Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».