LUPUS AND NUTRITION -THE FIRST STEP TO CONTROL YOUR FLARES?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".