sFlt-1 Is an Independent Predictor of Adverse Maternal Outcomes in Women With SARS-CoV-2 Infection and Hypertensive Disorders of Pregnancy

The Endothelial Glycocalyx as a Target of Excess Soluble Fms-like Tyrosine Kinase-1

Soluble fms-like tyrosine kinase-1 (sFlt-1) is a secreted protein that binds heparan sulfate expressed on the endothelial glycocalyx (eGC). In this paper we analyze how excess sFlt-1 causes conformational changes in the eGC, leading to monocyte adhesion, a key event triggering vascular dysfunction. In vitro exposure of primary human umbilical vein endothelial cells to excess sFlt-1 decreased eGC height and increased stiffness as determined by atomic force microscopy (AFM). Yet, structural loss of the eGC components was not observed, as indicated by Ulex europaeus agglutinin I and wheat germ agglutinin staining. Moreover, the conformation observed under excess sFlt-1, a collapsed eGC, is flat and stiff with unchanged coverage and sustained content. Functionally, this conformation increased the endothelial adhesiveness to THP-1 monocytes by about 35%. Heparin blocked all these effects, but the vascular endothelial growth factor did not. In vivo administration of sFlt-1 in mice also resulted in the collapse of the eGC in isolated aorta analyzed ex vivo by AFM. Our findings show that excess sFlt-1 causes the collapse of the eGC and favors leukocyte adhesion. This study provides an additional mechanism of action by which sFlt-1 may cause endothelial dysfunction and injury.

The sFlt-1/PlGF Ratio in Pregnant Patients Affected by COVID-19

COVID-19 in pregnant women increases the risk of adverse pregnancy outcomes, including preeclampsia. This meta-analysis aimed to examine the effect of SARS-CoV-2 infection on sFlt-1/PIGF ratio during pregnancy. The study was designed as a systematic review and meta-analysis. PubMed, Web of Science, Embase and Cochrane Library were searched for relevant studies reporting the sFlt-1/PlGF ratio in pregnant women with COVID-19. Results were compared using meta-analysis by the Mantel-Haenszel method. A total of 7 studies were included in the analysis. sFlt-1/PlGF ratios between COVID-19 positive vs. negative women were 45.8 ± 50.3 vs. 37.4 ± 22.5, respectively (SMD = 1.76; 95% CI: 0.43 to 3.09; p = 0.01). sFlt-1/PlGF ratios between asymptomatic vs. symptomatic patients were 49.3 ± 35.7 vs. 37.1 ± 25.6 (SMD = 0.30; 95% CI: -0.35 to 0.95; p = 0.36). sFlt-1/PlGF ratio in non-severe group was 30.7 ± 56.5, compared to 64.7 ± 53.5 for severe patients (SMD = -1.88; 95% CI: -3.77 to 0.01; p = 0.05). sFlt-1/PlGF ratios in COVID-19 patients, with and without hypertensive disease of pregnancy, were 187.0 ± 121.8 vs. 21.6 ± 8.6, respectively (SMD = 2.46; 95% CI: 0.99 to 3.93; p = 0.001). Conclusions: Patients with COVID-19, as compared to patients without COVID-19, were characterized by higher sFlt-1/PlGF ratio. Moreover, severe COVID-19 and SARS-CoV-2 infection in hypertensive pregnant women was related to significantly higher sFlt-1/PlGF ratio.

sFlt-1/PlGF Ratio Is Not a Good Predictor of Severe COVID-19 nor of Adverse Outcome in Pregnant Women with SARS-CoV-2 Infection-A Case-Control Study

BACKGROUND

Elevated serum levels of sFlt-1 were found in non-pregnant severe COVID-19 patients. The aim was to investigate sFlt-1/PlGF ratio as a predictor of severe disease and adverse outcome in pregnant women with COVID-19.

METHODS

A single-center case-control study was conducted in pregnant women with SARS-CoV-2 infection. SARS-CoV-2-negative pregnant women served as controls. Serum sFlt-1/PlGF ratio was assessed. The primary outcome was severe COVID-19 and the secondary outcome comprised adverse outcomes including severe COVID-19, intensive care unit admission, maternal multiple organ failure, preterm delivery, fetal demise, preeclampsia or hypertension diagnosed after COVID-19, maternal death.

RESULTS

138 women with SARS-CoV-2 infection and 140 controls were included. sFlt-1/PlGF ratio was higher in infected patients (11.2 vs. 24; p < 0.01) and in women with severe disease (50.8 vs. 16.2; p < 0.01). However, it was similar in women with adverse and non-adverse outcome (29.8 vs. 20; p = 0.2). The AUC of sFlt-1/PlGF ratio was 0.66 (95% CI 0.56-0.76) for the prediction of severe COVID-19, and 0.72 (95% CI 0.63-0.79) for the prediction of adverse outcome.

CONCLUSIONS

sFlt-1 and sFlt-1/PlGF ratio are related to SARS-CoV-2 infection and the severity of COVID-19 during pregnancy. However, sFlt-1/PlGF ratio is not a good predictor of severe COVID-19 or adverse outcome.

Diagnostic value of miR-101 levels in blood and urine of patients with hypertensive disorder complicating pregnancy

OBJECTIVES

This study explored the miR-101 clinical significance in hypertensive disorder complicating pregnancy (HDCP).

METHODS

Pregnant women with gestational hypertension (GH)/mild preeclampsia (mPE)/severe preeclampsia (sPE) were included. The miR-101 levels were measured. Correlation between miR-101 and soluble fmslike tyrosine kinase-1 (sFlt-1), miR-101 predictive value, and factors influencing HDCP grade were evaluated.

RESULTS

Serum miR-101 was down-regulated and negatively correlated with sFlt-1. miR-101 was an independent risk factor for HDCP and decreased with HDCP severity. The area under the curve of miR-101 in differentiating GH from mPE and mPE from sPE was 0.7764 and 0.8529.

CONCLUSION

Serum miR-101 level may be a biomarker for grading HDCP.

Integrating Health Data-Driven Machine Learning Algorithms to Evaluate Risk Factors of Early Stage Hypertension at Different Levels of HDL and LDL Cholesterol

Purpose: Cardiovascular disease (CVD) is a major worldwide health burden. As the risk factors of CVD, hypertension, and hyperlipidemia are most mentioned. Early stage hypertension in the population with dyslipidemia is an important public health hazard. This study was the application of data-driven machine learning (ML), demonstrating complex relationships between risk factors and outcomes and promising predictive performance with vast amounts of medical data, aimed to investigate the association between dyslipidemia and the incidence of early stage hypertension in a large cohort with normal blood pressure at baseline. Methods: This study analyzed annual health screening data for 71,108 people from 2005 to 2017, including data for 27 risk-related indicators, sourced from the MJ Group, a major health screening center in Taiwan. We used five machine learning (ML) methods—stochastic gradient boosting (SGB), multivariate adaptive regression splines (MARS), least absolute shrinkage and selection operator regression (Lasso), ridge regression (Ridge), and gradient boosting with categorical features support (CatBoost)—to develop a multi-stage ML algorithm-based prediction scheme and then evaluate important risk factors at the early stage of hypertension, especially for groups with high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) levels within or out of the reference range. Results: Age, body mass index, waist circumference, waist-to-hip ratio, fasting plasma glucose, and C-reactive protein (CRP) were associated with hypertension. The hemoglobin level was also a positive contributor to blood pressure elevation and it appeared among the top three important risk factors in all LDL-C/HDL-C groups; therefore, these variables may be important in affecting blood pressure in the early stage of hypertension. A residual contribution to blood pressure elevation was found in groups with increased LDL-C. This suggests that LDL-C levels are associated with CPR levels, and that the LDL-C level may be an important factor for predicting the development of hypertension. Conclusion: The five prediction models provided similar classifications of risk factors. The results of this study show that an increase in LDL-C is more important than the start of a drop in HDL-C in health screening of sub-healthy adults. The findings of this study should be of value to health awareness raising about hypertension and further discussion and follow-up research.