Commentary on a combined approach to the problem of developing biomarkers for the prediction of spontaneous preterm labor that leads to preterm birth

Introduction: Globally, preterm birth has replaced congenital malformation as the major cause of perinatal mortality and morbidity. The reduced rate of congenital malformation was not achieved through a single biophysical or biochemical marker at a specific gestational age, but rather through a combination of clinical, biophysical and biochemical markers at different gestational ages. Since the aetiology of spontaneous preterm birth is also multifactorial, it is unlikely that a single biomarker test, at a specific gestational age will emerge as the definitive predictive test. Methods: The Biomarkers Group of PREBIC, comprising clinicians, basic scientists and other experts in the field, with a particular interest in preterm birth have produced this commentary with short, medium and long-term aims: i) to alert clinicians to the advances that are being made in the prediction of spontaneous preterm birth; ii) to encourage clinicians and scientists to continue their efforts in this field, and not to be disheartened or nihilistic because of a perceived lack of progress and iii) to enable development of novel interventions that can reduce the mortality and morbidity associated with preterm birth. Results: Using language that we hope is clear to practising clinicians, we have identified 11 Sections in which there exists the potential, feasibility and capability of technologies for candidate biomarkers in the prediction of spontaneous preterm birth and how current limitations to this research might be circumvented. Discussion: The combination of biophysical, biochemical, immunological, microbiological, fetal cell, exosomal, or cell free RNA at different gestational ages, integrated as part of a multivariable predictor model may be necessary to advance our attempts to predict sPTL and PTB. This will require systems biological data using omics data and artificial intelligence/machine learning to manage the data appropriately. The ultimate goal is to reduce the mortality and morbidity associated with preterm birth.