SERUM PROTEOME PROFILING IDENTIFIES SEROLOGICALLY ACTIVE, CLINICALLY QUIESCENT LUPUS ENDOPHENOTYPES WITH DISTINCT DISEASE TRAJECTORIES
Bibliographic record
Abstract
PV040 / #601 Poster Topic: AS04 - Biomarkers Background/Purpose Serologically active clinically quiescent (SACQ) refers to a subset of systemic lupus erythematosus (SLE) patients with elevated serological markers but no apparent clinical symptoms. The variability within SACQ complicates disease management. This study aims to identify serum proteomic markers that characterize SACQ subgroups and predict clinical outcomes. Methods SLE patients were consecutively recruited from Peking Union Medical College Hospital (January 2018 - June 2024), including 58 SACQ patients and 25 controls (13 in remission, 12 in active phase). Clinical data and serum samples were collected during follow-ups. Primary endpoints were flares and new organ damage. Serum protein profiling used Olink PEA with 3 panels (‘Target 96 Inflammation’, ‘Cardiovascular III’, ‘Immune Response’) measuring 269 proteins. Differentially expressed proteins (DEPs) were identified via generalized linear models, and SACQ subtypes were classified using unsupervised clustering. Associations between proteins and outcomes were analyzed using Cox models and LASSO regression. Results SACQ patients and controls were comparable in demographics and past SLE manifestations. Seventy-four proteins showed differential abundance between SACQ and controls. SACQ patients were classified into 3 distinct endophenotypes, with significant differences in organ involvement and prognosis. Type 1 had higher rates of lupus nephritis (66.7%) and neurological involvement (33.3%), Type 3 had elevated lupus nephritis (47.8%), while Type 2 had fewer major organ involvements. These groups exhibited significant differences in flare rates, with Type 1 showing the highest flare rate (86.7%), severe flares (40.0%) and organ damage accumulation (46.7%). Key proteins distinguishing these subtypes included IL18, PCSK9, TNFRSF14, MILR1, and BTN3A2. IL18 was significantly elevated in Type 1 compared to Type 2 (coef = 6.175, SE = 2.218, p = 0.0054). For Type 3 vs Type 2, PCSK9 (coef = 2.005, SE = 0.899, p = 0.026), TNFRSF14 (coef = 2.521, SE = 0.954, p = 0.008), MILR1 (coef = 1.472, SE = 0.721, p = 0.041), and BTN3A2 (coef = -1.378, SE = 0.611, p = 0.024) showed significant differences. Clinical correlation analysis confirmed these proteins’ association with flare and organ damage accrual. Conclusions Serum proteome analysis in SACQ identifies distinct subgroups with differing clinical outcomes, offering potential biomarkers for personalized prognosis and targeted therapy in SLE management.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".