Levels of Transforming Growth Factor-β Are Low in Systemic Lupus Erythematosus Patients with Active Disease
Bibliographic record
Abstract
OBJECTIVE: Cytokines are central regulators of the immune response but the workings of this complex network in systemic lupus erythematosus (SLE) are not fully understood. We investigated a range of inflammatory and immune-modulating cytokines to determine their value as biomarkers for disease subsets in SLE. METHODS: This was a cross-sectional study in 102 patients with SLE (87% women, disease duration 10.6 yrs). Circulating concentrations of interleukin 1β (IL-1β), IL-4, IL-6, IL-10, IL-12, IL-17, monocyte chemotactic protein 1 (MCP-1), macrophage inflammatory protein 1 (MIP-1α), MIP-1β, interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α), and total transforming growth factor-β1 (TGF-β1) were related to disease activity (SLE Disease Activity Index; SLEDAI), lymphocyte subsets, autoantibody levels, accrued damage (Systemic Lupus International Collaborating Clinics/ACR Damage Index; SDI), and concomitant treatment. RESULTS: Patients with SLE had lower levels of TGF-β1 (p = 0.01) and IL-1β (p = 0.0004) compared to controls. TGF-β1 levels were lower in patients with SLEDAI scores 1-10 and SDI > 3; and were correlated with CD4+, CD8+, and natural killer cell counts; and were independent of steroid or cytotoxic drug use. Treatment with cardiovascular drugs was associated with lower IL-12 levels. No consistent disease associations existed for the other cytokines investigated. CONCLUSION: Lower TGF-β1 was the most consistent cytokine abnormality in patients with SLE. The associations with disease activity, lymphocyte subsets, and damage suggest that TGF-β1 may be a therapeutic target of interest in SLE.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".