Epigenome-wide association study of nuclear DNA methylation in relation to mitochondrial heteroplasmy
Bibliographic record
Abstract
We analyze 10,986 participants (mean age 77; 63% women; 54% non-White) across seven U.S. cohorts to study the relationship between mitochondrial DNA (mtDNA) heteroplasmy and nuclear DNA methylation. We identify 597 CpGs associated with heteroplasmy burden, generally showing lower methylation. These CpGs are enriched in dynamically regulated island shores and depleted in CpG islands, indicating involvement in context-specific rather than constitutive gene regulation. In HEK293T cells, we introduce a truncating mtDNA mutation (MT-COX3, mt.9979) and observe a positive correlation between variant allele fraction and methylation at cg04569152, supporting a direct mtDNA–nDNA epigenetic link. Many heteroplasmy-associated CpGs overlap with known methylation-trait associations for metabolic and behavioral traits. Composite CpG scores predict all-cause mortality and incident CVD, with one-unit increases associated with 1.27-fold and 1.12-fold higher hazards, respectively. These findings suggest an mtDNA–nDNA epigenetic connection in aging and disease, though its direction and mechanisms remain to be studied. Mitochondrial DNA heteroplasmy associates with context-specific nuclear CpG methylation, supported by a cell model. Heteroplasmy–CpG scores relate to mortality and cardiovascular risk, though direction and mechanisms remain unclear.
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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 | 0.001 |
| 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 | 0.000 | 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".