In recent years, in the study of preeclampsia, it has been found that blood hypercoagulability, chronic diffuse intravascular coagulation (DIC), lipid peroxidation, and vascular endothelial damage tend to form a prethrombotic state. Clinically, it is also often found that patients with preeclampsia often have hyperlipidemia, hyperglycemia or antiphospholipid syndrome, and the preeclampsia belongs to 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, diagnosis and even treatment.

 

Metabolomics is a discipline that studies biological metabolism. Different from previous genomics, proteomics, metabolomics to detect small molecule metabolite changes to understand the mechanisms of life physiology, pathology, help diagnose clinical diseases and improve prognosis, has become an emerging category in systems biology, More and more attention from scholars. At present, it is mainly used in the research of pharmacology and toxicology, and can also be used for the diagnosis and treatment of diseases in clinic. This article describes its application in the preeclampsia and its research progress, in order to help obstetric medical workers have a more comprehensive understanding of it.

 

1 Proposing and concept of metabolomics

 

The origin of metabolomics dates back to 1999 and was proposed by Nicholson et al. Compared to traditional transcriptomics and proteomics, it supports large sample detection with higher accuracy and economic benefits. Metabolomics service explores life from the level of metabolites. The analysis targets the pre-transcriptional, transcript and post-transcriptional levels of living organism genes and related downstream metabolites after protein modification. They are generally small molecules with a relative molecular mass of less than 1000u. It truly reflects the physiological and pathological state of various human systems and provides a powerful basis for clinical diagnosis and treatment.

 

 2 Metabolomics classification and research methods

 

At present, metabolomics is classified according to the subjects and differences in research purposes, and is roughly classified into four categories, specifically: (1) metabolite target analysis. (2) Metabolite profile analysis. (3) Metabolite fingerprint analysis. (4) Metabolomics analysis.

 

Metabolomics is studied in a variety of ways, including according to its technology platform, including: gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). At the same time, with the development of science and technology, on the basis of this, ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS), direct injection mass spectrometry (DIMS), Raman spectroscopy, capillary electrophoresis-mass spectrometry ( CE-MS), Fourier transform infrared spectroscopy (FTIR) and MRI (magnetic resonance). Among them, the most widely used is MRI and MS technology.

 

3 Metabolomics in the application of obstetric diseases

       

With the development of metabolomics, there are more and more applications in obstetrics. Biological tissues or body fluids can be used as specimens for the extraction of metabolic information. In the field of obstetrics, serological samples, amniotic fluid, fetal cord blood, villi, and urine are included. By detecting metabolites in fetal amniotic fluid, maternal urine and other tissue samples, the metabolic changes of fetal malformation (CFM), gestational diabetes mellitus (GDM), premature delivery (PTD) and other diseases can be revealed, providing new predictions for disease prediction. method. In the preeclamptic metabolomics study, available tissue specimens come from a variety of sources, including not only the above, but also vaginal secretions, milk, placenta, neonatal plasma and body fluids, with significant diversity.

 

In the field of obstetrics, metabolomics research is also the most commonly used MS and MRI technology platforms. The analysis principle is different. MS analysis is ionization separation based on the structural difference of metabolite components. The product is identified due to the difference in mass and relative abundance. MRI is based on the difference in response of the molecule to the electric field. The signal generated by the change of the direction of nuclear rotation generated by the molecule in the electric field is converted into a unique spectrum, which is matched to a specific database for identification. Compared with the two methods of analysis, MS has higher sensitivity and greater detection threshold, but it also has technical imperfections, that is, it requires metabolite derivatization to generate ions, so the sample preparation step is cumbersome and the sample is vulnerable. Destruction; while MRI avoids the problem of sample destruction. As a non-destructive technology, the sample reserve is not high, which is conducive to the rational use of resources.

 

In summary, each has its own advantages and disadvantages, so the clinical often adopts multiple platforms for joint analysis, because it is more comprehensive and accurate than the single method.

 

4 Metabolomics study in preeclampsia

 

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

 

4.1 Serum metabolomics The use 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 hydroxycaproylcarnitine in serum of patients with preeclampsia Metabolites were significantly elevated, combined with these indicators and a curve to predict the incidence of pre-eclampsia, the area under the curve (AUC) can reach 0.82, the AUC of early pre-eclampsia reached 0.85. Considering that cellular inflammation and endothelial cell dysfunction are one of the important mechanisms of preeclampsia, some scholars have used LC-MS to detect the metabolic specific factors related to this mechanism, and found that the serum is taurine. The decrease in asparagine levels is closely related to pre-eclampsia. Some scholars have discovered through metabolomics research that taurine as an antioxidant and cell membrane stabilizer, which is significantly low in pre-eclamptic placental trophoblast cells, with reduced activity, leading to regulation of uterine spiral arterial remodeling disorders, and then participate in preeclampsia Onset.

 

Bahado-Singh et al used MRI technique to detect differences in serum metabolomics between pre-eclampsia, pre-eclampsia and normal pregnant women in 11-11+6 weeks of pregnancy, and found that pregnant women with late onset preeclampsia There were significant differences in serum metabolites, with glycerol and carnitine being the most significant. Using these significantly altered indicators of metabolites and body weight, the sensitivity of preeclampsia was predicted to be 76.7% with an accuracy of 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 in the early and late preeclampsia serum differences, suggesting that these metabolites can distinguish between early onset and late Hair pre-eclampsia. Acilmis et al found that patients with pre-eclampsia had reduced serum choline levels, while low levels of choline increased the risk of preeclampsia, preterm and low birth weight infants. Studies have confirmed that serological metabolites are used in the prediction of pre-eclampsia, combined with a variety of metabolites can improve the pre-eclampsia detection rate to 75.9%, but can not rule out false positives, false positive rate is about 4.9%.

 

Austdal et al used MRI to detect 10 cases of preeclampsia, normal pregnancy and non-pregnant women between 17 and 20 weeks of pregnancy, and found that serum low density and very low density lipoprotein levels were significantly higher in the preeclampsia group than in the other two groups. And high-density lipoprotein is higher than the other two groups. The results of serum lipid metabolism in preeclampsia indicate that dyslipidemia has occurred in the early stage of preeclampsia, and may play an important role in its pathogenesis. The above studies suggest that changes in lipoprotein expression in serum lipid metabolism during early pregnancy may be used to predict early preeclampsia.

 

4.2 Urine Metabolomics In addition to the detection of serum metabolomics, the detection of urine in body fluids is also significant in preeclampsia studies. Austdal et al also detected changes in urinary metabolomics, suggesting that there are nine significantly different metabolites in the urine of preeclampsia patients, including cresol sulfate, histidine, glycine, and asparagine. , fenugreek and horse urate levels decreased, dimethylamine and isobutane levels increased. The study also found that the levels of choline in the urine of pregnant women with preeclampsia were abnormally elevated, and the analysis may be related to oxidative stress and renal insufficiency. Further studies have found that cresol sulfuric acid can be used to assess renal function, that is, in patients with pre-eclampsia complicated with renal insufficiency, the level of cresol sulfate in urine is decreased, but it is increased in kidney tissue. It is a cause of exacerbation of pre-eclampsia renal function damage. The mechanism of analysis may be related to the oxidative stress induced by cresol sulfate, which ultimately leads to damage of renal tubular cells and reduces renal excretion. In addition, Austdal et al also found that combined with urine levels of hippuric acid/creatinine, can significantly improve the predictive level of pre-eclampsia, suggesting that pre-eclampsia urine metabolites have a higher predictive value, worthy of study .

 

Paine et al found that the rapid increase of muscle glycophosphopeptide P (P-IPG) in the urine of patients with preeclampsia can be used as an indicator to predict 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 of different gestational weeks by ELISA. In the prospective study of 416 pregnant women, the final 34 cases progressed to preeclampsia, and the results showed P-IPG prediction. The sensitivity was 84.2%, the specificity was 83.6%, and it was able to predict 2 weeks before the onset.

 

4.3 Placental metabolomics Heazell et al intervene in placental villus tissue by different oxygen partial pressure, detect metabolite expression in culture medium and tissue lysate, simulate the regulation mechanism of hypoxia on preeclampsia, and find that differential expression Metabolites include 2-deoxyribose, triol or erythritol and palmitic acid. Dunn et al also described the pathogenesis of preeclampsia by UPLC-MS detection of placental metabonomics by in vitro hypoxic culture of preeclampsia and normal placental villus tissue. The study found that 47 metabolites are differentially expressed and preeclampsia. The incidence is closely related, such as glutamate, glutamine, tryptophan metabolism, leukotrienes or prostaglandins and other metabolic differences.

 

5 Advantages of metabolomics in predictive applications of preeclampsia

 

The choice of metabolomics to explore the pre-clinical disease, especially in the early prediction and pathogenesis has become a hot research topic.

 

       

 

Most of the subjects selected for metabolomics are serum, placental tissue, and urine. The recognition target is a small molecule metabolite, which has advantages that cannot be ignored compared with traditional research methods such as proteomics. The specific performance is: (1) The result is intuitive. As the final product of gene transcription or post-transcriptional modification, metabolites trace the origin of the results, and the metabolite-related marker factors can better reflect the overall state of the metabolic network, which is more intuitive. (2) High acceptability. Samples such as urine and serological sources are mostly non-invasive and simple, and are easily accepted by patients. It is difficult to apply clinically. (3) Simplicity of detection and analysis. The metabolomics research objective is metabolites, the species is significantly reduced relative to genomics, the detection is more convenient, and there is no need to establish a large number of databases for expressing sequence tags (EST) with low technical requirements. (4) Strong versatility. The differences in metabolites of different individuals are not large, which makes the technology more versatile and facilitates the unification of standards.

 

6 Prospects for the application of metabolomics in preeclampsia

 

In summary, we have seen the advantages of metabolomics in preeclampsia studies. Previous studies have shown that metabolomics is of great importance in the prediction of preeclampsia and disease pathogenesis, and it is worthy of further study. At the same time, we must also clarify the limitations of its existence, for example, the specificity of the matching requirements. Because each age level and constitutional state have an effect on individual metabolism, it is required to match factors such as age, weight, race and gestational age. In addition, metabolomics, as an emerging research discipline, is still in the preliminary exploration stage, so we are required to overcome some of the poor reproducibility of research data and enrich its data diversity to improve rigor and stability.

 

All in all, metabolomics is in the early stages of preeclampsia research, but we have seen its broad research prospects, which need to be further expanded in sample size and detection range, combined with the detection of many different products, using a variety of Technology platform to further enhance the role of metabolomics in predicting and diagnosing pre-eclampsia diseases.

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