Identification of Serum, Placental and Fetal Serum Metabolite Biomarkers for Preeclampsia Based on LC-MS

doi:10.21203/rs.3.rs-2472204/v1

Background

Preeclampsia is a common complication of pregnancy seriously affecting the health of mothers and babies. There is no independent methods for diagnosing and monitoring preeclampsia with sufficient accuracy, specificity and sensitivity. Changes in body metabolism usually occur before the appearance of clinical symptoms of the disease. Therefore, predicting the occurrence and development of preeclampsia by detecting changes in metabolites has become a research trend in this field.

Methods

In this study, a nontargeted metabolomics approach based on liquid chromatography (LC)-mass spectrometry (MS) was used to analyze the metabolites in the serum, placenta and fetal serum samples of 6 cases of late preeclampsia patients and 6 cases of normal pregnant women. Combine multivariate and univariate statistical methods to screen the endogenous differential metabolites in each sample, and use MetaboAnalyst software to perform enrichment analysis on the metabolic pathways involved in these differential metabolites. In addition, based on a binary logistic regression model to screen biomarkers that may be used to predict preeclampsia.

Results

Compared with normal pregnant women, there were significant differences in serum, placental and fetal serum metabolic profiles of late preeclampsia patients. We found that the late preeclampsia patients mainly had metabolic abnormalities in the metabolic pathways of ALA, taurine, glycine, serine and threonine; in addition, placenta samples showed abnormalities in the metabolic pathways of linolenic acid, taurine, α-linoleic acid and other unsaturated fatty acids, which in turn caused abnormalities in the metabolic pathways of glutamine and glutamate, ALA, alanine, aspartic acid and glutamate, and other polyunsaturated fatty acids in the fetuses of these patients. Moreover, we generated the ROC curves of differential metabolites using a binary logistic regression model and selected four serum metabolites with an AUC value greater than 0.9, most of which were polyunsaturated fatty acids.

Conclusions

There are significant differences in serum, placental, and fetal serum metabolic profiles between late preeclampsia patients and normal third trimester pregnant women in this study and influence the metabolic phenotypes of offspring. This result not only provides a meaningful biomarker for the diagnosis of preeclampsia, but also provides a basis for understanding the pathogenesis of preeclampsia.

liquid Chromatography-Mass Spectrometry

metabolite biomarkers

metabolic phenotypes

late preeclampsia

placental

serum

Preeclampsia is a common complication of pregnancy that causes the deaths of approximately 70,000 pregnant women and 500,000 perinatess annually [¹], seriously affecting the health of mothers and babies. The early prevention, diagnosis, recognition, and treatment of preeclampsia can effectively reduce the risk to mother and child. Changes in body metabolism usually occur before the appearance of clinical symptoms of the disease. Therefore, predicting the occurrence and development of preeclampsia by detecting changes in metabolites has become a research trend in this field. The search for potential biomarkers related to preeclampsia using metabolomics methods can not only help understand the pathogenesis of preeclampsia but can also provide an important material basis for early diagnosis and prognostic predictions. In this study, we investigated the pathogenesis of preeclampsia using nontargeted metabolomics techniques to clarify the changes in the metabolic phenotype of patients with preeclampsia and determine whether they affect offspring, and we screened and mined potential predictive biomarkers related to preeclampsia to provide references for clinical diagnosis and disease monitoring.

2.1 Subjects and groups

A total of six women with late preeclampsia and singleton pregnancies who were hospitalized at the Department of Obstetrics and Gynecology, the First Affiliated Hospital of Xi'an Jiaotong University, China, from December 2021 to February 2022 were included in the preeclampsia group (PE group) according to diagnostic criteria described in the ninth edition of Obstetrics and Gynecology from People's Medical Publishing House. Exclusion criteria: women in the first or second trimester of pregnancy; women with diabetes, anemia, dyslipidemia, and smoking or drinking habits; or use of insulin sensitizers. In addition, six pregnant women with normal singleton pregnancies during the same period were recruited as the control group. The pregnant women in both groups were delivered via cesarean section. In the control group, the indications for cesarean section were a history of previous cesarean section, abnormal fetal position, or abnormal birth canal. This study was approved by the Medical Ethics Committee of the First Affiliated Hospital of Xi'an Jiaotong University, and all patients signed the informed consent form.

2.2 Methods

2.2.1 Specimen collection

After overnight fasting (> 8 h), 5 ml of peripheral blood was collected from the subjects on the morning of the operation. The blood was placed in a centrifuge tube without anticoagulant, kept at 4°C for 1 h for coagulation and stratification, and then centrifuged at low temperature. The supernatant was transferred to a clean centrifuge tube, aliquoted, and stored in a -80°C freezer. During the cesarean section, 2 ml of cord venous blood was collected, treated as described above, and stored in a -80℃ freezer. During the operation, 1 cm³ of tissue from the central part of the placenta on the maternal side was also collected, rinsed with normal saline, drained, and placed in a cryotube; it was then quickly frozen with liquid nitrogen for 15 min and transferred to a -80℃ freezer for storage.

2.2.2 Sample pretreatment

All reagents were purchased from Thermo Fisher Company, USA. After slow thawing on ice, 100 µL of the above specimen was transferred to a centrifuge tube, 300 µL methanol was added to precipitate the protein. Then, 200 µL of the supernatant was removed and added to 5 µL internal standard (0.14 mg/mL dichloro-phenylalanine), and the solution was transferred to a sample vial for subsequent analytical analysis.

2.2.3 High-performance liquid chromatography (HPLC)-mass spectrometry (MS)

HPLC was performed using the ACQUITY UPLC system (Waters, USA) with an HSS T3 column (2.1*100 mm, 1.8 µm). MS was performed on the Q Exactive Model (Thermo, USA) to acquire both the positive and negative ions of small molecules eluted from the chromatographic column. An electrospray ionization (ESI) source was used, and positive and negative ion modes were detected simultaneously with a mass range of m/z 70–1500 at a resolution of 140,000. To monitor the stability and repeatability of the analysis results, the samples to be tested were mixed in equal amounts to create a quality control sample for each sample type. For each group, the original specimens (12 samples) and 3 quality control samples were analyzed.

2.2.4 Data processing

The raw MS data were exported to the CD software for spectrum processing and a database search to obtain the qualitative and quantitative results of metabolites. Quality control was then performed to ensure the accuracy and reliability of the data and results. Subsequently, multivariate statistical analyses, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), were conducted to determine the differences between the two groups in the metabolites from different samples. Intergroup comparison was performed with fold differences and an independent sample t test, in which P < 0.05 was considered statistically significant. Metabolites that satisfied both VIP > 1 and P < 0.05 were designated potential differential metabolites. The differential metabolites in the samples were identified by matching them with those in public databases, including the Human Metabolome Database (HMDB) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Then, metabolic pathway analysis was performed by exporting all of the differential metabolites in both positive and negative ion modes to the MetaboAnalyst software. The receiver operating characteristic (ROC) curve was generated using the binary logistic regression model to evaluate the metabolites’ diagnostic value for preeclampsia.

2.2.5 Statistical analysis

Statistical analysis was performed using the SPSS software, and measurement data with a normal distribution are expressed as mean ± standard deviation (χ ̅±S). Comparisons between two groups were performed using the independent sample t test, in which P < 0.05 was considered statistically significant. The area under the curve (AUC) was obtained by generating the ROC curve and was used to evaluate the diagnostic efficacy of the characteristic metabolites.

3.1 Comparison of the clinical data of the pregnant women in the two groups

In terms of age, primiparity, body mass index (BMI) and newborn birth weight, the PE and control groups showed no significant differences (P > 0.05). The PE group had a significantly lower gestational age than the control group (P < 0.05), which is consistent with clinical observation (Table 1).

3.2 Screening and identification of differential metabolites

Pattern recognition was performed on the serum, placenta, and umbilical serum samples of the two groups using the unsupervised PCA cluster analysis and the supervised OPLS-DA method to analyze the trends in metabolic changes of the two groups and determine their differences. As shown in the PCA graph, the quality control samples were closely clustered under both the positive and negative ion modes, indicating the stable state of the analysis process. The OPLS-DA model of each group of samples was evaluated using the parameters R²Y and Q². The R²Y and Q² values were 0.951and 0.502, respectively, for serum; 0.999 and 0.556, respectively, for the placenta; and 0.986 and 0.548, respectively, for umbilical serum, indicating that the model was reliable. As shown in the OPLS-DA score chart, the serum, placenta, and umbilical serum samples from the late preeclampsia patients and those in the control group showed distinctive clusters under both the positive ion and negative ion modes (Fig. 1; the legend uses the positive ion mode as an example), indicating that overall, serum, placenta and umbilical serum samples from the PE group and the control group had significant metabolic differences.

We then performed a multivariate statistical analysis of the data and a preliminary screening of the metabolite variables based on the VIP values in the OPLS-DA model. Metabolites that simultaneously satisfied the criteria VIP > 1 and P < 0.05 were regarded as potential differential metabolites; FC > 1 indicated that the metabolite was increased in the PE group, while FC < 1 indicated that the metabolite was decreased in the PE group. The numbers of differential metabolites identified in three samples types are shown in Table 2. A total of 70 differential metabolites were identified in the serum, of which 19 were increased and 51 were decreased in the PE patients; a total of 103 differential metabolites were identified in the placenta, of which 59 were increased and 44 were decreased in the PE patients; and a total of 77 differential metabolites were identified in the umbilical serum, of which 43 were increased and 34 were decreased in the PE patients (Table 2 − 1). To visualize the changes in the levels of differential metabolites in the samples from the PE group and the control group, we presented the cluster analysis results as a heat map (Fig. 2). Of the differential metabolites, α-linolenic acid (ALA) and 13S-hydroxyoctadecadienoic acid were increased in all three types of samples from PE patients, while decanamide was decreased in all three types of samples from PE patients.

Using a standard FC value of ≥ 2 or ≤ 0.5, the differential metabolites that were significantly increased or decreased in the samples from the PE group were screened. The results showed that for the PE group, 10 metabolites were significantly increased and 7 metabolites were significantly decreased in serum samples(Table <link rid="tb5">2</link>–2); 14 metabolites were significantly increased and 6 metabolites were significantly decreased in the placenta samples༈Table 2–3༉; and 10 metabolites were significantly increased and 6 metabolites were significantly decreased in the umbilical serum samples༈Table 2–4༉. Among the differential metabolites, ALA was increased in all three types of samples from the PE group.

3.3 Metabolic pathway analysis of the differential metabolites

The key regulatory pathways and network changes involved in the endogenous differential metabolites in the three types of samples from the two groups that were revealed by the enrichment analysis and the topological analysis were analyzed using the MetaboAnalyst open-source software. The key metabolic pathways were comprehensively determined based on the P value and the influence value. The results are shown in Fig. 3 and Tables 3 − 1, 3 − 2 and 3–3. In the PE group, changes in ALA metabolism were common among the metabolic pathways of the differential metabolites in the serum, placenta and umbilical serum samples.

3.4 Prediction of the efficacy potential biomarkers

To determine the potential diagnostic efficacy of the above-identified significantly differentiated serum metabolites, we generated an ROC curve based on the relative contents of each differential metabolite in the samples from the two groups. We identified four differential metabolites with an AUC value greater than 0.9: linolenic acid, gamma-glutamylphenylalanine, 13S-hydroxyoctadecadienoic acid and 13-L-hydroperoxylinoleic acid (Fig. 4). The AUC value of ALA, which was identified in all three types of samples (serum, placenta and umbilical serum) was 0.806, with a sensitivity of 66.67% and a specificity of 100% (Fig. 5).

The application of metabonomics to preeclampsia is still in the early exploratory stage. A comprehensive metabolomic analysis of mothers with preeclampsia, their fetuses and their appendages is of great significance for the pathogenesis and clinical diagnosis of preeclampsia. In this study, we found that the metabolic profiles of the serum, placenta, and fetal serum of late preeclampsia patients and normal pregnant women in the third trimester differed significantly. We then further examined the metabolic pathways involved in the significantly differentiated metabolites of each type of sample. We found that the late preeclampsia patients mainly had metabolic abnormalities in the metabolic pathways of ALA, taurine, glycine, serine and threonine; in addition, placenta samples showed abnormalities in the metabolic pathways of linolenic acid, taurine, α-linoleic acid and other unsaturated fatty acids, which in turn caused abnormalities in the metabolic pathways of glutamine and glutamate, ALA, alanine, aspartic acid and glutamate, and other polyunsaturated fatty acids in the fetuses of these patients.

We found that in the serum, placenta, and umbilical serum of patients with late preeclampsia, significant changes in ALA and metabolism were present. ALA is an essential omega-3 fatty acid and an important component of the cells of the human brain and tissues. Considerable evidence shows that ALA has many functions, such as lowering blood pressure, blood glucose, and blood lipids; inhibiting platelet aggregation; providing an anti-inflammatory function; preventing thrombosis; and improving vascular endothelial functions. Li et al. showed that ALA supplements can improve endothelial dysfunction and hypertension by inhibiting vascular oxidative stress through the reduction of superoxide dismutase 2 (SOD2) overacetylation and autophagy damage [²]. ALA can be converted to eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) by ∆6 and ∆5 desaturases, respectively. Dietary linoleic acid can inhibit the conversion of ALA to DHA, while stearic acid supplementation (18:3n-3) can increase the efficiency of ALA conversion to DHA, suggesting that the ∆6 desaturase-catalyzed reaction is a rate-limiting process [³,⁴]. In this study, we found that compared normal pregnant women in the third trimester, patients with late preeclampsia have significantly elevated levels of several polyunsaturated fatty acids in the serum, placenta, and umbilical blood, which is likely associated with the additional supplementation provided to preeclampsia patients due to fetal growth restriction or due to the decreased activities of ∆6 and ∆5 desaturases, which lead to rate limitation of the ALA metabolic pathway. Additionally, the maternal metabolic disorder can affect the metabolism of these substances by the fetus.

The serum and placentas of patients with late preeclampsia also showed taurine and hypotaurine metabolism disorders. Taurine is a sulfur-containing nonproteinogenic amino acid present in human body, mostly in free form; it acts as a cytoprotective agent and has various physiological functions, such as improving glucose and lipid metabolism, regulating the immune system, providing anti-inflammatory and antioxidant action, etc. [⁵]. It can reduce the overload of Ca²⁺ by regulating the ion channel and exchange system, thus protecting the myocardium [⁶]. Other studies have shown that taurine plays a protective role against liver damage from various causes, such as gestational diabetes, inflammation, etc.[⁷], as well as brain tissue and nervous system damage induced by oxidative stress [⁸]. In this study, we found decreased taurine levels in the serum and placental tissue of patients with preeclampsia, indicating that taurine was largely depleted and taurine metabolism was obstructed, which may be related to the organ protection function of taurine in patients with preeclampsia.

We also found that disorders of alanine, aspartic acid and glutamate metabolism were present in the serum of patients with preeclampsia and their fetuses, while disorders of glutamine and glutamate metabolism were observed in fetal serum. Glutamate plays a very important role in maintaining the balance of redox reactions and avoiding oxidative stress in cells by producing glutathione [⁹] , while glutamine and glutamate participate in cell energy metabolism through the tricarboxylic acid cycle (TCA)[¹⁰]. Alanine metabolism in the human body mainly involves the alanine-glucose cycle, which plays an important part in sustaining the body's energy metabolism [¹¹]. These results indicate that the redox balance and energy metabolism in patients with preeclampsia are abnormal, and these changes also affect the offspring.

It is worth mentioning that the incidence of fetal growth restriction in patients with preeclampsia is high. In this study, we found obvious disorders in arginine and proline metabolism in fetal serum. It has been found that the hydrolyzed product of arginine methylation modification can adversely affect fetal development by interfering with placental endothelial function and angiogenesis, which are related to fetal growth restriction [¹²]. The results of a meta-analysis showed that supplementation with L-arginine increases the birth weight of fetuses with intrauterine growth restriction (IUGR) and prolongs the gestational age at delivery [¹³].

Moreover, the linolenic acid content in the serum and placenta of patients with late preeclampsia was significantly higher than that of the individuals in the control group, and the ROC showed that this metabolite has high diagnostic value. Lee [¹⁴,¹⁵] showed that linolenic acid can attenuate nitric oxide (NO)-mediated vasodilation by inhibiting the release of NO from vascular endothelial cells, which is also in line with the clinical manifestations of hypertension in patients with preeclampsia. NO plays an important role in the regulation of vascular smooth muscle and blood pressure in normal pregnancy. Animal experiments have shown that inhibiting the release of NO can cause changes similar to those in preeclampsia patients. Therefore, NO deficiency or reductions may be a cause of preeclampsia [¹⁶]. The significantly elevated level of linolenic acid may be a reason for the decrease in the NO level.

In this study, we showed that the serum, placenta and fetal serum metabolic profiles of late preeclampsia patients and normal pregnant women in the third trimester differed significantly and that the ALA level and ALA metabolism showed significant differences in all types of samples from the two groups, indicating that the metabolic profile of patients with late preeclampsia undergoes significant changes that affect the metabolic phenotype of their offspring. In addition, four differential metabolites in serum, i.e., linolenic acid, γ-glutamylphenylalanine, 13S-hydroxyoctadecadienoic acid, and 13-L-hydroperoxylinoleic acid, showed high diagnostic value. The results of this study provide not only valuable biomarkers for the diagnosis of preeclampsia but a basis for an in-depth understanding of the pathogenesis of preeclampsia. These markers may be used for the clinical prediction and diagnosis of preeclampsia and for elucidation of its pathogenesis. Their efficacy will be further evaluated in future clinical trials with a large sample size.

Ethics approval and consent to participate

This study was approved by the Medical Ethics Committee of the First Affiliated Hospital of Xi 'an Jiaotong University. All patients provided written informed consent. All experiments were performed in accordance with the relevant guidelines and regulations of the Declaration of Helsinki, and all experimental protocols were approved by the ethics committee.

Consent for publication

Not applicable

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

Funding

The Key R&D Program of Shaanxi Province (General Project-Social Development Field) (No. 2022SF-055), Xi'an Science and Technology Projects（No. 21YXYJ0108), and Institutional Foundation of the First Affiliated Hospital of Xi'an Jiaotong University (No.2021ZYTS-16).

Authors’ contributions

YP, RL, CW, CL, GT, and WW conducted a literature search, assisted with study design, data collection, data analysis, data interpretation, and draft the manuscript. QM and WW participated in study design, and revised the manuscript. All authors contributed to the article and approved the submitted version.

Acknowledgments

This work was supported by the Key R&D Program of Shaanxi Province (General Project-Social Development Field) (No. 2022SF-055), the Xi'an Science and Technology Projects（No. 21YXYJ0108), and the Institutional Foundation of the First Affiliated Hospital of Xi'an Jiaotong University (No.2021ZYTS-16).

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Table 1. Demographics of study population

	Preeclampsia（n=6）	Control（n=6）	P Value
Mean maternal age (SD)	29.7(2.5)	30.8(4.6)	0.21
Nulliparous (%)	6(83.3)	5(66.7)	0.51
Mean BMI (SD)	27.9（2.9）	27.6（2.7）	0.88
Mean GA at delivery (weeks, SD)	36.3(2.2)	39.9(0.6)	0.01
Mean birth weight (g, SD)	2488.3(750.0)	3420.0(342.6)	0.11

GA, gestational age; SD, standard deviation.

Table 2-1. Number of differential metabolite

Metabolite	Maternal Serum	Placenta	Cord Serum
ESI+
FC＞1 FC＜1	17	20	17
FC＞1 FC＜1	33	26	29
ESI-
FC＞1 FC＜1	2	39	26
FC＞1 FC＜1	18	18	5
Total	70	103	77

FC，Fold Change. ＞1 indicated that the metabolite was increased in PE group, and < 1 indicated that the metabolite was decreased in PE group.

Table 2-2. Differential metabolites associated with preeclampsia in serum

No.	RT min	m/z	Metabolites	Formula	VIP	P	FC	Trend	HMDB ID	Scan mode
1	0.82		Dihydrothymine	C5 H8 N2 O2	1.97	*	2.69	↑	HMDB0000079	+
2	9.04		Linolenic acid	C18 H30 O2	1.81	*	2.61	↑	HMDB0001388	+
3	1.59		N1-Methyl-2-pyridone-5-carboxamide	C7 H8 N2 O2	1.79	*	2.47	↑	HMDB0004193	+
4	3.40		Tiglylcarnitine	C12 H21 N O4	1.90	*	2.41	↑	HMDB0002366	+
5	2.79		Pantothenic acid	C9 H17 N O5	1.78	*	2.39	↑	HMDB0000210	+
6	0.83		N-Acetylornithine	C7 H14 N2 O3	1.87	*	2.30	↑	HMDB0003357	+
7	11.09		13S-hydroxyoctadecadienoic acid	C18 H34 O4	1.56	*	2.24	↑	HMDB0004667	+
8	0.88		N-Acetylthreonine	C6 H11 N O4	1.47	*	2.16	↑	HMDB0062557	+
9	12.26		Alpha-Linolenic acid	C18 H30 O2	1.32	*	2.16	↑	HMDB0001388	+
10	3.42		gamma-Glutamylphenylalanine	C14 H18 N2 O5	1.75	*	2.05	↑	HMDB0000594	+
11	10.29		13-L-Hydroperoxylinoleic acid	C18 H32 O4	2.07	**	0.48	↓	HMDB0003871	-
12	1.41		L-Cystine	C6 H12 N2 O4 S2	1.50	*	0.47	↓	HMDB0000192	-
13	5.63		L-Pyrrolysine	C12 H21 N3 O3	2.23	***	0.46	↓	METPA1236	+
14	10.59		7alpha-Hydroxy-3-oxo-4-cholestenoate	C27 H42 O4	1.75	*	0.42	↓	HMDB0012458	-
15	11.57		7-Ketocholesterol	C27 H44 O2	1.52	*	0.36	↓	HMDB0000501	+
16 17	7.58 6.99		Dodecanoic acid Dehydroepiandrosterone sulfate	C12 H24 O2 C19 H28 O5 S	1.55 1.90	* *	0.34 0.20	↓ ↓	HMDB0000638 HMDB0001032	- -

“↓” or “↑” means the metabolite significantly decreased or increased in PE group compared with control group.

*p < 0.05, **p < 0.01, ***p < 0.001 compared with control group.

Table 2-3. Differential metabolites associated with preeclampsia in placenta

No.	RT min	m/z	Metabolites	Formula	VIP	P	FC	Trend	HMDB ID	Scan mode
1	1.31		Glycerophosphocholine	C8H20NO6P	1.93	*	3.37	↑	HMDB0000086	+
2	1.58		N1-Methyl-2-pyridone-5-carboxamide	C7 H8 N2 O2	1.71	*	3.02	↑	HMDB0004193	+
3	0.81		N-Formyl-L-methionine	C6 H11 N O3 S	1.69	**	2.90	↑	HMDB0001015	-
4	8.93		Linoleic acid	C18 H32 O2	1.70	*	2.73	↑	HMDB0000673	-
5	8.59		Gamma-Linolenic acid	C18 H30 O2	1.47	*	2.56	↑	HMDB0003073	-
6	8.14		Taurochenodeoxycholic acid	C26 H45 N O6 S	1.76	*	2.56	↑	HMDB0000951	-
7	9.91		Alpha-dimorphecolic acid	C18 H32 O3	1.72	*	2.49	↑	HMDB0004670	-
8	0.81		N-Acetylhistidine	C8H11N3O3	1.75	**	2.42	↑	HMDB0032055	-
9	9.21		8,11,14-Eicosatrienoic acid	C20 H34 O2	1.50	*	2.21	↑	HMDB0002925	-
10	12.26		Corticosterone	C18 H37 P3	1.78	**	2.16	↑	HMDB0001547	-
11	9.40		Oleic acid	C18 H34 O2	1.50	*	2.11	↑	HMDB0000207	-
12	12.26		Alpha-Linolenic acid	C18 H30 O2	2.30	***	2.07	↑	HMDB0001388	+
13	5.81		2-Phenylacetamide	C8 H9 N O	1.84	*	2.06	↑	HMDB0010715	-
14	0.97		Pyruvic acid	C3 H4 O3	1.68	*	2.06	↑	HMDB0000243	-
15	0.90		Ellagic acid	C14H6O8	1.93	*	0.48	↓	HMDB0002899	-
16	0.83		N-Acetylglucosamine 6-phosphate	C8H16NO9P	1.63	*	0.46	↓	HMDB0002817	-
17	6.63		8-Hydroxyguanine	C5 H5 N5 O2	1.61	**	0.44	↓	HMDB0002032	-
18	0.98		Alpha-D-Glucose 1,6-bisphosphate	C6 H14 O12 P2	1.97	*	0.38	↓	HMDB0003514	-
19	0.98		Acetylphosphate	C2 H5 O5 P	1.98	*	0.36	↓	HMDB0001494	-
20	0.80		O-Phosphoethanolamine	C2 H8 N O4 P	1.71	*	0.34	↓	HMDB0000224	+

“↓” or “↑” means the metabolite significantly decreased or increased in PE group compared with control group.

*p < 0.05, **p < 0.01, ***p < 0.001 compared with control group.

Table 2-4. Differential metabolites associated with preeclampsia in cord serum

No.	RT min	m/z	Metabolites	Formula	VIP	P	FC	Trend	HMDB ID	Scan mode
1	5.05		Methyl 2-octynoate	C9 H14 O2	2.08	*	4.77	↑	HMDB0031302	-
2	0.81		Trimethylamine N-oxide	C3 H9 N O	1.96	*	4.31	↑	HMDB0000925	+
3	13.96		9-Oxooctadecanoic acid	C18 H34 O3	1.87	*	3.31	↑	HMDB0030979	+
4	13.96		9E,11E-Octadecadienoic acid	C18 H32 O2	1.91	*	3.28	↑	HMDB0005047	+
5	0.82		D-Ribose	C5H10O5	1.97	*	2.86	↑	HMDB0000283	-
6	13.56		Pyrazinamide	C5 H5 N3 O	1.50	*	2.75	↑	HMDB0014483	+
7	6.86		Tauroursodeoxycholic acid	C26 H45 N O6 S	1.94	*	2.42	↑	HMDB0000874	-
8	13.96		Alpha-Linolenic acid	C18 H30 O2	1.85	*	2.40	↑	HMDB0001388	+
9	4.94		Vanillactic acid	C10 H12 O5	2.08	*	2.35	↑	HMDB0000913	-
10	13.46		Gingerol	C17H26O4	1.69	*	2.02	↑	HMDB0005783	-
11	6.63		2-Methoxyestrone	C19 H24 O3	1.40	*	0.50	↓	HMDB0000010	+
12	2.13		2-Piperidinone	C5 H9 N O	1.55	*	0.23	↓	HMDB0011749	+
13	3.23		Theophylline	C7 H8 N4 O2	1.95	*	0.19	↓	HMDB0001889	-

“↓” or “↑” means the metabolite significantly decreased or increased in PE group compared with control group.

*p < 0.05, **p < 0.01, ***p < 0.001 compared with control group.

Table 3-1. Results of ingenuity pathway ananlysis with MetaboAnalyst in serum

Pathway Name	Total	Hits	Raw p	-ln(P)	Holm P	FDR	Impact
alpha-Linolenic acid metabolism	13	1	0.3013	1.1997	1	1	0.33333
Taurine and hypotaurine metabolism	8	2	0.018102	4.0117	1	1	0.28571
Glycine, serine and threonine metabolism	33	1	0.59995	0.51092	1	1	0.21707
Aminoacyl-tRNA biosynthesis	48	2	0.3764	0.97711	1	1	0.16667
Tryptophan metabolism	41	2	0.30593	1.1844	1	1	0.15829
Alanine, aspartate and glutamate metabolism	28	2	0.17434	1.7467	1	1	0.11378

Table 3-2. Results of ingenuity pathway ananlysis with MetaboAnalyst in placenta

Pathway Name	Total	Hits	Raw p	-ln(P)	Holm P	FDR	Impact
Linoleic acid metabolism	5	1	0.21191	1.5516	1	1	1
Phenylalanine, tyrosine and tryptophan biosynthesis	4	1	0.17341	1.7521	1	1	0.5
Taurine and hypotaurine metabolism	8	1	0.3171	1.1485	1	1	0.42857
Phenylalanine metabolism	10	1	0.3794	0.96916	1	1	0.35714
alpha-Linolenic acid metabolism	13	1	0.46249	0.77113	1	1	0.33333
Biosynthesis of unsaturated fatty acids	36	6	0.0052326	5.2528	0.43954	0.43954	0

Table 3-3. Results of ingenuity pathway ananlysis with MetaboAnalyst in cord serum

Pathway Name	Total	Hits	Raw p	-ln(P)	Holm P	FDR	Impact
D-Glutamine and D-glutamate metabolism	6	2	0.013014	4.3418	1	0.98061	0.5
Phenylalanine, tyrosine and tryptophan biosynthesis	4	1	0.11834	2.1342	1	1	0.5
alpha-Linolenic acid metabolism	13	1	0.33672	1.0885	1	1	0.33333
Alanine, aspartate and glutamate metabolism	28	1	0.58881	0.52966	1	1	0.19712
Arginine and proline metabolism	38	2	0.33061	1.1068	1	1	0.14699
Valine, leucine and isoleucine degradation	40	2	0.35363	1.0395	1	1	0

*”Total” refers to the total number of metabolites in the pathways; “Hits” refers to the number of metabolite matches in the databases with the data uploaded by the user; the original P value was calculated through the enrichment analysis; the influence value was calculated through the pathway topology analysis.

No competing interests reported.

Identification of Serum, Placental and Fetal Serum Metabolite Biomarkers for Preeclampsia Based on LC-MS

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

1. Background

2. Materials And Methods

2.1 Subjects and groups

2.2 Methods

2.2.1 Specimen collection

2.2.2 Sample pretreatment

2.2.3 High-performance liquid chromatography (HPLC)-mass spectrometry (MS)

2.2.4 Data processing

2.2.5 Statistical analysis

3. Results

3.1 Comparison of the clinical data of the pregnant women in the two groups

3.2 Screening and identification of differential metabolites

3.3 Metabolic pathway analysis of the differential metabolites

3.4 Prediction of the efficacy potential biomarkers

4. Discussion

5. Conclusion

Declarations

References

tables

Additional Declarations

Status:

Version 1