URINARY PROTEOMIC ANALYSIS PROVIDES DIFFERENT PATTERNS BETWEEN LUPUS PATIENTS WITH AND WITHOUT NEPHRITIS: RESULTS OF A MULTICENTER STUDY IN 124 PATIENTS
Bibliographic record
Abstract
PV034 / #275 Poster Topic: AS04 - Biomarkers Background/Purpose Lupus nephritis is a common and serious manifestation in lupus patients. Biopsy is the gold standard to identify different patterns of disease, being not always accessible and with potential complications. A need emerges to identify biomarkers that reflect disease pathology in a noninvasive manner. Urine investigation by proteomics could in theory provide new biomarkers to better classify these patients. The objective of the study is to compare urine from lupus patients with and without nephritis using proteomics in order to find potential biomarkers of the disease Methods Multicentric and prospective proteomics study was conducted in 24-hour urine samples from SLE patients with and without renal involvement. The analysis has been performed by label free nLC MS/MS in 2 batches that included class I, II, III, IV and V nephropathology. SLE patients has been diagnosed according to the 2019 EULAR/ACR Classification Criteria. Limma test statistics were made using Prostar v.1.34.6 Results 124 samples of patients from 5 hospitals were collected and from those 109 was analyzed. There were no differences between groups according to race, gender and age (Table 1). 803 proteins were identified. 178 proteins were increased in lupus without nephritis and 190 in patients with nephritis. Making a different analysis by batches, results, it is confirmed that there is a linear correlation between proteins in patients with and without renal involvement (Figure 1). Proteins increased in urine of nephritis patients includes transport proteins as afamin but also others involved in B cell activation: Fc receptor-like protein 5, enzymes like beta-ala-his dipeptidase, immunoglobulin heavy constant gamma 4 involved in antibacterial humoral response and complement activation (Table 2). Table 1. Figure 1. Table 2. Conclusions This study shows different patterns of proteomic profile in lupus patients with renal involvement and opens a new field of investigation for better understanding of the disease and find potential biomarkers of different types and severity of nephritis. Funding: GSK-funded study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".