A CASE-CONTROL STUDY ON AUTOIMMUNE POLYENDOCRINE SYNDROMES IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS
Notice bibliographique
Résumé
PV211 / #363 Poster Topic: AS23 - SLE-Diagnosis, Manifestations, & Outcomes Background/Purpose Systemic lupus erythematosus (SLE) is a complex autoimmune disease that can impact multiple organs, including joints, kidneys, skin, heart, blood cells, lungs, and nervous system. SLE patients face a higher risk of various comorbidities and treatment-related complications, with an increased mortality rate compared to the general population. Studies on observational cohorts have identified cardiovascular diseases, diabetes mellitus type 2 (T2DM), osteoporosis, certain types of cancer, and autoimmune endocrine disease, such as Hashimoto’s thyroiditis (HT), Graves’ disease (GD), type 1 diabetes mellitus (T1DM) and hyperparathyroidism, as common cause of morbidity in SLE patients. However, to our knowledge, no data currently exist on the connection between SLE and Autoimmune Polyendocrine Syndromes (APS), which are rare diseases characterized by multiple autoimmune conditions affecting at least 1 endocrine organ. APS 1 is due to gene AIRE mutations; APS 2 is characterized by Addison’s disease (AD) associated with HT or GD and/or T1DM; HT or GD with any other autoimmune diseases (excluding AD and hypoparathyroidism) fall under APS 3; APS 4 includes remaining combinations of autoimmune forms having an impact on endocrine organs. This study aimed to investigate the prevalence of Autoimmune Polyendocrine Syndromes (APS) in patients with Systemic Lupus Erythematosus (SLE) and to assess whether APS predicts higher disease activity or worse outcomes. Methods Clinical charts of 417 SLE patients referring to our Center between 2021 and 2023 were analyzed. APS cases were identified using ORPHA code definitions; 185 APS-free SLE patients, randomly enrolled, served as controls. Demographic, clinical and serological data were collected. Results Forty-seven (11%) SLE patients have another autoimmune disease affecting the glands that allows the diagnosis of APS: 39 were diagnosed with HT, 6 with GD, and 3 with T1DM. Forty-five patients were affected by APS type 3, and 2 by APS type 4; no patients were diagnosed with APS type 1 or 2. Table 1 show the sequence in which autoimmune diseases manifest. SLE was the first manifestation of APS in 22 patients (47%). HT was the first autoimmune manifestation for 21 (45%) patients, GD was the first for 2 (4%) patients and 2 women started with rheumatoid arthritis (2%) and autoimmune urticaria (2%), respectively. SLE was the second manifestation in 23 (49%) patients and the fourth for 2 (4%) patients. The comparison between APS+ and APS- patients, as shown in Table 2, revealed no significant differences in clinical or serological features, except for pulmonary hypertension (p=0.044) and renal microangiopathy (p=0.044). At the last evaluation, approximately 80% of both groups’ patients were in clinical remission and approximately half of the patients were still on steroid therapy. APS+ patients had a slightly higher median damage index (SLICC-SDI), although this was not associated with increased disease activity. Table 1. Sequence of autoimmune diseases presentation in patients with SLE and APS Table 2. Comparision of data in SLE patients with and without APS Conclusions The prevalence of APS among SLE patients is significantly higher than in the general population (11% vs 0,005%), confirming the well-known association between autoimmune thyroiditis and SLE. However, APS+ patients do not appear to have a more aggressive disease or develop more complications. The only clinical conditions statistically associated with APS (renal microangiopathy and pulmonary hypertension) are so rare that no definite conclusions can be drawn. Limitations of the study include a small sample size and single-timepoint data, highlighting the need for larger multicenter studies to clarify the link between SLE and APS.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 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) | 0,000 | 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 tête enseignante, 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 ».