AUTOANTIBODIES IN A MULTIINSTITUTIONAL INDIAN INCEPTION COHORT (INSPIRE): PREVALENCE, CLUSTER ANALYSIS AND PHENOTYPE ASSOCIATION
Notice bibliographique
Résumé
PV189a / #296 Poster Topic: AS22 - SLE Heterogeneity Background/Purpose In SLE the prevalence of autoantibodies is variable across different ethnic group and data on Indian population is limited. Thus, we assessed the prevalence and association of different autoantibody clusters with clinical features in an Indian SLE inception cohort for research. Methods INSPIRE cohort is a cohort with 2503 patients accrued till October 2022 and currently 6 monthly follow-up is ongoing. At inclusion antibodies were assayed using Immunoline (Euroimmune, Germany) or ELISA. To determine autoantibody clusters, an unsupervised random forest algorithm was built with 10000 trees and the resulting proximity matrix was used to generate a distance matrix between individual autoantibodies. Odds ratios were used to identify associations between autoantibody/autoantibody clusters and clinical manifestations. Results A total of 2503 patients (mean age 27.69±10.19 years, 2292 [91.57%] females) were enrolled in the cohort. At the baseline, organ involvement (%) was as follows: constitutional features (68.23), alopecia (77.82), oral ulcers (49.74), acute cutaneous lupus (59.41), subacute/discoid lupus (12.4), arthritis (68.27), pleural effusion (20.94), pericarditis (12.39), nephritis as per active sediments and/or proteinuria (41.23), delirium (1.31), psychosis (2.27), seizures (7.39), autoimmune haemolysis (14.54), leukopenia (31.2), thrombocytopenia (24.85). Proliferative nephritis (class III, IV or combination of III/IV and V) was seen in 396, and non-proliferative lupus nephritis in 235. The median SLEDAI at baseline was 12 (IQR 6-18). Antibodies to the DNA nucleosome complex were the most common with anti-dsDNA in 70.19%, anti-nucleosome in 42.02% and anti-histone in 35.6%. This was followed by antibodies to the ribonuclear complex with anti-Sm (32.16%), anti-RNP (52.01%), anti-Ro52 (37.95%), anti-Ro60 (42.14%) and anti-La (12.26%). Other positive antibodies included anti-Ribosomal P (32.16%), anti-AMA-M2 (8.35%), anti-Scl70 (2.83%) anti-PCNA (4.55%), anti-PM/Scl (2.16%), anti-CENP-B (1.48%) and anti Jo-1 (0.99%). IgG autoantibodies (>40 GPL) to anticardiolipin and β2 glycoprotein1 were present in (10.06%) and (8.4%) patients respectively and 8.86% had lupus anticoagulant. Antibodies to dsDNA, histones and nucleosomes showed association with proliferative nephritis, oral ulcers and arthritis, anti-Ro antibodies had association with alopecia and serositis, antibodies to Sm, RNP and Ribosomal P showed association with mucocutaneous disease. Antibodies to Sm, nRNP, Ro and La were protective for proliferative nephritis. Four clusters of autoantibodies were identified. Cluster 1 had antibodies to dsDNA, histone and nucleosome and accounted for 932 (45.84%) patients. Cluster 2 had antibodies to Sm, nRNP, Ro52, Ro60 and Ribosomal P and accounted for 989 (48.65%) patients. Cluster 3 had autoantibodies to cardiolipin, β2GP1, lupus anticoagulant, La as well as AMA-M2 and accounted for 98 (4.62%) patients. Cluster 4 was a predominantly negative cluster which included antibodies to Scl-70, Jo-1, PCNA, PM-SCL and CENP-B and accounted for 18 (0.89 %) patients. Cluster 1 was associated (odds ratio) with clinically significant proteinuria (1.54) and proliferative lupus nephritis (2.06), pleural effusion (1.29), leukopenia (1.37) and with reduced risk of pericarditis (0.72). Cluster 2 was associated with increased seizures (1.36) and pericarditis (1.52) as well as lower risk of proteinuria (0.74), proliferative nephritis (0.56), leukopenia (0.82) and thrombocytopenia (0.78). Cluster 3 was associated with lower risk of proteinuria (0.57), proliferative nephritis (0.36), pleural effusion (0.41) and leukopenia (0.52). Conclusions The prevalence of anti-Sm and Ribosomal P antibodies is higher in Indian population, and they show association with mucocutaneous disease. While antibodies and Cluster 1 associated with DNA had an association with nephritis. Acknowledgment: The study was funded by a grant from the Department of Biotechnology.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».