Respiratory linked conditions associated with chronic obstructive pulmonary disease (COPD), asthma, and infection are increasing. The analyses of plasma samples from three biological states of varying phenotype (control, COPD and asthma patients) were conducted. Sample extraction was performed using a simple protein precipitation with a pre-cooled isopropanol. Lipid analysis was performed using a fast (<8min), comprehensive and high-throughput targeted method which is based on UPLC-HILIC lipid class separation followed by MRM quantitation of individual lipid species. It is integrated workflow for accurate and robust measurement of a carefully selected more than 500 lipid species. Data were processed using TargetLynx and Skyline. The biological samples were randomised and two technical replicates per sample were acquired. Statistical analysis of the data revealed clear separation between the various cohorts. Unsupervised PCA resulted in the separation of healthy controls, COPD and asthma patients. Application of the metadata also revealed significant differences between smoking status, with subsets readily observed within the COPD population. Loadings plot analysis revealed that FFA, LPC, PC and SM lipid classes to be the main contributors to sample type clustering. Additional ANOVA/t-test and hierarchical clustering showed all the lipid classes referenced to be up-regulated except for PC’s. A decrease in the level of PC’s was observed as significant for subjects associated with smoking. Overall, PCs are a potential marker for oxidative stress (immune activation). Pathway analysis revealed several components related to inflammation, oxidative and immunity processes were identified as significant and associated with signaling, metabolic and regulatory pathways.