Abstract
Background
Gas chromatography coupled with mass spectrometry (GC-MS) and liquid chromatography coupled with mass spectrometry (LC-MS) metabolomics have been deployed to detect novel differential metabolites in cases with recurrent spontaneous abortion (RSA).
Material/Methods
Fifty patients who had recurrent spontaneous abortions (RSAs) and 51 control patients (age, gestational age, and body mass index (BMI) match) were enrolled in this study. Untargeted GC-MS and targeted LC-MS were combined to discover and validate the different metabolomic profiles between groups. Score plots of orthogonal partial least-squares discriminant analysis (OPLS-DA) clearly separated the RSA group from the control group. The variable importance in projection (VIP) generated in OPLS-DA processing represented the contribution to the discrimination of each metabolite ion between groups. Variables with a VIP >1 and P<0.05 were considered to be different variables. We also used MetaboAnalyst 3.0 to analyze the pathway impact of potential metabolite biomarkers.
Results
Fifty-four metabolites were significantly different between the two groups, as indicated by a VIP >1 and P<0.05. The metabolic pathways involving glycine, serine, threonine (P=0.00529, impact=0.26), beta-alanine (P=0.0284, impact=0.27), and phenylalanine metabolism (P=0.0217, impact=0.17), along with the tricarboxylic acid (TCA) cycle (P=0.0113, impact=0.19) and the glycolysis pathway (P=0.037, impact=0.1) are obviously related to RSA. Verification by LC-MS showed that the concentration of lactic acid in RSA was higher than that in the control group (P<0.05), while the concentration of 5-methoxytryptamine was significantly lower in the RSA group (P<0.05).
Conclusions
In our study, untargeted GC-MS was used to detect disturbance of metabolism occurs in RSA and targeted LC-MS further was used to show that plasma concentrations of two metabolites (lactic acid and 5-methoxytryptamine) were different in the RSA compared to the control group.
MeSH Keywords: Abortion, Habitual; Chromatography, Liquid; Gas Chromatography-Mass Spectrometry; Metabolomics
Background
Recurrent spontaneous abortion (RSA) is a prevalent complication of pregnancy that occurs in 1–2% of all pregnant women, and it is defined as the occurrence of 2 or more consecutive spontaneous abortions [1]. A number of factors are thought to play a role in recurrent abortions, including genetic mutations, chromosomal abnormalities in the fetus, infectious agents, and a variety of environmental factors such as alcohol, tobacco, and caffeine [2]. Nevertheless, nearly 50% of RSA cases remain unexplained, and it is likely that this condition is multifactorial in nature [3]. Therefore, there is a critical need to identify factors involved in RSA to obtain a clear understanding of its causes. However, RSA is often asymptomatic, and therefore identifying RSA is very difficult. RSA sometimes represents a serious clinical condition that can cause physical and psychological trauma to patients. It would be clinically valuable to identify highly selective biomarkers for RSA to inform patients and minimize trauma. Thus far, a range of factors have been isolated from the plasma of RSA patients and evaluated as predictors of RSA, particularly indoleamine 2,3-dioxygenase and human chorionic gonadotropin (hCG) [4]. However, current studies have failed to investigate the role of metabolites in plasma as potential RSA biomarkers.
Metabolomics, which evolved from genomics, transcriptomics, and proteomics, is the systematic analysis of the inventory of metabolites to highlight disease-specific metabolic signatures, which could be used as biomarkers [5]. Several techniques can then be used to identify and characterize these metabolites, including nuclear magnetic resonance (NMR), liquid chromatography (LC) coupled with mass spectrometry (MS), and gas chromatography MS (GC-MS) [6]. Metabolomics has been successfully applied in the diagnosis of a wide range of diseases, including cancer, cardiovascular diseases, neurodegenerative diseases, and rheumatic diseases [7]. Mass-spectrometry-based metabolic profiling is increasingly used to uncover new biomarkers for diagnosis [8], prognosis [9], pathogenesis clarification and potential therapeutic targets for clinical treatment [10].
Chee et al. reported that metabolomics was used to detect the potential of urine metabolites as a non-invasive screening tool for predicting threatened miscarriage [11]. And metabolomics was also applied for non-invasive embryo assessment in vitro fertilization (IVF) [12]. However, this valuable tool has not yet been used for the discovery of new biomarkers for RSA. Consequently, the use of metabolomics might create new options for researchers to identify novel mechanisms and pathways involved in the pathogenesis of RSA.
In the present study, we collected plasma samples from patients with RSA and from women with normal pregnancies which have matched in maternal age, gestational age and body mass index (BMI). We used a variety of GC-MS techniques to discover the different metabolic compositions of the plasma samples. Then, differentially expressed metabolites were verified using a targeted metabolomics method via UPLC-MS. Our overall aim was to identify differential metabolites that could be used as new biomarkers for predicting the RSA.
Material and Methods
Clinical samples
Fifty RSA patients and 51 control patients were enrolled at the International Peace Maternity and Child Health Hospital of China Welfare Institute (IPMCH) between January 2016 and May 2017. The RSA group included patients who had experienced more than 2 unexplained and consecutive spontaneous abortions at less than 10 weeks of gestation. Those who had genital abnormalities, chronic hypertension; diabetes; liver, kidney, cardiovascular, and thyroid diseases; autoimmune diseases; or infectious diseases were excluded. The control group was composed of women undergoing induced abortions of a normal pregnancy at a gestational age matched to that of the RSA group. None of the study participants had experienced any previous spontaneous abortions and had delivered at least one healthy baby at term. The patients in the control group were not treated with any drugs and had no pregnancy risk factors.
Gestational age was determined by last menstruation period. All patients provided informed written consent, and this investigation was approved by the Ethics Committee of IPMCH. Plasma samples were kept at −80°C until analysis.
Sample preparation and analysis by GC-MS
Peripheral venous blood was collected from all study participants using ethylene diamine tetra-acetic acid (EDTA) for anticoagulation. Blood samples were allowed to coagulate for approximately 10–20 minutes and then centrifuged at 2000–3000 rpm for 20 minutes. Then, the supernatant (plasma) was stored at −80°C until analyzed.
Plasma samples were thawed and analyzed by GC-MS using the method described by Peng et al. [13]. A 100 μL aliquot of plasma was centrifuged at 15 000 rpm for 10 minutes at 4°C. Then, 50 μL of plasma was spiked with 10 μL of an internal standard (2-chlorophenylalanine, 0.3 mg/mL) and vortexed for 10 seconds. This mixture was then extracted with 150 μL of methanol-acetonitrile (2: 1, v/v) and vortexed for 30 seconds. Samples were cooled for 10 minutes at −20°C and then centrifuged at 15 000 rpm for 10 minutes at 4°C. An aliquot of the 150 μL supernatant was placed in a glass sampling vial and dried under vacuum at room temperature. The residue was first reconstituted in 80 μL of methoxyamine (15 mg/mL in pyridine), vortexed for 30 seconds, and kept at 37°C for 90 minutes. Finally, 80 μL of bis(trimethylsilyl)trifluoroacetamide (BSTFA) (1% trimethylchlorosilane) and 20 μL of n-hexane were added, and the extract was kept at 70°C for 60 minutes.
A 1-μL aliquot of the sample extract was injected in splitless mode onto an Agilent GC-MS system (Agilent, USA). A non-polar DB-5 capillary column (30 m×250 μm ID, J&W Scientific, Folsom, CA, USA) was used for separation, with high purity helium as the carrier gas at a constant flow rate of 1.0 mL/minute. GC temperature programming began at 50°C. This was then followed by ramps of 15°C/minute to 125°C, 5°C/minute to 210°C, 10°C/minute to 270°C, and 20°C/minute to 305°C, with final 5-minute incubation at 305°C. The electron impact (EI) ion source was held at 230°C with a filament bias of −70 V. We used full scan mode (m/z 50–600) and an acquisition rate of 20 spectrum/second in the MS setting.
GC-MS data analysis
Data acquired from GC-MS were then characterized by Chroma TOF software (v 4.34, LECO, St Joseph, MI, USA). The resulting data sets were then imported separately into the SIMCA-P+ 14.0 software package (Umetrics, Umeå, Sweden). After mean centering and unit variance scaling, we used principle component analysis (PCA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA) to identify differences between the RSA and control groups. The variable importance in projection (VIP) generated in OPLS-DA processing represents the contribution to the discrimination of each metabolite ion between groups. Variables with a VIP >1 and P<0.05 were considered to be different variables.
We used a variety of statistical techniques to investigate the significance of each of the metabolites identified, including multivariate analysis, the Student’s t-test, and the Wilcoxon-Mann-Whitney test. Metabolites responsible for differentiating the RSA group from the control group were those in which multivariate and univariate analysis showed statistical significance (VIP >1.0 and P<0.05). We then compared the extract masses of all selected metabolites using the Human Metabolome Database [14] (http://metlin.scripps.edu) to identify these compounds. Lastly, we used MetaboAnalyst 3.0 (http://www.metaboanalyst.ca/) to characterize the potential impact of our selected metabolites [15]. The P value and impact value threshold, as calculated via the analysis of pathway topology, were set to 0.05 and 0.1.
Validation of deferential metabolites between the RSA and control groups
We selected 8 metabolites (lactic acid, 5-methoxytryptamine, pyruvic acid, phenylalanine, proline, alanine, threonine, and 3-hydroxybutyric acid) that were quantitatively measured by LC-MS analysis. LC-MS analysis was performed using a Waters UPLC I-class system equipped with a binary solvent pump and a sample manager, coupled with a Waters TQ-XS mass spectrometer equipped with an electrospray interface (Waters, USA). Chromatographic separation was accomplished on an ACQUITY UPLC BEH Amide column (100×2.1 mm ID, packed with 1.7 μm particles, Waters, USA). The mobile phases consisted of 20 mM ammonium formate and 0.1% formic acid (A) and 95% ACN with 20 mM ammonium formate and 0.1% formic acid (B). The mobile phase gradient proceeded from 5% A: 95% B to 60% A: 40% B at a flow rate of 0.4 mL/minute. The autosampler was maintained at 4°C. In order to test the repeatability and stability of the system, the QC sample was randomly inserted into the sample sequence.
Results
Study participants
The participants’ detailed characters are shown in Table 1.
Table 1.
Demographic and clinical details of study participants with RSA compared to the control group.
| RSA | Control | P | |
|---|---|---|---|
| Sample numbers | 50 | 51 | |
| Maternal age (y) | 30.9±3.77 | 30.35±4.59 | NS |
| Gestational age (wk) | 9.7±2.34 | 8.5±2.25 | NS |
| BMI (kg/m2) | 22.3±3.42 | 21.5±2.51 | NS |
| Number of spontaneous abortions | 2.75±0.45 | 0 | <0.001 |
RSA – recurrent spontaneous abortion; BMI – body mass index; Data are expressed as mean ±SD. Gestational age of subjects represents the time at which plasma samples were collected. NS – not significant.
The average maternal age, gestational age, and the body mass index (BMI) of the patients in the RSA group were 30.9±3.77 years, 9.7±2.34 weeks, and 22.3±3.42 kg/m2, respectively. The average age, gestational age, and the BMI of the patients in the control group were 30.35±4.59 years, 8.5±2.25 weeks, and 21.5±2.51 kg/m2, respectively. No significant differences were shown in age, gestational age, or BMI between the 2 groups (P>0.05). Therefore, the 2 groups were suitable for the comparison studies. The mean number of spontaneous abortion in the RSA group was 2.78, compared to 0 in the control group (P<0.001).
Metabolomic profiling of plasma samples from the RSA and control groups
The PCA score plot, which used 5 components (R2Xcum=0.62, Q2cum=0.0996), showed separate trends when comparing maternal plasma from the RSA group and the control group, as shown in Figure 1A. Class separation improved following PLS-DA analysis (Figure 1B), which identified three key parameters: R2Xcum, R2Ycum, and Q2cum (0.376, 0.87, and 0.661, respectively). OPLS-DA identified an optimized class separation (R2Xcum=0.448, R2Ycum=0.945, Q2cum=0.592), which indicated that the model exhibited good fit and could predict RSA in an efficient manner (Figure 1C). Permutation testing (Figure 1D) was performed on the quality of the model and indicated that the model was not over-fitted.
Figure 1.
Score scatter plot of (A) PCA showing significant differences between the RSA and control groups, (B) PLS-DA showing improved discrimination, and (C) OPLS-DA showing optimized discrimination. (D) Comparison of the true model parameters in the validation test and those of permutated models. Y-axis intercepts: R2=(0.0, 0.891) and Q2=(0.0–0.314).
Fifty-four metabolites (VIP >1and P<0.05) are shown in Table 2. To more clearly characterize the plasma profile of RSA, we used a novel software called Heatmap Illustrator, version 1.0 (HemI) to draw a heat map based on the intensity levels of 54 markers between the 2 groups, and the heat map is presented in Figure 2 [16].
Table 2.
Potential metabolites associated with RSA and their metabolic pathways.
| Mass | R.T. (min) | Metabolites | Related pathway | VIP value | FC (R/C) | p Value |
|---|---|---|---|---|---|---|
| 234 | 7.29 | 3-hydroxybutyric acid | Fatty acid Biosynthesis | 1.23 | 0.41 | 0.02 |
| 180 | 19.40 | Oleic acid | Not available | 1.29 | 0.43 | 0.02 |
| 174 | 5.79 | Pyruvic acid | TCA cycle | 1.42 | 0.58 | 0.005 |
| 201 | 12.73 | Alpha-ketoglutaric acid | TCA cycle | 1.07 | 0.63 | 0.03 |
| 174 | 18.38 | 5-Methoxytryptamine | Not available | 1.38 | 0.67 | 0.01 |
| 218 | 10.00 | Serine | Glycine, serine, and threonine metabolism | 1.39 | 1.33 | 0.009 |
| 152 | 5.70 | 2-hydroxypyridine | Not available | 1.39 | 1.34 | 0.03 |
| 83 | 6.48 | 20alpha-Hydroxycholesterol | Steroidogenesis | 1.37 | 1.34 | 0.04 |
| 117 | 14.89 | 2-Deoxyerythritol | Not available | 1.38 | 1.34 | 0.03 |
| 185 | 22.99 | Methyl Palmitoleate | Not available | 1.40 | 1.34 | 0.04 |
| 218 | 10.34 | Threonine | Glycine, serine, and threonine metabolism | 1.17 | 1.35 | 0.02 |
| 218 | 13.38 | Phenylalanine | Phenylalanine and tyrosine metabolism | 1.10 | 1.35 | 0.02 |
| 149 | 21.68 | Dioctyl phthalate | Not available | 1.41 | 1.35 | 0.02 |
| 121 | 5.22 | p-benzoquinone | Pyrimidine metabolism | 1.40 | 1.36 | 0.04 |
| 285 | 9.55 | Phenylacetic acid | Phenylacetate metabolism | 1.37 | 1.37 | 0.03 |
| 215 | 10.07 | Pelargonic acid | Not available | 1.36 | 1.38 | 0.04 |
| 192 | 12.70 | L-kynurenine | Tryptophan metabolism | 1.06 | 1.41 | 0.04 |
| 218 | 8.06 | Valine | Valine, leucine, and isoleucine | 1.23 | 1.43 | 0.02 |
| 130 | 4.97 | N-Ethylglycine | Not available | 1.27 | 1.45 | 0.04 |
| 174 | 18.49 | Noradrenaline | Not available | 1.03 | 1.46 | 0.02 |
| 211 | 24.29 | D-(glycerol 1-phosphate) | Glycerolipid metabolism | 1.37 | 1.48 | 0.04 |
| 369 | 25.35 | Cholesterol | Steroid biosynthesis | 1.37 | 1.48 | 0.04 |
| 130 | 7.46 | N-Methyl-DL-alanine | Not available | 1.20 | 1.49 | 0.02 |
| 218 | 10.81 | Aminomalonic acid | Not available | 1.34 | 1.50 | 0.004 |
| 217 | 11.87 | Threitol | Not available | 1.38 | 1.51 | 0.007 |
| 245 | 9.91 | Fumaric acid | TCA cycle | 1.25 | 1.52 | 0.04 |
| 262 | 13.49 | 6-deoxy-D-glucose | Not available | 1.37 | 1.52 | 0.006 |
| 180 | 18.30 | Guanidinosuccinic acid | Not available | 1.25 | 1.54 | 0.02 |
| 217 | 17.09 | Galactinol | Galactose metabolism | 1.43 | 1.54 | 0.009 |
| 158 | 9.13 | Isoleucine | Valine, leucine, and isoleucine | 1.22 | 1.55 | 0.008 |
| 164 | 6.31 | 4-hydroxyphenylpyruvate | Phenylalanine and tyrosine metabolism | 1.27 | 1.55 | 0.04 |
| 216 | 13.05 | 3-hydroxy-L-proline | Not available | 1.46 | 1.56 | 0.002 |
| 117 | 5.19 | Lactic acid | Pyruvate metabolism | 1.09 | 1.57 | 0.02 |
| 217 | 13.91 | Threo-beta-hydroxyaspartate | Not available | 1.37 | 1.58 | 0.006 |
| 202 | 21.49 | Indolelactate | Not available | 1.26 | 1.65 | 0.015 |
| 218 | 16.13 | D-Arabitol | Not available | 1.26 | 1.66 | 0.017 |
| 158 | 12.49 | N-acetyl-L-aspartic acid | Aspartate metabolism | 1.43 | 1.67 | 0.004 |
| 369 | 7.36 | Gallic acid | Not available | 1.16 | 1.67 | 0.03 |
| 248 | 5.30 | Beta-Alanine | Beta-alanine metabolism | 1.13 | 1.68 | 0.04 |
| 212 | 7.74 | 4-Androsten-19-ol-3,17-dione | Not available | 1.27 | 1.70 | 0.03 |
| 179 | 19.75 | dl-p-Hydroxyphenyllactic acid | Not available | 1.22 | 1.72 | 0.01 |
| 212 | 7.67 | Phosphomycin | Not available | 1.39 | 1.73 | 0.02 |
| 156 | 9.66 | Pipecolinic acid | Not available | 1.17 | 1.74 | 0.02 |
| 248 | 14.72 | Aconitic Acid | TCA cycle | 1.72 | 1.75 | <0.001 |
| 217 | 15.68 | Xylitol | Not available | 1.42 | 1.86 | 0.006 |
| 116 | 6.51 | Alanine | Alanine metabolism | 1.47 | 1.87 | 0.005 |
| 262 | 11.22 | Erythrose | Not available | 1.63 | 1.89 | <0.001 |
| 221 | 10.51 | Malonic acid | Aspartate metabolism | 1.15 | 1.92 | 0.02 |
| 262 | 12.41 | N-Methyl-L-glutamic acid | Not available | 1.31 | 1.93 | 0.007 |
| 188 | 9.43 | 2,3-Dihydroxypyridine | Not available | 1.55 | 2.00 | <0.001 |
| 142 | 9.22 | Proline | Proline metabolism | 1.57 | 2.03 | 0.004 |
| 202 | 19.28 | Trehalose-6-phosphate | Not available | 1.76 | 2.56 | <0.001 |
| 102 | 6.77 | 3-Aminoisobutyric acid | Pyrimidine metabolism | 1.63 | 2.63 | <0.001 |
| 295 | 15.23 | Terephthalic acid | Not available | 1.57 | 2.64 | 0.002 |
VIP – variable importance in projection; FC (R/C) – the ratio of relative amounts of RSA group to control group; TCA cycle – tricarboxylic acid cycle.
Figure 2.
Heat map based on the normalized quantities of potential marker metabolites in the RSA and control groups.
Metabolic pathway and function analysis
Metabolic pathways involving glycine, serine and threonine metabolism (P=0.00529, impact=0.26) and beta-alanine (P=0.0284, impact=0.27) along with the TCA cycle (P=0.0113, impact=0.19), phenylalanine metabolism (P=0.0217, impact=0.17), and glycolysis (P=0.037, impact=0.1) were highlighted as targets for investigating the pathological mechanisms underlying RSA (showed in Figure 3).
Figure 3.
Summary of pathway analysis using MetaboAnalyst 3.0.
Validation of metabolites in the RSA and control groups
Eight amino acids and biogenic amines (lactic acid, 5-methoxytryptamine, pyruvic acid, phenylalanine, proline, alanine, threonine, and 3-hydroxybutyric acid) were quantitatively measured. The concentration of lactic acid in RSA was higher than that in the control group (P<0.05). Additionally, 5-methoxytryptamine concentration was significantly lower in the RSA group (P<0.05). There was no significant difference in pyruvic acid, phenylalanine, proline, alanine, threonine, and 3-hydroxybutyric acid between groups.
Discussion
Our study indicated that metabolic profiling in unexplained RSA were significantly different from the control group and 54 potential metabolites were identified by using GC-TOFMS. The OPLS-DA models derived from the metabonomic analysis showed clear separation between groups. Furthermore, targeted UPLC-MS/MS was performed to validate metabolites differentially expressed between the RSA and control groups in a more reliable and robust manner [17].
Feng et al. [18] discovered the metabolic profile in plasma in the threatened abortion of women with polycystic ovary syndrome (PCOS) was significantly altered. The metabolomic approach was also applied in our study, showing distinct profiles of metabolites between RSA and control group. Other research reported [19] that in missed abortion, metabolism was disturbed significantly and glyceric acid, indole and sphingosine were found satisfactory metabolites in diagnosis for missed abortion. While the metabolism in RSA is still not reported. In our present study, metabolic pathway impact analysis identified glycine, serine, threonine metabolism, beta-alanine metabolism, the TCA cycle, and phenylalanine metabolism as key biological pathways in the development of RSA. Furthermore, we found significant differences in 5-methoxytryptamine and lactic acid concentrations in RSA patients using LC-MS. Lactic acid arises as a byproduct of anaerobic metabolism. In cases involving sepsis and septic shock, there is a clear increase in the production and clearance of lactic acid leading to lactic acidosis. This condition can be caused by arterial hypotension, microcirculatory dysfunction, and the reduced extraction of oxygen from the peripheral tissues. Albright et al. found that elevated levels of lactic acid in pregnant women were associated with adverse maternal outcomes [20]. The process of implantation shares similarities with the pro-inflammatory response. Similar to the findings of Albright et al., we also found elevated lactic acid levels in our RSA group. Metabolic pathways showed that the impact of glycolysis was 0.1 (P=0.037), which implies that lactic acid may play a role in the pathological mechanisms underlying RSA. 5-methoxytryptamine is biosynthesized via the deacetylation of melatonin, which occurs naturally in the body at low levels [21]. It is a strong in vitro or in vivo antioxidant [22]. Lower 5-methoxytryptamine concentration may play a role in the pathogenesis of RSA. Further exploration is necessary to verify that.
There were some potential limitations of this study when interpreting our conclusions. First, the number of patients in this study was relatively small. Second, it is imperative that further validation experiments are performed in future prospective investigations that involve a larger number of people with RSA. Therefore, our findings can only provide indirect evidence for the pathogenesis of RSA.
To summarize, we investigated differences in metabolites in the plasma of RSA patients. This study suggests that plasma metabolic profiling has great potential in differentiating RSA patients from control patients, and the metabolism of lactic acid and 5-methoxytryptamine is significantly different between RSA and control groups, implying that the differential metabolites might be novel biomarkers of RSA.
Conclusions
Metabolic screening was conducted via GC-MS to identify clear metabolic differences between RSA patients and a control group. LC-MS quantitative analysis showed that lactic acid and 5-methoxytryptamine concentrations in RSA patients were significantly different from a control group.
Footnotes
Conflict of interests
None.
Source of support: This study was funded by the Interdisciplinary Program of Shanghai Jiao Tong University (project number YG2014MS35)
References
- 1.Toth B, Jeschke U, Rogenhofer N, et al. Recurrent miscarriage: Current concepts in diagnosis and treatment. J Reprod Immunol. 2010;85(1):25–32. doi: 10.1016/j.jri.2009.12.006. [DOI] [PubMed] [Google Scholar]
- 2.Krieg SA, Fan X, Hong Y, et al. Global alteration in gene expression profiles of deciduas from women with idiopathic recurrent pregnancy loss. Mol Hum Reprod. 2012;18(9):442–50. doi: 10.1093/molehr/gas017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Christiansen OB, Steffensen R, Nielsen HS, Varming K. Multifactorial etiology of recurrent miscarriage and its scientific and clinical implications. Gynecol Obstet Invest. 2008;66(4):257–67. doi: 10.1159/000149575. [DOI] [PubMed] [Google Scholar]
- 4.Liu Y, Liu Y, Li X, et al. Predictive value of serum beta-hCG for early pregnancy outcomes among women with recurrent spontaneous abortion. Int J Gynaecol Obstet. 2016;135(1):16–21. doi: 10.1016/j.ijgo.2016.03.007. [DOI] [PubMed] [Google Scholar]
- 5.Koek MM, Jellema RH, van der Greef J, et al. Quantitative metabolomics based on gas chromatography mass spectrometry: Status and perspectives. Metabolomics. 2011;7(3):307–28. doi: 10.1007/s11306-010-0254-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Tsugawa H, Tsujimoto Y, Arita M, et al. GC/MS based metabolomics: Development of a data mining system for metabolite identification by using soft independent modeling of class analogy (SIMCA) BMC Bioinformatics. 2011;12:131. doi: 10.1186/1471-2105-12-131. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Bengtsson AA, Trygg J, Wuttge DM, et al. Metabolic profiling of systemic lupus erythematosus and comparison with primary Sjogren’s syndrome and systemic sclerosis. PLoS One. 2016;11(7):e0159384. doi: 10.1371/journal.pone.0159384. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Mathe EA, Patterson AD, Haznadar M, et al. Noninvasive urinary metabolomic profiling identifies diagnostic and prognostic markers in lung cancer. Cancer Res. 2014;74(12):3259–70. doi: 10.1158/0008-5472.CAN-14-0109. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Chi Y, Pei L, Chen G, et al. Metabonomic profiling of human placentas reveals different metabolic patterns among subtypes of neural tube defects. J Proteome Res. 2014;13(2):934–45. doi: 10.1021/pr4009805. [DOI] [PubMed] [Google Scholar]
- 10.Spratlin JL, Serkova NJ, Eckhardt SG. Clinical applications of metabolomics in oncology: A review. Clin Cancer Res. 2009;15(2):431–40. doi: 10.1158/1078-0432.CCR-08-1059. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Ku CW, Tan ZW, Lim MK, et al. Spontaneous miscarriage in first trimester pregnancy is associated with altered urinary metabolite profile. BBA Clin. 2017;19(8):48–55. doi: 10.1016/j.bbacli.2017.07.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Botros L, Sakkas D, Seli E. Metabolomics and its application for non-invasive embryo assessment in IVF. Mol Hum Reprod. 2008;14(12):679–90. doi: 10.1093/molehr/gan066. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Peng ZX, Wang Y, Gu X, et al. Metabolic transformation of breast cancer in a MCF-7 xenograft mouse model and inhibitory effect of volatile oil from Saussurea lappa Decne treatment. Metabolomics. 2014;11(3):636–56. [Google Scholar]
- 14.Wishart DS, Knox C, Guo AC, et al. HMDB: A knowledgebase for the human metabolome. Nucleic Acids Res. 2009;37( Database issue):D603–10. doi: 10.1093/nar/gkn810. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Xia J, Sinelnikov IV, Han B, Wishart DS. MetaboAnalyst 3.0 – making metabolomics more meaningful. Nucleic Acids Res. 2015;43(W1):W251–57. doi: 10.1093/nar/gkv380. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Deng W, Wang Y, Liu Z, et al. HemI: A toolkit for illustrating heatmaps. PLoS One. 2014;9( 11):e111988. doi: 10.1371/journal.pone.0111988. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Zhu J, Djukovic D, Deng L, et al. Colorectal cancer detection using targeted serum metabolic profiling. J Proteome Res. 2014;13(9):4120–30. doi: 10.1021/pr500494u. [DOI] [PubMed] [Google Scholar]
- 18.Feng XL, Chen L, Li N, et al. Metabolomics biomarker analysis of threatened abortion in polycystic ovary syndrome: A clinical discovery study. RSC Adv. 2017;7:52923–29. [Google Scholar]
- 19.Fei H, Hou J, Wu Z, et al. Plasma metabolomic profile and potential biomarkers for missed abortion. Biomed Chromatogr. 2016;30(12):1942–52. doi: 10.1002/bmc.3770. [DOI] [PubMed] [Google Scholar]
- 20.Albright CM, Ali TN, Lopes V, et al. Lactic acid measurement to identify risk of morbidity from sepsis in pregnancy. Am J Perinatol. 2015;32(5):481–86. doi: 10.1055/s-0034-1395477. [DOI] [PubMed] [Google Scholar]
- 21.Gulcin I, Buyukokuroglu ME, Oktay M, Kufrevioglu OI. On the in vitro antioxidative properties of melatonin. J Pineal Res. 2002;33(3):167–71. doi: 10.1034/j.1600-079x.2002.20920.x. [DOI] [PubMed] [Google Scholar]
- 22.Herraiz T, Galisteo J. Endogenous and dietary indoles: A class of antioxidants and radical scavengers in the ABTS assay. Free Radic Res. 2004;38(3):323–31. doi: 10.1080/10611860310001648167. [DOI] [PubMed] [Google Scholar]