Anti-nucleosome antibodies outperform traditional biomarkers as longitudinal indicators of disease activity in systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine whether anti-nucleosome antibodies function as activity-specific biomarkers in SLE. METHODS: Fifty-one patients were recruited and followed prospectively with periodic clinical and biochemical assessments over a 14-month period. Disease activity was determined by the SLEDAI-2K. Anti-nucleosome antibody levels were measured by an ELISA and its utility as an activity-specific biomarker as compared with that of anti-dsDNA antibodies and C3 was assessed both at baseline and in longitudinal analysis. RESULTS: Anti-nucleosome antibodies were significantly elevated in SLE patients vs controls and showed a moderate positive correlation with disease activity. The utility of anti-nucleosome antibodies in identifying patients with active disease in a cross-sectional analysis was comparable to that of anti-dsDNA antibodies and C3. Analysis of variance demonstrated that the level of anti-nucleosome antibodies and C3 varied significantly with changes in disease activity over time. Changes in clinical state were not mirrored by changes in anti-dsDNA antibodies. In time-dependent analysis, anti-nucleosome antibodies showed a better fit over time than anti-dsDNA antibodies and C3. In pairwise comparisons, C3 and anti-nucleosome antibodies outperformed other models, including the conventional pairing of C3 and anti-dsDNA antibodies, however, no biomarker alone or as a group accurately predicted impending remissions or exacerbations. CONCLUSION: Anti-nucleosome antibodies demonstrate greater fidelity as a biomarker for changes in SLE disease activity than traditional biomarkers, supporting the routine monitoring of this antibody in clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".