Non-invasive prediction of preeclampsia using the maternal plasma cell-free DNA profile and clinical risk factors

作者：Yan Yu, Wenqiu Xu, Sufen Zhang, Suihua Feng, Feng Feng, Junshang Dai, Xiao Zhang, Peirun Tian, Shunyao Wang, Zhiguang Zhao, Wenrui Zhao, Liping Guan, Zhixu Qiu, Jian‐Guo Zhang, Huanhuan Peng, Jiawei Lin, Qun Zhang, Weiping Chen, Huahua Li, Huahua Li, Qiang Zhao, Gefei Xiao, Zhongzhe Li, Shihao Zhou, Can Peng, Zhen Xu, Jingjing Zhang, Rui Zhang, Xiaohong He, Hua Li, Hua Li, Jia Li, Xiaohong Ruan, Lijian Zhao, Jun Kit He · 发表于：Frontiers in Medicine · 年份：2024 · DOI：10.3389/fmed.2024.1254467 · 被引用次数：14 · 研究领域：Prenatal Screening and Diagnostics、Pregnancy and preeclampsia studies、Cancer Genomics and Diagnostics

Background: Preeclampsia (PE) is a pregnancy complication defined by new onset hypertension and proteinuria or other maternal organ damage after 20 weeks of gestation. Although non-invasive prenatal testing (NIPT) has been widely used to detect fetal chromosomal abnormalities during pregnancy, its performance in combination with maternal risk factors to screen for PE has not been extensively validated. Our aim was to develop and validate classifiers that predict early- or late-onset PE using the maternal plasma cell-free DNA (cfDNA) profile and clinical risk factors. Methods: We retrospectively collected and analyzed NIPT data of 2,727 pregnant women aged 24-45 years from four hospitals in China, which had previously been used to screen for fetal aneuploidy at 12 + 0 ~ 22 + 6 weeks of gestation. According to the diagnostic criteria for PE and the time of diagnosis (34 weeks of gestation), a total of 143 early-, 580 late-onset PE samples and 2,004 healthy controls were included. The wilcoxon rank sum test was used to identify the cfDNA profile for PE prediction. The Fisher's exact test and Mann-Whitney U-test were used to compare categorical and continuous variables of clinical risk factors between PE samples and healthy controls, respectively. Machine learning methods were performed to develop and validate PE classifiers based on the cfDNA profile and clinical risk factors. Results: By using NIPT data to analyze cfDNA coverages in promoter regions, we found the cfDNA profile,...