Establishing a Risk Prediction Model for Atherosclerosis in Systemic Lupus Erythematosus

Area of Science:

• Cardiovascular Research
• Rheumatology
• Genomics

Background:

• Systemic lupus erythematosus (SLE) patients exhibit a higher incidence of atherosclerosis compared to the general population.
• Existing atherosclerosis prediction models lack specificity for SLE patients, highlighting a critical gap in clinical risk assessment.
• Limited research exists on developing tailored risk prediction tools for atherosclerosis within the SLE cohort.

Purpose of the Study:

• To develop and validate a novel risk prediction model for atherosclerosis specifically in patients diagnosed with systemic lupus erythematosus.
• To identify key clinical and molecular factors that contribute to atherosclerosis development in SLE patients.
• To enhance early identification of atherosclerosis, including subclinical forms, in individuals with SLE.

Main Methods:

• RNA sequencing was performed on 67 SLE patients to analyze gene expression profiles.
• Differential gene expression analysis was conducted on 19 age-matched SLE patients with (AT) or without (Non-AT) atherosclerosis.
• Logistic regression models, including least absolute shrinkage and selection operator (LASSO) and stepwise backward selection, were employed to build the prediction model using DE genes and clinical data.

Main Results:

• A total of 106 differentially expressed genes were identified between the atherosclerosis and non-atherosclerosis groups.
• Pathway analysis indicated dysregulation in atherosclerosis signaling, oxidative phosphorylation, and IL-17 pathways in the AT group.
• The final prediction model incorporated Keratin 10, age, and hyperlipidemia, achieving an AUC of 0.922, demonstrating high predictive performance.

Conclusions:

• A robust atherosclerotic risk prediction model was successfully developed for SLE patients, integrating one gene (Keratin 10) and two clinical factors (age, hyperlipidemia).
• This model demonstrates significant potential in assisting clinicians to identify SLE patients at risk of atherosclerosis, particularly those with asymptomatic disease.
• The findings underscore the importance of molecular markers combined with clinical data for precise cardiovascular risk stratification in SLE management.