A role for maternally derived myokines to optimize placental function and fetal growth across gestation
Bibliographic record
Abstract
Exercise during pregnancy is associated with improved health outcomes for both mother and baby, including a reduced risk of future obesity and susceptibility to chronic diseases. Overwhelming evidence demonstrates a protective effect of maternal exercise against fetal birth weight extremes, reducing the rates of both large- and small-for-gestational-age infants. It is speculated that this protective effect is mediated in part through exercise-induced regulation of maternal physiology and placental development and function. However, the specific mechanisms through which maternal exercise regulates these changes remain to be discovered. We hypothesize that myokines, a collection of peptides and cytokines secreted from contracting skeletal muscles during exercise, may be an important missing link in the story. Myokines are known to reduce inflammation, improve metabolism and enhance macronutrient transporter expression and activity in various tissues of nonpregnant individuals. Little research to date has focused on the specific roles of the myokine secretome in the context of pregnancy; however, it is likely that myokines secreted from exercising skeletal muscles may modulate the maternal milieu and directly impact the vital organ of pregnancy-the placenta. In the current review, data in strong support of this potential role of myokines will be presented, suggesting myokine secretion as a key mechanism through which maternal exercise optimizes fetal growth trajectories. It is clear that further research is warranted in this area, as knowledge of the biological roles of myokines in the context of pregnancy would better inform clinical recommendations for exercise during pregnancy and contribute to the development of important therapeutic interventions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".