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Advances in Rheumatology

, 60:8 | Cite as

Evaluation of the association between KIR polymorphisms and systemic sclerosis: a meta-analysis

  • Elham Karimizadeh
  • Shayan Mostafaei
  • Saeed Aslani
  • Farhad Gharibdoost
  • Ricardo Machado Xavier
  • Patricia Hartstein Salim
  • Hoda Kavosi
  • Elham FarhadiEmail author
  • Mahdi MahmoudiEmail author
Open Access
Review
  • 92 Downloads

Abstract

Background

The results of investigations on the association between killer cell immunoglobulin-like receptor (KIR) gene polymorphisms and the risk of systemic sclerosis (SSc) are inconsistent. To comprehensively evaluate the influence of KIR polymorphisms on the risk of SSc, this meta-analysis was performed.

Methods

A systematic literature search was performed in electronic databases including Scopus and PubMed/MEDLINE to find all available studies involving KIR gene family polymorphisms and SSc risk prior to July 2019. Pooled odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) were measured to detect associations between KIR gene family polymorphisms and SSc risk.

Results

Five articles, comprising 571 patients and 796 healthy participants, evaluating the KIR gene family polymorphisms were included in the final meta-analysis according to the inclusion and exclusion criteria, and 16 KIR genes were assessed. None of the KIR genes were significantly associated with the risk of SSc.

Conclusions

The current meta-analysis provides evidence that KIR genes might not be potential risk factors for SSc risk.

Keywords

Killer immunoglobulin-like receptors Systemic sclerosis Polymorphism Meta-analysis 

Abbreviations

CI

Confidence interval

IFN

Interferon

IL

Interleukin

KIR

Killer cell immunoglobulin-like receptor

MHC

Major histocompatibility complex

MS

Multiple sclerosis

NK

Natural killer

OR

Odds ratios

RA

Rheumatoid arthritis

SLE

Systemic lupus erythematosus

SSc

Systemic sclerosis

TGF

Transforming growth factor

TNF

Tumor necrosis factor

Introduction

Systemic sclerosis (SSc) is a multisystem connective tissue disorder characterized by aberrant immune system activation, vascular abnormalities, inflammatory, and excessive extracellular matrix production, which results in skin and organ fibrosis [1].

Although the pathogenesis of SSc remains obscure, it is generally accepted that the complicated interplay between environmental agents and genetic predisposing factors can lead to the initiate autoimmune responses. Dysregulation of the innate immune system has been detected in autoimmune diseases such as SSc [2]. Natural killer (NK) cells are essential components of innate immune system that contribute to the early host defense. NK cells recognize cancerous and infected host cells through killer cell immunoglobulin-like receptor (KIR)- major histocompatibility complex (MHC) interactions and lyse them without antigen sensitization. In addition, NK cells produce various cytokines, including interferon (IFN)-γ, tumor necrosis factor (TNF)-α, granulocyte-macrophage colony stimulating factor, interleukin (IL)-5, IL-10, IL-13, and transforming growth factor (TGF)-β [3, 4]. TGF-β is defined as a profibrotic cytokine that provokes fibroblasts differentiation into myofibroblasts. Myofibroblasts are believed to be of major effector cells involved in SSc fibrosis [5, 6].

As mentioned above, genetic predisposition is associated with the onset and progression of SSc [7]. Studies have shown that the polymorphisms of genes, including human leukocyte antigen (HLA) [8], signal transducer and activator of transcription 4 (STAT4) [9], B cell scaffold protein with ankyrin repeats 1 (BANK1) [10], protein tyrosine phosphatase, non-receptor type 22 (PTPN22) [11], TNF alpha-induced protein 3 (TNFAIP3) [12], methyl-CpG binding protein 2 (MECP2) [13], interleukin 1 receptor-associated kinase 1 (IRAK1) [14], and killer immunoglobulin-like receptor (KIR) [15] increase the risk of SSc. Moreover, the first genome-wide association study (GWAS) performed in 2010 in a European ancestry population comprising 2296 SSc cases and 5171 controls disclosed the association of CD247 gene with SSc risk [16]. As well, the French SSc GWAS on 2921 SSc patients and 6963 healthy subjects unearthed the significant association of peroxisome proliferator-activated receptor gamma (PPARG) with increased disease risk [17],

KIR receptors are members of the immunoglobulin superfamily, which are expressed on the surface of NK cells and subsets of T cells [18], and are encoded by genes located on human chromosome 19q13.4. Up to date, 17 highly homologous KIR genes have been identified in human, which are divided into three different kinds; activating (2DS1 - 2DS5, and 3DS1), inhibitory (2DL1 - 2DL4, 2DL5A, 2DL5B, 3DL1, 3DL2, and 3DL3), and pseudogenes (2DP1 and 3DP1) [19]. Previous studies have shown that KIR gene polymorphisms are involved in etiopathogenesis of autoimmune diseases, such as rheumatoid arthritis (RA) [20], systemic lupus erythematosus (SLE) [21, 22], multiple sclerosis (MS) [23], and etc.

The association between KIR gene polymorphisms and the risk of SSc have been evaluated by several case-control studies. However, low statistical power, small sample size, clinical heterogeneity, and the extent of linkage disequilibrium between genotypes are elements which could be the cause of the inconsistent results of these studies. Meta-analysis has been proposed as an efficient method, which can integrate small studies and overcome the mentioned limitations [24]. Therefore, in this study, we perform a meta-analysis to clarify the association between KIR polymorphisms and susceptibility to the SSc.

Methods

The PRISMA guidelines were exerted to prepare this article [25].

Searches and data sources

We searched databases including PubMed/MEDLINE and Scopus to find all eligible case-control studies of KIR gene family polymorphisms and SSc risk up to July 2019. Moreover, we searched for non-digitally archived literature and interviewed relevant experts and research centers to identify any gray literature. The following keywords were used to search these databases: (“KIR” or “Killer cell immunoglobulin-like receptors) AND (“systemic sclerosis” OR “scleroderma”) with “OR” and “AND” and “NOT” Boolean operators in the Title/Abstract/Keywords fields. We reviewed all references to include any related studies on genotyping and polymorphisms in the KIR gene family. Only literature published in English and human population studies were included in the current meta-analysis.

Inclusion and exclusion criteria

The following criteria were considered for study inclusion in this meta-analysis: (1) case-control studies that evaluated the association of KIR gene family polymorphisms and SSc risk; and (2) studies with available KIR gene polymorphism frequencies to allow for calculation of odds ratios (ORs) with 95% confidence interval (CIs). The exclusion criteria were (1) duplication or overlapping subjects in any studies; (2) publications that were letters, reviews, comments, or abstract only; and (3) studies with inadequate data with respect to KIR gene polymorphism frequency.

Data extraction and quality assessment

All data were extracted according to the described criteria. The following information was included: first author’s last name, year of publication, and frequency of KIR genes in SSc patients and healthy controls. The Newcastle-Ottawa Scale was used for assessing methodological quality. Studies were graded as low, moderate, or high quality according to scores of 0–3, 4–6, and 7–9, respectively. Two independent investigators without knowledge of existing scores examined the selected studies based on the criteria described above to resolve any discrepancies.

Statistical methods

We used pooled ORs and corresponding 95% CIs for KIR genes to evaluate KIR gene family polymorphisms and SSc risk. In order to calculate the phenotypic frequency (pf %) in each group, the percentage of positive numbers between all samples was used. For calculating genotypic frequency (gf) among all participants, the formula gf = 1– (1 – pf) ½ was exerted. Cochran’s Q test was used to assess heterogeneity, and the I2 method was employed for calculating the variation in the pooled estimations. For the latter test, significance was considered at P < 0.1 [26]. The meta-analysis was performed with a random-effects model when heterogeneity between the individual studies was statistically significant. Otherwise, a fixed-effects model was used. Meanwhile, a sensitivity analysis was done by successively removing a particular study or group of studies (if any) that had the highest impact on the heterogeneity test. A funnel plot was established for checking the existence of publication bias. The funnel plot asymmetry was measured by Egger’s linear regression test and Begg’s test (P < 0.05 was considered to indicate statistically significant publication bias) [27]. All statistical analyses were conducted by using data analysis and statistical software (STATA) (version 11.0; Stata Corporation, College Station, TX) and MedCalc.

Results

Characteristics of the eligible studies

Figure 1 displays the inclusion/exclusion process of the potential studies with respect to the meta-analysis of KIR gene association with the risk of SSc. The initial search resulted in 19 related studies. Based on exclusion/inclusion criteria, 5 articles with 571 patients and 796 healthy participants were included in the final meta-analysis [15, 28, 29, 30, 31]. All included papers were case-control studies. One paper involved the Brazilian population, and the other four studies were conducted in Europe/Germany, Turkey, Mexico, and Iran. The range of publication years was 2004 to 2019 (Table 1). Based on the Newcastle-Ottawa Scale criteria, all included studies had a total score ranging from 7 to 9. The key characteristics and the KIR gene frequencies of the included studies in this meta-analysis are presented in Table 1.
Fig. 1

Flow chart of specifications and procedure for the literature search and study selection

Table 1

Specifications of the included studies in this meta-analysis

Author (Ref)

Published Year

Country/Race

Detection Technique

SSc Patients

Controls

KIR Polymorphisms

N

N

T. Momot [1]

2004

Germany/Caucasian

PCR

102

100

2DL1, 2DL2, 2DL3, 2DS1, 2DS2, 2DS3, 2DS4, 3DS1, 3DL1

P. H. Salim [2]

2013

Brazilian/Caucasian

PCR

115

115

2DL1, 2DL2, 2DL3, 2DL4, 2DL5, 2DS1,2DS2, 2DS3, 2DS4, 2DS5, 3DL1, 3DL2, 3DL3, 3DS1, 2DP1

JD. Tozkır [3]

2016

Turkey/Edirne

PCR-SSP

25

40

2DL1, 2DL2, 2DL3, 2DL4, 2DL5, 2DL5A, 2DL5B, 2DS1, 2DS2, 2DS3, 2DS4, 2DS5, 3DL1, 3DL2, 3DL3, 3DS1, 2DP1, 3DP1

M. Mahmoudi [4]

2017

Iranian/Caucasian

PCR-SSP

279

451

2DL1, 2DL2, 2DL3, 2DL4, 2DL5, 2DL5A, 2DL5B, 3DL1, 3DL2, 3DL3, 2DS1, 2DS2, 2DS3, 2DS4, 2DS4 (full), 2DS4 (var), 2DS5, 3DS1, 2DP1, 3DP1, 3DP1 (full), 3DP1 (var)

AC. Machado-Sulbaran [5]

2019

Mexico

PCR-SSP

50

90

2DL1, 2DL2, 2DL3, 2DL4, 2DL5, 2DS1, 2DS2, 2DS3, 2DS4, 2DS5, 3DL1, 3DL2, 3DL3, 3DS1, 2DP1, 3DP1

Main results and sensitivity analysis

Table 2 presents a summary of the frequency of 16 KIR genes, pooled ORs, and heterogeneity tests of the association between the KIR polymorphisms and susceptibility to SSc. The overall analysis did not show statistically significant association of KIR genes with SSc susceptibility. As examples, the forest plots of KIR2DP1 (A) and KIR3DL1 genes (B) are shown in Fig. 2.
Table 2

Meta-Analysis of the pooled association between KIR polymorphisms and SSc

KIR gene

No. of studies

SSc

Positive (%), Negative (%) / Total

Control

Positive (%), Negative (%) / Total

P-value

Pooled OR

(95% CI)

Heterogeneity Test

Q, I2%; P-value

Publication Bias

(Begg’s Test, P-value; Egger’s test, P-value)

Effect Model

2DL1

5

550 (96.3%), 21 (3.7%) / 571

765 (96.1%), 31 (3.9%) / 796

0.660

1.14 (0.64–2.0)

(3.17, 0.0%; P = 0.52)

(Begg’s Test, 0.99; Egger’s test, 0.43)

Fixed

2DL2

5

283 (49.5%), 288 (51.5%) / 571

444 (55.8%), 352 (44.2%) / 796

0.875

0.94 (0.42–2.07)

(39.87, 89.97%; P < 0.001)

(Begg’s Test, 0.99; Egger’s test, 0.86)

Random

2DL3

5

498 (87.2%), 73 (12.8%) / 571

710 (89.2%), 86 (10.8%) / 796

0.306

0.84 (0.60–1.17)

(4.02, 53%; P = 0.40)

(Begg’s Test, 0.70; Egger’s test, 0.59)

Fixed

2DL4

4

467 (99.6%), 2 (0.4%) / 469

696 (100%), 0 (0%) /696

0.335

0.42 (0.07–2.47)

(0.2, 0.0%; P = 0.97)

(Begg’s Test, 0.99; Egger’s test, 0.81)

Fixed

2DL5

4

180 (38.4%), 289 (61.6%) / 469

273 (39%), 423 (61%) / 696

0.465

0.91 (0.71–1.17)

(2.19, 0.0%; P = 0.53)

(Begg’s Test, 0.39; Egger’s test, 0.09)

Fixed

2DS1

5

285 (49.9%), 286 (50.1%) / 571

427 (53.6%), 369 (46.4%) / 796

0.92

0.98 (0.60–1.58)

(13.76, 70.9%; P = 0.008)

(Begg’s Test, 0.99; Egger’s test, 0.73)

Random

2DS2

5

322 (56.4%), 249 (43.6%) /571

445 (55.9%), 351 (44.1%) / 796

0.923

1.01 (0.81–1.26)

(3.29, 0.0%; P = 0.51)

(Begg’s Test, 0.29; Egger’s test, 0.17)

Fixed

2DS3

5

193 (24.3%), 378 (75.7%) / 571

260 (32.7%), 536 (67.3%) / 796

0.582

1.07 (0.85–1.34)

(7.25, 44.89%; P = 0.122)

(Begg’s Test, 0.12; Egger’s test, 0.16)

Fixed

2DS4

5

321 (56.2%), 250 (43.8%) / 571

395 (49.6%), 401 (50.4%) / 796

0.838

1.03 (0.75–1.45)

(2.61, 0.0%; P = 0.62)

(Begg’s Test, 0.85; Egger’s test, 0.19)

Fixed

2DS5

4

164 (35%), 305 (65%) /469

258 (37.1%), 438 (62.9%) / 696

0.444

0.81 (0.47–1.39)

(9.11, 67.10%; P = 0.02)

(Begg’s Test, 0.99; Egger’s test, 0.73)

Random

3DL1

5

535 (93.7%), 38 (6.3%) / 571

739 (92.8%), 57 (7.2%) / 796

0.683

1.09 (0.71–1.70)

(3.78, 0.0%; P = 0.43)

(Begg’s Test, 0.99; Egger’s test, 0.28)

Fixed

3DL2

4

467 (99.6%), 2 (0.4%) / 469

692 (99.4%), 4 (0.6%) / 696

0.902

1.09 (0.27–4.40)

(0.38, 0.0%; P = 0.94)

(Begg’s Test, 0.90; Egger’s test, 0.53)

Fixed

3DL3

4

467 (98.9%), 5 (1.1%) / 472

696 (99.6%), 3 (0.4%) /699

0.534

0.64 (0.155–2.63)

(0.001, 0.0%; P = 0.99)

(Begg’s Test, 0.99; Egger’s test, 0.60)

Fixed

3DS1

5

256 (44.8%), 315 (5.2%) / 571

342 (43%), 454 (57%) / 796

0.377

1.10 (0.88–1.37)

(2.68, 0.0%; P = 0.61)

(Begg’s Test, 0.19; Egger’s test, 0.11)

Fixed

2DP1

4

460 (98.1%), 9 (1.9%) / 469

672 (96.5%), 24 (3.5%) / 696

0.182

1.672 (0.79–3.56)

(6.99, 57.13%; P = 0.07)

(Begg’s Test, 0.73; Egger’s test, 0.56)

Fixed

3DP1

3

150 (42.4%), 204 (57.6%) / 354

230 (39.6%), 351 (60.4%) / 581

0.152

1.28 (0.91–1.81)

(0.94, 0.0%; P = 0.62)

(Begg’s Test, 0.90; Egger’s test, 0.47)

Fixed

1. Momot T, Koch S, Hunzelmann N, Krieg T, Ulbricht K, Schmidt RE, et al. Association of killer cell immunoglobulin-like receptors with scleroderma. Arthritis and rheumatism. 2004;50 (5):1561–5

2. Salim PH, Jobim M, Bredemeier M, Chies JA, Brenol JC, Jobim LF, et al. Characteristics of NK cell activity in patients with systemic sclerosis. Revista brasileira de reumatologia. 2013;53 (1):66–74

3. Tozkir JD, Tozkir H, Gurkan H, Donmez S, Eker D, Pamuk GE, et al. The investigation of killer cell immunoglobulin-like receptor genotyping in patients with systemic lupus erytematosus and systemic sclerosis. Clinical rheumatology. 2016;35 (4):919–25

4. Mahmoudi M, Fallahian F, Sobhani S, Ghoroghi S, Jamshidi A, Poursani S, et al. Analysis of killer cell immunoglobulin-like receptors (KIRs) and their HLA ligand genes polymorphisms in Iranian patients with systemic sclerosis. Clinical rheumatology. 2017;36 (4):853–62

5. Machado-Sulbaran AC, Ramirez-Duenas MG, Navarro-Zarza JE, Munoz-Valle JF, Mendoza-Carrera F, Banos-Hernandez CJ, et al. KIR/HLA Gene Profile Implication in Systemic Sclerosis Patients from Mexico. Journal of immunology research. 2019;2019:6808061

Fig. 2

Forest plot. The plot shows results of pooled OR for (a) 2DP1 and (b) 3DL1 genes

Sensitivity analysis

A sensitivity analysis was performed by sequential omission of individual and groups of studies. The pooled ORs did not deviate with the sequential omission of any participants or group of studies, indicating that our results were statistically robust (Fig. 3).
Fig. 3

Influence plot. The graph presents sensitivity analysis for (a) 2DP1 and (b) 3DL1 genes

Heterogeneity and publication bias

Heterogeneity between studies was observed for the KIR2DL3 (I2 = 53%; P = 0.40) gene, while other KIR genes did not indicate any heterogeneity. Accordingly, the random- and fixed-effects models were applied to pool the result.

Publication bias was examined by using a funnel plot and Egger’s and Begg’s tests. No publication bias was identified (Table 2, Fig. 4).
Fig. 4

Funnel plot. The plot displays publication bias and heterogeneity results between studies for (a) 2DP1 and (b) 3DL1 genes

Discussion

SSc is a multifactorial and systemic autoimmunity disorder that can lead to fibrosis and disturbance of regular organs function [32]. SSc has a strong dependency on both genetic and environment [33]. The multiplicity of genetic factors, environmental triggers, and their interactions involved in the development of SSc disease make its pathogenesis difficult to identify.

Up to now, several genes have been identified that may influence the risk of SSc development. HLA gene family is the most generally associated gene with SSc disease. An interaction between HLA molecules and KIR receptors on NK cells can mediate the recognition and elimination of defective and foreign cells [34]. Cytotoxic activity of NK cells and certain T cells are regulated with activating and inhibitory KIRs. Preceding studies revealed that the number of NK cells have been increased in the blood of SSc patients [35]. Moreover, T and NK cells phenotype and functional abnormalities were observed in SSc patients, suggesting that these cells may play a main role in the SSc pathogenesis [36, 37, 38].

There is a balance between inhibitory and activating KIRs in healthy individuals. The imbalance between activating and inhibitory KIR genes might influence the pathogenesis of SSc through upregulation of activation or downregulation of inhibition, or a combination of both [15].

Some investigations have been performed with regard to the KIR genes polymorphisms and SSc disease in populations. For instance, Momot et al. reported that the combination of KIR2DS2+/KIR2DL2 was associated with the risk of SSc disease [28]. The results of a study by Salim and colleagues also demonstrated the same results. In addition, they suggested that KIR2DL2+ might be a potential protective factor for SSc [39]. Likewise, an investigation showed that the frequency of KIR2DS3 gene polymorphism in SSc patients was more than healthy controls [30]. In another study, Mahmoudi et al. demonstrated that none of the single KIR genes affected the risk of SSc. Moreover, they reported that the combination of KIR3DL1 with HLA ligands can be a powerful marker for diagnosing of SSc [15].

As mentioned above, findings of the studies evaluating the association between KIR gene polymorphisms and the risk of SSc disease in populations are controversial. Consequently, the present meta-analysis was accomplished to quantitatively assess the relevance of KIR polymorphisms with susceptibility to SSc. In this meta-analysis, the results of five case-control studies with a total of 571 SSc cases and 796 healthy controls were integrated and evaluated. Contrary to what has been observed in previous association studies, no significant association was observed between KIR genes and the risk of SSc.

There are a number of limitations in the present meta-analysis. First, we could not perform further subgroup analysis with respect to ethnicity because of insufficient studies. Third, the meta-analysis was performed based on the data of a limited 5 studies. Therefore, this meta-analysis may have publication bias. In spite of mentioned limitations, this is the first meta-analysis focusing on the correlation between KIR genes polymorphisms and susceptibility to SSc.

Conclusion

In conclusion, this was the first meta-analysis of KIR genes in association with SSc. It was detected that KIR genes are not involved in conferring a susceptibility risk to SSc development.

Notes

Acknowledgements

Not applicable.

Authors’ contributions

EK; Performed literature search and prepared the draft of the paper. SM; Participated in manuscript preparation and draw the figures. SA; Participated in manuscript preparation and designed the Table. FG; Developed the main idea and read the manuscript critically. RMX; Read the manuscript critically. PHS; Read the manuscript critically. HK; Participated in manuscript preparation and designed the work. EF; Developed the main idea, designed the work, and read the manuscript critically. MM; Developed the main idea, designed the work, and read the manuscript critically. All authors read and approved the final manuscript.

Funding

This study was supported by a grant from Deputy of Research, Tehran University of Medical Sciences (Grant No. 98–01–41-42044).

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  • Elham Karimizadeh
    • 1
  • Shayan Mostafaei
    • 2
  • Saeed Aslani
    • 1
  • Farhad Gharibdoost
    • 1
  • Ricardo Machado Xavier
    • 3
  • Patricia Hartstein Salim
    • 3
  • Hoda Kavosi
    • 1
  • Elham Farhadi
    • 1
    • 4
    Email author
  • Mahdi Mahmoudi
    • 1
    • 4
    Email author
  1. 1.Rheumatology Research CenterTehran University of Medical SciencesTehranIran
  2. 2.Medical Biology Research Center, Health Technology InstituteKermanshah University of Medical SciencesKermanshahIran
  3. 3.Universidade Federal do Rio Grande do Sul, Serviço de Reumatologia, Hospital de Clínicas de Porto AlegrePorto AlegreBrazil
  4. 4.Inflammation Research CenterTehran University of Medical SciencesTehranIran

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