Vascular inflammation in moderate‐to‐severe atopic dermatitis is associated with enhanced Th2 response
Bibliographic record
Abstract
BACKGROUND: In atopic dermatitis (AD), some studies have shown an association with increased cardiovascular disease in certain populations. However, other investigations found modest or no association. Despite conflicting results, molecular profiling studies in both AD skin and blood have demonstrated upregulation of atherosclerosis and cardiovascular risk-related markers. However, the underlying mechanisms connecting AD to vascular inflammation/atherosclerosis are unknown. In this study, we aim to determine factors associated with vascular inflammation/atherosclerosis in AD patients. METHODS: We used 18-FDG PET-CT to characterize vascular inflammation in AD patients and healthy subjects. In parallel, we assessed their skin and blood immune profiles to determine AD-related immune biomarkers associated with vascular inflammation. We also assessed levels of circulating microparticles, which are known to be associated with increased cardiovascular risk. RESULTS: We found significant correlations between vascular inflammation and Th2-related products in skin and blood of AD patients as well as atherosclerosis-related markers that were modulated by dupilumab. Circulating levels of endothelial microparticles were significantly higher in severe AD patients and tended to correlate with vascular inflammation assessed by PET-CT. CONCLUSION: Vascular inflammation in AD is associated with enhanced Th2 response and clinical severity, which may explain cardiovascular comorbidities observed in select AD populations. Larger prospective studies are needed to further evaluate vascular inflammation and cardiovascular events and mortality in AD patients. Finally, as dupilumab treatment demonstrated significant modulation of atherosclerosis-related genes in AD patients compared to placebo, these data suggest that modulation of vascular inflammation with systemic treatment should be explored in patients with AD.
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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) | 0.002 | 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".