RELATIONSHIP BETWEEN AUTOANTIBODY-DEFINED SYSTEMIC LUPUS ERYTHEMATOSUS SUBGROUPS AND CLINICAL MANIFESTATIONS: PRELIMINARY FINDINGS FROM ILUPUS STUDY
Bibliographic record
Abstract
PV191 / #129 Poster Topic: AS22 - SLE Heterogeneity Background/Purpose Characterization of systemic lupus erythematosus (SLE) patients using autoantibody profiles has successfully identified less heterogeneous subgroups of patients with multifaceted clinical manifestations. These subgroups are associated with distinct clinical manifestations, immunological markers, and genetic factors, leading to the identification of more homogeneous, clinically actionable SLE subgroups. However, these findings have been predominantly derived from individuals of White European descent, underscoring the necessity to replicate these studies in populations with diverse ancestral backgrounds. Hence, we aimed to subgroup Malay SLE patients based on their autoantibody profiles. Methods This is cross-sectional study comprising a total of 191 Malay SLE patients meeting the 2019 EULAR/ACR Classification Criteria. The sera samples of the participants were subjected for autoantibody profiling based on the 15 SLE-associated autoantibodies (ie, anti-Rib.P_protein IgG /anti-histones IgG /anti-nucleosome IgG /anti-SSB IgG /anti-Ro52 IgG /anti-SSA IgG /anti-Sm IgG /anti-nRNP_Sm IgG /anti-cardiolipin IgG /anti-cardiolipin IgM /anti-β2glycoprotein IgG /anti-β2glycoprotein IgM /anti-phosphatidylserine IgG /anti-phosphatidylserine IgM) using immunoblot and ELISA methods. Unsupervised cluster analysis and logistic regression were used to define autoantibody-based SLE subgroups and explore their clinical associations. Results Our data showed 93% of the 191 SLE patients were female, with a mean age of 41.14 (±12.11) years. Four distinct clusters were identified: Cluster 1 (26.18%) was characterized by anti-Ro52 IgG (78%) and anti-SSA IgG (88%) autoantibodies; Cluster 2 (35.08%) by anti-nRNP_Sm IgG (68.1%); Cluster 3 (12.04%) by anti-histone IgG, anti-nucleosome IgG, and anti-nRNP_Sm IgG (91.3%); and Cluster 4 (26.70%) was autoantibody negative. Cluster 2 was associated with organ damage (OR 3.00, 95% CI 1.11-8.92), and Cluster 3 with active disease (SLEDAI-2K≥6) (OR 5.65, 95% CI 1.30-29.99), mucocutaneous manifestations (OR 9.94, 95% CI 2.29-55.12), and renal involvement (OR 6.67, 95% CI 1.14-54.87). Conclusions Our findings in Malay SLE patients reinforce the concept of subgrouping clinically heterogeneous SLE patients according to their autoantibody profiles is a promising strategy for precision medicine. Our results support subgrouping by autoantibody profiles and highlight the need for further research in diverse populations to validate these subgroups across ethnicities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".