Identifying Homogeneous Endophenotypes in Childhood-Onset Systemic Lupus Erythematosus with Data-Driven Methods
Notice bibliographique
Résumé
Objectives Childhood-onset Systemic Lupus Erythematosus (cSLE) is a clinically heterogeneous autoimmune disease. We hypothesized that data-driven methods would identify clinically homogeneous patient subgroups that may represent cSLE endotypes with distinct genetics. Methods We included patients diagnosed with cSLE between January 1992-October 2023. All patients met 2019 ACR-EULAR classification criteria and were genotyped on Illumina multiethnic arrays. Ungenotyped SNPs were imputed with TopMed as a referent. We extracted SLE manifestations, date of each manifestation onset and demographic characteristics from dedicated lupus databases. Ancestry was genetically inferred using principal components and ADMIXTURE with 1000 Genomes as a referent. We used time to each SLE manifestation onset from SLE diagnosis to identify patient subgroups using similarity network fusion (SNF), a data-driven method. We conducted Kaplan Meier analyses and Cox proportional-hazard models on the time from diagnosis to specific SLE manifestation onset between clusters. We tested cluster differences in demographic and manifestation prevalences using χ2 or Fisher’s exact test, and time to each SLE manifestation onset with log rank tests. We calculated additive, allelic weighted SLE non-HLA and HLA polygenic risk scores (PRS) using alleles from a trans-ancestral SLE GWAS. We tested the association between PRSs and cluster membership adjusting for sex and ancestry. Results Our cohort included 442 cSLE patients. 83% were female and the median age of SLE diagnosis was 13.6 years (Q1-Q3:12.0-15.8). Our cohort was primarily composed of patients of European (27%) and East Asian (26%) ancestry. SNF identified 2 clusters. Patients in cluster 1 (n=203) were predominantly of European ancestry (42%), while cluster 2 (n=239) was mainly composed of patients of East Asian (30%) and South Asian (22%) ancestry (P=3x10-9). Patients in cluster 2 had higher prevalence, and earlier onset of class III/IV lupus nephritis, hypocomplementemia, anti-dsDNA and anti-Sm antibodies compared to patients in cluster 1 (P<1×10-7) (Figure 1). The risk of developing any of 12 specific cSLE manifestations at any time was higher in patients in cluster 2 compared to cluster 1 (HR>1.4; P<4×10-3). SLE PRSs were not associated with cluster membership (Non-HLA PRS: OR 0.9, 95% CI 0.8-1.2; P=0.5; HLA PRS: OR 1.3, 95% CI 0.8-2.0; P=0.3). Figure 1. Clinical and Laboratory SLE Manifestations With Different Prevalence Between Patient Clusters . The number within each cell represents the prevalence of an SLE manifestation within cluster 1 and 2, respectively. “LN” stands for lupus nephritis and “Other Rashes” stands for maculopapular, discoid and cutaneous vasculitis rashes. Statistics performed with a Fisher’s Exact Test, Bonferroni corrected P <0.002. Conclusion In a large multiethnic cSLE cohort, data-driven methods identified 2 cSLE patient clusters. The cluster with more severe disease and earlier onset had a greater proportion of East Asian and South Asian patients compared to the cluster with milder disease. Next steps include regressing SLE variants by cluster membership with sequence kernel association tests.
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 | 0,022 | 0,053 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 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 ».