Longitudinal analysis of mRNA transcripts and plasma proteins to define a biomarker associated with lupus disease activity
Bibliographic record
Abstract
Lupus, a chronic autoimmune disease, is characterized by a variable clinical course, with periods of active disease. Identification of a biomarker or biomarker panel associated with clinical disease activity would be useful for disease management, assessment of response to therapeutic intervention in practice or clinical trials, and might suggest cellular or molecular targets for future therapies. To identify biomarkers that reflect lupus disease activity, we assessed longitudinal clinical, gene expression and proteomic data from SLE patients. One hundred and sixty-nine RNA extracts from PBMC and plasma samples were collected longitudinally (up to 3 years) from 23 SLE patients and five healthy donors (HD), and SLEDAI and BILAG scores were recorded. All SLE patients fulfilled ACR criteria for the disease. PBMC mRNA profiles for each visit were established using Affymetrix GeneChips. A panel of proinflammatory cytokines was evaluated using Multi-Analyte Profiling technology (Rules-Based Medicine, Austin, TX, USA). Longitudinal data analysis was performed using R (R Development Core Team) and the R packages lme4 and languageR. Data were analyzed using linear mixed-effects (LME) models. K-mean cluster analysis was first used to identify groups of gene transcripts that fluctuate in relation to disease activity, and representative transcripts were selected from each cluster. Thirteen plasma factors were identified as significantly increased in SLE patients compared with HD, and 14 plasma factors were significantly associated with disease activity. LME analysis was applied to the dataset to identify those transcripts and plasma factors that best define clinical disease activity. Statistical correlation with disease activity for this biomarker panel was compared with traditional measures of disease activity. A combination of mRNA transcripts and plasma factors, when assessed as a panel, shows a high correlation with clinical disease activity in patients with SLE. Validation of this biomarker panel in an extended patient group may provide support for measurement of these transcripts and proteins as an informative correlate of disease activity and a tool for patient 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".