Background: Aortic aneurysms and aortic dissections (AA/AD) are serious vascular conditions that often progress without symptoms and are associated with high mortality, highlighting the need for improved tools to predict the occurrence. This study aims to identify plasma proteins that can predict the risk of future AA/AD events and to combine these biomarkers with traditional risk factors to construct risk prediction model. Materials and methods: We analyzed plasma proteomic data from 22 416 participants in the UK Biobank, measuring 2911 proteins using the Olink Explore proximity extension assay. Plasma proteomics data were analyzed using Cox regression and machine learning techniques. Proteins significantly associated with AA/AD risk were identified, and predictive models were constructed by integrating these biomarkers with traditional risk factors such as age, sex, and blood pressure. Results: The Cox regression models identified 25 proteins significantly associated with AA/AD risk, after adjusting for demographic factors. Furthermore, light gradient-boosting machine was used to rank the importance of these proteins and applied forward stepwise selection to identify four key predictive proteins (cystatin 3 [CST3], matrix metallopeptidase 12 [MMP12], multiple EGF-like domains 9 [MEGF9], and C-X-C motif chemokine ligand 17 [CXCL17]). The protein panel demonstrated an overall predictive AUC of 0.725 for AA/AD. The demographic model achieved an AUC of 0.740. Integration of these biomarkers with demographic factors significantly enhanced predictive accuracy, achieving an AUC of 0.777 (DeLong test P <0.001). Temporal trajectory analysis revealed that elevated levels of CST3, MMP12, and CXCL17 were detectable up to 10 years prior to AA/AD diagnosis. Conclusion: Our study highlights the potential of plasma proteomics, particularly combination of four proteins (CST3, MMP12, MEGF9, and CXCL17), as a valuable strategy for predicting AA/AD risk. The integration of proteomic biomarkers with demographic factors enhances predictive accuracy and offers insights into the underlying molecular mechanisms, which could lead to improved early detection and personalized treatment for AA/AD.

Background: Abdominal aortic aneurysm (AAA) is a severe vascular disease that can lead to rupture and life-threatening hemorrhage. The role of ferroptosis in AAA pathogenesis remains insufficiently understood. This study aims to investigate the role of ferroptosis in AAA by identifying ferroptosis-associated molecular subtypes and examining their relationship with immunological characteristics using an artificial neural network (ANN) model. Methods: We analyzed three publicly available datasets (GSE7084, GSE47472, and GSE57691) to identify differentially expressed ferroptosis-related genes (FRGs) and employed consensus clustering to classify AAA samples into two subtypes. Immune infiltration was assessed with the CIBERSORT algorithm, and a diagnostic artificial neural network (ANN) model based on subtype-specific genes was developed to discriminate ferroptosis-associated molecular subtypes and derive the NeuraAAA score. Results: Nine differentially expressed FRGs were identified, and the model incorporated three key genes (oncostatin M, heme oxygenase-1, and interleukin-6), achieving high diagnostic accuracy (AUC = 0.988). Consensus clustering stratified AAA samples into two ferroptosis-associated subtypes with distinct immune profiles, with the C1 subtype showing higher immune infiltration and immune scores than C2. The derived NeuraAAA score was elevated in the immune-enriched subtype and correlated with immune-cell infiltration, and a nomogram integrating NeuraAAA and immune score showed good calibration. Immunofluorescence confirmed increased expression of all three genes in AAA specimens. Conclusion: Our study reveals the heterogeneous role of ferroptosis in AAA pathogenesis, demonstrating that ferroptosis-associated subtypes are linked to variations in the immune microenvironment. These findings provide new insights into AAA pathophysiology and suggest potential targets for subtype-specific therapeutic strategies, contributing to advances in precision medicine for AAA treatment.

Background: Right-sided aortic arch with Kommerell diverticulum (RAA-KD) has complex three-dimensional anatomy. Methods: We retrospectively analyzed computed tomography angiography data from 65 adults with RAA-KD using centerline-based reconstruction. Patients were stratified by conventional maximal KD diameter (D_KDmax) into <55 mm (n = 38) and ≥55 mm (n = 27) groups. Age associations, adjusted group differences, and continuous associations with centerline-derived KD diameter at maximal cross-sectional area (D₁₀) were examined. Results: After sex adjustment and false-discovery-rate correction, all aortic diameters increased with age; the ascending aorta, arch, and descending thoracic aorta also elongated, whereas aortic isthmus (L₄) and KD (L₅) lengths were not age-associated. After age and sex adjustment and false-discovery-rate correction, diameters of the ascending aorta, aortic arch, and descending thoracic aorta (D₂–D₈), as well as KD-related diameters (D₉–D₁₂), remained larger in the ≥55 mm group; L4 was shorter and L5 longer. In continuous models, D₁₀ was positively associated with all non-KD diameters, most strongly with the adjacent proximal descending aorta (D₇), and independently associated with shorter L₄ (B = −0.548; 95% CI, −0.828 to −0.268) and longer L₅ (B = 0.639; 95% CI, 0.412–0.866; both p < 0.001). Conclusions: RAA-KD demonstrates generalized age-associated thoracic aortic enlargement and localized KD-associated remodeling. Comprehensive three-dimensional assessment may inform evaluation of the adjacent aorta and proximal sealing zone for thoracic endovascular aortic repair.