Osteopontin and Disease Activity in Patients with Recent-onset Systemic Lupus Erythematosus: Results from the SLICC Inception Cohort
Bibliographic record
Abstract
OBJECTIVE: In cross-sectional studies, elevated osteopontin (OPN) levels have been proposed to reflect, and/or precede, progressive organ damage and disease severity in systemic lupus erythematosus (SLE). We aimed, in a cohort of patients with recent-onset SLE, to determine whether raised serum OPN levels precede damage and/or are associated with disease activity or certain disease phenotypes. METHODS: We included 344 patients from the Systemic Lupus International Collaborating Clinics (SLICC) Inception Cohort who had 5 years of followup data available. All patients fulfilled the 1997 American College of Rheumatology (ACR) criteria. Baseline sera from patients and from age- and sex-matched population-based controls were analyzed for OPN using ELISA. Disease activity and damage were assessed at each annual followup visit using the SLE Disease Activity Index 2000 (SLEDAI-2K) and the SLICC/ACR damage index (SDI), respectively. RESULTS: Compared to controls, baseline OPN was raised 4-fold in SLE cases (p < 0.0001). After relevant adjustments in a binary logistic regression model, OPN levels failed to significantly predict global damage accrual defined as SDI ≥ 1 at 5 years. However, baseline OPN correlated with SLEDAI-2K at enrollment into the cohort (r = 0.27, p < 0.0001), and patients with high disease activity (SLEDAI-2K ≥ 5) had raised serum OPN (p < 0.0001). In addition, higher OPN levels were found in patients with persistent disease activity (p = 0.0006), in cases with renal involvement (p < 0.0001) and impaired estimated glomerular filtration rate (p = 0.01). CONCLUSION: The performance of OPN to predict development of organ damage was not impressive. However, OPN associated significantly with lupus nephritis and with raised disease activity at enrollment, as well as over time.
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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".