Epigenetic and biological age acceleration in children with atopic dermatitis
Bibliographic record
Abstract
Background Atopic dermatitis (AD) is a chronic inflammatory skin disease resulting from the complex interplay of genetic and environmental factors, meriting exploration using temporally dynamic biomarkers. DNA methylation–based algorithms have been trained to accurately estimate biological age, and deviation of predicted age from true age (epigenetic age acceleration) has been implicated in several inflammatory diseases, including asthma. Objective We sought to determine the role of epigenetic and biological aging, telomere length, and epigenetically inferred abundance of 7 inflammatory biomarkers in AD. Methods We performed DNA methylation–based analyses in a pediatric AD cohort (n = 24, mean ± standard deviation [SD] age 2.56 ± 0.28 years) and age-matched healthy subjects (n = 24, age 2.09 [0.15] years) derived from blood using 5 validated algorithms that assess epigenetic age (Horvath, Skin&Blood) and biological age (PhenoAge, GrimAge), telomere length (TelomereLength), and inflammatory biomarker levels. Results Epigenetic and biological age, but not telomere length, were accelerated in AD patients for 4 algorithms: Horvath (+0.88 years; 95% confidence interval [CI], 0.33 to 1.4; P = 2.3 × 10 −3 ), Skin&Blood (+0.95 years; 95% CI, 0.67 to 1.2; P = 1.8 × 10 −8 ), PhenoAge (+8.2 years; 95% CI, 3.4 to 13.0; P = 1.3 × 10 −3 ), and GrimAge (+1.8 years 95% CI, 0.22 to 3.3; P = .026). Moreover, patients had increased levels of β 2 microglobulin (+47,584.4 ng/mL; P = .029), plasminogen activation inhibitor 1 (+3,432.9 ng/mL; P = 1.1 × 10 −5 ), and cystatin C (+31,691 ng/mL; P = 4.0 × 10 −5 ), while levels of tissue inhibitor metalloproteinase 1 (−370.7 ng/mL; P = 7.5 × 10 −4 ) were decreased compared to healthy subjects. Conclusion DNA methylation changes associated with epigenetic and biological aging, and inflammatory proteins appear early in life in pediatric AD and may be relevant clinical biomarkers of pathophysiology.
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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 | 0.000 |
| 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) | 0.000 | 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".