Investigating Transcriptional Age Acceleration in Inflammatory Skin Diseases
Bibliographic record
Abstract
Epigenetic age acceleration has previously been observed in inflammatory skin disease, however, less is known regarding recently described age-related gene expression patterns ("transcriptional clocks"). We investigated the role of transcriptional clocks in hidradenitis suppurativa (HS; n=37), atopic dermatitis (AD; n=27), plaque psoriasis (PP; n=28) and healthy subjects (n=38) using seven clock algorithms, to improve understanding of underlying pathophysiology and disease trajectory. Five of seven transcriptional clocks demonstrated moderate-to-strong accuracy in predicting age across groups (AD: ρ =0.40-0.86; HS: ρ =0.46-0.74; PP: ρ =0.50-0.80; healthy subjects: ρ =0.32-0.60, p <0.05). Age acceleration was observed in lesional versus healthy (AD: +3.9∼9.8y, t= 2.8∼5.9; HS: +5.0∼6.1y, t =2.5∼4.1; PP: +6.5∼12.5y, t =5.1∼8.0; p <0.05) and in lesional versus non-lesional skin in all diseases and less frequently observed in non-lesional versus healthy skin. In AD, loss-of-function mutations in the filaggrin gene were associated with transcriptional age acceleration, including FLGR244X/2282del4 dual carrier status ( t =2.3, p <0.05) and FLGR501X carrier status ( t =2.6, p <0.05). Pathway enrichment analyses revealed clock genes are enriched in signatures related to aging, inflammation, metabolism. Our study provides evidence for transcriptional age acceleration in inflammatory skin disease and sets a foundation for further investigation into the role of age-related transcriptional changes in the pathophysiology of these diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".