Increased Uric Acid, Gamma-Glutamyl Transpeptidase and Alkaline Phosphatase in Early-Pregnancy Associated With the Development of Gestational Hypertension and Preeclampsia.

Background: Previous studies have reported that biomarkers of liver injury and renal dysfunction were associated with hypertensive disorders of pregnancy (HDP). However, the associations of these biomarkers in early pregnancy with the risk of HDP and longitudinal blood pressure pattern during pregnancy were rarely investigated in prospective cohort studies. Methods: A total of 1,041 pregnant women were enrolled in this prospective cohort study. BP was assessed in four stages throughout pregnancy. The following biomarkers were measured at early pregnancy before 18 weeks gestation: lactate dehydrogenase (LDH), aspartate aminotransferase to alanine aminotransferase ratio (AST/ALT), gamma-glutamyl transpeptidase (GGT), alkaline phosphatase (ALP), uric acid (UA), and estimated glomerular filtration rate (eGFR). Linear mixed-effects and logistic regression models were used to examine the associations of these biomarkers with longitudinal BP pattern during pregnancy and HDP incidence, respectively. Results: In unadjusted models, higher serum UA, GGT, ALP, and LDH levels, as well as lower eGFR and AST/ALT, were associated with higher BP levels during pregnancy and an increased risk of HDP. After adjustment for maternal age, pre-pregnancy BMI and other potential confounders, UA, GGT, ALP, and LDH remained positively associated with both BP and HDP. However, eGFR and AST/ALT were not associated with HDP after adjusting for potential confounders. When including all 6 biomarkers simultaneously in multivariable analyses, increased UA, GGT, and ALP significantly associated with gestational hypertension and preeclampsia. Conclusion: This study suggests that increased UA, GGT, and ALP in early-pregnancy are independent risk factors of gestational hypertension and preeclampsia.

Uterine fibroids increase the risk of hypertensive disorders of pregnancy: a prospective cohort study.

OBJECTIVE: It is unclear whether uterine fibroids are associated with the occurrence of hypertensive disorders of pregnancy (HDP). Thus, this study aimed to evaluate the association between uterine fibroids and HDP in a prospective cohort. METHODS: Overall, 2404 pregnant women who received antenatal care were enrolled in a prospective cohort in China between 2014 and 2016; 2277 women met the inclusion criteria of this study. The clinical characteristics of participants were assessed via questionnaires and physical examinations at baseline (before the 20th week of gestation), 21st-27th, 28th-34th, and 35th-39th gestational weeks. Ultrasound examination was performed before the 20th week of pregnancy to determine the presence of uterine fibroids. Linear mixed-effect and Cox proportional hazard regression models were used to analyze the association of uterine fibroids with blood pressure and HDP. RESULTS: Of 2277 pregnant women, 242 (10.6%) had uterine fibroids, and 45 (2.0%) subsequently developed HDP. The incidence of HDP in women with and without uterine fibroids was 5% (n = 12) and 1.6% (n = 33), respectively. The longitudinal SBPs and DBPs were significantly higher in women with uterine fibroids than in those without. The multivariable Cox model showed that the presence of uterine fibroids was associated with increased HDP risk (adjusted hazard radio: 2.95, 95% confidence interval: 1.35-6.44). CONCLUSION: Uterine fibroids in early pregnancy were associated with an increased HDP risk. Blood pressure of women with uterine fibroids should be closely monitored, and HDP preventive measures are crucial.

The application of meteorological data and search index data in improving the prediction of HFMD: A study of two cities in Guangdong Province, China.

Hand, foot and mouth disease (HFMD) is a public health issue in China, and its incidence in Guangdong Province is higher than the national average. Previous studies have found climatic factors have an influential role in the transmission of HFMD. Internet search technology has been shown to predict some infectious disease epidemics and is a potential resource in tracking epidemics in countries where the use of Internet search index data is prevalent. This study aims to improve the prediction of HFMD in two Chinese cities, Shantou and Shenzhen in Guangdong Province, applying both meteorological data and Baidu search indices to create a HFMD forecasting model. To this end, the relationship between meteorological factors and HFMD was found to be linear in both cities, while the relationship between search engine data and HFMD was not consistent. The results of our study suggest that using both Internet search and meteorological data can improve the prediction of HFMD incidence. Using comparative analysis of both cities, we posit that improved quality search indices enhance prediction of HFMD.

An electrocardiographic diagnostic model for differentiating left from right ventricular outflow tract tachycardia origin.

INTRODUCTION: Although several electrocardiographic (ECG) algorithms have been proposed for differentiating the origins of outflow tract ventricular arrhythmias, the most optimal one has not been agreed on. The purpose of this study was to establish an ECG diagnostic model based on the previous ECG algorithms. METHODS AND RESULTS: The following ECG diagnostic model, Y=-1.15×( TZ )-0.494×(V2S/V3R), was developed by standard 12-lead ECG algorithms in 488 patients with idiopathic premature ventricular contractions or ventricular tachycardia with a left bundle branch block pattern and inferior axis QRS morphology. Binary logistic regression analysis was performed to establish the ECG diagnostic model. The ECG diagnostic model consisted of two ECG algorithms-the transition zone (TZ) index and V2S/V3R index. The area under the curve by receiver operating characteristic curve analysis for the ECG diagnostic model was 0.88, with a cut-off value of ≥ -0.76 predicting a left ventricular outflow tract (LVOT) origin with a sensitivity of 82% and a specificity of 86%, which was higher than other ECG algorithms in this study. The predictive accuracy of the ECG diagnostic model was also the best among all ECG algorithms in patients with a lead V3 precordial transition. This model was tested prospectively in 207 patients with a sensitivity of 90%, a specificity of 87%, and Youden index of 0.77. CONCLUSIONS: A highly accurate ECG diagnostic model for correctly differentiating LVOT origin from right ventricular outflow tract origin was developed.