ELEVATED SERUM BRAIN INJURY MARKERS CORRELATE WITH DISEASE FEATURES AND INTERFERONS IN CHILDREN WITH SYSTEMIC LUPUS ERYTHEMATOSUS
Notice bibliographique
Résumé
O044 / #255 Topic:AS05 - CNS Lupus ABSTRACT CONCURRENT SESSION 07: COGNITION IMPAIRMENT IN SLE – RECENT ADVANCEMENT AND EMERGING RESEARCH 23-05-2025 1:40 PM - 2:40 PM Background/Purpose Childhood-onset systemic lupus erythematosus (cSLE) involves interferon (IFN)-mediated inflammation emerging during the critical period of adolescent brain development. Neuropsychiatric lupus (NPSLE) manifests as syndromes like cognitive dysfunction, seizures, and psychiatric disorders, which negatively impact education, psychosocial functioning, and quality of life. While previous work indicates a type I IFN-α signature in cSLE, the levels of type II IFN-γ as well as the roles of both IFNs in the pathogenesis of brain inflammation have not been well established in literature. Moreover, clinicians face challenges diagnosing/treating brain inflammation in cSLE, due to suboptimal diagnostic tools. Neuronal/glial structural proteins may be useful biomarkers of brain injury in cSLE. We aimed to i) compare serum levels of brain injury markers and IFNs between children with cSLE and controls; and ii) investigate the relationship of brain injury markers to cSLE disease features and serum IFN levels. Methods We utilized prospectively-collected cross-sectional data from cSLE participants (ages 12-17 years) recruited from the Lupus Clinic at a Canadian tertiary children’s hospital from January 2020–December 2023, and age-, sex-matched healthy controls. Serum brain injury markers (serum neurofilament light (sNFL), glial fibrillary acidic protein (GFAP), Tau) were quantified using Simoa Human Neurology 4–Plex B assay; IFN-α and IFN-γ were also quantified with their respective Simoa assays (Quanterix, Billerca, MA, USA). Disease features included disease activity (SLEDAI-2K), damage (SLICC damage index, SDI > 0), glucocorticoid (GC) dose at study visit, and cumulative GC exposure (prednisone equivalent). Wilcoxon rank sum test was used to compare markers/IFNs between cSLE and controls, and Spearman correlation tested associations. Results 56 cSLE participants (mean age = 15.1 ± 1.8 years, 86% female) and 43 controls (mean age = 15.1 ± 1.7 years, 81% female) were included. For cSLE, median disease duration was 22.6 months (IQR 12.5-43.9), median SLEDAI-2K was 2.5 (IQR 2.0-5.3), 9% had disease damage, 41% were using glucocorticoids at study visit, and median cumulative GC exposure was 1.9 grams (IQR 0.6-6.9). One patient had a NPSLE diagnosis. GFAP (114.0 vs 74.3 pg/mL) and Tau (3.57 vs 2.58 pg/mL) serum levels were significantly higher in cSLE compared to controls (Figure 1), as were serum IFN-α (0.278 vs 0.018 pg/mL) and IFN-γ (0.100 vs 0.068 pg/mL) levels (all p < 0.05). All brain injury markers had significant positive correlations with SLEDAI-2K and GC dose; sNFL and Tau associated with disease damage (Table 1). Higher levels of sNFL and GFAP correlated with IFN-α, while GFAP also associated with IFN-γ (Table 1). No correlations were found between Tau and IFNs. Table 1: Relationship between Brain Injury Markers, Disease Characteristics, and Interferons in cSLE (n=56) Figure 1: Boxplots showing group differences in serum brain injury marker levels between cSLE and controls. GFAP and Tau were significantly elevated in cSLE group (Wilcoxon rank sum test, p < 0.05), with outliers also observed across all brain injuiy markers for cSLE. Conclusions Serum brain injury markers and type I and II IFNs were elevated in cSLE, with brain injury markers correlating with disease features, IFN-α, and IFN-γ. This suggests a link between IFN-mediated inflammation and neuronal/glial injury, and potential utility of sNFL, GFAP and Tau as diagnostic and monitoring biomarkers in cSLE. Also, these results indicate IFNs are potential therapeutic targets in cSLE. Future studies will explore relationships between brain injury markers and IFNs in larger cSLE cohorts over time.
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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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».