Systematic review of preterm birth multi-omic biomarker studies

Predictors of Inflammation-Mediated Preterm Birth

Preterm birth remains a worldwide health concern because of ongoing challenges in prediction and prevention. Current predictors are limited by poor performance, need for invasive sampling, and an inability to identify patients in a timely fashion to allow for effective intervention. The multiple etiologies of preterm birth often have an inflammatory component. Thus, a deeper understanding of the inflammatory mechanisms involved in preterm birth may provide opportunities to identify new predictors of preterm birth. This review discusses the multiple etiologies of preterm birth, their links to inflammation, current predictors available, and new directions for the field.

Protective Effect of EBF Transcription Factor 1 (EBF1) Polymorphism in Sporadic and Familial Spontaneous Preterm Birth: Insights from a Case-Control Study

This study investigated the potential role of specific single-nucleotide polymorphisms (SNPs) in the genes Astrotactin 1 (ASTN1), EBF Transcription Factor 1 (EBF1), Eukaryotic Elongation Factor, Selenocysteine-tRNA Specific (EEFSEC), Microtubule-Associated Serine/Threonine Kinase 1 (MAST1), and Tumor Necrosis Factor Alpha (TNF-α) to assess whether these genetic variants contribute to the risk of spontaneous preterm birth (sPTB). A case-control study was conducted involving 573 women from Croatia and Slovenia: 248 with sporadic sPTB (positive personal and negative family history of sPTB before 37 weeks' gestation), 44 with familial sPTB (positive personal and family history of sPTB before 37 weeks' gestation), and 281 control women. The analysis of ASTN1 rs146756455, EBF1 rs2963463, EBF1 rs2946169, EEFSEC rs201450565, MAST1 rs188343966, and TNF-α rs1800629 SNPs was performed using TaqMan real-time PCR. p-values were Bonferroni-adjusted for multiple comparisons. EBF1 SNP rs2963463 was significantly associated with sPTB (p adj = 0.03). Women carrying the CC genotype had a 3-4-times lower risk of sPTB (p adj < 0.0001). In addition, a significant difference in the frequency of the minor C allele was observed when comparing familial sPTB cases with controls (p adj < 0.0001). All other associations were based on unadjusted p-values. The minor T allele of EBF1 SNP rs2946169 was more frequent in sPTB cases overall than in controls, especially in sporadic sPTB (p = 0.045). Similarly, the CC genotype of ASTN1 SNP rs146756455 was more frequent in sporadic sPTB cases compared to controls (p = 0.019). Finally, the TNF-α SNP rs1800629 minor A allele and AA genotype were more common in the familial sPTB group compared to sporadic sPTB and controls (p < 0.05). The EBF1 SNP rs2963463 polymorphism showed a protective effect in the pathogenesis of sPTB, particularly in women carrying the CC genotype. Moreover, EBF1 SNP rs2946169 and ASTN1 SNP rs146756455, as well as TNF-α SNP rs1800629, were associated with an increased risk of sPTB, representing suggestive potential risk factors for sporadic and familial sPTB, respectively.

Predicting Preterm Birth Using Proteomics

The complexity of preterm birth (PTB), both spontaneous and medically indicated, and its various etiologies and associated risk factors pose a significant challenge for developing tools to accurately predict risk. This review focuses on the discovery of proteomics signatures that might be useful for predicting spontaneous PTB or preeclampsia, which often results in PTB. We describe methods for proteomics analyses, proteomics biomarker candidates that have so far been identified, obstacles for discovering biomarkers that are sufficiently accurate for clinical use, and the derivation of composite signatures including clinical parameters to increase predictive power.

Recent Advances and Challenges in the Early Diagnosis and Treatment of Preterm Labor

Preterm birth (PTB) is the primary cause of neonatal mortality and long-term disabilities. The unknown mechanism behind PTB makes diagnosis difficult, yet early detection is necessary for controlling and averting related consequences. The primary focus of this work is to provide an overview of the known risk factors associated with preterm labor and the conventional and advanced procedures for early detection of PTB, including multi-omics and artificial intelligence/machine learning (AI/ML)- based approaches. It also discusses the principles of detecting various proteomic biomarkers based on lateral flow immunoassay and microfluidic chips, along with the commercially available point-of-care testing (POCT) devices and associated challenges. After briefing the therapeutic and preventive measures of PTB, this review summarizes with an outlook.

Advances in mass spectrometry technologies to characterize cervicovaginal microbiome functions that impact spontaneous preterm birth

Preterm birth (PTB) is a leading cause of morbidity and mortality in young children. Infection is a major cause of this adverse outcome, particularly in PTBs characterised by spontaneous rupture of membranes, referred to as spontaneous (s)PTB. However, the aetiology of sPTB is not well defined and specific bacteria associated with sPTB differ between studies and at the individual level. This may be due to many factors including a lack of understanding of strain-level differences in bacteria that influence how they function and interact with each other and the host. Metaproteomics and metabolomics are mass spectrometry-based methods that enable the collection of detailed microbial and host functional information. Technological advances in this field have dramatically increased the resolution of these approaches, enabling the simultaneous detection of thousands of proteins or metabolites. These data can be used for taxonomic analysis of vaginal bacteria and other microbes, to understand microbiome-host interactions, and identify diagnostic biomarkers or therapeutic targets. Although these methods have been used to assess host proteins and metabolites, few have characterized the microbial compartment in the context of pregnancy. The utilisation of metaproteomic and metabolomic-based approaches has the potential to vastly improve our understanding of the mechanisms leading to sPTB.