CpG Methylation in<i>TGFβ1</i>and<i>IL-6</i>Genes as Surrogate Biomarkers for Diagnosis of IBD in Children
Bibliographic record
Abstract
BACKGROUND: Diagnostic markers for distinguishing between Crohn disease (CD) and ulcerative colitis (UC) remain elusive. We studied whether methylation marks across the promoters of the transforming growth factor beta 1 (TGFβ1) and interleukin-6 genes have diagnostic utility. METHODS: A case-control study was carried out. Cases were treatment-naïve, diagnosed before age 20, and recruited from 3 pediatric gastroenterology clinics across Canada. Control patients did not have inflammatory bowel disease and were recruited from orthopedic clinics within the same hospitals as the gastroenterology clinics. Patient DNA from peripheral blood was processed to identify methylation sites (CpG) across the promoter regions of the TGFβ1 and interleukin-6 genes. After initial nonparametric univariate analyses, multivariate logistic regression models were fit. Models with the best fit (Akaike information criteria) and strongest discriminatory capabilities (area under the curve [AUC]) were identified, and P values were adjusted for multiple comparisons using the false discovery rate method. RESULTS: A total of 67 CD, 31 UC, and 43 control patients were included. The age distribution of the 3 groups was similar. Most CD patients had ileocolonic disease (44.8%) and inflammatory disease (88.1%). Most UC patients had extensive (71%) and moderate disease (51.6%). Logistic regression analysis revealed the following: 14 TGFβ1 CpG sites discriminated between CD and control patients (AUC = 0.94), 9 TGFβ1 CpG sites discriminated between UC and control patients (AUC = 0.99), 3 TGFβ1 CpG sites discriminated between CD and UC (AUC = 0.81), and 6 TGFβ1 CpG sites distinguished colonic CD from UC (AUC = 0.91). CONCLUSIONS: We found that CpG methylation in the promoter of the TGFβ1 gene has high discriminative power for identifying CD and UC and could serve as an important diagnostic marker.
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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 | 0.000 |
| 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) | 0.001 | 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".