INVESTIGATING THE GENETICS OF DEPRESSION IN A MULTIANCESTRAL COHORT OF CHILDREN AND ADOLESCENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".