Circulating levels of angiogenic factors and their association with preeclampsia among pregnant women at Mulago National Referral Hospital in Uganda
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Preeclampsia (PE) is a major cause of maternal and new-born morbidity and mortality. Angiogenic factors contribute a major role in the vascular dysfunction associated with PE. We investigated the circulating levels of vascular endothelial growth factor (VEGF), placental growth factor (PlGF) and soluble Feline McDonough Sarcoma (fms)-like tyrosine kinase-1 (sFlt1), their association with PE and diagnostic performance of disease among pregnant women in Uganda. Using a case-control study design, 106 women with PE and 106 with normal pregnancy were enrolled. Demographic and clinical characteristics, and anticoagulated blood samples were collected from participants. Plasma VEGF, PlGF and sFlt1 levels were measured using Luminex and enzyme linked immunosorbent assays (ELISA). Conditional logistic regression was used to explore association of angiogenic factors with PE and receiver operating characteristic analysis was performed to investigate PE diagnostic performance. Levels of VEGF and PIGF were significantly lower in cases compared to controls (VEGF: median = 0.71 pg/ml (IQR = 0.38-1.11) Vs 1.20 pg/ml (0.64-1.91), p-value<0.001 and PlGF: 2.20 pg/ml (1.08-5.86) Vs 84.62 pg/ml (34.00-154.45), p-value<0.001). Plasma levels of sFlt1 were significantly higher in cases than controls (median = 141.13 (71.76-227.10) x103 pg/ml Vs 19.86 (14.20-29.37) x103 pg/ml). Increasing sFlt1 levels were associated with increased likelihood of PE (aOR = 4.73; 95% CI, 1.18-19.01; p-value = 0.0287). The sFlt1/PlGF ratio and sFlt1 had a better performance for diagnosis of PE, with AUC = 0.95 (95% CI, 0.93-0.98) followed by PlGF with AUC = 0.94 (95% CI, 0.91-0.97). Therefore, sFlt1, sFlt1/PlGF ratio and PlGF are potential candidates for incorporation into algorithms for PE diagnosis in the Ugandan population.
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基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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