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Enregistrement W3205447966 · doi:10.1113/ep090070

Exercise is medicine for chronic mountain sickness

2021· letter· en· W3205447966 sur OpenAlexaff
André L. Teixeira, J. Lang

Notice bibliographique

RevueExperimental Physiology · 2021
Typeletter
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueHigh Altitude and Hypoxia
Établissements canadiensUniversity of Guelph
Organismes subventionnairesnon disponible
Mots-clésPhlebotomyMedicineEffects of high altitude on humansBloodlettingIncidence (geometry)Altitude sicknessPhysical therapyPhysiologyInternal medicinePathology

Résumé

récupéré en direct d'OpenAlex

It is estimated that > 80 million people live above 2,500 m a.s.l. worldwide (Tremblay & Ainslie, 2021). Chronic exposure to low levels of oxygen causes several physiological adaptations and has been an active area of investigation for well over 100 years. However, ∼5–10% of individuals living at high altitude develop chronic mountain sickness (CMS), a syndrome characterized by hypoxaemia, excessive erythrocytosis and increased blood viscosity, which has several adverse neurological and cerebrovascular consequences (León-Velarde et al. 2005; Villafuerte & Corante, 2016). The incidence of CMS increases with elevation and with advancing age, with as many as one-third of older Andeans exhibiting CMS (Monge et al. 1989). Current treatments for CMS include therapeutic phlebotomy (bloodletting) and descent to lower altitudes. Phlebotomy has been shown to reduce haematocrit, improve oxygenation and, subsequently, to ameliorate CMS symptoms. Nonetheless, these improvements appear to be transient, receding a few weeks after the treatment (Villafuerte & Corante, 2016). In addition, the invasive nature of phlebotomy and the fact that it can also cause iron deficiency and induce pulmonary hypertension make it a difficult long-term treatment. Likewise, descent to lower altitudes or sea level is impractical for to social and economic reasons and is also not a permanent solution, because the CMS symptoms reappear after returning to a high altitude (León-Velarde et al. 2005; Villafuerte & Corante, 2016). A low-cost, non-invasive and non-pharmacological treatment for patients with CMS is currently unavailable. Regular physical exercise provides numerous physiological benefits, not only in healthy individuals, but also in a variety of clinical conditions. A previous report has demonstrated that native Andean athletes possess lower haematocrits than their sedentary counterparts (Cornolo et al. 2005). In this scenario, it is reasonable to speculate that exercise training could be beneficial for patients with CMS. Nevertheless, to date, the impact of exercise training on patients with CMS has not been studied. To fill this knowledge gap, in this issue of Experimental Physiology, Macarlupú et al. (2021) sought to determine the effects of moderate-intensity aerobic exercise training on markers of CMS in native Andean highlanders. Eight male participants diagnosed with CMS (pretraining haematocrit of ∼71%) performed 60 min of moderate-intensity cycling exercise (60% of peak oxygen uptake) 4 days per week for 8 weeks. The primary outcomes were the haematocrit and CMS signs and symptoms (Qinghai CMS questionnaire). Secondary outcomes included 24 h ambulatory blood pressure monitoring, in addition to blood levels of glucose, insulin, cholesterol and erythropoietin. Measurements were performed before and 4 and 8 weeks after exercise training. As expected, 8 weeks of moderate-intensity aerobic exercise increased the peak oxygen uptake by ∼10%, showing the effectiveness of the training protocol. In addition, exercise training decreased the haematocrit and CMS score, and these responses were already apparent after 4 weeks of training. The haematocrit decreased progressively by 5% and 7% and the CMS score decreased by ∼46% and ∼40% at weeks 4 and 8 of exercise training, respectively. In contrast, exercise training did not change 24 h blood pressure or resting glycaemia, insulinaemia, erythropoietin or lipid profile, with the exception of increased high-density lipoprotein–cholesterol after 8 weeks of exercise training. Collectively, these findings demonstrated that moderate-intensity aerobic exercise training can reduce haematocrit and alleviate symptoms of CMS in native Andean highlanders, providing the first evidence that exercise training might be used as a non-pharmacological therapy for CMS. It is important to consider that because the experiments were performed in young male Andeans, the impact of exercise training on female and/or older patients with CMS requires further investigation. In addition, Macarlupú et al. (2021) studied residents of Cerro de Pasco, Peru (4,340 m). Given that the prevalence and severity of CMS increase with elevation (León-Velarde et al. 2005; Villafuerte & Corante, 2016), the extent to which exercise training is effective in different highlander populations remains unknown. Furthermore, it is well accepted that different exercise intensities, frequencies and modalities (i.e., continuous aerobic, high-intensity interval training and resistance training) can induce different physiological responses and adaptations. Hence, future studies are required to determine the effects of different exercise training programmes in patients with CMS. Another open question is the impact of detraining on these patients. Do the haematocrit and CMS symptoms return to pretraining values after detraining? How long do the beneficial effects of 8 weeks of moderate-intensity aerobic training last? Notably, the mechanisms by which moderate-intensity aerobic training reduce haematocrit and CMS symptoms in native highlanders remain largely unknown. In conclusion, Macarlupú et al. (2021) are to be commended on performing a challenging training study in a unique clinical population. The authors not only advanced our current understanding on the beneficial effects of exercise training in patients with CMS, but also created new key research questions that broadly impact and determine the extent to which exercise training is medicine for patients with CMS. None declared.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,317
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,016
Tête enseignante GPT0,290
Écart entre enseignants0,274 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2021
Routes d'admission1
Résumé présentoui

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