Lupus & antiphospholipid syndrome
Stratification of primary antiphospholipid syndrome by mechanistic immunophenotype: machine learning identifies distinct T-cell and T-bet+CD11c+ B cell-driven patient clusters
Authors' conclusion
Our findings suggest that pAPS is not a monolithic disorder but a heterogeneous syndrome composed of distinct, mechanistically-defined immunophenotypes.
Study context
- Population
- Patients with primary APS, healthy controls and healthy pregnant controls
- Participants
- 54 / 26 / 16
- Exposure
- Primary antiphospholipid syndrome (pAPS) vs healthy controls (HC) and healthy pregnant (HP) controls
- pAPSn = 54
- HCn = 26reference
- HPn = 16
- Outcome
- Distribution of 16 T and B cell subsets, focus on T-bet+CD11c+ B cells
Methodological detailsflow cytometry, exploratory machine learning-based K-means clustering
- Flow cytometry
- Exploratory machine learning-based K-means clustering
Main finding
As reported in the abstract
T-bet+CD11c+ B cell population
P < 0.01
Observational data: associations, not effects.
Further findings
| Outcome | Cluster K2 | Cluster K1 |
|---|---|---|
| Immunophenotype clusters (K-means) and thrombosisExploratory | n = 17 | n = 19 |
| Venous thrombosis | 41.2% | — not stated in the abstract |
| Arterial thrombosis (highest rate) | — not stated in the abstract | 31.6% |
— not stated in the abstract
Immunophenotype clusters (K-means) and thrombosis
- Three distinct immunophenotypes identified; K2 had the highest T-bet+CD11c+ B cell levels
T-bet+CD11c+ B cells vs anti-β2GP1 IgG titres in patients with thrombotic events
- Positive correlation
Original publication
Stratification of primary antiphospholipid syndrome by mechanistic immunophenotype: machine learning identifies distinct T-cell and T-bet+CD11c+ B cell-driven patient clusters. Clin Exp Rheumatol. Published 24 November 2025. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →