Decoding preterm birth: Non-Invasive biomarkers and personalized multi-omics strategies

Area of Science:

• Biomarkers
• Genomics
• Proteomics
• Metabolomics
• Transcriptomics
• Epigenomics

Background:

• Preterm birth (PTB) before 37 weeks of gestation is a global health issue causing significant neonatal morbidity and mortality.
• Multi-omics technologies offer advanced tools for understanding PTB's complex molecular mechanisms.
• Identifying reliable biomarkers is crucial for early prediction and risk stratification of PTB.

Purpose of the Study:

• To review emerging liquid biomarkers from multi-omics studies for PTB prediction.
• To highlight the integration of various omics data for enhanced understanding of PTB pathogenesis.
• To discuss the potential of multi-omics for personalized prenatal care and PTB prevention.

Main Methods:

• Comprehensive review of proteomic, metabolomic, genomic, transcriptomic, and epigenomic studies on PTB.
• Analysis of liquid biomarkers in maternal and fetal compartments.
• Integration of multi-omics data using machine learning models for predictive accuracy assessment.

Main Results:

• Proteomics identified proteins linked to inflammation and extracellular matrix pathways.
• Metabolomics revealed lipid and metabolite profiles associated with energy metabolism.
• Genomics, epigenomics, and transcriptomics uncovered genetic variations, microRNAs, and ncRNAs involved in PTB.
• Multi-omics integration with machine learning showed superior predictive performance for PTB.

Conclusions:

• Multi-omics approaches provide deep insights into PTB's molecular underpinnings.
• Integrated multi-omics data and machine learning significantly improve PTB prediction.
• Future research requires longitudinal studies and diverse cohorts for clinical translation.
• Developing accessible biomarker panels and standardized guidelines is essential for clinical implementation and reducing the global PTB burden.