ACADM-mediated fatty acid β-oxidation pathway in atherosclerosis and abdominal aortic aneurysm

Atherosclerosis (AS) and abdominal aortic aneurysm (AAA) are both metabolism-associated vascular diseases, yet the role of lipid metabolic disturbances in their pathogenesis remains largely unknown. This study aimed to clarify the differential impact of lipid metabolic disturbances and their underlying mechanisms in AS and AAA.

Lipidomic analysis was performed to identify lipid metabolic differences between AS and AAA in various mouse models and different human cohorts. A multi-omics approach, integrated with functional assays, was utilized to elucidate the downstream mechanisms underlying disease-specific lipid metabolic features. Machine learning models were developed based on lipidomic features to differentiate AS from AAA.

Lipidomic analysis of mouse models and human samples revealed a predominant enrichment of neutral lipids (e.g. triglycerides and cholesterol esters) in AS, in contrast to phosphoglycerides in AAA. Consistently, large-scale longitudinal data from the UK Biobank showed strong positive associations of triglycerides, cholesterol, and fatty acid with the future risk of coronary atherosclerotic disease, while phosphoglycerides were negatively associated with the risk of AAA. Integrated transcriptomic and metabolomic analyses identified fatty acid metabolism, particularly Acadm-mediated fatty acid β-oxidation (FAO) pathway, as the most significantly altered lipid metabolic pathway contributing to the lipid metabolic differences between AS and AAA. Consistently, targeted restoration of the Acadm-mediated FAO pathway inhibited AS by reducing lipotoxic metabolites and preserving mitochondrial homeostasis but had little impact on AAA. Further validation with lipid droplets autophagy-tethering compound (LD·ATTEC), which selectively eliminates intracellular lipid droplets, significantly alleviated AS progression and improved FAO with no significant change observed in AAA. Finally, predictive models were developed based on lipidomic features using machine learning algorithms, facilitating accurate differentiation between these two vascular diseases.

These findings define previously unrecognized distinct lipid metabolic characteristics in the pathogenesis of AS vs AAA, providing a basis for differential diagnosis and targeted treatments.