BACKGROUND:This study aimed to determine the diagnostic role of serum levels of complement C1q, Bb, and H in nonpregnant women, women with normal pregnancy, and women with severe pre-eclampsia. MATERIAL AND METHODS:Healthy nonpregnant women (n=30), w...

Background: Globally, 10–15% of maternal deaths are statistically attributable to preeclampsia. Compared with late-onset PE, the severity of early-onset PE remains more harmful with higher morbidity and mortality. Objective: To establish an early-onset preeclampsia prediction model by clinical characteristics, risk factors and routine laboratory indicators were investigated from pregnant women at 6 to 10 gestational weeks. Methods: The clinical characteristics, risk factors, and 38 routine laboratory indicators (6–10 weeks of gestation) including blood lipids, liver and kidney function, coagulation, blood count, and other indicators of 91 early-onset preeclampsia patients and 709 normal controls without early-onset preeclampsia from January 2010 to May 2021 in Peking University Third Hospital (PUTH) were retrospectively analyzed. A logistic regression, decision tree model, and support vector machine (SVM) model were applied for establishing prediction models, respectively. ROC curves were drawn; area under curve (AUC^ROC), sensitivity, and specificity were calculated and compared. Results: There were statistically significant differences in the rates of diabetes, antiphospholipid syndrome (APS), kidney disease, obstructive sleep apnea (OSAHS), primipara, history of preeclampsia, and assisted reproductive technology (ART) (p < 0.05). Among the 38 routine laboratory indicators, there were no significant differences in the levels of PLT/LYM, NEU/LYM, TT, D-Dimer, FDP, TBA, ALP, TP, ALB, GLB, UREA, Cr, P, Cystatin C, HDL-C, Apo-A₁, and Lp(a) between the two groups (p > 0.05). The levels of the rest indicators were all statistically different between the two groups (p < 0.05). If only 12 risk factors of PE were analyzed with the logistic regression, decision tree model, and support vector machine (SVM), and the AUC^ROC were 0.78, 0.74, and 0.66, respectively, while 12 risk factors of PE and 38 routine laboratory indicators were analyzed with the logistic regression, decision tree model, and support vector machine (SVM), and the AUC^ROC were 0.86, 0.77, and 0.93, respectively. Conclusions: The efficacy of clinical risk factors alone in predicting early-onset preeclampsia is not high while the efficacy increased significantly when PE risk factors combined with routine laboratory indicators. The SVM model was better than logistic regression model and decision tree model in early prediction of early-onset preeclampsia incidence.

Background: Matrix Metalloproteinases (MMPs) have been found to have important roles in vascular pathology and may be involved in the occurrence of pre-eclampsia. In this study, the serum levels of MMP-2, -7, -9 in normal pregnant women and pre-eclampsia patients were analyzed to assess their predictive value. Methods: A total of 1563 pregnant women from Peking University Third Hospital, from February 2021 to October 2021, were enrolled. Serum samples were collected from patients one to three times, during the different trimesters. Among the 102 singleton pre-eclampsia patients, we collected samples from 33 patients in the first trimester (6–13 GW), 33 in the second trimester (14–28 GW), 41 in the third trimester (29–41 GW) and 28 after onset of pre-eclampsia. Samples from each trimester were collected before the onset of pre-eclampsia. Then we selected 35, 37, 43 and 25 samples from 124 healthy pregnant women by matching their age, BMI and gestational weeks, using these as the control groups. Serum levels of MMP-2, -7, -9 were detected by ELISA. The receiver operating characteristic (ROC) curve was used to evaluate their predictive value. Results: Except for the first trimester, MMP-2 and MMP-7 were significantly higher in the pre-eclampsia group (p < 0.5). Additionally, in the pre-eclampsia group, MMP-9 increased significantly in the first trimester and after the onset of pre-eclampsia but decreased significantly in the second and third trimesters (p < 0.5). The ROC curve indicated that MMP-9, MMP-2 and MMP-7 were the best indicators for predicting pre-eclampsia in the first, second and third trimesters, respectively. Conclusion: Increased MMP-2 and MMP-7 levels and a decreased MMP-9 level seem to be related to the pathogenesis of pre-eclampsia and are expected to be potential predictors of pre-eclampsia.

Background: Preeclampsia, affecting 2–4% of pregnancies worldwide, poses a substantial risk to maternal health. Late-onset preeclampsia, in particular, has a high incidence among preeclampsia cases. However, existing prediction models are limited in terms of the early detection capabilities and often rely on costly and less accessible indicators, making them less applicable in resource-limited settings. Objective: To develop and evaluate prediction models for late-onset preeclampsia using general information, maternal risk factors, and laboratory indicators from early gestation (6–13 weeks). Methods: A dataset of 2000 pregnancies, including 110 late-onset preeclampsia cases, was analyzed. General information and maternal risk factors were collected from the hospital information system. Relevant laboratory indicators between 6 and 13 weeks of gestation were examined. Logistic regression was used as the baseline model to assess the predictive performance of the support vector machine and extreme gradient boosting models for late-onset preeclampsia. Results: The logistic regression model, only considering general information and risk factors, identified 19.1% of cases, with a false positive rate of 0.4%. When selecting 15 factors encompassing general information, risk factors, and laboratory indicators, the false positive rate increased to 0.7% and the detection rate improved to 27.3%. The support vector machine model, only considering general information and risk factors, achieved a detection rate of 27.3%, with a false positive rate of 0.0%. After including all the laboratory indicators, the false positive rate increased to 7.7% but the detection rate significantly improved to 54.5%. The extreme gradient boosting model, only considering general information and risk factors, achieved a detection rate of 31.6%, with a false positive rate of 1.5%. After including all the laboratory indicators, the false positive rate remained at 0.7% but the detection rate increased to 52.6%. Additionally, after adding the laboratory indicators, the areas under the ROC curve for the logistic regression, support vector machine, and extreme gradient boosting models were 0.877, 0.839, and 0.842, respectively. Conclusion: Compared with the logistic regression model, both the support vector machine and extreme gradient boosting models significantly improved the detection rates for late-onset preeclampsia. However, the support vector machine model had a comparatively higher false positive rate. Notably, the logistic regression and extreme gradient boosting models exhibited high negative predictive values of 99.3%, underscoring their effectiveness in accurately identifying pregnant women less likely to develop late-onset preeclampsia. Additionally, logistic regression showed the highest areas under the ROC curve, suggesting that the traditional model has unique advantages in relation to prediction.

We aimed to define the relationships between the serum concentrations of complement factors B and H in mid-pregnancy and the risk of preeclampsia (PE) in patients with gestational diabetes mellitus (GDM). We performed a prospective nested case-control study of 503 patients with GDM who attended Peking University Third Hospital between March 2018 and December 2018. 456 patients were followed until delivery and blood samples were collected between gestational weeks 24 and 28. Thirty patients developed PE, 12 developed gestational hypertension (GH), and 42 matched cases were selected as a control group. The incidence of PE was 5.96 %. The serum concentrations of triacylglycerol (TG), FB, and FH of women with GDM who developed PE (GDM-PE group) in mid-pregnancy were significantly higher than those of the GDM group [TG: 3.60 (2.94-4.63) vs 2.54 (2.14-3.01) mmol/L, p < 0.001; FB: 346 (314-378) vs 284 (263-323) mg/L, p < 0.001; FH: 417 ± 45 vs 379 ± 47 mg/L, p = 0.003]. Multivariate regression analysis showed that high serum concentrations of TG and FB in mid-pregnancy were related to the risk of patients with GDM developing PE [TG: odds ratio (OR) 2.035 (95 % confidence interval (CI) 1.032-4.013); FB: OR 1.018 (95 % CI 1.001-1.035)]. The area under the curve (AUC) of FB for the prediction of PE was 0.821 (95 % CI 0.722-0.921) and that for a combination of TG, FB, and FH was 0.857 (95 % CI 0.770-0.944). High serum FB concentration during mid-pregnancy is a potential predictor of the risk of PE in patients with GDM, and the combination of FB, FH, and TG increased this predictive value.

Background: Prostate cancer (PCa) is the second most frequently diagnosed malignancy in men worldwide, whereas benign prostatic hyperplasia (BPH) is among the most prevalent non-malignant prostatic disorders. Differentiating between these two conditions remains challenging because of overlapping clinical manifestations and the limited specificity of currently available biomarkers. Methods: This retrospective study enrolled 200 male patients (53 PCa, 147 BPH) who underwent prostate biopsy or surgical resection based on pathological diagnosis. Clinical characteristics, laboratory parameters, and inflammatory indices were compared between the groups. Both univariate and multivariate logistic regression analyses were conducted to identify independent predictors. Nomograms were developed and validated through calibration curves, ROC analysis, and decision curve analysis (DCA). Results: PCa patients had significantly higher levels of inflammatory markers than BPH patients ( p < 0.05). In multivariate analysis, log2-SII (OR = 2.22, 95% CI: 1.13–4.37, p = 0.021) and log2-AISI (OR = 3.27, 95% CI: 1.83–5.85, p < 0.001) emerged as independent risk factors for PCa. The AISI-based model achieved superior diagnostic performance with an AUC of 0.878 (95% CI: 0.825–0.931) compared with the SII-based model (AUC = 0.855, 95% CI: 0.794–0.915). DCA showed greater clinical net benefit for the AISI model across a wide range of threshold probabilities. Conclusion: Blood inflammatory markers, particularly AISI, in combination with conventional biomarkers, offer enhanced diagnostic accuracy for differentiating BPH from PCa, representing a non-invasive and cost-effective approach for clinical decision-making.