Using serum metabolomics analysis to predict sub-clinical atherosclerosis in patients with SLE

Abstract

Background: Patients with systemic lupus erythematosus (SLE) have an increased risk of developing cardiovascular disease (CVD) and 30-40% have sub-clinical atherosclerosis on vascular ultrasound scanning. Standard measurements of serum lipids in clinical practice do not predict CVD risk in patients with SLE. We hypothesise that more detailed analysis of lipoprotein taxonomy could identify better predictors of CVD risk in SLE. Methods: Eighty patients with SLE and no history of CVD underwent carotid and femoral ultrasound scans; 30 had atherosclerosis plaques (SLE-P) and 50 had no plaques (SLE-NP). Serum samples obtained at the time of the scan were analysed using a lipoprotein-focused metabolomics platform assessing 228 metabolites by nuclear magnetic resonance spectroscopy. Data was analysed using logistic regression and five binary classification models with 10-fold cross validation; decision tree, random forest, support vector machine and lasso (Least Absolute Shrinkage and Selection Operator) logistic regression with and without interactions. Results: Univariate logistic regression identified four metabolites associated with the presence of sub-clinical plaque; three subclasses of very low density lipoprotein (VLDL) (percentage of free cholesterol in medium and large VLDL particles and percentage of phospholipids in chylomicrons and extremely large VLDL particles) and Leucine. Together with age, these metabolites were also within the top features identified by the lasso logistic regression (with and without interactions) and random forest machine learning models. Logistic regression with interactions differentiated between SLE-P and SLE-NP with greatest accuracy (0.800). Notably, percentage of free cholesterol in large VLDL particles and age were identified by all models as being important to differentiate between SLE-P and SLE-NP patients. Conclusion: Serum metabolites are a promising biomarker for prediction of sub-clinical atherosclerosis development in SLE patients and could provide novel insight into mechanisms of early atherosclerosis development.

Sex Differences in Lipid Metabolism: Implications for Systemic Lupus Erythematosus and Cardiovascular Disease Risk

“…Despite this, more detailed investigations into lipoprotein subsets using NMR technology have found that women with SLE have increases in smaller LDL subfractions compared to sex matched healthy controls (HCs) ( 47 ). Further to this, a serum NMR metabolomic study by Coelewij et al, incorporating detailed lipoprotein subclass evaluation, was able to confidently differentiate between adult women with SLE and sex matched HCs by use of machine learning ( 48 ). Here, the most influential metabolites in separating SLE from HCs were medium sized HDL measures, which were reduced in SLE, as well as small HDL, VLDL, and IDL particles, which were increased in SLE compared to HCs.…”