The Effect of Physical Activity/Exercise on miRNA Expression and Function in Non-Communicable Diseases—A Systematic Review
Bibliographic record
Abstract
Exercise may differently affect the expression of key molecular markers, including skeletal muscle and circulating miRNAs, involved in cellular and metabolic pathways' regulation in healthy individuals and in patients suffering from non-communicable diseases (NCDs). Epigenetic factors are emerging as potential therapeutic biomarkers in the prognosis and treatment of NCDs and important epigenetic factors, miRNAs, play a crucial role in cellular pathways. This systematic review aims to underline the potential link between changes in miRNA expression after different types of physical activity/exercise in some populations affected by NCDs. In June 2023, we systematically investigated the following databases: PubMed, MEDLINE, Scopus, and Web of Science, on the basis of our previously established research questions and following the PRISMA guidelines. The risk of bias and quality assessment were, respectively, covered by ROB2 and the Newcastle Ottawa scale. Of the 1047 records extracted from the initial search, only 29 studies were found to be eligible. In these studies, the authors discuss the association between exercise-modulated miRNAs and NCDs. The NCDs included in the review are cancer, cardiovascular diseases (CVDs), chronic obstructive pulmonary disease (COPD), and type 2 diabetes mellitus (T2DM). We evidenced that miR-146, miR-181, miR-133, miR-21, and miRNA-1 are the most reported miRNAs that are modulated by exercise. Their expression is associated with an improvement in health markers and they may be a potential target in terms of the development of future therapeutic tools.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".