Pendekatan Biomarker untuk Deteksi Dini Preeklamsia: Tinjauan Literatur Terbaru
DOI:
https://doi.org/10.55606/termometer.v4i1.5715Keywords:
Biomarkers, detection, preeclamsia, sFlt1, PIGFAbstract
Preeclampsia (PE) remains one of the leading causes of maternal and perinatal morbidity and mortality worldwide, making early detection a critical priority in modern obstetric practice. Although the pathophysiological mechanisms of PE, such as placental dysfunction, angiogenic imbalance, systemic inflammation, and immune dysregulation are increasingly understood, clinical diagnosis still relies on elevated blood pressure and proteinuria, which typically appear in the later stages of the disease. This diagnostic delay underscores the urgent need for biomarkers capable of identifying pathological changes long before clinical symptoms develop. This literature review aims to analyze the most recent scientific evidence (2020–2025) regarding potential biomarkers for the early detection of PE and to evaluate their strengths, limitations, and potential applications in clinical practice. Literature searches were conducted using PubMed, Google Scholar, and open-access journal repositories. The findings indicate that the sFlt-1/PlGF ratio is the most established biomarker, demonstrating high sensitivity and specificity. Omics approaches, including metabolomics and proteomics, present significant potential for first-trimester screening, although broader population validation is required. Inflammatory, genetic, epigenetic, and cardiovascular biomarkers further enrich the understanding of the biological complexity underlying PE. The integration of multiple biomarkers using machine learning algorithms has shown improved predictive accuracy; however, challenges remain regarding model generalizability and the need for large, diverse datasets. Overall, current evidence suggests that multiparameter biomarker combinations represent the most promising strategy to enhance early detection of PE at the population level.
Downloads
References
Ansbacher-Feldman, Z., Syngelaki, A., Meiri, H., Cirkin, R., Nicolaides, K. H., & Louzoun, Y. (2022). Machine-learning-based prediction of pre-eclampsia using first-trimester maternal characteristics and biomarkers. Ultrasound in Obstetrics & Gynecology : The Official Journal of the International Society of Ultrasound in Obstetrics and Gynecology, 60(6), 739–745. https://doi.org/10.1002/uog.26105
Chen, Q., Qian, Y., Feng, M., Zhang, H., & Xie, H. (2025). Integrating urine metabolomic biomarkers and machine learning algorithms to predict preeclampsia. European Journal of Medical Research, 30(1), 1103. https://doi.org/10.1186/s40001-025-03337-1
Chiang, Y., Seow, K., & Chen, K. (2024). The Pathophysiological , Genetic , and Hormonal Changes in Preeclampsia : A Systematic Review of the Molecular Mechanisms. International Journal of Molecular Sciences Review, 1–23.
Garrido-Giménez, C., Cruz-Lemini, M., Álvarez, F. V, Nan, M. N., Carretero, F., Fernández-Oliva, A., Mora, J., Sánchez-García, O., García-Osuna, Á., Alijotas-Reig, J., Llurba, E., & on behalf of the EuroPE Working Group. (2023). Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as Biomarkers. In Journal of Clinical Medicine (Vol. 12, Issue 2, p. 431). https://doi.org/10.3390/jcm12020431
Jena, M. K., Sharma, N. R., Petitt, M., Maulik, D., & Nayak, N. R. (2020). Pathogenesis of Preeclampsia and Therapeutic Approaches Targeting the Placenta. Biomolecules, 10(6), 1–25. https://doi.org/10.3390/biom10060953
Kalarani, I. B., Veerabathiran, R., & Mohammed, V. (2022). The role of biomarkers in early prediction and molecular mechanisms of preeclampsia. European Journal of Translational and Clinical Medicine, 5(2), 57–66. https://doi.org/10.31373/ejtcm/156837
Leal, C. R. V., Botezelli, H., Las Casas, J. F. do C., Simões E Silva, A. C., & Reis, F. M. (2025). Urinary biomarkers of preeclampsia: An update. Advances in Clinical Chemistry, 124, 197–211. https://doi.org/10.1016/bs.acc.2024.11.002
Lim, J. H., Lim, J. M., Lee, H. M., Lee, H. J., Kwak, D. W., Han, Y. J., Kim, M. Y., Jung, S. H., Kim, Y. R., Ryu, H. M., & Kim, K. P. (2024). Systematic Proteome Profiling of Maternal Plasma for Development of Preeclampsia Biomarkers. Molecular & Cellular Proteomics, 23(9), 100826. https://doi.org/https://doi.org/10.1016/j.mcpro.2024.100826
Liu, M., Yang, X., Chen, G., Ding, Y., Shi, M., Sun, L., Huang, Z., Liu, J., Liu, T., Yan, R., & Li, R. (2022). Development of a prediction model on preeclampsia using machine learning-based method: a retrospective cohort study in China. Frontiers in Physiology, 13, 896969. https://doi.org/10.3389/fphys.2022.896969
Liu, S., Fu, H., Ray, M., Heinsberg, L. W., Conley, Y. P., Anderson, C. M., Hubel, C. A., Roberts, J. M., Jeyabalan, A., Weeks, D. E., & Schmella, M. J. (2023). A longitudinal epigenome-wide association study of preeclamptic and normotensive pregnancy. Epigenetics Communications, 3(1). https://doi.org/10.1186/s43682-022-00014-w
Macedo, T. C. C., Montagna, E., Trevisan, C. M., Zaia, V., de Oliveira, R., Barbosa, C. P., Laganà, A. S., & Bianco, B. (2020). Prevalence of preeclampsia and eclampsia in adolescent pregnancy: A systematic review and meta-analysis of 291,247 adolescents worldwide since 1969. European Journal of Obstetrics, Gynecology, and Reproductive Biology, 248, 177–186. https://doi.org/10.1016/j.ejogrb.2020.03.043
Marek-Iannucci, S., Oliveros, E., Brailovsky, Y., Pirlamarla, P., Roman, A., & Rajapreyar, I. N. (2023). Natriuretic peptide biomarkers in the imminent development of preeclampsia. Frontiers in Cardiovascular Medicine, 10, 1203516. https://doi.org/10.3389/fcvm.2023.1203516
Miller, J. J., Higgins, V., Melamed, N., Hladunewich, M., Ma, L., Yip, P. M., & Fu, L. (2023). Clinical Validation of the sFlt-1:PlGF Ratio as a Biomarker for Preeclampsia Diagnosis in a High-Risk Obstetrics Unit. The Journal of Applied Laboratory Medicine, 8(3), 457–468. https://doi.org/10.1093/jalm/jfad003
Nguyen, T. X., Nguyen, V. T., Nguyen-Phan, H. N., & Hoang, B. B. (2022). Serum Levels of NT-Pro BNP in Patients with Preeclampsia. Integrated Blood Pressure Control, 15, 43–51. https://doi.org/10.2147/IBPC.S360584
Nikuei, P., Rajaei, M., Roozbeh, N., Mohseni, F., Poordarvishi, F., Azad, M., & Haidari, S. (2020). Diagnostic accuracy of sFlt1/PlGF ratio as a marker for preeclampsia. BMC Pregnancy and Childbirth, 20(1), 80. https://doi.org/10.1186/s12884-020-2744-2
Noonan, S. B., Brennecke, S. P., & Jones, G. D. (2025). Predicting Preeclampsia in Gestational Diabetes Mellitus Using the sFlt-1/PlGF Ratio. The Journal of Clinical Endocrinology & Metabolism, 110(10), e3343–e3352. https://doi.org/10.1210/clinem/dgaf069
Ogoyama, M., Takahashi, H., Suzuki, H., Ohkuchi, A., Fujiwara, H., & Takizawa, T. (2022). Non-Coding RNAs and Prediction of Preeclampsia in the First Trimester of Pregnancy. Cells, 11(15). https://doi.org/10.3390/cells11152428
Palm, K., Cluver, C., Langenegger, E., Tong, S., Walker, S., Imberg, H., Hastie, R., & Bergman, L. (2024). Circulating concentrations of pro-inflammatory cytokines in preeclampsia with varying disease severity. Pregnancy Hypertension, 38, 101168. https://doi.org/10.1016/j.preghy.2024.101168
Puttaiah, A., Kirthan, J. P. A., Sadanandan, D. M., & Somannavar, M. S. (2024). Inflammatory markers and their association with preeclampsia among pregnant women: A systematic review and meta-analysis. Clinical Biochemistry, 129, 110778. https://doi.org/10.1016/j.clinbiochem.2024.110778
Qiao, L., Zhang, Y., Wang, J., Wu, X., Zhang, C., Xue, Y., Cao, J., Jin, J., Wang, T., Li, J., & Liang, Y. (2025). Machine learning-based prediction of preeclampsia using first-trimester inflammatory markers and red blood cell indices. BMC Pregnancy and Childbirth, 25(1), 1083. https://doi.org/10.1186/s12884-025-08147-1
Rana, S., Burke, S. D., & Karumanchi, S. A. (2022). Imbalances in circulating angiogenic factors in the pathophysiology of preeclampsia and related disorders. American Journal of Obstetrics and Gynecology, 226(2S), S1019–S1034. https://doi.org/10.1016/j.ajog.2020.10.022
Rani Agarwal, N., Kachhawa, G., Oyeyemi, B. F., & Bhavesh, N. S. (2022). Metabolic profiling of serum and urine in preeclampsia and gestational diabetes in early pregnancy. Medicine in Drug Discovery, 16, 100143. https://doi.org/https://doi.org/10.1016/j.medidd.2022.100143
Rybak-krzyszkowska, M., Staniczek, J., Kondracka, A., Bogusławska, J., Kwiatkowski, S., Tomasz, G., Strus, M., & Wojciech, G. (2023). From Biomarkers to the Molecular Mechanism of Preeclampsia — A Comprehensive Literature Review. International Journal of Molecular Sciences Review, 1–19. https://doi.org/https://doi.org/10.3390/ ijms241713252
Tariq, M., Shaheen, G., David, M., Jahan, S., Afsar, T., Husain, F. M., Amor, H., & Razak, S. (2025). Evaluation of oxidative stress markers and the angiogenic factors in preeclampsia and associated features. BMC Pregnancy and Childbirth, 25(1), 1006. https://doi.org/10.1186/s12884-025-08165-z
Yang, M., Wang, M., & Li, N. (2024). Advances in pathogenesis of preeclampsia. Archives of Gynecology and Obstetrics, 309(5), 1815–1823. https://doi.org/10.1007/s00404-024-07393-6
Zaki-Dizaji, M., Ebrahimi, A., Saeedinia, R., & Heidary, Z. (2025). Epigenetic landscape of placental tissue in preeclampsia: a systematic review of DNA methylation profiles. BMC Pregnancy and Childbirth, 25(1), 953. https://doi.org/10.1186/s12884-025-07950-0
Zhang, L., Li, W., Chi, X., Sun, Q., Li, Y., Xing, W., & Ding, G. (2025). Predictive performance of sFlt-1, PlGF and the sFlt-1/PlGF ratio for preeclampsia: A systematic review and meta-analysis. Journal of Gynecology Obstetrics and Human Reproduction, 54(4), 102925. https://doi.org/10.1016/j.jogoh.2025.102925
Zhang, Y., Sylvester, K. G., Jin, B., Wong, R. J., Schilling, J., Chou, C. J., Han, Z., Luo, R. Y., Tian, L., Ladella, S., Mo, L., Marić, I., Blumenfeld, Y. J., Darmstadt, G. L., Shaw, G. M., Stevenson, D. K., Whitin, J. C., Cohen, H. J., McElhinney, D. B., & Ling, X. B. (2023). Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy. Metabolites, 13(6), 1–13. https://doi.org/10.3390/metabo13060715
Zhou, S., Li, J., Yang, W., Xue, P., Yin, Y., Wang, Y., Tian, P., Peng, H., Jiang, H., Xu, W., Huang, S., Zhang, R., Wei, F., Sun, H.-X., Zhang, J., & Zhao, L. (2023). Noninvasive preeclampsia prediction using plasma cell-free RNA signatures. American Journal of Obstetrics and Gynecology, 229(5), 553.e1-553.e16. https://doi.org/10.1016/j.ajog.2023.05.015
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Termometer: Jurnal Ilmiah Ilmu Kesehatan dan Kedokteran
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
