Bibliographic record
Abstract
In their interesting article, Ruiz et al. (2013) raise a number of points in disputing our contention that excess long-term exercise training can be harmful. First, they question the relevance of animal models. Our animal work, which exposed cardiac risks of high-level exercise training and defined underlying mechanisms, showed cardiac remodelling very similar to changes seen in man (Benito et al. 2011; Guasch et al. 2013). Our rat model roughly replicates running 11 km in one hour, 5 days a week, for 10 years (Guasch et al. 2013), well within the range of human high-intensity training. Our findings of atrial/right-ventricular dilatation and myocardial fibrosis are well supported by clinical data (O’Keefe et al. 2012). Ruiz et al. suggest that the arrhythmogenic risks may be due to ‘undiagnosed underlying cardiac arrhythmogenic diseases’. In studies of atrial fibrillation associated with high-level exercise training, the cardiac abnormalities seen are those typically caused by exercise itself (Sorokin et al. 2011). Individuals with ventricular arrhythmias often show structural abnormalities, but these may be caused by exercise effects rather than underlying disease per se (La Gerche et al. 2010), and may be due to an interaction between exercise effects and underlying vulnerabilities due to genetic variants. Ruiz et al. also argue that exercise-induced cardiac troponin release is physiological. Only long-term correlation with indices of myocardial damage can resolve this issue, but release-peaks both during and after exercise (Middleton et al. 2008) suggest possibly delayed deleterious effects. While a minority of marathon runners show myocardial scarring on CMR imaging, the prevalence remains ∼3-fold that of age-matched controls (Breuckmann et al. 2009), and may be underestimated because of limited test sensitivity (Jellis et al. 2010). Ruiz et al. emphasize that post-marathon cardiac damage appears greater in less-trained athletes. This should not mask the fact that sudden-death risk in young unscreened competitors remains 2.5-fold higher than in sedentary individuals (Corrado et al. 2006). Moreover, they overlook the evidence for significantly enhanced coronary artery pathology among super-marathoners completing ≥25 marathons over 25 years (Schwartz et al. 2010). We are certainly not arguing that exercise training is bad: on the contrary, we reiterate its well-established benefits. Nevertheless, there are few biological exposures that lack ceilings to their beneficial amplitude–response curves, beyond which they become harmful. The evidence strongly suggests that there are levels of exercise training that, when exceeded, are deleterious for the heart. The trick is to know how much exercise of what type is optimal for each individual, in order to maximize benefit and minimize risk. Readers are invited to give their views on this and the accompanying CrossTalk articles in this issue by submitting a brief comment. Comments may be posted up to 6 weeks after publication of the article, at which point the discussion will close and authors will be invited to submit a ‘final word’. To submit a comment, go to http://jp.physoc.org/letters/submit/jphysiol;591/20/4947 None. This work is supported by the Canadian Institutes of Health Research (MOP68929), the Heart and Stroke Foundation of Canada, and the Fondation Leducq. Disclaimer: Supplementary materials have been peer-reviewed but not copyedited. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.003 |
| 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".