Multiomics Data Analyses Identified CpG Sites and Genes That Mediate the Impact of Exposure to Environmental Chemicals on Cardiometabolic Traits
Bibliographic record
Abstract
Understanding the molecular mechanism whereby an environmental chemical causes a disease is important for downstream purposes. In this study, an analytical workflow was designed to combine several publicly available datasets in order to identify CpG sites and genes that mediate the impact of exposure to environmental chemicals on cardiometabolic traits. Organophosphate and prenatal lead exposure were previously reported to change methylation level at the cg23627948 site. I found as cg23627948 site becomes methylated, the expression of GNA12 gene decreases and this leads to higher body fat percentage. Prenatal perfluorooctane sulfonate expo-sure was reported to increase the methylation level at cg21153102 site. The outcome of analyses revealed higher methylation at this site contributes to higher diastolic blood by changing the expression of CHP1 and GCHFR genes. Furthermore, HKR1 mediates the impact of B12 supple-mentation-cg05280698 hypermethylation on higher kidney function; while, CTDNEP1 mediates the impact of air pollution-cg03186999 hypomethylation on higher systolic blood pressure. This study reports CpG sites and genes that mediate the impact of environmental chemicals on cardiometabolic traits and describes a framework to investigate the underlying molecular paths using publicly available data.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".