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Oxidation of PUFAs in LDLs trapped in the arterial intima plays a critical role in atherosclerosis. Though there have been many studies on the atherogenicity of oxidized derivatives of PUFA-esters of cholesterol, the effects of cholesteryl hemiesters (ChEs), the oxidation end products of these esters, have not been studied. Through lipidomics analyses, we identified and quantified two ChE types in the plasma of CVD patients and identified four ChE types in human endarterectomy specimens. Cholesteryl hemiazelate (ChA), the ChE of azelaic acid (n-nonane-1,9-dioic acid), was the most prevalent ChE identified in both cases. Importantly, human monocytes, monocyte-derived macrophages, and neutrophils exhibit inflammatory features when exposed to subtoxic concentrations of ChA in vitro. ChA increases the secretion of proinflammatory cytokines such as interleukin-1β and interleukin-6 and modulates the surface-marker profile of monocytes and monocyte-derived macrophage. In vivo, when zebrafish larvae were fed with a ChA-enriched diet, they exhibited neutrophil and macrophage accumulation in the vasculature in a caspase 1- and cathepsin B-dependent manner. ChA also triggered lipid accumulation at the bifurcation sites of the vasculature of the zebrafish larvae and negatively impacted their life expectancy. We conclude that ChA behaves as an endogenous damage-associated molecular pattern with inflammatory and proatherogenic properties.

Background: Localized stress and cell death in chronic inflammatory diseases may release tissue-specific lipids into the circulation causing the blood plasma lipidome to reflect the type of inflammation. However, deep lipid profiles of major chronic inflammatory diseases have not been compared. Methods: Plasma lipidomes of patients suffering from two etiologically distinct chronic inflammatory diseases, atherosclerosis-related vascular disease, including cardiovascular (CVD) and ischemic stroke (IS), and systemic lupus erythematosus (SLE), were screened by a top-down shotgun mass spectrometry-based analysis without liquid chromatographic separation and compared to each other and to age-matched controls. Lipid profiling of 596 lipids was performed on a cohort of 427 individuals. Machine learning classifiers based on the plasma lipidomes were used to distinguish the two chronic inflammatory diseases from each other and from the controls. Findings: Analysis of the lipidomes enabled separation of the studied chronic inflammatory diseases from controls based on independent validation test set classification performance (CVD vs control - Sensitivity: 0.94, Specificity: 0.88; IS vs control - Sensitivity: 1.0, Specificity: 1.0; SLE vs control – Sensitivity: 1, Specificity: 0.93) and from each other (SLE vs CVD ‒ Sensitivity: 0.91, Specificity: 1; IS vs SLE - Sensitivity: 1, Specificity: 0.82). Preliminary linear discriminant analysis plots using all data clearly separated the clinical groups from each other and from the controls, and partially separated CVD severities, as classified into five clinical groups. Dysregulated lipids are partially but not fully counterbalanced by statin treatment. Interpretation: Dysregulation of the plasma lipidome is characteristic of chronic inflammatory diseases. Lipid profiling accurately identifies the diseases and in the case of CVD also identifies sub-classes. Funding: Full list of funding sources at the end of the manuscript.