Flowchart of the study. (IMAGE)
Caption
This study included 12,715 pregnancies (with 140 cases of abnormal pregnancy excluded), among which 556 cases developed PE. Clinical data (age, BMI, plurality, parity, IVF, etc.), ultrasound parameters (UtA-PI), and laboratory markers (liver function, blood lipids, inflammation, trace elements, etc.) were collected before 14 weeks of gestation. Multivariate logistic regression, RCS analysis, and two-stage linear regression models were used to evaluate the association and threshold effects between laboratory markers and PE. Subsequently, the preterm PE and term PE cohorts were randomly divided into training and testing sets at an 8:2 ratio, respectively. The Boruta algorithm and seven machine learning models were employed for feature selection and the construction of laboratory markers prediction models. The optimal model was selected based on AUC and sensitivity, and SHAP values were used to quantify the contribution of features. Next, the prediction score of the optimal laboratory markers prediction model was integrated with the FMF model (maternal risk factors, MAP, PLGF, and UtA-PI) for the construction of the predictive modeling of PE, and the prediction performance was evaluated. Finally, cfDNA sequencing and TSS profiling were performed on 51 maternal plasma samples (18 cases of preterm PE and 33 controls), revealing early metabolic dysfunction and heightened inflammation in cases destined for PE. PE, preeclampsia; BMI, body mass index; IVF, in vitro fertilization; UtA-PI, uterine artery pulsatility index; RCS, restricted cubic spline; AUC, area under the receiver operating characteristic curve; SHAP, shapley additive explanations; FMF, fetal medicine foundation; MAP, mean arterial pressure; PLGF, placental growth factor; cfDNA, cell-free DNA; TSS, transcription start site; GGT, gamma-glutamyltransferase; ALT, alanine aminotransferase; ALP, alkaline phosphatase; CHE, cholinesterase; FBG, fasting blood glucose; UAR MoM, uric acid-albumin ratio multiple of the median; TC, total cholesterol; TyG MoM, triglyceride-glucose index multiple of the median; CRP, c-reactive protein; Cu, copper; Ca, calcium; Mg, magnesium; Fe, iron; CatBoost, categorical boosting; LR, logistic regression.
Credit
Yi Zhu, Yanqiu Zhang, Chao Huang, Sheng Zhang, Jun Cao, Nicole Miranda, Sarina Zhao, Yan Peng , Chao Yu, Bin Feng, Jieyu Jin, Qingqin Tang, Jiaming Fan, Longwei Qiao, Yuting Liang
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