Characterizing Arthritis Subtypes in SLE: Prevalence, Clinical Features, and the Role of Type I Interferon Signatures
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».