ArticleScientific reports2024
Exploring potential multiple molecular biomarkers that predict treatment response in patients with lupus nephritis.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transitioning from invasive to liquid biopsy techniques: a bibliometric analysis and prospective insights on biomarkers in lupus nephritis.Frontiers in medicine · 2026Pooled it
- Serum CXCL10 as a potential biomarker for lupus nephritis and disease activity: evidence from an Egyptian cohort.BMC nephrology · 2026Article
- Distinct Longitudinal Trajectories of SLEDAI-2K Scores Predict Prognosis in Systemic Lupus Erythematosus Based on Group-Based Trajectory Modeling.Journal of immunology research · 2026Article
- C1q CIC in Lupus Nephritis: An Analysis of 883 Patients.International journal of medical sciences · 2026Article
- Biomarkers in lupus nephritis: current challenges and future perspectives.Jornal brasileiro de nefrologiaArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Limited knowledge exists regarding biomarkers that predict treatment response in Lupus nephritis (LN). We aimed to identify potential molecular biomarkers to predict treatment response in patients with LN. We enrolled 66 patients with active LN who underwent renal biopsy upon enrollment. Serum and urine samples were collected longitudinally, and we measured 12 biomarkers in each sample using a multiplex immunofluorescence assay. These biomarkers included monocyte chemoattractant protein-1 (MCP-1), interferon gamma-induced protein 10 (IP-10), interferon-γ (IFN-γ), interleukin 6 (IL-6), interleukin 16 (IL-16), interleukin 17 (IL-17), interleukin 23 (IL-23), tumor necrosis factor receptor II (TNF-RII), vascular cell adhesion molecule 1 (VCAM-1), retinol-binding protein 4 (RBP 4), vitamin D binding protein (VDBP), and neutrophil gelatinase-associated lipocalin (NGAL). Patients were categorized into two groups based on their 1-year treatment response to Mycophenolate mofetil (MMF)-based therapy: 50 responders and 16 non-responders. Only urine IL-17 (uIL-17) showed baseline level differences between the two groups, with higher in responders. In ROC curve analyses assessing the predictive performance of biomarkers, baseline uIL-17 and changes in uIL-6 and uIL-23 levels at 3 months could predict the 1-year treatment response, showing AUC values of 0.70 (95% CI 0.54-0.87), 0.70 (0.54-0.86), and 0.71 (0.57-0.85), respectively. Combining uIL-6 and uIL-23 into a model improved predictability, achieving an AUC of 0.75 (0.61-0.90). Baseline uIL-17 levels and early changes in uIL-6 and uIL-23 could serve as potential biomarkers to predict 1-year treatment response in lupus nephritis patients receiving MMF-based therapy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.