Urine Biomarkers to Predict Response to Lupus Nephritis Therapy in Children and Young Adults
Bibliographic record
Abstract
OBJECTIVE: To delineate urine biomarkers that forecast response to therapy of lupus nephritis (LN). METHODS: Starting from the time of kidney biopsy, patients with childhood-onset systemic lupus erythematosus who were diagnosed with LN were studied serially. Levels of 15 biomarkers were measured in random spot urine samples, including adiponectin, α-1-acid glycoprotein (AGP), ceruloplasmin, hemopexin, hepcidin, kidney injury molecule 1, monocyte chemotactic protein-1, lipocalin-like prostaglandin D synthase (LPGDS), transforming growth factor-β (TGF-β), transferrin, and vitamin D binding protein (VDBP). RESULTS: Among 87 patients (mean age 15.6 yrs) with LN, there were 37 treatment responders and 50 nonresponders based on the American College of Rheumatology criteria. At the time of kidney biopsy, levels of TGF-β (p < 0.0001) and ceruloplasmin (p = 0.006) were significantly lower among responders than nonresponders; less pronounced differences were present for AGP, hepcidin, LPGDS, transferrin, and VDBP (all p < 0.05). By Month 3, responders experienced marked decreases of adiponectin, AGP, transferrin, and VDBP (all p < 0.01) and mean levels of these biomarkers were all outstanding (area under the receiver-operating characteristic curve ≥ 0.9) for discriminating responders from nonresponders. Patient demographics and extrarenal disease did not influence differences in biomarker levels between response groups. CONCLUSION: Low urine levels of TGF-β and ceruloplasmin at baseline and marked reduction of AGP, LPGDS, transferrin, or VDBP and combinations of other select biomarkers by Month 3 are outstanding predictors for achieving remission of LN. If confirmed, these results can be used to help personalize LN therapy.
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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 | 0.003 | 0.002 |
| 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 | 0.001 | 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".