Unsupervised clustering of clinical and laboratory features of systemic lupus erythematosus: insights from a multicentre cohort

Clinical Snapshot

Key Findings

• P Value: p=0.023 for cyclophosphamide response in neuropsychiatric cluster; p=0.019 for rituximab response in cardiopulmonary cluster

• Effect Size: LLDAS achievement: 46.42% in mucocutaneous cluster vs lower rates in other clusters

• Primary Outcome: Five distinct SLE clusters identified: renal (14.27%), mucocutaneous (39.53%), neuropsychiatric (14.74%), haematologic (22.63%), and cardiopulmonary (8.83%)

• Nnt Or Sensitivity: Cluster-specific treatment response correlations: r=0.284 (cyclophosphamide in Cluster 3), r=0.286 (rituximab in Cluster 5)

• Confidence Interval: Not reported in abstract

Clinical Application

Requires access to comprehensive laboratory testing and multi-omics capabilities; clustering approach may be implementable with standard clinical and laboratory data Relevant for Australian rheumatologists managing SLE patients; may inform PBS criteria for targeted therapies like rituximab; aligns with RACGP chronic disease management principles SLE patients in tertiary care settings, particularly those requiring phenotype-based treatment stratification

Abstract

OBJECTIVE: This study explored the relationship between clinical phenotypes and immuno-molecular features of systemic lupus erythematosus (SLE) using unsupervised machine learning and multi-omics integration. METHODS: A multicentre cohort of 1065 SLE patients from five teaching hospitals was studied. Unsupervised clustering was performed using integrated clinical and laboratory data to identify distinct phenotypic clusters. Multi-omics profiling, including proteomics and transcriptomics, was conducted for each cluster, comparing with healthy controls. In addition, 899 patients were prospectively followed for over 1 year to assess therapeutic responses and remission outcomes. RESULTS: Unsupervised clustering identified five distinct clusters of SLE based on clinical phenotypes and immunological signatures, and an intrinsic association was found between each cluster and immune-inflammatory features. Cluster 1 (renal; 14.27%) had predominant renal involvement (87.5%) and high disease activity. Cluster 2 (mucocutaneous; 39.53%) exhibited mainly cutaneous manifestations (72.45%) with mild visceral involvement and the highest rates of achieving low disease activity state (46.42%). Cluster 3 (neuropsychiatric; 14.74%) had a high frequency of neuropsychiatric manifestations (96.18%) and anti-U1 ribonucleoprotein antibodies positivity (42.04%). Cluster 4 (haematologic; 22.63%) was characterised by haematologic involvement, predominantly cytopenia (95.85%), with frequent antiphospholipid antibody positivity and direct Coombs positivity (87.55%). Cluster 5 (cardiopulmonary; 8.83%) exhibited prominent cardiopulmonary involvement (97.87%) and serositis, with enrichment of inflammatory CD161+ regulatory T cells. Transcriptomic and proteomic analyses confirmed distinct molecular signatures and cluster-specific enrichment of biological pathways. Key molecule apolipoprotein A4 was validated using enzyme-linked immunosorbent assay. Regarding therapeutic outcomes at 1 year follow-up, Cluster 2 had the highest proportions achieving lupus low disease activity state (46.42%) and tapering glucocorticoids. Cyclophosphamide use was associated with greater clinical improvement (r=0.284; p=0.023) in Cluster 3, while in Cluster 5, rituximab use was associated with favourable responses (r=0.286; p=0.019). CONCLUSIONS: We identified five SLE clusters with distinct clinical, immunological features and treatment outcomes, demonstrating an intrinsic link between clinical involvement and immune-inflammatory alterations.