Not all saliva samples are equal: The role of cellular heterogeneity in DNA methylation and epigenetic age analyses with biological and psychosocial factors
Bibliographic record
Abstract
Saliva is widely used in biomedical population research, including epigenetic analyses to investigate gene-environment interplay and identify biomarkers. Its minimally invasive collection procedure makes it ideal for studies in pediatric populations. Saliva is a heterogenous tissue composed of immune and buccal epithelial cells (BEC). Amongst the many epigenetic marks, DNA methylation (DNAm) is the most studied in human populations. DNAm profiles are often highly cell type (CT)-specific. CT composition can drive salivary DNAm associations with environments or health as well as epigenetic age acceleration (EAA), which is the discrepancy between chronological and biological age derived from DNAm. To address this, reference-based CT deconvolution and statistical adjustment with estimated CT in DNAm analyses have become a common practice. However, it remains unclear how different CT reference panels-constructed from adult versus pediatric samples-affect DNAm results. Additionally, whether DNAm and EAA associations in saliva primarily originate from immune cells or BECs, or if they persist across saliva samples despite varying CT proportions, still requires more investigations. The current study used salivary DNAm samples obtained from 529 children (mean age=7.26 years, SD=0.26 years) in a community-based cohort, the Family Life Project. Our results demonstrated that the child reference panel outperformed the adult one based on goodness of fit measure and highlighted the impact of estimated CT discrepancies across reference panels on DNAm associations. Upon stratifying the salivary DNAm samples into three subsamples-primarily BECs, primarily immune cells, and an approximately equal mix of both, we found significantly different EAAs across stratified samples when CT proportions were not accounted for. In both the contexts of DNAm and EAA associations, we detected stronger effects of cotinine concentrations, a tobacco smoke-exposure biomarker, in the subsample with primarily immune cells. We discussed the implications of our findings for the interpretation and replication of epigenetic research involving pediatric saliva samples.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".