Department of Pharmacy Practice, National Institute of Pharmaceutical Education and Research, Guwahati, Sila katamur Village, Changsari, Assam, India.
Applied Biology Division, CSIR-Indian Institute of Chemical Technology, Hyderabad, India.
Hypertens Res. 2023 Nov;46(11):2513-2526. doi: 10.1038/s41440-023-01348-1. Epub 2023 Jun 16.
Hypertensive disorders of pregnancy (HDP) result in major maternal and fetal complications. Our study aimed to find a panel of protein markers to identify HDP by applying machine-learning models. The study was conducted on a total of 133 samples, divided into four groups, healthy pregnancy (HP, n = 42), gestational hypertension (GH, n = 67), preeclampsia (PE, n = 9), and ante-partum eclampsia (APE, n = 15). Thirty circulatory protein markers were measured using Luminex multiplex immunoassay and ELISA. Significant markers were screened for potential predictive markers by both statistical and machine-learning approaches. Statistical analysis found seven markers such as sFlt-1, PlGF, endothelin-1(ET-1), basic-FGF, IL-4, eotaxin and RANTES to be altered significantly in disease groups compared to healthy pregnant. Support vector machine (SVM) learning model classified GH and HP with 11 markers (eotaxin, GM-CSF, IL-4, IL-6, IL-13, MCP-1, MIP-1α, MIP-1β, RANTES, ET-1, sFlt-1) and HDP with 13 markers (eotaxin, G-CSF, GM-CSF, IFN-gamma, IL-4, IL-5, IL-6, IL-13, MCP-1, MIP-1β, RANTES, ET-1, sFlt-1). While logistic regression (LR) model classified PE with 13 markers (basic FGF, IL-1β, IL-1ra, IL-7, IL-9, MIP-1β, RANTES, TNF-alpha, nitric oxide, superoxide dismutase, ET-1, PlGF, sFlt-1) and APE by 12 markers (eotaxin, basic-FGF, G-CSF, GM-CSF, IL-1β, IL-5, IL-8, IL-13, IL-17, PDGF-BB, RANTES, PlGF). These markers may be used to diagnose the progression of healthy pregnant to a hypertensive state. Future longitudinal studies with large number of samples are needed to validate these findings.
妊娠高血压疾病（HDP）会导致母婴严重并发症。我们的研究旨在通过应用机器学习模型找到一组蛋白质标志物来识别 HDP。该研究共纳入了 133 例样本，分为 4 组：健康妊娠组（HP，n=42）、妊娠期高血压组（GH，n=67）、子痫前期组（PE，n=9）和产前子痫组（APE，n=15）。采用 Luminex 多重免疫分析和 ELISA 法检测 30 种循环蛋白标志物。通过统计学和机器学习方法筛选出有潜在预测价值的显著标志物。统计分析发现，与健康妊娠组相比，疾病组中有 7 种标志物（sFlt-1、PlGF、内皮素-1（ET-1）、碱性成纤维细胞生长因子、IL-4、嗜酸性粒细胞趋化因子和 RANTES）发生显著改变。支持向量机（SVM）学习模型以 11 种标志物（嗜酸性粒细胞趋化因子、GM-CSF、IL-4、IL-6、IL-13、MCP-1、MIP-1α、MIP-1β、RANTES、ET-1、sFlt-1）区分 GH 和 HP，以 13 种标志物（嗜酸性粒细胞趋化因子、G-CSF、GM-CSF、IFN-γ、IL-4、IL-5、IL-6、IL-13、MCP-1、MIP-1β、RANTES、ET-1、sFlt-1）区分 HDP。而逻辑回归（LR）模型以 13 种标志物（碱性成纤维细胞生长因子、IL-1β、IL-1ra、IL-7、IL-9、MIP-1β、RANTES、TNF-α、一氧化氮、超氧化物歧化酶、ET-1、PlGF、sFlt-1）区分 PE，以 12 种标志物（嗜酸性粒细胞趋化因子、碱性成纤维细胞生长因子、G-CSF、GM-CSF、IL-1β、IL-5、IL-8、IL-13、IL-17、血小板衍生生长因子-BB、RANTES、PlGF）区分 APE。这些标志物可用于诊断健康妊娠向高血压状态的进展。需要进行更多大样本的纵向研究来验证这些发现。
