PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: By using NIPT data to analyze cfDNA coverages in promoter regions, we found the cfDNA profile, which was differential cfDNA coverages in gene promoter regions between PE and healthy controls, could be used to predict early- and late-onset PE. Maternal age, body mass index, parity, past medical histories and method of conception were significantly differential between PE and healthy pregnant women. With a false positive rate of 10%, the classifiers based on the combination of the cfDNA profile and clinical risk factors predicted early- and late-onset PE in four datasets with an average accuracy of 89 and 80% and an average sensitivity of 63 and 48%, respectively.
UNASSIGNED: Incorporating cfDNA profiles in classifiers might reduce performance variations in PE models based only on clinical risk factors, potentially expanding the application of NIPT in PE screening in the future.
UNASSIGNED: 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.
UNASSIGNED: By using NIPT data to analyze cfDNA coverages in promoter regions, we found the cfDNA profile, which was differential cfDNA coverages in gene promoter regions between PE and healthy controls, could be used to predict early- and late-onset PE. Maternal age, body mass index, parity, past medical histories and method of conception were significantly differential between PE and healthy pregnant women. With a false positive rate of 10%, the classifiers based on the combination of the cfDNA profile and clinical risk factors predicted early- and late-onset PE in four datasets with an average accuracy of 89 and 80% and an average sensitivity of 63 and 48%, respectively.
UNASSIGNED: Incorporating cfDNA profiles in classifiers might reduce performance variations in PE models based only on clinical risk factors, potentially expanding the application of NIPT in PE screening in the future.
摘要：
■先兆子痫（PE）是一种妊娠并发症，由妊娠20周后的新发高血压和蛋白尿或其他母体器官损害定义。尽管非侵入性产前检测（NIPT）已广泛用于检测怀孕期间的胎儿染色体异常，其与母体危险因素相结合筛查PE的表现尚未得到广泛验证.我们的目的是开发和验证使用母体无浆细胞DNA（cfDNA）谱和临床风险因素预测早期或晚期PE的分类器。
■我们回顾性收集并分析了来自中国四家医院的2,727名24-45岁孕妇的NIPT数据，先前已用于在妊娠120〜226周时筛查胎儿非整倍体。根据PE的诊断标准和诊断时间（妊娠34周），早期共有143个，包括580例晚发性PE样本和2,004例健康对照。wilcoxon秩和检验用于鉴定用于PE预测的cfDNA谱。Fisher精确检验和Mann-WhitneyU检验用于比较PE样本和健康对照之间的临床风险因素的分类和连续变量。分别。执行机器学习方法以基于cfDNA谱和临床风险因素开发和验证PE分类器。
■通过使用NIPT数据分析启动子区域的cfDNA覆盖率，我们找到了cfDNA图谱,这是PE和健康对照之间基因启动子区域的差异cfDNA覆盖，可用于预测早发性和晚发性PE。产妇年龄,身体质量指数,奇偶校验，过去的病史和受孕方法在PE和健康孕妇之间存在显着差异。假阳性率为10%，基于cfDNA谱和临床风险因素的组合的分类器在四个数据集中预测了早期和迟发性PE，平均准确率为89％和80％，平均灵敏度为63％和48％。分别。
■在分类器中合并cfDNA谱可能会减少仅基于临床风险因素的PE模型的性能差异，未来有可能扩大NIPT在PE筛查中的应用。
■我们回顾性收集并分析了来自中国四家医院的2,727名24-45岁孕妇的NIPT数据，先前已用于在妊娠120〜226周时筛查胎儿非整倍体。根据PE的诊断标准和诊断时间（妊娠34周），早期共有143个，包括580例晚发性PE样本和2,004例健康对照。wilcoxon秩和检验用于鉴定用于PE预测的cfDNA谱。Fisher精确检验和Mann-WhitneyU检验用于比较PE样本和健康对照之间的临床风险因素的分类和连续变量。分别。执行机器学习方法以基于cfDNA谱和临床风险因素开发和验证PE分类器。
■通过使用NIPT数据分析启动子区域的cfDNA覆盖率，我们找到了cfDNA图谱,这是PE和健康对照之间基因启动子区域的差异cfDNA覆盖，可用于预测早发性和晚发性PE。产妇年龄,身体质量指数,奇偶校验，过去的病史和受孕方法在PE和健康孕妇之间存在显着差异。假阳性率为10%，基于cfDNA谱和临床风险因素的组合的分类器在四个数据集中预测了早期和迟发性PE，平均准确率为89％和80％，平均灵敏度为63％和48％。分别。
■在分类器中合并cfDNA谱可能会减少仅基于临床风险因素的PE模型的性能差异，未来有可能扩大NIPT在PE筛查中的应用。
