Abstract B13: Adherence to cancer-specific prevention recommendations reduces risk of cancer in participants in Alberta's Tomorrow Project, Alberta, Canada
Notice bibliographique
Résumé
Abstract Background: The World Cancer Research Fund (WCRF) and the American Institute for Cancer Research (AICR) published specific recommendations on food and nutrition, physical activity, body composition, and other modifiable risk factors in 2007 with the aim of reducing risk for cancer. However, inconsistent results have been reported regarding the impact of following these recommendations on cancer incidence and cancer mortality. Further, the impact of adhering to cancer-specific recommendations issued by WCRF/AICR has not been evaluated in a Canadian population. Objective: The objective of this study was to estimate the association between adherence to cancer-specific prevention recommendations and subsequent cancer risk in a prospective Canadian cohort; Alberta's Tomorrow Project (ATP). Design: ATP is a large population-based cohort of 55,000 adults who will be followed for up to 50 years to study the etiology of cancer and chronic disease, providing information that will help establish targeted prevention strategies. In the present study, 25,100 adult Albertans (35-69 years) with no previous diagnosis of cancer were recruited into ATP from 2001 to 2009 by random digit dialing. Self-administered questionnaires (Health and Lifestyle Questionnaire, Canadian Diet History Questionnaire (past year food frequency questionnaire), and Past Year Total Physical Activity Questionnaire) were used to collect participants' health and lifestyle information. A composite score was constructed to reflect each participant's overall adherence to seven WCRF/AICR personal recommendations, including body fatness, physical activity, consumption of fruits and vegetables, consumption of red meat, alcoholic drinks, dietary supplements intake, and tobacco exposure (including second hand smoke). Incidence cancer cases (excluding non-melanoma skin cancer) were identified through linkage with the Alberta Cancer Registry in 2015. Multivariable Cox proportional hazard regression models were employed to assess the association (HRs and 95% CIs) between the adherence composite score and risk of developing cancer. Results: Over a median of 10.0 years (252,120 person-years) follow-up, 1,709 participants developed cancer. After adjustment for potential confounding covariates, participants who were most adherent to WCRF/AICR recommendations (composite score: 4-7) were 19% (HR 0.81, 95% CI: 0.71-0.91) less likely to develop cancer when compared to those who were least adherent to the same recommendations (composite score: 0-2). Each unit increase in the composite score corresponded to an 8% (HR 0.92, 95% CI: 0.88-0.96) reduction in risk of developing cancer. When stratified by sex, the associations remained significant for women, but not for men. Conclusions: Adherence to cancer-specific prevention recommendations may reduce the risk of developing cancer. The adherence composite score constructed in this study may serve as a surrogate lifestyle indictor for predicting those at high risk of developing cancer. Future work should focus on assessing contribution of each component of the overall risk reduction within the total score. Disclosure: This study was supported by Alberta Cancer Foundation, the Alberta Cancer Prevention Legacy Fund, and the Canadian Partnership Against Cancer. Citation Format: Jian-Yi Xu, Jennifer E Vena, Heather K Whelan, Paula J Robson. Adherence to cancer-specific prevention recommendations reduces risk of cancer in participants in Alberta's Tomorrow Project, Alberta, Canada. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr B13.
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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».