Abstract
Purpose
Systemic lupus erythematosus (SLE) is a prominent autoimmune disease highly linked to adverse pregnancy outcomes (APOs). Previous research on the risk factors for APOs in SLE pregnancies has been limited by regional constraints or inadequate sample sizes. Comprehensive systematic reviews on this topic remain scarce. To address these research gaps, we conducted a rigorous meta-analysis and systematic review to elucidate the risk factors for APOs in SLE pregnancies.
Methods
PubMed, Embase, Web of Science, and the Cochrane Library systematically searched for articles on risk factors for APOs in SLE pregnancy from initiation to March 25, 2025. Pooled odds ratios (ORs) were calculated using fixed-effect or random-effects models based on heterogeneity (I2). Egger’s test was used to assess publication bias.
Results
A total of 43 studies were reviewed. Patients with hypertension, lupus nephritis (LN), high disease activity, low complements, and antiphospholipid syndrome (APS)/positive antiphospholipid antibodies (aPL) were identified as having a higher risk for adverse pregnancy outcomes (APOs). Risk factors for preterm birth included LN, hypertension, disease flares, high disease activity, and APS/aPL. Risk factors for pregnancy loss included APS/aPL, low complements, disease flares, LN, hypertension, thrombocytopenia, and high disease activity. LN was also associated with an increased risk of intrauterine growth restriction and low birth weight.
Conclusions
This study identified risk factors for APOs in SLE pregnancies. These findings may support early identification of high-risk patients and guide timely interventions to improve maternal and fetal outcomes.
Supplementary Information
The online version contains supplementary material available at 10.1007/s00404-025-08106-3.
Keywords: Systemic lupus erythematosus, Pregnancy outcome, Risk factor
Background
Systemic lupus erythematosus (SLE), commonly known as lupus, is a prevalent chronic multisystemic autoimmune disease characterized by the presence of pathogenic autoantibodies and immune complexes, contributing to tissue damage. The estimated global incidence is 5.14 (from 1.4 to 15.3) per 100,000 person-years, with a corresponding prevalence estimated at 43.7 (from 15.87 to 108.92) per 100,000 individuals [1]. The chronic disease progression and the use of immunosuppressive therapy are significant contributors to life-threatening systemic organ damage. The mortality among SLE patients remains significantly higher, with rates two-to-three times greater than that among the general population [2]. Certain ethnic populations, like Black, Asian, and Hispanic, exhibit a pronounced predisposition [2].
SLE presents diverse phenotypes, spanning from mild skin symptoms to severe organ failures, affecting virtually any organ in the body. It has a distinct gender bias, predominantly affecting women of reproductive age, with a female/male ratio of roughly 9:1. The average age at which SLE is diagnosed is around 35 years [3]. SLE ranks among the top causes of mortality in young females [4]. The high susceptibility of reproductive-age women to SLE raises significant concerns for clinicians regarding childbearing. Despite comparable anti-Müllerian hormone levels and fertility potential as healthy controls [5], SLE women have heightened vulnerability to adverse pregnancy outcomes (APOs) [6]. Meta-analysis revealed that SLE subgroups exhibited significantly elevated rates of spontaneous abortion, stillbirth, premature birth, "small for gestational age” (SGA) infants, and infants with low birth weight [7, 8]. SLE-associated pregnancies are deemed high-risk pregnancies. Early identification of high-risk factors for APOs is of utmost importance. The careful monitoring of predictors and effective management of pregnancies in SLE women can improve outcomes.
The risk factors for APOs in SLE pregnancies have been extensively studied. However, these reports had limitations including a small sample size and limited diversity in terms of ethnicity and geographical regions. A systemic meta-analysis on this topic is currently absent. Therefore, our study aims to conduct a systemic review and meta-analysis to identify a set of risk factors that could potentially be targeted for interventions.
Methods
The conduct of this systematic review strictly followed the PRISMA Statement guidelines, with explicit application to all stages encompassing: (a) study design formulation, (b) search strategy implementation, (c) meta-analytical procedures, and (d) evidence synthesis reporting, as detailed in the methodology documentation.[9]
Search strategies
PubMed, Cochrane Library, Web of Science, and EMBASE were comprehensively searched for relevant articles published from initiation up to March 25, 2025. The search strategies encompassed three Medical Subject Headings (MeSH) terms (SLE, pregnancy outcome, risk factors) and free words. The detailed retrieval strategies used are displayed in Supplementary Table 1.
Study selection
After removing duplicates, two investigators (Sun C and Li XM) independently screened the potentially eligible articles by reviewing the titles and abstracts, and full texts when necessary. Any discrepancies between the two reviewers were resolved through discussion, with a third independent reviewer (Li X) acting as an arbitrator when consensus could not be reached.
The inclusion criteria were as follows: (1) studies involving pregnant women with SLE, with the diagnosis based on internationally accepted classification criteria; (2) use of specific diagnostic criteria to identify APOs; (3) application of multivariate analysis to determine risk factors for APOs in SLE pregnancies; and (4) study design limited to cohort or case–control studies.
The articles were excluded based on the following criteria: (1) publication types such as guidelines, reviews, editorials, case reports, abstracts, letters, conference summaries, or retracted articles; (2) incomplete or unavailable data; (3) inconsistent definitions of APOs; (4) studies reporting raw data without adjusting for confounding factors through regression analysis; and (5) studies that included participants with autoimmune diseases other than SLE, or that focused solely on specific SLE manifestations (e.g., lupus nephritis).
Quality evaluation
Each eligible study underwent independent evaluation using the Newcastle-Ottawa Scale (NOS; accessible at: https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp) in three domains: patient representation, exposure and outcome determination, and follow-up adequacy. A comprehensive assessment led to the overall NOS score for each study was 9, with scores of 0–5 indicating low quality, scores of 6–7 indicating moderate quality, and scores of 8–9 indicating high quality, signifying a low risk of bias.
Data extraction
Data extraction was conducted independently by two reviewers (Sun C and Li XM), including information on the first author, year of publication, study design, geographical location, sample size, mean maternal age, diagnostic criteria, and reported risk factors. Any discrepancies between the two reviewers were resolved through discussion or, when necessary, adjudicated by a third reviewer (Li X).
Terms definition
APOs were defined to encompass at least one of the following complications: pregnancy loss, preterm birth, SGA, intrauterine growth restriction (IUGR), low birth weight (LBW), preeclampsia/eclampsia, HELLP syndrome, neonatal asphyxia, fetal distress, or neonatal death.
-Pregnancy loss: Encompasses several distinct pathological outcomes, including spontaneous miscarriage (early pregnancy failure), therapeutic abortion (pregnancy termination necessitated by maternal lupus exacerbation or critical obstetric complications), stillbirth (intrauterine fetal demise not attributable to chromosomal aberrations or congenital anatomical malformations), and neonatal mortality (death of a viable infant born after 28 week gestation occurring within the initial 28 days post-partum).
-Preterm birth: Defined as the delivery of a live-born infant prior to the completion of 37 weeks of gestation.[10]
-IUGR: Defined as an estimated fetal weight below the 10th percentile for gestational age.[11]
-SGA: A neonate with a birthweight below the 10th percentile for their gestational age.[12]
-LBW: Defined as a birth weight of less than 2500 grams, regardless of gestational age.[13]
-Preeclampsia/eclampsia: preeclampsia is characterized by new-onset hypertension (≥140/90 mmHg) after 20 weeks of gestation accompanied by proteinuria (≥300 mg/24h) or evidence of maternal organ dysfunction; eclampsia refers to the occurrence of seizures in a woman with preeclampsia in the absence of other neurological conditions.[14]
-HELLP syndrome: A severe complication of preeclampsia characterized by hemolysis, elevated liver enzymes, and low platelet count.[14]
-Asphyxia neonatorum: Defined as failure to initiate or sustain breathing at birth, with evidence of metabolic acidosis (typically pH < 7.2), and an Apgar score <7 at 1 minute and/or 5 min.[15]
-Fetal distress: Refers to fetal hypoxia and acidosis, detected through abnormal fetal heart rate patterns or meconium-stained amniotic fluid, which could endanger the health of the fetus.[16]
-Neonatal death: Mortality occurring between birth and 28 days of postnatal life.[17]
Maternal risk factors were defined as follows:
-Lupus nephritis: Defined as biopsy-proven lupus nephritis or clinical evidence of renal involvement, characterized by persistent proteinuria (>0.5 g/day), active urinary sediment (hematuria, cellular casts), impaired renal function, or prior documented diagnosis and treatment of lupus nephritis.
-Hypertension: Defined as a documented prior diagnosis of chronic hypertension, gestational hypertension, or current treatment with antihypertensive medications.
-Disease activity: Evaluated using established clinical indices, including the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI), Physician Global Assessment (PGA), Lupus Activity Index-Pregnancy (LAI-P), Systemic Lupus Erythematosus Pregnancy Disease Activity Index (SLEPDAI), or Systemic Lupus International Collaborating Clinics Damage Index (SLICC-DI). Active lupus is defined as clinical or laboratory evidence of lupus activity reflected by elevated scores on established disease indices (SLEDAI, PGA, LAI-P, SLEPDAI, or SLICC-DI), new organ involvement, or intensified therapy. Flare is defined as a significant worsening in disease activity scores, new clinical symptoms, or laboratory abnormalities requiring medical interventions.
Data analysis
A meta-analysis was conducted to assess the connection between potential risk factors (consistently reported in at least three studies) and APOs in SLE patients. The odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were computed by Stata 15.1 software. To account for variability in baseline characteristics across studies, statistical models were selected based on heterogeneity levels: a fixed-effects model was applied when heterogeneity was low (I2 < 50%), and a random-effects model was used for substantial heterogeneity (I2 ≥ 50% or p value < 0.05), in accordance with the Cochrane Handbook for Systematic Reviews of Interventions [18]. An OR < 1 indicated reduced APO risk, whereas an OR > 1 suggested increased risk. Forest plots were employed to present the ORs from individual studies and the pooled OR estimate. Statistical significance was defined as a p < 0.05.
Sensitivity analyses were performed to assess the robustness of results, and potential publication bias was evaluated through Egger’s regression test [19].
Result
Article selection
The initial research on four databases retrieved 2098 records. After the removal of duplicates, 1614 records remained. After carefully reviewing the titles and abstracts, 1529 articles were excluded, resulting in 85 articles for further comprehensive evaluation in full-text format. Through this rigorous selection process, 43 articles were utilized for review, as depicted in Fig. 1.
Fig. 1.
Flow diagram representing the study selection
Study characteristics
Table 1 lists the key characteristics of 43 included articles, providing information on study type, geographical regions, and sample sizes. As for study types, 4 were retrospective case–control studies, 9 were retrospective cohort studies, and 30 were prospective cohort studies. The patient population was geographically diverse, spanning regions, such as Asia, North America, Europe, the Middle East, and others. Overall, the analysis involved a substantial sample size of 9225 pregnancies, ensuring reliable and generalizable findings.
Table 1.
Key characteristics of included articles
| Study | Year | Country | Study design | Sample size | Mean age of pregnancy |
|---|---|---|---|---|---|
| Andrade | 2008 | USA | Prospective cohort | 102 | _ |
| Arfaj | 2010 | Saudi Arabia | Retrospective cohort | 383 | 36.4 ± 7.4 |
| Al-Riyami | 2021 | Oman | Retrospective cohort | 149 | 30.6 ± 5 |
| Buyon | 2015 | USA | Prospective cohort | 385 | 30.93 ± 4.90 |
| Chen | 2018 | China | Retrospective cohort | 243 | 28.9 ± 3.9 |
| Chen | 2021 | China | Retrospective cohort | 85 | 27.4 |
| Chen | 2015 | China | Retrospective cohort | 83 | _ |
| Clowse | 2006 | USA | Prospective cohort | 166 | 30.2 ± 4.9 |
| Dai | 2024 | China | Retrospective cohort | 445 | 31 |
| Deguchi | 2018 | Japan | Prospective cohort | 56 | 33.9 ± 4.6 |
| Hiramatsu | 2021 | Japan | Retrospective case–control | 108 | 33 |
| Hamijoyo | 2019 | Indonesia | Retrospective case–control | 109 | 26 ± 6 |
| He | 2021 | China | Retrospective cohort | 223 | 27.8 ± 3.9 |
| Irino | 2021 | Japan | Retrospective cohort | 64 | 31.2 ± 4.9 |
| Jiang | 2021 | China | Retrospective cohort | 513 | 29.7 ± 4.0 |
| Kim | 2021 | Korea | Retrospective case–control | 163 | 31.9 ± 4.3 |
| Kalok | 2019 | Malaysia | Retrospective case–control | 71 | 30.5 ± 3.9 |
| Ko | 2011 | Korea | Retrospective cohort | 183 | 30.4 ± 3.2 |
| Kwok | 2011 | Hong Kong | Prospective cohort | 55 | 30.2 |
| Laíno-Pineiro | 2023 | Spain | Retrospective cohort | 1869 | _ |
| Larosa | 2022 | France | Prospective cohort | 238 | 31.6 ± 4.5 |
| Liu | 2017 | China | Retrospective cohort | 131 | 24.3 ± 2.8 |
| Liu | 2012 | China | Retrospective cohort | 111 | 29.2 ± 4.2 |
| Louthrenoo | 2021 | Thailand | Retrospective cohort | 90 | 26.94 ± 4.80 |
| Lu | 2021 | China | Retrospective cohort | 55 | _ |
| Lu | 2024 | China | Retrospective cohort | 126 | _ |
| Lv | 2015 | China | Retrospective cohort | 52 | 29.0 ± 3.7 |
| Madrazo | 2022 | Spain | Retrospective cohort | 64 | 32.1 ± 5.04 |
| Miranda | 2021 | Mexico | Prospective cohort | 351 | 28.3 ± 5.5 |
| Mokbel | 2023 | Egypt | Prospective cohort | 201 | 27.16 ± 4.8 |
| Natli | 2022 | Greece | Prospective cohort | 84 | 33.5 ± 6.8 |
| Oishi | 2021 | Japan | Retrospective cohort | 98 | 30 |
| Palma dos Reis | 2020 | Portugal | Retrospective cohort | 157 | 29.6 ±4.7 |
| Park | 2014 | Korea | Retrospective cohort | 62 | - |
| Shaharir | 2020 | Malaysia | Retrospective cohort | 240 | 29.9 ± 4.8 |
| Sugawara | 2019 | Japan | Retrospective cohort | 57 | 30 |
| Tani | 2021 | Germany | Retrospective cohort | 281 | 31.9 ± 4.5 |
| Tian | 2015 | China | Retrospective cohort | 347 | 31.9 ± 4.5 |
| Zamani | 2020 | Iran | Retrospective cohort | 121 | 33.74 ± 3.80 |
| Zhan | 2018 | China | Retrospective cohort | 180 | 29.4 ± 3.5 |
| Zhan | 2017 | China | Retrospective cohort | 263 | 28.6 ± 3.9 |
| Zhang | 2022 | China | Retrospective cohort | 123 | 27.1 ± 4.1 |
| Wu | 2019 | China | Retrospective cohort | 338 | 29.5 ± 4.0 |
Quality evaluation
The risk of bias was evaluated using NOS scores, and the results are presented in Supplementary Tables 2-3. Among the 43 studies analyzed, a significant majority of 76.74% (33/43) attained 8 scores or higher, indicating high quality. Additionally, ten studies were rated as moderate quality.
Analysis result
Lupus nephritis
Eleven studies revealed a significant association between lupus nephritis (LN) and APOs. The analysis, employing a random-effects model, indicated low heterogeneity (I2=58.9, P=0.007) and demonstrated that patients with LN exhibited a 3.08-fold higher risk of APO than those without LN (OR 3.08; 95% CI 1.69–5.61) (P<0.001) (Fig. 2).
Fig. 2.
OR for association between LN and APOs
In eight studies examining the association between LN and preterm birth, a fixed-effects model was utilized, showing low heterogeneity (I2=0.0, P=0.477). The analysis revealed LN as a prominent high-risk factor for preterm birth, with individuals with LN having a 3.43-fold increased risk compared to those without LN (OR, 3.43; 95% CI 2.31–5.10, P<0.001) (Fig. 3).
Fig. 3.
OR for association between LN and preterm birth
In six studies on pregnancy loss, a fixed-effects model was employed, indicating moderate heterogeneity (I2=32.5, P=0.180). The analysis identified LN as a high-risk factor for pregnancy loss (OR 2.88; 95% CI 1.73-4.82, P<0.001) (Fig. 4).
Fig. 4.
OR for association between LN and pregnancy loss
In three studies on IUGR, a heterogeneity test showed high heterogeneity (I2=69.4, P=0.038). The analysis revealed that LN was connected with a 3.51-fold elevated risk of IUGR (OR 3.51; 95% CI 1.30–9.51, P=0.013) (Fig. 5).
Fig. 5.
OR for association between LN and IGUR
In three studies on LBW, moderate heterogeneity was observed (I2=41.8, P=0.179). LN was significantly linked to a 5.55-fold elevated risk of LBW (OR 5.55; 95% CI 1.29–23.86, P=0.021) (Fig. 6).
Fig. 6.
OR for association between LN and LBW
Hypertension
In the analysis of six studies, a heterogeneity test revealed moderate heterogeneity (I2=57.4, P=0.029). Hypertension was strongly linked to a 5.23-fold enhanced risk of APOs (OR 5.23; 95% CI 2.76–9.91, P<0.001) (Fig. 7A).
Fig. 7.
A OR for association between hypertension and APOs; B OR for association between hypertension and preterm birth; C OR for association between hypertension and pregnancy loss
Similarly, in the analysis of six studies, moderate heterogeneity was observed (I2=48.5, P=0.084). Hypertension was uncovered as a high-risk factor for preterm birth, with a 4.50-fold raised risk (OR 3.27; 95% CI 2.12–5.03, P<0.001) (Fig. 7B).
However, the role of high blood pressure in pregnancy loss remains inconclusive (OR, 1.33; 95% CI 0.71–1.94, P<0.001) (Fig. 7C).
Disease activity
Based on eight studies, active lupus during pregnancy substantially increased the risk of APOs, with high heterogeneity (I2=78.2, P<0.000). The analysis revealed a 2.51-fold higher risk of APOs in patients with active lupus during pregnancy (OR 2.28; 95% CI 1.56–5.07, P<0.001).
Furthermore, active lupus related to a 3.92-fold higher risk of preterm birth (OR, 3.92; 95% CI 2.52–6.10, P<0.001) and a 10.56-fold higher risk of pregnancy loss (OR 10.56; 95% CI 4.81-23.17, P<0.001).
In three studies evaluating the impact of low disease activity state (LLDAS) during pregnancy on APOs, low heterogeneity was observed (I2=0.00, P=0.616). LLDAS was considered a protective factor against APOs (OR 0.26; 95% CI 0.12–0.57, P<0.001).
Based on seven studies, disease flare during pregnancy markedly enhanced the risk of preterm birth (OR 3.01; 95% CI 2.26–4.00, P<0.001), with moderate heterogeneity (I2=48.2, P=0.072).
Additionally, in the analysis of four studies, disease flare was connected with a 2.72-fold raised risk of pregnancy loss (OR 2.72; 95% CI 1.36–5.46, P=0.005), with moderate heterogeneity (I2=54.2, P=0.087) (Supplementary Figures 1-6).
Antiphospholipid syndrome (APS)/positive antiphospholipid antibody (aPL)
APS or positive aPL significantly elevated the risk of APOs by 4.97 times (OR 4.97; 95% CI 1.87–13.17, P<0.001), based on 5 studies with high heterogeneity (I2=71.6, P=0.007) (Supplementary Fig. 7).
APS or positive aPL was also a risk factor for preterm birth, with a 3.80-fold raised risk (OR 3.80; 95% CI 2.25–6.41, P<0.001), according to six studies with low heterogeneity (I2=0, P=0.450). It also elevated the risk of pregnancy loss by 3.46 times (OR 3.46; 95% CI 2.44–4.91, P<0.001), based on ten articles (I2=40.9, P=0.085) (Supplementary Figures 8-9).
Hypocomplementemia
Based on the analysis of six studies, there was a reported relationship between low complement levels and APOs (OR 1.99; 95% CI 1.43–2.76, P<0.001), with moderate heterogeneity (I2=35.6, P=0.170). Additionally, low complement levels were linked to an intensified risk of pregnancy loss (OR 2.60; 95% CI 1.08–6.27, P=0.033) (Supplementary Figures 10-11).
Thrombocytopenia
Based on the analysis of four studies, a relationship was found between low platelet count and pregnancy loss (OR, 2.20; 95% CI 1.00–4.81, P=0.049), with high heterogeneity (I2=81.8, P<0.000), indicating significant variability in the results (Supplementary Fig. 12).
Publication bias
As mentioned in the previous statistical analysis, we assessed the potential publication bias. The results of Egger's tests can be found in Table 2.
Table 2.
Summarized results
| Adverse pregnancy outcomes | Risk factors | Study (n) | Heterogeneity | OR (95%CI) | P | Egger’s test | |
|---|---|---|---|---|---|---|---|
| I2 (%) | P | ||||||
| APO | Hypertension | 6 | 57.4 | 0.029 | 5.23(2.76,9.91) | 0.001 | 0.038 |
| Lupus nephritis | 11 | 58.9 | 0.007 | 3.08(1.69,5.61) | 0.001 | 0.064 | |
| Hypocomplementemia | 6 | 35.6 | 0.170 | 1.99(1.43,2.76) | 0.001 | 0.140 | |
| LLDAS | 3 | 0.00 | 0.616 | 0.26 (0.12,0.57) | 0.001 | 0.351 | |
| Active disease | 8 | 78.2 | 0/0.001 | 2.28(1.56,5.07) | 0.001 | 0.001 | |
| APS/aPL | 5 | 71.6 | 0.007 | 4.97(1.87,13.17) | 0.001 | 0.753 | |
| Preterm birth | Lupus nephritis | 8 | 0.0 | 0.477 | 3.43(2.31, 5.10) | 0.001 | 0.375 |
| Hypertension | 6 | 48.5 | 0.084 | 3.27(2.12,5.03) | 0.001 | 0.048 | |
| Flare | 7 | 48.2 | 0.072 | 3.01(2.26,4.00) | 0.001 | 0.000 | |
| Active disease | 7 | 21.0 | 0.276 | 3.92(2.52,6.10) | 0.001 | 0.252 | |
| APS | 6 | 0.00 | 0.450 | 3.80(2.25,6.41) | 0.001 | 0.506 | |
| Pregnancy loss | Hypocomplementemia | 5 | 86.4 | 0 | 2.60(1.08,6.27) | 0.033 | 0.179 |
| Flare | 4 | 54.2 | 0.087 | 2.72(1.36,5.46) | 0.005 | 0.381 | |
| Thrombocytopenia | 4 | 81.8 | 0 | 2.20(1.00,4.81) | 0.049 | 0.004 | |
| Lupus nephritis | 6 | 32.5 | 0.180 | 2.88(1.73,4.82) | 0.001 | 0.856 | |
| Hypertension | 5 | 0.0 | 0.480 | 1.33(0.71,1.94) | 0.001 | 0.232 | |
| APS/aPL | 10 | 40.9 | 0.085 | 3.46(2.44,4.91) | 0.001 | 0.244 | |
| Active disease | 4 | 0.00 | 0.912 | 10.56(4.81,23.17) | 0.001 | 0.857 | |
| Low birth weight | Lupus nephritis | 3 | 69.4 | 0.038 | 5.55(1.29,23.86) | 0.021 | 0.594 |
| Intrauterine growth restriction | Lupus nephritis | 3 | 41.8 | 0.179 | 3.51(1.30,9.51) | 0.013 | 0.984 |
Discussion
Pregnancy in SLE patients is commonly regarded as a high-risk condition due to its well-documented connection with APOs [20]. SLE pregnancies are more susceptible to pregnancy loss, preterm births, and IUGR than the general population [21, 22]. A meta-analysis of 2751 pregnancies showed a 23.4% rate of unsuccessful pregnancy and a 39.4% rate of premature birth [23]. Our meta-analysis of 43 papers investigated the risk factors for APOs. The primary risk factors included hypertension, LN, hypocomplementemia, high disease activity, APS/positive aPL, and thrombocytopenia.
Pregnancy and autoimmunity influence each other. The concept of Th2 phenomenon in successful pregnancy, proposed in 1993, refers to the suppression of CD4+ T helper 1 (Th1) cells and a shift toward Th2 anti-inflammatory cytokines [24]. Extensive research has revealed the dynamic balance of cytokine expression during different stages of pregnancy [25]. Proinflammatory Th17 cells are associated with APOs, while regulatory T (Treg) cells increase in healthy pregnant women and are linked to immune tolerance toward the fetus [25]. In the third trimester of SLE pregnancy, the lower-than-anticipated decrease of Th1 cytokines was detected [26]. Both the quantity and functionality of Treg cells are reduced in SLE patients, suggesting impairment in placental development and fetal tolerance [27]. They also display increased secretions of activin A, IL-6, IL-17, IL-10, and TNF-α during pregnancy, indicating a hyper-reactive immune system [28]. A 16-year cohort study by Clowse ME et al. revealed that high-activity lupus during pregnancy augmented the risk of preterm birth, decreased live births, and brought about a significant rate of fetal loss [29]. Active lupus may impact the uteroplacental unit, leading to preterm labor and membrane rupture [29]. The activity indices in SLE pregnancy can be assessed using the SLE in Pregnancy Activity Index, Modified Lupus Activity Measurement, and Lupus Activity Index in Pregnancy [30]. American College of Rheumatology’s Reproductive Health Guideline 2020 recommends SLE women to conceive during a period of clinically mild or inactive disease activity in the 6 months before pregnancy and to continue taking pregnancy-compatible lupus medications throughout pregnancy [31].
Active LN during pregnancy is linked to a high risk of fetal loss, ranging from 35 to 50% [32]. Smyth A et al. have confirmed LN raises the risk of preterm delivery and unsuccessful pregnancy, while IUGR incidence is estimated to be 12.7% [23], consistent with the findings of another meta-analysis [33]. Patients with class III/IV LN exhibit a lower mean birthweight [34]. In patients with renal involvement, baseline renal function, proteinuria levels, and new onset have been associated with APOs [35]. European League Against Rheumatism recommends ongoing monitoring during pregnancy, including urine protein excretion, urine sediment analysis (assessing glomerular hematuria and urinary casts), serum creatinine levels, and glomerular filtration rate [36].
SLE women have noticeably higher rates of hypertension than healthy individuals [37]. It has been firmly established that hypertension has a significant impact on pregnancy outcomes [38]. Inadequate blood pressure regulation during pregnancy is linked to decreased gestational age at delivery [39], consistent with our results. Hypertension during pregnancy can trigger an imbalance of Th cells and the release of cytokines, leading to the reactive flare of lupus [40]. Conversely, active SLE can also serve as a predictor of hypertension [40].
The meta-analysis confirmed that aPL was connected with higher rates of APOs, including elevated fetal loss risk for Antiβ2GP1 and anticardiolipin antibody (aCL), and higher risk of preterm birth for lupus anticoagulant (LAC) [41]. LAC and aCL are predictors of APOs in aPL-positive patients [41–43]. In addition to causing thrombosis in the uteroplacental vasculature, aPL directly impairs the production of chorionic gonadotropin by influencing anionic phospholipids and β2GP1 in trophoblast cells during early pregnancy [44]. Despite aspirin and low-molecular-weight heparin treatment, APS still results in a 20-30% rate of pregnancy loss, suggesting that hydroxychloroquine and pravastatin should be explored as potential alternative options [39].
Complement activation contributes to abnormal placental development, as evidenced by the significant association between increased Bb and sC5b-9 levels in early pregnancy and APOs [45]. Complement fragments are detected in placental samples from complicated pregnancies, and C4d emerges as the prominent biomarker for complement activation in the placenta [46]. Physiological fluctuations in complement levels during gestation can be indicative of APOs in SLE pregnancy if there is a lack of anticipated increase in C3 and C4 levels [47, 48]. Appropriate surveillance and management of hypocomplementemia are critical for optimizing perinatal outcomes [49].
Thrombocytopenia during SLE pregnancy is linked to heightened disease activity, early onset preeclampsia, and elevated rates of pregnancy loss [50]. Plateletcrit is a valuable marker for predicting the risk of stillbirth in SLE pregnancies [51]. The decrease in platelet count could potentially signify early signs of preeclampsia, microthrombosis, or lupus activity. SLE pregnancies carry an elevated risk of preeclampsia, which is influenced by SLE circulating immune complexes and dysregulated cytokine expression [52].
This meta-analysis has several limitations that must be acknowledged when interpreting the results. First, substantial heterogeneity was observed among included studies, driven primarily by differences in study designs, patient populations, and definitions of adverse pregnancy outcomes. Although subgroup analyses and random-effects models were utilized to mitigate this effect, residual heterogeneity may still influence the robustness of the pooled estimates. Second, the predominance of observational cohort studies introduces inherent limitations, including potential selection bias, recall bias, and confounding factors, which could affect the accuracy and reliability of findings. Third, variability in the definitions and diagnostic criteria for hypertension, lupus nephritis, disease activity, and antiphospholipid syndrome across studies may compromise comparability and generalizability. Fourth, insufficient data limited the exploration of regional and ethnic differences, further restricting the generalizability of our findings to broader, more diverse populations. Additionally, inconsistent reporting across studies prevented comprehensive analyses of important risk factors, such as medication use (e.g., corticosteroids, immunosuppressants), socioeconomic status, and comorbidities. Notably, most primary studies did not provide detailed data on lupus nephritis activity or APS subtypes and treatment status, which limited the feasibility of subgroup analyses and reduced the clinical interpretability of our findings. Finally, although Egger’s test was conducted, the possibility of publication bias cannot be completely excluded. Future research should focus on adopting standardized outcome definitions, employing multicenter prospective study designs, and ensuring comprehensive and consistent reporting to validate and further refine these findings.
Conclusion
The current systematic review and meta-analysis examined the risk factors for APOs in SLE pregnancies and identified LN, hypertension, disease activity, aPL, hypocomplementemia, and thrombocytopenia as significant risk factors. These findings enhance comprehension regarding risk factors for APOs in SLE pregnancies and allow healthcare providers to take appropriate measures to optimize maternal and fetal health during pregnancy.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgments
Not applicable.
Author contributions
All authors contributed to the study conception and design. Writing—original draft preparation: CS; writing—review and editing: CS; conceptualization: CS; methodology: CS and Xi; formal analysis and investigation: CS and XL; resources: XL; supervision: XL, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Funding
The authors declare that no funds, grants, or other supports were received during the preparation of this manuscript.
Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.