Urinary metabolic variation analysis during pregnancy and application in Gestational Diabetes Mellitus and spontaneous abortion biomarker discovery

Xiaoyan Liu, Xiangqing Wang, Haidan Sun, Zhengguang Guo, Xiang Liu, Tao Yuan, Yong Fu, Xiaoyue Tang, Jing Li, Wei Sun, Weigang Zhao, Xiaoyan Liu, Xiangqing Wang, Haidan Sun, Zhengguang Guo, Xiang Liu, Tao Yuan, Yong Fu, Xiaoyue Tang, Jing Li, Wei Sun, Weigang Zhao

Abstract

Pregnancy is associated with the onset of many adaptation processes that are likely to change over the course of gestation. Understanding normal metabolites' variation with pregnancy progression is crucial for gaining insights of the key nutrients for normal fetal growth, and for comparative research of pregnancy-related complications. This work presents liquid chromatography-mass spectrum-based urine metabolomics study of 50 health pregnant women at three time points during pregnancy. The influence of maternal physiological factors, including age, BMI, parity and gravity to urine metabolome was explored. Additionally, urine metabolomics was applied for early prediction of two pregnancy complications, gestational diabetes mellitus and spontaneous abortion. Our results suggested that during normal pregnancy progression, pathways of steroid hormone biosynthesis and tyrosine metabolism were significantly regulated. BMI is a factor that should be considered during cross-section analysis. Application analysis discovered potential biomarkers for GDM in the first trimester with AUC of 0.89, and potential biomarkers for SA in the first trimester with AUC of 0.90. In conclusion, our study indicated that urine metabolome could reflect variations during pregnancy progression, and has potential value for pregnancy complications early prediction. The clinical trial number for this study is NCT03246295.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Study design.
Figure 2
Figure 2
Analysis of metabolic profiling variation of health pregnancy progression. (a) Score plot of PCA of urinary metabolic profiling in health controls. (b) Heatmap of relative intensity of differential metabolites with pregnancy progression. (c) Pathway analysis of differential metabolites with health pregnancy progression. (d) Correlation coefficient of BMI and urine metabolites. The absolute value above 0.4 was referred to have medium correlation. (e) Enriched pathway of metabolites correlated with BMI.
Figure 3
Figure 3
Early prediction of GDM and SA. (a) OPLS-DA score plot of urine metabolic of GDM and the health at first trimester. (b) ROC plot with 10-fold cross-Validation based on Logistic Regression Model based on metabolites L-phenylalanyl-L-proline, Hydroxylauroylcarnitine and levoglucosan. (c) OPLS-DA score plot of urine metabolic of SA and the health at first trimester. (d) ROC plot with 10-fold cross-validation based on logistic regression model of indolylacryloylglycine and L-histidine.

References

    1. Hadden DR, McLaughlin C. Normal and abnormal maternal metabolism during pregnancy. Seminars in fetal & neonatal medicine. 2009;14:66–71. doi: 10.1016/j.siny.2008.09.004.
    1. Stanley K, Fraser R, Bruce C. Physiological changes in insulin resistance in human pregnancy: longitudinal study with the hyperinsulinaemic euglycaemic clamp technique. British journal of obstetrics and gynaecology. 1998;105:756–759. doi: 10.1111/j.1471-0528.1998.tb10207.x.
    1. Lippi G, et al. Lipid and lipoprotein profile in physiological pregnancy. Clinical laboratory. 2007;53:173–177.
    1. Loke DF, et al. Lipid profiles during and after normal pregnancy. Gynecologic and obstetric investigation. 1991;32:144–147. doi: 10.1159/000293016.
    1. Luan H, et al. Pregnancy-induced metabolic phenotype variations in maternal plasma. Journal of proteome research. 2014;13:1527–1536. doi: 10.1021/pr401068k.
    1. Lindsay KL, et al. Longitudinal Metabolomic Profiling of Amino Acids and Lipids across Healthy Pregnancy. PloS one. 2015;10:e0145794. doi: 10.1371/journal.pone.0145794.
    1. Wang Q, et al. Metabolic profiling of pregnancy: cross-sectional and longitudinal evidence. BMC medicine. 2016;14:205. doi: 10.1186/s12916-016-0733-0.
    1. Trivedi DK, Iles RK. HILIC-MS-based shotgun metabolomic profiling of maternal urine at 9–23 weeks of gestation - establishing the baseline changes in the maternal metabolome. Biomedical chromatography: BMC. 2015;29:240–245. doi: 10.1002/bmc.3266.
    1. Sandler V, et al. Associations of maternal BMI and insulin resistance with the maternal metabolome and newborn outcomes. Diabetologia. 2017;60:518–530. doi: 10.1007/s00125-016-4182-2.
    1. Jacob S, et al. Targeted Metabolomics Demonstrates Distinct and Overlapping Maternal Metabolites Associated With BMI, Glucose, and Insulin Sensitivity During Pregnancy Across Four Ancestry Groups. Diabetes care. 2017;40:911–919. doi: 10.2337/dc16-2453.
    1. Liu XCX, et al. Investigation of the urinary metabolic variations and the application in bladder cancer biomarker discovery. International journal of cancer. 2018;143:408–418. doi: 10.1002/ijc.31323.
    1. Thevenot EA, Roux A, Xu Y, Ezan E, Junot C. Analysis of the Human Adult Urinary Metabolome Variations with Age, Body Mass Index, and Gender by Implementing a Comprehensive Workflow for Univariate and OPLS Statistical Analyses. Journal of proteome research. 2015;14:3322–3335. doi: 10.1021/acs.jproteome.5b00354.
    1. O’Leary P, Boyne P, Flett P, Beilby J, James I. Longitudinal assessment of changes in reproductive hormones during normal pregnancy. Clinical chemistry. 1991;37:667–672.
    1. Soldin OP, et al. Steroid hormone levels in pregnancy and 1 year postpartum using isotope dilution tandem mass spectrometry. Fertility and sterility. 2005;84:701–710. doi: 10.1016/j.fertnstert.2005.02.045.
    1. Mistry HD, et al. Gestation-specific reference intervals for comprehensive spot urinary steroid hormone metabolite analysis in normal singleton pregnancy and 6 weeks postpartum. Reproductive biology and endocrinology: RB&E. 2015;13:101. doi: 10.1186/s12958-015-0100-6.
    1. Tulchinsky D, Hobel CJ, Yeager E, Marshall JR. Plasma estrone, estradiol, estriol, progesterone, and 17-hydroxyprogesterone in human pregnancy. I. Normal pregnancy. American journal of obstetrics and gynecology. 1972;112:1095–1100. doi: 10.1016/0002-9378(72)90185-8.
    1. Makieva S, Saunders PT, Norman JE. Androgens in pregnancy: roles in parturition. Human reproduction update. 2014;20:542–559. doi: 10.1093/humupd/dmu008.
    1. Orczyk-Pawilowicz M, et al. Metabolomics of Human Amniotic Fluid and Maternal Plasma during Normal Pregnancy. PloS one. 2016;11:e0152740. doi: 10.1371/journal.pone.0152740.
    1. Whittaker PG, Lee CH, Cooper BG, Taylor R. Evaluation of phenylalanine and tyrosine metabolism in late human pregnancy. Metabolism: clinical and experimental. 1999;48:849–852. doi: 10.1016/S0026-0495(99)90217-2.
    1. Park S, Park JY, Lee JH, Kim SH. Plasma levels of lysine, tyrosine, and valine during pregnancy are independent risk factors of insulin resistance and gestational diabetes. Metabolic syndrome and related disorders. 2015;13:64–70. doi: 10.1089/met.2014.0113.
    1. Zanardo, V. et al. Prepregnancy Body Mass Index shift across gestation: primary evidence of an association with eating disorders. The journal of maternal-fetal & neonatal medicine: the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstet, 1–151, 10.1080/14767058.2018.1494709 (2018).
    1. Santos Ferreira DL, et al. Association of pre-pregnancy body mass index with offspring metabolic profile: Analyses of 3 European prospective birth cohorts. PLoS medicine. 2017;14:e1002376. doi: 10.1371/journal.pmed.1002376.
    1. Martin KE, Grivell RM, Yelland LN, Dodd JM. The influence of maternal BMI and gestational diabetes on pregnancy outcome. Diabetes research and clinical practice. 2015;108:508–513. doi: 10.1016/j.diabres.2014.12.015.
    1. Catalano PM, et al. The hyperglycemia and adverse pregnancy outcome study: associations of GDM and obesity with pregnancy outcomes. Diabetes care. 2012;35:780–786. doi: 10.2337/dc11-1790.
    1. Sachse D, et al. Metabolic changes in urine during and after pregnancy in a large, multiethnic population-based cohort study of gestational diabetes. PloS one. 2012;7:e52399. doi: 10.1371/journal.pone.0052399.
    1. Law KP, Mao X, Han TL, Zhang H. Unsaturated plasma phospholipids are consistently lower in the patients diagnosed with gestational diabetes mellitus throughout pregnancy: A longitudinal metabolomics study of Chinese pregnant women part 1. Clinica chimica acta; international journal of clinical chemistry. 2017;465:53–71. doi: 10.1016/j.cca.2016.12.010.
    1. Law KP, Han TL, Mao X, Zhang H. Tryptophan and purine metabolites are consistently upregulated in the urinary metabolome of patients diagnosed with gestational diabetes mellitus throughout pregnancy: A longitudinal metabolomics study of Chinese pregnant women part 2. Clinica chimica acta; international journal of clinical chemistry. 2017;468:126–139. doi: 10.1016/j.cca.2017.02.018.
    1. White SL, et al. Early Antenatal Prediction of Gestational Diabetes in Obese Women: Development of Prediction Tools for Targeted Intervention. PloS one. 2016;11:e0167846. doi: 10.1371/journal.pone.0167846.
    1. Bentley-Lewis R, et al. Metabolomic profiling in the prediction of gestational diabetes mellitus. Diabetologia. 2015;58:1329–1332. doi: 10.1007/s00125-015-3553-4.
    1. Lorenzo MP, et al. Optimization and validation of a chiral GC-MS method for the determination of free D-amino acids ratio in human urine: application to a gestational diabetes mellitus study. Journal of pharmaceutical and biomedical analysis. 2015;107:480–487. doi: 10.1016/j.jpba.2015.01.015.
    1. Zhang X, Zhang C, Chen L, Han X, Ji L. Human serum acylcarnitine profiles in different glucose tolerance states. Diabetes research and clinical practice. 2014;104:376–382. doi: 10.1016/j.diabres.2014.04.013.
    1. Nevalainen J, et al. First-Trimester Maternal Serum Amino Acids and Acylcarnitines Are Significant Predictors of GestationalDiabetes. The review of diabetic studies: RDS. 2016;13:236–245. doi: 10.1900/rds.2016.13.236.
    1. Shi R, et al. Cyp3a11-mediated testosterone-6beta-hydroxylation decreased, while UGT1a9-mediated propofol O-glucuronidation increased, in mice with diabetes mellitus. Biopharmaceutics & drug disposition. 2016;37:433–443. doi: 10.1002/bdd.2027.
    1. Fei H, et al. Plasma metabolomic profile and potential biomarkers for missed abortion. Biomedical chromatography: BMC. 2016;30:1942–1952. doi: 10.1002/bmc.3770.
    1. Feng RN, et al. Histidine supplementation improves insulin resistance through suppressed inflammation in obese women with the metabolic syndrome: a randomised controlled trial. Diabetologia. 2013;56:985–994. doi: 10.1007/s00125-013-2839-7.
    1. Zhang J, et al. An intelligentized strategy for endogenous small molecules characterization and quality evaluation of earthworm from two geographic origins by ultra-high performance HILIC/QTOF MS(E) and Progenesis QI. Analytical and bioanalytical chemistry. 2016;408:3881–3890. doi: 10.1007/s00216-016-9482-3.

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