Genetics of Neonatal Lupus Erythematosus Risk and Specific Manifestations
Bibliographic record
Abstract
Objective Neonatal lupus erythematosus (NLE) is a passively acquired autoimmune disease in infants born to anti-Ro and/or anti-La autoantibody-positive mothers. Genetics may affect NLE risk. We analyzed the genetics of infants and anti-Ro antibody-positive mothers, with NLE and NLE-specific manifestations. Methods Infants and mothers from a tertiary care clinic underwent genotyping on the Global Screening Array. We created additive non-HLA and HLA polygenic risk scores (PRS) for systemic lupus erythematosus (SLE), from one of the largest genome-wide association studies. Outcomes were any NLE manifestations, cardiac NLE, and cutaneous NLE. We tested the association between SLE-PRS in the infant, mother, and the PRS difference between the mother and infant with NLE outcomes, in logistic regression and generalized linear mixed models (BonferroniP< 0.02). We also performed HLA-wide analyses for the outcomes (P< 5.00 × 10–8). Results The study included 332 infants, 270 anti-Ro antibody-positive mothers, and 253 mother-infant pairs. A large proportion of mothers (40.4%) and infants (41.3%) were European, and 50% of infants were female. More than half of the infants had NLE (53%), including 7.2% with cardiac NLE and 11.7% with cutaneous NLE. We did not identify significant associations between infant PRS, maternal PRS, or maternal-infant PRS difference and any NLE outcomes. HLA-wide analyses did not identify NLE risk alleles. Conclusion In a multiethnic cohort of infants and anti-Ro antibody-positive mothers, we did not identify a significant association between SLE genetics and risk of NLE outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".