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Enregistrement W4210310992 · doi:10.1093/jncics/pkac011

Physical Activity to Improve Lung Cancer Survival: Promising Evidence

2022· letter· en· W4210310992 sur OpenAlexaff
Christine M. Friedenreich, Lin Yang

Notice bibliographique

RevueJNCI Cancer Spectrum · 2022
Typeletter
Langueen
DomaineMedicine
ThématiqueCancer survivorship and care
Établissements canadiensUniversity of CalgaryAlberta Health Services
Organismes subventionnairesnon disponible
Mots-clésMedicineLung cancerMEDLINEOncologyIntensive care medicinePhysical activityInternal medicinePhysical therapy

Résumé

récupéré en direct d'OpenAlex

We read with great interest the pooled analysis of 11 cohort studies by Yang et al. (1) in this issue of the Journal that examined how prediagnosis leisure-time (recreational) physical activity is associated with survival after lung cancer. The authors found a statistically significant 7% lower hazard of all-cause mortality among study participants who achieved 8.3 MET -h/wk or more of recreational physical activity compared with participants who were inactive. A stronger protective effect was found for those participants with localized cancer for whom a 20% lower mortality was observed for both all-cause and lung cancer–specific mortality. This study adds the most definitive evidence to date of an association between recreational physical activity done before lung cancer diagnosis and survival outcomes. There were 10 previous publications on the association between physical activity done either before or after lung cancer diagnosis and survival after lung cancer (2-11). In our published systematic review and meta-analysis (12) that we have recently updated with 1 additional study, we estimated that prediagnosis physical activity decreased risk of lung cancer-specific mortality by 19% (hazard ratio = 0.81, 95% confidence interval = 0.75 to 0.87) when comparing the highest with the lowest categories of activity level in 6 studies that provided estimates. For all cause-mortality, we estimated, based on 3 studies, there was a 27% decrease (hazard ratio = 0.73, 95% confidence interval = 0.60 to 0.94) with higher vs lower levels of postdiagnosis recreational physical activity. Of note, in the study by Yang and colleagues (1), only prediagnosis activity levels were available. Evidence from our systematic review (12) suggests that postdiagnosis activity levels have a greater impact on reducing both cancer-specific and all-cause mortality risks across cancer sites, including lung cancer. Hence, this evidence supports recommendations for cancer survivors to increase their levels of activity after diagnosis to improve their probability of survival (13). Several strengths in the pooled analysis conducted by Yang and colleagues (1) need to be highlighted. These investigators were able to combine 11 cohort studies conducted worldwide—7 in the USA, 2 across Europe, and 2 in China—which provides a fairly representative sample of the global population of lung cancer survivors. The sample size was considerable with more than 20 000 cases, of which nearly 17 000 had died, including nearly 14 000 lung cancer deaths; this enabled precise measures of the associations. Furthermore, data were available on tumor stage, grade, and histological type, which permitted a detailed assessment of mortality risk within these case subgroups that was not previously possible. The list of covariates selected a priori included major confounders and prognostic factors for lung cancer survival except for treatment data. The main limitations of this study (1) are related to the measurement of physical activity. Given the heterogeneity of methods used to assess physical activity across these cohorts, the authors were limited to examining only prediagnosis leisure-time physical activity. Furthermore, participants were categorized into 3 broad categories for these analyses (no activity, low active [>0-8.3 MET-h/wk], and moderately/highly active [≥8.3 MET-h/wk]). The moderately active group achieved the recommended levels for health benefits of 8.3-16.0 MET-h/wk equivalent to 150-300 minutes of moderate or 75-150 minutes of vigorous intensity activity per week. Although this study had a large sample size, too few participants were highly active (>16.0 MET-h/wk); hence, this level of activity could not be assessed separately from those who were moderately active. Yang et al. (1) have demonstrated the value of combining large cohort studies and conducting individual-level pooled data analyses that overcome many of the limitations of meta-analyses restricted to the published data. The next steps needed to overcome the limitations of their pooled analysis will be prospectively coordinated cohort studies conducted worldwide that include ethnically diverse populations and that use harmonized and standardized measures of physical activity that are a combination of direct measures and self-report questionnaires. These studies will require an extensive examination of confounders, effect modifiers, and a complete assessment of prognostic factors, including cancer treatments received. A key question that remains unresolved for both lung cancer risk and survival is the role of physical activity in mitigating the impact of smoking behaviors. Tobacco smoking is the most well-established lung cancer risk factor, with an estimated 72% of lung cancers and 17.5% of all cancers being directly attributable to this exposure and an additional 6% of lung cancers and 0.8% of all cancers attributable to passive tobacco smoking exposure (14). The paradox found with lung cancer risk is the apparent protective effect of physical activity apparent among ever smokers but not for nonsmokers (15). Yang and colleagues (1) noted a statistically significant protective effect of physical activity among former smokers, with a 20% reduction in all-cause mortality. For never smokers, a reduction in mortality risk was also observed, albeit not statistically significant and no association with physical activity was found among current smokers. These associations were not clearly observed for lung cancer–specific mortality, and residual confounding by smoking was acknowledged by the authors as a limitation. Yang and colleagues (1) have provided a clearer understanding of the role of prediagnosis recreational physical activity in improving survival after lung cancer. Although the evidence supporting a protective effect of physical activity on lung cancer risk appears weak (16), engaging in recreational physical activity before cancer diagnosis remains important for favorable survival outcomes among lung cancer survivors. Given the high morbidity and mortality associated with lung cancer, this pooled analysis provides welcome credibility to the rapidly accumulating observational evidence base that physical activity is a safe, effective adjuvant to conventional cancer treatments that can prolong both the quality and quantity of life after a cancer diagnosis. There remains an urgent need to delineate the exact type, dose, and timing of physical activity required to achieve those objectives by cancer site and by clinical-pathologic characteristics of each cancer patient. The ultimate objective is to be able to prescribe exercise interventions to cancer patients that are targeted and appropriate to them, their cancer, and their personal situation and that will improve their outcomes. This objective is rapidly coming closer to reality. None. Role of the funder: Not applicable. Disclosures: The authors have no disclosures. Author contributions: CMF, LY: Writing—original draft; writing—review and editing. CMF, who is a JNCI Cancer Spectrum Associate Editor and a co-author on this editorial, was not involved in the editorial review or decision to publish this editorial. No new data were generated or used for this editorial. All data cited can be found in the referenced sources.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,020
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,020
Score d'incertitude au seuil0,067

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,020
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0030,002
Science ouverte0,0010,001
Intégrité de la recherche0,0130,015
Charge utile insuffisante (le modèle a refusé de juger)0,0200,007

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 Opus0,043
Tête enseignante GPT0,359
Écart entre enseignants0,316 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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
Publié2022
Routes d'admission1
Résumé présentnon

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