Background: Worldwide, hypertensive disorders of pregnancy (HDP) are among the leading causes of maternal and fetal morbidity and mortality. Serum uric acid is a test that can evaluate the severity of HDP and the associated maternal and fetal morbidity and mortality.
Aim: To examine the relationship between maternal serum uric acid levels and the severity of HDP and overall pregnancy outcomes.
Material and methods: A retrospective study was conducted on women with a gestational age > 20 weeks and BP >140/90 mmHg over three years. A total of 134 patients were included in the study. Patients with chronic hypertension, hyperuricemia without hypertension, and other major illnesses were excluded. Data were collected from medical records, including age, gravida, parity, weight, height, gestational age, blood pressure at admission, urine albumin, and serum uric acid levels.
Results: Of the 134 enrolled women with HDP, 76 had gestational hypertension, 41 had preeclampsia, and 17 had eclampsia. Mean uric acid levels in mg/dL were 6.06±1.651, 6.20±0.824, and 7.38±1.26 in gestational hypertension, preeclampsia, and eclampsia, respectively, which was a significant association (p=0.002). Mean uric acid in mg/dL was 5.86±1.27 in intensive care unit (ICU) patients compared to 6.45±1.39 in ward patients (p=0.015). There was a significantly increased risk of ICU admission and preterm delivery (r=-0.401, p<0.001) in patients with elevated uric acid levels. There was a significantly increased risk of low-birth-weight babies with elevated uric acid levels (r=-0.278, p=0.001). However, there was no statistically significant increased risk of newborn intensive care unit admissions (p=0.264) with elevated uric acid levels.
Conclusion: Serum uric acid levels vary significantly in HDP and were found to be elevated in severe preeclampsia and eclampsia. It can be considered for risk stratification in HDP based on disease severity; however, its role in determining outcomes is debatable. Using serum uric acid levels in predictive models along with known biomarkers may determine its possible additional value in disease prediction and severity.