{"paper_id":"b38acffc-edd1-442b-a911-53c566b8bb1d","body_text":"Xiao et al. Advances in Rheumatology           (2023) 63:42  \nhttps://doi.org/10.1186/s42358-023-00323-1\nREVIEW Open Access\n© The Author(s) 2023. Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which \npermits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the \noriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or \nother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line \nto the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory \nregulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this \nlicence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.\nAdvances in Rheumatology\nRisk factors of systemic lupus \nerythematosus: an overview of systematic \nreviews and Mendelian randomization studies\nXin‑Yu Xiao1, Qian Chen1, Yun‑Zhou Shi1, Li‑Wen Li1, Can Hua1 and Hui Zheng1*   \nAbstract \nBackground The etiology of systemic lupus erythematosus is complex and incurable. A large number of systematic \nreviews have studied the risk factors of it. Mendelian randomization is an analytical method that uses genetic data \nas tool variables to evaluate the causal relationship between exposure and outcome.\nObjective To review the systematic reviews and Mendelian randomization studies that focused on the risk \nfactors of systemic lupus erythematosus and shed light on the development of treatments for its prevention \nand intervention.\nMethods From inception to January 2022, we systematically searched MEDLINE (via PubMed) and Embase for related \nsystematic reviews and Mendelian randomization studies. Extract relevant main data for studies that meet inclu‑\nsion criteria. The quality of systematic reviews was assessed by using Assessment of Multiple Systematic Reviews \n2 (AMSTAR‑2). Finally, the risk factors are scored comprehensively according to the results’ quantity, quality, \nand consistency.\nResults Our study involved 64 systematic reviews and 12 Mendelian randomization studies. The results of systematic \nreviews showed that diseases (endometriosis, atopic dermatitis, allergic rhinitis), lifestyle (smoking, drinking, vac‑\ncination), and gene polymorphism influenced the incidence of systemic lupus erythematosus. The results of Men‑\ndelian randomization studies identified the role of disease (periodontitis, celiac disease), trace elements (selenium, \niron), cytokines (growth differentiation factor 15), and gut microbiome in the pathogenesis of systemic lupus \nerythematosus.\nConclusion We should pay attention to preventing and treating systemic lupus erythematosus in patients \nwith endometriosis, celiac disease, and periodontitis. Take appropriate dietary supplements to increase serum iron \nand selenium levels to reduce the risk of systemic lupus erythematosus. There should be no excessive intervention \nin lifestyles such as smoking and drinking.\nKeywords SLE, Risk factors, Systematic reviews, Mendelian randomization\nIntroduction\nSystemic lupus erythematosus (SLE) is a chronic inflam -\nmatory autoimmune and multi-systemic disease that is \ncharacterized by the production of autoantibodies and \ntissue deposition of immune complexes. The clinical \nmanifestations range from slight fatigue and joint pain to \nsevere, catastrophic organ damage [1]. Owing to sex, age, \n*Correspondence:\nHui Zheng\nzhenghui@cdutcm.edu.cn\n1 Acupuncture and Tuina School, Chengdu University of Traditional \nChinese Medicine, No.1166 Liutai Avenue, Wenjiang District, \nChengdu 610000, China\n\nPage 2 of 15Xiao et al. Advances in Rheumatology           (2023) 63:42 \nethnicity, time, and environmental exposures the global \nprevalence of SLE varies widely, with the highest esti -\nmates of the prevalence of 241 cases per 100,000 persons \nin North America and the lowest in Northern Australia \n[2]. Mortality in patients with SLE is 2–3 times higher \nthan in the general population, and the most common \ncauses are infectious diseases and cardiovascular disease \n[3].\nDespite years of study, the etiology of SLE is still \nunclear. As reported previously, the development of SLE \nwas associated with hormonal, immunomodulatory, \nenvironmental, and genetic factors [4]. Some studies fur -\nther reported that allergic diseases and hormone-related \ndiseases may be associated with the incidence of SLE; for \nexample, the incidence of SLE in patients with endome -\ntriosis is higher than that in controls [5].\nLifestyle may be associated with the incidence of SLE, \nand its intervention is indispensable in the prevention \nand treatment of SLE. Studies linked the incidence of SLE \nto environmental factors, such as silica exposure, smok -\ning and drinking, infection, and vaccination [6]. Evidence \nfrom systematic reviews (SRs) suggests that endometrio -\nsis, allergic rhinitis, atopic dermatitis, smoking, and vac -\ncination are associated with an increased incidence of \nSLE.\nGene polymorphism plays an important role in eluci -\ndating the susceptibility to diseases and the diversity in \nthe clinical manifestations of the diseases. Single Nucle -\notide Polymorphism (SNP) is the most common DNA \nsequence variation in a population. Several studies have \nshown the key role of SNP in the development of SLE. \nFor example, SNP leads to abnormal T-cell function [4]. \nAnd it is known that the risk A allele of SNP (PPP2CA \nrs7704116) [7] is known to be one of the reasons for the \nincreased incidence of SLE.\nClinical observational studies can only show that the \ndisease and risk factors are related, but it is difficult to \nmake causal inferences. The correlation is likely to be a \n“false correlation” caused by a variety of confounding \nfactors, and the existence of reverse causality cannot be \nignored. Mendelian randomization (MR) study is an ana -\nlytical method used to evaluate the causal relationship \nbetween observable exposure or risk factors and clinical-\nrelated results [8]. The core of it is to use genetic data and \ntake genetic variables as instrumental variables, which \ncan effectively overcome the bias caused by confounding. \nIn genetic correlation, the direction of causality is deter -\nmined, which avoids the interference of reverse causality \nand thus provides more compelling evidence. At present, \nthere is no research to comprehensively summarize the \nrisk factors of SLE from SR and MR evidence.\nIn this paper, the reported risk factors of SLE are \nreviewed and summarized from the perspectives of \ndisease, lifestyle, gene polymorphism, and evidence from \nMR, to better understand the etiology and provide bet -\nter medical advice for disease management for the whole \npopulation.\nMethod\nSearch strategy\nWe searched MEDLINE (via PubMed), Embase from \ninception to January 27, 2022, by using the keywords \n“Risk factor” “Systemic lupus erythematosus” , “System -\natic review” , “Meta-analysis” , “Mendelian randomization \nstudy” with no restriction on language. For complete \nsearch strategies, see “ Appendix A” section.\nStudy selection\nAll retrieved studies were imported into Zotero (6.0.9), \nand duplicate studies were removed. Two independent \nreviewers (X-YX, QC) screened the title and abstract of \nthe article. After cross-checking, the 2 reviewers further \nindependently assessed the full text of the eligible stud -\nies. Disagreements about the inclusion of qualified stud -\nies were resolved through discussion. If they cannot be \nresolved, the third reviewer (HZ) would make the final \ndecision.\nInclusion: (1) SR of risk factors related to the incidence \nof SLE; (2) SR of autoimmune diseases including SLE; (3) \nMR study of SLE. Exclusion: (1) review; (2) case report; \n(3) original clinical research; (4) autoimmune-related but \nnot related to SLE or related to SLE but lacking corre -\nsponding data; (5) discoid lupus erythematosus; (6) lupus \nnephritis; (7) drug-induced lupus erythematosus.\nData extraction\nTwo researchers independently extracted data accord -\ning to predetermined extraction criteria. The following \ninformation was extracted from papers on disease and \nlifestyle, and some papers did not contain all informa -\ntion: Study ID, risk factor, outcome (relative risk or odds \nratio of a risk factor to SLE, 95% confidence interval, and \nP-value), type of study design (cohort, case–control, or \ncross-sectional), presence or absence of sensitivity and \nsubgroup analyses, publication bias, and quality assess -\nment tools (Table 1).\nMR studies extracted the following: Study ID, num -\nber of SNP , and main results (Table 2). Studies of genetic \npolymorphism were divided into four categories: risk fac-\ntors, protective factors, contradictory factors, and unre -\nlated factors. The following information was extracted: \nstudy ID, gene and SNP , and major significant results \n(Table 3).\n\nPage 3 of 15\nXiao et al. Advances in Rheumatology           (2023) 63:42 \n \nAssessment of methodological quality\nAssessment of Multiple Systematic Reviews 2 \n(AMSTAR-2) is a tool to assess the quality of included \nSRs. There were 16 items, including 7 critical items \n(items 2, 4, 7, 9, 11, 13, 15) and 9 non-critical items. Each \nitem was evaluated as ‘‘yes’’ (a positive result), ‘‘partial \nyes’’ (partial adherence to the standard), and ‘‘no’’ (no \ninformation is provided to rate an item) according to \nadherence to the standard. Based on these items, SR was \ndivided into four grades: high, moderate, low, and criti -\ncally low [9]. This review evaluates SRs with risk factors \nrelated to disease and lifestyle (“ Appendix B” section).\nDetermination of the magnitude of risk factors\nSR and MR Studies were scored and the magnitude was \ndetermined according to the number of studies on risk \nfactors, the average score of AMSTAR-2, the consistency \nof results from different studies, the consistency of results \nfrom different methods and sensitivity tests, and finally \nsummarize the risk factors and magnitude of SLE (Fig. 1).\nResults\nA total of 594 articles were retrieved on SRs of risk fac -\ntors. After removing duplicates, 493 articles remained. \nAfter screening the title and abstract, 119 articles \nTable 1 Main characteristics of SRs of disease and lifestyle\nOR odds ratio; RR relative risk; NM no meta-analysis; NA not available; CS cohort study; CCS case–control study; CSS cross-sectional study\nStudy ID Risk factor OR/RR\n(95% CI)\nP I2 Type of included \nstudies\nSensitivity/\nsubgroup \nanalysis\nPublication \nbias\nQuality \nassessment \ntool\nAgrawal [25] Immigrant NM NA NA Population‑based \nstudies\nNA NA NOS\nChua [21] Current smoking \nstatus\nOR = 1.54(1.06–2.25) NA NA CS CCS NA NA NA\nFormer smoking \nstatus\nOR = 1.39(0.95–2.08) NA NA\nCostenbader [18] Current smoker ver‑\nsus nonsmokers\nOR = 1.50(1.09–2.08) NA NA CS CCS Yes/Yes Yes NA\nFormer smoker \nversus nonsmoker\nOR = 0.98(0.75–1.27) NA NA\nJanowsky [23] Silicone breast \nimplants\nRR = 0.65(0.35–1.23) 0.530 NA CS CCS CSS NA No NA\nJiang [19] Current smokers \ncompared with non‑\nsmokers\nOR = 1.56(1.26–1.95) NA 56.3% CS CCS Yes/No No NA\nEx‑smokers ver‑\nsus nonsmokers\nOR = 1.23(0.93–1.63) NA 62.3% CS CCS Yes/Yes No NA\nParisis [20] Current smokers ver‑\nsus never‑smokers\nOR = 1.49(1.06–2.08) 0.010 78.0% CCS Yes/No Yes NA\nEver‑smokers ver‑\nsus never‑smokers\nOR = 1.54(1.06–2.23)  < 0.001 86.0%\nFormer‑smokers ver‑\nsus never‑smokers\nOR = 0.97(0.68–1.38) 0.010 63.0%\nPonvilawan [11] Atopic dermatitis OR = 1.46(1.05–2.04) 0.020 63.0% CS CCS No/No Yes NOS\nSakthiswary [26] Vitamin D levels NM NA NA CS CCS NA NA NA\nShigesi [5] Endometriosis OR = 1.36(1.07–1.73) 0.010 49.0% CCS Yes/No NA GRADE\nEndometriosis RR = 1.74(1.10–2.77) 0.020 0.0% CS\nWang [22] Vaccinations RR = 1.50(1.05–2.12) 0.024 71.7% CS CCS Yes/Yes No NOS\nShort vaccinated \ntime\nRR = 1.93(1.07–3.48) 0.028 NA\nWang [17] Alcohol intake CS CCS Yes/Yes Yes NOS\nMild OR = 0.85(0.53–1.38) 0.515 76.3%\nHeavy OR = 0.63(0.37–1.09) 0.102 58.3%\nModerate OR = 0.71(0.55–0.93) 0.012 49.1%\nWongtrakul [12] Allergic rhinitis OR = 1.36(1.08–1.72) 0.009 80.0% CS CCS No/No No NOS\nYoussefi [24] Helicobacter pylori \ninfection\nOR = 0.97(0.76–1.23) 0.820 94.9% CS CCS CSS No/No Yes NA\n\nPage 4 of 15Xiao et al. Advances in Rheumatology           (2023) 63:42 \nremained. 64 articles were finally included after read -\ning the full text. One study was related to the world -\nwide incidence of SLE, 3 were related to disease, 10 \nwere related to lifestyle, and 50 were related to gene \npolymorphism. In the study of gene polymorphism, 19 \ngenes or SNPs were risk factors.\nA total of 45 MR studies related to risk factors \nwere retrieved. After removing duplicates, 31 articles \nremained, and 12 were finally included in our review. \nAmong them, 2 were related to disease, 5 were related \nto lifestyle, 2 were related to cytokine, 2 were related \nto trace elements, and 1 was related to gut microbiome \n(Fig. 2 ).\nFinally, our study involved 5 diseases (endometriosis, \natopic dermatitis, allergic rhinitis, celiac disease, peri -\nodontitis), 9 lifestyles (smoking, drinking, vaccination, \nsilicone breast implants, helicobacter Pylori infection, \nimmigration, Vitamin D, coffee consumption, statins), \n2 cytokines (circulating adiponectin, growth differen -\ntiation factor 15), 2 trace elements (selenium, iron), \nthe gut microbiome (Bacillales, Coprobacter, Lach‑\nnospira, Actinobacteria, Bacilli, Lactobacillales, and \nEggerthella), and more than 10 gene polymorphisms \n(Table 4).\nRisk factors\nDisease\nEndometriosis\nOne SR and meta-analysis including cohort studies and \ncase–control studies in the study of endometriosis [5 ] \n(OR for case–control studies 1.36, 95% CI 1.07–1.73, \nP = 0.010; RR for cohort studies 1.74, 95% CI 1.10–2.77, \nP = 0.020), and show the prevalence of SLE in patients \nwith endometriosis was higher than that in the control \ngroup. It was rated moderate quality by AMSTAR-2.\nEndometriosis is an estrogen-dependent disor -\nder, and the increase in estrogen level can aggravate \nor induce SLE by suppressing cellular immunity and \nincreasing the formation of autoantibodies [10].\nAtopic dermatitis\nAtopic dermatitis is associated with an increased risk of \ncardiovascular, neurological, and autoimmune disease. \nMeta-analysis showed participants who had atopic der -\nmatitis were at an increased risk of SLE [11] (OR 1.46, \n95% CI 1.05–2.04, P = 0.020). The result of AMSTAR-2 \nwas critically low quality.\nTable 2 Mendelian randomized studies\nGDF-15 Growth differentiation factor 15\nStudy ID Number of SNP Factor Result\nBae [85] 3 Vitamin D β = 0.03, SE = 0.12, P = 0.789\nBae [86] 4 Coffee β = 0.59, SE = 0.44, P = 0.209\nBae [87] 20 Alcohol intake β = ‑0.41, SE = 0.51, P = 0.421\nBae [84] 20 Periodontitis β < 0.01, SE < 0.01, P = 0.046\nDan [81] 9 Circulating adiponectin OR = 1.38,95% CI = 0.90–1.35, P = 0.130\nInamo [88] 4 Celiac disease β = 0.29, SE = 0.06, P < 0.001\nWang [89] 9 Smoking cessation OR = 1.15, 95% CI = 0.63–2.11, P = 0.640\nWang [89] 56 Alcohol use OR = 0.90, 95% CI = 0.54–1.52, P = 0.707\nWang [89] 156 Smoking initiation OR = 0.97, 95% CI = 0.75–1.24, P = 0.778\nWang [89] 31 Heaviness of smoke OR = 1.00, 95% CI = 0.67–1.50, P = 0.982\nXiang [82] 16 Bacilli β = 0.34, SE = 0.16, OR = 1.40, 95% CI = 1.02–1.93, P = 0.037\nXiang [82] 14 Lactobacillale β = 0.34, SE = 0.17, OR = 1.40, 95% CI = 1.01–1.95, P = 0.045\nXiang [82] 10 Eggerthella β = 0.55, SE = 0.70, OR = 1.73, 95% CI = 1.01–1.95, P = 0.045\nXiang [82] 11 Bacillales β = ‑0.16, SE = 0.07, OR = 0.85, 95% CI = 0.74–0.98, P = 0.022\nXiang [82] 12 Coprobacter β = ‑0.25, SE = 0.10, OR = 0.78, 95% CI = 0.64–0.95, P = 0.014\nXiang [82] 7 Lachnospira β = ‑0.51, SE = 0.23, OR = 0.60, 95% CI = 0.38–0.94, P = 0.027\nYang [83] 6 Statins OR = 0.72, 95% CI = 0.33–1.58, P = 0.419\nYe [80] 3 GDF‑15 OR = 0.80, 95% CI = 0.68–0.92\nYe [78] 2 Selenium OR = 0.85, 95% CI = 0.77–0.93, P = 0.001\nYe [79] 3 Serum iron OR = 0.79, 95% CI = 0.66–0.94\n\nPage 5 of 15\nXiao et al. Advances in Rheumatology           (2023) 63:42 \n \nTable 3 Gene polymorphism from SRs\nStudy ID Gene, SNP Result\nRisk\nChang [33] BLK, rs4840568 A versus G: OR = 1.32, 95% CI = 1.22–1.43, P = 0.010\nSong [34] BLK, rs13277113 A allele: OR = 1.36, 95% CI = 1.29–1.43, P < 0.001\nXiong [31] VDR BsmI, rs1544410 BB + Bb versus bb: OR = 2.14, 95% CI = 1.20–3.82, P = 0.010\nLee [39] TNF‑α promoter‑308 A/G A/A: OR = 3.2, 95% CI = 2.0–5.3, P < 0.001\nZou [40] TNF‑α promoter‑308 A/G A allele: OR = 1.44, 95% CI = 1.04–2.01, P = 0.030\nPan [41] TNF‑α promoter‑308 A/G A/A versus G/G: OR = 3.69, 95% CI = 2.63–5.17, P < 0.001\nYang [42] TNF‑α promoter‑308 A/G A allele: OR = 2.18, 95% CI = 1.72–2.78, P < 0.001\nChen [43] TNF‑α promoter‑308 A/G A versus G: OR = 1.78, 95% CI = 1.45–2.19, P < 0.001\nFan [44] TNFAIP3, rs2230926 OR = 1.83, 95% CI = 1.55–2.16, P < 0.001\nLee [45] TNFAIP3, rs2230926 OR = 1.85, 95% CI = 1.55–2.21, P < 0.001\nLiu [47] TNFAIP3, rs2230926 G allele: OR = 1.64, 95% CI = 1.46–1.85, P < 0.010\nLu [48] TNFSF4, rs2205960 OR = 1.33, 95% CI = 1.23–1.44, P < 0.001\nWang [49] TNFSF4, rs2205960 OR = 1.93, 95% CI = 1.50–2.49, P < 0.001\nMoreno‑Eutimio [51] TNFSF4, rs2205960 G vs T: OR = 1.32, P = 0.004\nFu [50] TNFSF4, rs2205960 OR = 1.42, 95% CI = 1.36–1.49, P < 0.001\nXu [52] TRAF1/C5, rs10818488 OR = 1.25, 95% CI = 1.06–1.47, P = 0.008\nZhu [53] IL‑1B ‑31 T/C OR = 1.64, 95% CI = 1.03–2.62, P = 0.040\nWang [58] IL‑10‑1082A/G, rs1800896 OR = 1.20, 95% CI = 1.03–1.41\nYin [28] IL‑10‑1082A/G, rs1800896 GG versus AA: OR = 1.54, 95% CI = 1.14–2.07, P = 0.005\nNiu [62] HLA‑DR3, DR9, DR15 HLA‑DR3: OR = 1.88, 95% CI = 1.58–2.23, P < 0.001\nXue [63] HLA‑DR3, DR15 HLA‑DR15: OR = 1.68, 95% CI = 1.33–2.11, P = 0.001\nHu [65] IRF5, rs2004640 OR = 1.41, 95% CI = 1.34–1.49, P < 0.001\nWang [66] IRF5, rs2004640 OR = 1.39, 95% CI = 1.28–1.52, P < 0.001\nLi [67] IRF5, rs2070197 OR = 2.13, 95% CI = 1.86–2.44, P < 0.001\nJi [68] STAT4, rs7574865 OR = 1.57, 95% CI = 1.44–1.71\nYuan [69] STAT4, rs7574865 OR = 1.65, 95% CI = 1.56–1.75, P < 0.001\nWang [70] STAT4, rs7574865 OR = 1.56, 95% CI = 1.51–1.61, P < 0.001\nJi [73] MiR‑146a, rs2431697 OR = 1.24, 95% CI = 1.13–1.37\nLiu [75] MiR‑146a, rs2431697 OR = 1.56, 95% CI = 1.20–1.92, P = 0.001\nFan [77] ITGAM, rs1143679 AA versus GG: OR = 3.54, 95% CI = 2.77–4.52\nEbrahimiyan [76] ITGAM, rs1143679 OR = 1.97, 95% CI = 1.76–2.20, P < 0.001\nProtective\nNiu [62] HLA‑DR4, DR11, DR14 HLA‑DR14: OR = 0.47, 95% CI = 0.59–0.95, P < 0.050\nHu [65] IRF5, rs729302, rs2280714 rs729302: OR = 0.78, 95% CI = 0.74–0.83, P < 0.001\nEbrahimiyan [76] MBL, rs1800451 OR = 0.64, 95% CI = 0.42–0.99, P = 0.044\nContradictory\nYu [38] CTLA‑4, rs231775 No association\nChang [36] CTLA‑4, rs231775 GG versus AA: OR = 1.53, 95% CI = 1.12–2.10\nZhai [37] CTLA‑4, rs231775 GG + GA versus AA: OR = 0.85, 95% CI = 0.73–0.99, P = 0.040\nLee [35] CTLA‑4, rs231775 GG genotype: OR = 1.29, 95% CI = 1.03–1.56, P = 0.011\nLee [54] IL‑6–174 G/C, rs1800795 OR = 1.34, 95% CI = 1.05–1.72, P = 0.018\nYang [55] IL‑6–174 G/C, rs1800795 OR = 1.64, 95% CI = 1.10–2.45, P = 0.016\nCui [56] IL‑6–174 G/C, rs1800795 OR = 1.61, 95% CI = 1.16–2.24\nLiu [57] IL‑6–174 G/C, rs1800795 OR = 0.71, 95% CI = 0.56–0.88, P = 0.020\nYang [55] IL‑6–572 G/C, rs1800796 OR = 1.49,95% CI = 1.10–2.01, P = 0.009\nLiu [57] IL‑6–572 G/C, rs1800796 No association\nLee [54] IL‑6–572 G/C, rs1800796 No association\nWen [61] IL‑18‑137G/C, rs187238 OR = 1.21, 95% CI = 0.91–1.60\n\nPage 6 of 15Xiao et al. Advances in Rheumatology           (2023) 63:42 \nBLK B-cell lymphocyte kinase; VDR Vitamin D receptor; TNF-α Tumor necrosis factor-α; TNFAIP3 Tumor necrosis factor-α-induced protein 3; TNFSF4 tumor necrosis \nfactor ligand superfamily member 4; TRAF1/C5 tumor necrosis factor receptor-associated factor 1, complement component 5; IL interleukin; HLA human leukocyte \nantigen; IRF5 Interferon regulatory factor 5; STAT4 signal transducer and activator of transcription 4; ITGAM Integrin Subunit Alpha M; MBL Mannose-binding lectin; \nCTLA-4 Cytotoxic T lymphocyte-associated antigen-4\nTable 3 (continued)\nStudy ID Gene, SNP Result\nChen [60] IL‑18‑137G/C, rs187238 OR = 1.17, 95% CI = 0.95–1.44, P = 0.150\nJi [73] MiR‑146a, rs57095329 OR = 1.25, 95% CI = 1.17–1.35\nFu [74] MiR‑146a, rs57095329 OR = 1.26, 95% CI = 1.14–1.41\nLiu [75] MiR‑146a, rs57095329 OR = 1.17, 95% CI = 0.84–1.65, P = 0.360\nUnrelated\nYin [28] IL‑10, rs1800871, rs1800872 NA\nYuan [59] IL‑10, rs1800871, rs1800872 NA\nNiu [62] HLA‑DR8 NA\nFig. 1 Risk Factors for SLE and the consensus on the magnitude. Abbreviations: SLE, systemic lupus erythematosus. SR, systematic review. MR, \nMendelian randomization. AMSTAR‑2, Assessment of Multiple Systematic Reviews 2. TNF‑α, tumor necrosis factor‑α. TNFAIP3, Tumor necrosis \nfactor‑α‑induced protein 3. TNFSF4, Tumor necrosis factor ligand superfamily member 4. IL, Interleukin. HLA, Human leukocyte antigen. IRF5, \nInterferon regulatory factor 5. STAT4, Signal transducer and activator of transcription 4. ITGAM, Integrin Subunit Alpha M. Annotation: Determination \non the magnitude for the risk factors: a systematic reviews: based on a consensus after considering the evidence from (i). The number of literature, \n(ii). The average score of AMSTAR‑2 (no score added for e critically low, 1 score for low, 3 scores for moderate, and 5 scores for high quality), and iii. \nThe consistency of the results (1 score for factors with a number of ≥ 3 and consistent results, inconsistent results with a number of ≥ 2 minus 1 \nscore). b Mendelian randomization: based on a consensus after considering the evidence from (i). The number of literature, (ii). The consistency \nof results (2 scores if the results of inverse variance weighted, weighted median, and MR Egger are consistent, otherwise no score), and (iii). \nSensitivity tests (no horizontal pleiotropy is counted as 1 score, otherwise no score). If the risk factors involved both SR and MR studies, the scores \nwere added if the results were consistent and subtracted if the results were not consistent. Score the SR and MR studies according to the above \ncriteria and label low, medium, and high after the score (“low” for scores 1 and 2, “medium” for scores 3 and 4, and “high” for 5 and above.), and finally \nsummarize the risk factors and related magnitude of SLE\n\nPage 7 of 15\nXiao et al. Advances in Rheumatology           (2023) 63:42 \n \nAllergic rhinitis\nPatients with allergic rhinitis had a higher risk of SLE \nthan individuals without [12] (OR 1.36, 95% CI 1.08–\n1.72, P = 0.009). The result of AMSTAR-2 was critically \nlow quality.\nThe pathogenesis of atopic dermatitis, allergic rhi -\nnitis, and autoimmune disorders was similar and \nrelated to the increase of inflammatory mediators and \nimmune dysregulation [13]. The up-regulation of Th2 \nactivity and the increase of IgE production during the \nFig. 2 Flow chart showing study selection process. Abbreviations: SRs, systematic reviews. MR, Mendelian randomization\nTable 4 Risk factors of SLE\nBLK B-cell lymphocyte kinase; VDR Vitamin D receptor; TNF-α tumor necrosis factor-α; TNFAIP3 Tumor necrosis factor-α-induced protein 3; TNFSF4 Tumor necrosis \nfactor ligand superfamily member 4; TRAF1/C5 Tumor necrosis factor receptor-associated factor 1, complement component 5; IL interleukin; HLA human leukocyte \nantigen; IRF5 Interferon regulatory factor 5; STAT4 signal transducer and activator of transcription 4; ITGAM Integrin Subunit Alpha M; MBL Mannose-binding lectin; \nCTLA-4 Cytotoxic T lymphocyte-associated antigen-4; GDF-15 Growth differentiation factor 15\nRisk factors\nDisease Endometriosis, atopic dermatitis, allergic rhinitis\nLifestyle Smoking, drinking, vaccination, silicone breast implants, helicobacter pylori infection, immigration, Vitamin D\nGene polymorphisms Risk: BLK (rs4840568, rs13277113), VDR BsmI (rs1544410), TNF‑α promoter‑308 A/G, TNFAIP3 (rs2230926), TNFSF4 (rs2205960), \nTRAF1/C5 (rs10818488), IL‑1B‑31T/C, IL‑10‑1082A/G (rs1800896), HLA‑DR3, DR9, DR15, IRF5 (rs2004640, rs2070197), STAT4 \n(rs7574865), MiR‑146a (rs2431697), ITGAM (rs1143679)\nProtect: HLA‑DR4, DR11, DR14, IRF5(rs729302, rs2280714), MBL (rs1800451)\nContradictory: CTLA‑4 (rs231775), IL‑6‑174 G/C (rs1800795), IL‑6‑572 G/C (rs1800796), IL‑18 ‑137G/C (rs187238), MiR‑146a \n(rs57095329)\nUnrelated:IL‑10 (rs1800871, rs1800872), HLA‑DR8\nEvidence from Mende‑\nlian randomization\nDisease: celiac disease, Periodontitis\nLifestyle: coffee consumption, statins, smoking, drinking, Vitamin D\nCytokines: circulating adiponectin, GDF‑15\nTrace elements: selenium, iron\nGut microbiome\n\nPage 8 of 15Xiao et al. Advances in Rheumatology           (2023) 63:42 \ndevelopment of the disease may be the triggers for SLE \nin the future.\nLifestyle\nSmoking and drinking\nPrevious studies showed that moderate drinking reduced \nthe risk of SLE, and smoking increased it [6]. The main \nmechanisms include the anti-inflammatory mechanism \nrelated to alcohol consumption [14], including lower lev -\nels of C-reactive protein and fibrinogen in plasma, and \nCigarette-related pro-inflammatory mechanisms includ -\ning an increase in plasma C-reactive protein, oxidative \nstress, and apoptosis. The effects of drinking status and \nsmoking consumption on chemokine/cytokine concen -\ntrations in healthy female nurses in the United States \nin 2020 and 2021 showed that moderate drinking was \nrelated to lower stem cell factor levels [15]. The current \nsmoking status was related to the decrease of B-lympho -\ncyte stimulator and interleukin-10 (IL-10) [16].\nAn SR of drinking indicated that moderate alcohol con-\nsumption might be a protective factor (OR 0.71, 95% CI \n0.55–0.93, P = 0.012), while light and heavy alcohol con -\nsumption were not related to the risk of SLE [17].\nWe included four SRs related to smoking from 2004 to \n2020. The first [18] pointed out that the current smok -\ning status was correlated with the development of SLE \n(OR 1.50, 95% CI 1.09–2.08), but there was no correla -\ntion among former smokers (OR 0.98, 95% CI 0.75–1.27). \nThe second showed consistent results on the impact of \ncurrent smoking, but when analyzing non-smokers and \nformer smokers, different regions showed inconsistent \nresults [19]. The third stated that smoking was not only \na risk factor for SLE but also hampered disease treatment \nby reducing the curative effect of belimumab [20]. There-\nfore, it was suggested that smoking cessation should be \nthe first task in the prevention and treatment of SLE, \nwhich was supported in the fourth SR [21].\nVaccination\nVaccination can stimulate antigens to produce a specific \nimmune response, so it is considered the pathogenic fac -\ntor of SLE. One SR with 12 studies showed that vaccina -\ntion significantly increased the risk of SLE (RR 1.50, 95% \nCI 1.05–2.12, P = 0.024) [22]. The results of subgroup \nanalysis and sensitivity analysis both supported this \nconclusion.\nSilicone breast implants\nAutoimmune diseases caused by silicone breast implan -\ntation have long been a concern, but the SR results \nseemed to be reassuring. There was no evidence that sili -\ncone breast implantation was associated with connective \ntissue and autoimmune diseases, including SLE (RR 0.65, \n95% CI 0.35–1.23) [23].\nHelicobacter pylori infection\nThe molecular simulation, activation of polyclonal lym -\nphocytes, and cell damage produced by Helicobacter \npylori may be risk factors for autoimmune diseases. One \nSR published in 2020 showed that Helicobacter pylori \ninfection was not related to SLE susceptibility (OR 0.97, \n95% CI 0.76–1.23, P = 0.820), but its strains (H. pylori \ncagA positive strains) might be associated with a higher \nrisk of autoimmune diseases (OR 2.65, 95% CI 1.52–4.64, \nP = 0.001) [24].\nImmigrant\nIt was believed that environmental factors have made a \ngreat contribution to its incidence. Based on the findings \nof five population studies, the incidence of SLE was the \nhighest among immigrants from Africa, Iraq, and South \nAsia, especially among women and successive immigrant \ndescendants [25]. Assuming that “immigration” is a risk \nfactor for people in the appeal area, the impact of envi -\nronmental and lifestyle changes behind immigration on \nthe incidence of SLE cannot be ignored.\nOthers\nThe SR about vitamin D showed that vitamin D level was \nnegatively related to SLE disease activity [26]. Although \nthere were some reported risk factors, such as ultraviolet \nradiation, silica, air pollution, pesticides, heavy metals, \nand so on [6, 27], they were not covered in this paper due \nto the lack of SR evaluation.\nGene polymorphism\nGene polymorphism means that the structure or nucleo -\ntide sequence of the same gene is not the same in differ -\nent individuals, which is the variation of alleles. Human \ngene polymorphism plays an important role in elucidat -\ning the susceptibility and tolerance of the human body \nto disease, the diversity of clinical manifestations of dis -\neases, and the responsiveness to drug treatment.\nThere are three types of genes related to SLE: genes reg-\nulating the function of B cells and T cells, genes regulat -\ning interferon (IFN), and genes repairing DNA [28]. The \ngene polymorphism study of this article mainly includes \ntwo aspects of B Cell and T Cell Function-related genes \nand genes regulating IFN.\nB cell and T cell function‑related genes\nVitamin D The vitamin D receptor (VDR) is a member \nof the nuclear receptor superfamily. By binding to VDR, \nvitamin D can exert biological functions such as cell pro -\nliferation, differentiation, and immune response in the \n\nPage 9 of 15\nXiao et al. Advances in Rheumatology           (2023) 63:42 \n \nhuman body, and inhibit the pro-inflammatory activity of \nTh1 cells and the production of cytokines, such as IL-2, \nIFN-γ and TNF-α [29].\nThe polymorphism of the VDR gene was related to \nSLE, and its polymorphism mainly included the follow -\ning four types: VDR BsmI(rs1544410), Fok1(rs2228570), \nApaI(rs7975232), and TaqI(rs731236). In three studies \nfrom 2014 to 2016 [29–31], the results showed that the \npolymorphism of BsmI(rs1544410) and FokI(rs2228570) \nin the Asian population contributed to the pathogenesis \nof SLE, but the findings were not replicated in the Cauca-\nsian population.\nB‑cell lymphocyte kinase B-cell lymphocyte kinase \n(BLK), a member of the Src family, is involved in signal \ntransduction downstream of B-cell receptors, B-cell devel-\nopment, differentiation, and signal transduction, and fur-\nther affects B-cell function [32]. The risk allele variation of \nBLK may cause changes in the level of BLK protein, which \nmay affect the tolerance mechanism of B cells and induce \nimmune diseases. There was a significant correlation with \nBLK(rs4840568) (OR 1.32, 95% CI 1.22–1.43, P = 0.010) \n[33] and BLK(rs13277113) A-type allele (OR 1.36, 95% CI \n1.29–1.43, P < 0.001) [34].\nCytotoxic T lymphocyte‑associated Antigen‑4 Cytotoxic \nT lymphocyte-associated antigen-4 (CTLA-4), expressed \non T cells, is a key down-regulated molecule that inhib -\nits T cell activation and regulates its peripheral tolerance \n[35]. The relationship between rs231775(+49A/G) poly -\nmorphism [35–38] and SLE was studied, and the conclu -\nsions based on different races showed significant hetero -\ngeneity. The rs231775(+49A/G) polymorphism mainly \ncontributed to the SLE development in the Asian popula-\ntion.\nTumor necrosis factor‑α and related gene Tumor necro-\nsis factor-α (TNF-α) is a proinflammatory cytokine, \nwhich can stimulate the production of cytokines, enhance \nthe expression of adhesion molecules, increase neutro -\nphil activation, and act as a costimulatory factor for T cell \nactivation and antibody production. Its function-related \nproteins and receptors are actively involved in the patho -\ngenesis of SLE. Five SR-based meta-analyses reported \nTNF-α promoter-308A/G polymorphism [39–43]. These \nstudies were carried out in the China population, and \nwhether the conclusions could be extended to other pop-\nulations remained to be verified. There were also a series \nof genes related to TNF, such as tumor necrosis factor-α-\ninduced protein 3 (TNFAIP3) (rs2230926) [44–47], tumor \nnecrosis factor ligand superfamily member 4 (TNFSF4) \n(rs2205960) [48–51], and tumor necrosis factor receptor-\nassociated factor 1, complement component 5 (TRAF1/ \nC5)(rs10818488) [52] were also related to SLE susceptibil-\nity.\nInterleukin Interleukin (IL), a kind of cytokine produced \nby many kinds of cells, plays an important role in a series \nof processes such as the maturation, activation, prolif -\neration, and regulation of immune cells. IL-1 gene poly -\nmorphism, including IL-1A-889C/T, IL-1B-31T/C, and \nIL-1B-511C/T, might be associated with a higher risk of \nSLE [53], which needed more research to prove. IL-6 pol-\nymorphisms, rs1800796 and rs1800795, might be risk fac-\ntors in the previous studies [54–56]. However, one study \nin 2021 showed that rs1800795 was a protective factor, \nand rs1800796 was not associated with susceptibility [57], \nwhich was inconsistent with results from previous stud -\nies. IL-10 gene polymorphism rs1800896 was related to \nsusceptibility [28, 58, 59], while rs1800871 and rs1800872 \nwere not related to susceptibility. Although some of them \nwere different in subgroup analysis, we wrote the report \nbased on the results of the general population. The results \nof research on IL-18 rs187238 were inconsistent. One \nstudy showed that there was no relationship between \nrs187238 polymorphism and SLE in all populations, \nincluding the Chinese population [60]. Another study \nwith hierarchical analysis showed that rs187238 was a risk \nfactor for SLE, especially in the Asian population [61].\nHuman leukocyte antigen The frequency change of the \nhuman leukocyte antigen (HLA) allele is related to SLE. \nOne study focusing on the relationship between HLA-\nDRB1 allele polymorphism and SLE susceptibility dem -\nonstrated that HLADR3, DR9, and DR15 were risk factors \n[62]. Another study provided evidence to support HLA-\nDR3 and HLA-DR15 as the risk factors for SLE, and the \nstudy acknowledged the existence of inter-ethnic hetero -\ngeneity [63].\nInterferon regulatory genes\nInterferon regulatory factor 5 Interferon regulatory \nfactor 5 (IRF5) belongs to the transcription factor fam -\nily that regulates the activity of the immune system. It is \nexpressed in antigen-presenting cells (including dendritic \ncells, macrophages, and B cells) and monocytes, and it \nparticipates in the pathogenesis of SLE by influencing \nthe antigen-presenting cells [64]. IRF5 rs2070197 T allele \nand rs2004640 C allele were positively associated with the \npathogenesis of SLE [65, 66]. Although rs2070197 showed \nsusceptibility to SLE in the general population (OR 2.13, \n95% CI 1.86–2.44, P < 0.001), it had no effect in the Asian \npopulation [67]. Since this gene is monomorphic in China \nand Korea, the population in the two countries was not \naffected by the disease susceptibility caused by this gene \npolymorphism. It was speculated that it may be caused by \n\nPage 10 of 15Xiao et al. Advances in Rheumatology           (2023) 63:42 \nthe linkage imbalance between regions, and further stud -\nies on the specificity of ethnic populations are therefore \nneeded.\nSTAT4 The signal transducer and activator of tran -\nscription 4 (STAT4) is a transcription factor activated by \nIFN-α signal transduction, and the increase of IFN-α sig -\nnal transduction is the main pathogenic promotor of SLE. \nThe genetic variation of STAT4 is related to the risk of \nSLE. Three SRs showed that there was a significant rela -\ntionship between the STAT4 rs7574865 T allele and the \nrisk of developing SLE, and the OR value was around 1.60 \n[68–70].\nMiR‑146a MiR-146a is identified as a negative regula -\ntor of natural immunity, which directly inhibits the down-\nstream transactivation of type-I IFN at the molecular level \nand targets IRF5. There were some contradictions in the \nresults of miR-146a. No association between miR-146a \nand SLE susceptibility was found in 2015 [71] and 2017 \n[72], which might be attributed to the small number of \nincluded studies. MiR-146a rs57095329 was found to cor-\nrelate with the risk of SLE in two studies [73, 74], but this \ncorrelation was not supported in a recent study [75]; the \nrs2431697 and susceptibility to SLE were confirmed by \ntwo studies [73, 75].\nIntegrin Subunit Alpha M Complement dysfunction \nimpairs the ability to clear apoptotic cell fragments, which \nmay stimulate the production of autoantibodies in SLE. \nThe rs1143679 G/A polymorphism in Integrin Subunit \nAlpha M (ITGAM) severely impaired the phagocytosis of \ncomplement-coated particles and was positively associ -\nated with the risk of SLE [76], consistent with the findings \nof two studies in 2011 [77] and 2021 [76].\nEvidence from Mendelian randomization study\nThe evidence from SRs including case–control or \ncohort studies was meaningful to confirm the correla -\ntion between risk factors and SLE development, but it is \ninsufficient to make causal inferences. MR is an analytical \nmethod of causal estimation based on epidemiological \ndata. it has been widely used in the medical field in recent \nyears to assess the causal effect between exposure and \noutcome. We summarized the findings from the included \n13 MR studies on SLE below.\nSerum selenium, iron, growth differentiation factor 15, \nand circulating adiponectin\nThree MR analyses were performed [78–80], showing \nthat the rise in serum selenium (OR 0.85, 95% CI 0.77–\n0.93, P = 0.001) [78], serum iron (OR 0.79, 95% CI 0.66–\n0.94) [79] and growth differentiation factor 15 (GDF-15) \n(OR 0.80, 95% CI 0.68–0.92) [80] were all related to the \ndecrease of SLE risk, which provided the potential causal \nevidence of the protective effect of selenium, iron and \nGDF-15 on SLE.\nAnother study showed that there was no causal rela -\ntionship between circulating adiponectin levels and \nSLE (OR 1.38, 95% CI 0.91–1.35, P = 0.130) [81]. The \ntest of reverse causation in the study was also negative, \nand the several analyses performed also supported this \nconclusion.\nGut microbiome\nXiang et  al. [82] conducted a study on the composition \nof the gut microbiome (211 gut microbiota), supporting \na causal relationship between its positive and negative \neffects on SLE risk (Table  2). The results of inverse vari -\nance weighted, MR Egger, and weighted median methods \nwere slightly different. The levels of Bacillales, Coprobac‑\nter, Lachnospira, and Actinobacteria were negatively cor -\nrelated with the risk of SLE, while Bacilli, Lactobacillales, \nand Eggerthella might be risk factors for SLE. Although \nthe overlapping in the study samples—a limitation in \ntwo-sample MR studies—might affect the robustness of \nthe findings, these findings intrigue thoughts about the \ndevelopment of new treatment modalities, such as probi -\notics supplements, for the treatment of SLE.\nStatins\nRecently, an MR used HMGCR inhibition to genetically \nmimic statins and studied the relationship between the \ngenetic mimicry effect of statins and allergic diseases \nand immune-related diseases. The results showed that \ngenetic mimicry had little effect on allergic diseases or \nautoimmune diseases of men or women (OR 0.72, 95% CI \n0.33–1.58, P = 0.419) [83].\nPeriodontitis, celiac disease, vitamin D, coffee consumption, \nsmoking, and drinking\nFour studies on periodontitis [84], vitamin D [85], cof -\nfee consumption [86], and alcohol intake [87] were con -\nducted, respectively. The results showed that there might \nbe a causal relationship between periodontal inflamma -\ntion and SLE (β < 0.01, SE < 0.01, P = 0.046) [84]. Besides, \nceliac disease also showed a possible causal relationship \n(β = 0.29, SE = 0.06, P < 0.001) [88]. Vitamin D, coffee con-\nsumption, and alcohol intake had no causal relationship \nwith SLE. The common problem of the studies was the \ninsufficient number of SNP and the existence of weak \ninstrumental bias. One study showed that there was no \ncausal relationship between smoking or drinking and the \nrisk of SLE [89]. The analysis results based on the three \nmethods were consistent, and the corresponding tool \nvariables were appropriate.\n\nPage 11 of 15\nXiao et al. Advances in Rheumatology           (2023) 63:42 \n \nThe aforementioned MR studies adopted the above \ngenetic data from the European population only, which \nensured the genetic homogeneity and robustness of the \nresults but limited the generalization of the results to \nother populations. Therefore, the appeal of causality is \nworthy of further exploration.\nDiscussion\nCombined evidence from SR and MR showed that endo -\nmetriosis, atopic dermatitis, allergic rhinitis, periodon -\ntitis, and celiac disease were risk factors for SLE. The \nincreased levels of trace elements iron, selenium, and \nGDF-15 should be protective factors for SLE. Smoking, \nalcohol consumption, coffee consumption, and vitamin D \ndid not appear to have any effect on the risk of SLE.\nPeriodontitis and celiac disease should be paid more \nattention as observed risk factors. Early detection and \nscreening of antibodies in people with these diseases \nmay prevent the follow-up occurrence and development \nof SLE. More research evidence on the causal relation -\nship between endometriosis, atopic dermatitis, aller -\ngic rhinitis, and SLE is needed to further define disease \ninteractions.\nBased on the results of MR, serum iron, selenium, \nGDF-15, gut microbiomes Bacillales, Lachnospira, and \nActinobacteria might provide more options for the pre -\nvention and treatment of SLE, and relevant clinical trials \nare needed to verify their protective effects.\nThe pathogenesis of SLE is related to the reduction of \nT lymphocytes, the decline of the function of T suppres -\nsor cells, and the excessive proliferation of B cells to pro -\nduce a large number of antibodies. The existing emerging \ntherapies for SLE focus on the T-/B cell costimulatory \npathway as a target [90]. The risk factors confirmed by \nthe studies of gene polymorphism included STAT4, \nIFN-α, and IL-10, suggesting that future research might \nfocus on multi-target and multi-pathway precision ther -\napy, for example, using a combination of B cell activator \ninhibitor, IFN-α and STAT4 inhibitors, and recombinant \nhuman IL-2. The prevention and treatment effect may be \nachieved by regulating cytokines.\nContrary to the experience of routine care for SLE, \nsmoking, alcohol consumption, coffee consumption, and \nvitamin D, as shown by the MR studies, were not associ -\nated with SLE pathogenesis. This finding might indicate \nthat other confounding factors contribute more to SLE, \nfor example, patients who smoke or overdrink might be \nunder the status of stress, depression, higher frequency \nof staying up late, or less physical exercise. Such specific \nlifestyle interventions, such as smoking or overdrinking, \nmay not be emphasized in future medical advice to SLE \npatients and those at risk for the disease. Instead, more \nMR analyses should be conducted to rule out the real dis-\nease contributors behind the aforementioned unhealthy \nlifestyles.\nFinally, according to the studies of SR and MR, we \ndetermined the risk factors and their magnitude based \non the quality, quantity, and consistency of the results \n(Fig. 1). We summed up the most relevant risk factors for \nSLE. There was only one study of endometriosis, atopic \ndermatitis, allergic rhinitis, periodontitis, and celiac dis -\nease. The quality of evidence of endometriosis and celiac \ndisease was medium (4 scores), periodontitis, atopic \ndermatitis and allergic rhinitis were all low-quality evi -\ndence (1–2 scores), more research is needed to support \nit in the future. The MR results of smoking and drinking \nwere both negative and the evidence was of high qual -\nity. We tend to not correlate with drinking and SLE, so it \nwas not included. There were four SRs on smoking, but \nthe AMSTAR-2 score was very low, and MR did not sup -\nport it as a risk factor, so smoking was also low-quality \nevidence (1 score). All research on gene polymorphism \ncame from SR. We summarized the 9 genes with the larg-\nest number of literature and the most consistent results, \namong which the genes related to TNF had the highest \nscore (6 scores), which was high-quality evidence.\nHowever, some limitations in this review or included \nstudies should be noted. First, the search was relatively \nnot comprehensive, we only searched two databases. The \ntypes of included studies were limited, including only SRs \nand MR, without original studies and reviews, and the lit-\nerature was not up-to-date enough. Second, most of the \nSR quality assessment scores included in the studies were \ncritically low. Many of the included studies were small \nin scale and were likely to produce false associations. \nMR studies of endometriosis, atopic dermatitis, allergic \nrhinitis, and SLE are also needed to further determine \nthe causal relationship. Finally, there is a lack of official \nassessment tools for MR quality to confirm the reliability \nof the evidence.\nConclusion\nIn short, we should pay attention to preventing and treat-\ning SLE in patients with endometriosis, celiac disease, \nand periodontitis. Take appropriate dietary supplements \nto increase serum iron and selenium levels to reduce the \nrisk of SLE. There should be no excessive intervention \nin lifestyle such as smoking and drinking, as it does not \naffect the incidence of SLE.\n\nPage 12 of 15Xiao et al. Advances in Rheumatology           (2023) 63:42 \nAppendix A: search strategy\nPubMed\nSystematic review\n#1 “Lupus Erythematosus, Systemic”[Mesh]OR Systemic \nlupus erythematosus OR Systemic Lupus Erythemato -\nsus OR Lupus Erythematosus Disseminatus OR Libman-\nSacks Disease OR Disease, Libman-Sacks OR Libman \nSacks Disease.\n#2 risk factor[MeSH Terms] OR Factor, Risk OR Risk \nFactor OR Social Risk Factors OR Factor, Social Risk OR \nFactors, Social Risk OR Risk Factor, Social OR Risk Fac -\ntors, Social OR Social Risk Factor OR Health Correlates \nOR Correlates, Health OR Population at Risk OR Popu -\nlations at Risk OR Risk Scores OR Risk Score OR Score, \nRisk OR Risk Factor Scores OR Risk Factor Score OR \nScore, Risk Factor.\n#3 #1 AND #2 AND (meta-analysis[Filter] OR system -\natic review[Filter]).\nMendelian randomization study\n#1 “Lupus Erythematosus, Systemic”[Mesh]OR Systemic \nlupus erythematosus OR Systemic Lupus Erythematosus \nOR Lupus Erythematosus Disseminatus OR Libman-\nSacks Disease OR Disease, Libman-Sacks OR Libman \nSacks Disease.\n#2 Mendelian Randomization Analysis [MeSH Terms].\n#3 #1 AND #2.\nEmbase\nSystematic review\n#1’systemic lupus erythematosus’:ti,ab,kw OR sle:ti,ab,kw.\n#2’risk factors’:ti,ab,kw OR ’risk factor’:ti,ab,kw.\n#3’systematic review’:ti,ab,kw OR ’meta \nanalysis’:ti,ab,kw.\n#4 #1 AND #2\n#5 #4 AND #3.\nMendelian randomization study\n#1’systemic lupus erythematosus’:ti,ab,kw OR sle:ti,ab,kw.\n#2’mendelian randomization analysis’:ti,ab,kw OR \n’mendelian randomization study’:ti,ab,kw OR ’mendelian \nrandomization’:ti,ab,kw.\n#3 #1 AND #2.\nAppendix B: AMSTAR‑2 scale for the assessment of systematic reviews\nStudy ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Methodological quality\nAgrawal [25] N Y Y Y Y Y N Y PY N NM N NM Y NM Y Low\nChua [21] N N Y PY Y Y N PY PY N Y Y Y Y Y N Critically low\nCostenbader \n[18]\nN N Y PY Y Y N PY PY N Y Y Y Y Y N Critically low\nJanowsky [23] N N N PY Y Y N Y Y Y Y Y Y Y Y Y Critically low\nJiang [19] Y N Y PY N Y N PY PY N Y Y Y Y Y N Critically low\nParisis [20] N N Y N Y Y Y PY Y N Y Y N Y Y Y Critically low\nPonvilawan \n[11]\nN N Y PY Y N N PY N N NM N N Y Y Y Critically low\nRees [2] N N N Y N N N Y N N NM NM Y Y NM Y Critically low\nSakthiswary \n[26]\nN N Y PY Y N Y PY PY N NM NM Y Y NM Y Critically low\nShigesi [5] N Y Y PY Y Y Y PY PY N Y Y Y Y Y Y Moderate\nWang (22) Y Y Y PY Y Y Y Y Y Y Y Y Y Y Y Y High\nWang [17] Y N Y PY Y Y N Y Y N Y Y Y Y Y Y Critically low\nWongtrakul \n[12]\nN N Y PY Y Y N PY N N Y Y Y N Y N Critically low\nYoussefi [24] N N Y PY Y N N PY N N N N N Y N Y Critically low\nBold represents 7 critical items of the 16 items in the AMSTAR-2 scale (items 2, 4, 7, 9, 11, 13, 15). Y yes; PY  partial \nyes; N No; NM no meta-analysis\n\nPage 13 of 15\nXiao et al. Advances in Rheumatology           (2023) 63:42 \n \nAbbreviations\nSLE  Systemic lupus erythematosus\nSR  Systematic review\nMR  Mendelian randomization\nSNP  Single nucleotide polymorphism\nAMSTAR‑2  Assessment of Multiple Systematic Reviews 2\nIL  Interleukin\nIFN  Interferon\nVDR  Vitamin D receptor\nBLK  B‑cell lymphocyte kinase\nCTLA‑4  Cytotoxic T lymphocyte‑associated antigen‑4\nTNF‑α  Tumor necrosis factor‑α\nTNFAIP3  Tumor necrosis factor‑α‑induced protein 3\nTNFSF4  Tumor necrosis factor ligand superfamily member 4\nTRAF1/C5  Tumor necrosis factor receptor‑associated factor 1: complement \ncomponent 5\nHLA  Human leukocyte antigen\nIRF5  Interferon regulatory factor 5\nSTAT4  Signal transducer and activator of transcription 4\nITGAM  Integrin Subunit Alpha M\nAcknowledgements\nNot applicable.\nAuthor contributions\nAll authors had full access to all the data in the study and take responsibility \nfor the integrity of the data and the accuracy of the data analysis. Concept and \ndesign: HZ. Acquisition, analysis, or interpretation of data: X‑YX, QC, Y‑ZS, L‑WL, \nand CH. Drafting of the manuscript: X‑YX. Critical revision of the manuscript \nfor important intellectual content: all authors. Statistical analysis: X‑YX and HZ. \nAdministrative, technical, or material support: HZ. Supervision: HZ.\nFunding\nHui Zheng received a grant from the Sichuan Youth Science and Technology \nInnovation Research Team (No. 2021JDTD0007).\nAvailability of data and materials\nData sharing does not apply to this article as no datasets were generated or \nanalyzed during the current study.\nDeclarations\nEthics approval and consent to participate\nNot applicable.\nConsent for publication\nNot applicable.\nCompeting interests\nThe authors declare that they have no competing interests.\nReceived: 2 February 2023   Accepted: 2 August 2023\nReferences\n 1. Kiriakidou M, Ching CL. Systemic lupus erythematosus. Ann Intern Med. \n2020;172:ITC81–96. https:// doi. org/ 10. 7326/ AITC2 02006 020.\n 2. Rees F, Doherty M, Grainge MJ, Lanyon P , Zhang W. The worldwide \nincidence and prevalence of systemic lupus erythematosus: a systematic \nreview of epidemiological studies. Rheumatology. 2017;56:1945–61. \nhttps:// doi. org/ 10. 1093/ rheum atolo gy/ kex260.\n 3. Barber MRW, Drenkard C, Falasinnu T, Hoi A, Mak A, Kow NY, Svenungs‑\nson E, Peterson J, Clarke AE, Ramsey‑Goldman R. Global epidemiology \nof systemic lupus erythematosus. Nat Rev Rheumatol. 2021;17:515–32. \nhttps:// doi. org/ 10. 1038/ s41584‑ 021‑ 00668‑1.\n 4. Tsokos GC. Systemic lupus erythematosus. N Engl J Med. 2011. https:// \ndoi. org/ 10. 1056/ NEJMr a1100 359.\n 5. Shigesi N, Kvaskoff M, Kirtley S, Feng Q, Fang H, Knight JC, Missmer \nSA, Rahmioglu N, Zondervan KT, Becker CM. The association between \nendometriosis and autoimmune diseases: a systematic review and meta‑\nanalysis. Hum Reprod Update. 2019;25:486–503. https:// doi. org/ 10. 1093/ \nhumupd/ dmz014.\n 6. Barbhaiya M, Costenbader KH. Environmental exposures and the \ndevelopment of systemic lupus erythematosus. Curr Opin Rheumatol. \n2016;28:497–505. https:// doi. org/ 10. 1097/ BOR. 00000 00000 000318.\n 7. Tan W, Sunahori K, Zhao J, Deng Y, Kaufman KM, Kelly JA, Langefeld CD, \nWilliams AH, Comeau ME, Ziegler JT, et al. Association of PPP2CA poly‑\nmorphisms with systemic lupus erythematosus susceptibility in multiple \nethnic groups. Arthritis Rheum. 2011;63:2755–63. https:// doi. org/ 10. \n1002/ art. 30452.\n 8. Sekula P , Del Greco MF, Pattaro C, Köttgen A. Mendelian randomization \nas an approach to assess causality using observational data. J Am Soc \nNephrol. 2016;27:3253–65. https:// doi. org/ 10. 1681/ ASN. 20160 10098.\n 9. Shea BJ, Reeves BC, Wells G, Thuku M, Hamel C, Moran J, Moher D, Tug‑\nwell P , Welch V, Kristjansson E, et al. AMSTAR 2: a critical appraisal tool for \nsystematic reviews that include randomised or non‑randomised studies \nof healthcare interventions, or both. BMJ. 2017;358:j4008. https:// doi. org/ \n10. 1136/ bmj. j4008.\n 10. Bereshchenko O, Bruscoli S, Riccardi C. Glucocorticoids, sex hormones, \nand immunity. Front Immunol. 2018;9:1332. https:// doi. org/ 10. 3389/ \nfimmu. 2018. 01332.\n 11. Ponvilawan B, Charoenngam N, Wongtrakul W, Ungprasert P . Association \nof atopic dermatitis with an increased risk of systemic lupus erythemato‑\nsus: a systematic review and meta‑analysis. J Postgrad Med. 2021;67:139–\n45. https:// doi. org/ 10. 4103/ jpgm. JPGM_ 1270_ 20.\n 12. Wongtrakul W, Charoenngam N, Ponvilawan B, Ungprasert P . Allergic \nrhinitis and risk of systemic lupus erythematosus: a systematic review and \nmeta‑analysis. Int J Rheum Dis. 2020;23:1460–7. https:// doi. org/ 10. 1111/ \n1756‑ 185X. 13928.\n 13. Sin E, Anand P , Frieri M. A link: allergic rhinitis, asthma & systemic lupus \nerythematosus. Autoimmun Rev. 2016;15:487–91. https:// doi. org/ 10. \n1016/j. autrev. 2016. 02. 003.\n 14. Imhof A, Froehlich M, Brenner H, Boeing H, Pepys MB, Koenig W. Effect \nof alcohol consumption on systemic markers of inflammation. Lancet. \n2001;357:763–7. https:// doi. org/ 10. 1016/ S0140‑ 6736(00) 04170‑2.\n 15. Hahn J, Leatherwood C, Malspeis S, Liu X, Lu B, Roberts AL, Sparks JA, \nKarlson EW, Feldman CH, Munroe ME, et al. Associations between \ndaily alcohol consumption and systemic lupus erythematosus‑related \ncytokines and chemokines among US female nurses without SLE. Lupus. \n2020;29:976–82. https:// doi. org/ 10. 1177/ 09612 03320 929427.\n 16. Hahn J, Leatherwood C, Malspeis S, Liu X, Lu B, Roberts AL, Sparks JA, Karl‑\nson EW, Feldman CH, Munroe ME, et al. Associations between smoking \nand systemic lupus erythematosus‑related cytokines and chemokines \namong US female nurses. Arthritis Care Res. 2021;73:1583–9. https:// doi. \norg/ 10. 1002/ acr. 24370.\n 17. Wang J, Liu J, Pan L, Guo L, Liu C, Yang S. Association between alcohol \nintake and the risk of systemic lupus erythematosus: a systematic review \nand meta‑analysis. Lupus. 2021;30:725–33. https:// doi. org/ 10. 1177/ 09612 \n03321 991918.\n 18. Costenbader KH, Kim DJ, Peerzada J, Lockman S, Nobles‑Knight D, Petri M, \nKarlson EW. Cigarette smoking and the risk of systemic lupus erythema‑\ntosus: a meta‑analysis. Arthritis Rheum. 2004;50:849–57. https:// doi. org/ \n10. 1002/ art. 20049.\n 19. Jiang F, Li S, Jia C. Smoking and the risk of systemic lupus erythematosus: \nan updated systematic review and cumulative meta‑analysis. Clin Rheu‑\nmatol. 2015;34:1885–92. https:// doi. org/ 10. 1007/ s10067‑ 015‑ 3008‑9.\n 20. Parisis D, Bernier C, Chasset F, Arnaud L. Impact of tobacco smoking \nupon disease risk, activity and therapeutic response in systemic lupus \nerythematosus: a systematic review and meta‑analysis. Autoimmun Rev. \n2019;18:102393. https:// doi. org/ 10. 1016/j. autrev. 2019. 102393.\n 21. Chua MH, Ng IA, Mike WC, Mak A. Association between cigarette smok‑\ning and systemic lupus erythematosus: an updated multivariate Bayesian \nmeta‑analysis. J Rheumatol. 2020;47:1514–21. https:// doi. org/ 10. 3899/ \njrheum. 190733.\n 22. Wang B, Shao X, Wang D, Xu D, Zhang J‑A. Vaccinations and risk of sys‑\ntemic lupus erythematosus and rheumatoid arthritis: a systematic review \nand meta‑analysis. Autoimmun Rev. 2017;16:756–65. https:// doi. org/ 10. \n1016/j. autrev. 2017. 05. 012.\n\nPage 14 of 15Xiao et al. Advances in Rheumatology           (2023) 63:42 \n 23. Janowsky EC, Kupper LL, Hulka BS. Meta‑analyses of the relation between \nsilicone breast implants and the risk of connective‑tissue diseases. N Engl \nJ Med. 2000;342:781–90. https:// doi. org/ 10. 1056/ NEJM2 00003 16342 1105.\n 24. Youssefi M, Tafaghodi M, Farsiani H, Ghazvini K, Keikha M. Helicobacter \npylori infection and autoimmune diseases; is there an association with \nsystemic lupus erythematosus, rheumatoid arthritis, autoimmune \natrophy gastritis and autoimmune pancreatitis? A systematic review and \nmeta‑analysis study. J Microbiol Immunol Infect. 2021;54:359–69. https:// \ndoi. org/ 10. 1016/j. jmii. 2020. 08. 011.\n 25. Agrawal M, Shah S, Patel A, Pinotti R, Colombel J‑F, Burisch J. Changing \nepidemiology of immune‑mediated inflammatory diseases in immi‑\ngrants: a systematic review of population‑based studies. J Autoimmun. \n2019;105:102303. https:// doi. org/ 10. 1016/j. jaut. 2019. 07. 002.\n 26. Sakthiswary R, Raymond AA. The clinical significance of Vitamin D \nin systemic lupus erythematosus: a systematic review. PLoS ONE. \n2013;8:e55275. https:// doi. org/ 10. 1371/ journ al. pone. 00552 75.\n 27. Parks CG, de Souza Espindola Santos A, Barbhaiya M, Costenbader KH. \nUnderstanding the role of environmental factors in the development \nof systemic lupus erythematosus. Best Pract Res Clin Rheumatol. 2017; \n31:306–320. https:// doi. org/ 10. 1016/j. berh. 2017. 09. 005.\n 28. Yin Q, Wu L‑C, Zheng L, Han M‑Y, Hu L‑Y, Zhao P‑P , Bai W‑Y, Zhu X‑W, \nXia J‑W, Wang X‑B, et al. Comprehensive assessment of the Association \nbetween genes on JAK‑STAT pathway (IFIH1, TYK2, IL‑10) and systemic \nlupus erythematosus: a meta‑analysis. Arch Dermatol Res. 2018;310:711–\n28. https:// doi. org/ 10. 1007/ s00403‑ 018‑ 1858‑0.\n 29. Hu W, Niu G, Lin Y, Chen X, Lin L. Impact of the polymorphism in Vitamin \nD receptor gene BsmI and the risk of systemic lupus erythematosus: an \nupdated meta‑analysis. Clin Rheumatol. 2016;35:927–34. https:// doi. org/ \n10. 1007/ s10067‑ 015‑ 3157‑x.\n 30. Zhou T‑B, Jiang Z‑P , Lin Z‑J, Su N. Association of Vitamin D receptor gene \npolymorphism with the risk of systemic lupus erythematosus. J Recept \nSignal Transduct Res. 2015;35:8–14. https:// doi. org/ 10. 3109/ 10799 893. \n2014. 922577.\n 31. Xiong J, He Z, Zeng X, Zhang Y, Hu Z. Association of Vitamin D receptor \ngene polymorphisms with systemic lupus erythematosus: a meta‑analy‑\nsis. Clin Exp Rheumatol. 2014;32:174–81.\n 32. Zhang X, Mei D, Zhang L, Wei W. Src family protein kinase controls the \nfate of B Cells in autoimmune diseases. Inflammation. 2021;44:423–33. \nhttps:// doi. org/ 10. 1007/ s10753‑ 020‑ 01355‑1.\n 33. Zeng C, Fang C, Weng H, Xu X, Wu T, Li W. B‑cell lymphocyte kinase poly‑\nmorphisms rs13277113, rs2736340, and rs4840568 and risk of autoim‑\nmune diseases: a meta‑analysis. Medicine. 2017;96:e7855. https:// doi. org/ \n10. 1097/ MD. 00000 00000 007855.\n 34. Song GG, Lee YH. Association between BLK polymorphisms and suscep‑\ntibility to SLE: a meta‑analysis. Z Rheumatol. 2017;76:176–82. https:// doi. \norg/ 10. 1007/ s00393‑ 016‑ 0072‑8.\n 35. Lee YH, Harley JB, Nath SK. CTLA‑4 polymorphisms and systemic lupus \nerythematosus (SLE): a meta‑analysis. Hum Genet. 2005;116:361–7. \nhttps:// doi. org/ 10. 1007/ s00439‑ 004‑ 1244‑1.\n 36. Chang W‑W, Zhang L, Yao Y‑S, Su H. Association between CTLA‑4 \nexon‑1 +49A/G polymorphism and systemic lupus erythematosus: an \nupdated analysis. Mol Biol Rep. 2012;39:9159–65. https:// doi. org/ 10. 1007/ \ns11033‑ 012‑ 1788‑4.\n 37. Zhai J‑X, Zou L‑W, Zhang Z‑X, Fan W‑J, Wang H‑Y, Liu T, Ren Z, Dai R‑X, Ye \nD. CTLA‑4 polymorphisms and systemic lupus erythematosus (SLE): a \nmeta‑analysis. Mol Biol Rep. 2013;40:5213–23. https:// doi. org/ 10. 1007/ \ns11033‑ 012‑ 2125‑7.\n 38. Yu L, Shao M, Zhou T, Xie H, Wang F, Kong J, Xu S, Shuai Z, Pan F. \nAssociation of CTLA‑4 (+49 A/G) polymorphism with susceptibility to \nautoimmune diseases: a meta‑analysis with trial sequential analysis. Int \nImmunopharmacol. 2021;96:107617. https:// doi. org/ 10. 1016/j. intimp. \n2021. 107617.\n 39. Lee YH, Harley JB, Nath SK. Meta‑analysis of TNF‑alpha promoter ‑308 A/G \npolymorphism and SLE susceptibility. Eur J Hum Genet. 2006;14:364–71. \nhttps:// doi. org/ 10. 1038/ sj. ejhg. 52015 66.\n 40. Zou Y‑F, Feng X‑L, Tao J‑H, Su H, Pan F‑M, Liao F‑F, Fan Y, Ye D‑Q. Meta‑\nAnalysis of TNF‑α promoter ‑308A/G polymorphism and SLE susceptibil‑\nity in Asian populations. Rheumatol Int. 2011;31:1055–64. https:// doi. org/ \n10. 1007/ s00296‑ 010‑ 1392‑7.\n 41. Pan H‑F, Leng R‑X, Wang C, Qin W‑Z, Chen L‑L, Zha Z‑Q, Tao J‑H, Ye D‑Q. \nAssociation of TNF‑α promoter‑308 A/G polymorphism with susceptibility \nto systemic lupus erythematosus: a meta‑analysis. Rheumatol Int. \n2012;32:2083–92. https:// doi. org/ 10. 1007/ s00296‑ 011‑ 1924‑9.\n 42. Yang Z‑C, Xu F, Tang M, Xiong X. Association between TNF‑α promoter \n‑308 A/G polymorphism and systemic lupus erythematosus suscep‑\ntibility: a case‑control study and meta‑analysis. Scand J Immunol. \n2017;85:197–210. https:// doi. org/ 10. 1111/ sji. 12516.\n 43. Chen L, Huang Z, Liao Y, Yang B, Zhang J. Association between tumor \nnecrosis factor polymorphisms and rheumatoid arthritis as well as \nsystemic lupus erythematosus: a meta‑analysis. Br J Med Biol Res. \n2019;52:e7927. https:// doi. org/ 10. 1590/ 1414‑ 431X2 01879 27.\n 44. Fan Y, Tao J‑H, Zhang L‑P , Li L‑H, Ye D‑Q. The Association between BANK1 \nand TNFAIP3 gene polymorphisms and systemic lupus erythematosus: \na meta‑analysis. Int J Immunogenet. 2011;38:151–9. https:// doi. org/ 10. \n1111/j. 1744‑ 313X. 2010. 00990.x.\n 45. Lee YH, Song GG. Associations between TNFAIP3 gene polymorphisms \nand systemic lupus erythematosus: a meta‑analysis. Genet Test Mol \nBiomarkers. 2012;16:1105–10. https:// doi. org/ 10. 1089/ gtmb. 2012. 0096.\n 46. Zhang M‑Y, Yang X‑K, Pan H‑F, Ye D‑Q. Associations between TNFAIP3 \ngene polymorphisms and systemic lupus erythematosus risk: an updated \nmeta‑analysis. HLA. 2016;88:245–52. https:// doi. org/ 10. 1111/ tan. 12908.\n 47. Liu X, Qin H, Wu J, Xu J. Association of TNFAIP3 and TNIP1 polymor‑\nphisms with systemic lupus erythematosus risk: a meta‑analysis. Gene. \n2018;668:155–65. https:// doi. org/ 10. 1016/j. gene. 2018. 05. 062.\n 48. Lu M‑M, Xu W‑D, Yang J, Ye Q‑L, Feng C‑C, Li J, Pan H‑F, Tao J‑H, Wang J, Ye \nD‑Q. Association of TNFSF4 polymorphisms with systemic lupus erythe‑\nmatosus: a meta‑analysis. Mod Rheumatol. 2013;23:686–93. https:// doi. \norg/ 10. 1007/ s10165‑ 012‑ 0708‑8.\n 49. Wang J‑M, Yuan Z‑C, Huang A‑F, Xu W‑D. Association of TNFSF4 \nrs1234315, rs2205960 polymorphisms and systemic lupus erythematosus \nsusceptibility: a meta‑analysis. Lupus. 2019;28:1197–204. https:// doi. org/ \n10. 1177/ 09612 03319 862610.\n 50. Fu Y, Lin Q, Zhang Z‑R. Association of TNFSF4 polymorphisms with sys‑\ntemic lupus erythematosus: a meta‑analysis. Adv Rheumatol. 2021;61:59. \nhttps:// doi. org/ 10. 1186/ s42358‑ 021‑ 00215‑2.\n 51. Moreno‑Eutimio MA, Martínez‑Alemán CE, Aranda‑Uribe IS, Aquino‑Jar‑\nquin G, Cabello‑Gutierrez C, Fragoso JM, Barbosa‑Cobos RE, Saavedra MA, \nRamírez‑Bello J. TNFSF4 is a risk factor to systemic lupus erythematosus \nin a Latin American population. Clin Rheumatol. 2021;40:929–39. https:// \ndoi. org/ 10. 1007/ s10067‑ 020‑ 05332‑9.\n 52. Xu K, Peng H, Zhou M, Wang W, Li R, Zhu K‑K, Zhang M, Wen P‑F, Pan H‑F, \nYe D‑Q. Association study of TRAF1/C5 polymorphism (rs10818488) with \nsusceptibility to rheumatoid arthritis and systemic lupus erythematosus: \na meta‑analysis. Gene. 2013;517:46–54. https:// doi. org/ 10. 1016/j. gene. \n2012. 12. 092.\n 53. Zhu L, Chen P , Sun X, Zhang S. Associations between polymorphisms \nin the IL‑1 gene and the risk of rheumatoid arthritis and systemic lupus \nerythematosus: evidence from a meta‑analysis. Int Arch Allergy Immunol. \n2021;182:234–42. https:// doi. org/ 10. 1159/ 00051 0641.\n 54. Lee YH, Lee HS, Choi SJ, Ji JD, Song GG. The association between interleu‑\nkin‑6 polymorphisms and systemic lupus erythematosus: a meta‑analysis. \nLupus. 2012;21:60–7. https:// doi. org/ 10. 1177/ 09612 03311 422711.\n 55. Yang Z, Liang Y, Qin B, Zhong R. A meta‑analysis of the association of IL‑6 \n‑174 G/C and ‑572 G/C polymorphisms with systemic lupus erythema‑\ntosus risk. Rheumatol Int. 2014;34:199–205. https:// doi. org/ 10. 1007/ \ns00296‑ 013‑ 2855‑4.\n 56. Cui YX, Fu CW, Jiang F, Ye LX, Meng W. Association of the interleukin‑6 \npolymorphisms with systemic lupus erythematosus: a meta‑analysis. \nLupus. 2015;24:1308–17. https:// doi. org/ 10. 1177/ 09612 03315 588971.\n 57. Liu J, Liao M‑Q, Cao D‑F, Yang Y, Yang Y, Liu Y‑H, Zeng F‑F, Chen X‑H. The \nassociation between interleukin‑6 gene polymorphisms and risk of sys‑\ntemic lupus erythematosus: a meta‑analysis with trial sequential analysis. \nImmunol Invest. 2021;50:259–72. https:// doi. org/ 10. 1080/ 08820 139. 2020. \n17696 46.\n 58. Wang B, Zhu J‑M, Fan Y‑G, Xu W‑D, Cen H, Pan H‑F, Ye D‑Q. Association \nof the ‑1082G/A polymorphism in the interleukin‑10 gene with systemic \nlupus erythematosus: a meta‑analysis. Gene. 2013;519:209–16. https:// \ndoi. org/ 10. 1016/j. gene. 2013. 01. 026.\n 59. Yuan Y, Wang X, Ren L, Kong Y, Bai J, Yan Y. Associations between \ninterleukin‑10 gene polymorphisms and systemic lupus erythematosus \nrisk: a meta‑analysis with trial sequential analysis. Clin Exp Rheumatol. \n2019;37:242–53.\n\nPage 15 of 15\nXiao et al. Advances in Rheumatology           (2023) 63:42 \n \n•\n \nfast, convenient online submission\n •\n  \nthorough peer review by experienced researchers in your field\n• \n \nrapid publication on acceptance\n• \n \nsupport for research data, including large and complex data types\n•\n  \ngold Open Access which fosters wider collaboration and increased citations \n \nmaximum visibility for your research: over 100M website views per year •\n  At BMC, research is always in progress.\nLearn more biomedcentral.com/submissions\nReady to submit y our researc hReady to submit y our researc h  ?  Choose BMC and benefit fr om: ?  Choose BMC and benefit fr om: \n 60. Chen S, Jiang F, Ren J, Liu J, Meng W. Association of IL‑18 polymorphisms \nwith rheumatoid arthritis and systemic lupus erythematosus in Asian \npopulations: a meta‑analysis. BMC Med Genet. 2012;13:107. https:// doi. \norg/ 10. 1186/ 1471‑ 2350‑ 13‑ 107.\n 61. Wen D, Liu J, Du X, Dong J‑Z, Ma C‑S. Association of interleukin‑18 \n(‑137G/C) polymorphism with rheumatoid arthritis and systemic lupus \nerythematosus: a meta‑analysis. Int Rev Immunol. 2014;33:34–44. https:// \ndoi. org/ 10. 3109/ 08830 185. 2013. 816699.\n 62. Niu Z, Zhang P , Tong Y. Value of HLA‑DR genotype in systemic lupus \nerythematosus and lupus nephritis: a meta‑analysis. Int J Rheum Dis. \n2015;18:17–28. https:// doi. org/ 10. 1111/ 1756‑ 185X. 12528.\n 63. Xue K, Niu W‑Q, Cui Y. Association of HLA‑DR3 and HLA‑DR15 polymor‑\nphisms with risk of systemic lupus erythematosus. Chin Med J (Engl). \n2018;131:2844–51. https:// doi. org/ 10. 4103/ 0366‑ 6999. 246058.\n 64. Xu W‑D, Pan H‑F, Xu Y, Ye D‑Q. interferon regulatory factor 5 and autoim‑\nmune lupus. Expert Rev Mol Med. 2013;15:e6. https:// doi. org/ 10. 1017/ \nerm. 2013.7.\n 65. Hu W, Ren H. A meta‑analysis of the association of IRF5 polymorphism \nwith systemic lupus erythematosus. Int J Immunogenet. 2011;38:411–7. \nhttps:// doi. org/ 10. 1111/j. 1744‑ 313X. 2011. 01025.x.\n 66. Wang J‑M, Huang A‑F, Yuan Z‑C, Su L‑C, Xu W‑D. Association of IRF5 \nrs2004640 polymorphism and systemic lupus erythematosus: a meta‑\nanalysis. Int J Rheum Dis. 2019;22:1598–606. https:// doi. org/ 10. 1111/ \n1756‑ 185X. 13654.\n 67. Li Y, Chen S, Li P , Wu Z, Li J, Liu B, Zhang F, Li Y. Association of the IRF5 \nrs2070197 polymorphism with systemic lupus erythematosus: a meta‑\nanalysis. Clin Rheumatol. 2015;34:1495–501. https:// doi. org/ 10. 1007/ \ns10067‑ 015‑ 3036‑5.\n 68. Ji JD, Lee WJ, Kong KA, Woo JH, Choi SJ, Lee YH, Song GG. Association \nof STAT4 polymorphism with rheumatoid arthritis and systemic lupus \nerythematosus: a meta‑analysis. Mol Biol Rep. 2010;37:141–7. https:// doi. \norg/ 10. 1007/ s11033‑ 009‑ 9553‑z.\n 69. Yuan H, Feng J‑B, Pan H‑F, Qiu L‑X, Li L‑H, Zhang N, Ye D‑Q. A meta‑\nanalysis of the association of STAT4 polymorphism with systemic lupus \nerythematosus. Mod Rheumatol. 2010;20:257–62. https:// doi. org/ 10. \n1007/ s10165‑ 010‑ 0275‑9.\n 70. Wang J‑M, Xu W‑D, Huang A‑F. Association of STAT4 gene rs7574865, \nrs10168266 polymorphisms and systemic lupus erythematosus suscep‑\ntibility: a meta‑analysis. Immunol Invest. 2021;50:282–94. https:// doi. org/ \n10. 1080/ 08820 139. 2020. 17527 12.\n 71. Lee YH, Bae S‑C. The miR‑146a polymorphism and susceptibility to \nsystemic lupus erythematosus and rheumatoid arthritis: a meta‑analysis. \nZ Rheumatol. 2015;74:153–6. https:// doi. org/ 10. 1007/ s00393‑ 014‑ 1509‑6.\n 72. Sun H‑Y, Lv A‑K, Yao H. Relationship of miRNA‑146a to Primary Sjögren’s \nSyndrome and to Systemic Lupus Erythematosus: A Meta‑Analysis. Rheu‑\nmatol Int. 2017;37:1311–6. https:// doi. org/ 10. 1007/ s00296‑ 017‑ 3756‑8.\n 73. Ji JD, Cha ES, Lee WJ. Association of MiR‑146a polymorphisms with \nsystemic lupus erythematosus: a meta‑analysis. Lupus. 2014;23:1023–30. \nhttps:// doi. org/ 10. 1177/ 09612 03314 534512.\n 74. Fu L, Jin L, Yan L, Shi J, Wang H, Zhou B, Wu X. Comprehensive review of \ngenetic association studies and meta‑analysis on miRNA polymorphisms \nand rheumatoid arthritis and systemic lupus erythematosus susceptibil‑\nity. Hum Immunol. 2016;77:1–6. https:// doi. org/ 10. 1016/j. humimm. 2014. \n09. 002.\n 75. Liu F, Liang Y, Zhao Y, Chen L, Wang X, Zhang C. Meta‑analysis of associa‑\ntion of microRNAs genetic variants with susceptibility to rheumatoid \narthritis and systemic lupus erythematosus. Medicine. 2021;100:e25689. \nhttps:// doi. org/ 10. 1097/ MD. 00000 00000 025689.\n 76. Ebrahimiyan H, Mostafaei S, Aslani S, Faezi ST, Farhadi E, Jamshidi A, \nMahmoudi M. Association between complement gene polymorphisms \nand systemic lupus erythematosus: a systematic review and meta‑analy‑\nsis. Clin Exp Med. 2021. https:// doi. org/ 10. 1007/ s10238‑ 021‑ 00758‑0.\n 77. Fan Y, Li L‑H, Pan H‑F, Tao J‑H, Sun Z‑Q, Ye D‑Q. Association of ITGAM \npolymorphism with systemic lupus erythematosus: a meta‑analysis. J Eur \nAcad Dermatol Venereol. 2011;25:271–5. https:// doi. org/ 10. 1111/j. 1468‑ \n3083. 2010. 03776.x.\n 78. Ye D, Sun X, Guo Y, Shao K, Qian Y, Huang H, Liu B, Wen C, Mao Y. Geneti‑\ncally determined selenium concentrations and risk for autoimmune \ndiseases. Nutrition. 2021;91–92:111391. https:// doi. org/ 10. 1016/j. nut. \n2021. 111391.\n 79. Ye D, Zhu Z, Huang H, Sun X, Liu B, Xu X, He Z, Li S, Wen C, Mao Y. \nGenetically predicted serum iron status is associated with altered risk \nof systemic lupus erythematosus among european populations. J Nutr. \n2021;151:1473–8. https:// doi. org/ 10. 1093/ jn/ nxab0 15.\n 80. Ye D, Liu B, He Z, Huang L, Qian Y, Shao K, Wen C, Mao Y. Assessing the \nassociations of growth differentiation factor 15 with rheumatic diseases \nusing genetic data. Clin Epidemiol. 2021;13:245–52. https:// doi. org/ 10. \n2147/ CLEP . S3050 24.\n 81. Dan Y‑L, Wang P , Cheng Z, Wu Q, Wang X‑R, Wang D‑G, Pan H‑F. Circulat‑\ning adiponectin levels and systemic lupus erythematosus: a two‑sample \nmendelian randomization study. Rheumatology. 2021;60:940–6. https:// \ndoi. org/ 10. 1093/ rheum atolo gy/ keaa5 06.\n 82. Xiang K, Wang P , Xu Z, Hu Y‑Q, He Y‑S, Chen Y, Feng Y‑T, Yin K‑J, Huang J‑X, \nWang J, et al. Causal effects of gut microbiome on systemic lupus erythe‑\nmatosus: a two‑sample Mendelian randomization study. Front Immunol. \n2021;12:667097. https:// doi. org/ 10. 3389/ fimmu. 2021. 667097.\n 83. Yang G, Schooling CM. Investigating genetically mimicked effects of \nstatins via HMGCR inhibition on immune‑related diseases in men and \nwomen using Mendelian randomization. Sci Rep. 2021;11:23416. https:// \ndoi. org/ 10. 1038/ s41598‑ 021‑ 02981‑x.\n 84. Bae S‑C, Lee YH. Causal association between periodontitis and risk of \nrheumatoid arthritis and systemic lupus erythematosus: a Mendelian \nrandomization. Z Rheumatol. 2020;79:929–36. https:// doi. org/ 10. 1007/ \ns00393‑ 019‑ 00742‑w.\n 85. Bae S‑C, Lee YH. Vitamin D level and risk of systemic lupus erythematosus \nand rheumatoid arthritis: a Mendelian randomization. Clin Rheumatol. \n2018;37:2415–21. https:// doi. org/ 10. 1007/ s10067‑ 018‑ 4152‑9.\n 86. Bae S‑C, Lee YH. Coffee consumption and the risk of rheumatoid \narthritis and systemic lupus erythematosus: a Mendelian randomiza‑\ntion study. Clin Rheumatol. 2018;37:2875–9. https:// doi. org/ 10. 1007/ \ns10067‑ 018‑ 4278‑9.\n 87. Bae SC, Lee YH. Alcohol intake and risk of systemic lupus erythematosus: \na Mendelian randomization study. Lupus. 2019;28:174–80. https:// doi. \norg/ 10. 1177/ 09612 03318 817832.\n 88. Inamo J. Association between celiac disease and systemic lupus \nerythematosus: a mendelian randomization study. Rheumatology. \n2020;59:2642–4. https:// doi. org/ 10. 1093/ rheum atolo gy/ keaa0 71.\n 89. Wang P , Dan Y‑L, Wu Q, Tao S‑S, Yang X‑K, Wang D‑G, Ye D‑Q, Shuai Z‑W, \nPan H‑F. Non‑causal effects of smoking and alcohol use on the risk of \nsystemic lupus erythematosus. Autoimmun Rev. 2021;20:102890. https:// \ndoi. org/ 10. 1016/j. autrev. 2021. 102890.\n 90. Basta F, Fasola F, Triantafyllias K, Schwarting A. Systemic lupus erythema‑\ntosus (SLE) therapy: the old and the new. Rheumatol Ther. 2020;7:433–46. \nhttps:// doi. org/ 10. 1007/ s40744‑ 020‑ 00212‑9.\nPublisher’s Note\nSpringer Nature remains neutral with regard to jurisdictional claims in pub‑\nlished maps and institutional affiliations.","source_license":"CC-BY-4.0","license_restricted":false}