Miller Sarah, Lyell Deirdre, Maric Ivana, Lancaster Samuel, Sylvester Karl, Contrepois Kevin, Kruger Samantha, Burgess Jordan, Stevenson David, Aghaeepour Nima, Snyder Michael, Zhang Elisa, Badillo Keyla, Silver Robert, Einerson Brett D, Bianco Katherine
Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Brigham and Women's Hospital, Boston, Massachusetts; the Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, the Department of Pediatrics, the Metabolic Health Center, the Division of Pediatric Surgery, Department of General Surgery, the Department of Genetics, the Department of Anesthesiology, Peri-operative, and Pain Medicine, and the Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, and the Department of Physiology and Membrane Biology, University of California, Davis, Davis, California; and the Division of Maternal Fetal Medicine, University of Utah Health, Salt Lake City, Utah.
Obstet Gynecol. 2025 Jun 1;145(6):721-731. doi: 10.1097/AOG.0000000000005922. Epub 2025 May 1.
To perform metabolomic and lipidomic profiling with plasma samples from patients with placenta accreta spectrum (PAS) to identify possible biomarkers for PAS and to predict PAS with machine learning methods that incorporated clinical characteristics with metabolomic and lipidomic profiles.
This was a multicenter case-control study of patients with placenta previa with PAS (case group n=33) and previa alone (control group n=21). Maternal third-trimester plasma samples were collected and stored at -80°C. Untargeted metabolomic and targeted lipidomic assays were measured with flow-injection mass spectrometry. Univariate analysis provided an association of each lipid or metabolite with the outcome. The Benjamini-Hochberg procedure was used to control for the false discovery rate. Elastic net machine learning models were trained on patient characteristics to predict risk, and an integrated elastic net model of lipidome or metabolome with nine clinical features was trained. Performance using the area under the receiver operating characteristic curve (AUC) was determined with Monte Carlo cross-validation. Statistical significance was defined at P<.05.
The mean gestational age at sample collection was 33 3/7 weeks (case group) and 35 5/7 weeks (control group) (P<.01). In total, 786 lipid species and 2,605 metabolite features were evaluated. Univariate analysis revealed 31 lipids and 214 metabolites associated with the outcome (P<.05). After false discovery rate adjustment, these associations no longer remained statistically significant. When the machine learning model was applied, prediction of PAS with only clinical characteristics (AUC 0.685, 95% CI, 0.65-0.72) performed similarly to prediction with the lipidome model (AUC 0.699, 95% CI, 0.60-0.80) and the metabolome model (AUC 0.71, 95% CI, 0.66-0.76). However, integration of metabolome and lipidome with clinical features did not improve the model.
Metabolomic and lipidomic profiling performed similarly to, and not better than, clinical risk factors using machine learning to predict PAS among patients with PAS with previa and previa alone.
对胎盘植入谱系疾病（PAS）患者的血浆样本进行代谢组学和脂质组学分析，以确定PAS可能的生物标志物，并使用结合临床特征与代谢组学和脂质组学特征的机器学习方法预测PAS。
这是一项多中心病例对照研究，研究对象为前置胎盘合并PAS的患者（病例组n = 33）和单纯前置胎盘患者（对照组n = 21）。收集孕妇孕晚期血浆样本并储存在-80°C。采用流动注射质谱法进行非靶向代谢组学和靶向脂质组学检测。单因素分析确定了每种脂质或代谢物与研究结果之间的关联。采用Benjamini-Hochberg法控制错误发现率。基于患者特征训练弹性网络机器学习模型以预测风险，并训练了一个包含脂质组或代谢组以及九个临床特征的综合弹性网络模型。使用蒙特卡洛交叉验证法确定受试者工作特征曲线下面积（AUC）来评估模型性能。统计学显著性定义为P <.05。
样本采集时的平均孕周，病例组为33又3/7周，对照组为35又5/7周（P <.01）。总共评估了786种脂质种类和2605个代谢物特征。单因素分析显示31种脂质和214种代谢物与研究结果相关（P <.05）。经错误发现率调整后，这些关联不再具有统计学显著性。应用机器学习模型时，仅基于临床特征预测PAS（AUC 0.685，95% CI，0.65 - 0.72）与脂质组模型预测（AUC 0.699，95% CI，0.60 - 0.80）及代谢组模型预测（AUC 0.71，95% CI，0.66 - 0.76）表现相似。然而，将代谢组和脂质组与临床特征整合并未改善模型。
在前置胎盘合并PAS及单纯前置胎盘患者中，使用机器学习预测PAS时，代谢组学和脂质组学分析的表现与临床风险因素相似，且并不优于临床风险因素。
