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Background Blood biomarkers are a potential tool for early stroke diagnosis. We aimed to perform a pilot and exploratory study on untargeted blood biomarkers in patients with suspected stroke by using mass spectrometry analysis. Methods and Results This was a prospective observational study of consecutive patients with suspected stroke admitted within 6 hours of last being seen well. Blood samples were collected at admission. Patients were divided into 3 groups: ischemic stroke (IS), intracerebral hemorrhage (ICH), and stroke mimics. Quantitative analysis from mass spectrometry data was performed using a supervised approach. Biomarker‐based prediction models were developed to differentiate IS from ICH and ICH+stroke mimics. Models were built aiming to minimize misidentification of patients with ICH as having IS. We included 90 patients, one‐third within each subgroup. The median age was 71 years (interquartile range, 57–81 years), and 49 participants (54.4%) were women. In quantitative analysis, C3 (complement component 3), ICAM‐2 (intercellular adhesion molecule 2), PLGLA (plasminogen like A), STXBP5 (syntaxin‐binding protein 5), and IGHV3‐64 (immunoglobulin heavy variable 3‐64) were the 5 most significantly dysregulated proteins for both comparisons. Biomarker‐based models showed 88% sensitivity and 89% negative predictive value for differentiating IS from ICH, and 75% sensitivity and 95% negative predictive value for differentiating IS from ICH+stroke mimics. ICAM‐2, STXBP5, PLGLA, C3, and IGHV3‐64 displayed the highest importance score in our models, being the most informative for identifying patients with stroke. Conclusions In this proof‐of‐concept and exploratory study, our biomarker‐based prediction models, including ICAM‐2, STXBP5, PLGLA, C3, and IGHV3‐64, showed 75% to 88% sensitivity for identifying patients with IS, while aiming to minimize misclassification of ICH. Although our methodology provided an internal validation, these results still need validation in other cohorts and with different measurement techniques.

BackgroundDiabetes mellitus (DM) and cardiovascular disease are associated with dyslipidemia, but the detailed lipid molecular pattern in both diseases remains unknown. Methods and ResultsWe used shotgun mass spectrometry to determine serum levels of 255 molecular lipids in 316 controls, 171 DM, and 99 myocardial infarction (MI) events from a cohort derived from the Malmö Diet and Cancer study. Orthogonal projections to latent structures analyses were conducted between the lipids and clinical parameters describing DM or MI. Fatty acid desaturases (FADS) and elongation of very long chain fatty acid protein 5 (ELOVL5) activities were estimated by calculating product to precursor ratios of polyunsaturated fatty acids in complex lipids. FADS genotypes encoding these desaturases were then tested for association with lipid levels and ratios. Differences in the levels of lipids belonging to the phosphatidylcholine and triacylglyceride (TAG) classes contributed the most to separating DM from controls. TAGs also played a dominating role in discriminating MI from controls. Levels of C18:2 fatty acids in complex lipids were lower both in DM and MI versus controls (DM, P=0.004; MI, P=6.0E‐06) at least due to an acceleration in the metabolic flux from C18:2 to C20:4 (eg, increased estimated ELOVL5: DM, P=0.02; MI, P=0.04, and combined elongase‐desaturase activities: DM, P=3.0E‐06; MI, P=2.0E‐06). Minor allele carriers of FADS genotypes were associated with increased levels of C18:2 (P≤0.007) and lower desaturase activity (P≤0.002). ConclusionsWe demonstrate a possible relationship between decreased levels of C18:2 in complex lipids and DM or MI. We thereby highlight the importance of molecular lipids in the pathogenesis of both diseases.