Clément Andrée-Anne, Légaré Cécilia, Desgagné Véronique, Thibeault Kathrine, White Frédérique, Scott Michelle S, Jacques Pierre-Étienne, Fraser William D, Perron Patrice, Guérin Renée, Hivert Marie-France, Côté Anne-Marie, Bouchard Luigi
Department of Biochemistry and Functional Genomics, Faculty of Medicine and Health Sciences (FMHS), Université de Sherbrooke, Sherbrooke, Québec, Canada.
Plateforme de recherche, de valorisation, d'analyse et de liaison en informatique de la santé (PREVALIS), Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Québec, Canada.
BMC Pregnancy Childbirth. 2025 Mar 8;25(1):255. doi: 10.1186/s12884-025-07349-x.
Gestational hypertension (GH) is linked to an increased risk of cardiometabolic diseases for both mother and child, but we lack reliable biomarkers to identify high-risk women early in pregnancy. MicroRNAs (miRNAs) are small non-coding RNA that have emerged as promising biomarkers for pregnancy complications. We thus aimed to identify first trimester circulating miRNAs associated with GH and to build a miRNA-based algorithm to predict GH incidence.
We quantified miRNAs using next-generation sequencing in plasma samples collected at first trimester of pregnancy in Gen3G (N = 413, including 28 GH cases) and 3D (N = 281, including 21 GH cases) prospective birth cohorts. MiRNAs associated with GH in Gen3G (identified using DESeq2, p-value < 0.05) and replicated in 3D were included in a stepwise logistic regression model to estimate the probability of developing GH based on the miRNAs (normalized z-score counts) and maternal characteristics that contribute most to the model.
We identified 28 miRNAs associated with the onset of GH later in pregnancy (p < 0.05) in the Gen3G cohort. Among these, three were replicated in the 3D cohort (similar fold change and p < 0.1) and were included in stepwise logistic regression models with GH-related risk factors. When combined with first trimester mean arterial pressure (MAP), miR-208b-3p and miR-26a-1-3p achieve an AUC of 0.803 (95%CI: 0.512-0.895) in Gen3G and 0.709 (95%CI: 0.588-0.829) in 3D. The addition of miR-208b-3p, and miR-26a-1-3p to the model significantly improves the prediction performance over that of MAP alone (p = 0.03). We then proposed low and high-risk thresholds, which could help identify women at very low risk of GH and those who could benefit from prevention monitoring throughout their pregnancy.
The combination of circulating miR-208b-3p and miR-26a-1-3p with first trimester MAP offers good performance as early predictors of GH. Interestingly, these miRNAs target pathways related to the cardiovascular system and could thus be relevant to the pathophysiology of GH. These miRNAs thus provide a novel avenue to identify women at risk and could lead to even more adequate obstetrical care to reduce the risk of complications associated with GH.
妊娠期高血压（GH）与母婴发生心脏代谢疾病的风险增加有关，但我们缺乏可靠的生物标志物来在妊娠早期识别高危女性。微小RNA（miRNA）是一类小的非编码RNA，已成为妊娠并发症有前景的生物标志物。因此，我们旨在识别与妊娠期高血压相关的孕早期循环miRNA，并构建基于miRNA的算法来预测妊娠期高血压的发生率。
我们在Gen3G（N = 413，包括28例妊娠期高血压病例）和3D（N = 281，包括21例妊娠期高血压病例）前瞻性出生队列的妊娠早期采集的血浆样本中，使用下一代测序技术对miRNA进行定量。在Gen3G中与妊娠期高血压相关（使用DESeq2鉴定，p值<0.05）并在3D中得到验证的miRNA被纳入逐步逻辑回归模型，以根据miRNA（标准化z评分计数）和对模型贡献最大的母体特征来估计发生妊娠期高血压的概率。
我们在Gen3G队列中鉴定出28种与妊娠后期妊娠期高血压发病相关的miRNA（p < 0.05）。其中，三种在3D队列中得到验证（相似的倍数变化且p < 0.1），并被纳入与妊娠期高血压相关危险因素的逐步逻辑回归模型。当与孕早期平均动脉压（MAP）相结合时，miR-208b-3p和miR-26a-1-3p在Gen3G中的曲线下面积（AUC）为0.803（95%CI：0.512 - 0.895），在3D中为0.709（95%CI：0.588 - 0.829）。将miR-208b-3p和miR-26a-1-3p添加到模型中，与单独使用MAP相比，显著提高了预测性能（p = 0.03）。然后我们提出了低风险和高风险阈值，这有助于识别妊娠期高血压风险极低的女性以及那些在整个孕期可从预防监测中受益的女性。
循环miR-208b-3p和miR-26a-1-3p与孕早期MAP相结合，作为妊娠期高血压的早期预测指标具有良好的性能。有趣的是，这些miRNA靶向与心血管系统相关的途径，因此可能与妊娠期高血压的病理生理学相关。这些miRNA因此为识别高危女性提供了一条新途径，并可能带来更充分的产科护理，以降低与妊娠期高血压相关的并发症风险。
