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)

Nada Assi, Aurelie Moskal, Nadia Slimani, Vivian Viallon, Veronique Chajes, Heinz Freisling, Stefano Monni, Sven Knueppel, Jana Förster, Elisabete Weiderpass, Leila Lujan-Barroso, Pilar Amiano, Eva Ardanaz, Esther Molina-Montes, Diego Salmerón, José Ramón Quirós, Anja Olsen, Anne Tjønneland, Christina C. Dahm, Kim OvervadLaure Dossus, Agnès Fournier, Laura Baglietto, Renee Turzanski Fortner, Rudolf Kaaks, Antonia Trichopoulou, Christina Bamia, Philippos Orfanos, Maria Santucci De Magistris, Giovanna Masala, Claudia Agnoli, Fulvio Ricceri, Rosario Tumino, H. Bas Bueno de Mesquita, Marije F. Bakker, Petra H M Peeters, Guri Skeie, Tonje Braaten, Anna Winkvist, Ingegerd Johansson, Kay Tee Khaw, Nicholas J. Wareham, Tim Key, Ruth Travis, Julie A. Schmidt, Melissa A. Merritt, Elio Riboli, Isabelle Romieu, Pietro Ferrari

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Design: Nutrient patterns were derived with treelet transform (TT) and related to BC risk. TT was applied to twenty-three log-transformed nutrient densities from dietary questionnaires. Hazard ratios (HR) and 95 % confidence intervals computed using Cox proportional hazards models quantified the association between quintiles of nutrient pattern scores and risk of overall BC, and by hormonal receptor and menopausal status. Principal component analysis was applied for comparison.

Setting: The European Prospective Investigation into Cancer and Nutrition (EPIC).

Subjects: Women (n 334 850) from the EPIC study.

Results: The first TT component (TC1) highlighted a pattern rich in nutrients found in animal foods loading on cholesterol, protein, retinol, vitamins B12 and D, while the second TT component (TC2) reflected a diet rich in β-carotene, riboflavin, thiamin, vitamins C and B6, fibre, Fe, Ca, K, Mg, P and folate. While TC1 was not associated with BC risk, TC2 was inversely associated with BC risk overall (HRQ5 v. Q1=0·89, 95 % CI 0·83, 0·95, P trendQ5 v. Q1=0·89, 95 % CI 0·81, 0·98, P trend=0·02) and progesterone receptor-positive tumours (HRQ5 v. Q1=0·87, 95 % CI 0·77, 0·98, P trend

Conclusions: TT produces readily interpretable sparse components explaining similar amounts of variation as principal component analysis. Our results suggest that participants with a nutrient pattern high in micronutrients found in vegetables, fruits and cereals had a lower risk of BC.

Original languageEnglish
JournalPublic Health Nutrition
DOIs
Publication statusAccepted/In press - Feb 23 2015

Keywords

  • Breast cancer
  • European Prospective Investigationinto Cancer and Nutrition
  • Nutrient patterns
  • Principal component analysis
  • Treelet transform

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Nutrition and Dietetics
  • Public Health, Environmental and Occupational Health

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