Performance Characteristics of Current Biomarkers for the Prediction of Spontaneous Preterm Birth

生物标志物 医学 前瞻性队列研究 羊水 泌尿系统 危险分层 生物标志物发现 内科学 产科 试验预测值 诊断生物标志物 生物信息学 肿瘤科 疾病 怀孕 预测值 分子生物标志物 电流(流体) 尿 胎盘生长因子
作者
Gregory W. Kirschen,Kristin D. Gerson
出处
期刊:Clinical Chemistry [American Association for Clinical Chemistry]
卷期号:72 (1): 71-81
标识
DOI:10.1093/clinchem/hvaf141
摘要

BACKGROUND: Preterm birth (PTB), or birth occurring before 37 weeks' gestation, remains a significant public health burden, accounting for 10% of live births annually in the United States and incurring substantial healthcare expenditures. Our understanding of the molecular mechanisms underlying spontaneous preterm birth (sPTB) has advanced across the previous 4 decades, yet precise prediction tools and prevention strategies are lacking. CONTENT: Numerous studies have identified potential anatomical and molecular risk factors for sPTB, including sonographic characteristics of the cervix; maternal serum circulating RNA and proteins; maternal urine metabolic byproducts; cervicovaginal cytokine, microbiome, and metabolome composition; amniotic fluid cytokines; umbilical cord blood leukocyte DNA methylation status; and placental transcriptome profiles. This review focuses on recent developments in sPTB biomarker determination among singleton gestations. SUMMARY: Herein, we synthesize and evaluate the test characteristics of candidate biomarkers of sPTB, concluding that no single biomarker can accurately predict sPTB. However, several individual or combined panels of biomolecules, including some commercially available, carry clinically significant predictive information. These biomarkers include cervical ultrasonography, the ratio of insulin-like growth factor-binding protein 4 to sex-hormone binding globulin, panels of urinary metabolites and amniotic fluid proteins, and maternal circulating cell-free RNA. Future integration of select biomarkers drawn from prospective validation cohorts into existing risk stratification strategies may enhance sPTB prediction, thereby identifying patients at greatest risk.
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