Sjögren Syndrome in Systemic Lupus Erythematosus: A Subset Characterized by a Systemic Inflammatory State
Bibliographic record
Abstract
OBJECTIVE: An often-neglected subset of patients with systemic lupus erythematosus (SLE) is those with secondary Sjögren syndrome (SLE-sSS). Further, primary SS overlaps and can be difficult to delineate from SLE. To shed light on the SLE-sSS subset, we investigated a large and well-characterized SLE cohort, comparing patients with SLE-sSS and SLE patients without SS (SLE-nonsSS) and controls. METHODS: We included 504 consecutive patients with SLE, fulfilling the 1982 revised American College of Rheumatology criteria, and 319 controls from the general population, matched for age and sex to the first 319 patients. SLE-sSS was defined according to the American-European Consensus Criteria (AECC). A thorough clinical examination, including subjective and objective quantifications of sicca symptoms, was performed in all participants. Autoantibodies and 20 selected cytokines were measured by luminex and multiplex analysis, respectively. RESULTS: SLE-sSS, as defined by AECC, occurred in 23% of the patients with SLE. In comparison to SLE-nonsSS, the SLE-sSS group was older and more frequently female. Leukopenia and peripheral neuropathy were more frequent and nephritis less frequent. Circulating levels of 6/20 investigated proinflammatory cytokines [tumor necrosis factor-α, interleukin (IL) 6, monocyte chemoattractant protein 4, macrophage inflammatory protein 1β, IL-12/IL-23p40, and interferon γ-induced protein 10], total IgG, anti-SSA/Ro52, anti-SSA/Ro60, anti-SSB/La antibodies, and rheumatoid factor (IgM and IgA) were higher in the SLE-sSS group (p < 0.05 for all comparisons). CONCLUSION: The frequency of SLE-sSS increased with age and affected roughly one-quarter of all patients with SLE. Despite less internal organ involvement, a systemic inflammatory state with high levels of proinflammatory cytokines is present in the SLE-sSS subgroup. This is a novel observation that may affect future understanding and treatment of the SLE-sSS subset.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".