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      Systemic Lupus Erythematosus and Pregnancy Complications and Outcomes: A Mendelian Randomization Study and Retrospective Validation

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          Abstract

          Introduction

          Previous studies have shown that pregnant women with systemic lupus erythematosus (SLE) tend to have a higher risk of adverse pregnancy outcomes, but the potential causal role remained unclear. In this study, we aimed to investigate the causal relationship between SLE and some common pregnancy complications and outcomes using two-sample Mendelian randomization (MR).

          Methods

          The genetic tools were derived from genome-wide association studies of SLE and pregnancy complications and outcomes. MR analysis was performed using inverse variance weighting as primary method. Sensitivity analyses were performed to evaluate the robustness of the results. A retrospective analysis was conducted on 200 pregnant women with SLE and a control group of pregnant women delivering at Tongji Hospital.

          Results

          In the results, we found that genetic susceptibility to SLE was associated with a higher risk of gestational diabetes mellitus (OR = 1.028, 95% CI: 1.006–1.050), premature delivery (OR = 1.039, 95% CI: 1.013–1.066), polyhydramnios (OR = 1.075, 95% CI: 1.004–1.151) and premature rupture of membranes (OR = 1.030, 95% CI: 1.001–1.060). Some of the retrospective analysis results align with the findings from the MR analysis, indicating that pregnant women with SLE have a higher risk of developing gestational diabetes mellitus and preterm birth. Additionally, although MR analysis did not reveal a causal relationship between SLE and preeclampsia/eclampsia, retrospective analysis discovered that SLE pregnant women are more susceptible to developing preeclampsia/eclampsia (OR = 2.935, 95% CI: 1.118–7.620).

          Conclusion

          Our study findings suggest a potential causal relationship between SLE and increased risks of gestational diabetes and preterm delivery. Clinical data indicate that pregnant women with SLE are more prone to developing preeclampsia/eclampsia. Clinicians need to be vigilant about the occurrence of these conditions when managing pregnant women with SLE.

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          Most cited references53

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          IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

          Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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            Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

            Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.
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              Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

              ABSTRACT Developments in genome‐wide association studies and the increasing availability of summary genetic association data have made application of Mendelian randomization relatively straightforward. However, obtaining reliable results from a Mendelian randomization investigation remains problematic, as the conventional inverse‐variance weighted method only gives consistent estimates if all of the genetic variants in the analysis are valid instrumental variables. We present a novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate. This estimator is consistent even when up to 50% of the information comes from invalid instrumental variables. In a simulation analysis, it is shown to have better finite‐sample Type 1 error rates than the inverse‐variance weighted method, and is complementary to the recently proposed MR‐Egger (Mendelian randomization‐Egger) regression method. In analyses of the causal effects of low‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol on coronary artery disease risk, the inverse‐variance weighted method suggests a causal effect of both lipid fractions, whereas the weighted median and MR‐Egger regression methods suggest a null effect of high‐density lipoprotein cholesterol that corresponds with the experimental evidence. Both median‐based and MR‐Egger regression methods should be considered as sensitivity analyses for Mendelian randomization investigations with multiple genetic variants.
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                Author and article information

                Journal
                Int J Womens Health
                Int J Womens Health
                ijwh
                International Journal of Women's Health
                Dove
                1179-1411
                18 May 2024
                2024
                : 16
                : 891-902
                Affiliations
                [1 ]Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology , Wuhan, Hubei, 430030, People’s Republic of China
                [2 ]Department of Computer Science, Huazhong University of Science and Technology , Wuhan, 430074, People’s Republic of China
                Author notes
                Correspondence: Yi Jiang; Ling Feng, Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jiefang Avenue, Wuhan, Hubei, 430030, People’s Republic of China, Email einsmeer@foxmail.com; fltj007@163.com
                Author information
                http://orcid.org/0000-0002-6574-5949
                Article
                461640
                10.2147/IJWH.S461640
                11110830
                38779383
                eea9d25d-5b2f-40a4-b127-d33bf6841f97
                © 2024 Zhu et al.

                This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                History
                : 26 January 2024
                : 11 May 2024
                Page count
                Figures: 2, Tables: 3, References: 53, Pages: 12
                Funding
                Funded by: funding;
                There is no funding to report.
                Categories
                Original Research

                Obstetrics & Gynecology
                systemic lupus erythematosus,pregnancy complications,mendelian randomization,gestational diabetes mellitus,preeclampsia,retrospective analysis

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