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 Overvad; Laure 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

doi:10.1017/S1368980015000294

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^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

1 Citation (Scopus)

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 (HR_{Q5 v. Q1}=0·89, 95 % CI 0·83, 0·95, P _trend<0·01) and showed a significantly lower risk in oestrogen receptor-positive (HR_{Q5 v. Q1}=0·89, 95 % CI 0·81, 0·98, P _trend=0·02) and progesterone receptor-positive tumours (HR_{Q5 v. Q1}=0·87, 95 % CI 0·77, 0·98, P _trend<0·01).

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 language	English
Pages (from-to)	242-254
Journal	Public Health Nutrition
Volume	19
Issue number	2
DOIs	https://doi.org/10.1017/S1368980015000294
Publication status	Published - Feb 2016

Keywords

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

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Assi, N., Moskal, A., Slimani, N., Viallon, V., Chajes, V., Freisling, H., Monni, S., Knueppel, S., Förster, J., Weiderpass, E., Lujan-Barroso, L., Amiano, P., Ardanaz, E., Molina-Montes, E., Salmerón, D., Quirós, J. R., Olsen, A., Tjønneland, A., Dahm, C. C., ... Ferrari, P. (2016). 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). Public Health Nutrition, 19(2), 242-254. https://doi.org/10.1017/S1368980015000294

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title = "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)",

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 trend<0·01) and showed a significantly lower risk in oestrogen receptor-positive (HRQ5 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<0·01).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.",

keywords = "Breast cancer, European Prospective Investigationinto Cancer and Nutrition, Nutrient patterns, Principal component analysis, Treelet transform",

author = "Nada Assi and Aurelie Moskal and Nadia Slimani and Vivian Viallon and Veronique Chajes and Heinz Freisling and Stefano Monni and Sven Knueppel and Jana F{\"o}rster and Elisabete Weiderpass and Leila Lujan-Barroso and Pilar Amiano and Eva Ardanaz and Esther Molina-Montes and Diego Salmer{\'o}n and Quir{\'o}s, {Jos{\'e} Ram{\'o}n} and Anja Olsen and Anne Tj{\o}nneland and Dahm, {Christina C.} and Kim Overvad and Laure Dossus and Agn{\`e}s Fournier and Laura Baglietto and Fortner, {Renee Turzanski} and Rudolf Kaaks and Antonia Trichopoulou and Christina Bamia and Philippos Orfanos and {De Magistris}, {Maria Santucci} and Giovanna Masala and Claudia Agnoli and Fulvio Ricceri and Rosario Tumino and {Bueno de Mesquita}, {H. Bas} and Bakker, {Marije F.} and Peeters, {Petra H M} and Guri Skeie and Tonje Braaten and Anna Winkvist and Ingegerd Johansson and Khaw, {Kay Tee} and Wareham, {Nicholas J.} and Tim Key and Ruth Travis and Schmidt, {Julie A.} and Merritt, {Melissa A.} and Elio Riboli and Isabelle Romieu and Pietro Ferrari",

year = "2016",

month = feb,

doi = "10.1017/S1368980015000294",

language = "English",

volume = "19",

pages = "242--254",

journal = "Public Health Nutrition",

issn = "1368-9800",

publisher = "Cambridge University Press",

number = "2",

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Assi, N, Moskal, A, Slimani, N, Viallon, V, Chajes, V, Freisling, H, Monni, S, Knueppel, S, Förster, J, Weiderpass, E, Lujan-Barroso, L, Amiano, P, Ardanaz, E, Molina-Montes, E, Salmerón, D, Quirós, JR, Olsen, A, Tjønneland, A, Dahm, CC, Overvad, K, Dossus, L, Fournier, A, Baglietto, L, Fortner, RT, Kaaks, R, Trichopoulou, A, Bamia, C, Orfanos, P, De Magistris, MS, Masala, G, Agnoli, C, Ricceri, F, Tumino, R, Bueno de Mesquita, HB, Bakker, MF, Peeters, PHM, Skeie, G, Braaten, T, Winkvist, A, Johansson, I, Khaw, KT, Wareham, NJ, Key, T, Travis, R, Schmidt, JA, Merritt, MA, Riboli, E, Romieu, I & Ferrari, P 2016, '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)', Public Health Nutrition, vol. 19, no. 2, pp. 242-254. https://doi.org/10.1017/S1368980015000294

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 et al.
In: Public Health Nutrition, Vol. 19, No. 2, 02.2016, p. 242-254.

Research output: Contribution to journal › Article › Academic › peer-review

TY - JOUR

T1 - 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)

AU - Assi, Nada

AU - Moskal, Aurelie

AU - Slimani, Nadia

AU - Viallon, Vivian

AU - Chajes, Veronique

AU - Freisling, Heinz

AU - Monni, Stefano

AU - Knueppel, Sven

AU - Förster, Jana

AU - Weiderpass, Elisabete

AU - Lujan-Barroso, Leila

AU - Amiano, Pilar

AU - Ardanaz, Eva

AU - Molina-Montes, Esther

AU - Salmerón, Diego

AU - Quirós, José Ramón

AU - Olsen, Anja

AU - Tjønneland, Anne

AU - Dahm, Christina C.

AU - Overvad, Kim

AU - Dossus, Laure

AU - Fournier, Agnès

AU - Baglietto, Laura

AU - Fortner, Renee Turzanski

AU - Kaaks, Rudolf

AU - Trichopoulou, Antonia

AU - Bamia, Christina

AU - Orfanos, Philippos

AU - De Magistris, Maria Santucci

AU - Masala, Giovanna

AU - Agnoli, Claudia

AU - Ricceri, Fulvio

AU - Tumino, Rosario

AU - Bueno de Mesquita, H. Bas

AU - Bakker, Marije F.

AU - Peeters, Petra H M

AU - Skeie, Guri

AU - Braaten, Tonje

AU - Winkvist, Anna

AU - Johansson, Ingegerd

AU - Khaw, Kay Tee

AU - Wareham, Nicholas J.

AU - Key, Tim

AU - Travis, Ruth

AU - Schmidt, Julie A.

AU - Merritt, Melissa A.

AU - Riboli, Elio

AU - Romieu, Isabelle

AU - Ferrari, Pietro

PY - 2016/2

Y1 - 2016/2

N2 - 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 trend<0·01) and showed a significantly lower risk in oestrogen receptor-positive (HRQ5 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<0·01).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.

AB - 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 trend<0·01) and showed a significantly lower risk in oestrogen receptor-positive (HRQ5 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<0·01).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.

KW - Breast cancer

KW - European Prospective Investigationinto Cancer and Nutrition

KW - Nutrient patterns

KW - Principal component analysis

KW - Treelet transform

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U2 - 10.1017/S1368980015000294

DO - 10.1017/S1368980015000294

M3 - Article

C2 - 25702596

AN - SCOPUS:84923320575

SN - 1368-9800

VL - 19

SP - 242

EP - 254

JO - Public Health Nutrition

JF - Public Health Nutrition

IS - 2

ER -

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)

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