LUPUS AND NUTRITION -THE FIRST STEP TO CONTROL YOUR FLARES?
Notice bibliographique
Résumé
PV276 / #481 Poster Topic: AS24 - SLE-Treatment Background/Purpose Systemic lupus erythematosus (SLE) is an autoimmune disease, characterized by the presence of autoantibodies and diverse clinical manifestations, including 1 or even more organs. Still, the exact cause of Lupus is unknown, and symptoms include also fevers, rashes, swelling and pain. Some gastrointestinal symptoms in lupus patients, such as bloating or abdominal pain, could be related to food allergies or intolerance. Gluten intolerance or celiac disease may occur more frequently in lupus patients, which can increase gastrointestinal symptoms. Research even from 1993 shows that people with lupus are at a much higher risk of developing allergies to drugs, skin, and insects. Moreover, family members of individuals with systemic lupus erythematosus are also more likely to experience at least 1 type of allergy. Aim: To have a closer look on the antibody and autoantibody profile related to lifestyle and nutrition of SLE patients to improve disease management and quality of life. Methods A total of 17 patients with a clinical diagnosis of SLE were tested using AESKUBLOTS ® Allergy and AESKUBLOTS ® Gluten-Related Disorders (GRD) IgA. AESKUBLOTS ® Allergy is a membrane-based enzyme immunoassay for quantitative detection of allergen-specific IgE antibodies against allergens/allergen mixtures and total IgE in human plasma or serum. The AESKUBLOTS ® GRD IgA is a membrane-based enzyme immunoassay for quantitative detection of IgA subclass antibodies against gliadin, DGP, tTG (tissue Transglutaminase), tTG-neo (cross-linking of tTG with gliadin-specific peptides induces the formation of tTG-neo-epitopes), TG3 (epidermal Transglutaminase), mTG (microbial Transglutaminase), mTG-neo (cross-linking of mTG with gliadin-specific peptides induces the formation of mTG-neo-epitopes), Frazer’s Fraction in and total IgA in human serum or plasma. The antigens are positioned as parallel lines at precisely defined locations on a nitrocellulose membrane. Results A total of 52% of patients exhibited high IgE levels (class 5-6) against allergens such as wheat, spelt, egg white, casein, and various nuts including almond, hazelnut, peanut, pistachio, and cashew. Additionally, 5 out of 17 (30%) patients showed elevated antibodies against gliadin (>3-5x ULN (Upper Limit of Normal)), DGP (>3-5x ULN), mTG-neo (>2-3x ULN) and autoantibodies against tTG (>2x ULN) and tTG-neo (>3-5x ULN) antigens, which are highly associated with gastrointestinal disorders like celiac disease and non-celiac gluten sensitivity. Three SLE patients demonstrated significantly elevated food allergy-specific IgE levels (≥class 4) along with high levels of autoantibodies related to GRDs. Conclusions Even within this small cohort, antibodies and autoantibodies associated with food allergies and GRD are significantly elevated compared to the general population. A comprehensive understanding of SLE epidemiology is urgently needed to gain deeper insights into the disease and better manage healthcare resources. The close interaction between autoimmunity, inflammation, and allergies means that during a lupus flare, both gastrointestinal symptoms and allergic reactions can be exacerbated, which further complicates the treatment and management of SLE patients. Accurate identification, antibody monitoring via multiplex and treatment of gastrointestinal complaints and allergies during a lupus flare are crucial to improving the quality of life for affected patients and preventing complications.
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) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,030 | 0,009 |
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 ».