Association of systemic lupus erythematosus (SLE) genetic susceptibility loci with lupus nephritis in childhood-onset and adult-onset SLE
Bibliographic record
Abstract
OBJECTIVE: LN is one of the most common and severe manifestations of SLE. Our aim was to test the association of SLE risk loci with LN risk in childhood-onset SLE (cSLE) and adult-onset SLE (aSLE). METHODS: Two Toronto-based tertiary care SLE cohorts included cSLE (diagnosed <18 years) and aSLE patients (diagnosed ⩾18 years). Patients met ACR and/or SLICC SLE criteria and were genotyped on the Illumina Multi-Ethnic Global Array or Omni1-Quad arrays. We identified those with and without biopsy-confirmed LN. HLA and non-HLA additive SLE risk-weighted genetic risk scores (GRSs) were tested for association with LN risk in logistic models, stratified by cSLE/aSLE and ancestry. Stratified effect estimates were meta-analysed. RESULTS: Of 1237 participants, 572 had cSLE (41% with LN) and 665 had aSLE (30% with LN). Increasing non-HLA GRS was significantly associated with increased LN risk [odds ratio (OR) = 1.26; 95% CI 1.09, 1.46; P = 0.0006], as was increasing HLA GRS in Europeans (OR = 1.55; 95% CI 1.07, 2.25; P = 0.03). There was a trend for stronger associations between both GRSs and LN risk in Europeans with cSLE compared with aSLE. When restricting cases to proliferative LN, the magnitude of these associations increased for both the non-HLA (OR = 1.30; 95% CI 1.10, 1.52; P = 0.002) and HLA GRS (OR = 1.99; 95% CI 1.29, 3.08; P = 0.002). CONCLUSION: We observed an association between known SLE risk loci and LN risk in children and adults with SLE, with the strongest effect observed among Europeans with cSLE. Future studies will include SLE-risk single nucleotide polymorphisms specific to non-European ancestral groups and validate findings in an independent cohort.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".