Impact of age-related changes in buccal epithelial cells on pediatric epigenetic biomarker research
Bibliographic record
Abstract
Cheek swabs, heterogeneous samples consisting primarily of buccal epithelial cells, are widely used in pediatric DNA methylation studies and biomarker creation. However, the decrease in buccal proportion with age in adults remains unexamined in childhood. We analyzed cheek swabs from 4626 typically developing children 2-months to 20-years-old. Estimated buccal proportion declined throughout childhood with both increasing chronological and predicted epigenetic age. However, buccal proportion did not associate with age throughout adolescence. Variability in buccal proportion increased with age through the entire developmental range. These trends held inversely true for neutrophil proportions. Correcting for buccal proportion attenuated the weak association with PedBE age acceleration to non-significance during initial estimation. Notably, correcting for buccal proportion attenuated the association of PedBE age acceleration with obsessive-compulsive disorder and strengthened the association with diurnal cortisol slope. Thus, the age-related change in children’s oral cells is a crucial consideration for cell type-sensitive research. Cheek swabs are widely used in pediatric epigenetic studies, but changes in their cellular composition with age are unclear. Here the authors show that buccal epithelial cells decline with age until adolescence, then stabilize, while variability increases with age, impacting the precision of tools like the PedBE clock in pediatric epigenetics.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".