Abdominal aortic aneurysms (AAAs) are progressive dilations of the abdominal aorta that carry a high risk of rupture and mortality if untreated. Current management relies on imaging-based surveillance and surgical repair at diameter thresholds; however, marked inter-individual variability in growth underscores the lack of personalised prediction tools. This thesis develops and refines a mass spectrometry-based pipeline for the absolute quantification of circulating proteins and metabolic profiling in AAA.A bespoke isotopically labelled protein standard, the Oxford quantification conCATamer (OxConCAT), was designed to incorporate peptides from candidate AAA biomarkers. The construct was expressed in E. coli with complete ^15N incorporation, purified, and integrated into plasma proteomics workflows for absolute quantification via parallel reaction monitoring (PRM). Iterative optimisation addressed challenges in protein expression, digestion, and ion suppression, improving construct stability and proteotypic performance through computational redesign. In a dedicated pilot study of OxAAA plasma, OxConCAT peptides were successfully quantified, enabling evaluation of peptide chromatographic behaviour, heavy-to-light ratios, and protein-level fold changes. The combination of DIA-based discovery proteomics and PRM-based absolute quantification facilitated the selection of robust quantotypic peptides. It demonstrated the feasibility of multiplexed protein measurement in plasma, with sufficient precision for downstream clinical studies.In parallel, an optimised two-dimensional gas chromatography–mass spectrometry (GC×GC–MS) approach was developed for plasma metabolomics. Method development identified over 100 mass peaks (putatively assigned metabolites) across derivatised and non-derivatised fractions, and in a patient pilot study, 67 metabolites were confidently annotated. Comparative pre- and post-surgical analyses revealed significant systemic metabolic shifts involving lipid, amino acid, and oxidative metabolism, with cholesterol, octadecanoic acid, erythritol, glutamic acid, and citric acid emerging as key differentiators. These findings underscore the dynamic nature of the plasma metabolome during AAA surgery and suggest the potential of metabolomics for biomarker discovery and pathway-level insights.Together, these proteomic and metabolomic pilot studies establish a dual workflow for biomarker discovery and validation in AAA. The OxConCAT pipeline provides a scalable, standardised, and precise method for absolute protein quantification, while metabolomics captures complementary biochemical changes with clinical relevance. The integrated platform is being applied to international cohorts (>400 patients) to develop and validate prediction models for AAA growth at 12 and 24 months, advancing biomarker-informed risk stratification and personalised surveillance strategies. Beyond AAA, the workflows described here may be adaptable to other vascular, metabolic, and inflammatory conditions, contributing to the advancement of precision medicine.