Impact of Physical Activity and Exercise on the Epigenome in Skeletal Muscle and Effects on Systemic Metabolism
Why this work is in the frame
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Bibliographic record
Abstract
Exercise and physical activity induces physiological responses in organisms, and adaptations in skeletal muscle, which is beneficial for maintaining health and preventing and/or treating most chronic diseases. These adaptations are mainly instigated by transcriptional responses that ensue in reaction to each individual exercise, either resistance or endurance. Consequently, changes in key metabolic, regulatory, and myogenic genes in skeletal muscle occur as both an early and late response to exercise, and these epigenetic modifications, which are influenced by environmental and genetic factors, trigger those alterations in the transcriptional responses. DNA methylation and histone modifications are the most significant epigenetic changes described in gene transcription, linked to the skeletal muscle transcriptional response to exercise, and mediating the exercise adaptations. Nevertheless, other alterations in the epigenetics markers, such as epitranscriptomics, modifications mediated by miRNAs, and lactylation as a novel epigenetic modification, are emerging as key events for gene transcription. Here, we provide an overview and update of the impact of exercise on epigenetic modifications, including the well-described DNA methylations and histone modifications, and the emerging modifications in the skeletal muscle. In addition, we describe the effects of exercise on epigenetic markers in other metabolic tissues; also, we provide information about how systemic metabolism or its metabolites influence epigenetic modifications in the skeletal muscle.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it