Genome-Wide DNA Methylation Changes Associated With High-Altitude Acclimatization During an Everest Base Camp Trek
Bibliographic record
Abstract
The individual physiological response to high-altitude hypoxia involves both genetic and non-genetic factors, including epigenetic modifications. Epigenetic changes in hypoxia factor pathway (HIF) genes are associated with high-altitude acclimatization. However, genome-wide epigenetic changes that are associated with short-term hypoxia exposure remain largely unknown. We collected a series of DNA samples from 15 participants of European ancestry trekking to Everest Base Camp to identify DNA methylation changes associated with incremental altitude ascent. We determined genome-wide DNA methylation levels using the Illumina MethylationEPIC chip comparing two altitudes: baseline 1,400 m (day 0) and elevation 4,240 m (day 7). The results of our epigenome-wide association study revealed 2,873 significant differentially methylated positions (DMPs) and 361 significant differentially methylated regions (DMRs), including significant positions and regions in hypoxia inducible factor (HIF) and the renin–angiotensin system (RAS) pathways. Our pathway enrichment analysis identified 95 significant pathways including regulation of glycolytic process (GO:0006110), regulation of hematopoietic stem cell differentiation (GO:1902036), and regulation of angiogenesis (GO:0045765). Lastly, we identified an association between theACEgene insertion/deletion (I/D) polymorphism and oxygen saturation, as well as averageACEmethylation. These findings shed light on the genes and pathways experiencing the most epigenetic change associated with short-term exposure to hypoxia.
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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 | 0.000 | 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) | 0.001 | 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".