Intra‐individual differences in the effect of endurance versus resistance training on vascular function: A cross‐over study
Bibliographic record
Abstract
We used a within‐subject, cross‐over design study to compare the impact of 4‐weeks' resistance (RT) versus endurance (END) training on vascular function. We subsequently explored the association of intra‐individual effects of RT versus END on vascular function with a single nucleotide polymorphism (SNP) of theNOS3gene. Thirty‐five healthy males (21 ± 2 years old) were genotyped for theNOS3rs2070744 SNP and completed both training modalities. Participants completed 12 sessions over a 4‐week period, either RT (leg‐extension) or END (cycling) training in a randomized, balanced cross‐over design with a 3‐week washout period. Participants performed peak oxygen uptake (peak VO2) and leg‐extension single‐repetition maximum (1‐RM) testing, and vascular function assessment using flow‐mediated dilation (FMD) on 3 separated days pre/post‐training. Peak VO2increased after END (p < 0.001), while 1‐RM increased after RT (p < 0.001). FMD improved after 4‐weeks’ training (time effect:p = 0.006), with no difference between exercise modalities (interaction effect:p = 0.92). No relation was found between individual changes (delta, pre‐post) in FMD to both types of training (R2 = 0.06,p = 0.14). Intra‐individual changes in FMD following END and RT were associated with theNOS3SNP, with TT homozygotes significantly favoring only END (p = 0.016) and TC/CC tending to favor RT only (p = 0.056). Although both training modes improved vascular function, significant intra‐individual variation in the adaptation of FMD was found. The association withNOS3genotype suggests a genetic predisposition to FMD adapting to a specific mode of chronic exercise. This study therefore provides novel evidence for personalized exercise training to optimize vascular health.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".