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Subclinical Atherosclerosis Risk Can Be Predicted in Females with Systemic Lupus Erythematosus (SLE) Using Metabolomic Signatures

21 Pages Posted: 8 Feb 2024

See all articles by Laurel Woodridge

Laurel Woodridge

University College London

Maria Tektonidou

National and Kapodistrian University of Athens

George A. Robinson

University College London

Junjie Peng

University College London - Centre for Rheumatology Research

Leda Coelewij

University College London

Elvira Chocano Navarro

University College London

Maura Griffin

Vascular Noninvasive Diagnostic Centre

Andrew Nicolaides

Imperial College London

Coziana Ciurtin

University College London - Centre for Adolescent Rheumatology Versus Arthritis

Anisur Rahman

University College London

Ines Pineda Torra

University College London

Elizabeth C. Jury

University College London - GOSH and Centre for Rheumatology Research

More...

Abstract

Background: Cardiovascular disease (CVD) is a leading cause of mortality in people with systemic lupus erythematosus (SLE) yet established clinical scores fail to predict CVD-risk accurately.

Methods: Patients with SLE (n=44, 100% female) were assessed for CVD-risk using established tools and scanned for carotid intima-media thickness (CIMT) and carotid/femoral atherosclerotic plaques using vascular ultrasound. A lipid-focused nuclear magnetic resonance metabolomic platform was used to assess serum metabolites (n≥250). Classification and regression ML models were applied to metabolomic data comparing patients with (SLE-P, n=18) and without (SLE-NP, n=26) subclinical atherosclerotic plaque. The SLE/atherosclerosis-specific metabolite signature was validated in independent adult SLE (n=98) and juvenile-onset SLE (n=36) cohorts grouped by subclinical atherosclerosis. A clinical score for CVD-risk prediction in SLE was developed and applied to assess CVD-risk in an unscanned adult SLE cohort (n=38).

Findings: Existing CVD-risk assessment tools categorised 44.9-100% of patients with subclinical atherosclerotic plaque and SLE as low CVD-risk. However, a distinct 35-metabolite/5-clinical trait signature correctly classified patients with subclinical plaque with high accuracy (area under curve-receiver operating characteristic AUC-ROC=0.92). This signature outperformed individual features and lipid profiles measured in routine care in the model for CVD-risk prediction. The SLE CVD-risk signature was validated in an independent cohort and correctly predicted plaque status with high accuracy (AUC-ROC=0.79). Finally, a novel score system was developed comprising glycine, medium-sized low-density lipoprotein, intermediate density lipoprotein cholesterol, omega-6/omega-3 ratio and age that could also predict CIMT progression in post-pubertal juvenile SLE (AUC-ROC 0.69/0.79 with and without age respectively). This scoring system was also used to assess CVD-risk in an additional unscanned adult SLE cohort, revealing distinct high and low risk subgroups.

Interpretation: This novel CVD-risk score could improve CVD-risk assessment in SLE across age compared to current methods and/or be used to screen patients to improve CVD management in SLE.

Funding: BBSRC London Interdisciplinary Biosciences PhD consortium (BB/M009513/1), National Institute for Health Research University College London Hospital Biomedical Research Centre (BRC531/III/IPT/101350) and the Rosetrees Trust. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Declaration of Interest: The authors declare no competing interests.

Ethical Approval: All patients provided informed written consent/assent according to the declaration of Helsinki. All patient information was anonymised or pseudo-anonymised in accordance with relevant data protection legislation (EU General Data Protection Regulation (https://www.eugdpr.org/) and UK Data Protection Bill, 2018). This study was approved by the London - City & East Research Ethics Committee of the NHS 15-LO-2065, LondonHarrow Research Ethics Committee (REC11/LO/0330) and by the Institutional Review Board of Laiko Hospital, Athens, Greece.

Keywords: Systemic lupus erythematosus, cardiovascular risk, metabolomics, atherosclerosis, machine learning

Suggested Citation

Woodridge, Laurel and Tektonidou, Maria and Robinson, George A. and Peng, Junjie and Coelewij, Leda and Chocano Navarro, Elvira and Griffin, Maura and Nicolaides, Andrew and Ciurtin, Coziana and Rahman, Anisur and Pineda Torra, Ines and Jury, Elizabeth C., Subclinical Atherosclerosis Risk Can Be Predicted in Females with Systemic Lupus Erythematosus (SLE) Using Metabolomic Signatures. Available at SSRN: https://ssrn.com/abstract=4719176 or http://dx.doi.org/10.2139/ssrn.4719176

Laurel Woodridge

University College London ( email )

Gower Street
London, WC1E 6BT
United Kingdom

Maria Tektonidou

National and Kapodistrian University of Athens ( email )

George A. Robinson

University College London ( email )

Gower Street
London, WC1E 6BT
United Kingdom

Junjie Peng

University College London - Centre for Rheumatology Research ( email )

Leda Coelewij

University College London ( email )

Gower Street
London, WC1E 6BT
United Kingdom

Elvira Chocano Navarro

University College London ( email )

Gower Street
London, WC1E 6BT
United Kingdom

Maura Griffin

Vascular Noninvasive Diagnostic Centre ( email )

Andrew Nicolaides

Imperial College London ( email )

South Kensington Campus
Exhibition Road
London, SW7 2AZ
United Kingdom

Coziana Ciurtin

University College London - Centre for Adolescent Rheumatology Versus Arthritis ( email )

Anisur Rahman

University College London ( email )

Gower Street
London, WC1E 6BT
United Kingdom

Ines Pineda Torra

University College London ( email )

Gower Street
London, WC1E 6BT
United Kingdom

Elizabeth C. Jury (Contact Author)

University College London - GOSH and Centre for Rheumatology Research ( email )

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