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A treelet transform analysis to relate nutrient patterns to the risk of hormonal receptor-defined breast cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC)

Assi, Nada; Moskal, Aurelie; Slimani, Nadia; Viallon, Vivian; Chajes, Veronique; Freisling, Heinz; Monni, Stefano; Knueppel, Sven; Förster, Jana; Weiderpass, Elisabete; Lujan-Barroso, Leila; Amiano, Pilar; Ardanaz, Eva; Molina-Montes, Esther; Salmeron, Diego; Quiros, Jose Ramon; Olsen, Anja; Tjonneland, Anne; Dahm, Christina C; Overvad, Kim; Dossus, Laure; Fournier, Agnes; Baglietto, Laura; Fortner, Renee Turzanski; Kaaks, Rudolf; Trichopoulou, Antonia; Bamia, Christina; Orfanos, Philippos; de Magistris, Maria Santucci; Masala, Giovanna; Agnoli, Claudia; Ricceri, Fulvio; Tumino, Rosario; Bueno De Mesquita, H Bas; Bakker, Marije F; Peeters, Petra HM; Skeie, Guri; Braaten, Tonje; Winkvist, Anna; Johansson, Ingegerd; Khaw, Kay-Tee; Wareham, Nicholas J; Key, Tim; Travis, Ruth; Schmidt, Julie A; Merritt, Melissa A; Riboli, Elio; Romieu, Isabelle; Ferrari, Pietro (Journal article; Tidsskriftartikkel; Peer reviewed, 2015-02-23)

Objective: Pattern analysis has emerged as a tool to depict the role of multiple nutrients/foods in relation to health outcomes. The present study aimed at extracting nutrient patterns with respect to breast cancer (BC) aetiology.<br> Design: Nutrient patterns were derived with treelet transform (TT) and related to BC risk. TT was applied to twenty-three log-transformed nutrient densities ...

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A treelet transform analysis to relate nutrient patterns to the risk of hormonal receptor-defined breast cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC)