The effect of exercise mode on inflammation markers during pregnancy: A narrative review
Bibliographic record
Abstract
Excessive inflammation during pregnancy can be influenced by maternal factors (e.g., pre-pregnancy obesity, excessive gestational weight gain) and is associated with adverse pregnancy outcomes. Pro-inflammatory biomarkers such as tumor necrosis factor-alpha (TNF-α), interleukin 6 (IL-6), interleukin 1 beta (IL-1ß), interleukin-8 (IL-8), and c-reactive protein (CRP) are increased in individuals with gestational diabetes, preeclampsia, intrauterine growth restriction, and preterm birth. Maternal-fetal interface pathologies, such as recurrent spontaneous miscarriages, are also associated with increased pro-inflammatory biomarkers. Prolonged excessive inflammation can also increase fetal exposure to high inflammation in utero , contributing to the increased risk of cardiometabolic conditions later in life. Exercise is an effective method for reducing pro-inflammatory and increasing anti-inflammatory biomarkers in non-gravid adults and has differing effects depending on exercise mode. Exercise during pregnancy is associated with reduced risk for adverse pregnancy outcomes (APOs), enhanced placental function and structure, lower neonatal adiposity, and improved maternal and offspring cardiovascular measures. Although there are other methods for reducing inflammation during pregnancy (e.g., medications, dietary interventions), there is evidence to support that exercise during pregnancy may have a lasting effect on infant health, potentially improving the risk for chronic disease later in life. Recent research suggests that exercise during pregnancy reduces pro-inflammatory biomarkers, however, research is limited, and remains unclear whether there is an exercise mode effect similar to non-gravid adults. This review outlines the effect excessive inflammation has during pregnancy and explores existing literature on how different exercise modes during pregnancy may impact maternal, placental, and fetal inflammation.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".