Genetic and epigenetic factors shape phenotypes and outcomes in systemic lupus erythematosus – focus on juvenile-onset systemic lupus erythematosus
Bibliographic record
Abstract
PURPOSE OF REVIEW: Systemic lupus erythematosus (SLE) is a severe autoimmune/inflammatory disease. Patients with juvenile disease-onset and those of non-European ancestry are most severely affected. While the exact pathophysiology remains unknown, common and rare gene variants in the context of environmental exposure and epigenetic alterations are involved. This manuscript summarizes the current understanding of genetic and epigenetic contributors to SLE risk, manifestations and outcomes. RECENT FINDINGS: Though SLE is a mechanistically complex disease, we are beginning to understand the impact of rare and common gene variants on disease expression and associated outcomes. Recent trans -ancestral and multigenerational studies suggest that differential genetic and environmental impacts shape phenotypic variability between age-groups and ancestries. High genetic burden associates with young age at disease-onset, organ involvement, and severity. Additional epigenetic impact contributes to disease-onset and severity, including SLE-phenotypes caused by rare single gene variants. Studies aiming to identify predictors of organ involvement and disease outcomes promise future patient stratification towards individualized treatment and care. SUMMARY: An improved understanding of genetic variation and epigenetic marks explain phenotypic differences between age-groups and ancestries, promising their future exploitation for diagnostic, prognostic and therapeutic considerations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".