Unsupervised machine learning identifies distinct SLE patient endotypes with differential response to belimumab
Three distinct SLE endotypes were identified based on B cell immunophenotyping and serological profiles, showing differential benefit from belimumab therapy.
Authors
- Mariele Gatto (ORCID: https://orcid.org/0000-0003-4012-1248)
- Julius Lindblom
- Leonardo Palazzo (ORCID: https://orcid.org/0000-0002-2652-7447)
- Roberto Depascale
- Luca Iaccarino
- Dionysis Nikolopoulos (ORCID: https://orcid.org/0000-0002-9894-6966)
- Andrea Doria (ORCID: https://orcid.org/0000-0003-0548-4983)
- Raffaele Da Mutten (ORCID: https://orcid.org/0000-0002-2645-0865)
- Ioannis Parodis (ORCID: https://orcid.org/0000-0002-4875-5395)
- Nurşen Çetrez (ORCID: https://orcid.org/0000-0001-9874-841X)
Institutions
- Karolinska University Hospital (SE)
- University of Padua (IT)
- Örebro University (SE)
- Karolinska Institutet (SE)
- A. O. Ordine Mauriziano di Torino (IT)
- University of Turin (IT)
Publication Details
- Journal
- Lara D. Veeken
- Published
- 2025-04-17
- DOI
- https://doi.org/10.1093/rheumatology/keaf215
- Citations
- 3
- Primary Topic
- Systemic Lupus Erythematosus Research
- Type
- article
- Field-Weighted Citation Impact
- 5.00
Funders
- Knowledge Foundation
- AstraZeneca
- GlaxoSmithKline
- Örebro Universitet
- Karolinska Institutet
- Stiftelsen Konung Gustaf V:s 80-årsfond
- Reumatikerförbundet