Blood-based Biomarkers Used to Predict Disease Activity in Crohnʼs Disease and Ulcerative Colitis
Bibliographic record
Abstract
BACKGROUND: Identifying specific genes that are differentially expressed during inflammatory bowel disease flares may help stratify disease activity. The aim of this study was to identify panels of genes to be able to distinguish disease activity in Crohn's disease (CD) and ulcerative colitis (UC). METHODS: Patients were grouped into categories based on disease and severity determined by histological grading. Whole blood was collected by PAXgene Blood RNA collection tubes, (PreAnalytiX) and gene expression analysis using messenger RNA was conducted. Logistic regression was performed on multiple combinations of common probe sets, and data were evaluated in terms of discrimination by computing the area under the receiving operator characteristic curve (ROC-AUC). RESULTS: Nine inactive CD, 8 mild CD, 10 moderate-to-severe CD, 9 inactive UC, 8 mild UC, 10 moderate-to-severe UC, and 120 controls were hybridized to Affymetrix U133 Plus 2 microarrays. Panels of 6 individual genes discriminated the stages of disease activity: CD with mild severity {ROC-AUC, 0.89 (95% confidence interval [CI], 0.84%-0.95%)}, CD with moderate-to-severe severity (ROC-AUC 0.98 [95% CI, 0.97-1.0]), UC with mild severity (ROC-AUC 0.92 [95% CI, 0.87-0.96]), and UC with moderate-to-severe severity (ROC-AUC 0.99 [95% CI, 0.97-1.0]). Validation by real-time reverse transcription-PCR confirmed the Affymetrix microarray data. CONCLUSIONS: The specific whole blood gene panels reliably distinguished CD and UC and determined the activity of disease, with high sensitivity and specificity in our cohorts of patients. This simple serological test has the potential to become a biomarker to determine the activity of disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".