908 The association between Systemic lupus Erythematosus (SLE) and bone mineral density (BMD) polygenic risk scores with lumbar spine BMD z-score: a retrospective cohort study
Notice bibliographique
Résumé
Background Systemic lupus erythematosus (SLE) is a chronic autoimmune disease. Genetics play a role in SLE susceptibility, with >100 risk single nucleotide polymorphisms (SNPs) from genome wide association studies (GWAS). Approximately 20% of SLE patients have childhood- onset SLE (cSLE) diagnosed <18 years. These patients are at risk for reduced bone mineral density (BMD) due to disease activity and chronic glucocorticoid exposure. Our aim was to assess the genetic contribution to bone mineral density among a multi-ethnic cohort of patients with cSLE.Methods All patients were diagnosed and followed for cSLE at the SickKids Lupus Clinic. Patients were genotyped on the multiethnic Multiethnic Genotyping Array or the Infinium Global Screening Array. Those with baseline Lumbar Spine (LS) BMD dual-energy X-ray absorptiometry (DXA) scan were included in analysis. Baseline was defined as 1 month prior, or up to one year after cSLE diagnosis. Patients with bony abnormalities and with DXA scans due to medical conditions other than SLE were excluded. We extracted demographics, clinical features, and medication use from the Lupus database. The main outcome of interest was LS (L1-L4) BMD z scores. Two weighted polygenic risk scores (PRSs) were calculated. 1.) BMD PRS was calculated using alleles associated with low LS from the largest LS BMD meta-GWAS of BMD to date. 2.) SLE PRS was also calculated using one of the largest SLE GWAS. We regressed BMD and SLE PRSs with baseline BMD z-scores in linear models adjusted for sex, ancestry, glucocorticoid exposure, height percentile, and an indicator for lupus nephritis and/or neuropsychiatric lupus.Results Our study included 285 patients, 82% female, 30% of European and 28% of East Asian ancestry. The median age of cSLE diagnosis was 13.3 years [IQR 10.8, 15.1]. In univariate and multivariate adjusted models, a higher BMD PRS was significantly associated with low BMD z- score (β: -0.73; 95%CI: -1.30, -0.16; P = 0.01, multivariable model). Using steroids prior to DXA was significantly associated with low BMD at a univariate level but was not significant in the adjusted model. Height percentile was significantly associated with BMD z-score (β: 0.01; 95%CI: 0.01, 0.02; P=5.09e-10), yet the presence of LN and/or NPSLE was not (β: 0.06; 95%CI: 0.21, 0.33; P=0.67).Conclusions Our study found that a low-BMD PRS was significantly associated with lower LS BMD z-score in cSLE patients at baseline. BMD PRS may be used to stratify patients with cSLE who are at greatest risk of reduced BMD. We hope to expand this to long term LS BMD z-scores and explore BMD PRS predicts long term BMD z scores among cSLE patients.Lay Summary Systemic lupus erythematosus, commonly known as lupus is chronic, life-threatening autoimmune disease. Up to 20% of all people with lupus are diagnosed during childhood. Treatment for lupus involves long-term steroids which can have devastating impacts on children’s bones. However, different people respond differently to steroid treatment, with some patients having more severe negative side effects than others. We aimed to explore the genetic basis of bone mineral density (BMD) in children and adolescents diagnosed with SLE. We calculated genetic risk scores for: 1. low bone density (BMD); 2. Lupus risk. We tested the association between these genetic risk scores and BMD in a multiethnic group of children and adolescents with lupus. We found that genetics for low bone density was significantly associated with lower bone density in these lupus patients within 1 year of lupus diagnosis. This was true even when we accounted for steroid exposure and the presence of kidney and/or brain involvement. Our work has the potential to identify lupus patients at high risk of developing low bone density.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».