Changes in nuclear and mitochondrial DNA methylation in cow blood associated with age and disease
Bibliographic record
Abstract
DNA methylation is among the most promising biomarkers for age prediction, enabling the development of epigenetic clocks that correlate methylation profiles with chronological age. In this study, we investigated the relationship between ageing and disease susceptibility, focusing on both nuclear and mitochondrial DNA methylation in dairy cows. Genome-wide DNA methylation profiling was performed using enzymatic methyl-seq, covering 53 million CpG sites. The dataset included 96 cows with different phenotypes, sampled cross-sectionally and ranging from 2 to 9 years of age. We applied elastic net regression to identify the most predictive CpG sites for age estimation, achieving a mean absolute error of 111 days with a strong correlation to chronological age r = 0.97. Beyond chronological age prediction, we assessed the impact of disease status on epigenetic ageing. Our results revealed accelerated epigenetic ageing in cows susceptible to diseases, suggesting a link between health-related stress and disrupted DNA methylation dynamics. We further identified age-associated promoter methylation changes, particularly in MAB21L1, which may play a role in molecular ageing mechanisms. Additionally, we observed a decline in mitochondrial DNA methylation with age, notably in genes encoding Cytochrome c oxidase (COX), indicating a possible connection between mitochondrial dysfunction and epigenetic regulation. An inverse correlation between D-loop methylation and mtDNA copy number was also observed. This study demonstrates the potential of epigenetic models for biological age prediction in livestock, while recognizing that their accuracy may vary among species with different lifespans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".