Int J Med Sci 2022; 19(11):1672-1679. doi:10.7150/ijms.71047 This issue Cite
1. Institute of Clinical Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
2. Department of Laboratory Medicine, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan.
3. Department of Obstetrics and Gynecology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
4. Department of Public Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
5. Biostatistics Consulting Center, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Preeclampsia is one of the most serious pregnancy complications. It may be caused by immunological changes in the early placental microenvironment. The contents of small EVs may serve as biomarkers of pregnancy complications. Evidence suggests that the balance between T helper 17 (Th17) and regulatory T (Treg) cells are critical for preventing preeclampsia. The study recruited 39 pregnant women with preeclampsia and 127 healthy pregnant women. We assessed the levels of both Th17 and Treg cytokines (IL-10, IL-17, IL-21, IL-22, and TGF-β) in their plasma and small EVs. We found significant differences in the levels of all cytokines in the plasma between the two groups during the second trimester. We also observed significant differences between the two groups in the levels of EV-encapsulated cytokines IL-21, IL-22, and TGF-β, as well as in total small EVs, during the second trimester. The ROC analysis showed that the classification efficiency (AUC) of TGF-β in small EVs was 0.81. TGF-β had the best discriminant ability of all the single EV biomarkers tested, the cross-validation of the accuracy was 0.89. Th17 and Treg cytokines in plasma and small EVs may contribute to maternal immune activation and clarify the potential mechanisms of small EVs and cytokines in preeclampsia.
Keywords: preeclampsia, Th17, Treg, cytokine, small extracellular vesicles, predictive biomarker, discriminant analysis, machine learning, support vector machine