Influential Serum Kinases (Non-sFlt-1) and Phosphatases in Preeclampsia—Systematic Review and Metanalysis

The early identification of women with an increased risk of preeclampsia (PE) is desirable, but apart from soluble fms-like tyrosine kinase-1 (sFlt-1), few biomarkers have previously been identified as relevant for predicting preeclampsia. Since kinases and phosphatases regulate critical biological processes and previous evidence suggests a potential role of these molecules in preeclampsia, we performed this systematic review and metanalysis. The objective was to determine if there are kinases and phosphatases whose serum levels are different between women with and without PE, being relevant biomarkers of PE. We followed the recommendations of Cochrane and the Preferred Reported Items for Systematic Reviews and Metanalysis (PRISMA) to perform this study. The MESH terms preeclampsia, kinases, phosphatases, angiopoietins, soluble tyrosine protein kinase receptor (sTIE2), and cellular-mesenchymal-epithelial transition factor (c-MET) were combined to find relevant articles in the PubMed, PROSPERO, and Cochrane databases. Then, a qualitative and quantitative analysis was performed in R Studio software. From 580 abstracts identified, 37 were included in the final analysis, which comprised 24,211 pregnant women (2879 with PE and 21,332 women without PE [HP]. The pooled analysis showed that serum creatine kinase (CK) (SMD: 2.43, CI 95% 0.25–4.62) was significantly higher in PE, whereas sTIE2 and anti-angiogenic factor soluble c-Met (sMet)were significantly lower in PE than in HP (SMD: −0.23, CI95% −0.37 to −0.09; and SMD:0.24, CI95% 0.01–0.47, respectively). Adenosine monophosphate-activated protein kinase (AMPK), angiopoietin-1 (ANG-1), angiopoietin-2 (ANG-2), the ratio angiopoietin-1/angiopoietin-2, acid phosphatase, and alkaline phosphatase were not different between women with PE and HP. In summary CK, sTIE2, and c-MET are relevant biomarkers of PE. It is desirable to incorporate them into current models for PE prediction to evaluate their utility as biomarkers.