Samsu Hidayat
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Pendekatan Biomarker untuk Deteksi Dini Preeklamsia: Tinjauan Literatur Terbaru Risqiyani, Siti Zulaikha; Samsu Hidayat; Yonas Hadisubroto
Termometer: Jurnal Ilmiah Ilmu Kesehatan dan Kedokteran Vol. 4 No. 1 (2026): Januari : Termometer: Jurnal Ilmiah Ilmu Kesehatan dan Kedokteran
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/termometer.v4i1.5715

Abstract

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.