Prediction of gestational age using urinary metabolites in term and preterm pregnancies.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
16 05 2022
16 05 2022
Historique:
received:
12
08
2021
accepted:
25
04
2022
entrez:
16
5
2022
pubmed:
17
5
2022
medline:
20
5
2022
Statut:
epublish
Résumé
Assessment of gestational age (GA) is key to provide optimal care during pregnancy. However, its accurate determination remains challenging in low- and middle-income countries, where access to obstetric ultrasound is limited. Hence, there is an urgent need to develop clinical approaches that allow accurate and inexpensive estimations of GA. We investigated the ability of urinary metabolites to predict GA at time of collection in a diverse multi-site cohort of healthy and pathological pregnancies (n = 99) using a broad-spectrum liquid chromatography coupled with mass spectrometry (LC-MS) platform. Our approach detected a myriad of steroid hormones and their derivatives including estrogens, progesterones, corticosteroids, and androgens which were associated with pregnancy progression. We developed a restricted model that predicted GA with high accuracy using three metabolites (rho = 0.87, RMSE = 1.58 weeks) that was validated in an independent cohort (n = 20). The predictions were more robust in pregnancies that went to term in comparison to pregnancies that ended prematurely. Overall, we demonstrated the feasibility of implementing urine metabolomics analysis in large-scale multi-site studies and report a predictive model of GA with a potential clinical value.
Identifiants
pubmed: 35577875
doi: 10.1038/s41598-022-11866-6
pii: 10.1038/s41598-022-11866-6
pmc: PMC9110694
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
8033Subventions
Organisme : NICHD NIH HHS
ID : T32 HD075731
Pays : United States
Organisme : NIH HHS
ID : R35GM138353
Pays : United States
Organisme : Bill & Melinda Gates Foundation
ID : OPP1203327
Pays : United States
Organisme : Bill & Melinda Gates Foundation
ID : OPP1113682
Pays : United States
Organisme : NIGMS NIH HHS
ID : R35 GM138353
Pays : United States
Organisme : NIH HHS
ID : 2RM1HG00773506
Pays : United States
Organisme : NIH HHS
ID : T32 HD075731
Pays : United States
Organisme : NIAID NIH HHS
ID : P30AI050410
Pays : United States
Organisme : NIAID NIH HHS
ID : P30 AI050410
Pays : United States
Organisme : FIC NIH HHS
ID : K01 TW010857
Pays : United States
Investigateurs
Fyezah Jehan
(F)
Sunil Sazawal
(S)
Abdullah H Baqui
(AH)
Muhammad I Nisar
(MI)
Usha Dhingra
(U)
Rasheda Khanam
(R)
Muhammad Ilyas
(M)
Arup Dutta
(A)
Usma Mehmood
(U)
Saikat Deb
(S)
Aneeta Hotwani
(A)
Said M Ali
(SM)
Sayedur Rahman
(S)
Ambreen Nizar
(A)
Shaali M Ame
(SM)
Sajid Muhammad
(S)
Aishwarya Chauhan
(A)
Waqasuddin Khan
(W)
Rubhana Raqib
(R)
Sayan Das
(S)
Salahuddin Ahmed
(S)
Tarik Hasan
(T)
Javairia Khalid
(J)
Mohammed H Juma
(MH)
Nabidul H Chowdhury
(NH)
Furqan Kabir
(F)
Fahad Aftab
(F)
Abdul Quaiyum
(A)
Alexander Manu
(A)
Sachiyo Yoshida
(S)
Rajiv Bahl
(R)
Anisur Rahman
(A)
Jesmin Pervin
(J)
Joan T Price
(JT)
Monjur Rahman
(M)
Margaret P Kasaro
(MP)
James A Litch
(JA)
Patrick Musonda
(P)
Bellington Vwalika
(B)
Jeffrey S A Stringer
(JSA)
Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2022. The Author(s).
Références
Soma-Pillay, P., Nelson-Piercy, C., Tolppanen, H. & Mebazaa, A. Physiological changes in pregnancy. Cardiovasc. J. Afr. 27, 89–94. https://doi.org/10.5830/CVJA-2016-021 (2016).
doi: 10.5830/CVJA-2016-021
pubmed: 27213856
pmcid: 4928162
Karl, S. et al. Preterm or not—An evaluation of estimates of gestational age in a cohort of women from Rural Papua New Guinea. PLoS ONE 10, e0124286. https://doi.org/10.1371/journal.pone.0124286 (2015).
doi: 10.1371/journal.pone.0124286
pubmed: 25945927
pmcid: 4422681
Committee on Obstetric Practice, the American Institute of Ultrasound in Medicine, and the Society for Maternal-Fetal Medicine. Committee Opinion No 700: Methods for estimating the due date. Obstet. Gynecol. 129, e150–e154. https://doi.org/10.1097/AOG.0000000000002046 (2017).
doi: 10.1097/AOG.0000000000002046
Kim, E. T., Singh, K., Moran, A., Armbruster, D. & Kozuki, N. Obstetric ultrasound use in low and middle income countries: A narrative review. Reprod. Health 15, 129. https://doi.org/10.1186/s12978-018-0571-y (2018).
doi: 10.1186/s12978-018-0571-y
pubmed: 30029609
pmcid: 6053827
Blencowe, H. et al. Born too soon: The global epidemiology of 15 million preterm births. Reprod. Health 10(Suppl 1), S2. https://doi.org/10.1186/1742-4755-10-S1-S2 (2013).
doi: 10.1186/1742-4755-10-S1-S2
pubmed: 24625129
pmcid: 3828585
The Alliance for Maternal and Newborn Health Improvement (AMANHI) mortality study group. Population-based rates, timing, and causes of maternal deaths, stillbirths, and neonatal deaths in south Asia and sub-Saharan Africa: A multi-country prospective cohort study. Lancet Glob. Health 6, e1297–e1308. https://doi.org/10.1016/S2214-109X(18)30385-1 (2018).
doi: 10.1016/S2214-109X(18)30385-1
Pan, W. et al. Simultaneously monitoring immune response and microbial infections during pregnancy through plasma cfRNA sequencing. Clin. Chem. 63, 1695–1704. https://doi.org/10.1373/clinchem.2017.273888 (2017).
doi: 10.1373/clinchem.2017.273888
pubmed: 28904056
Aghaeepour, N. et al. An immune clock of human pregnancy. Sci. Immunol. 2, eaan2946. https://doi.org/10.1126/sciimmunol.aan2946 (2017).
doi: 10.1126/sciimmunol.aan2946
pubmed: 28864494
pmcid: 5701281
Aghaeepour, N. et al. A proteomic clock of human pregnancy. Am. J. Obstet. Gynecol. 218(347), e341-347. https://doi.org/10.1016/j.ajog.2017.12.208 (2018).
doi: 10.1016/j.ajog.2017.12.208
Ghaemi, M. S. et al. Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy. Bioinformatics 35, 95–103. https://doi.org/10.1093/bioinformatics/bty537 (2019).
doi: 10.1093/bioinformatics/bty537
pubmed: 30561547
Liang, L. et al. Metabolic dynamics and prediction of gestational age and time to delivery in pregnant women. Cell 181, 1680–1692. https://doi.org/10.1016/j.cell.2020.05.002 (2020).
doi: 10.1016/j.cell.2020.05.002
pubmed: 32589958
pmcid: 7327522
Contrepois, K., Jiang, L. & Snyder, M. Optimized analytical procedures for the untargeted metabolomic profiling of human urine and plasma by combining hydrophilic interaction (HILIC) and reverse-phase liquid chromatography (RPLC)-mass spectrometry. Mol. Cell Proteom. 14, 1684–1695. https://doi.org/10.1074/mcp.M114.046508 (2015).
doi: 10.1074/mcp.M114.046508
Stevens, V. L., Hoover, E., Wang, Y. & Zanetti, K. A. Pre-analytical factors that affect metabolite stability in human urine, plasma, and serum: A review. Metabolites 9, 156. https://doi.org/10.3390/metabo9080156 (2019).
doi: 10.3390/metabo9080156
pmcid: 6724180
Chang, H. H. et al. Preventing preterm births: Analysis of trends and potential reductions with interventions in 39 countries with very high human development index. Lancet 381, 223–234. https://doi.org/10.1016/S0140-6736(12)61856-X (2013).
doi: 10.1016/S0140-6736(12)61856-X
pubmed: 23158883
Kuijper, E. A., Ket, J. C., Caanen, M. R. & Lambalk, C. B. Reproductive hormone concentrations in pregnancy and neonates: A systematic review. Reprod. Biomed. Online 27, 33–63. https://doi.org/10.1016/j.rbmo.2013.03.009 (2013).
doi: 10.1016/j.rbmo.2013.03.009
pubmed: 23669015
Reddy, D. S. Is there a physiological role for the neurosteroid THDOC in stress-sensitive conditions?. Trends Pharmacol. Sci. 24, 103–106. https://doi.org/10.1016/S0165-6147(03)00023-3 (2003).
doi: 10.1016/S0165-6147(03)00023-3
pubmed: 12628349
Brunton, P. J. Neuroactive steroids and stress axis regulation: Pregnancy and beyond. J. Steroid Biochem. Mol. Biol. 160, 160–168. https://doi.org/10.1016/j.jsbmb.2015.08.003 (2016).
doi: 10.1016/j.jsbmb.2015.08.003
pubmed: 26259885
Coussons-Read, M. E. Effects of prenatal stress on pregnancy and human development: Mechanisms and pathways. Obstet. Med. 6, 52–57. https://doi.org/10.1177/1753495X12473751 (2013).
doi: 10.1177/1753495X12473751
pubmed: 27757157
pmcid: 5052760
Schiffer, L. et al. Human steroid biosynthesis, metabolism and excretion are differentially reflected by serum and urine steroid metabolomes: A comprehensive review. J. Steroid Biochem. Mol. Biol. 194, 105439. https://doi.org/10.1016/j.jsbmb.2019.105439 (2019).
doi: 10.1016/j.jsbmb.2019.105439
pubmed: 31362062
pmcid: 6857441
Ngo, T. T. M. et al. Noninvasive blood tests for fetal development predict gestational age and preterm delivery. Science 360, 1133–1136. https://doi.org/10.1126/science.aar3819 (2018).
doi: 10.1126/science.aar3819
pubmed: 29880692
pmcid: 7734383
Vwalika, B. et al. Maternal and newborn outcomes at a tertiary care hospital in Lusaka, Zambia, 2008–2012. Int. J. Gynaecol. Obstet. 136, 180–187. https://doi.org/10.1002/ijgo.12036 (2017).
doi: 10.1002/ijgo.12036
pubmed: 28099725
Carter, R. A., Pan, K., Harville, E. W., McRitchie, S. & Sumner, S. Metabolomics to reveal biomarkers and pathways of preterm birth: A systematic review and epidemiologic perspective. Metabolomics 15, 124. https://doi.org/10.1007/s11306-019-1587-1 (2019).
doi: 10.1007/s11306-019-1587-1
pubmed: 31506796
pmcid: 7805080
AMANHI (Alliance for Maternal and Newborn Health Improvement) Bio–banking Study group) et al. Understanding biological mechanisms underlying adverse birth outcomes in developing countries: Protocol for a prospective cohort (AMANHI bio-banking) study. J. Glob. Health 7, 021202. https://doi.org/10.7189/jogh.07.021202 (2017).
doi: 10.7189/jogh.07.021202
Murphy, M. S. Q. et al. Incidental screen positive findings in a prospective cohort study in Matlab, Bangladesh: Insights into expanded newborn screening for low-resource settings. Orphanet. J. Rare Dis. 14, 25. https://doi.org/10.1186/s13023-018-0993-1 (2019).
doi: 10.1186/s13023-018-0993-1
pubmed: 30700313
pmcid: 6354381
Castillo, M. C. et al. The Zambian Preterm Birth Prevention Study (ZAPPS): Cohort characteristics at enrollment. Gates Open Re.s 2, 25. https://doi.org/10.12688/gatesopenres.12820.3 (2018).
doi: 10.12688/gatesopenres.12820.3
Papageorghiou, A. T. et al. International standards for early fetal size and pregnancy dating based on ultrasound measurement of crown-rump length in the first trimester of pregnancy. Ultrasound Obstet. Gynecol. 44, 641–648. https://doi.org/10.1002/uog.13448 (2014).
doi: 10.1002/uog.13448
pubmed: 25044000
pmcid: 4286014
Hadlock, F. P., Shah, Y. P., Kanon, D. J. & Lindsey, J. V. Fetal crown-rump length: Reevaluation of relation to menstrual age (5–18 weeks) with high-resolution real-time US. Radiology 182, 501–505. https://doi.org/10.1148/radiology.182.2.1732970 (1992).
doi: 10.1148/radiology.182.2.1732970
pubmed: 1732970
Contrepois, K. et al. Molecular choreography of acute exercise. Cell 181, 1112–1130. https://doi.org/10.1016/j.cell.2020.04.043 (2020).
doi: 10.1016/j.cell.2020.04.043
pubmed: 32470399
pmcid: 7299174
Rosen Vollmar, A. K. et al. Normalizing untargeted periconceptional urinary metabolomics data: A comparison of approaches. Metabolites 9, 198. https://doi.org/10.3390/metabo9100198 (2019).
doi: 10.3390/metabo9100198
pmcid: 6835889
Shen, X. et al. Metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics. Nat. Commun. 10, 1516. https://doi.org/10.1038/s41467-019-09550-x (2019).
doi: 10.1038/s41467-019-09550-x
pubmed: 30944337
pmcid: 6447530
Stein, S. E. & Scott, D. R. Optimization and testing of mass spectral library search algorithms for compound identification. J. Am. Soc. Mass. Spectrom. 5, 859–866. https://doi.org/10.1016/1044-0305(94)87009-8 (1994).
doi: 10.1016/1044-0305(94)87009-8
pubmed: 24222034
Blazenovic, I. et al. Structure annotation of all mass spectra in untargeted metabolomics. Anal. Chem. 91, 2155–2162. https://doi.org/10.1021/acs.analchem.8b04698 (2019).
doi: 10.1021/acs.analchem.8b04698
pubmed: 30608141
Li, S. et al. Predicting network activity from high throughput metabolomics. PLoS Comput. Biol. 9, e1003123. https://doi.org/10.1371/journal.pcbi.1003123 (2013).
doi: 10.1371/journal.pcbi.1003123
pubmed: 23861661
pmcid: 3701697
Chong, J. et al. MetaboAnalyst 4.0: Towards more transparent and integrative metabolomics analysis. Nucleic Acids Res. 46, W486–W494. https://doi.org/10.1093/nar/gky310 (2018).
doi: 10.1093/nar/gky310
pubmed: 29762782
pmcid: 6030889
Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30. https://doi.org/10.1093/nar/28.1.27 (2000).
doi: 10.1093/nar/28.1.27
pubmed: 10592173
pmcid: 102409
Kanehisa, M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 28, 1947–1951. https://doi.org/10.1002/pro.3715 (2019).
doi: 10.1002/pro.3715
pubmed: 31441146
pmcid: 6798127
Kanehisa, M., Furumichi, M., Sato, Y., Ishiguro-Watanabe, M. & Tanabe, M. KEGG: Integrating viruses and cellular organisms. Nucleic Acids Res. 49, D545–D551. https://doi.org/10.1093/nar/gkaa970 (2021).
doi: 10.1093/nar/gkaa970
pubmed: 33125081