Systematic Review Maps Emerging Biomarkers for Neonatal Respiratory Distress Syndrome Diagnosis and Prognosis

Neonatal respiratory distress syndrome (NRDS), caused by pulmonary surfactant deficiency, remains a leading cause of morbidity and mortality in preterm infants. Current diagnostic and therapeutic strategies often fall short in providing early warning and precise prognostic stratification, leading to suboptimal outcomes. This critical gap highlights the urgent need for more sensitive and specific tools to identify infants at risk and guide personalized interventions. Biomarker research, exploring novel molecular indicators, offers a promising avenue to address these limitations and improve the management of NRDS.

Researchers conducted a systematic review of PubMed, Web of Science, and Embase, analyzing studies published from January 2010 to December 2025. The search focused on evaluating the application of various biomarker categories in NRDS: inflammatory markers, microRNAs, protein and peptide biomarkers, and lung ultrasound score (LUS). The review synthesized findings across these categories to assess their diagnostic and prognostic potential, as well as their readiness for clinical translation. The aim was to identify promising candidates and highlight areas requiring further research and validation.

The systematic review revealed several emerging biomarkers with potential for NRDS. For inflammatory biomarkers, IL-6 and IL-17 were consistently associated with disease severity, although their specificity for NRDS remained limited. Notably, IL-37 showed promise as a potential anti-inflammatory therapeutic target. In the realm of microRNAs, miR-375 and miR-363 demonstrated improved diagnostic performance when combined with lung ultrasound and blood gas parameters, though these findings were based on preliminary single-center data. Among protein biomarkers, ANGPTL4 exhibited both diagnostic and prognostic value, with its combination with lung ultrasound increasing the AUC (Area Under the Curve), suggesting enhanced predictive accuracy. However, evidence for ANGPTL4 is still early and lacks external validation. Clinical tools like lung ultrasound (LUS) were recognized for their good diagnostic performance, non-invasiveness, and suitability for repeated bedside exams.

Overall, while combined biomarker strategies and tools like lung ultrasound show promise in some studies, current evidence is insufficient for broader clinical use, with most emerging biomarkers facing challenges in validation. Machine learning approaches demonstrated advantages in multimodal data integration but are hampered by a lack of external validation, concerns about overfitting, and limited interpretability, restricting their clinical implementation.