Segmental hair metabolomics analysis in pregnant women with pregnancy complications.
GC-MS
HELLP syndrome
Hair
Metabolomics
Preeclampsia
Journal
Metabolomics : Official journal of the Metabolomic Society
ISSN: 1573-3890
Titre abrégé: Metabolomics
Pays: United States
ID NLM: 101274889
Informations de publication
Date de publication:
21 04 2023
21 04 2023
Historique:
received:
17
04
2022
accepted:
11
04
2023
medline:
25
4
2023
pubmed:
21
4
2023
entrez:
21
04
2023
Statut:
epublish
Résumé
Pregnancy complications, as preeclampsia (PE) and HELLP syndrome, occurring with similar pathophysiological mechanisms, have adverse effects on the health of both mother and fetus during pregnancy and thereafter, they are leading causes of maternal and fetal morbidity and mortality. The hair metabolome has been recognized as a valuable source of information in pregnancy research, as it provides stable metabolite information to be able to assist with studying biomarkers or metabolic mechanisms of pregnancy and its complications. The aim of this study was to investigate the hair metabolome profile of pregnant women with PE, HELLP syndrome and healthy women. Hair samples of new-borns' mothers (patients and controls) were investigated segmentally relevant to each trimester using a proper sample preparation and gas chromatography-mass spectrometry (GC-MS) to identify robust biomarkers that can be useful for screening, early detection, follow-up and treatment of PE and HELLP syndrome, the etiology of which are still unknown. The results showed a significant change in the metabolome profiles of the patient and control groups regarding the trimesters. A striking decrease was observed in all 100 metabolites investigated in the patient group (p < 0.000). The metabolic pathways associated with significant metabolites have also been investigated, and the most affected pathways were observed to be the urea cycle, glycine, serine, aspartate, methionine and purine metabolism, ammonia cycle, and phosphatidylethanolamine biosynthesis. The found metabolites provide us with extensive data on the ability to establish biomarkers for predicting, early detection and monitoring of PE.
Identifiants
pubmed: 37084096
doi: 10.1007/s11306-023-02009-7
pii: 10.1007/s11306-023-02009-7
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
45Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Références
Lam, C., Lim, K. H., & Karumanchi, S. A. (2005). Circulating angiogenic factors in the pathogenesis and prediction of preeclampsia. Hypertension, 46(5), 1077–1085.
doi: 10.1161/01.HYP.0000187899.34379.b0
pubmed: 16230516
Khong, Y., & Brosens, I. (2011). Defective deep placentation. Best Practice & Research Clinical Obstetrics & Gynaecology, 25(3), 301–311.
doi: 10.1016/j.bpobgyn.2010.10.012
Anderson, U. D., Olsson, M. G., Kristensen, K. H., Akerstrom, B., & Hansson, S. R. (2012). Review: Biochemical markers to predict preeclampsia. Placenta, 33(Suppl), S42–S47.
doi: 10.1016/j.placenta.2011.11.021
pubmed: 22197626
Burton, G. J., Redman, C. W., Roberts, J. M., & Moffett, A. (2019). Pre-eclampsia: Pathophysiology and clinical implications. BMJ, 366, l2381.
doi: 10.1136/bmj.l2381
pubmed: 31307997
Romero, R., & Chaiworapongsa, T. (2013). Preeclampsia: A link between trophoblast dysregulation and an antiangiogenic state. The Journal of Clinical Investigation, 123(7), 2775–2777.
doi: 10.1172/JCI70431
pubmed: 23934119
pmcid: 3999621
Hypertension in pregnancy. (2013). Report of the American College of Obstetricians and Gynecologists’ Task Force on hypertension in pregnancy. Obstetrics and Gynecology, 122(5), 1122–1131.
Wallace, K., Harris, S., Addison, A., & Bean, C. (2018). HELLP syndrome: Pathophysiology and current therapies. Current Pharmaceutical Biotechnology, 19(10), 816–826.
doi: 10.2174/1389201019666180712115215
pubmed: 29998801
Wahid, B., Rafique, S., Ali, A., Waqar, M., Nabi, G., Wasim, M., & Idrees, M. (2018). Biomarkers for diagnosis of pre-eclampsia and endometriosis. Biomarkers In Medicine, 12(10), 1161–1173.
doi: 10.2217/bmm-2018-0058
pubmed: 30191726
Delplancke, T. D. J., de Seymour, J. V., Tong, C., Sulek, K., Xia, Y., Zhang, H., Han, T. L., & Baker, P. N. (2018). Analysis of sequential hair segments reflects changes in the metabolome across the trimesters of pregnancy. Scientific Reports, 8(1), 36.
doi: 10.1038/s41598-017-18317-7
pubmed: 29311683
pmcid: 5758601
Sulek, K., Han, T. L., Villas-Boas, S. G., Wishart, D. S., Soh, S. E., Kwek, K., Gluckman, P. D., Chong, Y. S., Kenny, L. C., & Baker, P. N. (2014). Hair metabolomics: Identification of fetal compromise provides proof of concept for biomarker discovery. Theranostics, 4(9), 953.
doi: 10.7150/thno.9265
pubmed: 25057319
pmcid: 4107295
He, X., de Seymour, J. V., Sulek, K., Qi, H., Zhang, H., Han, T. L., Villas-Bôas, S. G., & Baker, P. N. (2016). Maternal hair metabolome analysis identifies a potential marker of lipid peroxidation in gestational diabetes mellitus. Acta Diabetologica, 53(1), 119–122.
doi: 10.1007/s00592-015-0737-9
pubmed: 25904507
Eisenbeiss, L. (2020). Towards best practice in hair metabolomic studies: Systematic investigation on the impact of hair length and color. Metabolites, 10(10), 381.
doi: 10.3390/metabo10100381
pubmed: 32993123
pmcid: 7601250
Yin, P., Lehmann, R., & Xu, G. (2015). Effects of pre-analytical processes on blood samples used in metabolomics studies. Analytical and Bioanalytical Chemistry, 407, 4879–4892.
doi: 10.1007/s00216-015-8565-x
pubmed: 25736245
pmcid: 4471316
Smart, K. F., Aggio, R. B., Van Houtte, J. R., & Villas-Boas, S. G. (2010). Analytical platform for metabolome analysis of microbial cells using methyl chloroformate derivatization followed by gas chromatography-mass spectrometry. Nature Protocols, 5(10), 1709–1729.
doi: 10.1038/nprot.2010.108
pubmed: 20885382
Reisetter, A. C., Muehlbauer, M. J., Bain, J. R., Nodzenski, M., Stevens, R. D., Ilkayeva, O., Metzger, B. E., Newgard, C. B., Lowe, W. L., & Scholtens, D. M. (2017). Mixture model normalization for non-targeted gas chromatography/mass spectrometry metabolomics data. Bmc Bioinformatics, 18(1), 84.
doi: 10.1186/s12859-017-1501-7
pubmed: 28153035
pmcid: 5290663
Borzychowski, A. M., Sargent, I. L., & Redman, C. W. (2006). Inflammation and pre-eclampsia. Seminars in Fetal and Neonatal Medicine, 11(5), 309–316.
doi: 10.1016/j.siny.2006.04.001
pubmed: 16828580
Visser, N., van Rijn, B. B., Rijkers, G. T., Franx, A., & Bruinse, H. W. (2007). Inflammatory changes in preeclampsia: Current understanding of the maternal innate and adaptive immune response. Obstetrical & Gynecological Survey, 62(3), 191–201.
doi: 10.1097/01.ogx.0000256779.06275.c4
Roberts, J. M. (1998). Endothelial dysfunction in preeclampsia. Seminars in Reproductive Endocrinology, 16(1), 5.
doi: 10.1055/s-2007-1016248
pubmed: 9654603
Zhang, A., Sun, H., & Wang, X. (2012). Serum metabolomics as a novel diagnostic approach for disease: A systematic review. Analytical and Bioanalytical Chemistry, 404(4), 1239–1245.
doi: 10.1007/s00216-012-6117-1
pubmed: 22648167
Kuc, S., Koster, M. P., Pennings, J. L., Hankemeier, T., Berger, R., Harms, A. C., et al. (2014). Metabolomics profiling for identification of novel potential markers in early prediction of preeclampsia. PLoS ONE, 9(5), e98540.
doi: 10.1371/journal.pone.0098540
pubmed: 24873829
pmcid: 4038585
Alonso, A., Marsal, S., & Julia, A. (2015). Analytical methods in untargeted metabolomics: State of the art in 2015. Frontiers in Bioengineering and Biotechnology, 3, 23.
doi: 10.3389/fbioe.2015.00023
pubmed: 25798438
pmcid: 4350445