Woodridge, Laurel; Tektonidou, Maria G; Robinson, George A; Peng, Junjie; Coelewij, Leda; Martin-Gutierrez, Lucia; Navarro, Elvira Chocano; ... Jury, Elizabeth C; + view all Woodridge, Laurel; Tektonidou, Maria G; Robinson, George A; Peng, Junjie; Coelewij, Leda; Martin-Gutierrez, Lucia; Navarro, Elvira Chocano; Griffin, Maura; Nicolaides, Andrew; Ciurtin, Coziana; Rahman, Anisur; Torra, Ines Pineda; Jury, Elizabeth C; - view fewer (2025) Subclinical Atherosclerosis Risk Can Be Predicted in Female Patients With Systemic Lupus Erythematosus Using Metabolomic Signatures: An Observational Study. Journal of the American Heart Association , 14 (8) , Article e036507. 10.1161/JAHA.124.036507.

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Abstract

BACKGROUND: Cardiovascular disease (CVD) is a leading cause of death in women with systemic lupus erythematosus (SLE) due to accelerated atherosclerosis that is not predicted by established CVD risk scores. This study aimed to develop, validate, and test a female-focused predictive atherosclerosis risk signature based on serum metabolites in patients with SLE. METHODS AND RESULTS: Female patients with SLE were assessed for the presence (SLE-P; n=18) or absence (SLE-NP; n=26) of subclinical atherosclerosis using vascular ultrasound for carotid/femoral intima-media thickness. CVD risk was assessed using QRISK3 (which includes SLE diagnosis as a risk factor) and Framingham Risk Score. Serum metabolomics (n≥250) was performed and analyzed using machine learning pipelines. Despite having subclinical atherosclerosis, 44.8% to 100% of patients with SLE-P had low CVD risk according to QRISK3/Framlingham Risk Score scores. Using a lipid-focused metabolomic analysis, an improved atherosclerosis risk predictive signature was developed comprising 35 metabolites/5 clinical traits that classified patients with SLE-P and outperformed CVD risk assessment tools, lipid profiles measured in routine care, and clinical features alone. This “atherosclerosis risk signature” was validated in a second adult female SLE cohort (n=98) that predicted plaque status with moderate accuracy (area under the receiver operating characteristic curve, 0.79). The signature was then refined into a 5-feature subclinical plaque-predictive score that not only stratified the combined SLE-P/SLE-NP cohorts (n=142; area under the receiver operating characteristic curve, 0.84) but also predicted 3-year atherosclerosis progression in female postpubertal patients with juvenile-onset SLE (n=36; area under the receiver operating characteristic curve, 0.79). Finally, the 5-feature score identified distinct high and low subclinical atherosclerosis risk subgroups in a “real-world” setting of unscanned adult patients with SLE (n=38). CONCLUSIONS: This atherosclerosis risk score could improve CVD risk assessment/management in female patients with SLE across age. Validation in non-SLE and healthy cohorts could further substantiate these findings.

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