ANTI-HISTONE ANTIBODIES CLINICAL SIGNIFICANCE IN PATIENTS WITH SYSTEMIC LUPUS: A MOROCCAN COHORT OF 80 PATIENTS
Notice bibliographique
Résumé
PV198 / #384 Poster Topic: AS23 - SLE-Diagnosis, Manifestations, & Outcomes Background/Purpose Systemic lupus erythematosus (SLE) is a complex and heterogeneous autoimmune disease, presenting a number of significant challenges in both its understanding and management. While classic antibodies have been the focus, anti-histone antibodies are crucial markers warranting further exploration. The aim of our study was to examine a wide range of clinical and biological features and analyze their correlation with this antibody, providing valuable insights into the complexities of this multifaceted condition. Methods In this case-control study, carried out in the internal medicine department of the Cheikh Khalifa International University Hospital in Casablanca between January 2017 and July 2024, 80 patients with SLE were included, of which 30 were positive for anti-histone antibodies (37.5%) and 50 were negative (62.5%), along with 2 patients with induced lupus. Ethics committee approval was obtained. Results The mean age of our patients was 38,5 years (± 15,2). A female predominance was observed at 90% (female-to-male ratio of 9 :1). By analyzing the 2 groups, positive and negative for the antibody, we highlighted significant differences in clinical presentation and biological profiles. Anti-histone antibodies correlated with fever(p-value=0,018), weight loss(p-value=0,019), renal markers such as hematuria (p-value=0,003) and proteinuria (p-value=0,002) and the development of lupus nephropathy (p-value=0,003). Hematologically, their positivity correlated with anemia(p-value=0,0002) and lymphopenia(p-value=0,024). A more pronounced inflammatory profile and increased disease activity were observed among the positive patients. The SLEDAI (Systemic Lupus Erythematosus Disease Activity Index) score was significantly higher in the group positive for anti-histone antibodies(p-value=0,0001). Additionally, there was a strong association between anti-histone and anti-nucleosome antibodies (p-value<0,00001). Lupus patients with anti-histone antibodies would also be significantly more likely to have anti u1-snRNP(p-value=0,0005), anti-ribosomal protein P (p-value=0,049) and anti DSF70 antibodies(p-value<0,0001). Among the 2 reported cases of induced lupus, 1 was linked to anti-TNF alpha treatment and was negative for anti-histone antibodies, while the other was associated with an adenovirus vaccine for COVID-19 and tested positive for these antibodies. Conclusions These data highlight the essential role of measuring these autoantibodies in SLE and drug-induced lupus. These antibodies could be a valuable asset as a diagnostic tool and a marker of disease activity, helping to enhance follow-up and management. Their detection, along with anti-nucleosome and anti-DNA antibodies, reinforces the importance of triple positivity as a predictor of the severity of renal damage. Considering the tuberculosis-endemic situation in our country, isoniazid-induced lupus might be underdiagnosed among the Moroccan population, and assessing anti-histone antibodies could provide additional value for clinicians.
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 | 0,001 | 0,000 |
| Communication savante | 0,000 | 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 ».