Serum proteomic and metabolomic analyses from patients with <scp>IBD</scp> identify biological pathways associated with treatment success with anti‐integrin therapy
Bibliographic record
Abstract
Crohn's disease (CD) and ulcerative colitis (UC) are chronic inflammatory diseases of the gastrointestinal tract believed to arise from an imbalance between its epithelial, immune and microbial components. It has been shown that biological differences (e.g. genetic, epigenetic, microbial, environmental) exist between patients with IBD. It is also known that there is important heterogeneity in the response to therapies that target very specific biological pathways (e.g. TNF-alpha signaling, IL-23R signaling, immune cell trafficking). The aim of this study was to identify potential biological differences associated with differential treatment response to the anti α4β7 integrin therapy known as vedolizumab. We performed targeted analyses of > 150 proteins and metabolites, and nontargeted analyses of > 1100 lipid entities in serum samples from 92 IBD patients (42 CD, 50 UC) immediately prior to initiation of therapy with vedolizumab (baseline samples) and at their first clinical assessment (week 14 samples). We detected that the baseline levels of multiple serum cytokines, amino acids, acylcarnitines and triglycerides were different between responders and nonresponders to treatment with vedolizumab. We also noted changes in serum analytes between baseline and week 14 samples that were different between these two groups of patients. Many of these serum analytes are markers of biological pathways that are involved in the activation, proliferation and metabolism of pro-inflammatory cells. This study provides support for the hypothesis that biological differences between individuals not only impact the risk to develop IBD and IBD-related clinical phenotypes but also an IBD patient's likelihood of responding to a biological therapy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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) | 0.001 | 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".