Relationship Between Genetic Risk and Age of Diagnosis in Systemic Lupus Erythematosus
Bibliographic record
Abstract
Objective. Specific risk alleles for childhood-onset systemic lupus erythematosus SLE (cSLE) vs adult-onset SLE (aSLE) patients have not been identified. The aims of this study were to determine if there is an association (1) between non-HLA–related genetic risk score (GRS) and age of SLE diagnosis, and (2) between HLA-related GRS and age of SLE diagnosis. Methods. Genomic DNA was obtained from 2001 multiethnic patients and genotyped using the Immunochip. Following quality control, genetic risk counting (GRCS), weighted (GRWS), standardized counting (GRSCS), and standardized weighted (GRSWS) scores were calculated based on independent single-nucleotide polymorphisms from validated SLE loci. Scores were analyzed in a regression model and adjusted by sex and ancestral population. Results. The analyzed cohort consisted of 1540 patients: 1351 females and 189 males (675 cSLE and 865 aSLE). There were significant negative associations between all non-HLA GRS and age of SLE diagnosis: P = 0.011 and r2 = 0.175 for GRWS; P = 0.008 and r2 = 0.178 for GRSCS; P = 0.002 and r2 = 0.176 for GRSWS (higher GRS correlated with lower age of diagnosis.) All HLA GRS showed significant positive associations with age of diagnosis: P = 0.049 and r2 = 0.176 for GRCS; P = 0.022 and r2 = 0.176 for GRWS; P = 0.022 and r2 = 0.176 for GRSCS; P = 0.011 and r2 = 0.177 for GRSWS (higher GRS correlated with higher age of diagnosis). Conclusion. Our data suggest that there is a linear relationship between genetic risk and age of SLE diagnosis and that HLA and non-HLA GRS are associated with age of diagnosis in opposite directions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".