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.
Département de Biologie, Faculté de Sciences, Université de Sherbrooke, Sherbrooke, Québec, Canada.
Pregnancy Hypertens. 2023 Oct 27;34:95-103. doi: 10.1016/j.preghy.2023.10.011.
To identify first trimester circulating microRNAs associated with preeclampsia (PE) and assess their predictive value in two independent cohorts METHODS: Circulating microRNAs were quantified from plasma samples collected at first trimester of pregnancy in women from Gen3G (discovery; N = 385 normotensives/22 PE) and 3D (replication; N = 260 normotensives/24 PE) prospective birth cohorts. MicroRNAs associated with PE in Gen3G were identified using DESeq2 (p-value ≤ 0.05). Replicated microRNAs (selection criteria: p-value ≤ 0.05 in Gen3G, same direction of association, nominal one-side p-value ≤ 0.1 in 3D) were included in a stepwise logistic regression model to assess their predictive values alone and in combination with PE risk factors.
Seventy-three circulating microRNAs were associated with PE (p-value ≤ 0.05) in Gen3G. Five microRNAs were replicated in 3D and included in a stepwise logistic regression model with PE clinical risk factors (maternal age, body mass index and mean arterial pressure (MAP) at first trimester, parity, and smoking status) and gestational age at first visit. The best model included miR-194-5p, miR-1278, maternal age, MAP at first trimester and parity and results in an area under the curve (AUC) of 0.861 [CI 95 %: 0.787-0.935] in Gen3G. Compared to risk factors only, the addition of microRNAs improves the AUC (from 0.826 to 0.861; p = 0.03). In 3D, the best model reached an AUC of 0.842 [CI 95 %: 0.769-0.914].
Circulating miR-194-5p and miR-1278 enhance early detection of women at risk of PE and offer great potential as predictors of PE in combination with classic risk factors.
鉴定与子痫前期（PE）相关的孕早期循环微小RNA，并在两个独立队列中评估其预测价值。方法：从Gen3G（发现队列；N = 385例血压正常者/22例PE患者）和3D（验证队列；N = 260例血压正常者/24例PE患者）前瞻性出生队列中收集的孕早期血浆样本中定量检测循环微小RNA。使用DESeq2（p值≤0.05）鉴定Gen3G中与PE相关的微小RNA。将重复验证的微小RNA（选择标准：在Gen3G中p值≤0.05，关联方向相同，在3D中名义单侧p值≤0.1）纳入逐步逻辑回归模型，以单独评估其预测价值，并与PE风险因素联合评估。
在Gen3G中，73种循环微小RNA与PE相关（p值≤0.05）。5种微小RNA在3D中得到重复验证，并与PE临床风险因素（母亲年龄、体重指数、孕早期平均动脉压（MAP）、产次和吸烟状况）以及首次就诊时的孕周一起纳入逐步逻辑回归模型。最佳模型包括miR-194-5p、miR-1278、母亲年龄、孕早期MAP和产次，在Gen3G中的曲线下面积（AUC）为0.861 [95%置信区间：0.787 - 0.935]。与仅使用风险因素相比，添加微小RNA可提高AUC（从0.826提高到0.861；p = 0.03）。在3D中，最佳模型的AUC为0.842 [95%置信区间：0.769 - 0.914]。
循环miR-194-5p和miR-1278可增强对有PE风险女性的早期检测，并与经典风险因素联合使用时作为PE预测指标具有巨大潜力。
