Abnormal glucose and lipid metabolism promotes disrupted differentiation of T and B cell subsets in Behçet’s disease
Bibliographic record
Abstract
Abstract Introduction Behçet’s disease (BD) is a chronic, systemic inflammatory condition characterized by recurrent immune dysregulation. Materials & Methods This study conducted a comprehensive analysis of immune cell subsets, metabolic markers, and their interplay in BD patients. Using multiparametric flow cytometry, we identified elevated Th1 cells, senescent CD8+ T cells, and abnormal B cell activation as hallmarks of the chronic inflammatory state in BD. Results Despite immunotherapy, innate immune activation persisted, with increased mature NK cells, γδT1 cells, and conventional dendritic cells (cDCs), alongside reduced plasmacytoid dendritic cells (pDCs). Elevated glucose (GLU) and triacylglycerol (TAG) levels in BD patients correlated with increased Th1 cells, functional CD8+ T cells, and B cell activation. In vitro experiments demonstrated that GLU and TAG promote Th1 differentiation, CD8+ T cell activation, and B cell antibody production, revealing their role as drivers of immune dysregulation. Conclusion These findings underscore the intricate relationship between metabolic dysregulation and immune dysfunction in BD, highlighting potential diagnostic and therapeutic targets. Our study provides critical insights into BD pathogenesis, offering a foundation for optimizing disease management and monitoring immune and metabolic markers for improved 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 | 0.000 | 0.000 |
| 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".