Anti-C1q antibodies in systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVE: Anti-C1q has been associated with systemic lupus erythematosus (SLE) and lupus nephritis in previous studies. We studied anti-C1q specificity for SLE (vs rheumatic disease controls) and the association with SLE manifestations in an international multicenter study. METHODS: Information and blood samples were obtained in a cross-sectional study from patients with SLE (n = 308) and other rheumatologic diseases (n = 389) from 25 clinical sites (84% female, 68% Caucasian, 17% African descent, 8% Asian, 7% other). IgG anti-C1q against the collagen-like region was measured by ELISA. RESULTS: Prevalence of anti-C1q was 28% (86/308) in patients with SLE and 13% (49/389) in controls (OR = 2.7, 95% CI: 1.8-4, p < 0.001). Anti-C1q was associated with proteinuria (OR = 3.0, 95% CI: 1.7-5.1, p < 0.001), red cell casts (OR = 2.6, 95% CI: 1.2-5.4, p = 0.015), anti-dsDNA (OR = 3.4, 95% CI: 1.9-6.1, p < 0.001) and anti-Smith (OR = 2.8, 95% CI: 1.5-5.0, p = 0.01). Anti-C1q was independently associated with renal involvement after adjustment for demographics, ANA, anti-dsDNA and low complement (OR = 2.3, 95% CI: 1.3-4.2, p < 0.01). Simultaneously positive anti-C1q, anti-dsDNA and low complement was strongly associated with renal involvement (OR = 14.9, 95% CI: 5.8-38.4, p < 0.01). CONCLUSIONS: Anti-C1q was more common in patients with SLE and those of Asian race/ethnicity. We confirmed a significant association of anti-C1q with renal involvement, independent of demographics and other serologies. Anti-C1q in combination with anti-dsDNA and low complement was the strongest serological association with renal involvement. These data support the usefulness of anti-C1q in SLE, especially in lupus nephritis.
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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) | 0.000 | 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 | 0.001 | 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".