Association of a Healthy Diet Score with prostate cancer severity in newly diagnosed men: A cross-sectional analysis of RADICAL PC
Notice bibliographique
Résumé
Background: Prostate cancer remains the second most common cause of cancer-related death in men in the United States (Siegel et al. 2017). Observational studies of patients with prostate cancer have found associations between diet and prostate cancer severity, but the evidence is inconsistent or inconclusive. The purpose of this thesis is to implement a validated international healthy diet score and evaluate whether or not it is associated with prostate cancer severity. Objective: The objectives of this thesis were: Chapter 1: examine whether an association exists between diet quality, using the validated Healthy Diet Score, and the severity of prostate cancer, and Chapter 2: examine the agreement between two methods of dietary data collection (an abridged FFQ and a longer previously validated FFQ) with respect to macronutrients and main food groups. Methods: We used observational data from the Randomized Intervention for Cardiovascular and Lifestyle Risk Factors in Prostate Cancer Patients (RADICAL PC), a multi-centre Canadian prospective cohort study into which men with a new diagnosis of prostate cancer or who were being treated with androgen deprivation therapy were enrolled. To complete objective 1 (Chapter 1) of this dissertation, a cross-sectional analysis was completed using baseline data collected in the RADICAL PC study. In order to evaluate the association of diet with prostate cancer severity, the relationship between the Healthy Diet Score and prostate cancer severity (stage and grade) was assessed. The second objective (Chapter 2) is a comparability sub-study comparing an abridged FFQ with a long, validated FFQ in a subgroup of participant (N=130) enrolled in the RADICAL PC study. Results: Chapter 1: In the cross-sectional analysis of baseline data collected in RADICAL PC, a higher diet score was not significantly associated with prostate cancer severity. An association between age and the high-risk prostate cancer category was found to be statistically significant (OR: 1.04, 95%CI 1.02-1.05, p<0.00). Chapter 2: There was good agreement between the abridged FFQ and long FFQ for carbohydrates, proteins, whole wheat, refined grains, fish, dairy, potatoes, fruits, nuts, and soft drinks (Spearman rank correlation >0.5). Food groups including fried foods, processed meats, vegetables and total fats (nutrients) were found to have moderate correlation (Spearman rank correlation between 0.3-0.5). There was low correlation for legumes, sugars and oils. Bland-Altman plots showed good absolute agreements between the two methods, and reliability test using Spearman’s correlation showed moderate to good correlation (0.45 to 0.75 among most food groups. Conclusion: There was no clear association between a healthier diet and prostate cancer severity in men with newly diagnosed prostate cancer. There was adequate agreement between the abridged SFFQ and the long FFQ of the expected food groups, and thus the SFFQ can be considered an appropriate tool to use for measuring diet among prostate cancer patients for some food groups and nutrients.
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) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 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 ».