Phenotype, serotype, and data-driven clustering reveal complementary dimensions of heterogeneity in ANCA-associated vasculitis: a multicentre Japanese cohort (J-CANVAS).

OBJECTIVE

To compare clinicopathological phenotype-based, anti-neutrophil cytoplasmic antibody (ANCA) serotype-based, and unsupervised data-driven classifications in relation to clinical outcomes and patient heterogeneity in a large Japanese cohort with ANCA-associated vasculitis (AAV).

METHODS

This multicentre, retrospective cohort study analysed data from a nationwide Japanese registry of 729 newly diagnosed patients with granulomatosis with polyangiitis (GPA) or microscopic polyangiitis (MPA), all positive for myeloperoxidase (MPO)- or proteinase 3 (PR3)-ANCA. Patients were classified by phenotype, serotype, combined phenotype-serotype groupings, and data-driven clustering based on baseline clinical and laboratory features. Associations with clinical outcomes-including mortality, relapse, and response to rituximab (RTX) versus cyclophosphamide (IVCY)-were evaluated using inverse probability of treatment weighting (IPW).

RESULTS

Phenotype-based classification more accurately distinguished all-cause mortality risk (MPA vs. GPA: hazard ratio [HR] 2.53, 95% CI 1.34-4.76). Combined phenotype-serotype analysis identified MPO-MPA patients with the highest mortality (HR 3.45, 95% CI 1.09-11.0, vs. PR3-GPA) and PR3-GPA with the highest severe relapse. Discordant groups, such as MPO-GPA, demonstrated unique clinical characteristics. After IPW adjustment, no significant difference in 24-week remission rates was observed between RTX and IVCY across classifications, both overall (RR 1.02, 95% CI 0.95-1.09) and within subgroups. Unsupervised clustering identified four distinct clinical subgroups, with limited concordance with conventional phenotype or serotype classifications.

CONCLUSION

Phenotype and serotype classifications provide complementary, not competing, prognostic insights in Japanese patients with AAV. Data-driven clustering revealed additional clinical heterogeneity not captured by traditional systems, underscoring the need for integrated, multi-dimensional stratification approaches to improve personalised risk assessment and treatment strategies.