The Saliva Exposome for Monitoring of Individuals’ Health Trajectories
Bibliographic record
Abstract
BACKGROUND: There is increasing evidence that environmental, rather than genetic, factors are the major causes of most chronic diseases. By measuring entire classes of chemicals in archived biospecimens, exposome-wide association studies (EWAS) are being conducted to investigate associations between a myriad of exposures received during life and chronic diseases. OBJECTIVES: Because the intraindividual variability in biomarker levels, arising from changes in environmental exposures from conception onwards, leads to attenuation of exposure-disease associations, we posit that saliva can be collected repeatedly in longitudinal studies to reduce exposure-measurement errors in EWAS. METHODS: From the literature and an open-source saliva-metabolome database, we obtained concentrations of 1,233 chemicals that had been detected in saliva. We connected salivary metabolites with human metabolic pathways and PubMed Medical Subject Heading (MeSH) terms, and performed pathway enrichment and pathway topology analyses. RESULTS: One hundred ninety-six salivary metabolites were mapped into 49 metabolic pathways and connected with human metabolic diseases, central nervous system diseases, and neoplasms. We found that the saliva exposome represents at least 14 metabolic pathways, including amino acid metabolism, TCA cycle, gluconeogenesis, glutathione metabolism, pantothenate and CoA biosynthesis, and butanoate metabolism. CONCLUSIONS: Saliva contains molecular information worthy of interrogation via EWAS. The simplicity of specimen collection suggests that saliva offers a practical alternative to blood for measurements that can be used to characterize individual exposomes. https://doi.org/10.1289/EHP1011.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".