Preterm birth (PTB) and its associated complications remain the leading cause of death in children under 5 years of age, worldwide. Despite extensive research, our understanding of the underlying causes of PTB and our ability to predict women at risk remain limited. However, 40% of all PTB cases are linked with an infectious aetiology. Recent studies have established an association between vaginal bacterial composition and risk of PTB. Depletion of Lactobacillus species and increased bacterial diversity associate with increased PTB risk. Current techniques used to study vaginal microbiota are costly, time-consuming, and do not provide information on host:microbiota interactions, which are crucial for understanding PTB phenotypes. The biochemical composition of cervicovaginal fluid (CVF) has been shown to reflect the microbial composition of the vagina. The work presented in this thesis set out to test the hypothesis that CVF metabolic profiles rapidly captured by a novel platform, direct on swab analysis by Desorption Electrospray Ionisation-Mass Spectrometry (DESI-MS), can be used for PTB risk stratification and outcome prediction. To test this, women at high risk of PTB were recruited and vaginal swabs analysed using DESI-MS. Integration of the resulting metabolic profiles with metataxonomics and immunological data enabled the identification of metabolic signatures capable of simultaneously predicting bacterial composition and host inflammatory status. Results also indicated that the CVF metabolome as assessed by DESI-MS is partly influenced by prior pregnancy history and can moderately predict cervical shortening. Analysis of samples with cervical sutures in situ showed that DESI-MS -based prediction of bacterial composition is not affected by the procedure. A sub analysis of vaginal swabs highlighted a limitation of DESI-MS to detect progesterone and other steroid hormones in CVF...