A rapid diagnostic technique based on metabolomics to differentiate between preeclampsia (PE) and chronic kidney disease (CKD) using maternal urine

Abstract

Similar clinical manifestations between preeclampsia and chronic kidney diseases can lead to potential misdiagnosis. Therefore, it is crucial to investigate effective diagnostic approaches that can reduce misdiagnosis and ensure the well-being of pregnant women. In this study, urine samples collected from 44 individuals with preeclampsia, 37 individuals with chronic kidney disease, and 37 healthy pregnant women were analyzed using metabolomic and proteomic strategies to distinguish between these two diseases. A total of 15 small molecules were tentatively identified as biomarkers to differentiate these two diseases, including potential internally exposed drugs and their metabolites like labetalol and SN-38, metabolites of exogenous substances like 3-phenylpropyl glucosinolate, and endogenous substances related to metabolism such as isoglobotriaose and chitobiose. Metabolic differences between preeclampsia from healthy pregnant women, as well as the differences between chronic kidney disease and healthy pregnant women were also investigated. Major mechanistic pathways were investigated based on the combination of metabolomic and proteomic, amino acid metabolisms and folate metabolism play key roles in distinguishing preeclampsia and chronic kidney disease. Two patients who were initially diagnosed with chronic kidney disease were found to have a closer association with preeclampsia following metabolomic analysis. Subsequent clinical symptoms and manifestations further supported the diagnosis of preeclampsia, and one of patient's pregnancy was ultimately terminated due to severe preeclampsia. Results of this study contribute to a better understanding of the pathogenesis and clinical diagnosis of preeclampsia, offering insights that could potentially improve future diagnostic and management approaches.

Keywords

• Preeclampsia
• Chronic kidney disease
• Metabolomics
• Clinical diagnosis
• Biomarkers