Correlation of uric acid and lipid levels with preeclampsia and final pregnancy outcome in late pregnancy.

OBJECTIVE

To investigate the correlation between uric acid (UA), lipid levels, and preeclampsia (PE), as well as their effect on pregnancy outcome in women in late pregnancy.

METHODS

A retrospective analysis was conducted on the clinical data from 126 pregnant women with PE who were admitted to the First Affiliated Hospital of Xi'an Medical University from June 2021 to January 2024 (research group). Additionally, clinical data from 130 healthy pregnant women who gave birth during the same period were served as controls. General information, UA levels, blood lipid levels [total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), free fatty acids (FFA), lipoprotein-a (Lp-a), apolipoprotein-a1 (ApoA1), apolipoprotein B (ApoB), LDL-C/HDL-C, and ApoA1/ApoB] and pregnancy outcomes were compared between the two groups. A logistic regression model was used to identify the influencing factors for PE. The predictive value of UA and lipid levels for PE diagnosis and prognosis was evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

No significant differences were observed between the groups in terms of age, parity, mode of delivery, neonatal gender, gestational cardiac disease, HDL-C, FFA, ApoA1, or ApoA1/ApoB (all P>0.05). However, the research group exhibited significantly higher body mass index (BMI), prevalence of gestational diabetes, and gestational hypertension, UA, TC, TG, LDL-C, Lp-a, ApoB, and LDL-C/HDL-C ratio compared to the control group, but lower neonatal weight (all P<0.05). Furthermore, the research group had a higher incidence of gestational diabetes, gestational hypertension, postpartum hemorrhage, fetal growth retardation, preterm delivery, and neonatal asphyxia (all P<0.05). Multivariate logistic regression analysis identified BMI, neonatal weight, UA, TC, TG, and LDL-C as independent influencing factors for PE. ROC curve analysis demonstrated high diagnostic accuracy for BMI (AUC=0.835), neonatal weight (AUC=0.755), UA (AUC=0.765), TC (AUC=0.706), and LDL-C (AUC=0.792) in predicting PE.

CONCLUSION

Maternal BMI, neonatal weight, serum UA, TC, TG, and LDL-C levels are risk factors for the development of PE. Among these, BMI, neonatal weight, serum UA, TC, and LDL-C levels have a high predictive value for PE and can serve as valuable indicators for its early prediction and management.