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. 2024 Dec 27;103(52):e40893. doi: 10.1097/MD.0000000000040893

The effects of coagulation factors on the risk of autoimmune diseases: A Mendelian randomization study

Shuxuan Li a, Chunlei Zhou a, Wenjing Li a, Lichun Kang a, Hong Mu a,*
PMCID: PMC11688059  PMID: 39969330

Abstract

The objective of this study was to investigate the potential causal relationship between coagulation factors and autoimmune diseases (ADs). We employed Mendelian randomization to investigate the associations between selected 7 coagulation factors and 10 ADs, leveraging genetic variants as instrumental variables to assess causal relationships between exposures of interest and outcomes. Within the scope of this investigation, coagulation factors were designated as the exposure source, while ADs were observed to manifest as the consequent outcome. Our analysis using the inverse-variance weighted (IVW) method revealed that Factor VIII (FVIII) (P = .0067) exhibited significant causal associations with a reduced risk of multiple sclerosis. In contrast, fibrinogen (P = .0004) was associated with an increased risk of multiple sclerosis. The analysis also indicated that activated partial thromboplastin time (P = .0047) was implicated in elevating the risk of urticaria. The results also showed that protein C (P = .0188) was inversely associated with the risk of systemic lupus erythematosus. The results unveiled a significant positive correlation between fibrinogen (P = .0318) and the risk of rheumatoid arthritis. Similarly, Factor VII (P = .0119), FVIII (P = .0141), and von Willebrand Factor (P = .0494) were also found to be positively associated with the risk of inflammatory bowel disease. The IVW analysis demonstrated a causal relationship between von Willebrand Factor (P = .0316) and FVIII (P = .0408) and a decreased risk of primary sclerosing cholangitis. IVW results confirmed that protein C (P = .0409) had a protective effect on vitiligo. No significant associations were found between psoriatic arthritis, rosacea, and the 7 coagulation factors in this study. This is of significant importance for advancing the prevention, diagnosis, and treatment of ADs.

Keywords: autoimmune diseases, Mendelian randomization, multiple sclerosis, urticaria

1. Introduction

Autoimmune diseases (ADs) constitute a spectrum of intricate chronic illnesses distinguished by an aberrant immune response targeting the body’s own tissues, with a global prevalence of approximately 5% to 8%.[1–3] The critical role of inflammatory mediators in the pathogenesis of ADs is acknowledged. Inflammatory factors and inflammasomes, among other factors, are implicated in the development of ADs by influencing both innate and adaptive immune cells.[4,5] Circumstantial evidence suggests that the shared evolutionary origin between the innate immune system and the coagulation system leads to extensive interplay between inflammatory cytokines and coagulation factors. This interplay renders many components crucial for both systems.[6] Inflammation has the capacity to impact both the coagulation and fibrinolytic systems. Heightened inflammation can precipitate thrombosis, while conversely, thrombosis can further escalate inflammation.[7,8] Previous research has revealed that the majority of patients with ADs exhibit disruptions in either the coagulation or fibrinolysis systems. Hematological abnormalities are common in systemic lupus erythematosus (SLE). Elevated D-dimer levels have been noted in SLE patients, indicating a positive correlation with the disease activity of SLE.[9] The clinical manifestations of rheumatoid arthritis (RA) also encompass a diverse range of extra-articular symptoms, among which is a hypercoagulable state.[10] Increased levels of fibrinogen (Fg) have been detected in the plasma of patients diagnosed with axial spondyloarthritis, which includes both ankylosing spondylitis and nonradiographic axial spondyloarthritis. Moreover, disease activity shows a significant association with activated coagulation.[11] Patients diagnosed with inflammatory bowel disease (IBD) exhibit notable abnormalities in procoagulant, anticoagulant, and fibrinolytic factors, as well as impaired function of the fibrinolytic system.[12]

Evidence suggests that systemic inflammation consistently triggers the activation of the coagulation system. Conversely, activated coagulation proteases can modulate the inflammatory response by targeting specific cellular receptors present on inflammatory cells and endothelial cells.[13] Hence, further research into the correlation between the coagulation system and ADs is deemed valuable for advancing endeavors in ADs prevention, diagnosis, and treatment. Currently, the existence of a definitive causal connection between coagulation factors and ADs remains uncertain. In order to mitigate confounding bias and the potential for reverse causality, we utilized Mendelian randomization (MR) analysis, as genetic variants are randomly allocated at conception.[14] MR stands as a novel statistical method, founded upon its fundamental principle of leveraging genetic variations as instrumental variables (IVs) to assess causal relationships between an exposure and an outcome within an observational context.[15]

In this study, a comprehensive genome-wide association analysis (GWAS) dataset was employed to conduct a two-sample MR study. The objective was to investigate the causal relationship between 7 coagulation factors: namely von Willebrand Factor (vWF), activated partial thromboplastin time (aPTT), Factor VII (FVII), Factor VIII (FVIII), Fg, plasminogen activator inhibitor-1 (PAI-1), and protein C, and the risk of 10 major autoimmune diseases (ADs), which could potentially offer novel insights and evidence within this realm of research.[16–22]

2. Materials and methods

2.1. Study design

In our study, MR analysis was employed to estimate the causal relationship between 7 coagulation factors and SLE, RA, multiple sclerosis (MS), IBD, primary sclerosing cholangitis (PSC), rosacea, vitiligo, psoriasis (PsO), psoriatic arthritis (PsA), and urticaria. The 7 coagulation factors encompassed vWF, aPTT, FVII, FVII, Fg, PAI-1, and protein C. Our analysis is based on publicly accessible summary statistics derived from GWAS. Within the scope of this investigation, coagulation factors were designated as the exposure source, while ADs were observed to manifest as the consequent outcome. The research workflow is depicted in Figure 1.

Figure 1.

Figure 1.

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The flowchart of the study.

2.2. ADs GWAS summary statistics

Summary-level data for GWAS of ADs were extracted from a meta-analysis involving participants of European ancestry. The sample sizes of 10 ADs were as follows: SLE[23] (5201 cases and 9066 controls), RA[24] (14,361 cases and 42,923 controls), MS[25] (47,429 cases and 68,374 controls), IBD[26] (12,882 cases and 21,770 controls), PSC[27] (2871 cases and 12,091 controls), rosacea[28] (1195 cases and 211,139 controls), vitiligo[28] (131 cases and 207,482 controls), PsO[28] (4510 cases and 212,242 controls), PsA (1455 cases and 217,377 controls), and urticaria (5066 cases and 212,464 controls), which originate from the IEU OpenGWAS project. The summary data for MR of coagulation factors predominantly originated from European populations. All GWAS meta-analyses included in the study are publicly accessible. Additional information regarding the GWAS datasets utilized in this study is provided in Table 1.

Table 1.

Autoimmune diseases GWAS samples used in this study.

Trait N. cases N. controls Population GWAS ID in IEU
SLE 5201 9066 Europeans ebi-a-GCST003156
RA 14,361 43,923 Europeans ieu-a-832
MS 47,429 68,374 Europeans ieu-b-18
IBD 12,882 21,770 Europeans ieu-a-31
PSC 2871 12,019 Europeans ieu-a-1112
PsO 4510 212,242 Europeans finn-b-L12_PSORIASIS
PsA 1455 217,337 Europeans finn-b-M13_PSORIARTH_ICD10
Rosacea 1195 211,139 Europeans finn-b-L12_ROSACEA
Urticaria 5066 212,464 Europeans finn-b-L12_URTICARIA
Vitiligo 131 207,482 Europeans finn-b-L12_VITILIGO
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IBD = inflammatory bowel disease, MS = multiple sclerosis, PSC = primary sclerosing cholangitis, PsO = psoriasis, PsA = psoriatic arthritis, RA = rheumatoid arthritis, SLE = systemic lupus erythematosus.

2.3. Genetic instrumental variable selection

The selected IVs adhere to 3 fundamental assumptions: (1) genetic variation associated with the exposure, (2) the IVs should be independent of potential confounders, and (3) genetic variation has no effect on the outcome except through its correlation with the exposure factor.[29] Following the 3 fundamental assumptions of MR analysis, firstly, we applied a genome-wide significance threshold of P < 5E-8 to identify single-nucleotide polymorphisms (SNPs) significantly associated with specific coagulation factors. Linkage disequilibrium analysis was conducted to verify the independence of SNPs, using criteria of r2 < 0.1 and a distance of 10,000 kb.[30] Then, data on SNPs associated with the mentioned coagulation factors were retrieved from the summary GWAS data on 4 ADs. We excluded missing SNPs and those with a minor allele frequency <0.01.[31] Third, in order to mitigate potential weak instrument bias, F-statistics were computed for each SNP to evaluate the strength of the IVs. Following this, SNPs with F-statistics below 10 were removed.[32] Fourth, through consultation of pertinent literature, SNPs significantly associated with the outcomes or confounding factors were manually excluded from the analysis. Finally, we excluded palindromic SNPs, referring to SNPs with A/T or G/C alleles, to prevent distortion of strand orientation or allele coding.

2.4. MR analysis

All analyses were performed using R version 4.3.3. The R packages “TwoSampleMR” were employed for MR analysis. Additionally, R was utilized for data visualization, enabling the generation of charts to present the findings. To delve deeper into the correlation between 7 coagulation factors and SLE, RA, MS, IBD, PSC, PsO, PsA, rosacea, urticarial, and vitiligo. We conducted 5 separate MR analyses. We also utilized 5 methods, such as the inverse-variance weighted (IVW), MR-Egger, weighted median, simple mode, and weighted mode. According to available guidelines, the IVW method is recommended as the primary analysis due to its robustness.[33]

Heterogeneity in causal estimates was assessed using Cochran Q test, with a P-value >.05 indicating the absence of significant heterogeneity among the instruments. In cases where significant heterogeneity (P < .05) is detected for the Q statistic, we employ the utilization of random-effects IVW MR analysis. In order to uphold the reliability and precision of the findings, the MR-Egger intercept and the global MR pleiotropy residual sum and outlier test to identify potential outliers that may bias the results.[34] Any remaining peripheral SNPs were removed using LDtrait, and the analysis was repeated. We utilized leave-one-out analysis to assess whether removing each instrumental SNP individually had a significant impact on the results.[35]

3. Results

This study investigated the causal relationship between 7 coagulation factors and SLE, RA, MS, IBD, PSC, rosacea, vitiligo, PsO, PsA, and urticaria. The IVW analysis revealed that FVIII (OR = 0.7367, 95% CI 0.5907–0.9187, P = .0067) exhibited significant causal associations with a reduced risk of MS (Fig. 2C, Fig. 3C, Fig. 4C, and Table S3, Supplemental Digital Content, http://links.lww.com/MD/O200). After Bonferroni correction, P-values remained significant (Padjusted = .0025). In contrast, Fg (OR = 5.3709, 95% CI 2.1354–13.5089, P = .0004) was associated with an increased risk of MS. After Bonferroni correction, P-values remained significant (Padjusted = .0233) (Fig. 2D, Fig. 3D, Fig. 4D, and Table S3, Supplemental Digital Content, http://links.lww.com/MD/O200). The analysis also indicated that aPTT (OR = 1.0259, 95% CI 1.0079–1.0442, P = .0047) was implicated in elevating the risk of urticaria (Fig. 2J, Fig. 3J, Fig. 4J, and Table S7, Supplemental Digital Content, http://links.lww.com/MD/O200). After Bonferroni correction, P-values remained significant (Padjusted = .0326). The findings highlighted that Fg level (OR = 0.4235, 95% CI 0.1789–1.0022, P = .0506) demonstrated a borderline significant association with the risk of urticaria (Table S7, Supplemental Digital Content, http://links.lww.com/MD/O200). Furthermore, the results also showed that protein C (OR = 0.7991, 95% CI 0.6626–0.9635, P = .0188) was inversely associated with the risk of SLE (Fig. 2A, Fig. 3A, Fig. 4A, and Table S1, Supplemental Digital Content, http://links.lww.com/MD/O200). The results unveiled a significant positive correlation between Fg (OR = 2.8692, 95% CI 1.0961–7.5104, P = .0318) and the risk of RA (Fig. 2B, Fig. 3B, Fig. 4B, and Table S2, Supplemental Digital Content, http://links.lww.com/MD/O200). Similarly, FVII (OR = 1.4684, 95% CI 1.0882–1.9815, P = .0119), FVIII (OR = 1.3247, 95% CI 1.0582–1.6583, P = .0141) and vWF (OR = 1.1991, 95% CI 1.0005–1.4371, P = .0494) were also found to be positively associated with the risk of IBD (Fig. 2E–G, Fig. 3E–G, Fig. 4E–G, and Table S4, Supplemental Digital Content, http://links.lww.com/MD/O200). Our analysis using the IVW method demonstrated a causal relationship between vWF (OR = 0.7168, 95% CI 0.5291–0.9711, P = .0316) and FVIII (OR = 0.6578, 95% CI 0.4403–0.9826, P = .0408) and a decreased risk of PSC (Fig. 2H and I, Fig. 3H and I, Fig. 4H and I, and Table S5, Supplemental Digital Content, http://links.lww.com/MD/O200). IVW results confirmed that protein C (OR = 0.5103, 95% CI 0.2677–0.9728, P = .0409) had a protective effect on vitiligo (Fig. 2K, Fig. 3K, Fig. 4K, and Table S8, Supplemental Digital Content, http://links.lww.com/MD/O200). Additionally, we found that vWF (OR = 1.2341, 95% CI 0.9997–1.5234, P = .0504) demonstrated a borderline significant association with the risk of PsO (Table S6, Supplemental Digital Content, http://links.lww.com/MD/O200). The IVW analysis revealed a causal relationship between PAI-1 and PsO, while the MR-Egger method demonstrated the opposite direction, suggesting the invalidity of the causal relationship (Table S6, Supplemental Digital Content, http://links.lww.com/MD/O200). No significant associations were found between PsA, rosacea, and the 7 coagulation factors in this study.

Figure 2.

Figure 2.

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Scatter plots for MR analyses of the causal effect of coagulation factors on autoimmune diseases in a meta-analysis. (A) Scatter plots of the causal effect of protein C on SLE. (B) Scatter plots of the causal effect of Fg on RA. (C) Scatter plots of the causal effect of FVIII on MS. (D) Scatter plots of the causal effect of Fg on MS. (E) Scatter plots of the causal effect of FVII on IBD. (F) Scatter plots of the causal effect of FVIII on IBD. (G) Scatter plots of the causal effect of vWF on IBD. (H) Scatter plots of the causal effect of FVIII on PSC. (I) Scatter plots of the causal effect of vWF on PSC. (J) Scatter plots of the causal effect of aPTT on urticaria. (K) Scatter plots of the causal effect of protein C on vitiligo. aPTT = activated partial thromboplastin time; FVII = Factor VII; FVIII = Factor VIII; IBD = inflammatory bowel disease; MR = Mendelian randomization; MS = multiple sclerosis; PsA = psoriatic arthritis; PSC = primary sclerosing cholangitis; PsO = psoriasis; RA = rheumatoid arthritis; SLE = systemic lupus erythematosus.

Figure 3.

Figure 3.

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Plots of leave-one-out analyses for the causal associations. (A) Leave-one-out analysis of the causal effect of protein C on SLE. (B) Leave-one-out analysis of the causal effect of Fg on RA. (C) Leave-one-out analysis of the causal effect of FVIII on MS. (D) Leave-one-out analysis of the causal effect of Fg on MS. (E) Leave-one-out analysis of the causal effect of FVII on IBD. (F) Leave-one-out analysis of the causal effect of FVIII on IBD. (G) Leave-one-out analysis of the causal effect of vWF on IBD. (H) Leave-one-out analysis of the causal effect of FVIII on PSC. (I) Leave-one-out analysis of the causal effect of vWF on PSC. (J) Leave-one-out analysis of the causal effect of aPTT on urticaria. (K) Leave-one-out analysis of the causal effect of protein C on vitiligo. aPTT = activated partial thromboplastin time; FVII = Factor VII; FVIII = Factor VIII; IBD = inflammatory bowel disease; MR = Mendelian randomization; MS = multiple sclerosis; PsA = psoriatic arthritis; PSC = primary sclerosing cholangitis; PsO = psoriasis; RA = rheumatoid arthritis; SLE = systemic lupus erythematosus.

Figure 4.

Figure 4.

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Causal estimates of coagulation factors on autoimmune diseases by MR analysis. (A) Forest plots showing causal estimates of coagulation factors on SLE. (B) Forest plots showing causal estimates of coagulation factors on RA. (C) Forest plots showing causal estimates of coagulation factors on MS. (D) Forest plots showing causal estimates of coagulation factors on IBD. (E) Forest plots showing causal estimates of coagulation factors on PSC. (F) Forest plots showing causal estimates of coagulation factors on urticaria. (G) Forest plots showing causal estimates of coagulation factors on vitiligo. aPTT = activated partial thromboplastin time; FVII = Factor VII; FVIII = Factor VIII; IBD = inflammatory bowel disease; MR = Mendelian randomization; MS = multiple sclerosis; PsA = psoriatic arthritis; PSC = primary sclerosing cholangitis; PsO = psoriasis; RA = rheumatoid arthritis; SLE = systemic lupus erythematosus.

4. Discussion

ADs constitute a spectrum of complex chronic illnesses characterized by an aberrant immune response against the body’s own tissues.[1–3] The pivotal role of inflammatory mediators in the pathogenesis of ADs is widely acknowledged. Inflammatory factors and inflammasomes, among other mediators, contribute to the development of ADs by influencing both innate and adaptive immune cells.[4,5] Circumstantial evidence supports the notion that the shared evolutionary origin of the innate immune system and the coagulation system fosters extensive interplay between inflammatory cytokines and coagulation factors, thereby underscoring the importance of many components in both systems.[6] The present study represents the first MR aimed at assessing the causal association between SLE, RA, MS, IBD, PSC, PsO, PsA, rosacea, urticaria, vitiligo, and 7 coagulation factors.

In our study, we found that Fg displayed significant causal associations, resulting in an elevated risk of RA and MS. Fg, pivotal in the blood clotting process, is among the primary factors to notably decrease in various coagulopathic events.[36] Fg is a critical protein in the coagulation cascade, playing a central role in fibrin clot formation and platelet aggregation.[37] We observe that Fg, along with other coagulation factors, plays crucial roles in the response to tissue injury, serving as an inflammatory mediator. Dysregulated acute phase responses triggered by inappropriate coagulation-mediated inflammation may adversely affect tissue repair. Therefore, precise coordination of fibrin deposition and degradation post-injury is crucial for controlling inflammation and promoting tissue repair.[38] Insufficient Fg is a significant risk factor for bleeding, while elevated Fg levels correlate with an increased risk of thrombosis.[39,40] Inflammation leads to an increase in the blood Fg content, as evidenced by the high concentration of Fg found in the blood of patients with RA, a condition known as hyperfibrinogenemia.[41] It has been reported that the clotting factor Fg promotes neuroinflammation, leading to the occurrence of MS, with Fg levels correlated with the severity of MS, consistent with the results of our MR study.[42]

Genetically predicted, FVIII exhibited significant causal associations, resulting in a reduced risk of MS and PSC, while indicating an increased risk of IBD. Furthermore, genetically predicted, vWF exhibited significant causal associations, resulting in a reduced risk of PSC, while indicating an increased risk of IBD. Due to the genetic correlation of VWF and FVIII being 83.5%, with VWF serving as the carrier for FVIII, VWF levels are typically positively correlated with FVIII activity.[43] The stability of FVIII’s heterodimeric structure is upheld by VWF, allowing it to evade premature proteolytic inactivation.[44] FVIII participates in the proteolytic cleavage of membrane-bound ultra-large VWF multimers.[45] Previous studies have demonstrated that patients with IBD exhibit elevated levels of VWF antigen and active VWF compared to healthy controls, resulting in increased peak thrombin generation.[46] Furthermore, we observed significant causal associations with FVII, leading to an increased risk of IBD. Previous studies have indicated that non-cirrhotic PSC patients exhibit systemic hypercoagulability along with significant systemic inflammatory activity.[47,48] Additionally, we discovered a significant causal effect of activated aPTT on the increased risk of urticaria. A case–control study revealed the presence of coagulation dysfunction in chronic urticaria, with elevated levels of D-dimer, CRP, aPTT, and Fg during the acute phase of infection-related acute urticaria, with a positive correlation observed for CRP.[49] IVW analysis revealed significant causal associations between protein C and a reduced risk of SLE and vitiligo. Activated protein C (APC), a plasma serine protease dependent on vitamin K, originates from protein C, which demonstrates various activities. Serving as an intrinsic anticoagulant, APC functions by inhibiting thrombin generation in the coagulation cascade.[50] APC functions as an anti-inflammatory factor by downregulating thrombin, serves as a natural anticoagulant protein by inactivating coagulation factors Va and VIIIa, and acts as an antiapoptotic protein by inhibiting p53-mediated apoptosis.[51] Furthermore, APC can modulate the immune response by reducing leukocyte adhesion to endothelial cells, suppressing the production involved in inflammation, and inhibiting the release of pro-inflammatory cytokines.[52] In addition to the structural and functional changes observed in platelets and erythrocytes, hemostatic disorders may also play a role in the pathogenesis of SLE.[53–56] It has long been recognized that patients with SLE exhibit elevated levels of protein C and D-dimer.[57] The transcriptional activation of matrix metalloproteinases (MMPs) is considered the primary cause of repigmentation in vitiligo, with their regulation positively influenced by ETS-1.[58–60] Previous studies have indicated the absence of ETS-1 expression in perilesional active vitiligo skin, leading to decreased expression of MMP-2 and MMP-9.[61] APC plays a central role in enhancing the expression and activation of MMP-2 in keratinocytes, suggesting its potential to promote re-epithelialization during wound healing.[62] Our findings revealed a borderline significant association between Fg level and the risk of urticaria, as well as vWF and the risk of PsO.

This study has several limitations that warrant acknowledgments. First, the study may be subject to bias given the predominantly European ancestry of the exposed GWAS and the European and Finnish ancestry of the outcome GWAS. Further investigation of MR results is warranted in other ethnic populations. Second, only 7 coagulation factors were selected for analysis in this MR study, and not all coagulation factors included in the GWAS studies were analyzed. Third, due to data limitations, we were unable to conduct subgroup analyses based on sex to further investigate the causal relationship between coagulation factors and ADs. Fourth, despite conducting heterogeneity analysis, pleiotropy analysis, and sensitivity analysis to ensure the reliability of the results, further validation of our findings through larger-scale GWAS studies is warranted.

5. Conclusions

To our knowledge, the present study represents the first MR aimed at assessing the causal association between SLE, RA, MS, IBD, PSC, PsO, PsA, rosacea, urticaria, vitiligo, and 7 coagulation factors. The genetically predicted a significant association between Fg and the risk of RA and MS, FVII, vWF, FVIII, and the risk of IBD, as well as aPTT and the risk of urticaria. Conversely, the genetically predicted negative association between protein C and the risk of SLE and vitiligo, FVIII and the risk of MS, as well as vWF and FVIII and the risk of urticaria. This is of significant importance for advancing the prevention, diagnosis, and treatment of ADs.

Acknowledgments

All the authors of this article should be appreciated sincerely.

Author contributions

Conceptualization: Shuxuan Li.

Data curation: Shuxuan Li, Wenjing Li, Lichun Kang.

Supervision: Chunlei Zhou, Hong Mu.

Writing – original draft: Shuxuan Li.

Supplementary Material

medi-103-e40893-s001.xlsx (41.3KB, xlsx)

Abbreviations:

ADs
autoimmune diseases
APC
activated protein C
aPTT
activated partial thromboplastin time
Fg
fibrinogen
FVII
Factor VII
FVIII
Factor VIII
GWAS
genome-wide association analysis
IBD
inflammatory bowel disease
IVs
instrumental variables
IVW
inverse-variance weighted
MMPs
matrix metalloproteinases
MR
Mendelian randomization
MS
multiple sclerosis
PAI-1
plasminogen activator inhibitor-1
PsA
psoriatic arthritis
PSC
primary sclerosing cholangitis
PsO
psoriasis
RA
rheumatoid arthritis
SLE
systemic lupus erythematosus
SNPs
single-nucleotide polymorphisms
vWF
von Willebrand Factor

This work was supported by the Tianjin Key Medical Discipline (Specialty) Construction Project (No. TJYXZDXK-015A) and Tianjin Key Science and Technology Project of Tianjin Science and Technology Bureau (21ZXJBSY00040).

All data used in this study are publicly available and do not contain any personally identifiable information. Therefore, informed consent was not required for this study.

The authors have no conflicts of interest to disclose.

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

Supplemental Digital Content is available for this article.

How to cite this article: Li S, Zhou C, Li W, Kang L, Mu H. The effects of coagulation factors on the risk of autoimmune diseases: A Mendelian randomization study. Medicine 2024;103:52(e40893).

Contributor Information

Shuxuan Li, Email: 18238446@qq.com.

Chunlei Zhou, Email: tj_zcl@hotmail.com.

Wenjing Li, Email: 18238446@qq.com.

Lichun Kang, Email: kanglichun1118@163.com.

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