Role of C reactive protein at early gestation in prediction of preeclampsia Abstract

Abstract

Preeclampsia is a major form of hypertensive disorder of pregnancy which causes an enormous morbidity and mortality for both the mother and the newborn child worldwide. The objective of this study is to determine whether C-reactive protein (CRP), a biomarker of systemic inflammation, can be used as a predictor marker for the development of preeclampsia in early gestation. Like any good research, a cohort study was conducted with 150 pregnant women in their first trimester. Additionally, we measured CRP levels and followed participants until delivery to see if they developed preeclampsia. Logistic regression and receiver operating characteristic (ROC) curve analysis were performed to assess association between CRP level and the risk of preeclampsia. Preeclampsia was diagnosed among 13.3 (n = 20) of the participants. Women who subsequently developed preeclampsia had significantly higher mean CRP level (7.2 ± 2.1 mg/L) than those without preeclampsia (3.8 ± 1.5 mg/L, p < 0.001). Logistic regression analysis revealed that each 1 mg/L increase in CRP levels was associated with an 85% increase in the odds of developing preeclampsia (OR: 1.They found that, compared to those in the placebo groups whose bone density was measured after 12 months, those who were given combination therapy had greater decreases (85, 95% CI: 1.35–2.54, p < 0.001). Good predictive accuracy for CRP was shown by ROC analysis, with AUC of 0.82, sensitivity of 75%, and specificity of 78% at optimal cutoff value of 5.0 mg/L. The potential for cost-effective and accessible biomarker detection of preeclampsia with CRP is explored in this study. Higher early gestation CRP levels were significantly associated with higher preeclampsia risk. In resource limited settings these finding suggest that CRP testing may be integrated into routine antenatal screen protocols. Further research, however, is needed to confirm these findings and to examine the use of CRP in combination with other biomarkers in order to increase predictive accuracy.