CE-21 The prevalence and determinants of ANTI-DFS70 antibodies in an international inception cohort of systemic lupus erythematosus (SLE) patients
Notice bibliographique
Résumé
Background When found in the absence of antibodies to extractable nuclear antigens (ENA) or anti-double-stranded DNA (dsDNA) (i.e., monospecific), autoantibodies to the nuclear autoantigen dense fine speckles 70 (DFS70) are purported to rule out SLE. The reported frequency of anti-DFS70 by chemiluminescence (CIA) in SLE is low compared to healthy individuals (0–5.7% vs. 1.3–23.2%), while the frequency of monospecific anti-DFS70 in SLE is even lower at 0–0.4%. There are no studies examining the frequency of anti-DFS70 in an early inception SLE cohort. This study determined the prevalence of anti-DFS70 in a multi-national, multi-ethnic early inception SLE cohort and examined demographic, clinical, and autoantibody associations. Materials and methods Patients fulfilling ACR Classification Criteria for SLE were enrolled in the Systemic Lupus International Collaborating Clinics (SLICC) inception cohort within 15 months of diagnosis. Demographic and clinical data were collected at enrollment. ANAs were detected by indirect immunofluorescence on HEp-2 cells (ImmunoConcepts, Sacramento) and ENAs and dsDNA by an addressable laser bead immunoassay (FIDIS Connective13, TheraDiag, Paris). Anti-DFS70 antibodies were measured by CIA (Inova Diagnostics, San Diego). The association between anti-DFS70 and baseline demographic, clinical, and autoantibody profiles was assessed using univariate and multivariate logistic regression. For the most informative model, only the remaining statistically significant predictors at the 95% CI: were included, after eliminating other potential predictors individually, starting with the least likely to be associated with the outcome. Results 1137 patients were included; 89.9% were female and 93.8% were ANA positive (Table 1). The frequency of anti-DFS70 was 7.1% [95% CI: 5.7–8.8%]. 13 of 1137 (1.1%) [95% CI: 0.6–1.9%] were positive for anti-DFS70 only (monospecific). In univariate analysis, patients with musculoskeletal activity (based on SLEDAI items) or anti-β−2 glycoprotein-1 (anti-β2GP1) were more likely to have anti-DFS70, whereas those with anti-dsDNA, anti-SSA/Ro60, anti-SSB/La, or anti-U1RNP were less likely to have anti-DFS70. In multivariate analysis, patients with musculoskeletal activity (Odd Ratio (OR) 1.25 [95% CI: 1.10, 1.41]) or anti-β2GP1 (OR 2.15, 95% CI: 1.21, 3.84) were more likely to have anti-DFS70, while those with anti-dsDNA (OR 0.53, 95% CI: 0.31, 0.92) or anti-SSB/La (OR 0.25, 95% CI:0.08, 0.82) were less likely to have anti-DFS70. Conclusions The prevalence of anti-DFS70 in newly diagnosed SLE patients was at the high end of the range previously ?published for SLE (7.1% vs. 0–5.7%) and was associated with musculoskeletal activity and anti-β2GP1. However, ‘monospecific’ anti-DFS70 was rare (1.1%) and is potentially useful to discriminate between ANA positive healthy individuals and SLE.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».