Prediction and early diagnosis of preeclampsia: scientific perspectives and clinical opportunities

1. Nan L., Yu-Na G., Li-Kun G., Bing-Shun W. Advances in biomarker development and potential application for preeclampsia based on pathogenesis. Eur. J. Obstet. Gynecol. Reprod. Biol. X. 2021; 9: 100-19. https://dx.doi.org/10.1016/j.eurox.2020.100119.
2. WHO recommendations for prevention and treatment of pre-eclampsia and eclampsia. WHO; 2015: 1-48.
3. Kucukgoz U.G., Ozgunen F.T., Buyukkurt S., Guzel A.B., Urunsak I.F., Demir S.C., Evruke I.C. Comparison of clinical and laboratory findings in early- and late-onset preeclampsia. J. Matern. Fetal Neonatal Med. 2013; 26(12): 1228-33. https://dx.doi.org/10.3109/14767058.2013.776533.
4. Raymond D., Peterson E. A critical review of early-onset and late-onset preeclampsia. Obstet. Gynecol. Surv. 2011; 66(8): 497-506.https://dx.doi.org/10.1097/OGX.0b013e3182331028.
5. Stubert J., Ullmann S., Dieterich M., Diedrich D., Reimer T. Clinical differences between early- and late-onset severe preeclampsia and analysis of predictors for perinatal outcome. J. Perinat. Med. 2014; 42(5): 617-27.https://dx.doi.org/10.1515/jpm-2013-0285.
6. Ходжаева З.С., Холин А.М., Вихляева Е.М. Ранняя и поздняя преэклампсия: парадигмы патобиологии и клиническая практика. Акушерство и гинекология. 2013; 10: 4-11. Khodzhaeva Z.S., Kholin A.M., Vikhlyaeva E.M. Early and late preeclampsia: Pathobiology paradigms and clinical practice. Obstetrics and Gynegology. 2013; 10: 4-11.(in Russian)].
7. Sun C.J., Li L., Li X.Y., Zhang W.Y., Liu X.W. Associations of polymorphisms of CYP2D6 and CYP2C9 with early onset severe pre-eclampsia and response to labetalol therapy. Arch. Gynecol. Obstet. 2018; 298(1): 125-32. https://dx.doi.org/10.1007/s00404-018-4791-8.
8. Youssef L., Simões R.V., Jezid Miranda J., García-Martín M.L., Paules С., Crovetto F. et al. Paired maternal and fetal metabolomics reveal a differential fingerprint in preeclampsia versus fetal growth restriction. Sci. Rep. 2021; 11(1): 14422. https://dx.doi.org/10.1038/s41598-021-93936-9.
9. Муминова К.Т., Ходжаева З.С., Шмаков Р.Г., Баранов И.И., Кононихин А.С.,Стародубцева Н.Л., Франкевич В.Е., Холин А.М. Факторы риска и возможные предикторы преэклампсии на основании протеомного (пептидомного) анализа мочи. Акушерство и гинекология: новости, мнения, обучение. 2020; 8(4): 8-12. [Muminova K.Т., Khodzhaeva Z.S., Shmakov R.G., Baranov I.I., Kononikhin A.S. Risk factors and possible predictors of PE based on proteomic (peptidomic) urine analysis. Obstetrics and Gynegology. 2020; 8(4): 8-12. (in Russian)].
10. Opichka M.A., Rappelt M.W., Gutterman D.D., Grobe J.L., McIntosh J.J. Review vascular dysfunction in preeclampsia. Cells. 2021; 10(11): 3055.https://dx.doi.org/10.3390/cells10113055.
11. Сидорова И.С., Никитина Н.А. Научно обоснованная система прогнозирования преэклампсии. Акушерство и гинекология. 2017; 3: 55-61. https://dx.doi.org/10.18565/aig.2017.3.55-61. [Sidorova I.S., Nikitina N.A. A scientifically based system for predicting preeclampsia. Obstetrics and Gynegology. 2017; 3: 55-61. (in Russian)]. https://dx.doi.org/10.18565/aig.2017.3.55-61.
12. Chaemsaithong P., Sahota D.S., Poon L.C. First trimester preeclampsia screening and prediction. Am. J. Obstet. Gynecol. 2022; 226(2): 1071-97.https://doi.org/10.1016/j.ajog.2020.07.020.
13. Zeisler H., Llurba E., Chantraine F., Vatish M., Staff A.C., Sennström M. et al. Predictive value of the sFlt-1:PlGF ratio in women with suspected preeclampsia. N. Engl. J. Med. 2016; 374(1): 13-22. https://dx.doi.org/10.1056/NEJMoa1414838.
14. Hadker N., Garg S., Costanzo C., van der Helm W., Creeden J. Are there financial savings associated with supplementing current diagnostic practice for preeclampsia with a novel test? Learnings from a modeling analysis from a German payer perspective. Hypertens. Pregnancy. 2013; 32(2): 105-19.https://dx.doi.org/10.3109/10641955.2011.638958.
15. Tan M.Y., Syngelaki А., Poon L.C., Rolnik D.L., O'Gorman N. Delgado J.L. et al. Screening for pre-eclampsia by maternal factors and biomarkers at 11–13 weeks’ gestation. Ultrasound Obstet. Gynecol. 2018; 52(2): 186-95.https://dx.doi.org/10.1002/uog.19112.
16. O’Gorman N., Wright D., Syngelaki A., Akolekar R., Wright А., Poon L.C., Nicolaides K.H. Competing risks model in screening for preeclampsia by maternal factors and biomarkers at 11-13 weeks gestation. Am. J. Obstet. Gynecol. 2016; 214(1): 103.E1-12. https://dx.doi.org/10.1016/j.ajog.2015.08.034.
17. O’Gorman N., Wright D., Poon L.C., Rolnik D.L., Syngelaki A., Wright A. et al. Accuracy of competing-risks model in screening for pre-eclampsia by maternal factors and biomarkers at 11–13 weeks’ gestation. Ultrasound Obstet. Gynecol. 2017; 49(6): 751-5. https://dx.doi.org/10.1002/uog.17399.
18. Deshpande J.S., Sundrani D.P., Sahay A.S., Gupte S.A., Joshi S.R. Unravelling the potential of angiogenic factors for the early prediction of preeclampsia. Hypertens. Res. 2021; 44(7): 756-69. https://dx.doi.org/10.1038/s41440-021-00647-9.
19. Gray K.J., Kovacheva V.P., Mirzakhani H., Bjonnes A.C., Almoguera B.,Wilson M.L. et al. Risk of pre-eclampsia in patients with a maternal genetic predisposition to common medical conditions: a case–control study. BJOG. 2021; 128(1): 55-65. https://dx.doi.org/10.1111/1471-0528.16441.
20. Gray K.J., Saxena R., Karumanchi S.A. Genetic predisposition to preeclampsia is conferred by fetal DNA variants near FLT1, a gene involved in the regulation of angiogenesis. Am. J. Obstet. Gynecol. 2018; 218(2): 211-8.https://dx.doi.org/10.1016/j.ajog.2017.11.562.
21. McGinnis R., Steinthorsdottir V., Williams N., Thorleifsson G., Shooter S., Hjartardottiret S. et al. Variants in the fetal genome near FLT1 are associated with risk of preeclampsia. Nat. Genet. 2017; 49(8): 1255-60.https://dx.doi.org/10.1038/ng.3895.
22. Gray K.J., Kovacheva V.P., Mirzakhani H., Bjonnes A.C., Almoguera B., DeWan A.T. et al. Gene-centric analysis of preeclampsia identifies maternal association at PLEKHG1. Hypertension. 2018; 72(2): 408-16.https://dx.doi.org/10.1161/HYPERTENSIONAHA.117.10688.
23. Reidy K.J., Hjorten R.C., Simpson C.L., Rosenberg A.Z., Rosenblum S.D., Kovesdy C.P. et al. Fetal–Not Maternal–APOL1 genotype associated with risk for preeclampsia in those with African Ancestry. Am. J. Hum. Genet. 2018; 103(3): 367-76. https://dx.doi.org/10.1016/j.ajhg.2018.08.002.
24. Johnson M.P., Roten L.T., Dyer T.D., East C.E., Forsmoet S., Blangero J. et al. The ERAP2 gene is associated with preeclampsia in Australian and Norwegian populations. Hum. Genet. 2009; 126(5): 655-66. https://dx.doi.org/10.1007/s00439-009-0714-х.
25. Sun C.J., Li L., Li X., Zhang W.Y., Liu X.W. Novel SNPs of WNK1 and AKR1C3 are associated with preeclampsia. Gene. 2018; 668: 27-32. https://dx.doi.org/10.1016/j.gene.2018.05.055.
26. Zhao L., Bracken M.B., DeWan A.T. Genome-wide association study of pre-eclampsia detects novel maternal single nucleotide polymorphisms and copy-number variants in subsets of the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study cohort. Ann. Hum. Genet. 2013; 77(4): 277-87. https://dx.doi.org/10.1111/ahg.12021.
27. Steinthorsdottir V., McGinnis R., Williams N.O., Stefansdottir L., Thorleifsson G., Shooter S. et al. Genetic predisposition to hypertension is associated with preeclampsia in European and Central Asian women. Nat. Commun. 2020; 11(1): 59-76. https://dx.doi.org/10.1038/s41467-020-19733-6.
28. Zhang L., Cao Z., Feng F., Xu Y.N., Li L., Gao H. A maternal GOT1 novel variant associated with early-onset severe preeclampsia identified by whole-exome sequencing. BMC Med. Genet. 2020; 20(1): 49. https://dx.doi.org/10.1186/s12881-020-0989-2.
29. Whitehead C.L., Walker S.P., Ye L., Mendis S., Kaitu'u-Lino T.J., Lappas M., Tong S. Placental specific mRNA in the maternal circulation are globally dysregulated in pregnancies complicated by fetal growth restriction. J. Clin. Endocrinol. Metab. 2013; 98(3): 429-36. https://dxdoi.org/10.1210/jc.2012-2468.
30. Song X., Luo X., Gao Q., Wang Y.,Gao O., Long W. Dysregulation of LncRNAs in placenta and pathogenesis of preeclampsia. Curr. Drug Targets. 2017; 18(10): 1165-70. https://dx.doi.org/10.2174/1389450118666170404160000.
31. Lv. Y., Lu Ch., Ji X., Miao Z., Long W., Ding H., Lv M. Roles of microRNAs in preeclampsia. J. Cell. Physiol. 2019; 234(2): 1052-61.https://dx.doi.org/10.1002/jcp.27291.
32. Xu P., Ma Y., Wu H., Wang Y.L. Placenta-derived microRNAs in the pathophysiology of human pregnancy. Front. Cell Dev. Biol. 2021; 9: 646326. https://dx.doi.org/10.3389/fcell.2021.646326.
33. Morales-Prieto D.M., Ospina-Prieto S., Schmidt A., Chaiwangyen W., Markert U.R. Elsevier Trophoblast Research Award Lecture: origin, evolution and future of placenta miRNAs. Placenta. 2014; 35(Suppl. 1): S39-45.https://dx.doi.org/10.1016/j.placenta.2013.11.017.
34. Khodzhaeva Z., Kogan E., Demura T., Akatyeva A., Vavina O., Savonova A., Kholin A., Sukhikh G. [206-POS]: Morphological/immunohistochemical alterations of endometrial and placental site areas in preeclampsia. Pregnancy Hypertens. 2015; 1(5): 103-4. https://dx.doi.org/10.1016/j.preghy.2014.10.212.
35. Biró O., Nagy B., Rigó J. Identifying miRNA regulatory mechanisms in preeclampsia by systems biology approaches. Hypertens. Pregnancy. 2017; 36(1): 90-9. https://dx.doi.org/10.1080/10641955.2016.1239736.
36. Luo R., Wang Y., Xu P., Cao G., Zhao Y. Shao X. et al. Hypoxia-inducible miR-210 contributes to preeclampsia via targeting thrombospondin type I domain containing 7A. Sci. Rep. 2016; 6: 19588. https://dx.doi.org/10.1038/srep19588.
37. Brooks S.A., Martin E., Smeester L., Grace M.R., Boggess K., Fry R.C. miRNAs as common regulators of the transforming growth factor (TGF)-β pathway in the preeclamptic placenta and cadmium-treated trophoblasts: Links between the environment, the epigenome and preeclampsia. Food Chem. Toxicol. 2016; 98(Pt A): 50-7. https://dx.doi.org/10.1016/j.fct.2016.06.023.
38. Srinivasan S., Treacy R., Herrero T., Olsen R., Leonardo T.R. et al. Discovery and verification of extracellular miRNA biomarkers for Non-invasive prediction of pre-eclampsia in asymptomatic women. Cell Reports Med. 2020; 1(2): 100-13. https://dx.doi.org/10.1016/j.xcrm.2020.100013.
39. Rasmussen M., Reddy M., Nolan R., Camunas-Soler J., Khodursky A, Scheller N.M. et al. RNA profiles reveal signatures of future health and disease in pregnancy. Nature. 2022; 601(7893): 422-7. https://dx.doi.org/10.1038/s41586-021-04249-w.
40. Hemmatzadeh M., Shomali N., Yousefzadeh Y., Mohammadi H., Ghasemzadeh A., Yousefi М. MicroRNAs: Small molecules with a large impact on pre-eclampsia. J. Cell. Physiol. 2020; 235(4): 3235-48. https://dx.doi.org/10.1002/jcp.29286.
41. Tsang J.C.H., Vong J.S.L., Ji L., Poon L.C.Y., Jiang P., Lui K. et al. Integrative single-cell and cell-free plasma RNA transcriptomics elucidates placental cellular dynamics. Proc. Natl. Acad. Sci. USA. 2017; 114(37): E7786-95. https://dx.doi.org/10.1073/pnas.1710470114.
42. Tarca A.L., Romero R., Erez O., Gudicha D.W., Gabor Than N., Benshalom-Tirosh N. et al. Maternal whole blood mRNA signatures identify women at risk of early preeclampsia: a longitudinal study. J. Matern. Neonatal Med. 2021; 34(21): 3463-74. https://dx.doi.org/10.1080/14767058.2019.1685964.
43. Dupont C., Armant D.R., Brenner C.A. Epigenetics: definition, mechanisms and clinical perspective. Semin. Reprod. Med. 2009; 27(5): 351-7.https://dx.doi.org/10.1055/s-0029-1237423.
44. Yeung K.R., Chiu C.L., Pidsley R., Makris A., Hennessy A., Lind J.M. DNA methylation profiles in preeclampsia and healthy control placentas. Am. J. Physiol. Heart Circ. Physiol. 2016; 310(10): H1295-303.https://dx.doi.org/10.1152/ajpheart.00958.2015.
45. Feil R., Fraga M.F. Epigenetics and the environment: emerging patterns and implications. Nat. Rev. Genet. 2012; 13(2): 97-109. https://dx.doi.org/10.1038/nrg3142.
46. Walsh S.W., Nugent W.H., Archer K.J., Dulaimi M.A., Washingtone S.L., Strauss J.F. 3rd. Epigenetic regulation of interleukin-17-related genes and their potential roles in neutrophil vascular infiltration in preeclampsia. Reprod. Sci. 2022; 29(1): 154-62. https://dx.doi.org/10.1007/s43032-021-00605-3.
47. Cruz J. de O., Conceição I., Sandrim V.S., Luizon M.R. Comprehensive analyses of DNA methylation of the TIMP3 promoter in placentas from early-onset and late-onset preeclampsia. Placenta. 2022; 117: 118-21.https://dx.doi.org/10.1016/j.placenta.2021.12.003.
48. Liang L., Rasmussen M.-L., Bening B., Shen X., Chen S, Röst H. et al. Metabolic dynamics and prediction of gestational age and time to delivery in pregnant women. Cell. 2020; 181(7): 1680-92. https://dx.doi.org/10.1016/j.cell.2020.05.002.
49. Nikolov A., Popovski N. Role of gelatinases mmp-2 and mmp-9 in healthy and complicated pregnancy and their future potential as preeclampsia biomarkers. Diagnostics. 2021; 11(3): 480. https://dx.doi.org/10.3390/diagnostics11030480.
50. Law K.P., Han T.-L., Tong Ch., Baker P. Mass spectrometry-based proteomics for pre-eclampsia and preterm birth. International J. Mol. Sci. 2015; 16(5): 10952- 85. https://dx.doi.org/10.3390/ijms160510952.
51. Bujold E., Fillion A., Roux-Dalvai F., Scott-Boyer M.P., Giguère Y.,Forest J.C. et al. Proteomic analysis of maternal urine for the early detection of preeclampsia and fetal growth restriction. J. Clin. Med. 2021; 10(20):4679. https://dx.doi.org/10.3390/jcm10204679.
52. Tarca A.L., Romero R., Benshalom-Tirosh N., Gabor Than N., Gudicha D.W., Erez O. The prediction of early preeclampsia: results from a longitudinal proteomics study. PLoS One. 2019; 14(6): 0217273. https://dx.doi.org/10.1371/journal.pone.0217273.
53. Erez O., Romero R., Maymon E., Chaemsaithong P., Done B., Pacora P. et al.The prediction of late-onset preeclampsia: results from a longitudinal proteomics study. PLoS One. 2017; 12(7): 0181468. https://dx.doi.org/10.1371/journal.pone.0181468.
54. Муминова К.Т., Кононихин А.С., Ходжаева З.С., Шмаков Р.Г., Сергеева В.А. Дифференциальная диагностика гипертензивных состояний во время беременности при помощи анализа пептидомного профиля мочи. Акушерство и гинекология. 2018; 8: 66-75. https://dx.doi.org/10.18565/aig.2018.8.66-75. [Muminova К.Т., Kononikhin А.S., Khodzhaeva Z.S., Shmakov R.G., Sergeeva V.A. Differential diagnosis hypertensive disorders in pregnancy based on urine peptidome profiling. Obstetrics and Gynegology. 2018; 8: 66-75. (in Russian)]. https://dx.doi.org/10.18565/aig.2018.8.66-75.
55. Starodubtseva N., Nizyaeva N., Baev O., Bugrova A., Gapaeva M., Muminova K., Kononikhin A., Frankevich V., Nikolaev E., Sukhikh G. SERPINA1 peptides in urine as a potential marker of preeclampsia severity. Int. J. Mol. Sci. 2020; 21(3): 914. https://dx.doi.org/10.3390/ijms21030914.
56. Bahado-Singh R.O., Syngelaki A., Mandal R., Graham S.F., Akolekar R., Han B. et al. Metabolomic determination of pathogenesis of late-onset preeclampsia. J. Matern. Fetal Neonatal Med. 2017; 30(6): 658-64. https://dx.doi.org/10.1080/14767058.2016.1185411.
57. Austdal M., Tangerås L.H., Skråstad R.B., Salvesen K.Å., Austgulen R., Iversen A.C., Bathen T.F. First trimester urine and serum metabolomics for prediction of preeclampsia and gestational hypertension: a prospective screening study. Int. J. Mol. Sci. 2015; 16(9): 21520-38. https://dx.doi.org/10.3390/ijms160921520.
58. Koster M.P.H., Vreeken R.J., Harms A.C., Dane A.D., Kuc S., Schielen P.C. et al. First-Trimester serum acylcarnitine levels to predict preeclampsia: a metabolomics approach. Dis. Markers. 2015; 2015: 8571108.https://dx.doi.org/10.1155/2015/857108.
59. Bahado-Singh R.O., Syngelaki А., Akolekar R., Mandal R., Bjondahl T.C., Han B. et al. Validation of metabolomic models for prediction of early-onset preeclampsia. Am. J. Obstet. Gynecol. 2015; 213(4): 530.e1-530.e10.https://dx.doi.org/10.1016/j.ajog.2015.06.044.
60. Odibo A.O., Goetzinger K.R., Odibo L., Cahill A.G., Macones G.A.,Dietzen D.J. First-trimester prediction of preeclampsia using metabolomic biomarkers: a discovery phase study. Prenat. Diagn. 2011; 31(10): 990-4.https://dx.doi.org/10.1002/pd.2822.
61. Austdal M., Thomsen L.C.V., Tangerås L.H., Skei B., Mathew S., Bjørge L. et al. Metabolic profiles of placenta in preeclampsia using HR-MAS MRS metabolomics. Placenta. 2015; 36(12): 1455-62. https://dx.doi.org/10.1016/j.placenta.2015.10.019.
62. Walejko J.M., Chelliah A., Keller-Wood M., Gregg A., Edison A.S. Global metabolomics of the placenta reveals distinct metabolic profiles between maternal and fetal placental tissues following delivery in non-labored women. Metabolites. 2018; 8(1): 10. https://dx.doi.org/10.3390/metabo8010010.
63. Bahado-Singh R., Poon L.C., Yilmaz A., Syngelaki A., Turkoglu O., Kumar P. et al. Integrated proteomic and metabolomic prediction of term preeclampsia. Sci. Rep. 2017; 7(1). 16189. https://dx.doi.org/10.1038/s41598-017-15882-9.
64. Youssef L., Crovetto F., Simoes R.V., Miranda J., Paules C. et al. The interplay between pathophysiological pathways in early-onset severe preeclampsia unveiled by metabolomics. Life (Basel). 2022; 12(1): 86.https://dx.doi.org/10.1038/s41598-017-15882-9.
65. Baylis C., Beinder E., Sütö T., August P. Recent insights into the roles of nitric oxide and renin angiotensin in the pathophysiology of preeclamptic pregnancy. Semin. Nephrol. 1998; 18(2): 208-30.
66. Turner E., Brewster J.A., Simpson N., Walker J.J., Fisher J. Aromatic amino acid biomarkers of preeclampsia - a nuclear magnetic resonance investigation. Hypertens. Pregnancy. 2008; 27(3): 225-35.https://dx.doi.org/10.1080/10641950801955725.
67. Prokopieva V.D., Yarygina E.G., Bokhan N.A., Ivanova S.A. Use of Carnosine for Oxidative Stress Reduction in Different Pathologies. Oxid. Med. Cell. Longev. 2016; 2016: 2939087. https://dx.doi.org/10.1155/2016/2939087.
68. Youssef L., Erlandsson L., Åkerström B., Miranda J., Paules Ch. et al. Hemopexin and α1-microglobulin heme scavengers with differential involvement in preeclampsia and fetal growth restriction. PLoS One. 2020; 15(9) e 0239030.. https://dx.doi.org/10.1371/journal.pone.0239030.
69. Brosnan M.E., Brosnan J.T. Hepatic glutamate metabolism: A tale of 2 hepatocytes. Am. J. Clin. Nutr. 2009; 90(3): 857-61. https://dx.doi.org/10.3945/ajcn.2009.27462Z.
70. Wang T.J., Larson M.G., Vasan R.S., Cheng S., Rhee E.P. et al. Metabolite profiles and the risk of developing diabetes. Nat. Med. 2011; 17(4): 448-53. https://dx.doi.org/10.1038/nm.2307.
71. Feig D.S., Shah B.R., Lipscombe L.L., Wu C.F., Ray J.G. et al. Preeclampsia as a risk factor for diabetes: a population-based cohort study. PLoS Med. 2013; 10(4): e1001425. https://dx.doi.org/10.1371/journal.pmed.1001425.
72. Nobakht M.Gh.B.F. Application of metabolomics to preeclampsia diagnosis. Syst. Biol. Reprod. Med. 2018; 64(5): 324-39. https://dx.doi.org/10.1080/19396368.2018.1482968.

Received 15.09.2022

Accepted 14.10.2022