Peroxisome proliferator-activated receptors in inflammatory bowel disease: linking immunometabolism, lipid signaling, and therapeutic potential
Bibliographic record
Abstract
Inflammatory bowel disease (IBD), encompassing Crohn's disease (CD) and ulcerative colitis, is a chronic condition marked by immune dysregulation, genetic predisposition, and metabolic disturbances. Emerging evidence highlights the role of lipid metabolism and peroxisome proliferator-activated receptor (PPAR) signaling in modulating immune responses in IBD. PPAR-γ and PPAR-α regulate macrophage polarization, T-cell differentiation, and epithelial barrier integrity, influencing disease severity and progression. Alterations in PPAR activity contribute to metabolic stress and inflammation, linking IBD pathophysiology to immunometabolism. Studies suggest that targeting PPARs may mitigate inflammation through modulation of cytokine production, immune cell function, and gut microbiota interactions. In this review, we focus specifically on CD and explore how PPAR signaling intersects with mesenteric adipose tissue dysfunction and microbial dysbiosis, 2 hallmark features of CD. PPAR agonists, already used in metabolic-inflammatory diseases such as metabolic-associated liver disease, have demonstrated antiinflammatory effects in experimental colitis models. Translating these findings into clinical applications could offer novel treatment strategies for CD. Future research should focus on clinical trials, genetic studies, and microbiota-targeted approaches to elucidate PPAR-driven mechanisms in CD pathogenesis. Understanding the interplay between PPARs, lipid metabolism, and immune responses may lead to innovative therapeutic strategies, improving disease management and patient outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".