CE-21 The prevalence and determinants of ANTI-DFS70 antibodies in an international inception cohort of systemic lupus erythematosus (SLE) patients
Bibliographic record
Abstract
Background When found in the absence of antibodies to extractable nuclear antigens (ENA) or anti-double-stranded DNA (dsDNA) (i.e., monospecific), autoantibodies to the nuclear autoantigen dense fine speckles 70 (DFS70) are purported to rule out SLE. The reported frequency of anti-DFS70 by chemiluminescence (CIA) in SLE is low compared to healthy individuals (0–5.7% vs. 1.3–23.2%), while the frequency of monospecific anti-DFS70 in SLE is even lower at 0–0.4%. There are no studies examining the frequency of anti-DFS70 in an early inception SLE cohort. This study determined the prevalence of anti-DFS70 in a multi-national, multi-ethnic early inception SLE cohort and examined demographic, clinical, and autoantibody associations. Materials and methods Patients fulfilling ACR Classification Criteria for SLE were enrolled in the Systemic Lupus International Collaborating Clinics (SLICC) inception cohort within 15 months of diagnosis. Demographic and clinical data were collected at enrollment. ANAs were detected by indirect immunofluorescence on HEp-2 cells (ImmunoConcepts, Sacramento) and ENAs and dsDNA by an addressable laser bead immunoassay (FIDIS Connective13, TheraDiag, Paris). Anti-DFS70 antibodies were measured by CIA (Inova Diagnostics, San Diego). The association between anti-DFS70 and baseline demographic, clinical, and autoantibody profiles was assessed using univariate and multivariate logistic regression. For the most informative model, only the remaining statistically significant predictors at the 95% CI: were included, after eliminating other potential predictors individually, starting with the least likely to be associated with the outcome. Results 1137 patients were included; 89.9% were female and 93.8% were ANA positive (Table 1). The frequency of anti-DFS70 was 7.1% [95% CI: 5.7–8.8%]. 13 of 1137 (1.1%) [95% CI: 0.6–1.9%] were positive for anti-DFS70 only (monospecific). In univariate analysis, patients with musculoskeletal activity (based on SLEDAI items) or anti-β−2 glycoprotein-1 (anti-β2GP1) were more likely to have anti-DFS70, whereas those with anti-dsDNA, anti-SSA/Ro60, anti-SSB/La, or anti-U1RNP were less likely to have anti-DFS70. In multivariate analysis, patients with musculoskeletal activity (Odd Ratio (OR) 1.25 [95% CI: 1.10, 1.41]) or anti-β2GP1 (OR 2.15, 95% CI: 1.21, 3.84) were more likely to have anti-DFS70, while those with anti-dsDNA (OR 0.53, 95% CI: 0.31, 0.92) or anti-SSB/La (OR 0.25, 95% CI:0.08, 0.82) were less likely to have anti-DFS70. Conclusions The prevalence of anti-DFS70 in newly diagnosed SLE patients was at the high end of the range previously ?published for SLE (7.1% vs. 0–5.7%) and was associated with musculoskeletal activity and anti-β2GP1. However, ‘monospecific’ anti-DFS70 was rare (1.1%) and is potentially useful to discriminate between ANA positive healthy individuals and SLE.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".