LIFESTYLE AND BREAST CANCER RISK FACTORS IN POSTMENOPAUSAL CAUCASIAN AND CHINESE-CANADIAN WOMEN
Notice bibliographique
Résumé
Striking differences exist between countries in the incidence of breast cancer, with rates higher in the West than in Asian countries. The causes of these differences are unknown, but because incidence rates change in migrants, they are thought to be due to lifestyle rather than genetic differences. The objective of this thesis was to compare established breast cancer risk factors, physical activity, and diet in three groups of postmenopausal women at substantially different risks of developing breast cancer – Caucasians (N = 413), Chinese born in the West or who migrated to the West before age 21 (N = 216), and recent Chinese migrants, 99% of whom coming from urban China (N = 421). In this cross-sectional study, information on risk factors and diet were collected by telephone, and physical activity and anthropometric data were obtained at a home visit. Compared to Caucasians, recent Chinese migrants weighed on average 14 kg less, were 6 cm shorter, had menarche a year later, were more often parous, and less often had a family history of breast cancer or a benign breast biopsy. Estimating 5-year absolute breast cancer risks using the Gail Model showed that risk estimates in Caucasians would be reduced by only 11% if they had the risk factor profile of recent Chinese migrants for the variables in the Gail Model. Compared to Caucasians, recent Chinese migrants had lower average total physical activity over lifetime, and also spent less time on moderate- and vigorous-intensity activity. Compared to Caucasians, recent Chinese migrants consumed per day on average 175 fewer calories, 6 more grams of energy-adjusted protein, 16 more grams of energy-adjusted carbohydrates, and 5 fewer grams of energy-adjusted fat. Also, recent Chinese migrants consumed higher amounts of grains, fruits, vegetables, fish, and soy products, and lower amounts of alcohol, meat, dairy products, and sweets than Caucasians. Western born Chinese and early Chinese migrants had values intermediate between the other two groups for most of the variables. These results suggest that in addition to the established risk factors, some dietary factors may also contribute to the lower breast cancer risk in urban Chinese women.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».