AUTHOR=Xing Haiping , Pang Haiyu , Du Tian , Yang Xufei , Zhang Jing , Li Mengtao , Zhang Shuyang
TITLE=Establishing a Risk Prediction Model for Atherosclerosis in Systemic Lupus Erythematosus
JOURNAL=Frontiers in Immunology
VOLUME=12
YEAR=2021
URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2021.622216
DOI=10.3389/fimmu.2021.622216
ISSN=1664-3224
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.