mortality rates decreased by ∼15% between 1990 and 2015 in some high-income and some middle-income countries (11), while mortality rates have plateaued in high-income regions such as Western Europe, North America, and Australia. Overall, middle-to-low-income countries appear to be disproportionately burdened by CVD mortality. Additionally, CVD occurs approximately 7–10 years later in females than in males, although it remains a major cause of death in females over 65 years. For instance, recent data from the National Health and Nutrition Examination revealed that the prevalence of MI has increased in females aged between 35 and 45 years over the past two decades while decreasing in similarly aged males (12). Deaths due to CVD are most amenable to rapid intervention, and preventing deaths from CVD requires reliable data on CVD risk factors to inform effective treatment and prevention. Individual predisposition to CVD is determined by both environmental and genetic risk factors, the most prevalent environmental factors being hypertension, hypercholesteremia, diabetes, obesity, smoking, stress, gender, ethnic origin, and a sedentary lifestyle (13–15).

CVD is an umbrella term for different heart diseases including coronary artery disease (CAD), myocardial infarction (MI), heart failure (HF), coronary artery abnormalities (CAA), acute coronary syndrome (ACS), and rheumatic heart disease (RHD) (Figure 1). CHD is defined as the narrowing of the coronary arteries leading to a reduced luminal diameter and hence a decrease in blood flow, and it is the most common cause of MI (16). Hypertension and hypercholesteremia accelerate atherosclerotic plaque development and formation due to endothelial injury, which increases endothelial permeability and allows plasma components to infiltrate (17). Plaque hemorrhage ultimately activates thrombus formation initiated by platelet aggregation. Furthermore, cholesterol and triglycerides contribute to plaque formation, both positively and negatively (18). Prolonged and silent atherosclerosis and plaque formation involve chronic inflammatory reactions that influence immune cell, platelet, and complement recruitment to the site of injury (18). Recent compelling evidence highlights a central role for macrophage proliferation within atherosclerotic lesions in driving disease progression. These macrophages, initially immune cells, undergo multiplication, contributing significantly to the pool of foam cells within arterial walls (19). As part of this process, myocardium-produced MIF is highly associated with the development of various CVDs (20), and there is increasing evidence that MIF plays a major role in atheroma formation and CVD progression.

Figure 1

Figure 1. Cardiovascular disease (CVD) is an umbrella term for different heart diseases that include abnormalities of the pericardial layer (e.g., pericarditis and pericardial effusion), myocardial layer [e.g., myocarditis, cardiomyopathy, coronary artery disease (CAD), myocardial infarction (MI), heart failure (HF), coronary artery abnormalities (CAA), acute coronary syndrome (ACS)], the endocardial layer [e.g., valvular heart diseases including endocarditis and rheumatic heart disease (RHD)], abnormalities of the cardiac conductive system including all cardiac arrythmias, and lastly arterial abnormalities such as hypertension and aneurysm.

MIF is a short gene (<0.7 kb) at 22q11.2 composed of three short exons of 107, 172, and 66 base pairs (21). The MIF promoter harbors two polymorphisms that have a regulatory effect on gene transcription (22): the -974 CATT tetranucleotide repeat, which exists in 5–8 repeats (rs5844572), and the -173 G-to-C polymorphism (rs755622). The CATT₅ repeat is associated with low MIF expression compared with the CATT₆, CATT₇, and CATT₈ repeat alleles. By contrast, -173C allele is associated with high MIF gene expression (23). Both polymorphisms have been reported to be associated with different autoimmune and inflammatory diseases. A meta-analysis of 23 articles from different populations representing 5,559 cases and 7,335 controls reported an association between the -173G/C polymorphism and susceptibility to a wide range of different autoimmune diseases (24). Karakaya et al. reported an association between the MIF -173C allele and erythema nodosum in Löfgren syndrome patients but not sarcoidosis, indicating a role for MIF after the sarcoid inflammatory response has begun (25). Interestingly, MIF demonstrated a specific role in the recruitment and accumulation of inflammatory macrophages in an animal model of polymicrobial sepsis (26). Moreover, MIF was found to play an important role as a stress molecule counteracting the immunosuppressive effect of glucocorticoids in renal inflammation (27), and MIF deficiency suppressed apoptosis and protected the liver from ischemia-reperfusion injury (28). Conversely, MIF has been shown to play a protective role in Parkinson's disease (29).

Therefore, there is evidence that MIF is associated with CVD, with an association between the MIF -173C/G polymorphism and CVD reported in some but not all populations. To clarify this association, here we conducted a meta-analysis based on a systematic literature review to confirm whether MIF -173G/C (rs755622) is associated with the risk of developing CVD.

2 Materials and methods

2.1 Study design and objectives

This review followed Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines (Figure 2) (30) and was registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD42023441139) database. A PICO strategy was used to guide the study design: population, patients with CVD; intervention, association between the MIF -173G/C variant and CVD; and primary outcome, the association between MIF -173G/C and CVD.

Figure 2

Figure 2. Flow chart of the literature review.

2.2 Search strategy

The PubMed, Science Direct, and Embase databases were searched from inception of these databases to June 2023 using the following terms: macrophage migration inhibitory factor or MIF [TEXT WORD] and cardiovascular disease or coronary artery disease [TEXT WORD] or cardiomyopathy [TEXT WORD] or cardiac surgery [TEXT WORD] or HF [TEXT WORD] or rheumatic heart disease [TEXT WORD] or kawasaki disease [TEXT WORD], MI [TEXT WORD] or intracoronary thrombosis [TEXT WORD] or acute coronary syndrome [TEXT WORD] or sudden cardiac death [TEXT WORD]. In addition, the reference lists of compatible articles and conferences were reviewed. Two authors individually screened each article by title and abstract and then evaluated the full text to fully assess eligibility for inclusion.

2.3 Inclusion and exclusion criteria

The inclusion criteria were: (1) all conditions affecting the heart or blood vessels were included as CVD; (2) evaluated MIF -173 G/C polymorphisms and cardiovascular risk; (3) case-control or nested case-control design; (4) included the genotypes for the MIF -173G/C gene polymorphism in CVD cases and controls; and (5) the study reported that the distribution of genotypes among controls was in Hardy-Weinberg equilibrium (HWE). Exclusion criteria were: (1) failed to provide detailed data in the abstract and review; (2) the study was a duplicate; (3) failed to report the genotype frequency; and (4) the controls failed to meet HWE.

2.4 Data extraction and models

The author's details, date of publication, region of study, population ethnicity, number of genotypes analyzed, and the total number of cases and controls were recorded from each article. Two authors individually extracted the required data for each study article, and any disagreement was resolved by consensus or by consultation with a senior author.

Three different genetic models were used to assess the association between the genetic variant and the outcome: the dominant model (Model 1) was defined as the presence of the common allele (CC + CG vs. GG); the recessive genetic model (Model 2) was the presence of rare allele (GG + GC vs. CC); and the allelic model assessed the association between the alleles (C vs. G) and the outcome, regardless of whether it was dominant or recessive.

2.5 Quality assessment

Quality was assessed using the Newcastle-Ottawa quality assessment scale (NOS) for case-control studies. Data quality was judged based on comparability, selection, and outcome of interest for case-control study articles and was noted using a “star system”. To compare study quality, star counts were totaled (Table 1). Data validity was assessed by senior authors based on the provided criteria.

Table 1

Table 1. Characteristics of studies included in the meta-analysis.

2.6 Statistical analysis

All statistical analyses were performed in STATA v17 (StataCorp, College Station, TX, United States). Heterogeneity between studies was assessed with the I² statistic. The pooled odds ratio (OR) with 95% CI in the forest plot was analyzed using a random-effects model [restricted maximum likelihood (REML) method] with the subgroup option in Stata. Begg's funnel plot was used to qualitatively assess the risk of publication bias. All analyses were performed using Stata 18. A p-value <0.05 (two-sided) was considered statistically significant in all analyses.

3 Results

3.1 Study characteristics

Figure 1 summarizes the search process. Forty articles were found in the initial search, 29 of which were excluded after applying exclusion criteria. Eleven articles met the inclusion criteria and were used in the meta-analysis, representing 4,906 controls and 4,141 cases.

The characteristics of the included articles are summarized in Table 1. Of the included studies, three were performed in Arab populations, six in Asian populations, and two in European populations (31–41). All included studies were cross-sectional case-control studies that included the necessary data to calculate the possible association between the MIF -173G/C polymorphism and CVD. One study was published in Chinese, while the remaining studies were published in English.

The individual studies' quality was appraised utilizing the Newcastle-Ottawa Scale (NOS) scoring system. According to the NOS results, 73% of the included studies achieved a score of 5 or higher out of 7 on the NOS scale, indicating an overall good level of quality (Table 1).

3.2 Quantitative data synthesis

The distribution of the MIF -173 genotype in CVD is shown in Table 2, and the meta-analysis results are shown in Table 3. There was no significant impact of the MIF -173G/C polymorphism and the risk of CVD in the three models assessed: Model 1 (dominant): CC + CG vs. GG (Figure 3), Model 2 (recessive): GG + GC vs. CC (Figure 4), and Model 3 (allelic): C vs. G (Figure 5).

Table 2

Table 2. Genotypes and allele frequencies of MIF -173G/C genes in CVD patients and controls.

Table 3

Table 3. Summary of different meta-analysis results.

Figure 3

Figure 3. Forest plot of cardiovascular disease risk associated with the MIF -173 G/C polymorphism in Model 1 (CC + CG vs. GG) in Arab, Asian, and European ethnicities.

Figure 4

Figure 4. Forest plot of cardiovascular disease risk associated with the MIF -173 G/C polymorphism in model 2 (GG + GC vs. CC) in Arab, Asian, and European ethnicities.

Figure 5

Figure 5. Forest plot of cardiovascular disease risk associated with the MIF -173 G/C polymorphism in model 3 (C vs. G) alleles in Arab, Asian, and European ethnicities.

In the stratified analysis by ethnicity, Model 1 (CC + CG vs. GG) and Model 2 (GG + GC vs. CC) demonstrated significant associations between the MIF -173G/C polymorphism and CVD in the Arab population (OR = 0.42, CI 0.30 to 0.61, p < 0.001 and OR = 3.84, CI 1.57 to 9.41, p < 0.001) (Figures 3, 4) but not in the European and Asian populations. Model 3 (C vs. G) also demonstrated a significant association between the MIF -173G/C polymorphism and the risk of CVD in the Arab population (OR = 0.56, CI 0.42 to 0.75, p < 0.001) (Figure 5) and in the Asian population (OR = 1.28, CI 1.12 to 1.46, p < 0.001) but not in the European population.

3.3 Publication bias analyses

Begg's funnel plot and Egger's test were performed to assess publication bias (Figure 6). For all three models, there was no obvious asymmetry and there was no evidence of publication bias for Model 1 (p = 0.851), Model 2 (p = 0.154), or Model 3 (p = 0.687).

Figure 6

Figure 6. Begg's funnel plot of the MIF -173 polymorphism and CVD for all models; Model 1: CC + CG vs. GG, Model 2: GG + GC vs. CC, and Model 3: C vs. G. Egger's test was used to provide statistical evidence of funnel plot symmetry for the three models, and no evidence of publication bias was found in Model 1 (p = 0.851), Model 2 (p = 0.154), and Model 3 (p = 0.687).

4 Discussion

In this meta-analysis, we aimed to comprehensively review and quantify the literature to establish whether the MIF -173G/C (rs755622) polymorphism is associated with a risk of CVD development. By meta-analyzing eleven studies representing 4,279 cases and 5,069 controls, we found no significant association between the MIF -173G/C polymorphism and the risk of CVD in the overall study population in the three models assessed: CC + CG vs. GG (OR = 1.03), GG + GC vs. CC (OR = 0.86), and C vs. G allele (OR = 1.13). In addition, due to overall heterogeneity and variability in study outcomes between different studies, we conducted a subgroup analysis of the different ethnicities, which revealed that the MIF -173G/C polymorphism is significantly associated with a decreased risk of CVD in the Arab population but not the Asian or European populations in the CC + CG vs. GG model. In the second GG + GC vs. CC model, there was again a significant association between the MIF -173G/C polymorphism and the risk of CVD in the Arab population but not the Asian or European populations. Finally, for the C vs. G allele model, a significant association was observed in the Arab population (OR = 0.56) and the Asian population (OR = 1.28) but not the European population for the MIF -173G/C polymorphism and CVD risk. Our findings are similar to other meta-analyses of MIF, where the C allele was found to be more common within CAD patients (42) and those with chronic kidney diseases (43). However, in systemic lupus erythematosus, while serum MIF levels were associated with the disease, a meta-analysis found no association between the -173G/C polymorphism and the disease (44).

In atherosclerosis, macrophages play a pivotal role, undergoing proliferation and apoptosis. Macrophage proliferation contributes to plaque inflammation, while apoptosis, if excessive, may lead to plaque instability (45). MIF influences macrophage functions, promoting recruitment and inhibiting migration. This interplay is crucial in atherosclerotic plaque development. Within plaques, activated macrophages release pro-inflammatory signals and transform into foam cells by engulfing oxidized LDL cholesterol. The balance between macrophage dynamics and MIF's influence determines the progression and severity of atherosclerosis. Blockade of MIF reduces the aortic inflammatory response and is associated with reduction in aortic plaque and foam cell formation (46). In addition to its direct effects on inflammation and plaque stability, MIF interacts intricately with CXCL4L1, leading to the formation of prothrombotic and proinflammatory MIF-CXCL4L1 heterocomplexes (47, 48). These heterocomplexes have been implicated in promoting endothelial dysfunction, thrombosis, and exacerbation of inflammatory responses within the vascular environment (49). The presence of the -173 polymorphism in the MIF gene may modulate the formation or activity of these heterocomplexes, potentially influencing the progression and severity of CAD. Consequently, understanding the interplay between MIF protein levels, genetic variations, and the formation of MIF-CXCL4L1 heterocomplexes is crucial for deciphering the multifaceted molecular mechanisms underlying CAD and devising targeted therapeutic strategies aimed at disrupting these detrimental interactions (50).

Studies of patients with MI have demonstrated dual functions for the MIF polymorphism depending on disease severity and the patient's age. For instance, when cardiac ischemia is brief, MIF secreted by cardiomyocytes is cardioprotective through activation of AMP-activated protein kinase (AMPK) (51). Phosphorylation of AMPK stimulates glucose uptake through glucose transporter-4 (GLUT4). Conversely, when myocardial ischemia is prolonged, MIF activates immune cells, thereby increasing inflammation and cardiac remodeling by utilizing myofibroblasts to promote matrix protein synthesis (51). Similarly, Abdallah et al. (34) reported a similar dual function for MIF in RHD patients, with a lower frequency of the MIF -173C allele in RHD patients compared with controls and in those with later disease onset. Their findings suggested that MIF may help to clear pathogens and apoptotic cells during the early stages of RHD, perhaps protecting cardiomyocytes and delaying valvular damage. Conversely, after repetitive rheumatic insults, MIF may accelerate the recruitment of inflammatory cells and pro-inflammatory mediators, increasing regional inflammation and cardiac tissue damage. Similar studies on other diseases have also demonstrated that MIF is age-dependent. For instance, Das et al. (52) reported that adults expressing the low MIF (CATT₅) allele were more susceptible to Gram-negative bacterial infections, while Lehmann et al. (53) found that adults expressing high levels of MIF polymorphisms were protected from sepsis mortality. In animal models, the protective role of MIF was lost in aged animals after ischemic heart injury, with low MIF expression impairing AMPK activation (54). These data suggest that it is important to consider the patient's age and disease stage when analyzing MIF polymorphisms.

Sex has also been reported to be associated with MIF polymorphisms. The MONICA/KORA Augsburg study concluded that female carriers of the MIF -173C polymorphism were at higher risk of coronary heart disease (55). This result was later confirmed in two studies of Chinese populations (37, 38). In inflammatory diseases, MIF -173 was found to be a disease severity marker for male multiple sclerosis patients (56), while the minor homozygous genotype for both the 974 CATT repeat and the -173G/C polymorphism were reported to protect female patients from major depressive disorder (57). However, another study showed that the MIF -173C allele is a susceptibility factor for depression in type 2 diabetes patients (58). These data suggest that these two polymorphisms are sex-specific disease modifiers.

There are several limitations to our meta-analysis. First, we identified relatively few studies for inclusion, and independent validation is now needed, especially for different ancestries. The number of studies for certain diseases and demographic subgroups was small, and the control group in one study was not in HWE. This precluded meaningful subgroup analysis with specific genotypes. In addition, several records without available original data were excluded from the final analysis. The chance of publication bias is high, as studies with statistically significant results are more likely to be published. The lack of representation of certain ethnicities leads to a reduction in the overall heterogeneity of the study samples, so the results require cautious interpretation. The influence of the MIF -173G/C variant on CVD may be affected by genetic, lifestyle, or environmental factors that were inconsistently measured across studies. This lack of consistent measurement may have led to underreporting of these phenomena in the context of MIF variants and CVD within the scope of this meta-analysis. Finally, variations in diagnostic methodology and criteria for CVD can contribute to inconsistencies, compromising the data integrity of published results. Consequently, these discrepancies may restrict the relevance of this meta-analysis.

In conclusion, our meta-analysis suggests that the MIF -173G/C polymorphism is not significantly associated with the risk of cardiovascular disease in the overall population. In subgroup analysis by ethnicity, the polymorphism was associated with a decreased risk of CVD in the Arab population. Future meta-analyses should consider the dual effect of MIF and the other promoter polymorphisms as well as disease status, sex, and patient age.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Author contributions

HF: Data curation, Methodology, Writing – original draft. WI: Formal Analysis, Investigation, Resources, Supervision, Validation, Writing – review & editing. ZS: Methodology, Formal Analysis, Writing – review & editing. FA: Validation, Visualization, Writing – review & editing. YA: Validation, Visualization, Writing – review & editing. AA: Validation, Writing – review & editing, Funding acquisition, Project administration, Resources. AA: Resources, Validation, Conceptualization, Investigation, Supervision, Writing – review & editing.

Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article.

This publication was supported by Qatar University, internal grant no. QUCP-CHS-2022-551. The findings achieved herein are solely the responsibility of the authors.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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