SERUM BIOMARKERS AND INTERFERON-ALPHA CONCENTRATION AND THEIR CORRELATION TO DISEASE ACTIVITY IN 62 ESTONIAN SYSTEMIC LUPUS ERYTHEMATOSUS (SLE) PATIENTS
Notice bibliographique
Résumé
PV030 / #282 Poster Topic: AS04 - Biomarkers Background/Purpose SLE is a rare chronic autoimmune disease with polymorphic clinical manifestation. With a complex disease in pathoetiology and heterogenicity in organ involvement, understanding disease activity and treating patients can be difficult. Identifying certain cytokine profiles in different disease states can give additional information in characterizing SLE and relieving the burden of disease. Methods Consecutive outpatient and inpatient patients with rheumatologist diagnosed SLE (≥20 years) were enrolled. Evaluation for disease activity, current treatment, organ involvement, immunological findings and comorbidities were done. In addition, data from medical records were collected: organ involvement and immunological findings at the time of diagnosis and initial treatment. SLE disease activity was measured using SLEDAI 2K (Systemic Lupus Erythematosus Disease Activity Index 2K) score. Blood tests were taken to measure interferon levels using Simoa (Single Molecul Array) method and Olink proximity extension assay was used to measure inflammatory proteins. To evaluate the extent of the deviation of IFNα and marked protein levels in patients compared to non-autoimmune individuals, age and gender matched control group was used. Interferon score was calculated according to interferon induced gene expression in blood cells collected to RNA stabilizing tubes. Principal component analysis, volcano plot and analysis of variance (ANOVA), Kruskal and Wilcoxon test were used to evaluate significant differences between controls and patients, different disease activity groups and glucocorticosteroid dosage in serum protein pattern. Heatmap to visualize different SLE clusters in relation to organ involvement, biomarker pattern and used treatment was done. Results Among 62 patients (mean age 49 (SD ±12.4) years, mean disease duration 12 (±10.1) years, mean SLEDAI 2K at diagnosis 11 (±5.5)) 90% were females. Mean SLEDAI 2K value at study visit was 4 (±4.0), 39% of patients had SLEDAI 2K >4 and 42% of patient were positive for anti-dsDNA antibodies and 48% had hypocomplementemia. Glucocorticosteroids were used in 71% of patients and 29% had Rituximab treatment. Olink assay highlighted 25 upregulated biomarkers for patients in comparison to controls with IL-10 and IL15RA upregulated in inactive patients (SLEDAI 2K=0) vs controls. We found significant differences in cytokine levels for patients in methylprednisolone treatment groups high dose (>4 mg/day), low dose (1-4 mg/day) and no treatment in SLEDAI high (SLEDAI 2K>4) group for IL18R1, PD-L1, FGF5; in SLEDAI low group (SLEDAI 2K £4) for FGF-5 and SLEDAI inactive group for IL-12B. With identifying 4 different disease clusters, it was highlighted that CXCL10 and CXCL11 were related with more active disease while MCP-1 could predict arthritis in patients with low cytokine activity. Conclusions With measuring biomarkers activity in Estonian SLE patients, we have identified 4 disease clusters, confirming CXCL10 and CXCL11 role in more active disease and giving additional insight to low cytokine activity driven disease with MCP-1 as a potential predictor for arthritis as it has been highlighted in rheumatoid arthritis studies.[1,2] However further studies are needed to provide insight to changes in FGF-5 levels in relation to glucocorticosteroids. References: [1.] Reynolds JA. Arthritis Res Ther 2018;20(1):173. [2.] Ellingsen T. J Rheumatol 2001;28(1):41-6.
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 | 0,000 | 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 ».