Soluble urokinase plasminogen activator receptor (suPAR) levels predict damage accrual in patients with recent-onset systemic lupus erythematosus
Bibliographic record
Abstract
The soluble urokinase plasminogen activator receptor (suPAR) has potential as a prognosis and severity biomarker in several inflammatory and infectious diseases. In a previous cross-sectional study, suPAR levels were shown to reflect damage accrual in cases of systemic lupus erythematosus (SLE). Herein, we evaluated suPAR as a predictor of future organ damage in recent-onset SLE. Included were 344 patients from the Systemic Lupus International Collaborating Clinics (SLICC) Inception Cohort who met the 1997 American College of Rheumatology classification criteria with 5-years of follow-up data available. Baseline sera from patients and age- and sex-matched controls were assayed for suPAR. Organ damage was assessed annually using the SLICC/ACR damage index (SDI). The levels of suPAR were higher in patients who accrued damage, particularly those with SDI≥2 at 5 years (N = 32, 46.8% increase, p = 0.004), as compared to patients without damage. Logistic regression analysis revealed a significant impact of suPAR on SDI outcome (SDI≥2; OR = 1.14; 95% CI 1.03–1.26), also after adjustment for confounding factors. In an optimized logistic regression to predict damage, suPAR persisted as a predictor, together with baseline disease activity (SLEDAI-2K), age, and non-Caucasian ethnicity (model AUC = 0.77). Dissecting SDI into organ systems revealed higher suPAR levels in patients who developed musculoskeletal damage (SDI≥1; p = 0.007). Prognostic biomarkers identify patients who are at risk of acquiring early damage and therefore need careful observation and targeted treatment strategies. Overall, suPAR constitutes an interesting biomarker for patient stratification and for identifying SLE patients who are at risk of acquiring organ damage during the first 5 years of disease.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".