Characterizing Arthritis Subtypes in SLE: Prevalence, Clinical Features, and the Role of Type I Interferon Signatures
Bibliographic record
Abstract
Objectives To study the prevalence of SLE arthritis subtypes, deforming and non-deforming arthritis, and determine the association with clinical features, serology, and the influence of type I interferon. Methods This is a retrospective study of patients with arthritis defined by the ACR or EULAR/ACR SLE classification criteria at presentation and SLEDAI 2K over follow-up identified from a single-center SLE database (July 1970-Aug 2024) from both inception and prevalent cohorts. Demographic, clinical, laboratory (including interferon signature), radiographic features, and treatment variables were retrieved from the database. Descriptive statistics were used to outline features across 3 subtypes of arthritis: non-deforming arthritis (determined by clinical examination), arthritis with reducible deformities or Jaccoud’s Arthropathy (JA), and arthritis with non-reducible deformities or rhupus. Factors associated with deforming arthritis were determined using multivariate Fine and Gray modeling for the inception cohort. Results Arthritis was observed in 1,248 of 2264 (55.12%) patients. 908 (72.6%) had non-deforming and 340 (27.2%) had deforming arthritis- 239 (19.2%) had JA, 101 (8.1%) had rhupus. The median age at diagnosis of SLE was comparable, though a higher proportion of females was observed in JA (p=0.03). The distribution of organ involvement and antibodies was similar across the 3 subtypes, except nervous system involvement(p=0.03) and anti-Ro antibodies (p=0.04) being more frequent in rhupus. There was a trend toward higher mean SLEDAI-2K scores in JA (p=0.07), and the SDI was highest in rhupus (p<0.01). The distribution of rheumatoid factor and anti-CCP positivity did not differ significantly. The proportion of patients with high interferon signature was the greatest in JA, followed by non-deforming arthritis, and lastly, rhupus (p<0.01). Radiographs (n, 95) revealed erosive disease in 10 of 43 (23.2%) with JA, 12 of 36 (33.3%) with rhupus, and 2 of 16 (12.5%) with non-deforming arthritis. The use of glucocorticoids, mycophenolate, and belimumab was most prevalent in JA, while methotrexate was higher in rhupus (Table 1). In the multivariate analysis, JA was associated with higher average mean SLEDAI 2K [1.09(1.01-1.19)] and females [3.3(1.14-12.5)]. No associations were observed with rhupus. Table 1: Baseline demographic, clinical, laboratory, and treatment characteristics of patients with arthritis (n=1248) Conclusion Arthritis was observed in half the cohort, with the majority being non-deforming (72.6%). Among deforming arthritis, JA (19%) was more common than rhupus (8%). JA was associated with a high interferon signature, high disease activity, and female sex compared to rhupus. This sheds light on 2 different mechanisms for deforming arthritis with JA associated with SLE disease burden in contrast to rhupus. Erosions were observed in both types of deforming arthritis blurring the line of radiologic differences historically outlined between them.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".