205 Longitudinal immune changes during and after recent flares in lupus
Notice bibliographique
Résumé
Background Previous studies suggest substantial immunologic heterogeneity in lupus. However, the majority of these studies were cross-sectional in nature. Here we followed flaring and quiescent patients longitudinally to determine how their immunologic profile changes over time. Methods Forty-seven SLE patients with a recent flare (change in clinical SLEDAI ≥ 2 in the past month that prompted a change in therapy), 25 quiescent SLE patients (clinical SLEDAI = 0 for ≥ 1 year with no increase in immunosuppressive treatment , ≤ 10 mg prednisone, matched for disease duration) and 16 healthy controls (HC) were recruited. The peripheral blood immunologic profile at baseline and follow-up (every 6 months for 1 year, COVID permitting) was examined by multi-parameter flow cytometry. Expression of interferon (IFN)-induced proteins that correlated with gene expression was examined in immune populations of interest using CyTOF. Results Using unsupervised clustering, incorporating all subjects and visits, four distinct immunologic profiles were seen: Cluster 1, with increased levels of activated B cells and age- associated B cells (ABCs); Cluster 2, with Tfh and Tph expansion; Cluster 3, with reduced levels of innate, naïve B, and Tfh cells; and Cluster 4 with expansion of Th1 and innate immune cells relative to other clusters. Although patients with new-onset flares were found in all clusters, Cluster 1 had the highest number of these patients, whereas Cluster 4 has the highest number of patients who were inactive at baseline, as well as HC. Patients moved between clusters over time and/or in response to treatment. A substantial proportion of flaring patients in Cluster 3 transitioned to Cluster 1 on follow-up, suggesting that B cell changes accumulate post-flare. Similar findings were seen for myeloid populations in a smaller subset of patients that transitioned from Cluster 3 to 4. In general, patients in Cluster 1, 2, or 4 at baseline tended to remain in the same cluster subsequently, with a notable exception being patients with early disease (< 6 months duration), where switching between clusters was frequent. Patients in Cluster 1 at follow-up were more likely to remain active or flare than those in Cluster 4. Analysis of IFN-induced protein expression, revealed considerable variability in the levels of these proteins between immune populations in the same patient and between patients, with significantly higher levels in flaring than in quiescent patients in most immune populations. Cluster 1 visits tended to have higher levels of IFN-induced proteins than Cluster 4 visits, particularly within B cell populations and the T helper cell populations that support their activation. Conclusion Accumulation of activated B cells and ABCs can occur during or after flare, is associated with high levels of IFN-induced proteins in these populations, and defines patients who are more likely to have ongoing disease activity or subsequent flares.
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 | 0,000 |
| 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 ».