Genetic characterization of physical activity behaviours in university students enrolled in kinesiology degree programs
Bibliographic record
Abstract
Studies of physical activity behaviours have increasingly shown the importance of heritable factors such as genetic variation. Nonsynonymous polymorphisms of alpha-actinin 3 (ACTN3) and the β-adrenergic receptors 1 and 3 (ADRB1 and ADRB3) have been previously associated with exercise capacity and cardiometabolic health. We thus hypothesized that these polymorphisms are also related to physical activity behaviours in young adults. To test this hypothesis we examined relationships between ACTN3 (R577X), ARDB1 (Arg389Gly), ADRB3 (Trp64Arg), and physical activity behaviours in university students. We stratified for student enrollment in kinesiology degree programs compared with nonmajors as we previously found this to be a predictor of physical activity. We did not identify novel associations between physical activity and ACTN3. However, the minor alleles of ADRB1 and ADRB3 were significantly underrepresented in kinesiology students compared with nonmajors. Furthermore, carriers of the ADRB1 minor allele reported reduced participation in moderate physical activity and increased afternoon fatigue compared with ancestral allele homozygotes. Together, these findings suggest that the heritability of physical activity behaviours in young adults may be linked to nonsynonymous polymorphisms within β-adrenergic receptors.
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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 | 0.001 | 0.001 |
| 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".