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Large-Scale Longitudinal Metabolomics Study Reveals Different Trimester-specific Alterations of Metabolites in Relation to Gestational Diabetes Mellitus Hongzhi Zhao, Han Li, Arthur Chi Kong Chung, Li Xiang, Xiaona Li, Yuanyuan Zheng, Hemi Luan, Lin Zhu, Wenyu Liu, Yang Peng, Yaxing Zhao, Shunqing Xu, Yuanyuan Li, and Zongwei Cai J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.8b00602 • Publication Date (Web): 29 Nov 2018 Downloaded from http://pubs.acs.org on November 30, 2018

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Journal of Proteome Research

Large-Scale Longitudinal Metabolomics Study Reveals Different Trimester-specific Alterations of Metabolites in Relation to Gestational Diabetes Mellitus

Hongzhi Zhao1#, Han Li2#, Arthur Chi Kong Chung1, Li Xiang1, Xiaona Li1, Yuanyuan Zheng1, Hemi Luan1, Lin Zhu1, Wenyu Liu2, Yang Peng2, Yaxing Zhao2, Shunqing Xu2, Yuanyuan Li2*, Zongwei Cai1*

1State

Key Laboratory of Environmental and Biological Analysis, Department of Chemistry,

Hong Kong Baptist University, Hong Kong SAR, China. 2Key

Laboratory of Environment and Health, Ministry of Education & Ministry of

Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

#Both authors contributed equally to this work. *Corresponding author. E-mail: [email protected] (Z. Cai), Tel: +852-34117070, FAX: +852-34117348; [email protected] (Y. Li), Tel: +86-27-83657705, FAX: +86-2783657781.

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ABSTRACT Despite the increasing research attention paid to gestational diabetes mellitus (GDM) due to its high prevalence, limited knowledge is available about its pathogenesis. In this study, 428 serum samples were collected from 107 pregnant women suffering GDM and 107 matched healthy controls. The non-targeted metabolomics data of maternal serum samples from the first (T1, n = 214) and second trimesters (T2, n = 214) were acquired by using ultra-high performance liquid chromatography coupled with Orbitrap mass spectrometry (MS). A total of 93 differential metabolites were identified based on the accurate mass and MS/MS fragmentation. After false discovery rate correction, the levels of 31 metabolites were significantly altered by GDM in the first trimester. The differential metabolites were mainly attributed to purine metabolism, fatty acid β-oxidation, urea cycle and tricarboxylic acid cycle pathways. The fold changes across pregnancy (T2/T1) of six amino acids (serine, proline, leucine/isoleucine, glutamic acid, tyrosine and ornithine), a lysophosphatidylcholine (LysoPC(20:4)) and uric acid in GDM group were significantly different from those in the control groups, suggesting that these 8 metabolites might have contributed to the occurrence and progression of GDM. The findings revealed that the amino acid metabolism, lipid metabolism, and other pathways might be disturbed prior to GDM onset and during the period from the first to the second trimester of pregnancy. KEYWORDS: Non-targeted metabolomics study; High-resolution mass spectrometry; Gestational diabetes mellitus; Metabolic pathways


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1. INTRODUCTION Gestational diabetes mellitus (GDM) is one of common metabolic diseases during pregnancy with worldwide distribution 1. The high prevalence of GDM is of great concern because pregnant women suffering from GDM have a higher risk for type 2 diabetes 2, cardiovascular diseases 3 and female malignancies 4. In addition, GDM may increase the risk of macrosomia in neonates

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and other adverse health outcomes of the offspring 6. GDM is

often diagnosed at 24-28th gestational weeks (the late second or early third trimester of pregnancy) 7. Some metabolic disturbances, such as an imbalance between insulin resistance and insulin secretion, has been proposed as the cause of GDM 8. Despite many research efforts, the development and pathological mechanism of GDM have not been fully clearly characterized. Mass spectrometry (MS)-based metabolomics, a powerful approach for the investigation of metabolite profiles, has been applied in the study of GDM pathogenesis. The profiling of maternal serum may reveal the metabolite signatures of pregnant women. Recently, 35 serum metabolites including lipids and amino acids were differentiated between the GDM women and healthy pregnant women from a small-scale study (n = 22) with maternal serum samples of the third trimester by using ultra-high performance liquid chromatography (UHPLC) coupled with quadrupole time-of-flight MS

9.

Some prognostic markers, such as

lysophosphatidylethanolamines (LysoPEs) and taurine-bile acids, have been identified in the plasma of GDM women (n = 20) in the second trimester of gestation based on a multiplatform MS system 10. On the other hand, metabolite alterations may occur along with substantial metabolic changes in the maternal body in different trimesters of pregnancy

11,

highlighting the value of longitudinal metabolomics research of different pregnancy stages. The comparison of trimester-specific alterations in metabolites between the women diagnosed with GDM and the control group may provide a better understanding about the 3 ACS Paragon Plus Environment


occurrence and development of GDM. However, metabolomics studies of GDM mostly focused on the cross-sectional case study by comparing GDM and normal women in one-spot samples and the sample sizes of pregnant women were often small. The report on the longitudinal study through different trimesters during the developing process of GDM, especially with large sample size, is rare. In this study, a non-targeted longitudinal metabolomics study was performed on 428 serum samples from 214 pregnant women in the first and second trimesters by using UHPLC coupled with high-resolution MS. The trimester-specific alterations of metabolites and metabolic pathways related to GDM were investigated. 2. EXPERIMENTAL SECTION 2.1. Chemicals and Reagents HPLC grade solvents, such as methanol and acetonitrile, were provided by VWR chemicals (France). Pure water was obtained from Synergy® Water Purification System (Merck Millipore). Formic acid was from Riedel-de Haen (Germany). The metabolites standards were from Sigma-Aldrich, Cambridge Isotope Laboratories and Toronto Research Chemicals. 2.2. Sample Collection This study was approved by the ethics committee of the Tongji Medical College, Huazhong University of Science and Technology. The volunteers were enrolled from Women and Children Medical and Healthcare Center of Wuhan, Hubei Province, P. R. China. All participants signed consent forms. The face-to-face interviews were conducted by trained nurses to collect demographic information, including socioeconomic data and lifestyle factors during pregnancy. The clinical information was from the medical records. None of the pregnant women had abnormal kidney function based on medical record. The 75 g oral glucose tolerance test (OGTT) was used for the diagnosis of GDM between 24th and 28th 4 ACS Paragon Plus Environment

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gestational weeks, according to the criteria of American Diabetes Association

12.

The

diagnosis of GDM was established when any of the following results were obtained: fasting plasma glucose ≥ 5.1 mmol/L, 1 h glucose level ≥ 10.0 mmol/L or 2 h glucose level ≥ 8.5 mmol/L

12.

In this study, 107 women diagnosed with GDM were included and an equal

number of healthy controls were selected and matched by the gender of the neonate, maternal age and the gestational week of sample collection. The number of each gender of the neonate was equal in two groups; the maternal ages were matched within a deviation of 1 year and the gestational weeks of sample collection were matched within a deviation of 1 week. The median value of gestational week for the first trimester (T1) serum sample collection was 12.8, while that for the second trimester (T2) serum sample collection was 26.1. A total of 428 fasting blood samples in the first and second trimesters were collected in the same way (e.g. the same clotting time) and centrifuged immediately to separate the serum. The four groups’ maternal serum samples (GDM-T1, GDM-T2, CON-T1 and CON-T2) were all stored at -80 °C. 2.3. Sample Preparation The serum samples were prepared using the protocol in our previously published method with small modifications

13.

For each sample, 50 μL serum was added to 200 μL 4-chloro-

phenylalanine (1 μg/mL, internal standard) in methanol. The mixture was vortexed for 60 s and centrifuged for 10 min at 13,000 rpm and 4 °C. The supernatant was dried under nitrogen, and re-dissolved in 100 μL methanol/water (1/1, v/v) for analysis. The commercial metabolites standards were firstly dissolved in methanol and diluted to 1 μg/mL in methanol/water (1/1, v/v) for the verification analysis. 2.4. Instrument Analysis and Quality Control The metabolomics analyses were performed on Dionex Ultimate 3000 UHPLC system (Dionex, Sunnyvale, CA, USA) coupled with Thermo Scientific™ Q Exactive™ Focus mass 5 ACS Paragon Plus Environment


spectrometer (Thermo Scientific, Bremen, Germany). An ACQUITY UPLC HSS T3 column (100 × 2.1 mm, 1.8 µm, Waters) was utilized for the chromatographic separation. The MS analysis was carried out with heated-electrospray ionization source in both positive and negative ion modes. The UHPLC conditions and MS parameters are provided in the supporting information. All the samples were injected in a random order. Quality control (QC) samples were prepared as aliquots of a pool of 10 μL of each sample, and analyzed at the beginning, every 10 injections and at the end of each batch to monitor instrument stability. StatTarget

14, 15,

an approach based on the Quality Control-

Robust Loess Signal Correction algorithm (QC-RLSC) 16, was used to correct signal drift and remove the batch effect from the large-scale metabolomics data. The peaks that are not detected in 80% of the samples (or more) were removed from the resulting matrix (80% rule) 17.

2.5. Statistical Analysis Data pretreatments including peak picking alignment were achieved by using XCMS package with R statistical language (Version 3.4.0) 18. The parameters of XCMS were set as follows: method=“obiwarp”, snthresh=3, mzdiff=-0.005, noise=500000, ppm=5. SIMCA-P software (Version 13.0, Umetrics, Umea, Sweden) was employed for multivariate analyses, such as orthogonal partial least squares discriminant analysis (OPLS-DA). Statistical power analysis was performed to determine the minimum sample size by using metaboanalyst (http://www.metaboanalyst.ca/) after log transformation and mean centering. Fold changes were computed as the ratio of peak area between two groups (GDM-T1 vs. CON-T1, GDMT2 vs. CON-T2, GDM-T2 vs. GDM-T1 and CON-T2 vs. CON-T1). Mann-Whitney U test and t-test were used for significance test. The p values were adjusted with BenjaminiHochberg false discovery rate (FDR). 2.6. Identification of Differential Metabolites 6 ACS Paragon Plus Environment

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MS/MS analysis was performed with higher energy collisional-dissociation (HCD) cell using the collision energy of 10 eV, 20 eV, 40 eV. Metabolites were identified by matching the accurate mass and MS/MS fragmentation with online database, such as Human Metabolome Database (HMDB, http://www.hmdb.ca/), metlin (https://metlin.scripps.edu) and mzcloud (https://www.mzcloud.org/). The retention times and MS/MS fragment of metabolites were verified whenever the commercial standards are available. 2.7. Pathway Analysis and Metabolic Network Analysis Pathway analyses were conducted based on Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Database. The metabolic network was built by using MetScape 19, a plugin for Cytoscape 3.5.1. 3. RESULTS 3.1. Demographic Characteristics and Clinical Information Table 1 presents demographic characteristics and clinical information of the participants. In total, 97% of participants (207 of 214) had more than one-year residency in Wuhan, a city in Central China. None of the 214 participants had a family history of diabetes mellitus. More than 87% of individuals were primiparous. The participants diagnosed with GDM are wellmatched with the controls in terms of maternal age, the gender of neonate and the gestational weeks of sample collection. No significant difference was found in maternal age, employment and education, the household yearly income and parity between the two study groups (p > 0.05).



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Table 1 Demographic characteristics and clinical information of the GDM women and matched control group.

Variables

GDM Group (n = 107) 30.7 ± 4.55 12.97 ± 1.00 26.47 ± 2.28 22.41 ± 2.79 6 73 28

Control Group (n = 107) 29.63 ± 3.98 13.11 ± 0.92 26.22 ± 1.46 20.98 ± 2.56 9 88 10

p value

Maternal age (years) 0.72 Gestational week at collection (T1) / Gestational week at collection (T2) / Pre-pregnancy BMI (kg/m2) < 0.01 Underweight (< 18.5) / Normal (18.5-23.9) / Overweigh (≥ 24) / Household yearly income (Yuan) 0.09 < 30, 000 / 13 17 30,000 - 49,000 / 53 53 5,000 - 99,000 / 35 28 10,000 - 199,000 / 2 1 ≥ 200,000 / 4 8 Employment status before pregnancy / 68 63 Unemployed / 39 44 Employed / Maternal education / / Junior middle school 1 5 / Senior high school 7 6 / Secondary vocational school 16 9 / Secondary technical school 0 1 / Junior college 39 25 / University 38 51 Postgraduate 6 10 / Parity Primiparous (n = 1) 94 97 / Multiparous (n ≥ 2) 13 10 / Gender of neonates (n) (male/female) 58/49 58/49 / The t-test p value < 0.05 was considered as significant difference. The continuous numerical values are presented as mean ± standard deviation. Abbreviation: GDM, gestational diabetes mellitus; T1, first trimester; T2, second trimester; BMI, body mass index.


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3.2. Multivariate Statistical Analysis Retention time-exact mass pairs (metabolic features) were extracted from each sample profile in positive and negative ion modes, respectively. Data precision was significantly improved after the correction by QC-RLSC method (Fig. S1). Significant differences in the profiles between GDM group and control group were shown in OPLS-DA score plots of both positive and negative ion modes (Fig. 1). The obvious separations of metabolic profiles in different trimesters (GDM-T2 vs. GDM-T1; CON-T2 vs. CON-T1) were shown in Fig. S2. As shown in Fig. S3, the study achieved powers of 69% and 57% for detecting differences between GDM and control groups at the FDR level of 0.05, in first- and second-trimester samples (with sample size of 107), respectively.

Figure 1 OPLS-DA score plots (each point represents an individual). (a) GDM-T1 vs. CON-T1, positive ion mode, cumulative R2X = 0.22 and R2Y = 0.35; (b) GDM-T1 vs. CON-T1, negative ion mode, cumulative R2X = 0.17and R2Y = 0.45; (c) GDMT2 vs. CON-T2, positive ion mode, cumulative R2X = 0.22 and R2Y = 0.35; (d) GDM-T2 vs. CON-T2, negative ion mode, cumulative R2X = 0.19 and R2Y = 0.47; (Abbreviation: OPLS-DA, orthogonal partial least squares discriminant analysis; CON, control; GDM, gestational diabetes mellitus) 9 ACS Paragon Plus Environment


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Differential metabolites (fold change > 1.2 or < 0.8, p < 0.05) were identified according to volcano plots (Fig. S4), with the negative log10 of the p value on the vertical axis versus the log2 of the fold change on the horizontal axis. As shown in Fig. S4, the levels of 1428, 1175, 2053 and 1508 features were found to increase in the comparisons of GDM-T1 vs. CON-T1, GDM-T2 vs. CON-T2, GDM-T2 vs. GDM-T1 and CON-T2 vs. CON-T1, respectively. Meanwhile, the levels of 955, 459, 1407, 1212 metabolites decreased in the comparison of the above four pairs. 3.3. Identification of Differential Metabolites All the differential features were searched in online databases. A total of 93 differential metabolites (Table S1) were identified based on the accurate mass and MS/MS fragmentation pathway. Other differential metabolites were also identified by positive or negative MS/MS spectra. More identification information, such as accurate m/z value, MS/MS fragment and retention time, was shown in Table S1. About 95% of relative errors were within 2 ppm. In addition, 31 metabolites were confirmed by available commercial standards (Table S1) and mainly classified into amino acids (and derivatives), lipids (and lipid-like molecules) and others. For example, based on the MS/MS fragmentations (Fig. S5), the metabolites with the m/z values of 116.07030 and 468.30678 were considered to be proline and lysophosphatidylcholine (LysoPC)(14:0), respectively. 3.4. Metabolites Variations between Different Groups Fold change between GDM women relative to normal matched control groups (Table S2) was used to assess the variations of the metabolites. A total of 48 differential metabolites (GDM-T1 vs. CON-T1, fold change > 1.2 or < 0.8, p < 0.05)

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were identified in the first-

trimester samples (Table S2). Among them, the levels of 38 metabolites increased and 10 metabolites decreased in GDM-T1 samples compared with CON-T1. In total, 13 identified metabolites were related to GDM in both first trimester (GDM-T1 vs. CON-T1) and second10 ACS Paragon Plus Environment

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trimester (GDM-T2 vs. CON-T2) (Fig. 2ab and Table S4). The levels of 35 and two metabolites were significantly altered only in the first trimester (GDM-T1 vs. CON-T1) or second trimester (GDM-T2 vs. CON-T2), respectively. After FDR correction (Fig. 2cd), the levels of 22 metabolites increased and 9 metabolites decreased in GDM-T1 (vs. CON-T1); no significant difference of metabolites was observed between GDM-T2 and CON-T2.

Figure 2 Venn diagram of identified metabolites numbers in relation to GDM (fold change > 1.2 or < 0.8 and p < 0.05) in different groups (a) GDM-T1 vs. CON-T1; (b) GDM-T2 vs. CON-T2; (c) GDM-T1 vs. CON-T1 (after FDR correction); (d) GDM-T2 vs. CON-T2 (after FDR correction). The significances were calculated with t-test (when normal distribution) or Mann-Whitney U test (when nonnormal distribution). (Abbreviation: FDR, false discovery rate; Pal, Palmitoylcarnitine; Vac, Vaccenyl carnitine) The trends of pregnancy-related changes of some metabolites in the GDM group (GDMT2 vs. GDM-T1) were different from those in the control group (CON-T2 vs. CON-T1). As shown in Table S3, 27 metabolites, including four amino acids, 13 lipids and other compounds, decreased in GDM group during pregnancy (GDM-T2 vs. GDM-T1) but no significant decrease was observed in the comparison of CON-T2 and CON-T1 serum samples. 11 ACS Paragon Plus Environment


For example, the levels of leucine/isoleucine, methionine and tyrosine decreased (GDM-T2 vs. GDM-T1) in GDM group with fold changes at 0.75, 0.79 and 0.49, respectively, but no significant difference was observed between CON-T1 and CON-T2 samples. After FDR correction, the alterations of the above metabolites (leucine/isoleucine, methionine and tyrosine) remained significant in GDM group (Table S3). The fold changes (T2 vs. T1) of serine, proline, leucine/isoleucine, glutamic acid, tyrosine, ornithine and LysoPC(20:4) in GDM group were significantly (p < 0.05) lower than those in healthy controls (Fig. 3). However, uric acid (urate) was elevated during pregnancy in GDM group (GDM-T2 vs. GDM-T1), and the fold changes of GDM-T2 vs. GDM-T1 were significantly higher than the values of CON-T2 vs. CON-T1 (Fig. 3).

Figure 3 Fold changes of CON-T2 vs. CON-T1 and GDM-T2 vs. GDM-T1. (Abbreviation: C, CON-T2 vs. CON-T1; D, GDM-T2 vs. GDM-T1); error bar represents standard error of the mean (SEM), * represents mann-whitney U test p < 0.05; ** represents mann-whitney U test p < 0.01) Heatmap (Fig. 4a) shows two main clusters (clustering of rows): The first includes the amino acids and the second includes amino acids derivatives. The highest relative levels of amino acids were observed in GDM-T1 samples. The amino acid levels in GDM-T2 and CON-T2 samples were lower compared with the other two groups. In the cluster dendrogram 12 ACS Paragon Plus Environment

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of columns of lipids and lipid-like compounds (Fig. 4b), a clear separation between the profiles of GDM-T1 and CON-T1 samples were observed.

Figure 4 Heatmaps of metabolites. (a) Amino acids and derivatives and (b) Lipids and lipidlike molecules (The data distribution was normalized between 0-1.). 3.5. Metabolic Network Analysis of Differential Metabolites The global metabolic network (GDM-T1 vs. CON-T1) formed by the identified metabolites revealed the relationship of the metabolites and provided a holistic view of the alterations in metabolic pathways related to GDM in the first trimester (Fig. S6). Sixteen metabolites, such as proline, ornithine, glutamate, leucine/isoleucine and histidine, were found at critical junctures. The key nodes were related to "tricarboxylic acid (TCA) cycle", "purine metabolism", "urea cycle and metabolism of arginine, proline, glutamate, aspartate and asparagine", and other pathways. In the case of the second-trimester samples (Fig. S7), five metabolites, such as palmitoylcarnitine, acetylcarnitine, were found at critical junctures, which were associated with "glycerophospholipid metabolism", "saturated fatty acids β-oxidation", "urea cycle and metabolism of arginine, proline, glutamate, aspartate and asparagine". Among the pathways, "urea cycle and metabolism of arginine, proline, glutamate, aspartate and asparagine" overlapped in both pairs of comparison groups (GDM-T1 vs. CON-T1 as well as GDM-T2 vs. 13 ACS Paragon Plus Environment


CON-T2), indicating that this pathway may be altered in the development progress of GDM. 4. DISCUSSION This work described a longitudinal metabolomics evaluation of 107 GDM women compared with 107 healthy pregnant women at two different time points. Large sample size and QC-RLSC signal correction may provide more reliable results. Although a large number of endogenous metabolites could be associated with physiological factors such as maternal age, gestational week and others

20,

such possibility has been ruled out in this study with

well-matched GDM and control groups and large-scale sample sets. Metabolic phenotypes revealed significant differences of either between GDM women and the matched control groups or between different trimesters. After FDR correction, the levels of 31 metabolites were found to be significantly altered associated with GDM in the first-trimester serum samples, suggesting that major alterations in metabolic pathways occurred in GDM. In this study, purine metabolism, fatty acid β-oxidation pathway, urea cycle and TCA cycle pathways, and other pathways were identified to be potentially involved with GDM (Fig. 5).


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Figure 5 Proposed metabolic pathways based on differential compounds. The fill color in the left of oblong represents the fold change of GDM-T1 vs. CON-T1, while the color in the right of oblong represents the fold change of GDM-T2 vs. CON-T2. The arrow in the left of oblong represents the fold change of GDM-T2 vs. GDM-T1, while the arrow in the right of oblong represents the fold change of CON-T2 vs. CON-T1. (Abbreviation: TCA, tricarboxylic acid; CPT1, carnitine palmitoyltransferase I; XO, Xanthine oxidase; CON, control; GDM, gestational diabetes mellitus) Red (up) represents fold change > 1.2 and p < 0.05, and the green (down) represents fold change < 0.8 and p < 0.05.) Purines are key components of cellular energy systems. For instance, hypoxanthine is a degradation product of adenosine triphosphate 21, which is a major biological energy source. To the best of our knowledge, this is the first report on the purine metabolism altered with GDM in the maternal serum samples. In this study, it is found that hypoxanthine and xanthosine were both significantly (FDR adjusted p < 0.05) elevated in GDM group compared with control group in the first trimester (Fig. 6). The up-regulation of the metabolites in purine metabolism in GDM groups compared with controls may be explained by the fact that women who subsequently developed GDM often had a higher energy intake 15 ACS Paragon Plus Environment


22.

The detection of differential endogenous metabolites in first-trimester serum samples,

which were collected before the onset of GDM, may advance the understanding of the occurrence and development progress of GDM.

Figure 6 Differential metabolites related to purine metabolism in response to GDM. (* represents FDR adjusted p < 0.05 (GDM-T1 vs. CON-T1), ** represents FDR adjusted p < 0.01 (GDM-T1 vs. CON-T1) # represents FDR adjusted p < 0.05 (CON-T2 vs. CON-T1 or GDM-T2 vs. GDM-T1). ## represents FDR adjusted p < 0.01 (CON-T2 vs. CON-T1 or GDM-T2 vs. GDM-T1). Manu-Whitney U test was used for significant test. The data distribution was normalized between 0-1.) On the other hand, levels of hypoxanthine, xanthine and xanthosine significantly (FDR adjusted p value < 0.05) decreased in the second trimester compared with the first trimester, while the level of uric acid exhibited an increased trend (T2 vs. T1) in both GDM and control groups (FDR adjusted p < 0.05). However, the fold change (Fig. 3) of uric acid in GDM groups (GDM-T2 vs. GDM-T1) was significantly higher (p < 0.05) than that in control groups (CON-T2 vs. CON-T1). Thus, uric acid, which is the end oxidation product of purine metabolism, might accumulate in women who subsequently developed GDM. Hypoxanthine 16 ACS Paragon Plus Environment

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is transformed to xanthine by xanthine oxidase (XO), which can also convert xanthine to uric acid 23. The excessive accumulation of uric acid might be attributed to the elevated activity of XO in maternal serum of GDM group, which was consistent with the previous report that the XO activity was elevated in maternal blood collected from GDM women in 38th-40th gestation weeks compared with healthy women

24, 25.

Uric acid is associated with insulin

resistance 26, which is a cause and a feature of GDM. High levels of uric acid are considered as a risk factor for GDM in the first 20 weeks of pregnancy 27 and for type 2 diabetes mellitus 28.

In this study, the degree of increase of uric acid in GDM group compared with control

group elevated in a longitudinal manner (Fig. 3). The accelerated accumulation of uric acid in GDM group suggested that it may contribute to GDM occurrence and progression. Fatty acid β-oxidation is a process during which fatty acids are broken down by various tissues to produce energy

29.

Pantothenic acid and long-chain acylcarnitines are involved in

fatty acid β-oxidation pathway

30.

Pantothenic acid, which is the precursor of CoA and

participates in the formation of long-chain acyl-CoAs, has been reported to increase in type 2 diabetes serum samples 31, but there is no previous report about it in relation to GDM. Longchain acylcarnitines and their precursors long-chain acyl-CoAs associated with insulin resistance

33, 34.

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are reported to be

In this study, pantothenic acid was elevated (FDR

adjusted p < 0.05) in GDM-T1 samples compared with CON-T1 samples. In addition, longchain acylcarnitines (such as palmitoylcarnitine and vaccenyl carnitine) were found to increase in GDM women compared with controls in both first and second trimester (Table S2). Both results suggested an alteration of the fatty acid β-oxidation pathway during GDM occurred in both first and second trimesters, and the lipid metabolism might be disturbed prior to the time of GDM diagnosis. The comparison of time-dependent metabolic trajectories between GDM and control groups may provide information about the metabolites in the development progress of GDM. 17 ACS Paragon Plus Environment


However, only a few longitudinal metabolomics studies of GDM were reported. In a recent study

35,

2-hydroxybutyrate and 3-hydroxybutyrate were identified as potential prognostic

biomarkers to predict the early onset of diabetic complications after the delivery in the comparison of maternal blood from GDM women (n = 24) and control women (n = 24) from the second and the third trimesters. Another longitudinal study 36 demonstrated that a number of phospholipids were lower in plasma of GDM women (n = 27) compared with the healthy controls (n = 34) in all three trimesters. Tryptophan and purine metabolism in maternal urine was reported to be associated with GDM 23. In this study, metabolic dynamic signatures were described by comparing the first- and second-trimester serum metabolomes (Table S2). The levels of serine, proline, leucine/isoleucine, glutamic acid, tyrosine, ornithine and LysoPC(20:4) decreased more in GDM group than those in controls as the pregnancy progressed (Fig. 3). Among them, the levels of proline, leucine/isoleucine, glutamic acid and ornithine, increased in the comparison of GDM with control of the first-trimester serum (Table S2), while the levels of other two metabolites (serine and tyrosine) also showed an increased trend in GDM-T1. The levels of leucine and isoleucine are associated with insulin resistance and lower insulin sensitivity 37, while the concentration of tyrosine is positively associated with the modifications of insulin resistance and secretion

38.

The imbalance between insulin resistance and insulin secretion

during pregnancy is considered as one of the possible causes of GDM 8. On the other hand, the levels of glycerophosphocholine, LysoPE(20:1) and 13 LysoPCs (such as LysoPC(20:4) and LysoPC(20:5)) decreased in GDM-T2 samples in comparison with GDM-T1, but not in control group (CON-T2 vs. CON-T1) (Table S2). Among them, the fold changes of LysoPC(20:4) in GDM group (GDM-T2 vs. GDM-T1) were significantly lower than those in healthy controls (CON-T2 vs. CON-T1) (Fig. 3). The levels of lysophospholipids, including LysoPCs and LysoPEs, were previously reported to change considerably related to the 18 ACS Paragon Plus Environment

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glycemic state of pregnant women

10.

LysoPCs have been found to induce glucose-induced

insulin secretion from pancreatic β-cell 39. Thus, the decrease of the LysoPC levels in secondtrimester serum of GDM group (GDM-T2 vs. GDM-T1) may be associated with glucose intolerance through altered β-cell function. Thus, the difference in longitudinal changes of the amino acids levels (serine, proline, leucine/isoleucine, glutamic acid, tyrosine and ornithine) and above-mentioned lipids between GDM group and healthy controls provided the evidence that the metabolisms of amino acid and lipid were affected by GDM. The findings also suggest that metabolic profiling approach might have the potential for the early prediction of GDM and the potential utility of the differential metabolites to find the high-risk population in the first trimester, supplementing the traditional indicators for GDM, such as maternal body mass index

40.

However, one limitation of this study is the lack of

detailed information on medication and diet of the participants. Some substances, such as amino acid and lipids, could be taken from the diet and the metabolism also might be influenced by some medicine. Such issues need to be investigated in further studies. 5. CONCLUSIONS This study reported so far the largest MS-based non-targeted longitudinal metabolomics study of GDM. The identified differential metabolites revealed that the pathways, including purine metabolism, fatty acid β-oxidation, urea cycle and TCA cycle, were altered in relation to GDM. The longitudinal study with samples of different time points indicated that the metabolites that had different trimester-specific alterations between GDM group and healthy controls might contribute to GDM progression. The results of this study revealed that the amino acid metabolism, lipid metabolism and some other physiological processes might be disturbed in women who subsequently developed GDM prior to the diagnosis, although there was no obvious clinical symptom. The findings may offer valuable information about GDM development and facilitate the early prediction of GDM. It is demonstrated that large-scale 19 ACS Paragon Plus Environment


longitudinal metabolomics studies may advance the understanding of GDM pathogenesis. ACKNOWLEDGMENTS This work was supported by National Natural Science Foundation of China (21437002 and 91543202) and the General Research Fund (12319716) and Collaborative Research Fund (C2014-14E) from Research Grants Council of Hong Kong. Dr. Simon Wang at the Language Centre of HKBU has improved the linguistic presentation of the manuscript. ASSOCIATED CONTENT The following supporting information is available free of charge at ACS website http://pubs.acs.org Figure S1. The comparison of the cumulative frequency of RSD% of features in metabolomics data. Figure S2. OPLS-DA score plots. Figure S3. Power analysis results Figure S4. Volcano plots based on the comparisons of different groups. Figure S5. Mass spectra and proposed fragmentation pattern of (a) proline and (b) LysoPC (14:0). Figure S6. Network of the significantly differential metabolites (GDM-T2 vs. CON-T2). Figure S7. Network of the significantly differential metabolites (GDM-T2 vs. CON-T2). Table S1. The identification information of differential metabolites. Table S2. Statistical analysis of differential metabolites between GDM and control groups. Table S3. Statistical analysis of differential metabolites in different trimesters. Table S4. Metabolites in relation to GDM in both first trimester and second trimester. REFERENCES (1) Leng, J.; Shao, P.; Zhang, C.; Tian, H.; Zhang, F.; Zhang, S.; Dong, L.; Li, L.; Yu, Z.; Chan, J. C., Prevalence of gestational diabetes mellitus and its risk factors in Chinese pregnant women: a prospective population-based study in Tianjin, China. PLoS ONE 2015, 10, (3), e0121029. (2) Allalou, A.; Nalla, A.; Prentice, K. J.; Liu, Y.; Zhang, M.; Dai, F. F.; Ning, X.; Osborne, L. R.; Cox, B. J.; Gunderson, E. P.; Wheeler, M. B., A predictive metabolic signature for the transition from gestational diabetes mellitus to type 2 diabetes. Diabetes 2016, 65, (9), 2529-2539. (3) Harreiter, J.; Dovjak, G.; Kautzky-Willer, A., Gestational diabetes mellitus and cardiovascular risk after pregnancy. Women’s Health 2014, 10, (1), 91-108. (4) Fuchs, O.; Sheiner, E.; Meirovitz, M.; Davidson, E.; Sergienko, R.; Kessous, R., The association between a history of gestational diabetes mellitus and future risk for female malignancies. Arch. Gynecol. Obstet. 2017, 295, (3), 731-736. 20 ACS Paragon Plus Environment

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