Preterm birth is a persistent and expensive global health problem accounting for almost 8% of all live births in the UK. There are some effective interventions available however, due to the heterogeneous nature of the condition; it is still difficult to tailor the correct management for each woman. A major obstacle to the development of effective treatment strategies is a limited understanding of the molecular events preceding preterm labour. Using SCOPE, a prospectively acquired global cohort, this MD investigated the three areas of clinical risk factors, biomarker discovery using proteomic technology and directed candidate cytokine analysis.
Clinical risk factor algorithms have been developed with the most clinically relevant group, those delivering less than 34 weeks, exhibiting the best predictive performance. The algorithm has an area under the ROC curve of 0.74, negative predictive value of 99%, with a positive predictive value of 33%. This is likely to be indicative of the best performance achievable using clinical data to predict preterm birth in a healthy nulliparous population.
A proteomic discovery study was performed comparing term and preterm birth. The proteins that were discovered appeared to be mainly plasma proteins related to systemic inflammation and therefore were not specific enough as predictors of spontaneous preterm birth.
As there is strong evidence to support a role for cytokines in the initiation of inflammation/infection-induced preterm labour, a panel of 27 were assessed as predictive markers for preterm birth. Of these, five cytokines (IL-4, IFN-γ, IL-6, IL-17α and MIP-1α) appeared to be the most sensitive with a predictive accuracy of 71.25%.
The data from this thesis have provided further understanding into preterm birth and provides a pathway for future investigation into the prediction and prevention of spontaneous preterm birth.
