Abstract
Background and aims: Patients with systemic lupus erythematosus (SLE) have a significantly higher incidence of atherosclerosis than the general population. Studies on atherosclerosis prediction models specific for SLE patients are very limited. This study aimed to build a risk prediction model for atherosclerosis in SLE.Methods: RNA sequencing was performed on 67 SLE patients. Subsequently, differential expression analysis was carried out on 19 pairs of age-matched SLE patients with (AT group) or without (Non-AT group) atherosclerosis using peripheral venous blood. We used logistic least absolute shrinkage and selection operator regression to select variables among differentially expressed (DE) genes and clinical features and utilized backward stepwise logistic regression to build an atherosclerosis risk prediction model with all 67 patients. The performance of the prediction model was evaluated by area under the curve (AUC), calibration curve, and decision curve analyses.Results: The 67 patients had a median age of 42.7 (Q1–Q3: 36.6–51.2) years, and 20 (29.9%) had atherosclerosis. A total of 106 DE genes were identified between the age-matched AT and Non-AT groups. Pathway analyses revealed that the AT group had upregulated atherosclerosis signaling, oxidative phosphorylation, and interleukin (IL)-17-related pathways but downregulated T cell and B cell receptor signaling. Keratin 10, age, and hyperlipidemia were selected as variables for the risk prediction model. The AUC and Hosmer–Lemeshow test p-value of the model were 0.922 and 0.666, respectively, suggesting a relatively high discrimination and calibration performance. The prediction model had a higher net benefit in the decision curve analysis than that when predicting with age or hyperlipidemia only.Conclusions: We built an atherosclerotic risk prediction model with one gene and two clinical factors. This model may greatly assist clinicians to identify SLE patients with atherosclerosis, especially asymptomatic atherosclerosis.
Highlights
Background and aimsPatients with systemic lupus erythematosus (SLE) have a significantly higher incidence of atherosclerosis than the general population
At the gene expression level, studies have determined that multiple proteins, including inflammatory cytokines [interleukin (IL)-1, IL-6, IL-10, interferon (IFN)-γ, tumor necrosis factor (TNF)-α, transforming growth factor (TGF)-β] and vascular endothelial related molecules [highmobility group box protein (HMGB)1, vascular cell adhesion molecule (VCAM)-1, intercellular adhesion molecule (ICAM)1, vascular endothelial growth factor (VEGF)], are closely related to atherosclerosis in SLE patients [10, 11]
We compared the net benefits of our prediction model and the models built with age, hyperlipidemia, or age plus hyperlipidemia and found the combination of age, hyperlipidemia, and KRT10 in our model had the highest net benefit (Figure 3)
Summary
Patients with systemic lupus erythematosus (SLE) have a significantly higher incidence of atherosclerosis than the general population. Studies on atherosclerosis prediction models specific for SLE patients are very limited. The incidence of cardiovascular events in SLE patients is significantly higher than that in the general population [2,3,4]. Patients with SLE exhibit a higher prevalence of atherosclerosis than the general population [8, 9]. At the gene expression level, studies have determined that multiple proteins, including inflammatory cytokines [interleukin (IL)-1, IL-6, IL-10, interferon (IFN)-γ, tumor necrosis factor (TNF)-α, transforming growth factor (TGF)-β] and vascular endothelial related molecules [highmobility group box protein (HMGB), vascular cell adhesion molecule (VCAM)-1, intercellular adhesion molecule (ICAM), vascular endothelial growth factor (VEGF)], are closely related to atherosclerosis in SLE patients [10, 11]. Some interesting findings have been reported, the number of studies is relatively small, and the biological pathways that contribute to the increased atherosclerosis in SLE are not fully understood
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