Monitoring of Systemic Lupus Erythematosus Pregnancies: A Systematic Literature Review

Autoimmune diseases and adverse pregnancy outcomes: an umbrella review

BACKGROUND

There is a high prevalence of autoimmune conditions in women specially in the reproductive years; thus, the association with adverse pregnancy outcomes has been widely studied. However, few autoimmune conditions/adverse outcomes have been studied more than others, and this umbrella review aims to consolidate existing knowledge in this area with the aim to provide new knowledge and also identify gaps in this research area.

METHODS

Medline, Embase, and Cochrane databases were searched from inception to December 2023. Screening, data extraction, and quality appraisal (AMSTAR 2) were done by two independent reviewers. Data were synthesised narratively and quantitatively. Relative risks (RR)/odds ratio (OR) with 95% confidence intervals were reported.

RESULTS

Thirty-two reviews were included consisting of 709 primary studies. The review reported the association between 12 autoimmune conditions and 16 adverse pregnancy outcomes. Higher risk of miscarriage is reported in women with Sjögren's syndrome RR 8.85 (95% CI 3.10-25.26) and systemic lupus erythematosus (SLE) OR 4.90 (3.10-7.69). Pre-eclampsia was reported higher in women with type 1 diabetes mellitus (T1DM) OR 4.19 (3.08-5.71) and SLE OR 3.20 (2.54-4.20). Women reported higher risk of diabetes during pregnancy with inflammatory bowel disease (IBD) OR 2.96 (1.47-5.98). There was an increased risk of intrauterine growth restriction in women with systemic sclerosis OR 3.20 (2.21-4.53) and coeliac disease OR 1.71 (1.36-2.14). Preterm birth was associated with T1DM OR 4.36 (3.72-5.12) and SLE OR 2.79 (2.07-3.77). Low birth weight babies were reported in women with women with SLE or systemic sclerosis OR 5.95 (4.54-7.80) and OR 3.80 (2.16-6.56), respectively. There was a higher risk of stillbirth in women with T1DM OR 3.97 (3.44-4.58), IBD OR 1.57 (1.03-2.38), and coeliac disease OR 1.57 (1.17-2.10). T1DM in women was associated with 32% lower odds of small for gestational age baby OR 0.68 (0.56-0.83).

CONCLUSIONS

Pregnant women with autoimmune conditions are at a greater risk of developing adverse pregnancy outcomes. Further research is required to develop better preconception to postnatal care for women with autoimmune conditions.

Prediction by uterine artery Doppler screening of small-for-gestational-age neonates at 19-24 weeks' gestation

OBJECTIVE

Small-for-gestational-age (SGA) neonates are at increased risk of perinatal mortality and morbidity. We aimed to investigate the performance of uterine artery pulsatility index (UtA-PI) at 19-24 weeks' gestation to predict the delivery of a SGA neonate in a Chinese population.

METHODS

This was a retrospective cohort study using data obtained between January 2010 and June 2018. Doppler ultrasonography was performed at 19-24 weeks' gestation. SGA was defined as birth weight below the 10th centile according to the INTERGROWTH-21st fetal growth standards. The performance of UtA-PI to predict the delivery of a SGA neonate was assessed using receiver-operating-characteristics (ROC)-curve analysis.

RESULTS

We included 6964 singleton pregnancies, of which 748 (11%) delivered a SGA neonate, including 115 (15%) women with preterm delivery. Increased UtA-PI was associated with an elevated risk of SGA, both in neonates delivered at or after 37 weeks' gestation (term SGA) and those delivered before 37 weeks (preterm SGA). The areas under the ROC curve (AUCs) for UtA-PI were 64.4% (95% CI, 61.5-67.3%) and 75.8% (95% CI, 69.3-82.3%) for term and preterm SGA, respectively. The performance of combined screening by maternal demographic/clinical characteristics and estimated fetal weight in the detection of term and preterm SGA was improved significantly by the addition of UtA-PI, although the increase in AUC was modest (2.4% for term SGA and 4.9% for preterm SGA).

CONCLUSIONS

This is the first Chinese study to evaluate the role of UtA-PI at 19-24 weeks' gestation in the prediction of the delivery of a neonate with SGA. The addition of UtA-PI to traditional risk factors improved the screening performance for SGA, and this improvement was greater in predicting preterm SGA compared with term SGA. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Updates on the dermatopathology of pregnancy-associated skin conditions

Pathologists provide valuable input in the dermatological care of pregnant patients in various contexts. This article provides dermatopathology updates on cutaneous changes associated with pregnancy, organized based on the following classification system: physiological skin changes in pregnancy, specific dermatoses of pregnancy, dermatoses modified in pregnancy, and skin neoplasms in pregnancy. Awareness of the impact of pregnancy on the skin by pathologists is important, as this is an opportunity to contribute to diagnostic precision in this patient population.

Machine Learning Models for Predicting Adverse Pregnancy Outcomes in Pregnant Women with Systemic Lupus Erythematosus

Predicting adverse outcomes is essential for pregnant women with systemic lupus erythematosus (SLE) to minimize risks. Applying statistical analysis may be limited for the small sample size of childbearing patients, while the informative medical records could be provided. This study aimed to develop predictive models applying machine learning (ML) techniques to explore more information. We performed a retrospective analysis of 51 pregnant women exhibiting SLE, including 288 variables. After correlation analysis and feature selection, six ML models were applied to the filtered dataset. The efficiency of these overall models was evaluated by the Receiver Operating Characteristic Curve. Meanwhile, real-time models with different timespans based on gestation were also explored. Eighteen variables demonstrated statistical differences between the two groups; more than forty variables were screened out by ML variable selection strategies as contributing predictors, while the overlap of variables were the influential indicators testified by the two selection strategies. The Random Forest (RF) algorithm demonstrated the best discrimination ability under the current dataset for overall predictive models regardless of the data missing rate, while Multi-Layer Perceptron models ranked second. Meanwhile, RF achieved best performance when assessing the real-time predictive accuracy of models. ML models could compensate the limitation of statistical methods when the small sample size problem happens along with numerous variables acquired, while RF classifier performed relatively best when applied to such structured medical records.

TMT-based quantitative proteomics analysis and potential serum protein biomarkers for systemic lupus erythematosus

Systemic lupus erythematosus (SLE) was not only a typical systemic autoimmune disease, but also one of the most challenging heterogeneous diseases for physicians. Currently, the pathogenesis of SLE was unclear, and there were no accurate, universal or easy-to-use diagnostic criteria for assessing SLE activity and predicting SLE severity. Proteins were direct effectors of biological mechanisms, and were closer to clinical phenotypes than the other discovered biomarkers. Moreover, proteins were widely used as biomarkers for clinical diagnosis and mechanism research of many diseases. Herein, we compared the proteins profiles of healthy individuals (HCs) and SLE patients to reveal the pathogenesis and provide evidence for diagnosis and management of persons with SLE. Serum samples were collected from 28 SLE patients and 30 HCs. Tandem mass tag (TMT)-based quantitative proteomics method was used to identify, screen and detect differentially expressed proteins (DEPs) in the collected serum samples. A total of 744 proteins were identified, and 84 of them were considered as DEPs with 71 upregulated and 13 downregulated. Bioinformatics analysis suggested that these DEPs were mainly involved in many biological processes, including immune response, signal transduction, inflammatory response, proteolysis, innate immune response and apoptosis, which were closely related to the pathogenesis of SLE. After comprehensive analysis, serum amyloid A1 (SAA1) and endothelin (CD248) were identified as specific biomarkers for the diagnosis of SLE, and were confirmed by subsequent enzyme-linked immunosorbent assays (ELISA), indicating a high reliability of TMT-based quantitative proteomics results. Areas under the ROC curve (AUC) results confirmed that SAA1 and CD248 combination as early immune diagnosis biomarkers of SLE presented excellent sensitivity and specificity.

2020 Chinese guidelines for the diagnosis and treatment of systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that represents a prodigious challenge of diagnosis and treatment. In 2019, under the leadership of the Chinese Rheumatology Association, a multidisciplinary guideline development group was established to develop an evidence-based diagnosis and treatment guideline for patients with SLE in PR China. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach was used to evaluate the quality of evidence and the strength of recommendations. The guideline was reported following the Reporting Items for Practice Guidelines in Healthcare (RIGHT) checklist. In this guideline, we provided recommendations for SLE classification criteria, disease activity monitoring and assessment, medication administration and considerations for SLE patients with organs and systems involved, and management of special populations such as SLE patients in the setting of pregnancy. This guideline serves as an evidence-based tool for Chinese clinicians to diagnose and treat patients with SLE.

Leveraging Heterogeneity in Systemic Lupus Erythematosus for New Therapies

Systemic lupus erythematosus (SLE) is a multisystem, chronic autoimmune disease where treatment varies by patient and disease activity. Strong preclinical results and clinical correlates have motivated development of many drugs, but many of these have failed to achieve efficacy in clinical trials. FDA approval of belimumab in 2011 was the first successful SLE drug in nearly six decades. In this article, we review insights into the molecular and clinical heterogeneity of SLE from transcriptomics studies and detail their potential impact on drug development and clinical practices. We critically examine the pipeline of SLE drugs, including past failures and their associated lessons and current promising approaches. Finally, we identify opportunities for integrating these findings and drug development with new multidisciplinary advances to enhance future SLE treatment.

Insights From Twitter Conversations on Lupus and Reproductive Health: Protocol for a Content Analysis

Background

Systemic lupus erythematosus (SLE) is the most common form of lupus. It is a chronic autoimmune disease that predominantly affects women of reproductive age, impacting contraception, fertility, and pregnancy. Although clinic-based studies have contributed to an increased understanding of reproductive health care needs of patients with SLE, misinformation abounds and perspectives on reproductive health issues among patients with lupus remain poorly understood. Social networks such as Twitter may serve as a data source for exploring how lupus patients communicate about their health issues, thus adding a dimension to enrich our understanding of communication regarding reproductive health in this unique patient population.

Objective

The objective of this study is to conduct a content analysis of Twitter data published by users in English in the United States from September 1, 2017, to October 31, 2018, in order to examine people’s perspectives on reproductive health among patients with lupus.

Methods

This study will analyze user-generated posts that include keywords related to lupus and reproductive health from Twitter. To access public Twitter user data, we will use Symplur Signals, a health care social media analytics platform. Text classifiers will be used to identify topics in posts. Posts will be classified manually into the a priori and emergent categories. Based on the information available in a user’s Twitter profile (ie, username, description, and profile image), we will further attempt to characterize the user who generated the post. We will use descriptive statistics to analyze the data and identify the most prevalent topics in the Twitter content among patients with lupus.

Results

This study has been funded by the National Center for Advancing Translational Science (NCATS) through their Clinical and Translational Science Awards program. The Institutional Review Board at the University of Southern California approved the study (HS-18-00912). Data extraction and cleaning are complete. We obtained 47,715 Twitter posts containing terms related to “lupus” from users in the United States, published in English between September 1, 2017, and October 31, 2018. We will include 40,885 posts in the analysis, which will be completed in fall 2020. This study was supported by funds from the has been funded by the National Center for Advancing Translational Science (NCATS) through their Clinical and Translational Science Awards program.

Conclusions

The findings from this study will provide pilot data on the use of Twitter among patients with lupus. Our findings will shed light on whether Twitter is a promising data source for learning about reproductive health issues expressed among patients with lupus. The data will also help to determine whether Twitter can serve as a potential outreach platform for raising awareness of lupus and reproductive health and for implementing relevant health interventions.

International Registered Report Identifier (IRRID)

DERR1-10.2196/15623

Pregnancy and lactation interfere with the response of autoimmunity to modulation of gut microbiota

BACKGROUND

Dysbiosis of gut microbiota exists in the pathogenesis of many autoimmune diseases, including systemic lupus erythematosus (lupus). Lupus patients who experienced pregnancy usually had more severe disease flares post-delivery. However, the possible role of gut microbiota in the link between pregnancy and exacerbation of lupus remains to be explored.

RESULTS

In the classical lupus mouse model MRL/lpr, we compared the structures of gut microbiota in pregnant and lactating individuals vs. age-matched naïve mice. Consistent with studies on non-lupus mice, both pregnancy and lactation significantly changed the composition and diversity of gut microbiota. Strikingly, modulation of gut microbiota using the same strategy resulted in different disease outcomes in postpartum (abbreviated as "PP," meaning that the mice had undergone pregnancy and lactation) vs. control (naïve; i.e., without pregnancy or lactation) MRL/lpr females; while vancomycin treatment attenuated lupus in naïve mice, it did not do so, or even exacerbated lupus, in PP mice. Lactobacillus animalis flourished in the gut upon vancomycin treatment, and direct administration of L. animalis via oral gavage recapitulated the differential effects of vancomycin in PP vs. control mice. An enzyme called indoleamine 2,3-dioxygenase was significantly inhibited by L. animalis; however, this inhibition was only apparent in PP mice, which explained, at least partially, the lack of beneficial response to vancomycin in these mice. The differential production of immunosuppressive IL-10 and proinflammatory IFNγ in PP vs. control mice further explained why the disease phenotypes varied between the two types of mice bearing the same gut microbiota remodeling strategy.

CONCLUSIONS

These results suggest that pregnancy and lactation interfere with the response of autoimmunity to modulation of gut microbiota. Further studies are necessary to better understand the complex relationship between pregnancy and lupus.

Canadian Rheumatology Association Recommendations for the Assessment and Monitoring of Systemic Lupus Erythematosus

OBJECTIVE

To develop recommendations for the assessment of people with systemic lupus erythematosus (SLE) in Canada.

METHODS

Recommendations were developed using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach. The Canadian SLE Working Group (panel of Canadian rheumatologists and a patient representative from Canadian Arthritis Patient Alliance) was created. Questions for recommendation development were identified based on the results of a previous survey of SLE practice patterns of members of the Canadian Rheumatology Association. Systematic literature reviews of randomized trials and observational studies were conducted. Evidence to Decision tables were prepared and presented to the panel at 2 face-to-face meetings and online.

RESULTS

There are 15 recommendations for assessing and monitoring SLE, with varying applicability to adult and pediatric patients. Three recommendations focus on diagnosis, disease activity, and damage assessment, suggesting the use of a validated disease activity score per visit and annual damage score. Strong recommendations were made for cardiovascular risk assessment and measuring anti-Ro and anti-La antibodies in the peripartum period and conditional recommendations for osteoporosis and osteonecrosis. Two conditional recommendations were made for peripartum assessments, 1 for cervical cancer screening and 2 for hepatitis B and C screening. A strong recommendation was made for annual influenza vaccination.

CONCLUSION

These are considered the first guidelines using the GRADE method for the monitoring of SLE. Existing evidence is largely of low to moderate quality, resulting in more conditional than strong recommendations. Additional rigorous studies and special attention to pediatric SLE populations and patient preferences are needed.