INVESTIGATING THE GENETICS OF DEPRESSION IN A MULTIANCESTRAL COHORT OF CHILDREN AND ADOLESCENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS
Notice bibliographique
Résumé
PV161 / #509 Poster Topic: AS18 - Pediatric SLE Background/Purpose Patients with childhood-onset systemic lupus erythematosus (cSLE) have a higher prevalence of depression compared to healthy peers. Patients with cSLE also have a 10% greater risk of major depressive disorder (MDD) compared to those with adult-onset SLE (39% vs. 29%). Genetics plays a role in mood disorders and SLE susceptibility. Genome-wide association studies (GWAS) have identified >100 genetic risk variants for each of depression and SLE. The cumulative effects of these variants can be combined into polygenic risk scores (PRS). Our study aims to test the association between genetic risk variants for (1) SLE and (2) MDD with depression, in a multiancestral cohort of children and adolescents with SLE. Methods We included patients followed in a tertiary care Lupus clinic from January 1, 2000, to December 31, 2023. All patients met ≥4 American College of Rheumatology (ACR) and/or Systemic Lupus International Collaborative Clinics (SLICC) criteria for SLE with data prospectively collected in a dedicated lupus database. Patients were genotyped on an Illumina multiethnic array, with un-genotyped single nucleotide polymorphisms (SNPs) imputed using TopMed as a referent. Ancestry was genetically inferred using principal components (PCs) and ADMIXTURE. We calculated weighted, additive PRSs for: 1) SLE (HLA and non-HLA) and 2) MDD using risk SNPs from the largest GWAS to date. We identified patients with depression as those with a depression diagnosis and/or persistent depressive symptoms over a course of at least 2 months prior to or following SLE diagnosis. We tested the association between each PRS and depression in univariate and multivariable-adjusted logistic regression models, adjusted for sex and 5 PCs (P<0.017). We additionally carried out a sensitivity analysis focusing only on patients with a clinical depression diagnosis. Results Our study included 491 patients, 84% were female, with a median age of SLE diagnosis of 14 years (IQR: 11-15). There were 64 (13%) patients with a depression diagnosis, 130 (26%) with a depression diagnosis and/or persistent depressive symptoms. The majority of patients were of European (29%) and East Asian (27%) ancestry (Table 1). We did not observe a significant association between PRSs for either SLE (HLA and non-HLA) or MDD and depression (Table 2). Regarding SLE clinical features, the most common was arthritis (68%), followed by lupus nephritis (39%) and neuropsychiatric SLE (NPSLE; 25%). NPSLE was significantly associated with depression in univariate and multivariable-adjusted models (OR 2.37, 95% CI 1.51-3.73; P =0.0002). Sensitivity analyses demonstrated similar associations between the PRSs and clinical depression. Table 1: Demographic characteristics and clinical and laboratory featares of cohort (n=491) Table 2: Univariate and multivariable logistic regression results (n=491) Conclusions In a multiancestral cohort of children and adolescents with SLE, we did not observe a significant association between genetic loci for SLE and MDD and depression. This may be due to the limited generalizability of European SLE and MDD risk loci to a multiancestral population. Our cohort is comparable to prior studies of mood in cSLE as we found a significant association between NPSLE and depression. Future work will examine anxiety.
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 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| 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) | 0,001 | 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 ».