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Rheumatoid arthritis and adverse pregnancy outcomes: a bidirectional two-sample mendelian randomization study

BMC Pregnancy and Childbirth volume 24, Article number: 517 (2024) Cite this article

Abstract

Background

There is growing evidence of bidirectional associations between rheumatoid arthritis and adverse pregnancy outcomes (APOs) in observational studies, but little is known about the causal direction of these associations. Therefore, we explored the potential causal relationships between rheumatoid arthritis and APOs using a bidirectional two-sample Mendelian randomization (MR) in European and Asian populations.

Methods

We conducted a bidirectional two-sample Mendelian randomization analysis using available summary statistics from released genome-wide association studies. Summary statistics for instrument–outcome associations were retrieved from two separate databases for rheumatoid arthritis and adverse pregnancy outcomes, respectively. The inverse-variance weighted method was used as the primary MR analysis, and cML-MA-BIC was used as the supplementary analysis. MR-Egger, MR pleiotropy residual sum and outlier (MR-PRESSO), and Cochran Q statistic method were implemented as sensitivity analyses approach to ensure the robustness of the results.

Results

Our study showed that a higher risk of rheumatoid arthritis in the European population was associated with gestational hypertension (OR: 1.04, 95%CI: 1.02–1.06), pre-eclampsia (OR: 1.06, 95%CI: 1.01–1.11), fetal growth restriction (OR: 1.08, 95%CI: 1.04–1.12), preterm delivery (OR:1.04, 95%CI: 1.01–1.07). Furthermore, we found no evidence that APOs had causal effects on rheumatoid arthritis in the reverse MR analysis. No association between rheumatoid arthritis and APOs was found in East Asian population. There was no heterogeneity or horizontal pleiotropy.

Conclusions

This MR analysis provides the positive causal association from rheumatoid arthritis to gestational hypertension, pre-eclampsia, fetal growth restriction and preterm delivery genetically. It highlights the importance of more intensive prenatal care and early intervention among pregnant women with rheumatoid arthritis to prevent potential adverse obstetric outcomes.

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Introduction

Rheumatoid arthritis is a common chronic autoimmune disease, which characterized by synovial inflammation, autoantibody production, cartilage and bone destruction, and systemic features, including cardiovascular, pulmonary, psychological, and skeletal disorders [1]. Certain risk factors are known to be associated with an increased likelihood of developing rheumatoid arthritis [2]. First, a positive family history of rheumatoid arthritis increases the risk by approximately three to five times, implicating genetic factors in its pathogenesis [3]. Then, the development of rheumatoid arthritis is associated with environmental factors. Known risk factors include smoking and low socioeconomic status or education. In addition, rheumatoid arthritis can be linked to hormone levels. The global prevalence of rheumatoid arthritis is approximately 0.5–1.0%, and 2 to 3 times more common in women than in men. Several studies have shown that autoimmune diseases are more prevalent in females and are one of the fourth leading causes of disability for women [4, 5]. Rheumatoid arthritis can affect individuals of any age [2] and its incidence of increases with age [6]. Rheumatoid arthritis is a significant global public health challenge. Therefore, early identification and treatment of the disease is vital, especially among females, to minimize the persistent impact of this condition.

Poor maternal and neonatal health remain a recognized public issue and is crucial for sustainable development worldwide [7]. Adverse pregnancy outcomes (APOs) significantly contribute to maternal and neonatal mortality. APOs refer to all pathological complications related to pregnancy and childbirth [8], such as gestational hypertension, pre-eclampsia, preterm delivery, gestational diabetes, fetal growth restriction, delivering a small-for-gestational age baby, placental abruption, and pregnancy loss [9]. The World Health Organization (WHO) reported that in 2020, nearly 800 women died each day due to complications during pregnancy or childbirth [10], and approximately 13.4 million babies were born prematurely [11]. It is clear that preterm birth is currently the primary cause of child deaths [11]. Furthermore, it is estimated that 23 million miscarriages occur worldwide each year, which was equivalent to 44 miscarriages every minute. The pooled risk of miscarriage was 15.3% (95% CI: 12.5–18.7%) of all recognized pregnancies [12]. However, a significant number of adverse maternal and neonatal outcomes can be prevented in advance. Preventing APOs are therefore essential for preserving and extending a healthy lifespan among women.

Previous studies have shown inconsistent results regarding pregnancy-related hypertension and pre-eclampsia/eclampsia in pregnant women with rheumatoid arthritis. Some studies have found a positive association with these complications in pregnant women with rheumatoid arthritis [5, 13, 14], and a retrospective study suggested that women with rheumatoid arthritis had a modestly increased risk for preterm birth and pre-eclampsia [15], while other studies did not find the same associations [16, 17]. Furthermore, there are no articles examining the relationship between rheumatoid arthritis and ectopic pregnancy. Previous studies have shown an association between APOs, such as hyperemesis gravidarum, pre-eclampsia and gestational hypertension, and an increased subsequent risk of developing rheumatoid arthritis [18, 19]. In contrast, an analysis of a prospective case-control study of women who had been pregnant found no statistically significant differences in any APOs, including spontaneous abortion and stillbirth, between rheumatoid arthritis cases and controls [20]. Associations between other APOs and the subsequent risk of rheumatoid arthritis have not been explored. Therefore, a clear assessment of the causality and direction of these associations will help in understanding the disease and contribute to more targeted treatment.

The most important advantage of Mendelian randomization (MR) is that it uses genetic variation as an instrumental variable (IV) to estimate the causal relationship directly and accurately between the exposure phenotype and the outcome phenotype. This approach is independent of external environmental and social behavioral factors, thus overcoming the confounding factors inherent in observational studies, and is a long-term and stable exposure factor [21]. This approach is essentially equivalent to a randomized clinical trial [22].

In this study, we applied a bidirectional two-sample MR analysis to investigate the potential bidirectional causal association between rheumatoid arthritis and APOs, so as to provide evidence for the prevention and control of these diseases.

Methods

Study design

We performed a bidirectional two-sample MR analysis using data from genome-wide association studies (GWAS) to investigate the relationships between rheumatoid arthritis and APOs, including gestational hypertension, gestational diabetes, pre-eclampsia, hyperemesis gravidarum, ectopic pregnancy, fetal growth restriction, preterm delivery and spontaneous abortion. Single nucleotide polymorphisms (SNPs) linked to exposure were used as genetic instrumental variables (IVs). MR analysis is based on three core assumptions [23]: (a) There is a strong link between IVs and exposure; (b) There are no unmeasured confounders of the associations between genetic IVs and outcome; (c) The genetic IVs influence the outcome only through the exposures and not via other biological pathways. The three prerequisites for IVs in our study were summarized in Fig. 1A. The flowchart illustrating the study design and the process of our MR analysis in this study was shown in Fig. 1B.

Fig. 1
figure 1

Schematic overview of the study design. [A] Mendelian randomization illustration. [B] The research design and framework of our study

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Data sources

We used large-scale publicly available genome-wide association studies (GWAS) summary data to conduct MR analyses of European and East Asian populations. In European populations, GWAS dataset for rheumatoid arthritis was obtained from the IEU Open GWAS project (https://gwas.mrcieu.ac.uk/), with corresponding GWAS IDs of ebi-a-GCST90013534. The GWAS study conducted a large-scale meta-analysis on RA with summary association data from East Asian and European cohorts comprising 22,628 rheumatoid arthritis cases and 288,664 controls (14,361 cases and 43,923 controls in the European population) [24]. Summary-level genetic data for APOs were obtained from the Finnegan study (https://www.finngen.fi/en, R9 released in 2023). FinnGen combines imputed genotype data generated from newly collected and legacy samples from Finnish biobanks and digital health record data from Finnish health registries, with the aim of providing new insights into disease genetics [25]. Among East Asian populations, Summary data on rheumatoid arthritis were obtained from a GWAS study in China [26]. APOs including pre-eclampsia (ID: ebi-a-GCST90018686) [27], ectopic pregnancy (ID: ebi-a-GCST90018617) [27], and spontaneous abortion (ID: ebi-a-GCST90018566) [27] were obtained from the IEU project, using the GWAS study conducted by the BioBank Japan. Table 1 shows the summary statistics of the genetic variants associated with these traits.

Table 1 The summary of rheumatoid arthritis and adverse pregnancy outcomes
Full size table

Instruments selection

SNPs were used as instrumental variables (IVs). When rheumatoid arthritis was used as exposure, the significance threshold for SNPs was set at P < 5 × 10− 8. However, when APOs were used as exposures, the significance threshold was expanded to P < 5 × 10− 6. This adjustment was made to ensure that an adequate number of SNPs were available for the heterogeneity test and pleiotropy test. Secondly, to exclude SNPs that were in strong linkage disequilibrium (LD), we performed the clumping procedure with R2 < 0.01 and clump distance = 10,000 kb. The strength of IVs was assessed by calculating the F-statistic using the formula F = R2(N-2)/(1-R2), where R2 represents the proportion of variance in the phenotype explained by the genetic variants, and N is the sample size of the GWAS for the exposure [28]. The R2 of instrumental variables can be calculated using the formula below [29]: R2 = 2 × (1-MAF)×MAF × β2. whereas for the extended 10 SNP instruments, we used: R2=[2×EAF×(1-EAF)×β2]/[2×EAF×(1-EAF)×β2 + 2×EAF×(1-EAF)×N×SE2], where β indicates the estimated genetic effect of the SNP on exposure, EAF denotes the effect allele frequency, MAF represents the other effect allele frequency, Ν is the sample size of the GWAS, and SE is the standard error of the genetic effect. The data mentioned above can be obtained from the original summary data. We retained the SNPs with F > 10 as the final genetic variables to avoid the risk of selecting weak instrumental variables.

Statistical analysis

To investigate the causal relationship between exposure and outcome, bidirectional two-sample MR analyses were performed using several methods, including inverse variance weighting (IVW), weighted median, MR-Egger regression, simple mode, and weighted modal methods. We used the fixed-effects inverse-variance weighting (IVW) as the primary analytical method. However, when heterogeneity is observed, the random-effects IVW was used. In addition, Mendelian randomization based on constrained maximum likelihood and model averaging Bayesian information criterion (cML-MA-BIC) was employed to control correlated and uncorrelated pleiotropic effects. Because this method combines the advantages of maximum likelihood estimation and model averaging, it allows better control the Type-I error rate in the estimation [30]. The heterogeneity was quantified using the P-value of Cochran’s Q-statistics test, which quantifies the extent to which any differences in the individual effect sizes among the selected genetic variants are due to actual differences between SNPs rather than sampling error. A P-value of less than 0.05 implies the presence of heterogeneity.

In addition, we conducted a sensitivity analysis to evaluate the robustness of the association. To assess the influence of horizontal pleiotropy, the P-value of the MR-Egger regression intercept was used to identify and adjust for bias resulting from directional pleiotropy. When P < 0.05, it indicated that there was significant pleiotropy bias. Then, the MR pleiotropy residual sum and outlier (MR-PRESSO) test was conducted to detect and rectify any horizontal pleiotropic outliers. This was done to obtain accurate results by removing any outliers.

The associations between rheumatoid arthritis and APOs were presented using ORs along with their 95% confidence intervals (CIs). We adjusted for multiple testing using a Bonferroni-corrected threshold of P < 0.0063 (P < 0.05/8). The P-values ranging from 0.0063 to 0.05 were considered to indicate suggestive associations. The study was adhered to the STROBE-MR guidelines [31]. All analyses were performed using R statistical software (Version 4.2.3; https://www.r-project.org/). MR analyses were performed using the R-based “TwoSampleMR”,“MRPROSSO” and “MRcML” packages, and forest plots were generated using the “forestplot” package.

Results

Causal effects of rheumatoid arthritis on APOs risk in the European population

For IVs used for rheumatoid arthritis, all the F-statistics were greater than 10, indicating no presence of weak instrumental bias. After removing linkage disequilibrium (LD) and anomalous outliers, we incorporated 78 SNPs with a significant P-value less than 5 × 10− 8 as IVs for rheumatoid arthritis, Detailed information about exposure was listed in the Table S1.

Figure 2 illustrated the primary results of the causal relationship between rheumatoid arthritis and APOs. Among the main results of using IVW, we observed a causal association between rheumatoid arthritis and gestational hypertension, pre-eclampsia, fetal growth restriction and preterm delivery, with corresponding odds ratio (OR) =1.04 (95%CI: 1.02–1.06; P = 9.89 × 10− 5), 1.06 (95%CI: 1.01–1.11; P = 6.47 × 10− 5), 1.08 (95%CI: 1.04–1.12; P = 7.20 × 10− 5), 1.04 (95%CI: 1.01–1.07; P = 0.001). respectively, none of them changed their significance levels after applying the Bonferroni correction. Detailed results of the MR analyses using different methods, such as MR Egger, weighted median, simple mode, weighted mode, were presented in the Table S2. The methods mentioned above are consistent with IVW, except for the Simple model. In addition, the complementary method cML-MA-BIC also confirmed the causal association of the above rheumatoid arthritis with hypertension in pregnancy, pre-eclampsia, and foetal growth restriction (Table S3).

Fig. 2
figure 2

Total causal effects of rheumatoid arthritis on APOs.

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In the sensitivity analyses (Table S4), heterogeneity was evaluated using Cochran’s Q test, which was not found to be significant (P > 0.05). The MR Egger intercept test did not observe a significant pleiotropy, with P-values ranging from 0.053 to 0.891. And no outlier SNPs were identified by using MR-PRESSO in our study, with P-values ranging from 0.088 to 0.444.

Causal effects of APOs on rheumatoid arthritis risk in the European population

Based on the same screening criteria, 25 SNPs were selected as IVs for gestational hypertension, 52 SNPs for gestational diabetes, 31 SNPs for pre-eclampsia, 19 SNPs for hyperemesis gravidarum, 9 SNPs for ectopic pregnancy, 10 SNPs for fetal growth restriction, 8 SNPs for preterm delivery, and 11 SNPs for spontaneous abortion. A summary and detailed information about the SNPs for each exposure were presented in the Table S5.

In general, the primary analysis using IVW did not reveal a statistically significant relationship between an increase in the risk of having APOs and an increased risk of rheumatoid arthritis (Table S6, Fig. 3). While the other five MR methods (weighted median, MR-Egger regression, simple mode, weighted modal methods and cML-MA-BIC) affirmed the identical causal effect of APOs on rheumatoid arthritis (Table S7). Neither the MR-Egger intercept test nor Cochran’s Q statistic revealed any evidence of directional pleiotropy or heterogeneity. In addition, the MR-PRESSO global test did not reveal any evidence of horizontal pleiotropy (all P > 0.18). The results of the sensitivity analyses were shown in Table S8.

Fig. 3
figure 3

Total causal effects of APOs on rheumatoid arthritis

Full size image

Rheumatoid arthritis and APOs risk in the east Asian population

There were a total of 5 SNPs with P < 5 × 10− 6, and a total of 3 SNPs were included after removing the LD (rs12525220, rs7748270). A summary and detailed information about the SNPs were presented in the Table S9. The results failed to reveal any discernible causal relationship between RA and APOs, as confirmed by the statistical insignificance shown in Table S10 (P > 0.0063). cML-MA-BIC Similarly did not find any significant difference. Cochran’s Q test and MR-Egger (Table S11) showed no significant heterogeneity or horizontal pleiotropy.

In the reverse Mendelian randomization analysis, we excluded the effect of LD and merged the results with APOs. However, we did not find any SNPs that were closely associated with rheumatoid arthritis. Due to the lack of the related SNPs we were unable to conduct analysis.

Discussion

Main findings

In this study, we used a bidirectional two-sample Mendelian randomization analysis to investigate the causal relationship between rheumatoid arthritis and adverse pregnancy outcomes in European and East Asian populations. This study indicated that in European population, rheumatoid arthritis was associated with the increased risk of gestational hypertension, pre-eclampsia, fetal growth restriction and preterm delivery. And these associations remained consistent even after multiple corrections were applied to the data. Furthermore, our study did not find evidence of causal associations of APOs on the increased risk of rheumatoid arthritis. It was also evident from the sensitivity analysis that the results of this study were robust and reliable. In the East Asian population, we did not find any association between rheumatoid arthritis and APOs.

Interpretation

Our findings in bidirectional Mendelian randomization of the association of RA with gestational hypertension and preeclampsia are consistent with previous observational studies [5, 32]. A systematic review [33] also found that women with rheumatoid arthritis tended to have a higher risk of maternal and neonatal complications compared to the general pregnant population. There have been observational studies [34, 35] and a meta-analysis [36] demonstrated that maternal rheumatoid arthritis during pregnancy was associated with a significantly increased risk of preterm birth and low birth weight in the fetus. The aforementioned studies provide support for our findings regarding the causal associations between genetically predicted rheumatoid arthritis and gestational hypertension, pre-eclampsia, fetal growth restriction, and preterm delivery. There was certainly a good deal of evidence that rheumatoid arthritis was the risk factor for small for gestational age infants [5, 13, 37]. However, due to the limited number of GWAS studies, we have been unable to find a suitable database.

Several possible mechanisms have been proposed to explain the association between rheumatoid arthritis and gestational hypertension, pre-eclampsia, fetal growth restriction and preterm delivery. First, in patients with rheumatoid arthritis, the CD4 protein on helper T lymphocytes is activated, which then stimulates monocytes, macrophages, and fibroblast-like synoviocytes. This activation can result in an increased release of proinflammatory cytokines, such as interleukin-1 (IL-1), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α). Additionally, it can lead to a decrease in the release of regulatory and anti-inflammatory cytokines [38]. High levels of proinflammatory cytokines, as well as chronic stress states, can reduce the activity of 11β-HSD2 and result in elevated maternal cortisol levels, which may have potentially deleterious effects on the placenta, leading to preterm birth, low birth weight, and small for gestational age [39, 40]. Secondly, endothelial dysfunction is a common complication of active rheumatoid arthritis. Vasculopathy resulting from endothelial dysfunction may contribute to placental maldevelopment. Maldevelopment of the placenta is associated with unfavorable pregnancy outcomes, such as lower birth weight and hypertension [41]. Endothelial dysfunction is considered the initial stage of atherosclerosis [42], and proinflammatory cytokines implicated in rheumatoid arthritis also contribute to the development of atherosclerosis [43]. Furthermore, in patients with rheumatoid arthritis, the upregulated expression of vascular endothelial growth factor (VEGF), a crucial regulator of endothelial dysfunction [44], may lead to the development of pre-eclampsia during pregnancy [45].

Epidemiologic data suggests that pregnancy-related hormones may influence the link between reproduction and the risk of rheumatoid arthritis. Excessive level of female hormones, such as estrogen and progesterone, may be protective against the development of rheumatoid arthritis [46]. During pregnancy, when estradiol and progesterone levels are high, women have a reduced risk of developing rheumatoid arthritis [47]. Hormone levels return to a non-pregnant state rapidly after delivery [48], especially during the first 3 months, which appears to be a period of increased risk. A nationwide cohort study in Denmark found that women with hyperemesis gravidarum, gestational hypertension, or pre-eclampsia have a significantly higher risk of developing rheumatoid arthritis [18]. However, a population-based prospective study found that preterm delivery and small-for-gestational-age infants did not appear to have a significant association with subsequent rheumatoid arthritis [49]. Our study also did not find the causal relationship from the gene perspective. This may be due to the lack of individual-level information in the pooled data used, and the fact that the data on rheumatoid arthritis did not include data on its typing, due to which limitation it is difficult to determine a causal association of APOs on rheumatoid arthritis progression.

Strengths and limitations

This bidirectional two-sample MR study, which investigated the causal association between rheumatoid arthritis and APOs risk, had several strengths. First, the bidirectional two-sample Mendelian randomization analysis can reduce the effects of unknown confounders and small-sample selection bias. The bidirectional analysis ensures the inference of causality between rheumatoid arthritis and APOs in both directions. Furthermore, the study used a large sample size and SNPs from GWAS in European population, which provided sufficient statistical validity to estimate causality. Finally, we applied a series of sensitivity analyses to ensure the consistency and the robustness of causal estimates.

Inevitably, there were several limitations in our study. First, considering the problem of sample overlap, this study did not find a large sample of rheumatoid arthritis -related GWAS studies in East Asian populations, and APOs-related GWAS studies were limited, making it impossible to draw the same clear conclusions as European populations. Secondly, when examining the risk of rheumatoid arthritis associated with APOs, we set the P-value threshold for SNPs at P < 5 × 10− 6 to ensure an adequate number of instrumental variables for heterogeneity and horizontal multivariate tests. This threshold may explain only a small portion of the variability in exposures and could affect the statistical efficacy of the causal estimates. Finally, we were unable to perform subgroup analyses due to the lack of specific information describing the severity of the disease and specific information at the individual level.

Conclusions

In conclusion, this study confirmed the causal relationship between rheumatoid arthritis and adverse pregnancy outcomes in the European population, that is, rheumatoid arthritis is associated with an increased risk of gestational hypertension, pre-eclampsia, fetal growth restriction, and preterm delivery. It highlights the importance of more intensive prenatal care and early intervention among pregnant women with rheumatoid arthritis to prevent potential adverse obstetric outcomes. However, due to the limitations of the study, further research is warranted.

Data availability

Data is provided within the manuscript or supplementary information files. This study uses publicly available datasets, which can be found in the IEU Open Project (https://gwas.mrcieu.ac.uk/), and the FinnGen study (https://www.finngen.fi/en).

Abbreviations

APOs:

Adverse Pregnancy Outcomes

LD:

Linkage Disequilibrium

MR:

Mendelian randomization

SNP:

Single-nucleotide polymorphism

GWAS:

Genome-Wide Association Studies

IV:

Instrumental Variable

IVW:

Inverse Variance-Weighted

MR-PRESSO:

MR pleiotropy residual sum and outlier

References

  1. IB M. The pathogenesis of rheumatoid arthritis. N Engl J Med. 2011;365(23):2205–19.

    Article  Google Scholar 

  2. Smith MH, Berman JR. What Is Rheumatoid Arthritis? Jama. 2022;327(12):1194.

    PubMed  Google Scholar 

  3. Silman AJ, Pearson JE. Epidemiology and genetics of rheumatoid arthritis. Arthritis Res. 2002;4(Suppl 3):S265–72.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Pennell LM, Galligan CL, Fish EN. Sex affects immunity. J Autoimmun. 2012;38(2–3):J282–91.

    Article  CAS  PubMed  Google Scholar 

  5. Lin HC, Chen SF, Lin HC, Chen YH. Increased risk of adverse pregnancy outcomes in women with rheumatoid arthritis: a nationwide population-based study. Ann Rheum Dis. 2010;69(4):715–7.

    Article  PubMed  Google Scholar 

  6. Conrad N, Misra S, Verbakel JY, Verbeke G, Molenberghs G, Taylor PN, et al. Incidence, prevalence, and co-occurrence of autoimmune disorders over time and by age, sex, and socioeconomic status: a population-based cohort study of 22 million individuals in the UK. Lancet (London England). 2023;401(10391):1878–90.

    Article  PubMed  Google Scholar 

  7. Cui Y, Liao M, Xu A, Chen G, Liu J, Yu X, et al. Association of maternal pre-pregnancy dietary intake with adverse maternal and neonatal outcomes: a systematic review and meta-analysis of prospective studies. Crit Rev Food Sci Nutr. 2023;63(19):3430–51.

    Article  CAS  PubMed  Google Scholar 

  8. Li C, Liu C, Li N. Causal associations between gut microbiota and adverse pregnancy outcomes: a two-sample mendelian randomization study. Front Microbiol. 2022;13:1059281.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Farland LV, Prescott J, Sasamoto N, Tobias DK, Gaskins AJ, Stuart JJ, et al. Endometriosis and risk of adverse pregnancy outcomes. Obstet Gynecol. 2019;134(3):527–36.

    Article  PubMed  PubMed Central  Google Scholar 

  10. WHO U, UNFPA, World Bank Group and UNDESA/Population Division. Trends in maternal mortality 2000 to 2020: estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA., /Population Division 2023 [ https://www.who.int/publications/i/item/9789240068759.

  11. WHO. 152 million babies born preterm in the last decade 2023 [ https://www.who.int/news/item/09-05-2023-152-million-babies-born-preterm-in-the-last-decade.

  12. Quenby S, Gallos ID, Dhillon-Smith RK, Podesek M, Stephenson MD, Fisher J, et al. Miscarriage matters: the epidemiological, physical, psychological, and economic costs of early pregnancy loss. Lancet (London England). 2021;397(10285):1658–67.

    Article  CAS  PubMed  Google Scholar 

  13. Nørgaard M, Larsson H, Pedersen L, Granath F, Askling J, Kieler H, et al. Rheumatoid arthritis and birth outcomes: a Danish and Swedish nationwide prevalence study. J Intern Med. 2010;268(4):329–37.

    Article  PubMed  Google Scholar 

  14. Aljary H, Czuzoj-Shulman N, Spence AR, Abenhaim HA. Pregnancy outcomes in women with rheumatoid arthritis: a retrospective population-based cohort study. The journal of maternal-fetal & neonatal medicine: the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstet. 2020;33(4):618–24.

  15. Eudy AM, McDaniel G, Clowse MEB. Pregnancy in rheumatoid arthritis: a retrospective study. Clin Rheumatol. 2018;37(3):789–94.

    Article  PubMed  Google Scholar 

  16. Hunter TM, Boytsov NN, Zhang X, Schroeder K, Michaud K, Araujo AB. Prevalence of rheumatoid arthritis in the United States adult population in healthcare claims databases, 2004–2014. Rheumatol Int. 2017;37(9):1551–7.

    Article  PubMed  Google Scholar 

  17. Yu KH, See LC, Kuo CF, Chou IJ, Chou MJ. Prevalence and incidence in patients with autoimmune rheumatic diseases: a nationwide population-based study in Taiwan. Arthritis Care Res. 2013;65(2):244–50.

    Article  Google Scholar 

  18. Jørgensen KT, Pedersen BV, Jacobsen S, Biggar RJ, Frisch M. National cohort study of reproductive risk factors for rheumatoid arthritis in Denmark: a role for hyperemesis, gestational hypertension and pre-eclampsia? Ann Rheum Dis. 2010;69(2):358–63.

    Article  PubMed  Google Scholar 

  19. Camacho EM, Verstappen SM, Lunt M, Bunn DK, Symmons DP. Multiple adverse pregnancy outcomes before symptom onset are associated with a worse disease outcome in women with recent-onset inflammatory polyarthritis. Ann Rheum Dis. 2012;71(4):528–33.

    Article  CAS  PubMed  Google Scholar 

  20. Nelson JL, Voigt LF, Koepsell TD, Dugowson CE, Daling JR. Pregnancy outcome in women with rheumatoid arthritis before disease onset. J Rhuematol. 1992;19(1):18–21.

    CAS  Google Scholar 

  21. Bowden J, Holmes MV. Meta-analysis and mendelian randomization: a review. Res Synthesis Methods. 2019;10(4):486–96.

    Article  Google Scholar 

  22. Smith GD. Mendelian randomization for strengthening causal inference in Observational studies: application to gene × environment interactions. Perspect Psychol Science: J Association Psychol Sci. 2010;5(5):527–45.

    Article  Google Scholar 

  23. Davies NM, Holmes MV, Davey Smith G. Reading mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ (Clinical Res ed). 2018;362:k601.

    Article  Google Scholar 

  24. Ha E, Bae SC, Kim K. Large-scale meta-analysis across east Asian and European populations updated genetic architecture and variant-driven biology of rheumatoid arthritis, identifying 11 novel susceptibility loci. Ann Rheum Dis. 2021;80(5):558–65.

    Article  CAS  PubMed  Google Scholar 

  25. Kurki MI, Karjalainen J, Palta P, Sipilä TP, Kristiansson K, Donner KM, et al. Author correction: FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023;615(7952):E19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Jiang L, Yin J, Ye L, Yang J, Hemani G, Liu AJ et al. Novel risk loci for rheumatoid arthritis in Han Chinese and congruence with risk variants in Europeans. Arthritis & rheumatology (Hoboken, NJ). 2014;66(5):1121-32.

  27. Sakaue S, Kanai M, Tanigawa Y, Karjalainen J, Kurki M, Koshiba S, et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet. 2021;53(10):1415–24.

    Article  CAS  PubMed  Google Scholar 

  28. Burgess S, Thompson SG. Avoiding bias from weak instruments in mendelian randomization studies. Int J Epidemiol. 2011;40(3):755–64.

    Article  PubMed  Google Scholar 

  29. Papadimitriou N, Dimou N, Tsilidis KK, Banbury B, Martin RM, Lewis SJ, et al. Physical activity and risks of breast and colorectal cancer: a mendelian randomisation analysis. Nat Commun. 2020;11(1):597.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Xue H, Shen X, Pan W. Constrained maximum likelihood-based mendelian randomization robust to both correlated and uncorrelated pleiotropic effects. Am J Hum Genet. 2021;108(7):1251–69.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, et al. Strengthening the reporting of Observational studies in Epidemiology using mendelian randomization: the STROBE-MR Statement. JAMA. 2021;326(16):1614–21.

    Article  PubMed  Google Scholar 

  32. Chakravarty EF, Nelson L, Krishnan E. Obstetric hospitalizations in the United States for women with systemic lupus erythematosus and rheumatoid arthritis. 2006;54(3):899–907.

  33. Sim BL, Daniel RS, Hong SS, Matar RH, Ganiel I, Nakanishi H, et al. Pregnancy outcomes in women with rheumatoid arthritis: a systematic review and Meta-analysis. J Clin Rheumatology: Practical Rep Rheumatic Musculoskelet Dis. 2023;29(1):36–42.

    Article  Google Scholar 

  34. Harris N, Eudy A, Clowse M. Patient-reported disease activity and adverse pregnancy outcomes in systemic Lupus Erythematosus and Rheumatoid Arthritis. Arthritis Care Res. 2019;71(3):390–7.

    Article  Google Scholar 

  35. Tsai YC, Chang HC, Chiou MJ, Luo SF, Kuo CF. Fetal-neonatal and maternal pregnancy outcomes in women with rheumatoid arthritis: a population-based cohort study. BMJ open. 2022;12(10):e059203.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Huang W, Wu T, Jin T, Zhang Y, Wang J, Qi J, et al. Maternal and fetal outcomes in pregnant women with rheumatoid arthritis: a systematic review and meta-analysis. Clin Rheumatol. 2023;42(3):855–70.

    Article  PubMed  Google Scholar 

  37. Tian L, Zhang Z, Mao Y, Zong M. Association between maternal rheumatoid arthritis and small for gestational age neonates: a systematic review and meta-analysis. Front Public Health. 2023;11:1075946.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Chen Z, Bozec A, Ramming A, Schett G. Anti-inflammatory and immune-regulatory cytokines in rheumatoid arthritis. Nat Rev Rheumatol. 2019;15(1):9–17.

    Article  CAS  PubMed  Google Scholar 

  39. Kossintseva I, Wong S, Johnstone E, Guilbert L, Olson DM, Mitchell BF. Proinflammatory cytokines inhibit human placental 11beta-hydroxysteroid dehydrogenase type 2 activity through Ca2 + and cAMP pathways. Am J Physiol Endocrinol Metabolism. 2006;290(2):E282–8.

    Article  CAS  Google Scholar 

  40. Fowden AL, Valenzuela OA, Vaughan OR, Jellyman JK, Forhead AJ, Suppl. S121–32.

  41. Jaddoe VW, Witteman JC. Hypotheses on the fetal origins of adult diseases: contributions of epidemiological studies. Eur J Epidemiol. 2006;21(2):91–102.

    Article  PubMed  Google Scholar 

  42. Smolen JS, Aletaha D, McInnes IB. Rheumatoid arthritis. Lancet (London England). 2016;388(10055):2023–38.

    Article  CAS  PubMed  Google Scholar 

  43. Hansson GK. Inflammation, atherosclerosis, and coronary artery disease. D – 0255562. 2005;352(16):1685–95.

    CAS  Google Scholar 

  44. Zhan H, Li H, Liu C, Cheng L, Yan S, Li Y. Association of circulating vascular endothelial growth factor levels with Autoimmune diseases: a systematic review and Meta-analysis. Front Immunol. 2021;12:674343.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Zhang D, Hu Y, Guo W, Song Y, Yang L, Yang S, et al. Mendelian randomization study reveals a causal relationship between rheumatoid arthritis and risk for pre-eclampsia. Front Immunol. 2022;13:1080980.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Karlson EW, Mandl LA, Hankinson SE, Grodstein F. Do breast-feeding and other reproductive factors influence future risk of rheumatoid arthritis? Results from the nurses’ Health Study. Arthritis Rheum. 2004;50(11):3458–67.

    Article  PubMed  Google Scholar 

  47. Silman A, Kay A, Brennan P. Timing of pregnancy in relation to the onset of rheumatoid arthritis. Arthritis Rheum. 1992;35(2):152–5.

    Article  CAS  PubMed  Google Scholar 

  48. Mastorakos G, Ilias I. Maternal hypothalamic-pituitary-adrenal axis in pregnancy and the postpartum period. Postpartum-related disorders. Ann N Y Acad Sci. 2000;900:95–106.

    Article  CAS  PubMed  Google Scholar 

  49. Ma KK, Nelson JL, Guthrie KA, Dugowson CE, Gammill HS. Adverse pregnancy outcomes and risk of subsequent rheumatoid arthritis. Arthritis Rheumatol (Hoboken NJ). 2014;66(3):508–12.

    Article  Google Scholar 

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Acknowledgements

The authors acknowledge the participants and investigators of the IEU Open Project study and FinnGen study.

Funding

This work was supported by the Shandong Provincial Medical Association (YXH2022PT06001), the National Key Research and Development Program of China (2021YFF1201101) and ECCM Program of Clinical Research Center of Shandong University (2021SDUCRCE001).

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Authors and Affiliations

  1. School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China

    Tongmin Chang, Zengle Zhao, Xuening Zhang, Yuan Zhang, Xinjie Liu & Ming Lu

  2. Department of Gynecology and Obstetrics, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, China

    Xiaoyan Liu

  3. Clinical Epidemiology Unit, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, China

    Yuan Zhang & Ming Lu

  4. Clinical Research Center, Shandong University, Jinan, Shandong, 250012, China

    Yuan Zhang & Ming Lu

Authors
  1. Tongmin Chang
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  2. Zengle Zhao
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  3. Xiaoyan Liu
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  4. Xuening Zhang
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  5. Yuan Zhang
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  6. Xinjie Liu
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  7. Yuan Zhang
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  8. Ming Lu
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Contributions

Data collection, data curation, Methodology, Formal analysis, Software, and Visualization were performed by ZZ, TC, XL, XZ, JC, YZ, and XL. Writing original draft: ZZ, YZ. Funding acquisition: ML. Writing review and editing: YZ, XL, and ML.

Corresponding authors

Correspondence to Yuan Zhang or Ming Lu.

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Ethics approval and consent to participate

All the data were publicly available GWAS summary statistics Ethical approval and participant consent were obtained in the original studies.

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The authors declare no competing interests.

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Chang, T., Zhao, Z., Liu, X. et al. Rheumatoid arthritis and adverse pregnancy outcomes: a bidirectional two-sample mendelian randomization study. BMC Pregnancy Childbirth 24, 517 (2024). https://doi.org/10.1186/s12884-024-06698-3

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  • DOI: https://doi.org/10.1186/s12884-024-06698-3

Keywords

BMC Pregnancy and Childbirth

ISSN: 1471-2393

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