Abstract：
Pregnancy-induced hypertension (PIH), a prominent determinant of maternal mortality and morbidity worldwide, is hindered by the absence of efficacious biomarkers for early diagnosis, contributing to suboptimal outcomes. Here, we explored potential causal relationships between blood metabolites and the risk of PIH using Mendelian randomization (MR). We employed a two-sample univariable MR approach to empirically estimate the causal relationships between 249 circulating metabolites and PIH. Inverse variance weighted, MR-egger, weight median, simple mode, and weighted mode methods were used for causal estimates. The exposure-to-outcome directionality was confirmed with the MR Steiger test. The Bayesian model averaging MR (MR-BMA) method was applied to detect the predominant causal metabolic traits with alignment for pleiotropy effects. In the primary analysis, analyzing 249 metabolites, we identified 25 causally linked to PIH, including 11 lipid-related traits and 6 associated with fatty acid (un)saturation. Importantly, MR-BMA analyses corroborated the total concentration of branched-chain amino acids(total-BCAA) to be the highest rank causal metabolite, followed by leucine (Leu), phospholipids to total lipids ratio in medium LDL (M-LDL-PL-pct), and Val (all P < 0.05). The directionality of causality predicted by univariable MR and MR-BMA for these metabolites remained consistent. This study highlights the causal connection between metabolites and PIH risk. It highlighted BCAAs as the strongest causal candidates warranting further investigation. Since PIH typically occurs in the second and third trimesters, extending these findings could inform earlier strategies to reduce its risk. Directed acyclic graph of the MR framework investigating the causal relationship between metabolites and PIH. MR: Mendelian randomization; GIVs: genetic instrument variables; SNPs: single-nucleotide polymorphism; IVW: inverse variance weighted; WM: weighted median; PIH: pregnancy-induced hypertension; SM: significant metabolite; MR-BMA: Bayesian model averaging MR.
摘要：
妊娠高血压综合征(PIH),全球孕产妇死亡率和发病率的一个重要决定因素，由于缺乏有效的早期诊断生物标志物而受到阻碍，导致次优结果。这里,我们使用孟德尔随机化(MR)研究了血液代谢物与PIH风险之间的潜在因果关系.我们采用了双样本单变量MR方法来经验估计249个循环代谢物与PIH之间的因果关系。反向方差加权，艾格先生,体重中位数，简单模式，和加权模式方法用于因果估计。通过MRSteiger测试确认了暴露于结果的方向性。贝叶斯模型平均MR（MR-BMA）方法用于检测主要的因果代谢性状，并对多效性效应进行对齐。在初步分析中，分析249种代谢物，我们确定了25个与PIH有因果关系，包括11个与脂质相关的性状和6个与脂肪酸(不)饱和相关的性状。重要的是,MR-BMA分析证实支链氨基酸的总浓度（总-BCAA）是最高等级的因果代谢物，其次是亮氨酸（Leu），中等LDL中磷脂与总脂质的比率（M-LDL-PL-pct），和Val(均P<0.05)。单变量MR和MR-BMA预测的这些代谢物的因果关系的方向性保持一致。这项研究强调了代谢物与PIH风险之间的因果关系。它强调BCAA是需要进一步调查的最强因果候选人。由于PIH通常发生在第二和第三三个月，扩展这些发现可以为降低其风险的早期策略提供信息.MR框架的有向无环图，研究代谢物与PIH之间的因果关系。MR：孟德尔随机化；GIV：遗传工具变量；SNP：单核苷酸多态性；IVW：方差逆加权；WM：加权中位数；PIH：妊娠高血压；SM：显著代谢物；MR-BMA：贝叶斯模型平均MR。
