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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".