SERUM BIOMARKERS AND INTERFERON-ALPHA CONCENTRATION AND THEIR CORRELATION TO DISEASE ACTIVITY IN 62 ESTONIAN SYSTEMIC LUPUS ERYTHEMATOSUS (SLE) PATIENTS
Bibliographic record
Abstract
PV030 / #282 Poster Topic: AS04 - Biomarkers Background/Purpose SLE is a rare chronic autoimmune disease with polymorphic clinical manifestation. With a complex disease in pathoetiology and heterogenicity in organ involvement, understanding disease activity and treating patients can be difficult. Identifying certain cytokine profiles in different disease states can give additional information in characterizing SLE and relieving the burden of disease. Methods Consecutive outpatient and inpatient patients with rheumatologist diagnosed SLE (≥20 years) were enrolled. Evaluation for disease activity, current treatment, organ involvement, immunological findings and comorbidities were done. In addition, data from medical records were collected: organ involvement and immunological findings at the time of diagnosis and initial treatment. SLE disease activity was measured using SLEDAI 2K (Systemic Lupus Erythematosus Disease Activity Index 2K) score. Blood tests were taken to measure interferon levels using Simoa (Single Molecul Array) method and Olink proximity extension assay was used to measure inflammatory proteins. To evaluate the extent of the deviation of IFNα and marked protein levels in patients compared to non-autoimmune individuals, age and gender matched control group was used. Interferon score was calculated according to interferon induced gene expression in blood cells collected to RNA stabilizing tubes. Principal component analysis, volcano plot and analysis of variance (ANOVA), Kruskal and Wilcoxon test were used to evaluate significant differences between controls and patients, different disease activity groups and glucocorticosteroid dosage in serum protein pattern. Heatmap to visualize different SLE clusters in relation to organ involvement, biomarker pattern and used treatment was done. Results Among 62 patients (mean age 49 (SD ±12.4) years, mean disease duration 12 (±10.1) years, mean SLEDAI 2K at diagnosis 11 (±5.5)) 90% were females. Mean SLEDAI 2K value at study visit was 4 (±4.0), 39% of patients had SLEDAI 2K >4 and 42% of patient were positive for anti-dsDNA antibodies and 48% had hypocomplementemia. Glucocorticosteroids were used in 71% of patients and 29% had Rituximab treatment. Olink assay highlighted 25 upregulated biomarkers for patients in comparison to controls with IL-10 and IL15RA upregulated in inactive patients (SLEDAI 2K=0) vs controls. We found significant differences in cytokine levels for patients in methylprednisolone treatment groups high dose (>4 mg/day), low dose (1-4 mg/day) and no treatment in SLEDAI high (SLEDAI 2K>4) group for IL18R1, PD-L1, FGF5; in SLEDAI low group (SLEDAI 2K £4) for FGF-5 and SLEDAI inactive group for IL-12B. With identifying 4 different disease clusters, it was highlighted that CXCL10 and CXCL11 were related with more active disease while MCP-1 could predict arthritis in patients with low cytokine activity. Conclusions With measuring biomarkers activity in Estonian SLE patients, we have identified 4 disease clusters, confirming CXCL10 and CXCL11 role in more active disease and giving additional insight to low cytokine activity driven disease with MCP-1 as a potential predictor for arthritis as it has been highlighted in rheumatoid arthritis studies.[1,2] However further studies are needed to provide insight to changes in FGF-5 levels in relation to glucocorticosteroids. References: [1.] Reynolds JA. Arthritis Res Ther 2018;20(1):173. [2.] Ellingsen T. J Rheumatol 2001;28(1):41-6.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".