Physiological response during exercise is preserved in children exposed to a suboptimal gestational environment
Bibliographic record
Abstract
Abstract Objective This study investigated whether exposure to suboptimal gestational factors (SGFs) alters mechanical efficiency (ME) and substrate oxidation during rest and exercise in children as a mechanism contributing to obesity. Methods Data from the Quebec Adiposity and Lifestyle Investigation in Youth cohort were used. Children aged 8 to 10 years performed an incremental maximal cycling test with indirect calorimetry. Their ME was measured during submaximal and maximal effort. The substrate oxidation during rest and submaximal effort was also computed. ME and substrate oxidation results between children exposed to each SGF during pregnancy (gestational diabetes mellitus: n = 68; hypertensive disorders: n = 49; maternal smoking: n = 77) and nonexposed children (n = 370) were compared. Results No difference was observed for ME during submaximal (F[3,540] = 0.46, p = 0.713) and maximal effort (F[3,545] = 0.86, p = 0.463) between exposed and nonexposed children. The percentage contributions of lipids and carbohydrates did not differ during rest (F[3,545] =1.68, p = 0.169) or submaximal exercise (F[3,544] = 0.31, p = 0.534) between exposed and nonexposed children. Conclusions Children exposed to investigated SGFs display a similar physiological response regarding ME and substrate oxidation during rest and exercise compared to nonexposed children. Future studies should confirm these novel results and continue investigating other research avenues to explain the higher risk of obesity in this population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".