Identifying Homogeneous Endophenotypes in Childhood-Onset Systemic Lupus Erythematosus with Data-Driven Methods
Bibliographic record
Abstract
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.
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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 | 0.022 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".