Simona Bogdanova

Background: Patients with spondyloarthritis (SpA) have an increased risk of cardiovascular disease that is not fully explained by traditional cardiovascular risk factors. Chronic inflammation may alter lipid metabolism and contribute to the lipid paradox, limiting the value of conventional lipid parameters for cardiovascular risk assessment. Composite atherogenic lipid indices have emerged as potential markers of residual cardiovascular risk, but their relationship with subclinical vascular damage in SpA remains incompletely understood. Objective: To investigate the association between atherogenic lipid indices and markers of subclinical atherosclerosis and arterial stiffness in patients with spondyloarthritis. Methods: This cross-sectional study included 150 patients with SpA, comprising 109 patients with ankylosing spondylitis and 41 patients with psoriatic arthritis. Lipid profile, atherogenic index of plasma (AIP), Castelli risk indices I and II, non-high-density lipoprotein cholesterol (non-HDL-C), and atherogenic coefficient were assessed. Carotid intima-media thickness (CIMT) and arterial stiffness parameters, including local carotid pulse wave velocity (PWV), β-stiffness, pressure-strain elastic modulus (EP), arterial compliance (AC), and augmentation index (AI), were evaluated using carotid ultrasonography with echo-tracking technology. Associations were analyzed using Spearman correlation and multivariable linear regression models. Results: Triglycerides demonstrated the strongest and most consistent correlations with vascular parameters, including CIMT (ρ = 0.241, p = 0.003), β-stiffness (ρ = 0.314, p < 0.001), local carotid PWV (ρ = 0.325, p < 0.001), and EP (ρ = 0.318, p < 0.001). Non-HDL-C was significantly associated with CIMT (ρ = 0.248, p = 0.002), β-stiffness (ρ = 0.195, p = 0.017), local carotid PWV (ρ = 0.249, p = 0.002), and EP (ρ = 0.225, p = 0.006). AIP correlated positively with CIMT, β-stiffness, local carotid PWV, and EP and remained independently associated with local carotid PWV after multivariable adjustment (B = 0.534, 95% CI 0.005–1.063; β = 0.150, p = 0.048), whereas non-HDL-C remained independently associated with CIMT (B = 0.027, 95% CI 0.002–0.053; β = 0.149, p = 0.033). In contrast, LDL-C and HDL-C demonstrated limited associations with vascular parameters. Conclusions: Atherogenic lipid indices are associated with carotid structural and functional vascular parameters in patients with spondyloarthritis. Non-HDL-C and AIP were independently associated with CIMT and PWV, respectively, suggesting that these indices may provide complementary information regarding vascular characteristics in patients with SpA.

Background: Patients with ankylosing spondylitis (AS) and psoriatic arthritis (PsA) have increased cardiovascular risk beyond traditional factors. Endothelial activation is an early and potentially reversible stage of atherogenesis, and circulating adhesion molecules such as intercellular adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1) have been proposed as markers of vascular involvement in inflammatory diseases. This study aimed to compare serum ICAM-1 and VCAM-1 levels in patients with ankylosing spondylitis and psoriatic arthritis versus controls, and to examine their associations with disease activity, systemic inflammatory burden, cardiovascular risk profile, and treatment status. Methods: This cross-sectional study included 154 participants recruited between 2023 and 2024, including 83 patients with AS, 40 with PsA, and 31 controls. Disease activity was assessed using ASDAS-CRP (AS) and DAS28-CRP (PsA). Inflammatory markers, lipid profile, cardiovascular risk scores, and treatment history were recorded. Serum ICAM-1 and VCAM-1 concentrations were measured by enzyme-linked immunosorbent assay. Group comparisons used Kruskal–Wallis and Mann–Whitney U tests; correlations used Spearman or Pearson coefficients as appropriate. Multivariable linear regression was applied to identify independent predictors of VCAM-1. Results: Serum VCAM-1 and ICAM-1 levels did not differ significantly between AS, PsA, and controls. VCAM-1 showed positive correlations with C-reactive protein (ρ = 0.247, p = 0.002) and erythrocyte sedimentation rate (ρ = 0.163, p = 0.044), whereas ICAM-1 showed no significant associations with inflammatory, metabolic, or vascular parameters. In multivariable analysis, body mass index was the only independent predictor of VCAM-1 (β = 0.198, p = 0.027). In AS, higher disease activity defined by ASDAS-CRP > 2.1 was associated with higher CRP ( p = 0.002), ESR ( p = 0.04), and VCAM-1 concentrations ( p = 0.002). Biologic-naïve patients had higher VCAM-1 levels than treated patients ( p = 0.046), while those receiving Janus kinase inhibitors had lower VCAM-1 compared with biologic-naïve individuals ( p = 0.014). Conclusions: Although ICAM-1 and VCAM-1 levels were comparable between patients and controls, VCAM-1 reflected inflammatory activity and treatment status, particularly in AS. VCAM-1 may represent a more sensitive marker of endothelial activation than ICAM-1 in treated spondyloarthritis.

Artificial intelligence (AI) is rapidly transforming rheumatology, particularly in imaging and laboratory diagnostics where data complexity challenges traditional interpretation. This narrative review summarizes current evidence on AI-driven tools across musculoskeletal ultrasound, radiography, MRI, CT, capillaroscopy, and laboratory analytics. A structured literature search (PubMed, Scopus, Web of Science; 2020–2025) identified 90 relevant publications addressing AI applications in diagnostic imaging and biomarker analysis in rheumatic diseases, while twelve supplementary articles were incorporated to provide contextual depth and support conceptual framing. Deep learning models, notably convolutional neural networks and vision transformers, have demonstrated expert-level accuracy in detecting synovitis, bone marrow edema, erosions, and interstitial lung disease, as well as in quantifying microvascular and structural damage. In laboratory diagnostics, AI enhances the integration of traditional biomarkers with high-throughput omics, automates serologic interpretation, and supports molecular and proteomic biomarker discovery. Multi-omics and explainable AI platforms increasingly enable precision diagnostics and personalized risk stratification. Despite promising performance, widespread implementation is constrained by significant domain-specific validation gaps, data heterogeneity, lack of external validation, ethical concerns, and limited workflow integration. Clinically meaningful progress will depend on transparent, validated, and interoperable AI systems supported by robust data governance and clinician education. The transition from concept to clinic is under way—AI will likely serve as an augmenting rather than replacing partner, standardizing interpretation, accelerating decision-making, and ultimately facilitating precision, data-driven rheumatologic care.