**2.7 Biochemical and urine analysis**

Sandwich enzyme-linked immunosorbent assay technique (Elabscience Biotechnology Co. Ltd., Wu Han, People's Republic of China) was used to analyze adiponectin, leptin, resistin, and visfatin in the baseline samples of both cases and controls, while the lipid profiles were performed using the Vitros dry chemistry analyzer (Ortho-Clinical Diagnostics, Johnson & Johnson, High Wycombe, UK). None of the samples in this investigation had been thawed and frozen before.

For less than 2 seconds, a urine strip was put into a urine sample up to the test area. To remove surplus urine, the strips' margins were drawn around the brims of the

vessels, ensuring that the test areas did not come into contact with them. To eliminate any residual urine, the strips were held vertically and tapped on absorbent papers [59]. Under bright light, the urine strip was horizontally held and compared to the color chart on the vial label.

The intensity of the blue-green color, which was related to the quantity of protein in the urine, was then used to determine the amount of protein. Proteinuria was defined as the presence of urine protein at concentrations of "+" or higher [60].

#### **2.8 Study variables and outcome measurement**

After the twentieth week of pregnancy, every pregnant woman in this hospital is screened for PE. PE occurrence (yes/no), as determined by PE diagnosis criteria, was the primary outcome. Urine protein was measured using the dip-stick qualitative/semi-quantitative method (Urit Medical Electronic Co., Ltd., Guangxi, People's Republic of China) after 20 weeks of pregnancy. PE was diagnosed by a qualified Obstetrician/Gynecologist based on systolic and diastolic blood pressures of 140 mmHg or more on two occasions at least 4 hours apart (or both) in addition to proteinuria of + or more.

#### **2.9 Statistical analysis**

The SPSS software, version 20, and Graph Pad Prism, version 5.0, San Diego, California, USA, and Systat, Inc. Germany were used to analyze the data. The Shapiro-Wilk test was used to determine the normality of the variables under investigation, followed by a Mann-Whitney *U*-test to compare those with PE to those without. A value of *p* < 0.05 was considered significant in all of the statistical analyses. The AUC (area under the receiver operating characteristic (ROC) curve) is commonly used to assess a test's/accuracy. When the AUC is less than 50%, the result is considered random guessing and thus not meaningful. This is represented by a diagonal line in the ROC plot [61]. The adipokines and lipids were evaluated for their accuracy (AUC 60%) in predicting preeclampsia-like pregnancies.

After correcting for potential confounding variables, multivariate analysis was performed on the individual adipokines as predictors of PE (age, BMI, relatives with hypertension, family history of diabetes mellitus, family history of preeclampsia, and parity). After correcting for confounders, the goal was to determine the independent contribution of each adipokine in predicting PE.

The parameters for the goodness of fit test for the models were −2Log (Likelihood), R2 (Cox and Snell), R2 (Nagelkerke), Akaike Information Criterion (AIC), and Correct Classification Rate (CCR).

The −2Log (Likelihood) statistic indicates how well a model predicts a certain occurrence, the lower the number, the better the model.

The coefficients of determination Cox and Snell R2 and Nagelkerke R2 are used to measure the amount of variation in the dependent variable that is explained by the independent variable. The Cox and Snell R2 has been modified to create the Nagelkerke R2. The AIC is also a relative quality estimator for statistical models. The better the model, the smaller the estimate. The Correct Classification Rate is another valuable metric for evaluating the utility of a logistic regression model (CCR).

*Pathophysiology of Preeclampsia: The Role of Adiposity and Serum Adipokines DOI: http://dx.doi.org/10.5772/intechopen.104752*
