Author's Response * Dietary patterns and the risk of mortality: impact of cardiorespiratory fitness
Notice bibliographique
Résumé
We would like to thank Drs Ding, Hu and Pischon for their insights and commentaries on our paper ‘Dietary patterns and the risk of mortality: impact of cardiorespiratory fitness’.1 In the spirit of a healthy debate, there are a few issues that we would like to address in our response. We would first like to highlight that the purpose of our paper was not to argue against the role of an unhealthy diet as a risk factor for morbidity and mortality. We are in full agreement that a healthy diet is important and that there is an abundance of evidence to support this position. Rather, our goal was to highlight that previous studies examining the relation between dietary patterns and health have not properly controlled for the confounding effects of physical activity, which, without exception, have been measured by self-report. Validation studies have consistently found that self-reported physical activity measures are biased (e.g. most people overestimate their activity) and only modestly associated with objective measures of physical activity.2–4 The imprecision of these estimates would result in an underestimated effect of physical activity on the morbidity and mortality outcomes, and in studies of unhealthy dietary patterns and poor health, would result in residual confounding for physical activity. Our study attempted to overcome this limitation by including an objective marker of physical activity as a covariate in the analyses. Indeed, the risk estimates for dietary patterns were substantively smaller when cardiorespiratory fitness was the covariate compared with when self-reported physical activity was the covariate. Next, we would like to address the concern regarding the use of cardiorespiratory fitness as a marker of physical activity participation. We appreciate that fitness is not a direct measure of physical activity and that other factors such as genetics, age and sex play a role in determining one’s fitness. However, by using age- and sex-specific cut-points to define the different fitness groups, our analyses accounted for some of the most meaningful non-activity determinants of fitness. Furthermore, several studies have shown that fitness is highly related to physical activity participation in recent months2,5,6 and that fitness is responsive to changes in physical activity.7–9 Thus, while we recognize that there is not a perfect relation between physical activity and fitness, it is clear that physical activity is a major driver of fitness. We, therefore, are confident that cardiorespiratory fitness can be used as a proxy and objective measure of physical activity. Both commentaries argued that diet is an important determinant of cardiorespiratory fitness. We do not support this position. Randomized controlled trials have clearly demonstrated that diet-induced weight loss is not associated with improvements in fitness, whereas exercise with or without weight loss is.6,8 Dr Pischon is correct in that dietary factors affect lipid and glucose metabolism. However, while these changes in metabolism would alter long-distance endurance performance (e.g. distance run in 1 h), they would not have a meaningful impact on performance in a relatively short (e.g. 15 min) cardiorespiratory fitness test, such as that performed in our study, as the utilization of fats relative to carbohydrates is not a determinant of success in a test of this duration. Thus, we do not feel the comments that diet is a determinant of cardiorespiratory fitness are supported by research findings or biological plausibility. Subsequently, we do not agree that dietary associations are mediated rather than confounded by fitness, or that fitness is an intermediary variable in the association of an unhealthy dietary pattern and mortality risk. Another important point that was raised in the commentaries was about the choice of biomarker response variables for the reduced rank regression (RRR) analysis. While the biomarkers chosen for this study are primarily considered as risk factors related to cardiovascular disease, we would like to point out that they are also related to numerous other chronic diseases such as type 2 diabetes and some cancers. Nonetheless, we agree that the number of risk factors considered was limited and does not represent a complete global index of all dietary effects on cardiovascular or all-cause mortality. In summary, while no study or analysis is perfect, we strongly believe that the results of this article are important and provide strong evidence for the need to use objective measures of physical activity in studies examining the relation between diet and health. We agree that it would be interesting to examine the confounding effects of objectively measured physical activity using accelerometers in comparison with cardiorespiratory fitness. However, at the present time we are unaware of any large, prospective studies in adults that have obtained such measures. Conflict of interest: 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 machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,114 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,015 | 0,019 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,061 | 0,024 |
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 source (Gemma direct ou Codex distillé), 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 ».