SERUM S100A8/A9, MMP-9 AND IL-6 ARE ASSOCIATED WITH IMPAIRMENT IN EXECUTIVE FUNCTION, SIMPLE ATTENTION AND PROCESSING SPEED IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS
Notice bibliographique
Résumé
O045 / #659 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 Cognitive impairment (CI) is a common manifestation in patients with systemic lupus erythematosus (SLE). Despite its impact on patient quality of life, treatments remain limited as its pathogenesis is poorly understood. The Automated Neuropsychological Assessment Metrics (ANAM) has superior patient acceptability and feasibility in ambulatory settings compared to the American College of Rheumatology Neuropsychological Battery (ACR-NB) [gold-standard test] and is validated in screening for CI in SLE. Data from our laboratory have revealed that serum S100A8/A9 and MMP-9 are associated with CI measured by the ACR-NB. However, the relationship between these serum analytes, ANAM subtests and CI has not been elucidated. We therefore aimed to determine if serum analytes are associated with CI measured by the ANAM. Methods We cross-sectionally analyzed the data of 327 adults aged 18-65 who were followed longitudinally between January 2016 and October 2019 at a single SLE center. All participants fulfilled the 2019 EULAR/ACR SLE classification criteria. Cognitive function was measured using ANAM throughput scores, and serum levels of 9 analytes (IL-10, IL-6, IFN-γ, TNF-α, TWEAK, S100B, S100A8/A9, NGAL and MMP-9) were measured using ELISA. The K-means clustering algorithm was used to cluster the patient data, and the Principal Component Analysis (PCA) characterized the clusters. The silhouette coefficient(s) was calculated for 2 to 15 clusters to determine the optimal number of clusters. Results PCA identified 2 principal components explaining 36.2% of the variance in ANAM throughputs and serum analytes. The first component (26.7% of the variance) was correlated with ANAM throughputs, with the strongest contribution from procedural reaction time. The second component (9.44% of the variance) was correlated with serum analyte measurements, with the strongest contribution from TNF-alpha. (Figure 1) The highest silhouette value was found for 2 (s = 0.177) and 3 (s = 0.176) clusters. Only 4% of patients were classified in the 3 cluster model, so a 2 cluster model was selected. Cluster 1 had low throughput scores representing CI, and Cluster 2 had higher throughput scores representing no CI. A significant difference was observed in mean serum S100A8/A9 (SMD = 0.362), MMP-9 (SMD = 0.178) and IL-6 (SMD = 0.311) between the clusters, reflected by their correlation with the first principal component. (Figure 2) Serum levels of S100A8/A9, MMP-9 and IL-6 had a strongly negative correlation between the Go No Go and Running Memory throughputs. Figure 1: Correlation matrix for individual ANAM throughputs and serum analyte levels Figure 2: Biplot of the first 2 principal components, with 2 clusters and association with analytes Conclusions Serum S100A8/A9, MMP-9, and IL-6 are associated with CI in SLE as measured by the ANAM. Patient clusters with elevated serum S100A8/A9, MMP-9, and IL-6 had strongly negative associations with throughputs representing impairment in executive function, simple attention and processing speed. Further studies are needed to uncover mechanistic relationships between these analytes and CI in SLE, and whether they may represent valuable therapeutic targets for further exploration.
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) | 0,001 | 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 ».