Noninvasive, microbiome-based diagnosis of inflammatory bowel disease
Bibliographic record
Abstract
Despite recent progress in our understanding of the association between the gut microbiome and inflammatory bowel disease (IBD), the role of microbiome biomarkers in IBD diagnosis remains underexplored. Here we developed a microbiome-based diagnostic test for IBD. By utilization of metagenomic data from 5,979 fecal samples with and without IBD from different geographies and ethnicities, we identified microbiota alterations in IBD and selected ten and nine bacterial species for construction of diagnostic models for ulcerative colitis and Crohn’s disease, respectively. These diagnostic models achieved areas under the curve >0.90 for distinguishing IBD from controls in the discovery cohort, and maintained satisfactory performance in transethnic validation cohorts from eight populations. We further developed a multiplex droplet digital polymerase chain reaction test targeting selected IBD-associated bacterial species, and models based on this test showed numerically higher performance than fecal calprotectin in discriminating ulcerative colitis and Crohn’s disease from controls. Here we discovered universal IBD-associated bacteria and show the potential applicability of a multibacteria biomarker panel as a noninvasive tool for IBD diagnosis. Using ethnically and geographically diverse metagenomic data, the authors identify microbiota alterations associated with inflammatory bowel disease (IBD). They discover universal IBD-associated bacteria, which serve as the basis for a multibacteria biomarker panel that could support a noninvasive tool for IBD diagnosis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".