The association of risk factors, circulatory biomarkers and antihypertensive medications with preeclampsia: a Mendelian randomisation analysis

The association of risk factors, circulatory biomarkers and antihypertensive medications with preeclampsia: a Mendelian randomisation analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The association of risk factors, circulatory biomarkers and antihypertensive medications with preeclampsia: a Mendelian randomisation analysis Sze Sen Wang, Devendra Meena, Jingxian Huang, Georg W Otto, Abbas Dehghan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9202748/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background While many risk factors for pre-eclampsia (PE) have been identified, their causal role and the potential preventive effects of specific antihypertensive drug classes remain unclear. Methods We examined the potential causal effect of 22 risk factors and two genetically proxied antihypertensive drugs (Beta-blockers and Calcium channel blockers) on PE development and its two related conditions, PE-HTP (PE or other maternal hypertension) and PE-SGA (PE or delivery of a small for gestational age neonate). Using summary statistics from published genome-wide association studies, we employed two-sample Mendelian randomization (MR) using Wald ratio or inverse variance weighted. MR Egger, weighted median and MR-PRESSO (Pleiotropy RESidual Sum and Outlier) were applied and Multivariable MR (MVMR) was also used. Results Maternal body mass index (BMI), systolic blood pressure (SBP) and diastolic BP (DBP) were significantly associated with all PE outcomes. Higher BMI was associated with increased risk of PE (Odds ratio (OR) = 1.50, P = 1.58×10⁻⁸), PE-HTP (OR = 1.72, P = 1.04×10⁻¹⁷) and PE-SGA (OR = 1.35, P = 2.06×10⁻⁵). Elevated SBP showed strong associations with PE (OR = 2.74, P = 3.90×10⁻²⁶), PE-HTP (OR = 3.64, P = 4.23×10⁻³⁷) and PE-SGA (OR = 2.21, P = 5.01×10⁻¹⁸). Increased DBP was significantly linked to PE (OR = 2.33, P = 7.03×10⁻¹⁷), PE-HTP (OR = 3.08, P = 9.59×10⁻²⁷) and PE-SGA (OR = 2.04, P = 8.38 × 10⁻¹⁴). Age at menarche was associated with PE-HTP, but this effect was mediated by BMI. Genetically proxied calcium channel blockers were linked to reduced risk of PE and PE-HTP (OR per DBP mmHg reduction: 0.37, P = 0.004 and 0.23, P = 1.90×10 − 4 , respectively). Conclusions The findings validate the significant role of cardiovascular factors in the PE pathogenesis and highlight new therapeutic possibilities. Mendelian randomization pre-eclampsia maternal hypertension small for gestational age neonate cardiovascular factors antihypertensive drugs Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Pre-eclampsia (PE) affects 2–5% of pregnancies globally and one of the leading causes of maternal and perinatal morbidity and mortality. 1 In addition to acute complications such as eclampsia and preterm delivery, PE is associated with long-term maternal risk including stroke, type 2 diabetes mellitus (T2DM) and cardiovascular disease. 2 Pre-eclampsia, is often linked to impaired fetal growth and delivery of a small for gestational age (SGA). Offsprings born to affected mothers are also at an increased risk of cardiovascular disease later in life. 3 The pathogenesis of PE remains largely elusive. While several biological pathways including angiogenic/anti-angiogenic imbalance, 4 inflammation 5 and endothelial dysfunction 6 are likely to be involved, the precise causal mechanisms remain to be clarified. Epidemiologic studies have identified several maternal risk factors, including advanced maternal age, high body mass index (BMI) (≥ 30kg/m 2 ), pre-existing chronic hypertension, renal disease, DM, history of PE or multifetal gestation, 7,8–10 but, their causal nature has often been questioned due to the potential of residual confounding and reverse causation. Observational studies have also shown associations between circulating biomarkers such as placental growth factor (PlGF), vascular endothelial growth factor (VEGF) 4 , interleukins 5 and PE risk. However, whether these observational associations reflect causal pathways remains unknown. Management of PE primarily involves symptomatic control of blood pressure (BP) or seizure prophylaxis. 11–13 Antihypertensive agents, such as calcium channel blockers (CCBs) and beta blockers (BBs), are commonly used, yet their comparative effectiveness in preventing PE remains uncertain. Mendelian randomization (MR)uses genetic variants as proxies for modifiable exposures and can strengthen causal inference by reducing confounding and reverse causation 14 MR relies on the random allele assortment during meiosis, which results in a random distribution of genetic variants across the population and is independent of confounding effects. 15 Previous MR studies have supported causal roles for BMI, T2DM and hyperthyroidism in PE. 16 However, these studies were largely limited to metabolic risk factors and did not comprehensively evaluate the roles of circulating biomarkers, placental proteins, or antihypertensive drug mechanisms. Moreover, most analyses used sex-combined GWAS datasets, leaving uncertainty about causal pathways specific to the female population. To address these gaps, we aim to build on previous findings by: (1) conducting a comprehensive MR analysis to evaluate the potential causal relationships between 22 maternal demographic, cardiometabolic and biophysical traits (including angiogenic/anti-angiogenic, inflammatory, adipokine and endothelial factors) 7 , 8 , 17 and development of PE; (2) extending the analysis to other placental-related conditions, including PE or other maternal hypertension (PE-HTP) and PE or delivery of a SGA neonate (PE-SGA); and (3) assessing the genetically predicted effects of two commonly used antihypertensive drug classes, beta blockers (BB) and calcium channel blockers (CCB), on PE risk, using a drug-target MR approach to model the pharmacological effects of BP lowering. 11 Methods Mendelian Randomization The study is based on a two-sample MR approach, which uses summary-level data from genome-wide association studies (GWAS) to estimate the potential causal associations between the exposures (cardiometabolic traits) and the outcome (PE) using genetic variants as instrumental variables (IVs). 14 Since individuals inherit their genetic variants from birth in a randomly assorted manner, the effects estimated are less likely to be impacted by confounders and as the genetic variants are constant over lifetime, reverse causation is not likely (Figure 1). 15 Data source GWAS summary statistics for the 22 exposures (risk factors of PE) were downloaded from multiple sources (Table 1). Since the outcome of interest, PE, is a disease that only affects females, sex-specific data of exposures were downloaded. Notably, for plasma proteins such as placental growth factor (PlGF), vascular endothelial growth factors (VEGF) and endoglin, GWAS was performed with the BOLT-LMM 18 pipeline to select only female participants using the Olink proteomic assay (Olink Explore 1536 platform) 19 and the genotypic data from 54,306 UK Biobank participants. 20 Covariates including age, Olink batch and the first 10 principal components were adjusted. Since the GWAS data for most exposures originate from the UK biobank cohort, UK biobank data is avoided for the outcome to reduce bias caused by overlapped samples. Therefore, the GWAS data for PE was downloaded from the recent study of Tyrmi et al. , which performed meta-analysis on multiple cohorts, including Finnish Genetics of Pre-eclampsia Consortium (FINNPEC), 21 Finnish FinnGen project, 22 Estonian Biobank 23 and InterPregGen 24 consortium. Together, they provide summary statistics of 16,743 individuals with PE and 271,306 controls. 24 In addition to PE, the GWAS data for the two other outcomes, PE-HTP ( N cases =15,200, N controls =115,007) and PE-SGA ( N cases =10,800, N controls =119,225), is also downloaded from the same study. 24 PE is defined as systolic BP (SBP) ≥140 mm Hg or diastolic BP(DBP) ≥90 mm Hg, and proteinuria (≥0.3 g/24 hours, or two ≥1+ dipstick readings) that occurs after 20 weeks of gestation. 17 PE-HTP is defined as other maternal hypertension including hypertension occurring after 20 weeks of gestation in the absence of other organ involvement, or pre-existing (chronic) hypertension. PE-SGA is defined as PE and/or delivery of an SGA neonate; birth weight below 2.0 standard deviation units, based on the Finnish standards. 25 Instrumental variable selection The following criteria were used to select IVs for the analysis. Single nucleotide polymorphisms (SNPs) with genome-wide significant association ( P 0.01 and F-statistics ≥ 10 (to avoid weak instrument bias) were selected as potential IVs. If less than five IVs were left after clumping, the threshold was relaxed to P < 5.0 × 10 -5 (to increase significant power of the IVs, for exposures in which both sex-combined and sex-specific data were available, SNPs were extracted from sex-combined data with larger sample size but the beta estimate and standard error from the female-only data set were used for statistical analysis). We harmonized the exposure and outcome data using the ‘harmonise data’ function as implemented in the TwoSampleMR v.0.5.7 package. IVs Clumping was performed using the ‘ld_clump’ function from the ieugwasr v.0.1.5 package using the 1000 Genomes European only data as reference panel (10,000 kb window, r 2 = 0.001). Drug target mendelian randomisation To model the on-target effect of BBs and CCBs, we conducted drug-target MR using SNPs within or near genes encoding drug targets, identified using the DrugBank database 26 including the promoter and enhancer regions for each gene identified using the GeneHancer database. 27 SNPs were selected based on association with DBP ( P < 5.0 × 10 -5 to increase the number of valid SNPs) and LD clumped using the 1000 Genomes linkage disequilibrium (LD) reference panel with a threshold of R 2 < 0.3. MR analyses were performed accounting for the correlation between variants using a correlation matrix. 28 The genetic associations were scaled to reflect 1 mmHg reduction in DBP, as a proxy for drug effect. Statistical analysis Inverse variance weighted (IVW) MR was used as the primary method to investigate the potential causal associations between the exposures and PE. It was a meta-analysis of the Wald ratios for each SNP (weighted by inverse variance of each individual SNP-outcome association) that combines them to obtain an overall estimate of the effect of the exposure on the outcome. 15,29 When there was only one instrumental variable, Wald ratio was used for the analysis instead of IVW. MR-Egger 30 and weighted median 31 MR were used for sensitivity analysis. MR-Egger is based on the InSIDE (INstrument Strength Independent of Direct Effect) assumption that the strength of association between exposure and outcome is unrelated to the magnitude of the pleiotropic effect. 32 The intercept of MR-Egger regression can be used to detect directional pleiotropy (if the p-value of the intercept term is lower than 0.05, pleiotropy may exist). The weighted median method can provide a consistent estimation of the causal effect even when up to 50% of the IV are invalid and can provide results with less bias when IV assumptions are violated. 33 For the risk factors, the significant threshold P < 0.0023 was used after Bonferroni correction. 34 Similarly, the significant threshold P < 0.025 was used for the antihypertensive drugs (BBs and CCBs). Odds ratios (OR) were used to represent the association between the exposures and outcomes. Heterogeneity tests were performed to obtain Q-statistics for the meta-analysis. For exposures with significant IVW estimates, MR-PRESSO was performed to detect possible outliers and to reduce horizontal pleiotropy by removing significant outliers. 35 The IVW, MR-Egger and weighted median analysis were performed again for the outlier-corrected data. To investigate the mediation effects between possibly related exposures, where relevant, we conducted multivariable MR (MVMR) to evaluate mediation between exposures (e.g., BMI and BP). 36 All statistical analyses were performed using R version 4.3.0. The allele harmonization, clumping, and MR analysis were performed using the TwoSampleMR 37 (version 0.5.7) and MRPRESSO (version 1.0) R packages. MVMR was performed using the MVMR R package. Data visualization was performed using the ggplot2 (version 3.4.3) 38 and ggforestplot (version 0.1.0) packages and the wesanderson (version 0.3.6) color palette generator. Results Effect of risk factors on PE, PE-HTP and PE-SGA Of the 22 risk factors, four - BMI, SBP, DBP and age at menarche (AAM) - were significantly associated with at least one PE outcome based on the IVW MR method and after correction for multiple testing (P < 0.0023). These associations were further explored using robust sensitivity methods and MVMR where applicable (Fig. 2 & Supplementary Tables 1–3)). Waist-hip ratio (WHR) (PE: OR = 1.36; PE-HTP: OR = 1.42), T2DM (PE-HTP: beta = 17.92), PlGF and C-reactive protein (CRP) level (PE: OR = 1.11; PE-SGA: OR = 1.10) showed nominal significance at least with one of the outcomes, however, none of them survived Bonferroni correction (Fig. 3 – 5 and supplementary Tables 1–3). All effect estimates and supporting sensitivity results are presented in Supplementary Tables 1–6 and Figs. 1 – 5 . Body mass index showed a robust association with all three PE outcomes (Figs. 3 a-c). IVW estimates showed OR of 1.50 for PE ( P = 1.58 × 10 − 8 ), 1.72 for PE-HTP ( P = 1.04 × 10 − 17 ), and 1.35 for PE-SGA ( P = 2.06 ×10 − 5 ) and were further supported by consistent MR Egger and Weighted Median estimates (Fig. 3 , Supplementary Table 1–3). The Q statistics was significant for all three outcomes, and the MR Egger intercept was significant for PE and PE-HTP (Supplementary Table 5). After two outliers were detected by MR-PRESSO and were removed from analysis, the IVW estimates did not change materially (PE: OR = 1.43; PE-HTP: OR = 1.71; PE-SGA: OR = 1.36) (Supplementary Table 4), however, the MR Egger intercept became not significant for PE. In contrast, WHR demonstrated only nominal ( P < 0.05) associations with PE (OR = 1.36, P = 0.020) and PE-HTP (OR = 1.42, P = 0.01) based on IVW estimates (Fig. 3ab, Supplementary Tables 1 and 2). However, these associations did not survive Bonferroni correction and were not supported by MR Egger or Weighted Median methods. The results of both Q statistics and MR Egger intercept were not significant. The IVW analysis indicated a negative association between AAM and PE (OR = 0.90, P = 0.002), PE-HTP (OR = 0.87, P = 0.0002), and PE-SGA (OR = 0.93, P = 0.047) (Fig. 3 d-f). Sensitivity analyses were less consistent, with only PE-HTP association supported by the weighted median method (Fig. 3 b & Supplementary Tables 1 and 3). For all three outcomes, the Q statistics of AAM was significant (PQ-stat < 0.05), while the MR Egger intercept was not significant. After removing the outliers of IV detected by MR-PRESSO, no significant change of the IVW estimate was observed and the p values were < 0.05. MVMR adjusting for BMI revealed that the AAM with PE-HTP association was no longer significant (OR = 0.95; P = 0.20), suggesting mediation via BMI (Supplementary Table 7). SBP and DBP were strongly associated with all outcomes. SBP showed significant results with PE (OR = 2.74 per mmHg, P = 3.90 × 10 − 26 ), PE-HTP (OR = 3.64, P = 4.23 × 10 − 37 ), and PE-SGA (OR = 2.21, P = 5.01 × 10 − 18 ). DBP showed similarly elevated ORs of 2.33 for PE (P = 7.03 × 10⁻¹⁷), 3.08 for PE-HTP (P = 9.59 × 10⁻²⁷), and 2.04 for PE-SGA (P = 8.38 × 10⁻¹⁴) (Fig. 3 , Supplementary Tables 1–3). MR Egger and Weighted Median results agreed with IVW and further complemented the findings. IVW estimates were nominally significant for the association of CRP with PE (OR = 1.11, P = 0.018) and PE-SGA (OR = 1.10, P = 0.01) (Fig. 3 ) but did not survive Bonferroni correction and were not confirmed in sensitivity analyses (Supplementary Table 1&3). MR-PRESSO identified outliers in the PE model but showed no substantial effect on the estimate (OR = 1.09) (Supplementary Tables 4 & 6). For PlGF level, only one instrumental variable was left after filtering and clumping. The Wald ratio analysis suggested a protective effect on PE (OR = 0.46, P = 0.007) (Supplementary table 1), however, this was not supported by sensitivity tests. Type 2 diabetes mellitus showed a nominal association with PE-HTP (beta = 17.92, standard error = 8.13, P = 0.027) (Fig. 3 c, Supplementary table 2), but not PE or PE-SGA. However, this result did not survive multiple testing. Multivariate Mendelian Randomisation We conducted an MVMR analysis to disentangle the effect of BMI, SBP and DBP and test possible mediation between them. SBP and DBP remained significant with all three outcomes (Supplementary Table 7) after adjustment for BMI, indicating their independent effect. BMI was not significant when adjusted for DBP in the case of PE (OR = 1.18, P = 0.1), and when adjusted for both SBP and DBP in the case of PE-SGA. Furthermore, the effect of AAM on PE-HTP was not significant, when adjusted for BMI (OR = 0.95, P = 0.2), further supporting the effect of BMI as a mediator. A summary of the key causal relationships across exposures, mediators, and PE phenotypes is illustrated in Fig. 5 . Effect of beta-blockers and calcium channel blockers Calcium channel blockers (CCBs), instrumented via DBP-lowering SNPs within their gene targets, were associated with lower risk of PE (OR per mmHg = 0.31, P = 0.001) and PE-HTP (OR = 0.17, P = 1.12 × 10 − 4 ) (Fig. 4 a–b and Supplementary Table 6). The results of weighted median aligned with the IVW result for both PE and PE-HTP. For PE-SGA, the result of IVW were not significant (OR = 0.39, P = 0.045) (Fig. 4 c–e). The BBs-related findings did not meet Bonferroni-corrected significance and showed weaker support in sensitivity analyses (Fig. 4 ). Discussion This MR study provides evidence that elevated maternal BMI, SBP and DBP are causally associated with an increased risk of PE, PE-HTP and PE-SGA. Age at menarche, WHR, CRP, PlGF and T2DM were not independently associated with PE, PE-HTP and PE-SGA. In addition, the analysis suggested that CCBs may reduce the risk of PE and PE-HTP, while the role of BBs remains less clear. Our findings are consistent with previous observational studies and MR studies that implicate that maternal metabolic and vascular health strongly influence PE risk. Our study showed that genetically predicted BMI levels are positively associated with development of PE (OR = 1.50 per 1 kg/m 2 increase in BMI), PE-HTP and PE-SGA. Compared with a pre-pregnancy BMI of 21 kg/m 2 , pregnant people with BMI≥25kg/m 2 , ≥ 30kg/m 2 and ≥ 50kg/m 2 have twice, three- and four-times higher risk of developing PE, respectively. 39,40 The effect of BMI on PE has been found to be greater in milder rather than severe forms of PE, 41 which is consistent with our findings as the OR for BMI was slightly higher in the PE compared to the PE-SGA (a proxy for more severe PE) group. The association of BMI with PE development remained significant in MVMR analysis and in accordance with previous studies, which have shown that one year increase in AAM is associated with a 0.38 kg/m 2 reduction in adult BMI, we also found that the effect of AAM on the risk of PE-HTP is mediated through BMI. 42, 43 Multiple observational studies have demonstrated an association between BP traits and risk of PE. Systolic BP has been linked to PE development with higher pre-pregnancy SBP in PE patients associated with low birth weight, a proxy for more severe form of PE. 44 Similarly, elevated DBP in early to mid-gestation is associated with an increased risk of PE (OR = 1.8) and delivery of SGA neonates (OR = 1.3). 45 In accordance, two recent multicentre trials have demonstrated that a lower threshold for initiating of antihypertensive treatment in pregnant women with mild chronic hypertension and tighter BP control in women with hypertension during pregnancy resulted in 18% reduction of PE, severe hypertension, preterm birth, placental abruption and fetal death. 46 , 47 A published MR study has reported an association between genetically predicted BP traits and PE (using GWAS data from round 7 of Finngen consortium) providing evidence for a causal relationship (SBP: OR = 1.81; DBP: OR = 2.54; Pulse Pressure: OR = 1.68). 48 However, our study used summary statistics from a recent and larger meta-analysis on PE 24 and included sex-specific (female only) GWAS datasets for SBP and DBP, which are representative of the population affected by PE, PE-HTP and PE-SGA. It is also worth noting that a recent MR study examined the causal effects of hypertensive disorders (including PE/eclampsia) on cardiovascular disease risk factors and reported significant associations with BMI, SBP, and DBP). 49 The bidirectional relationship between cardiovascular diseases and PE suggests the possibility of shared underlying biological pathway. In the MVMR analysis, for PE, the association between BMI and PE was no longer significant, when DBP was considered suggesting that the effect of BMI is likely mediated through DBP, rather than SBP. This implies that among the BP traits, DBP may play a more important role in the pathogenesis of PE. This finding is consistent with a population-based study showing a higher risk of PE and delivery of a SGA neonate for pregnant people with elevated DBP (aOR = 1.8, 95% CI = 1.6–2.0 and aOR = 1.3, 95% CI = 1.2–1.5, respectively) rather than those with elevated SBP (aOR = 1.3, 95% CI = 1.2–1.5 and aOR = 1.0, 95% CI = 0.9–1.1, respectively). 50 In our study, the OR for most of the exposures was highest for PE-HTP and lowest for PE-SGA. The difference in clinical definition of the three PE groups could possibly account for this observation as PE-HTP is a broader term and comprises all types of gestational hypertension, including PE, and as such, the larger OR for PE-HTP may be driven by BMI and other BP traits that are known to be associated with hypertension. On the other hand, PE-SGA is a more rigorous definition of PE that includes delivery of a SGA neonate and could be used as a proxy of the more severe, early-onset PE (which is associated with placental dysfunction) 42 , 43 and hypertension-related risk factors may play a less important role in its pathogenesis. Our study evaluated the effects of common antihypertensive drug classes, including BBs and CCBs, on the PE risk. While our findings indicate that CCBs lower the risk of PE and PE-HTP, the causal relevance of BBs was not significant. This is in agreement with a previous study that used SNPs in the drug target regions of SBP GWAS data to mimic the pharmacological effects of BBs and CCBs, showing that genetically-proxied BBs were associated with a borderline reduction in PE, whereas the association between CCBs and reduction in PE risk was statistically significant. 51 Given the potential negative effects of BBs on birthweight, 51,52 our results, together with existing evidence suggest that CCBs may represent a more suitable option for the management of hypertensive disorders in pregnancy. Strengths and limitations Key strengths of this study include the use of the largest and most up-to-date GWAS meta-analysis of PE and related phenotypes (16,743 cases and 271,306 controls) 24 integration of sex-specific (female-only) exposure data to ensure biological relevance, and the application of complementary MR methods (IVW, MR-Egger, weighted median, MR-PRESSO and MVMR) to test causal robustness. The inclusion of both risk factor and drug-target analyses provides translational context, linking genetic evidence to potential therapeutic mechanisms. Nevertheless, several limitations should be acknowledged. First, despite the large sample size, some exposures, particularly circulating biomarkers, had a limited number of genetic instruments, which may reduce statistical power. Second, horizontal pleiotropy cannot be completely ruled out in MR analysis, however, we applied sensitivity methods including weighted median, MR-Egger and MR-PRESSO to explore the potential risk of horizontal pleiotropy. Third, all analyses were restricted to participants of European ancestry, which may limit generalisability to other populations. Finally, the drug-target MR analysis assumes that genetic variation near target genes mimics pharmacological inhibition; thus, the estimates should be interpreted as mechanistic proxies rather than direct equivalents of clinical trial effects. Conclusions The results of this study provide evidence supporting a causal role of BMI and BP traits with regards to PE, PE-HTP and PE-SGA, underscoring the importance of BMI, and particularly BP, management in the prevention and management of PE. The results also support a causal relevance of genetically proxied CCBs and a reduced risk of PE and PE-HTP, offering new insights into the potential management options of PE. Abbreviations AAM Age at menarche BMI Body mass index CI Confidence interval CRP C reactive protein DBP Diastolic blood pressure FG Fasting glucose FI Fasting insulin FLT1 FMS-like tyrosine kinase 1 GWAS Genome-wide association studies IL Interleukin IVW Inverse variance weighted IV Instrumental variable MR Mendelian randomization OR Odds ratio PAI1 Plasminogen activator inhibitor-1 PAPPA Pappalysin 1 PE Pre-eclampsia PE-HTP Pre-eclampsia or other maternal hypertension during pregnancy PE-SGA Pre-eclampsia or small for gestational age neonate PlGF Placental growth factor pQTL Protein quantitative trait loci SBP Systolic blood pressure SLE Systemic lupus erythematosus SNP Single nucleotide polymorphism UKB-PPP UK Biobank Pharma Proteomics Project VEGF Vascular endothelial growth factor VEGFA Vascular endothelial growth factor A WHR Waist-hip ratio Declarations Ethical approval: Not required Availability of data and materials: All data used in this study were obtained from publicly available or controlled-access genome-wide association study summary statistics, as detailed in Table 1. Disclosures: The authors report no conflicts Sources of funding: None Authors’ contributions: M. Savvidou and A. Dehghan conceptualized and supervised the study. S.S. Wang was responsible for data collection, formal analysis and interpretation of the results. D. Meena, J. Huang and G. Otto helped with data curation, formal analysis, resource collection and visualision of the study. S.S. Wang wrote the first draft. All authors wrote, reviewed and edited the manuscript. Acknowledgments. The authors would like to thank the consortiums and individual studies for making their genome-wide association study summary statistics publicly available. References Dimitriadis E, Rolnik DL, Zhou W, Estrada-Gutierrez G, Koga K, Francisco RPV, et al. Pre-eclampsia. Nat Rev Dis Primers. 2023; 9 (1): 8. Amaral LM, Cunningham MW, Cornelius DC, LaMarca B. Preeclampsia: long-term consequences for vascular health. Vasc Health Risk Manag . 2015; 11 403–415. 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Int J Epidemiol . 2015; 44 (4): 1137–47. Tyrmi JS, Kaartokallio T, Lokki AI, Jääskeläinen T, Kortelainen E, Ruotsalainen S, et al. Genetic Risk Factors Associated With Preeclampsia and Hypertensive Disorders of Pregnancy. JAMA Cardiol . 2023; 8 (7): 674–83. Pihkala J, Hakala T, Voutilainen P, Raivio K. [Characteristic of recent fetal growth curves in Finland]. Duodecim . 1989; 105 (18): 1540–46. Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res . 2006; 34 (Database issue): 668-72. Fishilevich S, Nudel R, Rappaport N, Hadar R, Plaschkes I, Iny Stein T, et al. GeneHancer: genome-wide integration of enhancers and target genes in GeneCards. Database (Oxford). 2017; 2017 bax028. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol . 2013; 37 (7): 658–65. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol . 2013; 37 (7): 658–65. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Intern J Epidemiol . 2015; 44 (2): 512–25. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genetic Epidemiology. 2016; 40 (4): 304–14. Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. European Journal of Epidemiology. 2017; 32 (5): 377–89. Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Intern J Epidemiol . 2017; 46 (6): 1985–98. Bland JM, Altman DG. Multiple significance tests: the Bonferroni method. BMJ . 1995; 310 (6973): 170. Verbanck M, Chen C, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet . 2018; 50 (5): 693–8. Sanderson E. Multivariable Mendelian Randomization and Mediation. Cold Spring Harb Perspect Med . 2021; 11 (2): a038984. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. ELife . 2018; 7 e34408. Hadley Wickham. ggplot2: Elegant Graphics for Data Analysis. https://ggplot2.tidyverse.org/ [Accessed Aug 30, 2023]. Bodnar LM, Ness RB, Markovic N, Roberts JM. The risk of preeclampsia rises with increasing prepregnancy body mass index. Ann Epidemiol . 2005; 15 (7): 475–82. Knight M, Kurinczuk JJ, Spark P, Brocklehurst P. Extreme obesity in pregnancy in the United Kingdom . Obstet Gynecol. 2010; 115 (5): 989–97. Wang Z, Wang P, Liu H, He X, Zhang J, Yan H, et al. Maternal adiposity as an independent risk factor for pre-eclampsia: a meta-analysis of prospective cohort studies. Obes Rev . 2013; 14 (6): 508–21. Gill D, Brewer CF, Del Greco M F, Sivakumaran P, Bowden J, Sheehan NA, et al. Age at menarche and adult body mass index: a Mendelian randomization study. Int J Obes (Lon). 2018; 42 (9): 1574–81. Au Yeung SL, Jiang C, Cheng KK, Xu L, Zhang W, Lam TH, et al. Age at menarche and cardiovascular risk factors using Mendelian randomization in the Guangzhou Biobank Cohort Study. Prev Med . 2017; 101 142–48. Gan B, Wu X, Lu L, Li X, Li J. The Value of Prenatal First Systolic Blood Pressure Can Predict Severe Preeclampsia and Birth Weight in Patients With Preeclampsia. Front Med (Lausanne) . 2022; 8 771738. Gunnarsdottir J, Akhter T, Högberg U, Cnattingius S, Wikström AK. Elevated diastolic blood pressure until mid-gestation is associated with preeclampsia and small-for-gestational-age birth: a population-based register study. BMC Pregnancy Childbirth . 2019; 19 (1): 186. Tita AT, Szychowski JM, Boggess K, Dugoff L, Sibai B, Lawrence K, et al. Treatment for Mild Chronic Hypertension during Pregnancy. N Engl J Med . 2022; 386 (19): 1781–92. Magee LA, von Dadelszen P, Rey E, Ross S, Asztalos E, Murphy KE, et al. Less-tight versus tight control of hypertension in pregnancy. N Engl J Med . 2015; 372 (5): 407–17. Ardissino M, Reddy RK, Slob EAW, Griffiths J, Girling J, Ng FS. Maternal hypertensive traits and adverse outcome in pregnancy: a Mendelian randomization study. Journal of Hypertension. 2023; 41 (9): 1438–45. Tschiderer L, van der Schouw YT, Burgess S, Bloemenkamp KW, Seekircher L, Willeit P, et al. Hypertensive disorders of pregnancy and cardiovascular disease risk: A Mendelian Randomisation study. Eur Heart J . 2023; 44 (Suppl 2): 655.2726. Gunnarsdottir J, Akhter T, Högberg U, Cnattingius S, Wikström AK. Elevated diastolic blood pressure until mid-gestation is associated with preeclampsia and small-for-gestational-age birth: a population-based register study. BMC Pregnancy Childbirth . 2019; 19(1): 186. Ardissino M, Slob EAW, Rajasundaram S, Reddy RK, Woolf B, Girling J, et al. Safety of beta-blocker and calcium channel blocker antihypertensive drugs in pregnancy: a Mendelian randomization study. BMC Med . 2022; 20(1): 288. Duan L, Ng A, Chen W, Spencer HT, Lee M. Beta-blocker subtypes and risk of low birth weight in newborns. J Clin Hypertens (Greenwich) . 2018; 20 (11): 1603–9. Table Table 1. Data source of the genome-wide association studies (GWAS) summary statistics used in the Mendelian randomization analysis. GWAS trait Data source Ancestry Sample Size Sex-specific data Maternal demographic and biophysical markers Age at menarche Day et al, 2018 (21) European 329,345 Yes Miscarriage Laisk et al, 2020 (31) European 359,469 Yes Waist-hip ratio GIANT consortium European 194,174 No BMI, SBP, DBP, T1DM, T2DM, Systemic lupus erythematosus UK BioBank-GWAS done by Neale Lab (27) European 361,194 Yes Other biomarkers PlGF, VEGFA, sFLT-1, endoglin, PAPP-A, IL-4, IL-6, IL-10, PAI-1, leptin UKBPPP (20-21） European 54,306 Yes Fasting glucose MAGIC（23-25) European 140,595 Yes Fasting insulin MAGIC（23-25） European 98,210 Yes CRP UK BioBank-GWAS done by Neale Lab (27) European 361,194 Yes BMI: body mass index; CRP: c-reactive protein; DBP: diastolic blood pressure; IL: interleukin; PAI1: plasminogen activator inhibitor-1; PAPPA: pappalysin 1; PlGF: placental growth factor; SBP: systolic blood pressure; sFLT1: soluble fms-like tyrosine kinase 1; T1DM: type 1 diabetes mellitus; T2DM: type 2 diabetes mellitus; VEGFA: vascular endothelial growth factor A. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 01 May, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers invited by journal 08 Apr, 2026 Editor assigned by journal 24 Mar, 2026 Submission checks completed at journal 24 Mar, 2026 First submitted to journal 23 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9202748","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622866550,"identity":"aed7411e-9d09-4775-b3dc-5101e5f8c87a","order_by":0,"name":"Sze Sen Wang","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Sze","middleName":"Sen","lastName":"Wang","suffix":""},{"id":622866551,"identity":"fdc40193-d532-48fc-a4cb-c09553bdc842","order_by":1,"name":"Devendra Meena","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Devendra","middleName":"","lastName":"Meena","suffix":""},{"id":622866553,"identity":"bc956dde-c6f7-42a2-af72-d65596aa6b99","order_by":2,"name":"Jingxian Huang","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Jingxian","middleName":"","lastName":"Huang","suffix":""},{"id":622866554,"identity":"175de8a1-205f-41f0-8d17-20b78d66e287","order_by":3,"name":"Georg W Otto","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Georg","middleName":"W","lastName":"Otto","suffix":""},{"id":622866555,"identity":"a72b341e-3b5f-49ed-93f9-72b6d0ff8f1c","order_by":4,"name":"Abbas Dehghan","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Abbas","middleName":"","lastName":"Dehghan","suffix":""},{"id":622866556,"identity":"d525732a-81dc-41eb-892d-88c5c944201e","order_by":5,"name":"Makrina Savvidou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYPACCcYGBuYDB0BMNgbmBogYYS1sCVAtjERpASnjMUCw8WnRbWB/+Jm3zUK2XyLn46GbO2zy+dgPNjD8qGFInNmAXYvZAR5jad42CeOZM3I3HM49k2bZxpPYwNhzjCFxNg5bgFoYJGeckUjccAOkpe2wARtDYgMDbwND4jycWtgf/4RoyXkA1PLfgI3/YQPjX7xaGMwkPlSAtTAAtRwwYJNIbGAG2YLTYYd5zCyAWoxn9jwzAPolGajlYcNhmWNAEVzeP97++EaCQZ1sP3vy48+5O+wM5PuTDz58U2MjO+MADmuYkTnQGGE4QDgi0bWMglEwCkbBKEAGAE2cXlMlkuWBAAAAAElFTkSuQmCC","orcid":"","institution":"Imperial College London","correspondingAuthor":true,"prefix":"","firstName":"Makrina","middleName":"","lastName":"Savvidou","suffix":""}],"badges":[],"createdAt":"2026-03-23 16:09:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9202748/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9202748/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107245770,"identity":"a5e09ba6-1467-42dc-b530-6f2696c2210f","added_by":"auto","created_at":"2026-04-19 08:06:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":134955,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy design. \u003c/strong\u003e(a) A two-sample univariable Mendelian randomization (UVMR) framework is used to study the potential causal association between 22 risk factors and pre-eclampsia; IV1 - IV3: Assumptions for instrumental variables (IVs); IV1: Relevance assumption: the genetic variant must be significantly associated with the exposure; IV2: Exchangeability: the genetic variant must be independent of (unmeasured) confounders; IV3: Exclusion restriction: there’s no other pathway between the genetic variant and the outcome other than via the exposure (no horizontal pleiotropy) (b) Study workflow, the risk factor marked with * denotes \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05 but \u0026gt; 0.0023;\u003c/p\u003e\n\u003cp\u003eAAM: age at menarche; BMI: body mass index; CRP: c-reactive protein; DBP: diastolic blood pressure; IL: Interleukin; MVMR: multi-variable Mendelian randomization; PAI1: plasminogen activator inhibitor-1; PAPPA: pappalysin 1; PE: pre-eclampsia; PE:HTP: pre-eclampsia or other maternal hypertension during pregnancy; PE-SGA: pre-eclampsia and/or small for gestational age neonate; PlGF: placental growth factor; sFLT1: soluble FMS-like tyrosine kinase SBP: systolic blood pressure; VEGFA: vascular endothelial growth factor A; WHR: waist-hip ratio.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9202748/v1/c6908d4a8055b456ca82f109.png"},{"id":107484335,"identity":"67c20ceb-9caa-486a-9245-21398d554807","added_by":"auto","created_at":"2026-04-22 02:31:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":89568,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap displaying the effect estimates of genetically predicted risk factors on the clinical outcomes of pre-eclampsia (PE), pre-eclampsia or other maternal hypertension during pregnancy (PE-HTP), and pre-eclampsia or small for gestational age neonate (PE-SGA), using IVW or Wald ratio method.\u003c/strong\u003e The x-axis (exposure) shows the risk factors, and the y-axis (outcome) shows the pre-eclampsia diagnoses. The colour of the squares indicates the direction of the causal estimates (protective as green and risk as red). The size of the coloured squares represents the magnitude p-value: a full square represents significance after Bonferroni correction (P\u0026lt; 0.0023), a 0.75 square represents p-value \u0026lt; 0.05, a half square represents p-value \u0026lt; 0.1 and a 0.25 square represents p-value ≥ 0.1. * Represents robust associations supported by either WM or MR-Egger (p-value \u0026lt; 0.05). ** Represents robust associations supported by both WM and MR-Egger (p-value \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eCRP: c-reactive protein; IL: interleukin; FLT1: fms-like tyrosine kinase 1; PAI1: plasminogen activator inhibitor-1; PAPPA: pappalysin 1; PlGF: placental growth factor; VEGFA: vascular endothelial growth factor A.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9202748/v1/e3e641544266c3bf44696dec.png"},{"id":107483097,"identity":"ce6346ea-c591-4572-a252-0cf9ec7e9d93","added_by":"auto","created_at":"2026-04-22 02:26:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60751,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot displaying the risk factors that have significant results for association with pre-eclampsia (PE), pre-eclampsia or other maternal hypertension during pregnancy (PE-HTP), and pre-eclampsia or small for gestational age neonate (PE-SGA) using the inverse variance weighted approach in a Mendelian randomization study.\u003c/strong\u003e (a) PE; (b)(c) PE-HTP; (d) PE-SGA. The effect estimates from weighted median and MR Egger are also displayed. The effect of the risk factors presented as odds ratio in (a)(b)(d) and as beta in (c). Filled dots represent \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05. Risk factors enclosed with a red bucket has OR with\u003cem\u003e P\u003c/em\u003e \u0026lt; 0.05 but \u0026gt; 0.0023 (the significant threshold after Bonferroni correction); Wald ratio results were presented for risk factors marked with an asterisk*\u003c/p\u003e\n\u003cp\u003eDBP: diastolic blood pressure; SBP: systolic blood pressure\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9202748/v1/86236ad42a12ef568b12f49f.png"},{"id":107245774,"identity":"eea02d79-c3a7-4166-bb00-f242ea3262ed","added_by":"auto","created_at":"2026-04-19 08:06:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":58867,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot displaying the effect of genetically proxied beta blocker and calcium channel blocker usage on pre-eclampsia (PE), pre-eclampsia or other maternal hypertension during pregnancy (PE-HTP), and pre-eclampsia or small for gestational age neonate (PE-SGA) in a Mendelian randomization (MR) analysis. \u003c/strong\u003e(a) PE as outcome; (b) PE-HTP as outcome; (c) PE-SGA as outcome; The effect estimates are displayed as OR per mmHg genetically-predicted diastolic blood pressure reduction. MR analyses are performed using IVW, MR Egger, and weighted median methods. Filled dots represent statistical significance (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0025).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9202748/v1/e6d9369858d01ea62def7ab4.png"},{"id":107245773,"identity":"f7a58f2b-b899-4cb9-b7d3-1bb9a2acab5d","added_by":"auto","created_at":"2026-04-19 08:06:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":125737,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot displaying the effect of systolic blood pressure (SBP), body mass index (BMI), DBP (diastolic blood pressure) and AAM (age at menarche) on pre-eclampsia (PE), pre-eclampsia or other maternal hypertension during pregnancy (PE-HTP), and pre-eclampsia or small for gestational age neonate (PE-SGA) in a multivariate mendelian randomization (MVMR) analysis. \u003c/strong\u003e(a) PE as outcome; (b) PE-HTP as outcome; (c) PE-SGA as outcome; The effect estimates are displayed as OR and MR analyses are performed using the IVW method. Filled dots represent statistical significance (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9202748/v1/f345e26263208c2edeac5aeb.png"},{"id":107486987,"identity":"0dc0a82e-65db-4fef-bb38-fb91e21c013f","added_by":"auto","created_at":"2026-04-22 02:39:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1253876,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9202748/v1/a56f1154-41b0-41ff-8a09-cd1769c6a9f6.pdf"},{"id":107245769,"identity":"73845ace-7980-46c2-9846-29e4986b3bb2","added_by":"auto","created_at":"2026-04-19 08:06:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":74917,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-9202748/v1/b00d1a46343dd82f2d7f6a95.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association of risk factors, circulatory biomarkers and antihypertensive medications with preeclampsia: a Mendelian randomisation analysis","fulltext":[{"header":"Background","content":"\u003cp\u003ePre-eclampsia (PE) affects 2\u0026ndash;5% of pregnancies globally and one of the leading causes of maternal and perinatal morbidity and mortality.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e In addition to acute complications such as eclampsia and preterm delivery, PE is associated with long-term maternal risk including stroke, type 2 diabetes mellitus (T2DM) and cardiovascular disease.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Pre-eclampsia, is often linked to impaired fetal growth and delivery of a small for gestational age (SGA). Offsprings born to affected mothers are also at an increased risk of cardiovascular disease later in life.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe pathogenesis of PE remains largely elusive. While several biological pathways including angiogenic/anti-angiogenic imbalance,\u003csup\u003e4\u003c/sup\u003e inflammation\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e and endothelial dysfunction\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e are likely to be involved, the precise causal mechanisms remain to be clarified. Epidemiologic studies have identified several maternal risk factors, including advanced maternal age, high body mass index (BMI) (\u0026ge;\u0026thinsp;30kg/m\u003csup\u003e2\u003c/sup\u003e), pre-existing chronic hypertension, renal disease, DM, history of PE or multifetal gestation,\u003csup\u003e7,8\u0026ndash;10\u003c/sup\u003e but, their causal nature has often been questioned due to the potential of residual confounding and reverse causation.\u003c/p\u003e \u003cp\u003eObservational studies have also shown associations between circulating biomarkers such as placental growth factor (PlGF), vascular endothelial growth factor (VEGF) \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, interleukins\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e and PE risk. However, whether these observational associations reflect causal pathways remains unknown.\u003c/p\u003e \u003cp\u003eManagement of PE primarily involves symptomatic control of blood pressure (BP) or seizure prophylaxis. \u003csup\u003e11\u0026ndash;13\u003c/sup\u003e Antihypertensive agents, such as calcium channel blockers (CCBs) and beta blockers (BBs), are commonly used, yet their comparative effectiveness in preventing PE remains uncertain.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR)uses genetic variants as proxies for modifiable exposures and can strengthen causal inference by reducing confounding and reverse causation \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e MR relies on the random allele assortment during meiosis, which results in a random distribution of genetic variants across the population and is independent of confounding effects.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Previous MR studies have supported causal roles for BMI, T2DM and hyperthyroidism in PE.\u003csup\u003e16\u003c/sup\u003e However, these studies were largely limited to metabolic risk factors and did not comprehensively evaluate the roles of circulating biomarkers, placental proteins, or antihypertensive drug mechanisms. Moreover, most analyses used sex-combined GWAS datasets, leaving uncertainty about causal pathways specific to the female population.\u003c/p\u003e \u003cp\u003eTo address these gaps, we aim to build on previous findings by: (1) conducting a comprehensive MR analysis to evaluate the potential causal relationships between 22 maternal demographic, cardiometabolic and biophysical traits (including angiogenic/anti-angiogenic, inflammatory, adipokine and endothelial factors) \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e and development of PE; (2) extending the analysis to other placental-related conditions, including PE or other maternal hypertension (PE-HTP) and PE or delivery of a SGA neonate (PE-SGA); and (3) assessing the genetically predicted effects of two commonly used antihypertensive drug classes, beta blockers (BB) and calcium channel blockers (CCB), on PE risk, using a drug-target MR approach to model the pharmacological effects of BP lowering.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eMendelian Randomization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study is based on a two-sample MR approach, which uses summary-level data from genome-wide association studies (GWAS) to estimate the potential causal associations\u0026nbsp;between the exposures\u0026nbsp;(cardiometabolic traits) and the outcome (PE) using genetic variants as instrumental variables (IVs).\u003csup\u003e14\u003c/sup\u003e Since individuals inherit their genetic variants from birth in a randomly assorted manner, the effects estimated are less likely to be impacted by confounders and as the genetic variants are constant over lifetime, reverse causation is not likely (Figure 1).\u0026nbsp;\u003csup\u003e15\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGWAS summary statistics for the 22 exposures (risk factors of PE) were downloaded from multiple sources (Table 1). Since the outcome of interest, PE, is a disease that only affects females, sex-specific data of exposures were downloaded. Notably, for plasma proteins such as placental growth factor (PlGF), vascular endothelial growth factors (VEGF) and endoglin, GWAS was performed with the BOLT-LMM\u0026nbsp;\u003csup\u003e18\u003c/sup\u003e pipeline to select only female participants using the Olink proteomic assay (Olink Explore 1536 platform)\u0026nbsp;\u003csup\u003e19\u003c/sup\u003e and the genotypic data from 54,306 UK Biobank participants.\u0026nbsp;\u003csup\u003e20\u003c/sup\u003e Covariates including age, Olink batch and the first 10 principal components were adjusted.\u003c/p\u003e\n\u003cp\u003eSince the GWAS data for most exposures originate from the UK biobank cohort, UK biobank data is avoided for the outcome to reduce bias caused by overlapped samples. Therefore, the GWAS data for PE was downloaded from the recent study of Tyrmi \u003cem\u003eet al.\u003c/em\u003e, which performed meta-analysis on multiple cohorts, including Finnish Genetics of Pre-eclampsia Consortium (FINNPEC),\u0026nbsp;\u003csup\u003e21\u003c/sup\u003e Finnish FinnGen project,\u0026nbsp;\u003csup\u003e22\u003c/sup\u003e Estonian Biobank\u0026nbsp;\u003csup\u003e23\u003c/sup\u003e and InterPregGen\u003csup\u003e24\u003c/sup\u003e consortium. Together, they provide summary statistics of 16,743 individuals with PE and 271,306 controls.\u0026nbsp;\u003csup\u003e24\u003c/sup\u003e In addition to PE, the GWAS data for the two other outcomes, PE-HTP (\u003cem\u003eN\u003csub\u003ecases\u003c/sub\u003e\u003c/em\u003e=15,200,\u003cem\u003e\u0026nbsp;N\u003csub\u003econtrols\u003c/sub\u003e\u003c/em\u003e=115,007) and PE-SGA (\u003cem\u003eN\u003csub\u003ecases\u003c/sub\u003e\u003c/em\u003e=10,800,\u003cem\u003e\u0026nbsp;N\u003csub\u003econtrols\u003c/sub\u003e\u003c/em\u003e=119,225), is also downloaded from the same study.\u0026nbsp;\u003csup\u003e24\u003c/sup\u003e PE is defined as systolic BP (SBP) \u0026ge;140 mm Hg or diastolic BP(DBP) \u0026ge;90 mm Hg, and proteinuria (\u0026ge;0.3 g/24 hours, or two \u0026ge;1+ dipstick readings) that occurs after 20 weeks of gestation.\u0026nbsp;\u003csup\u003e17\u003c/sup\u003e PE-HTP is defined as other maternal hypertension including hypertension occurring after 20 weeks of gestation in the absence of other organ involvement, or pre-existing (chronic) hypertension. PE-SGA is defined as PE and/or delivery of an SGA neonate; birth weight below 2.0 standard deviation units, based on the Finnish standards.\u0026nbsp;\u003csup\u003e25\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstrumental variable selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following criteria were used to select IVs for the analysis. Single nucleotide polymorphisms (SNPs) with genome-wide significant association (\u003cem\u003eP\u003c/em\u003e \u0026lt; 5 \u0026times; 10\u003csup\u003e-8\u003c/sup\u003e) with the exposures, effect allele frequency \u0026gt; 0.01 and F-statistics \u0026ge; 10 (to avoid weak instrument bias) were selected as potential IVs. If less than five IVs were left after clumping, the threshold was relaxed to \u003cem\u003eP\u003c/em\u003e \u0026lt; 5.0 \u0026times; 10\u003csup\u003e-5\u003c/sup\u003e (to increase significant power of the IVs, for exposures in which both sex-combined and sex-specific data were available, SNPs were extracted from sex-combined data with larger sample size but the beta estimate and standard error from the female-only data set were used for statistical analysis). We harmonized the exposure and outcome data using the \u0026lsquo;harmonise data\u0026rsquo; function as implemented in the TwoSampleMR v.0.5.7 package. IVs Clumping was performed using the \u0026lsquo;ld_clump\u0026rsquo; function from the ieugwasr v.0.1.5 package using the 1000 Genomes European only data as reference panel (10,000 kb window, r\u003csup\u003e2\u003c/sup\u003e = 0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrug target mendelian randomisation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo model the on-target effect of BBs and CCBs, we conducted drug-target MR using SNPs within or near genes encoding drug targets, identified using the DrugBank database\u0026nbsp;\u003csup\u003e26\u003c/sup\u003e including the promoter and enhancer regions for each gene identified using the GeneHancer database.\u0026nbsp;\u003csup\u003e27\u003c/sup\u003e SNPs were selected based on association with DBP (\u003cem\u003eP\u003c/em\u003e \u0026lt; 5.0 \u0026times; 10\u003csup\u003e-5\u003c/sup\u003e to increase the number of valid SNPs) and LD clumped using the 1000 Genomes linkage disequilibrium (LD) reference panel with a threshold of \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;\u003c/em\u003e\u0026lt; 0.3. MR analyses were performed accounting for the correlation between variants using a correlation matrix.\u0026nbsp;\u003csup\u003e28\u003c/sup\u003e The genetic associations were scaled to reflect 1 mmHg reduction in DBP, as a proxy for drug effect.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInverse variance weighted (IVW) MR was used as the primary method to investigate the potential causal associations between the exposures and PE. It was a meta-analysis of the Wald ratios for each SNP (weighted by inverse variance of each individual SNP-outcome association) that combines them to obtain an overall estimate of the effect of the exposure on the outcome.\u0026nbsp;\u003csup\u003e15,29\u003c/sup\u003e When there was only one instrumental variable, Wald ratio was used for the analysis instead of IVW. MR-Egger\u0026nbsp;\u003csup\u003e30\u003c/sup\u003e and weighted median\u0026nbsp;\u003csup\u003e31\u003c/sup\u003e MR were used for sensitivity analysis. MR-Egger is based on the InSIDE (INstrument Strength Independent of Direct Effect) assumption that the strength of association between exposure and outcome is unrelated to the magnitude of the pleiotropic effect.\u0026nbsp;\u003csup\u003e32\u003c/sup\u003e The intercept of MR-Egger regression can be used to detect directional pleiotropy (if the p-value of the intercept term is lower than 0.05, pleiotropy may exist). The weighted median method can provide a consistent estimation of the causal effect even when up to 50% of the IV are invalid and can provide results with less bias when IV assumptions are violated.\u0026nbsp;\u003csup\u003e33\u003c/sup\u003e For the risk factors, the significant threshold P \u0026lt; 0.0023 was used after Bonferroni correction.\u0026nbsp;\u003csup\u003e34\u003c/sup\u003e Similarly, the significant threshold P \u0026lt; 0.025 was used for the antihypertensive drugs (BBs and CCBs). Odds ratios (OR) were used to represent the association between the exposures and outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHeterogeneity tests were performed to obtain Q-statistics for the meta-analysis. For exposures with significant IVW estimates, MR-PRESSO was performed to detect possible outliers and to reduce horizontal pleiotropy by removing significant outliers.\u0026nbsp;\u003csup\u003e35\u003c/sup\u003e The IVW, MR-Egger and weighted median analysis were performed again for the outlier-corrected data. To investigate the mediation effects between possibly related exposures, where relevant, we conducted multivariable MR (MVMR) to evaluate mediation between exposures (e.g., BMI and BP).\u0026nbsp;\u003csup\u003e36\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using R version 4.3.0. The allele harmonization, clumping, and MR analysis were performed using the TwoSampleMR\u0026nbsp;\u003csup\u003e37\u003c/sup\u003e (version 0.5.7) and MRPRESSO (version 1.0) R packages. MVMR was performed using the MVMR R package. Data visualization was performed using the ggplot2 (version 3.4.3)\u0026nbsp;\u003csup\u003e38\u003c/sup\u003e and ggforestplot (version 0.1.0) packages and the wesanderson (version 0.3.6) color palette generator.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eEffect of risk factors on PE, PE-HTP and PE-SGA\u003c/h2\u003e \u003cp\u003eOf the 22 risk factors, four - BMI, SBP, DBP and age at menarche (AAM) - were significantly associated with at least one PE outcome based on the IVW MR method and after correction for multiple testing (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0023). These associations were further explored using robust sensitivity methods and MVMR where applicable (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026amp; Supplementary Tables\u0026nbsp;1\u0026ndash;3)). Waist-hip ratio (WHR) (PE: OR\u0026thinsp;=\u0026thinsp;1.36; PE-HTP: OR\u0026thinsp;=\u0026thinsp;1.42), T2DM (PE-HTP: beta\u0026thinsp;=\u0026thinsp;17.92), PlGF and C-reactive protein (CRP) level (PE: OR\u0026thinsp;=\u0026thinsp;1.11; PE-SGA: OR\u0026thinsp;=\u0026thinsp;1.10) showed nominal significance at least with one of the outcomes, however, none of them survived Bonferroni correction (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e and supplementary Tables\u0026nbsp;1\u0026ndash;3). All effect estimates and supporting sensitivity results are presented in Supplementary Tables\u0026nbsp;1\u0026ndash;6 and Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBody mass index showed a robust association with all three PE outcomes (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-c). IVW estimates showed OR of 1.50 for PE (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.58 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e), 1.72 for PE-HTP (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.04 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;17\u003c/sup\u003e), and 1.35 for PE-SGA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.06 \u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) and were further supported by consistent MR Egger and Weighted Median estimates (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary Table\u0026nbsp;1\u0026ndash;3). The Q statistics was significant for all three outcomes, and the MR Egger intercept was significant for PE and PE-HTP (Supplementary Table\u0026nbsp;5). After two outliers were detected by MR-PRESSO and were removed from analysis, the IVW estimates did not change materially (PE: OR\u0026thinsp;=\u0026thinsp;1.43; PE-HTP: OR\u0026thinsp;=\u0026thinsp;1.71; PE-SGA: OR\u0026thinsp;=\u0026thinsp;1.36) (Supplementary Table\u0026nbsp;4), however, the MR Egger intercept became not significant for PE.\u003c/p\u003e \u003cp\u003eIn contrast, WHR demonstrated only nominal (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) associations with PE (OR\u0026thinsp;=\u0026thinsp;1.36, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020) and PE-HTP (OR\u0026thinsp;=\u0026thinsp;1.42, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) based on IVW estimates (Fig.\u0026nbsp;3ab, Supplementary Tables\u0026nbsp;1 and 2). However, these associations did not survive Bonferroni correction and were not supported by MR Egger or Weighted Median methods. The results of both Q statistics and MR Egger intercept were not significant.\u003c/p\u003e \u003cp\u003eThe IVW analysis indicated a negative association between AAM and PE (OR\u0026thinsp;=\u0026thinsp;0.90, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), PE-HTP (OR\u0026thinsp;=\u0026thinsp;0.87, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0002), and PE-SGA (OR\u0026thinsp;=\u0026thinsp;0.93, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed-f). Sensitivity analyses were less consistent, with only PE-HTP association supported by the weighted median method (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb \u0026amp; Supplementary Tables\u0026nbsp;1 and 3). For all three outcomes, the Q statistics of AAM was significant (PQ-stat\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while the MR Egger intercept was not significant. After removing the outliers of IV detected by MR-PRESSO, no significant change of the IVW estimate was observed and the p values were \u0026lt;\u0026thinsp;0.05. MVMR adjusting for BMI revealed that the AAM with PE-HTP association was no longer significant (OR\u0026thinsp;=\u0026thinsp;0.95; P\u0026thinsp;=\u0026thinsp;0.20), suggesting mediation via BMI (Supplementary Table\u0026nbsp;7).\u003c/p\u003e \u003cp\u003eSBP and DBP were strongly associated with all outcomes. SBP showed significant results with PE (OR\u0026thinsp;=\u0026thinsp;2.74 per mmHg, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.90 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;26\u003c/sup\u003e), PE-HTP (OR\u0026thinsp;=\u0026thinsp;3.64, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.23 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;37\u003c/sup\u003e), and PE-SGA (OR\u0026thinsp;=\u0026thinsp;2.21, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.01 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;18\u003c/sup\u003e). DBP showed similarly elevated ORs of 2.33 for PE (P\u0026thinsp;=\u0026thinsp;7.03 \u0026times; 10⁻\u0026sup1;⁷), 3.08 for PE-HTP (P\u0026thinsp;=\u0026thinsp;9.59 \u0026times; 10⁻\u0026sup2;⁷), and 2.04 for PE-SGA (P\u0026thinsp;=\u0026thinsp;8.38 \u0026times; 10⁻\u0026sup1;⁴) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary Tables\u0026nbsp;1\u0026ndash;3). MR Egger and Weighted Median results agreed with IVW and further complemented the findings.\u003c/p\u003e \u003cp\u003eIVW estimates were nominally significant for the association of CRP with PE (OR\u0026thinsp;=\u0026thinsp;1.11, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) and PE-SGA (OR\u0026thinsp;=\u0026thinsp;1.10, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) but did not survive Bonferroni correction and were not confirmed in sensitivity analyses (Supplementary Table\u0026nbsp;1\u0026amp;3). MR-PRESSO identified outliers in the PE model but showed no substantial effect on the estimate (OR\u0026thinsp;=\u0026thinsp;1.09) (Supplementary Tables\u0026nbsp;4 \u0026amp; 6).\u003c/p\u003e \u003cp\u003eFor PlGF level, only one instrumental variable was left after filtering and clumping. The Wald ratio analysis suggested a protective effect on PE (OR\u0026thinsp;=\u0026thinsp;0.46, P\u0026thinsp;=\u0026thinsp;0.007) (Supplementary table 1), however, this was not supported by sensitivity tests.\u003c/p\u003e \u003cp\u003eType 2 diabetes mellitus showed a nominal association with PE-HTP (beta\u0026thinsp;=\u0026thinsp;17.92, standard error\u0026thinsp;=\u0026thinsp;8.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, Supplementary table 2), but not PE or PE-SGA. However, this result did not survive multiple testing.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMultivariate Mendelian Randomisation\u003c/h3\u003e\n\u003cp\u003eWe conducted an MVMR analysis to disentangle the effect of BMI, SBP and DBP and test possible mediation between them. SBP and DBP remained significant with all three outcomes (Supplementary Table\u0026nbsp;7) after adjustment for BMI, indicating their independent effect. BMI was not significant when adjusted for DBP in the case of PE (OR\u0026thinsp;=\u0026thinsp;1.18, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1), and when adjusted for both SBP and DBP in the case of PE-SGA. Furthermore, the effect of AAM on PE-HTP was not significant, when adjusted for BMI (OR\u0026thinsp;=\u0026thinsp;0.95, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.2), further supporting the effect of BMI as a mediator. A summary of the key causal relationships across exposures, mediators, and PE phenotypes is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEffect of beta-blockers and calcium channel blockers\u003c/h2\u003e \u003cp\u003eCalcium channel blockers (CCBs), instrumented via DBP-lowering SNPs within their gene targets, were associated with lower risk of PE (OR per mmHg\u0026thinsp;=\u0026thinsp;0.31, P\u0026thinsp;=\u0026thinsp;0.001) and PE-HTP (OR\u0026thinsp;=\u0026thinsp;0.17, P\u0026thinsp;=\u0026thinsp;1.12 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u0026ndash;b and Supplementary Table\u0026nbsp;6). The results of weighted median aligned with the IVW result for both PE and PE-HTP. For PE-SGA, the result of IVW were not significant (OR\u0026thinsp;=\u0026thinsp;0.39, P\u0026thinsp;=\u0026thinsp;0.045) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ec\u0026ndash;e). The BBs-related findings did not meet Bonferroni-corrected significance and showed weaker support in sensitivity analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis MR study provides evidence that elevated maternal BMI, SBP and DBP are causally associated with an increased risk of PE, PE-HTP and PE-SGA. Age at menarche, WHR, CRP, PlGF and T2DM were not independently associated with PE, PE-HTP and PE-SGA. In addition, the analysis suggested that CCBs may reduce the risk of PE and PE-HTP, while the role of BBs remains less clear. Our findings are consistent with previous observational studies and MR studies that implicate that maternal metabolic and vascular health strongly influence PE risk.\u003c/p\u003e \u003cp\u003eOur study showed that genetically predicted BMI levels are positively associated with development of PE (OR\u0026thinsp;=\u0026thinsp;1.50 per 1 kg/m\u003csup\u003e2\u003c/sup\u003e increase in BMI), PE-HTP and PE-SGA. Compared with a pre-pregnancy BMI of 21 kg/m\u003csup\u003e2\u003c/sup\u003e, pregnant people with BMI\u0026ge;25kg/m\u003csup\u003e2\u003c/sup\u003e, \u0026ge;\u0026thinsp;30kg/m\u003csup\u003e2\u003c/sup\u003e and \u0026ge;\u0026thinsp;50kg/m\u003csup\u003e2\u003c/sup\u003e have twice, three- and four-times higher risk of developing PE, respectively. \u003csup\u003e39,40\u003c/sup\u003e The effect of BMI on PE has been found to be greater in milder rather than severe forms of PE, \u003csup\u003e41\u003c/sup\u003e which is consistent with our findings as the OR for BMI was slightly higher in the PE compared to the PE-SGA (a proxy for more severe PE) group. The association of BMI with PE development remained significant in MVMR analysis and in accordance with previous studies, which have shown that one year increase in AAM is associated with a 0.38 kg/m\u003csup\u003e2\u003c/sup\u003e reduction in adult BMI, we also found that the effect of AAM on the risk of PE-HTP is mediated through BMI. \u003csup\u003e42, 43\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMultiple observational studies have demonstrated an association between BP traits and risk of PE. Systolic BP has been linked to PE development with higher pre-pregnancy SBP in PE patients associated with low birth weight, a proxy for more severe form of PE.\u003csup\u003e44\u003c/sup\u003e Similarly, elevated DBP in early to mid-gestation is associated with an increased risk of PE (OR\u0026thinsp;=\u0026thinsp;1.8) and delivery of SGA neonates (OR\u0026thinsp;=\u0026thinsp;1.3).\u003csup\u003e45\u003c/sup\u003e In accordance, two recent multicentre trials have demonstrated that a lower threshold for initiating of antihypertensive treatment in pregnant women with mild chronic hypertension and tighter BP control in women with hypertension during pregnancy resulted in 18% reduction of PE, severe hypertension, preterm birth, placental abruption and fetal death.\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e A published MR study has reported an association between genetically predicted BP traits and PE (using GWAS data from round 7 of Finngen consortium) providing evidence for a causal relationship (SBP: OR\u0026thinsp;=\u0026thinsp;1.81; DBP: OR\u0026thinsp;=\u0026thinsp;2.54; Pulse Pressure: OR\u0026thinsp;=\u0026thinsp;1.68).\u003csup\u003e48\u003c/sup\u003e However, our study used summary statistics from a recent and larger meta-analysis on PE\u003csup\u003e24\u003c/sup\u003e and included sex-specific (female only) GWAS datasets for SBP and DBP, which are representative of the population affected by PE, PE-HTP and PE-SGA. It is also worth noting that a recent MR study examined the causal effects of hypertensive disorders (including PE/eclampsia) on cardiovascular disease risk factors and reported significant associations with BMI, SBP, and DBP).\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e The bidirectional relationship between cardiovascular diseases and PE suggests the possibility of shared underlying biological pathway.\u003c/p\u003e \u003cp\u003eIn the MVMR analysis, for PE, the association between BMI and PE was no longer significant, when DBP was considered suggesting that the effect of BMI is likely mediated through DBP, rather than SBP. This implies that among the BP traits, DBP may play a more important role in the pathogenesis of PE. This finding is consistent with a population-based study showing a higher risk of PE and delivery of a SGA neonate for pregnant people with elevated DBP (aOR\u0026thinsp;=\u0026thinsp;1.8, 95% CI\u0026thinsp;=\u0026thinsp;1.6\u0026ndash;2.0 and aOR\u0026thinsp;=\u0026thinsp;1.3, 95% CI\u0026thinsp;=\u0026thinsp;1.2\u0026ndash;1.5, respectively) rather than those with elevated SBP (aOR\u0026thinsp;=\u0026thinsp;1.3, 95% CI\u0026thinsp;=\u0026thinsp;1.2\u0026ndash;1.5 and aOR\u0026thinsp;=\u0026thinsp;1.0, 95% CI\u0026thinsp;=\u0026thinsp;0.9\u0026ndash;1.1, respectively).\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn our study, the OR for most of the exposures was highest for PE-HTP and lowest for PE-SGA. The difference in clinical definition of the three PE groups could possibly account for this observation as PE-HTP is a broader term and comprises all types of gestational hypertension, including PE, and as such, the larger OR for PE-HTP may be driven by BMI and other BP traits that are known to be associated with hypertension. On the other hand, PE-SGA is a more rigorous definition of PE that includes delivery of a SGA neonate and could be used as a proxy of the more severe, early-onset PE (which is associated with placental dysfunction) \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e and hypertension-related risk factors may play a less important role in its pathogenesis.\u003c/p\u003e \u003cp\u003eOur study evaluated the effects of common antihypertensive drug classes, including BBs and CCBs, on the PE risk. While our findings indicate that CCBs lower the risk of PE and PE-HTP, the causal relevance of BBs was not significant. This is in agreement with a previous study that used SNPs in the drug target regions of SBP GWAS data to mimic the pharmacological effects of BBs and CCBs, showing that genetically-proxied BBs were associated with a borderline reduction in PE, whereas the association between CCBs and reduction in PE risk was statistically significant.\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e Given the potential negative effects of BBs on birthweight,\u003csup\u003e51,52\u003c/sup\u003e our results, together with existing evidence suggest that CCBs may represent a more suitable option for the management of hypertensive disorders in pregnancy.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eKey strengths of this study include the use of the largest and most up-to-date GWAS meta-analysis of PE and related phenotypes (16,743 cases and 271,306 controls) \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e integration of sex-specific (female-only) exposure data to ensure biological relevance, and the application of complementary MR methods (IVW, MR-Egger, weighted median, MR-PRESSO and MVMR) to test causal robustness. The inclusion of both risk factor and drug-target analyses provides translational context, linking genetic evidence to potential therapeutic mechanisms.\u003c/p\u003e \u003cp\u003eNevertheless, several limitations should be acknowledged. First, despite the large sample size, some exposures, particularly circulating biomarkers, had a limited number of genetic instruments, which may reduce statistical power. Second, horizontal pleiotropy cannot be completely ruled out in MR analysis, however, we applied sensitivity methods including weighted median, MR-Egger and MR-PRESSO to explore the potential risk of horizontal pleiotropy. Third, all analyses were restricted to participants of European ancestry, which may limit generalisability to other populations. Finally, the drug-target MR analysis assumes that genetic variation near target genes mimics pharmacological inhibition; thus, the estimates should be interpreted as mechanistic proxies rather than direct equivalents of clinical trial effects.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe results of this study provide evidence supporting a causal role of BMI and BP traits with regards to PE, PE-HTP and PE-SGA, underscoring the importance of BMI, and particularly BP, management in the prevention and management of PE. The results also support a causal relevance of genetically proxied CCBs and a reduced risk of PE and PE-HTP, offering new insights into the potential management options of PE.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eAAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eAge at menarche\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eBody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eConfidence interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eC reactive protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eDiastolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFasting glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFasting insulin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFLT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eFMS-like tyrosine kinase\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGWAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eGenome-wide association studies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eInterleukin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eInstrumental variable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eMendelian randomization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eOdds ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003ePAI1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003ePlasminogen activator inhibitor-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003ePAPPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003ePappalysin 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003ePE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003ePre-eclampsia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003ePE-HTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003ePre-eclampsia or other maternal hypertension during pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003ePE-SGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003ePre-eclampsia or small for gestational age neonate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003ePlGF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003ePlacental growth factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003epQTL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eProtein quantitative trait loci\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eSystolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eSLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eSystemic lupus erythematosus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eSNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eSingle nucleotide polymorphism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eUKB-PPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eUK Biobank Pharma Proteomics Project\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eVEGF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eVascular endothelial growth factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eVEGFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eVascular endothelial growth factor A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eWHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 312px;\"\u003e\n \u003cp\u003eWaist-hip ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval:\u0026nbsp;\u003c/strong\u003eNot required\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eAll data used in this study were obtained from publicly available or controlled-access genome-wide association study summary statistics, as detailed in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures:\u0026nbsp;\u003c/strong\u003eThe authors report no conflicts\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSources of funding:\u0026nbsp;\u003c/strong\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u0026nbsp;\u003c/strong\u003eM. Savvidou and A. Dehghan conceptualized and supervised the study. S.S. Wang was responsible for data collection, formal analysis and interpretation of the results. D. Meena, J. Huang and G. Otto helped with data curation, formal analysis, resource collection and visualision of the study. \u0026nbsp;S.S. Wang wrote the first draft. All authors wrote, reviewed and edited the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments.\u0026nbsp;\u003c/strong\u003eThe authors would like to thank the consortiums and individual studies for making their genome-wide association study summary statistics publicly available.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDimitriadis E, Rolnik DL, Zhou W, Estrada-Gutierrez G, Koga K, Francisco RPV, et al. Pre-eclampsia. Nat Rev Dis Primers. 2023; 9 (1): 8. \u003c/li\u003e\n\u003cli\u003eAmaral LM, Cunningham MW, Cornelius DC, LaMarca B. Preeclampsia: long-term consequences for vascular health. Vasc Health Risk Manag\u003cem\u003e. \u003c/em\u003e2015; 11 403\u0026ndash;415. \u003c/li\u003e\n\u003cli\u003eDavis EF, Lazdam M, Lewandowski AJ, Worton SA, Kelly B, Kenworthy Y, et al. Cardiovascular risk factors in children and young adults born to preeclamptic pregnancies: a systematic review. Pediatrics\u003cem\u003e. \u003c/em\u003e2012; 129 (6): 1552-61. \u003c/li\u003e\n\u003cli\u003eFlint EJ, Cerdeira AS, Redman CW, Vatish M. The role of angiogenic factors in the management of preeclampsia. Acta Obstet Gynecol Scand\u003cem\u003e. \u003c/em\u003e2019; 98 (6): 700\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eHarmon AC, Cornelius DC, Amaral LM, Faulkner JL, Cunningham MW, Wallace K, et al. The role of inflammation in the pathology of preeclampsia. Clin Sci (Lond). 2016; 130 (6): 409\u0026ndash;19. \u003c/li\u003e\n\u003cli\u003eYoshizumi M, Perrella MA, Burnett JC, Lee ME. Tumor necrosis factor downregulates an endothelial nitric oxide synthase mRNA by shortening its half-life. Circ Res\u003cem\u003e. \u003c/em\u003e1993; 73 (1): 205\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eBrown MA, Magee LA, Kenny LC, Karumanchi SA, McCarthy FP, Saito S, et al. The hypertensive disorders of pregnancy: ISSHP classification, diagnosis \u0026amp; management recommendations for international practice. Pregnancy Hypertens\u003cem\u003e. \u003c/em\u003e2018; 13 291\u0026ndash;310. \u003c/li\u003e\n\u003cli\u003eWebster K, Fishburn S, Maresh M, Findlay SC, Chappell LC. Diagnosis and management of hypertension in pregnancy: summary of updated NICE guidance. BMJ\u003cem\u003e. \u003c/em\u003e2019; 366:l5119. \u003c/li\u003e\n\u003cli\u003eElawad T, Scott G, Bone JN, Elwell H, Lopez CE, Filippi V, et al. Risk factors for pre-eclampsia in clinical practice guidelines: Comparison with the evidence. BJOG\u003cem\u003e. \u003c/em\u003e2024;131(1): 46-62. \u003c/li\u003e\n\u003cli\u003eAskie LM, Duley L, Henderson-Smart DJ, Stewart LA. Antiplatelet agents for prevention of pre-eclampsia: a meta-analysis of individual patient data. Lancet\u003cem\u003e. \u003c/em\u003e2007; 369 (9575): 1791\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eNij Bijvank SW, Hengst M, Cornette JC, Huigen S, Winkelen Av, Edens MA, et al. Nicardipine for treating severe antepartum hypertension during pregnancy: Nine years of experience in more than 800 women. Acta Obstet Gynecol Scand\u003cem\u003e. \u003c/em\u003e2022; 101 (9): 1017\u0026ndash;25. \u003c/li\u003e\n\u003cli\u003eMagee LA, Namouz-Haddad S, Cao V, Koren G, von Dadelszen P. Labetalol for hypertension in pregnancy. Expert Opin Drug Saf\u003cem\u003e. \u003c/em\u003e2015; 14 (3): 453\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003eAbalos E, Duley L, Steyn DW, Henderson-Smart DJ. Antihypertensive drug therapy for mild to moderate hypertension during pregnancy. Cochrane Database Syst Rev\u003cem\u003e. \u003c/em\u003e2001; (2): CD002252. \u003c/li\u003e\n\u003cli\u003eSmith GD, Ebrahim S. \u0026apos;Mendelian randomization\u0026apos;: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol\u003cem\u003e. \u003c/em\u003e2003; 32 (1): 1\u0026ndash;22. \u003c/li\u003e\n\u003cli\u003eSanderson E, Glymour MM, Holmes MV, Kang H, Morrison J, Munaf\u0026ograve; MR, et al. Mendelian randomization. Nat Rev Methods Primers\u003cem\u003e. \u003c/em\u003e2022; 2: 6. \u003c/li\u003e\n\u003cli\u003eTan Z, Ding M, Shen J, Huang Y, Li J, Sun A, et al. Causal pathways in preeclampsia: a Mendelian randomization study in European populations. Front Endocrinol (Lausanne)\u003cem\u003e. \u003c/em\u003e2024; 15: 1453277\u003c/li\u003e\n\u003cli\u003eACOG Practice Bulletin No. 203: Chronic Hypertension in Pregnancy. 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Int J Epidemiol\u003cem\u003e. \u003c/em\u003e2015; 44 (4): 1137\u0026ndash;47. \u003c/li\u003e\n\u003cli\u003eTyrmi JS, Kaartokallio T, Lokki AI, J\u0026auml;\u0026auml;skel\u0026auml;inen T, Kortelainen E, Ruotsalainen S, et al. Genetic Risk Factors Associated With Preeclampsia and Hypertensive Disorders of Pregnancy. JAMA Cardiol\u003cem\u003e. \u003c/em\u003e2023; 8 (7): 674\u0026ndash;83. \u003c/li\u003e\n\u003cli\u003ePihkala J, Hakala T, Voutilainen P, Raivio K. [Characteristic of recent fetal growth curves in Finland]. Duodecim\u003cem\u003e. \u003c/em\u003e1989; 105 (18): 1540\u0026ndash;46. \u003c/li\u003e\n\u003cli\u003eWishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res\u003cem\u003e. \u003c/em\u003e2006; 34 (Database issue): 668-72. \u003c/li\u003e\n\u003cli\u003eFishilevich S, Nudel R, Rappaport N, Hadar R, Plaschkes I, Iny Stein T, et al. 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Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. \u003cem\u003eGenetic Epidemiology. \u003c/em\u003e2016; 40 (4): 304\u0026ndash;14. \u003c/li\u003e\n\u003cli\u003eBurgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. European Journal of Epidemiology.\u003cem\u003e \u003c/em\u003e2017; 32 (5): 377\u0026ndash;89. \u003c/li\u003e\n\u003cli\u003eHartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Intern J Epidemiol\u003cem\u003e. \u003c/em\u003e2017; 46 (6): 1985\u0026ndash;98. \u003c/li\u003e\n\u003cli\u003eBland JM, Altman DG. Multiple significance tests: the Bonferroni method. BMJ\u003cem\u003e. \u003c/em\u003e1995; 310 (6973): 170. \u003c/li\u003e\n\u003cli\u003eVerbanck M, Chen C, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet\u003cem\u003e. \u003c/em\u003e2018; 50 (5): 693\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eSanderson E. Multivariable Mendelian Randomization and Mediation. Cold Spring Harb Perspect Med\u003cem\u003e. \u003c/em\u003e2021; 11 (2): a038984. \u003c/li\u003e\n\u003cli\u003eHemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. ELife\u003cem\u003e. \u003c/em\u003e2018; 7 e34408. \u003c/li\u003e\n\u003cli\u003eHadley Wickham. ggplot2: Elegant Graphics for Data Analysis.\u003cem\u003e \u003c/em\u003ehttps://ggplot2.tidyverse.org/ [Accessed Aug 30, 2023].\u003c/li\u003e\n\u003cli\u003eBodnar LM, Ness RB, Markovic N, Roberts JM. The risk of preeclampsia rises with increasing prepregnancy body mass index. Ann Epidemiol\u003cem\u003e. \u003c/em\u003e2005; 15 (7): 475\u0026ndash;82. \u003c/li\u003e\n\u003cli\u003eKnight M, Kurinczuk JJ, Spark P, Brocklehurst P. Extreme obesity in pregnancy in the United Kingdom\u003cem\u003e. Obstet Gynecol. \u003c/em\u003e2010; 115 (5): 989\u0026ndash;97. \u003c/li\u003e\n\u003cli\u003eWang Z, Wang P, Liu H, He X, Zhang J, Yan H, et al. Maternal adiposity as an independent risk factor for pre-eclampsia: a meta-analysis of prospective cohort studies. Obes Rev\u003cem\u003e. \u003c/em\u003e2013; 14 (6): 508\u0026ndash;21. \u003c/li\u003e\n\u003cli\u003eGill D, Brewer CF, Del Greco M F, Sivakumaran P, Bowden J, Sheehan NA, et al. Age at menarche and adult body mass index: a Mendelian randomization study. Int J Obes (Lon).\u003cem\u003e \u003c/em\u003e2018; 42 (9): 1574\u0026ndash;81.\u003c/li\u003e\n\u003cli\u003eAu Yeung SL, Jiang C, Cheng KK, Xu L, Zhang W, Lam TH, et al. Age at menarche and cardiovascular risk factors using Mendelian randomization in the Guangzhou Biobank Cohort Study. Prev Med\u003cem\u003e. \u003c/em\u003e2017; 101 142\u0026ndash;48. \u003c/li\u003e\n\u003cli\u003eGan B, Wu X, Lu L, Li X, Li J. The Value of Prenatal First Systolic Blood Pressure Can Predict Severe Preeclampsia and Birth Weight in Patients With Preeclampsia. Front Med (Lausanne)\u003cem\u003e. \u003c/em\u003e2022; 8 771738. \u003c/li\u003e\n\u003cli\u003eGunnarsdottir J, Akhter T, H\u0026ouml;gberg U, Cnattingius S, Wikstr\u0026ouml;m AK. Elevated diastolic blood pressure until mid-gestation is associated with preeclampsia and small-for-gestational-age birth: a population-based register study. BMC Pregnancy Childbirth\u003cem\u003e. \u003c/em\u003e2019; 19 (1): 186. \u003c/li\u003e\n\u003cli\u003eTita AT, Szychowski JM, Boggess K, Dugoff L, Sibai B, Lawrence K, et al. Treatment for Mild Chronic Hypertension during Pregnancy. N Engl J Med\u003cem\u003e. \u003c/em\u003e2022; 386 (19): 1781\u0026ndash;92. \u003c/li\u003e\n\u003cli\u003eMagee LA, von Dadelszen P, Rey E, Ross S, Asztalos E, Murphy KE, et al. Less-tight versus tight control of hypertension in pregnancy. N Engl J Med\u003cem\u003e. \u003c/em\u003e2015; 372 (5): 407\u0026ndash;17. \u003c/li\u003e\n\u003cli\u003eArdissino M, Reddy RK, Slob EAW, Griffiths J, Girling J, Ng FS. Maternal hypertensive traits and adverse outcome in pregnancy: a Mendelian randomization study. Journal of Hypertension.\u003cem\u003e \u003c/em\u003e2023; 41 (9): 1438\u0026ndash;45.\u003c/li\u003e\n\u003cli\u003eTschiderer L, van der Schouw YT, Burgess S, Bloemenkamp KW, Seekircher L, Willeit P, et al. Hypertensive disorders of pregnancy and cardiovascular disease risk: A Mendelian Randomisation study. Eur Heart J\u003cem\u003e. \u003c/em\u003e2023; 44 (Suppl 2): 655.2726. \u003c/li\u003e\n\u003cli\u003eGunnarsdottir J, Akhter T, H\u0026ouml;gberg U, Cnattingius S, Wikstr\u0026ouml;m AK. Elevated diastolic blood pressure until mid-gestation is associated with preeclampsia and small-for-gestational-age birth: a population-based register study. BMC Pregnancy Childbirth\u003cem\u003e. \u003c/em\u003e2019; 19(1): 186. \u003c/li\u003e\n\u003cli\u003eArdissino M, Slob EAW, Rajasundaram S, Reddy RK, Woolf B, Girling J, et al. Safety of beta-blocker and calcium channel blocker antihypertensive drugs in pregnancy: a Mendelian randomization study. BMC Med\u003cem\u003e. \u003c/em\u003e2022; 20(1): 288. \u003c/li\u003e\n\u003cli\u003eDuan L, Ng A, Chen W, Spencer HT, Lee M. Beta-blocker subtypes and risk of low birth weight in newborns. J Clin Hypertens (Greenwich)\u003cem\u003e. \u003c/em\u003e2018; 20 (11): 1603\u0026ndash;9. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Data source of the genome-wide association studies (GWAS) summary statistics used in the Mendelian randomization analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGWAS trait\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eData source\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAncestry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample Size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex-specific data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal demographic and biophysical markers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAge at menarche\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eDay et al, 2018 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e329,345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMiscarriage\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLaisk et al, 2020 (31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e359,469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eWaist-hip ratio\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGIANT consortium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e194,174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBMI, SBP, DBP,\u003c/p\u003e\n \u003cp\u003eT1DM, T2DM,\u003c/p\u003e\n \u003cp\u003eSystemic lupus erythematosus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eUK BioBank-GWAS done by Neale Lab (27)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e361,194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther biomarkers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePlGF, VEGFA, sFLT-1, endoglin, PAPP-A, IL-4, IL-6, IL-10, PAI-1, leptin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eUKBPPP (20-21）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e54,306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFasting glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMAGIC（23-25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e140,595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFasting insulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMAGIC（23-25）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e98,210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eUK BioBank-GWAS done by Neale Lab (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e361,194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBMI: body mass index; CRP: c-reactive protein; DBP: diastolic blood pressure; IL: interleukin; PAI1: plasminogen activator inhibitor-1; PAPPA: pappalysin 1; PlGF: placental growth factor; SBP: systolic blood pressure; sFLT1: soluble fms-like tyrosine kinase 1; T1DM: type 1 diabetes mellitus; T2DM: type 2 diabetes mellitus; VEGFA: vascular endothelial growth factor A.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mendelian randomization, pre-eclampsia, maternal hypertension, small for gestational age neonate, cardiovascular factors, antihypertensive drugs","lastPublishedDoi":"10.21203/rs.3.rs-9202748/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9202748/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eWhile many risk factors for pre-eclampsia (PE) have been identified, their causal role and the potential preventive effects of specific antihypertensive drug classes remain unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe examined the potential causal effect of 22 risk factors and two genetically proxied antihypertensive drugs (Beta-blockers and Calcium channel blockers) on PE development and its two related conditions, PE-HTP (PE or other maternal hypertension) and PE-SGA (PE or delivery of a small for gestational age neonate). Using summary statistics from published genome-wide association studies, we employed two-sample Mendelian randomization (MR) using Wald ratio or inverse variance weighted. MR Egger, weighted median and MR-PRESSO (Pleiotropy RESidual Sum and Outlier) were applied and Multivariable MR (MVMR) was also used.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMaternal body mass index (BMI), systolic blood pressure (SBP) and diastolic BP (DBP) were significantly associated with all PE outcomes. Higher BMI was associated with increased risk of PE (Odds ratio (OR)\u0026thinsp;=\u0026thinsp;1.50, P\u0026thinsp;=\u0026thinsp;1.58\u0026times;10⁻⁸), PE-HTP (OR\u0026thinsp;=\u0026thinsp;1.72, P\u0026thinsp;=\u0026thinsp;1.04\u0026times;10⁻\u0026sup1;⁷) and PE-SGA (OR\u0026thinsp;=\u0026thinsp;1.35, P\u0026thinsp;=\u0026thinsp;2.06\u0026times;10⁻⁵). Elevated SBP showed strong associations with PE (OR\u0026thinsp;=\u0026thinsp;2.74, P\u0026thinsp;=\u0026thinsp;3.90\u0026times;10⁻\u0026sup2;⁶), PE-HTP (OR\u0026thinsp;=\u0026thinsp;3.64, P\u0026thinsp;=\u0026thinsp;4.23\u0026times;10⁻\u0026sup3;⁷) and PE-SGA (OR\u0026thinsp;=\u0026thinsp;2.21, P\u0026thinsp;=\u0026thinsp;5.01\u0026times;10⁻\u0026sup1;⁸). Increased DBP was significantly linked to PE (OR\u0026thinsp;=\u0026thinsp;2.33, P\u0026thinsp;=\u0026thinsp;7.03\u0026times;10⁻\u0026sup1;⁷), PE-HTP (OR\u0026thinsp;=\u0026thinsp;3.08, P\u0026thinsp;=\u0026thinsp;9.59\u0026times;10⁻\u0026sup2;⁷) and PE-SGA (OR\u0026thinsp;=\u0026thinsp;2.04, P\u0026thinsp;=\u0026thinsp;8.38 \u0026times; 10⁻\u0026sup1;⁴). Age at menarche was associated with PE-HTP, but this effect was mediated by BMI. Genetically proxied calcium channel blockers were linked to reduced risk of PE and PE-HTP (OR per DBP mmHg reduction: 0.37, P\u0026thinsp;=\u0026thinsp;0.004 and 0.23, P\u0026thinsp;=\u0026thinsp;1.90\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, respectively).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe findings validate the significant role of cardiovascular factors in the PE pathogenesis and highlight new therapeutic possibilities.\u003c/p\u003e","manuscriptTitle":"The association of risk factors, circulatory biomarkers and antihypertensive medications with preeclampsia: a Mendelian randomisation analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-19 08:06:40","doi":"10.21203/rs.3.rs-9202748/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-01T14:00:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318795766037760614834709162256187393829","date":"2026-04-20T09:21:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-08T18:27:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-24T07:56:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-24T06:24:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medicine","date":"2026-03-23T15:59:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7ddd2396-8373-4990-9680-1116afbbfbe7","owner":[],"postedDate":"April 19th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-01T14:00:45+00:00","index":87,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-19T08:06:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-19 08:06:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9202748","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9202748","identity":"rs-9202748","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}