Risk factors of systemic lupus erythematosus: an overview of systematic reviews and Mendelian randomization studies

Background The etiology of systemic lupus erythematosus is complex and incurable. A large number of systematic reviews have studied the risk factors of it. Mendelian randomization is an analytical method that uses genetic data as tool variables to evaluate the causal relationship between exposure and outcome.

To review the systematic reviews and Mendelian randomization studies that focused on the risk factors of systemic lupus erythematosus and shed light on the development of treatments for its prevention and intervention.

From inception to January 2022, we systematically searched MEDLINE (via PubMed) and Embase for related systematic reviews and Mendelian randomization studies. Extract relevant main data for studies that meet inclu‑ sion criteria. The quality of systematic reviews was assessed by using Assessment of Multiple Systematic Reviews 2 (AMSTAR‑2). Finally, the risk factors are scored comprehensively according to the results’ quantity, quality, and consistency.

Our study involved 64 systematic reviews and 12 Mendelian randomization studies. The results of systematic reviews showed that diseases (endometriosis, atopic dermatitis, allergic rhinitis), lifestyle (smoking, drinking, vac‑ cination), and gene polymorphism influenced the incidence of systemic lupus erythematosus. The results of Men‑ delian randomization studies identified the role of disease (periodontitis, celiac disease), trace elements (selenium, iron), cytokines (growth differentiation factor 15), and gut microbiome in the pathogenesis of systemic lupus erythematosus.

We should pay attention to preventing and treating systemic lupus erythematosus in patients with endometriosis, celiac disease, and periodontitis. Take appropriate dietary supplements to increase serum iron and selenium levels to reduce the risk of systemic lupus erythematosus. There should be no excessive intervention in lifestyles such as smoking and drinking.

SLE, Risk factors, Systematic reviews, Mendelian randomization

Systemic lupus erythematosus (SLE) is a chronic inflam - matory autoimmune and multi-systemic disease that is characterized by the production of autoantibodies and tissue deposition of immune complexes. The clinical manifestations range from slight fatigue and joint pain to severe, catastrophic organ damage [1]. Owing to sex, age, *Correspondence: Hui Zheng [email protected] 1 Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, No.1166 Liutai Avenue, Wenjiang District, Chengdu 610000, China Page 2 of 15Xiao et al. Advances in Rheumatology (2023) 63:42 ethnicity, time, and environmental exposures the global prevalence of SLE varies widely, with the highest esti - mates of the prevalence of 241 cases per 100,000 persons in North America and the lowest in Northern Australia [2]. Mortality in patients with SLE is 2–3 times higher than in the general population, and the most common causes are infectious diseases and cardiovascular disease [3]. Despite years of study, the etiology of SLE is still unclear. As reported previously, the development of SLE was associated with hormonal, immunomodulatory, environmental, and genetic factors [4]. Some studies fur - ther reported that allergic diseases and hormone-related diseases may be associated with the incidence of SLE; for example, the incidence of SLE in patients with endome - triosis is higher than that in controls [5]. Lifestyle may be associated with the incidence of SLE, and its intervention is indispensable in the prevention and treatment of SLE. Studies linked the incidence of SLE to environmental factors, such as silica exposure, smok - ing and drinking, infection, and vaccination [6]. Evidence from systematic reviews (SRs) suggests that endometrio - sis, allergic rhinitis, atopic dermatitis, smoking, and vac - cination are associated with an increased incidence of SLE. Gene polymorphism plays an important role in eluci - dating the susceptibility to diseases and the diversity in the clinical manifestations of the diseases. Single Nucle - otide Polymorphism (SNP) is the most common DNA sequence variation in a population. Several studies have shown the key role of SNP in the development of SLE. For example, SNP leads to abnormal T-cell function [4]. And it is known that the risk A allele of SNP (PPP2CA rs7704116) [7] is known to be one of the reasons for the increased incidence of SLE. Clinical observational studies can only show that the disease and risk factors are related, but it is difficult to make causal inferences. The correlation is likely to be a “false correlation” caused by a variety of confounding factors, and the existence of reverse causality cannot be ignored. Mendelian randomization (MR) study is an ana - lytical method used to evaluate the causal relationship between observable exposure or risk factors and clinical- related results [8]. The core of it is to use genetic data and take genetic variables as instrumental variables, which can effectively overcome the bias caused by confounding. In genetic correlation, the direction of causality is deter - mined, which avoids the interference of reverse causality and thus provides more compelling evidence. At present, there is no research to comprehensively summarize the risk factors of SLE from SR and MR evidence. In this paper, the reported risk factors of SLE are reviewed and summarized from the perspectives of disease, lifestyle, gene polymorphism, and evidence from MR, to better understand the etiology and provide bet - ter medical advice for disease management for the whole population.

Search strategy We searched MEDLINE (via PubMed), Embase from inception to January 27, 2022, by using the keywords “Risk factor” “Systemic lupus erythematosus” , “System - atic review” , “Meta-analysis” , “Mendelian randomization study” with no restriction on language. For complete search strategies, see “ Appendix A” section. Study selection All retrieved studies were imported into Zotero (6.0.9), and duplicate studies were removed. Two independent reviewers (X-YX, QC) screened the title and abstract of the article. After cross-checking, the 2 reviewers further independently assessed the full text of the eligible stud - ies. Disagreements about the inclusion of qualified stud - ies were resolved through discussion. If they cannot be resolved, the third reviewer (HZ) would make the final decision. Inclusion: (1) SR of risk factors related to the incidence of SLE; (2) SR of autoimmune diseases including SLE; (3) MR study of SLE. Exclusion: (1) review; (2) case report; (3) original clinical research; (4) autoimmune-related but not related to SLE or related to SLE but lacking corre - sponding data; (5) discoid lupus erythematosus; (6) lupus nephritis; (7) drug-induced lupus erythematosus. Data extraction Two researchers independently extracted data accord - ing to predetermined extraction criteria. The following information was extracted from papers on disease and lifestyle, and some papers did not contain all informa - tion: Study ID, risk factor, outcome (relative risk or odds ratio of a risk factor to SLE, 95% confidence interval, and P-value), type of study design (cohort, case–control, or cross-sectional), presence or absence of sensitivity and subgroup analyses, publication bias, and quality assess - ment tools (Table 1). MR studies extracted the following: Study ID, num - ber of SNP , and main results (Table 2). Studies of genetic polymorphism were divided into four categories: risk fac- tors, protective factors, contradictory factors, and unre - lated factors. The following information was extracted: study ID, gene and SNP , and major significant results (Table 3). Page 3 of 15 Xiao et al. Advances in Rheumatology (2023) 63:42 Assessment of methodological quality Assessment of Multiple Systematic Reviews 2 (AMSTAR-2) is a tool to assess the quality of included SRs. There were 16 items, including 7 critical items (items 2, 4, 7, 9, 11, 13, 15) and 9 non-critical items. Each item was evaluated as ‘‘yes’’ (a positive result), ‘‘partial yes’’ (partial adherence to the standard), and ‘‘no’’ (no information is provided to rate an item) according to adherence to the standard. Based on these items, SR was divided into four grades: high, moderate, low, and criti - cally low [9]. This review evaluates SRs with risk factors related to disease and lifestyle (“ Appendix B” section). Determination of the magnitude of risk factors SR and MR Studies were scored and the magnitude was determined according to the number of studies on risk factors, the average score of AMSTAR-2, the consistency of results from different studies, the consistency of results from different methods and sensitivity tests, and finally summarize the risk factors and magnitude of SLE (Fig. 1).

A total of 594 articles were retrieved on SRs of risk fac - tors. After removing duplicates, 493 articles remained. After screening the title and abstract, 119 articles Table 1 Main characteristics of SRs of disease and lifestyle OR odds ratio; RR relative risk; NM no meta-analysis; NA not available; CS cohort study; CCS case–control study; CSS cross-sectional study Study ID Risk factor OR/RR (95% CI) P I2 Type of included studies Sensitivity/ subgroup analysis Publication bias Quality assessment tool Agrawal [25] Immigrant NM NA NA Population‑based studies NA NA NOS Chua [21] Current smoking status OR = 1.54(1.06–2.25) NA NA CS CCS NA NA NA Former smoking status OR = 1.39(0.95–2.08) NA NA Costenbader [18] Current smoker ver‑ sus nonsmokers OR = 1.50(1.09–2.08) NA NA CS CCS Yes/Yes Yes NA Former smoker versus nonsmoker OR = 0.98(0.75–1.27) NA NA Janowsky [23] Silicone breast implants RR = 0.65(0.35–1.23) 0.530 NA CS CCS CSS NA No NA Jiang [19] Current smokers compared with non‑ smokers OR = 1.56(1.26–1.95) NA 56.3% CS CCS Yes/No No NA Ex‑smokers ver‑ sus nonsmokers OR = 1.23(0.93–1.63) NA 62.3% CS CCS Yes/Yes No NA Parisis [20] Current smokers ver‑ sus never‑smokers OR = 1.49(1.06–2.08) 0.010 78.0% CCS Yes/No Yes NA Ever‑smokers ver‑ sus never‑smokers OR = 1.54(1.06–2.23) < 0.001 86.0% Former‑smokers ver‑ sus never‑smokers OR = 0.97(0.68–1.38) 0.010 63.0% Ponvilawan [11] Atopic dermatitis OR = 1.46(1.05–2.04) 0.020 63.0% CS CCS No/No Yes NOS Sakthiswary [26] Vitamin D levels NM NA NA CS CCS NA NA NA Shigesi [5] Endometriosis OR = 1.36(1.07–1.73) 0.010 49.0% CCS Yes/No NA GRADE Endometriosis RR = 1.74(1.10–2.77) 0.020 0.0% CS Wang [22] Vaccinations RR = 1.50(1.05–2.12) 0.024 71.7% CS CCS Yes/Yes No NOS Short vaccinated time RR = 1.93(1.07–3.48) 0.028 NA Wang [17] Alcohol intake CS CCS Yes/Yes Yes NOS Mild OR = 0.85(0.53–1.38) 0.515 76.3% Heavy OR = 0.63(0.37–1.09) 0.102 58.3% Moderate OR = 0.71(0.55–0.93) 0.012 49.1% Wongtrakul [12] Allergic rhinitis OR = 1.36(1.08–1.72) 0.009 80.0% CS CCS No/No No NOS Youssefi [24] Helicobacter pylori infection OR = 0.97(0.76–1.23) 0.820 94.9% CS CCS CSS No/No Yes NA Page 4 of 15Xiao et al. Advances in Rheumatology (2023) 63:42 remained. 64 articles were finally included after read - ing the full text. One study was related to the world - wide incidence of SLE, 3 were related to disease, 10 were related to lifestyle, and 50 were related to gene polymorphism. In the study of gene polymorphism, 19 genes or SNPs were risk factors. A total of 45 MR studies related to risk factors were retrieved. After removing duplicates, 31 articles remained, and 12 were finally included in our review. Among them, 2 were related to disease, 5 were related to lifestyle, 2 were related to cytokine, 2 were related to trace elements, and 1 was related to gut microbiome (Fig. 2 ). Finally, our study involved 5 diseases (endometriosis, atopic dermatitis, allergic rhinitis, celiac disease, peri - odontitis), 9 lifestyles (smoking, drinking, vaccination, silicone breast implants, helicobacter Pylori infection, immigration, Vitamin D, coffee consumption, statins), 2 cytokines (circulating adiponectin, growth differen - tiation factor 15), 2 trace elements (selenium, iron), the gut microbiome (Bacillales, Coprobacter, Lach‑ nospira, Actinobacteria, Bacilli, Lactobacillales, and Eggerthella), and more than 10 gene polymorphisms (Table 4). Risk factors Disease Endometriosis One SR and meta-analysis including cohort studies and case–control studies in the study of endometriosis [5 ] (OR for case–control studies 1.36, 95% CI 1.07–1.73, P = 0.010; RR for cohort studies 1.74, 95% CI 1.10–2.77, P = 0.020), and show the prevalence of SLE in patients with endometriosis was higher than that in the control group. It was rated moderate quality by AMSTAR-2. Endometriosis is an estrogen-dependent disor - der, and the increase in estrogen level can aggravate or induce SLE by suppressing cellular immunity and increasing the formation of autoantibodies [10]. Atopic dermatitis Atopic dermatitis is associated with an increased risk of cardiovascular, neurological, and autoimmune disease. Meta-analysis showed participants who had atopic der - matitis were at an increased risk of SLE [11] (OR 1.46, 95% CI 1.05–2.04, P = 0.020). The result of AMSTAR-2 was critically low quality. Table 2 Mendelian randomized studies GDF-15 Growth differentiation factor 15 Study ID Number of SNP Factor Result Bae [85] 3 Vitamin D β = 0.03, SE = 0.12, P = 0.789 Bae [86] 4 Coffee β = 0.59, SE = 0.44, P = 0.209 Bae [87] 20 Alcohol intake β = ‑0.41, SE = 0.51, P = 0.421 Bae [84] 20 Periodontitis β < 0.01, SE < 0.01, P = 0.046 Dan [81] 9 Circulating adiponectin OR = 1.38,95% CI = 0.90–1.35, P = 0.130 Inamo [88] 4 Celiac disease β = 0.29, SE = 0.06, P < 0.001 Wang [89] 9 Smoking cessation OR = 1.15, 95% CI = 0.63–2.11, P = 0.640 Wang [89] 56 Alcohol use OR = 0.90, 95% CI = 0.54–1.52, P = 0.707 Wang [89] 156 Smoking initiation OR = 0.97, 95% CI = 0.75–1.24, P = 0.778 Wang [89] 31 Heaviness of smoke OR = 1.00, 95% CI = 0.67–1.50, P = 0.982 Xiang [82] 16 Bacilli β = 0.34, SE = 0.16, OR = 1.40, 95% CI = 1.02–1.93, P = 0.037 Xiang [82] 14 Lactobacillale β = 0.34, SE = 0.17, OR = 1.40, 95% CI = 1.01–1.95, P = 0.045 Xiang [82] 10 Eggerthella β = 0.55, SE = 0.70, OR = 1.73, 95% CI = 1.01–1.95, P = 0.045 Xiang [82] 11 Bacillales β = ‑0.16, SE = 0.07, OR = 0.85, 95% CI = 0.74–0.98, P = 0.022 Xiang [82] 12 Coprobacter β = ‑0.25, SE = 0.10, OR = 0.78, 95% CI = 0.64–0.95, P = 0.014 Xiang [82] 7 Lachnospira β = ‑0.51, SE = 0.23, OR = 0.60, 95% CI = 0.38–0.94, P = 0.027 Yang [83] 6 Statins OR = 0.72, 95% CI = 0.33–1.58, P = 0.419 Ye [80] 3 GDF‑15 OR = 0.80, 95% CI = 0.68–0.92 Ye [78] 2 Selenium OR = 0.85, 95% CI = 0.77–0.93, P = 0.001 Ye [79] 3 Serum iron OR = 0.79, 95% CI = 0.66–0.94 Page 5 of 15 Xiao et al. Advances in Rheumatology (2023) 63:42 Table 3 Gene polymorphism from SRs Study ID Gene, SNP Result Risk Chang [33] BLK, rs4840568 A versus G: OR = 1.32, 95% CI = 1.22–1.43, P = 0.010 Song [34] BLK, rs13277113 A allele: OR = 1.36, 95% CI = 1.29–1.43, P < 0.001 Xiong [31] VDR BsmI, rs1544410 BB + Bb versus bb: OR = 2.14, 95% CI = 1.20–3.82, P = 0.010 Lee [39] TNF‑α promoter‑308 A/G A/A: OR = 3.2, 95% CI = 2.0–5.3, P < 0.001 Zou [40] TNF‑α promoter‑308 A/G A allele: OR = 1.44, 95% CI = 1.04–2.01, P = 0.030 Pan [41] TNF‑α promoter‑308 A/G A/A versus G/G: OR = 3.69, 95% CI = 2.63–5.17, P < 0.001 Yang [42] TNF‑α promoter‑308 A/G A allele: OR = 2.18, 95% CI = 1.72–2.78, P < 0.001 Chen [43] TNF‑α promoter‑308 A/G A versus G: OR = 1.78, 95% CI = 1.45–2.19, P < 0.001 Fan [44] TNFAIP3, rs2230926 OR = 1.83, 95% CI = 1.55–2.16, P < 0.001 Lee [45] TNFAIP3, rs2230926 OR = 1.85, 95% CI = 1.55–2.21, P < 0.001 Liu [47] TNFAIP3, rs2230926 G allele: OR = 1.64, 95% CI = 1.46–1.85, P < 0.010 Lu [48] TNFSF4, rs2205960 OR = 1.33, 95% CI = 1.23–1.44, P < 0.001 Wang [49] TNFSF4, rs2205960 OR = 1.93, 95% CI = 1.50–2.49, P < 0.001 Moreno‑Eutimio [51] TNFSF4, rs2205960 G vs T: OR = 1.32, P = 0.004 Fu [50] TNFSF4, rs2205960 OR = 1.42, 95% CI = 1.36–1.49, P < 0.001 Xu [52] TRAF1/C5, rs10818488 OR = 1.25, 95% CI = 1.06–1.47, P = 0.008 Zhu [53] IL‑1B ‑31 T/C OR = 1.64, 95% CI = 1.03–2.62, P = 0.040 Wang [58] IL‑10‑1082A/G, rs1800896 OR = 1.20, 95% CI = 1.03–1.41 Yin [28] IL‑10‑1082A/G, rs1800896 GG versus AA: OR = 1.54, 95% CI = 1.14–2.07, P = 0.005 Niu [62] HLA‑DR3, DR9, DR15 HLA‑DR3: OR = 1.88, 95% CI = 1.58–2.23, P < 0.001 Xue [63] HLA‑DR3, DR15 HLA‑DR15: OR = 1.68, 95% CI = 1.33–2.11, P = 0.001 Hu [65] IRF5, rs2004640 OR = 1.41, 95% CI = 1.34–1.49, P < 0.001 Wang [66] IRF5, rs2004640 OR = 1.39, 95% CI = 1.28–1.52, P < 0.001 Li [67] IRF5, rs2070197 OR = 2.13, 95% CI = 1.86–2.44, P < 0.001 Ji [68] STAT4, rs7574865 OR = 1.57, 95% CI = 1.44–1.71 Yuan [69] STAT4, rs7574865 OR = 1.65, 95% CI = 1.56–1.75, P < 0.001 Wang [70] STAT4, rs7574865 OR = 1.56, 95% CI = 1.51–1.61, P < 0.001 Ji [73] MiR‑146a, rs2431697 OR = 1.24, 95% CI = 1.13–1.37 Liu [75] MiR‑146a, rs2431697 OR = 1.56, 95% CI = 1.20–1.92, P = 0.001 Fan [77] ITGAM, rs1143679 AA versus GG: OR = 3.54, 95% CI = 2.77–4.52 Ebrahimiyan [76] ITGAM, rs1143679 OR = 1.97, 95% CI = 1.76–2.20, P < 0.001 Protective Niu [62] HLA‑DR4, DR11, DR14 HLA‑DR14: OR = 0.47, 95% CI = 0.59–0.95, P < 0.050 Hu [65] IRF5, rs729302, rs2280714 rs729302: OR = 0.78, 95% CI = 0.74–0.83, P < 0.001 Ebrahimiyan [76] MBL, rs1800451 OR = 0.64, 95% CI = 0.42–0.99, P = 0.044 Contradictory Yu [38] CTLA‑4, rs231775 No association Chang [36] CTLA‑4, rs231775 GG versus AA: OR = 1.53, 95% CI = 1.12–2.10 Zhai [37] CTLA‑4, rs231775 GG + GA versus AA: OR = 0.85, 95% CI = 0.73–0.99, P = 0.040 Lee [35] CTLA‑4, rs231775 GG genotype: OR = 1.29, 95% CI = 1.03–1.56, P = 0.011 Lee [54] IL‑6–174 G/C, rs1800795 OR = 1.34, 95% CI = 1.05–1.72, P = 0.018 Yang [55] IL‑6–174 G/C, rs1800795 OR = 1.64, 95% CI = 1.10–2.45, P = 0.016 Cui [56] IL‑6–174 G/C, rs1800795 OR = 1.61, 95% CI = 1.16–2.24 Liu [57] IL‑6–174 G/C, rs1800795 OR = 0.71, 95% CI = 0.56–0.88, P = 0.020 Yang [55] IL‑6–572 G/C, rs1800796 OR = 1.49,95% CI = 1.10–2.01, P = 0.009 Liu [57] IL‑6–572 G/C, rs1800796 No association Lee [54] IL‑6–572 G/C, rs1800796 No association Wen [61] IL‑18‑137G/C, rs187238 OR = 1.21, 95% CI = 0.91–1.60 Page 6 of 15Xiao et al. Advances in Rheumatology (2023) 63:42 BLK 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 factor ligand superfamily member 4; TRAF1/C5 tumor necrosis factor receptor-associated factor 1, complement component 5; IL interleukin; HLA human leukocyte antigen; IRF5 Interferon regulatory factor 5; STAT4 signal transducer and activator of transcription 4; ITGAM Integrin Subunit Alpha M; MBL Mannose-binding lectin; CTLA-4 Cytotoxic T lymphocyte-associated antigen-4 Table 3 (continued) Study ID Gene, SNP Result Chen [60] IL‑18‑137G/C, rs187238 OR = 1.17, 95% CI = 0.95–1.44, P = 0.150 Ji [73] MiR‑146a, rs57095329 OR = 1.25, 95% CI = 1.17–1.35 Fu [74] MiR‑146a, rs57095329 OR = 1.26, 95% CI = 1.14–1.41 Liu [75] MiR‑146a, rs57095329 OR = 1.17, 95% CI = 0.84–1.65, P = 0.360 Unrelated Yin [28] IL‑10, rs1800871, rs1800872 NA Yuan [59] IL‑10, rs1800871, rs1800872 NA Niu [62] HLA‑DR8 NA Fig. 1 Risk Factors for SLE and the consensus on the magnitude. Abbreviations: SLE, systemic lupus erythematosus. SR, systematic review. MR, Mendelian randomization. AMSTAR‑2, Assessment of Multiple Systematic Reviews 2. TNF‑α, tumor necrosis factor‑α. TNFAIP3, Tumor necrosis factor‑α‑induced protein 3. TNFSF4, Tumor necrosis factor ligand superfamily member 4. IL, Interleukin. HLA, Human leukocyte antigen. IRF5, Interferon regulatory factor 5. STAT4, Signal transducer and activator of transcription 4. ITGAM, Integrin Subunit Alpha M. Annotation: Determination on the magnitude for the risk factors: a systematic reviews: based on a consensus after considering the evidence from (i). The number of literature, (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. The 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 score). b Mendelian randomization: based on a consensus after considering the evidence from (i). The number of literature, (ii). The consistency of results (2 scores if the results of inverse variance weighted, weighted median, and MR Egger are consistent, otherwise no score), and (iii). Sensitivity tests (no horizontal pleiotropy is counted as 1 score, otherwise no score). If the risk factors involved both SR and MR studies, the scores were added if the results were consistent and subtracted if the results were not consistent. Score the SR and MR studies according to the above criteria 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 summarize the risk factors and related magnitude of SLE Page 7 of 15 Xiao et al. Advances in Rheumatology (2023) 63:42 Allergic rhinitis Patients with allergic rhinitis had a higher risk of SLE than individuals without [12] (OR 1.36, 95% CI 1.08– 1.72, P = 0.009). The result of AMSTAR-2 was critically low quality. The pathogenesis of atopic dermatitis, allergic rhi - nitis, and autoimmune disorders was similar and related to the increase of inflammatory mediators and immune dysregulation [13]. The up-regulation of Th2 activity and the increase of IgE production during the Fig. 2 Flow chart showing study selection process. Abbreviations: SRs, systematic reviews. MR, Mendelian randomization Table 4 Risk factors of SLE BLK 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 factor ligand superfamily member 4; TRAF1/C5 Tumor necrosis factor receptor-associated factor 1, complement component 5; IL interleukin; HLA human leukocyte antigen; IRF5 Interferon regulatory factor 5; STAT4 signal transducer and activator of transcription 4; ITGAM Integrin Subunit Alpha M; MBL Mannose-binding lectin; CTLA-4 Cytotoxic T lymphocyte-associated antigen-4; GDF-15 Growth differentiation factor 15 Risk factors Disease Endometriosis, atopic dermatitis, allergic rhinitis Lifestyle Smoking, drinking, vaccination, silicone breast implants, helicobacter pylori infection, immigration, Vitamin D Gene polymorphisms Risk: BLK (rs4840568, rs13277113), VDR BsmI (rs1544410), TNF‑α promoter‑308 A/G, TNFAIP3 (rs2230926), TNFSF4 (rs2205960), TRAF1/C5 (rs10818488), IL‑1B‑31T/C, IL‑10‑1082A/G (rs1800896), HLA‑DR3, DR9, DR15, IRF5 (rs2004640, rs2070197), STAT4 (rs7574865), MiR‑146a (rs2431697), ITGAM (rs1143679) Protect: HLA‑DR4, DR11, DR14, IRF5(rs729302, rs2280714), MBL (rs1800451) Contradictory: CTLA‑4 (rs231775), IL‑6‑174 G/C (rs1800795), IL‑6‑572 G/C (rs1800796), IL‑18 ‑137G/C (rs187238), MiR‑146a (rs57095329) Unrelated:IL‑10 (rs1800871, rs1800872), HLA‑DR8 Evidence from Mende‑ lian randomization Disease: celiac disease, Periodontitis Lifestyle: coffee consumption, statins, smoking, drinking, Vitamin D Cytokines: circulating adiponectin, GDF‑15 Trace elements: selenium, iron Gut microbiome Page 8 of 15Xiao et al. Advances in Rheumatology (2023) 63:42 development of the disease may be the triggers for SLE in the future. Lifestyle Smoking and drinking Previous studies showed that moderate drinking reduced the risk of SLE, and smoking increased it [6]. The main mechanisms include the anti-inflammatory mechanism related to alcohol consumption [14], including lower lev - els of C-reactive protein and fibrinogen in plasma, and Cigarette-related pro-inflammatory mechanisms includ - ing an increase in plasma C-reactive protein, oxidative stress, and apoptosis. The effects of drinking status and smoking consumption on chemokine/cytokine concen - trations in healthy female nurses in the United States in 2020 and 2021 showed that moderate drinking was related to lower stem cell factor levels [15]. The current smoking status was related to the decrease of B-lympho - cyte stimulator and interleukin-10 (IL-10) [16]. An SR of drinking indicated that moderate alcohol con- sumption might be a protective factor (OR 0.71, 95% CI 0.55–0.93, P = 0.012), while light and heavy alcohol con - sumption were not related to the risk of SLE [17]. We included four SRs related to smoking from 2004 to 2020. The first [18] pointed out that the current smok - ing status was correlated with the development of SLE (OR 1.50, 95% CI 1.09–2.08), but there was no correla - tion among former smokers (OR 0.98, 95% CI 0.75–1.27). The second showed consistent results on the impact of current smoking, but when analyzing non-smokers and former smokers, different regions showed inconsistent

[19]. The third stated that smoking was not only a risk factor for SLE but also hampered disease treatment by reducing the curative effect of belimumab [20]. There- fore, it was suggested that smoking cessation should be the first task in the prevention and treatment of SLE, which was supported in the fourth SR [21]. Vaccination Vaccination can stimulate antigens to produce a specific immune response, so it is considered the pathogenic fac - tor of SLE. One SR with 12 studies showed that vaccina - tion significantly increased the risk of SLE (RR 1.50, 95% CI 1.05–2.12, P = 0.024) [22]. The results of subgroup analysis and sensitivity analysis both supported this conclusion. Silicone breast implants Autoimmune diseases caused by silicone breast implan - tation have long been a concern, but the SR results seemed to be reassuring. There was no evidence that sili - cone breast implantation was associated with connective tissue and autoimmune diseases, including SLE (RR 0.65, 95% CI 0.35–1.23) [23]. Helicobacter pylori infection The molecular simulation, activation of polyclonal lym - phocytes, and cell damage produced by Helicobacter pylori may be risk factors for autoimmune diseases. One SR published in 2020 showed that Helicobacter pylori infection was not related to SLE susceptibility (OR 0.97, 95% CI 0.76–1.23, P = 0.820), but its strains (H. pylori cagA positive strains) might be associated with a higher risk of autoimmune diseases (OR 2.65, 95% CI 1.52–4.64, P = 0.001) [24]. Immigrant It was believed that environmental factors have made a great contribution to its incidence. Based on the findings of five population studies, the incidence of SLE was the highest among immigrants from Africa, Iraq, and South Asia, especially among women and successive immigrant descendants [25]. Assuming that “immigration” is a risk factor for people in the appeal area, the impact of envi - ronmental and lifestyle changes behind immigration on the incidence of SLE cannot be ignored. Others The SR about vitamin D showed that vitamin D level was negatively related to SLE disease activity [26]. Although there were some reported risk factors, such as ultraviolet radiation, silica, air pollution, pesticides, heavy metals, and so on [6, 27], they were not covered in this paper due to the lack of SR evaluation. Gene polymorphism Gene polymorphism means that the structure or nucleo - tide sequence of the same gene is not the same in differ - ent individuals, which is the variation of alleles. Human gene polymorphism plays an important role in elucidat - ing the susceptibility and tolerance of the human body to disease, the diversity of clinical manifestations of dis - eases, and the responsiveness to drug treatment. There are three types of genes related to SLE: genes reg- ulating the function of B cells and T cells, genes regulat - ing interferon (IFN), and genes repairing DNA [28]. The gene polymorphism study of this article mainly includes two aspects of B Cell and T Cell Function-related genes and genes regulating IFN. B cell and T cell function‑related genes Vitamin D The vitamin D receptor (VDR) is a member of the nuclear receptor superfamily. By binding to VDR, vitamin D can exert biological functions such as cell pro - liferation, differentiation, and immune response in the Page 9 of 15 Xiao et al. Advances in Rheumatology (2023) 63:42 human body, and inhibit the pro-inflammatory activity of Th1 cells and the production of cytokines, such as IL-2, IFN-γ and TNF-α [29]. The polymorphism of the VDR gene was related to SLE, and its polymorphism mainly included the follow - ing four types: VDR BsmI(rs1544410), Fok1(rs2228570), ApaI(rs7975232), and TaqI(rs731236). In three studies from 2014 to 2016 [29–31], the results showed that the polymorphism of BsmI(rs1544410) and FokI(rs2228570) in the Asian population contributed to the pathogenesis of SLE, but the findings were not replicated in the Cauca- sian population. B‑cell lymphocyte kinase B-cell lymphocyte kinase (BLK), a member of the Src family, is involved in signal transduction downstream of B-cell receptors, B-cell devel- opment, differentiation, and signal transduction, and fur- ther affects B-cell function [32]. The risk allele variation of BLK may cause changes in the level of BLK protein, which may affect the tolerance mechanism of B cells and induce immune diseases. There was a significant correlation with BLK(rs4840568) (OR 1.32, 95% CI 1.22–1.43, P = 0.010) [33] and BLK(rs13277113) A-type allele (OR 1.36, 95% CI 1.29–1.43, P < 0.001) [34]. Cytotoxic T lymphocyte‑associated Antigen‑4 Cytotoxic T lymphocyte-associated antigen-4 (CTLA-4), expressed on T cells, is a key down-regulated molecule that inhib - its T cell activation and regulates its peripheral tolerance [35]. The relationship between rs231775(+49A/G) poly - morphism [35–38] and SLE was studied, and the conclu - sions based on different races showed significant hetero - geneity. The rs231775(+49A/G) polymorphism mainly contributed to the SLE development in the Asian popula- tion. Tumor necrosis factor‑α and related gene Tumor necro- sis factor-α (TNF-α) is a proinflammatory cytokine, which can stimulate the production of cytokines, enhance the expression of adhesion molecules, increase neutro - phil activation, and act as a costimulatory factor for T cell activation and antibody production. Its function-related proteins and receptors are actively involved in the patho - genesis of SLE. Five SR-based meta-analyses reported TNF-α promoter-308A/G polymorphism [39–43]. These studies were carried out in the China population, and whether the conclusions could be extended to other pop- ulations remained to be verified. There were also a series of genes related to TNF, such as tumor necrosis factor-α- induced protein 3 (TNFAIP3) (rs2230926) [44–47], tumor necrosis factor ligand superfamily member 4 (TNFSF4) (rs2205960) [48–51], and tumor necrosis factor receptor- associated factor 1, complement component 5 (TRAF1/ C5)(rs10818488) [52] were also related to SLE susceptibil- ity. Interleukin Interleukin (IL), a kind of cytokine produced by many kinds of cells, plays an important role in a series of processes such as the maturation, activation, prolif - eration, and regulation of immune cells. IL-1 gene poly - morphism, including IL-1A-889C/T, IL-1B-31T/C, and IL-1B-511C/T, might be associated with a higher risk of SLE [53], which needed more research to prove. IL-6 pol- ymorphisms, rs1800796 and rs1800795, might be risk fac- tors in the previous studies [54–56]. However, one study in 2021 showed that rs1800795 was a protective factor, and rs1800796 was not associated with susceptibility [57], which was inconsistent with results from previous stud - ies. IL-10 gene polymorphism rs1800896 was related to susceptibility [28, 58, 59], while rs1800871 and rs1800872 were not related to susceptibility. Although some of them were different in subgroup analysis, we wrote the report based on the results of the general population. The results of research on IL-18 rs187238 were inconsistent. One study showed that there was no relationship between rs187238 polymorphism and SLE in all populations, including the Chinese population [60]. Another study with hierarchical analysis showed that rs187238 was a risk factor for SLE, especially in the Asian population [61]. Human leukocyte antigen The frequency change of the human leukocyte antigen (HLA) allele is related to SLE. One study focusing on the relationship between HLA- DRB1 allele polymorphism and SLE susceptibility dem - onstrated that HLADR3, DR9, and DR15 were risk factors [62]. Another study provided evidence to support HLA- DR3 and HLA-DR15 as the risk factors for SLE, and the study acknowledged the existence of inter-ethnic hetero - geneity [63]. Interferon regulatory genes Interferon regulatory factor 5 Interferon regulatory factor 5 (IRF5) belongs to the transcription factor fam - ily that regulates the activity of the immune system. It is expressed in antigen-presenting cells (including dendritic cells, macrophages, and B cells) and monocytes, and it participates in the pathogenesis of SLE by influencing the antigen-presenting cells [64]. IRF5 rs2070197 T allele and rs2004640 C allele were positively associated with the pathogenesis of SLE [65, 66]. Although rs2070197 showed susceptibility to SLE in the general population (OR 2.13, 95% CI 1.86–2.44, P < 0.001), it had no effect in the Asian population [67]. Since this gene is monomorphic in China and Korea, the population in the two countries was not affected by the disease susceptibility caused by this gene polymorphism. It was speculated that it may be caused by Page 10 of 15Xiao et al. Advances in Rheumatology (2023) 63:42 the linkage imbalance between regions, and further stud - ies on the specificity of ethnic populations are therefore needed. STAT4 The signal transducer and activator of tran - scription 4 (STAT4) is a transcription factor activated by IFN-α signal transduction, and the increase of IFN-α sig - nal transduction is the main pathogenic promotor of SLE. The genetic variation of STAT4 is related to the risk of SLE. Three SRs showed that there was a significant rela - tionship between the STAT4 rs7574865 T allele and the risk of developing SLE, and the OR value was around 1.60 [68–70]. MiR‑146a MiR-146a is identified as a negative regula - tor of natural immunity, which directly inhibits the down- stream transactivation of type-I IFN at the molecular level and targets IRF5. There were some contradictions in the

of miR-146a. No association between miR-146a and SLE susceptibility was found in 2015 [71] and 2017 [72], which might be attributed to the small number of included studies. MiR-146a rs57095329 was found to cor- relate with the risk of SLE in two studies [73, 74], but this correlation was not supported in a recent study [75]; the rs2431697 and susceptibility to SLE were confirmed by two studies [73, 75]. Integrin Subunit Alpha M Complement dysfunction impairs the ability to clear apoptotic cell fragments, which may stimulate the production of autoantibodies in SLE. The rs1143679 G/A polymorphism in Integrin Subunit Alpha M (ITGAM) severely impaired the phagocytosis of complement-coated particles and was positively associ - ated with the risk of SLE [76], consistent with the findings of two studies in 2011 [77] and 2021 [76]. Evidence from Mendelian randomization study The evidence from SRs including case–control or cohort studies was meaningful to confirm the correla - tion between risk factors and SLE development, but it is insufficient to make causal inferences. MR is an analytical

of causal estimation based on epidemiological data. it has been widely used in the medical field in recent years to assess the causal effect between exposure and outcome. We summarized the findings from the included 13 MR studies on SLE below. Serum selenium, iron, growth differentiation factor 15, and circulating adiponectin Three MR analyses were performed [78–80], showing that the rise in serum selenium (OR 0.85, 95% CI 0.77– 0.93, P = 0.001) [78], serum iron (OR 0.79, 95% CI 0.66– 0.94) [79] and growth differentiation factor 15 (GDF-15) (OR 0.80, 95% CI 0.68–0.92) [80] were all related to the decrease of SLE risk, which provided the potential causal evidence of the protective effect of selenium, iron and GDF-15 on SLE. Another study showed that there was no causal rela - tionship between circulating adiponectin levels and SLE (OR 1.38, 95% CI 0.91–1.35, P = 0.130) [81]. The test of reverse causation in the study was also negative, and the several analyses performed also supported this conclusion. Gut microbiome Xiang et  al. [82] conducted a study on the composition of the gut microbiome (211 gut microbiota), supporting a causal relationship between its positive and negative effects on SLE risk (Table  2). The results of inverse vari - ance weighted, MR Egger, and weighted median methods were slightly different. The levels of Bacillales, Coprobac‑ ter, Lachnospira, and Actinobacteria were negatively cor - related with the risk of SLE, while Bacilli, Lactobacillales, and Eggerthella might be risk factors for SLE. Although the overlapping in the study samples—a limitation in two-sample MR studies—might affect the robustness of the findings, these findings intrigue thoughts about the development of new treatment modalities, such as probi - otics supplements, for the treatment of SLE. Statins Recently, an MR used HMGCR inhibition to genetically mimic statins and studied the relationship between the genetic mimicry effect of statins and allergic diseases and immune-related diseases. The results showed that genetic mimicry had little effect on allergic diseases or autoimmune diseases of men or women (OR 0.72, 95% CI 0.33–1.58, P = 0.419) [83]. Periodontitis, celiac disease, vitamin D, coffee consumption, smoking, and drinking Four studies on periodontitis [84], vitamin D [85], cof - fee consumption [86], and alcohol intake [87] were con - ducted, respectively. The results showed that there might be a causal relationship between periodontal inflamma - tion and SLE (β < 0.01, SE < 0.01, P = 0.046) [84]. Besides, celiac disease also showed a possible causal relationship (β = 0.29, SE = 0.06, P < 0.001) [88]. Vitamin D, coffee con- sumption, and alcohol intake had no causal relationship with SLE. The common problem of the studies was the insufficient number of SNP and the existence of weak instrumental bias. One study showed that there was no causal relationship between smoking or drinking and the risk of SLE [89]. The analysis results based on the three

were consistent, and the corresponding tool variables were appropriate. Page 11 of 15 Xiao et al. Advances in Rheumatology (2023) 63:42 The aforementioned MR studies adopted the above genetic data from the European population only, which ensured the genetic homogeneity and robustness of the

but limited the generalization of the results to other populations. Therefore, the appeal of causality is worthy of further exploration.

Combined evidence from SR and MR showed that endo - metriosis, atopic dermatitis, allergic rhinitis, periodon - titis, and celiac disease were risk factors for SLE. The increased levels of trace elements iron, selenium, and GDF-15 should be protective factors for SLE. Smoking, alcohol consumption, coffee consumption, and vitamin D did not appear to have any effect on the risk of SLE. Periodontitis and celiac disease should be paid more attention as observed risk factors. Early detection and screening of antibodies in people with these diseases may prevent the follow-up occurrence and development of SLE. More research evidence on the causal relation - ship between endometriosis, atopic dermatitis, aller - gic rhinitis, and SLE is needed to further define disease interactions. Based on the results of MR, serum iron, selenium, GDF-15, gut microbiomes Bacillales, Lachnospira, and Actinobacteria might provide more options for the pre - vention and treatment of SLE, and relevant clinical trials are needed to verify their protective effects. The pathogenesis of SLE is related to the reduction of T lymphocytes, the decline of the function of T suppres - sor cells, and the excessive proliferation of B cells to pro - duce a large number of antibodies. The existing emerging therapies for SLE focus on the T-/B cell costimulatory pathway as a target [90]. The risk factors confirmed by the studies of gene polymorphism included STAT4, IFN-α, and IL-10, suggesting that future research might focus on multi-target and multi-pathway precision ther - apy, for example, using a combination of B cell activator inhibitor, IFN-α and STAT4 inhibitors, and recombinant human IL-2. The prevention and treatment effect may be achieved by regulating cytokines. Contrary to the experience of routine care for SLE, smoking, alcohol consumption, coffee consumption, and vitamin D, as shown by the MR studies, were not associ - ated with SLE pathogenesis. This finding might indicate that other confounding factors contribute more to SLE, for example, patients who smoke or overdrink might be under the status of stress, depression, higher frequency of staying up late, or less physical exercise. Such specific lifestyle interventions, such as smoking or overdrinking, may not be emphasized in future medical advice to SLE patients and those at risk for the disease. Instead, more MR analyses should be conducted to rule out the real dis- ease contributors behind the aforementioned unhealthy lifestyles. Finally, according to the studies of SR and MR, we determined the risk factors and their magnitude based on the quality, quantity, and consistency of the results (Fig. 1). We summed up the most relevant risk factors for SLE. There was only one study of endometriosis, atopic dermatitis, allergic rhinitis, periodontitis, and celiac dis - ease. The quality of evidence of endometriosis and celiac disease was medium (4 scores), periodontitis, atopic dermatitis and allergic rhinitis were all low-quality evi - dence (1–2 scores), more research is needed to support it in the future. The MR results of smoking and drinking were both negative and the evidence was of high qual - ity. We tend to not correlate with drinking and SLE, so it was not included. There were four SRs on smoking, but the AMSTAR-2 score was very low, and MR did not sup - port it as a risk factor, so smoking was also low-quality evidence (1 score). All research on gene polymorphism came from SR. We summarized the 9 genes with the larg- est number of literature and the most consistent results, among which the genes related to TNF had the highest score (6 scores), which was high-quality evidence. However, some limitations in this review or included studies should be noted. First, the search was relatively not comprehensive, we only searched two databases. The types of included studies were limited, including only SRs and MR, without original studies and reviews, and the lit- erature was not up-to-date enough. Second, most of the SR quality assessment scores included in the studies were critically low. Many of the included studies were small in scale and were likely to produce false associations. MR studies of endometriosis, atopic dermatitis, allergic rhinitis, and SLE are also needed to further determine the causal relationship. Finally, there is a lack of official assessment tools for MR quality to confirm the reliability of the evidence.

In short, we should pay attention to preventing and treat- ing SLE in patients with endometriosis, celiac disease, and periodontitis. Take appropriate dietary supplements to increase serum iron and selenium levels to reduce the risk of SLE. There should be no excessive intervention in lifestyle such as smoking and drinking, as it does not affect the incidence of SLE. Page 12 of 15Xiao et al. Advances in Rheumatology (2023) 63:42 Appendix A: search strategy PubMed Systematic review #1 “Lupus Erythematosus, Systemic”[Mesh]OR Systemic lupus erythematosus OR Systemic Lupus Erythemato - sus OR Lupus Erythematosus Disseminatus OR Libman- Sacks Disease OR Disease, Libman-Sacks OR Libman Sacks Disease. #2 risk factor[MeSH Terms] OR Factor, Risk OR Risk Factor OR Social Risk Factors OR Factor, Social Risk OR Factors, Social Risk OR Risk Factor, Social OR Risk Fac - tors, Social OR Social Risk Factor OR Health Correlates OR Correlates, Health OR Population at Risk OR Popu - lations at Risk OR Risk Scores OR Risk Score OR Score, Risk OR Risk Factor Scores OR Risk Factor Score OR Score, Risk Factor. #3 #1 AND #2 AND (meta-analysis[Filter] OR system - atic review[Filter]). Mendelian randomization study #1 “Lupus Erythematosus, Systemic”[Mesh]OR Systemic lupus erythematosus OR Systemic Lupus Erythematosus OR Lupus Erythematosus Disseminatus OR Libman- Sacks Disease OR Disease, Libman-Sacks OR Libman Sacks Disease. #2 Mendelian Randomization Analysis [MeSH Terms]. #3 #1 AND #2. Embase Systematic review #1’systemic lupus erythematosus’:ti,ab,kw OR sle:ti,ab,kw. #2’risk factors’:ti,ab,kw OR ’risk factor’:ti,ab,kw. #3’systematic review’:ti,ab,kw OR ’meta analysis’:ti,ab,kw. #4 #1 AND #2 #5 #4 AND #3. Mendelian randomization study #1’systemic lupus erythematosus’:ti,ab,kw OR sle:ti,ab,kw. #2’mendelian randomization analysis’:ti,ab,kw OR ’mendelian randomization study’:ti,ab,kw OR ’mendelian randomization’:ti,ab,kw. #3 #1 AND #2. Appendix B: AMSTAR‑2 scale for the assessment of systematic reviews Study ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Methodological quality Agrawal [25] N Y Y Y Y Y N Y PY N NM N NM Y NM Y Low Chua [21] N N Y PY Y Y N PY PY N Y Y Y Y Y N Critically low Costenbader [18] N N Y PY Y Y N PY PY N Y Y Y Y Y N Critically low Janowsky [23] N N N PY Y Y N Y Y Y Y Y Y Y Y Y Critically low Jiang [19] Y N Y PY N Y N PY PY N Y Y Y Y Y N Critically low Parisis [20] N N Y N Y Y Y PY Y N Y Y N Y Y Y Critically low Ponvilawan [11] N N Y PY Y N N PY N N NM N N Y Y Y Critically low Rees [2] N N N Y N N N Y N N NM NM Y Y NM Y Critically low Sakthiswary [26] N N Y PY Y N Y PY PY N NM NM Y Y NM Y Critically low Shigesi [5] N Y Y PY Y Y Y PY PY N Y Y Y Y Y Y Moderate Wang (22) Y Y Y PY Y Y Y Y Y Y Y Y Y Y Y Y High Wang [17] Y N Y PY Y Y N Y Y N Y Y Y Y Y Y Critically low Wongtrakul [12] N N Y PY Y Y N PY N N Y Y Y N Y N Critically low Youssefi [24] N N Y PY Y N N PY N N N N N Y N Y Critically low Bold 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 yes; N No; NM no meta-analysis Page 13 of 15 Xiao et al. Advances in Rheumatology (2023) 63:42 Abbreviations SLE Systemic lupus erythematosus SR Systematic review MR Mendelian randomization SNP Single nucleotide polymorphism AMSTAR‑2 Assessment of Multiple Systematic Reviews 2 IL Interleukin IFN Interferon VDR Vitamin D receptor BLK B‑cell lymphocyte kinase CTLA‑4 Cytotoxic T lymphocyte‑associated antigen‑4 TNF‑α Tumor necrosis factor‑α TNFAIP3 Tumor necrosis factor‑α‑induced protein 3 TNFSF4 Tumor necrosis factor ligand superfamily member 4 TRAF1/C5 Tumor necrosis factor receptor‑associated factor 1: complement component 5 HLA Human leukocyte antigen IRF5 Interferon regulatory factor 5 STAT4 Signal transducer and activator of transcription 4 ITGAM Integrin Subunit Alpha M

Not applicable. Author contributions All authors had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: HZ. Acquisition, analysis, or interpretation of data: X‑YX, QC, Y‑ZS, L‑WL, and CH. Drafting of the manuscript: X‑YX. Critical revision of the manuscript for important intellectual content: all authors. Statistical analysis: X‑YX and HZ. Administrative, technical, or material support: HZ. Supervision: HZ. Funding Hui Zheng received a grant from the Sichuan Youth Science and Technology Innovation Research Team (No. 2021JDTD0007). Availability of data and materials Data sharing does not apply to this article as no datasets were generated or analyzed during the current study. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Received: 2 February 2023 Accepted: 2 August 2023

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