Metabolomics in Preeclampsia Prediction and Diagnosis

Posted by beauty33 on January 14th, 2020

In recent years, research on preeclampsia found that blood coagulation, chronic diffuse intravascular coagulation (DIC), lipid peroxidation, and vascular endothelial injury are prone to form a prethrombotic state. Clinically, it is often found that patients with preeclampsia often have hyperlipidemia, hyperglycemia, or antiphospholipid syndrome. Preeclampsia is a metabolic syndrome. Therefore, we need to re-examine and study the disease from the direction of metabolomics, and provide new ideas for its prediction and diagnosis and even treatment.

The application of metabolomics in the field of preeclampsia is still in the early stages of exploration, mainly focusing on the three major metabolisms of fat, protein and carbohydrate. The current research is divided into the following directions.

Serum metabolomics The application of serum metabolomics in the field of preeclampsia is not uncommon and has begun to take shape. Odibo et al. detected serum acylcarnitine and amino acid levels by UPLC-MS method, and found that alanine, glutamate, phenylalanine, and hydroxyhexanoylcarnitine in the serum of preeclampsia patients Metabolites were significantly increased. Combined detection of these indicators and drawing a curve to predict the incidence of preeclampsia, the area under the curve (AUC) can reach 0.82, and the AUC of early preeclampsia can reach 0.85. Considering that cell inflammation and endothelial cell dysfunction are one of the important mechanisms of pre-eclampsia, some scholars have used LC-MS to detect metabolic production related to this mechanism to find specific differential factors. As a result, it was found that taurine in serum And lower asparagine levels are closely related to early preeclampsia. Some scholars have found through metabolomics studies that taurine, as an antioxidant and a cell membrane stabilizer, has significantly lower expression and reduced activity in placental trophoblasts in preeclampsia, leading to the regulation of uterine spiral arterial remodeling disorders and further participation in preeclampsia Onset.

Bahado-Singh et al. used MRI to detect differences in serum metabolomics between the three groups of late preeclampsia, preeclampsia, and normal pregnant women at 11 ~ 13 + 6 weeks of pregnancy. There are significant differences in serum metabolites, with glycerol and carnitine increasing most significantly. Using these significantly altered metabolites and weight, the sensitivity of comprehensive prediction of preeclampsia reaches 76.7%, and the accuracy is as high as 100%. The involvement of carnitine in the pathogenesis of preeclampsia may be related to its oxidative stress and lipid peroxidation. In addition to carnitine, the researchers also found that glycerol, acetate, trimethylamine, succinate and other metabolites have differences in serum between early and late onset of preeclampsia, suggesting that these metabolites can distinguish between early and late onset. Preeclampsia. Acilmis et al. research found that serum choline levels in patients with preeclampsia decreased, and low levels of choline can increase the risk of preeclampsia, premature birth and low birth weight infants. Studies have confirmed that serological metabolites are used for the prediction of preeclampsia, and the combination of multiple metabolites can improve the detection rate of preeclampsia to 75.9%, but it cannot rule out false positives, which is about 4.9%.

Austdal et al. used MRI to detect 10 cases of preeclampsia, normal pregnancy and non-pregnant women from 17 to 20 weeks of pregnancy. , While HDL was higher than the other two groups. Preeclampsia serum lipid metabolism research results have shown that dyslipidemia has occurred in the early stages of preeclampsia and may play an important role that cannot be ignored in its pathogenesis. The above studies suggest that changes in the expression of lipoproteins in serum lipid metabolism in early pregnancy may be used for early prediction of preeclampsia.

Urine metabolomics 

In addition to serum metabolomics, urine detection in body fluids has significant significance in preeclampsia studies. Austdal et al. also detected changes in urine metabolomics, and the results suggest that there are 9 significantly different metabolites in the urine of preeclampsia patients, including cresol sulfate, histidine, glycine, asparagine , Trigonelline and hippurate levels decreased, dimethylamine and isobutane levels increased. The study also found that the abnormally elevated choline levels in the urine of preeclampsia pregnant women may be related to oxidative stress and renal insufficiency. Further research found that cresol sulfate can be used to evaluate renal function, that is, in patients with preeclampsia and renal insufficiency, the level of cresol sulfate in urine decreases, but it increases in the kidney tissue. It is one of the reasons for the increase of preeclampsia renal function damage. The analysis mechanism may be related to cresol sulfuric acid-induced oxidative stress response, which eventually leads to damage of renal tubular cells and reduces renal excretory function. In addition, research by Austdal et al. also found that combined with the level of uric acid / creatinine in urine can significantly improve the pre-eclampsia prediction level, suggesting that pre-eclampsia urine metabolites have a higher predictive value and are worth studying .

Paine et al. found that the rapid rise of inositol phosphoglycan P-type (P-IPG) in urine of patients with preeclampsia can be used as an index for predicting preeclampsia, with a sensitivity of 88.9% and an accuracy of 62.7%. However, the number of cases is too small and needs further confirmation. Dawonauth et al. sequentially detected the expression of P-IPG in the urine of pregnant women at different gestational weeks by ELISA. In a prospective study of 416 pregnant women, 34 cases progressed to preeclampsia, and the results found that P-IPG prediction The sensitivity was 84.2% and the specificity was 83.6%, and it could be predicted 2 weeks before the onset.

Placenta metabolomics Heazell et al. interfered with placental villus tissue by different oxygen partial pressures, detected the expression of metabolites in the culture medium and tissue lysate, and simulated the mechanism of hypoxia on preeclampsia diseases. Metabolites include 2-deoxyribose, triol or erythritol, and hexadecanoic acid. Dunn et al. also elaborated the pathogenesis of preeclampsia through in vitro hypoxia culture of preeclampsia and normal placental villi, and UPLC-MS method to detect placenta metabolomics. The study found that 47 metabolites are differentially expressed and preeclampsia. The pathogenesis is closely related to metabolic differences such as glutamate, glutamine, tryptophan metabolism, leukotriene or prostaglandin.

Advantages of Metabolomics in Preeclampsia Prediction

Choosing to use metabolomics to explore the early stage of sub-diseases, especially in terms of early prediction and pathogenesis, has become a current research hotspot.

Most of the analysis objects selected for metabolomics are serum, placental tissue, and urine. The identification objects are metabolites of small molecules. Compared with traditional research methods such as proteomics, it has an advantage that cannot be ignored. The specific performance is as follows: (1) The result is intuitive. As the final product of gene transcription or post-transcriptional modification, metabolites can be traced back to the origin. Metabolite-related marker factors can better reflect the overall state of the metabolic network and are more intuitive. (2) Highly acceptable. Samples such as urine and serological sources are mostly non-invasive, simple, easy to accept by patients, and difficult to apply in clinical applications. (3) Simplicity of detection and analysis. The target of metabolomics research analysis is metabolites. Compared with genomics, the number of species is significantly reduced, and the detection is more convenient. There is no need to establish a large database of expressed sequence tags (EST), and the technical requirements are low. (4) Strong versatility. The metabolites of different individuals are not very different, which makes the technology more versatile and conducive to the unification of standards.

Application prospects of metabolomics in preeclampsia

In summary, we have seen the advantages of metabolomics that cannot be ignored in preeclampsia research. Previous studies have shown that metabolomics is of great significance in the prediction of preeclampsia and the pathogenesis of diseases and deserves further study. At the same time we need to be clear about its limitations, such as the specificity of the matching requirements. As each age level and physical condition has an effect on individual metabolism, it is required to match factors such as age, weight, race and gestational week. In addition, metabolomics, as a new research discipline, is still in the preliminary exploration stage, so we are required to overcome the poor repetitiveness of some research data and enrich its data diversity to improve rigor and stability.

All in all, metabolomics is in its infancy in the preeclampsia research field, but we have seen its broad research prospects, which needs to further expand the sample size and detection range, and combine the detection of multiple different products, using multiple technology platform to further improve the role of targeted metabolomics in predicting and diagnosing preeclampsia.

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