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Public Health Nutr. 2016 Feb;19(2):242-54. doi: 10.1017/S1368980015000294. Epub 2015 Feb 23.

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).

Author information

1
1International Agency for Research on Cancer,150 Cours Albert Thomas,69372 Lyon Cedex 08,France.
2
3Université de Lyon,Lyon,France.
3
6Division of Cancer Epidemiology,German Cancer Research Center (DKFZ),Heidelberg,Germany.
4
7Department of Epidemiology,German Institute of Human Nutrition Potsdam-Rehbruecke,Nuthetal,Germany.
5
8Department of Community Medicine, Faculty of Health Sciences,University of Tromsø,The Arctic University of Norway,Tromsø,Norway.
6
12Unit of Nutrition,Environment and Cancer,Cancer Epidemiology Research Program,Catalan Institute of Oncology (ICO),Bellvitge Biomedical Research Institute (IDIBELL),Barcelona,Spain.
7
13CIBER de Epidemiología y Salud Pública (CIBERESP),Madrid,Spain.
8
19Public Health Directorate,Asturias,Oviedo,Spain.
9
20Danish Cancer Society Research Center,Copenhagen,Denmark.
10
21Section for Epidemiology,Department of Public Health,Aarhus University,Aarhus,Denmark.
11
22Inserm,Centre for Research in Epidemiology and Population Health (CESP),Nutrition,Hormones and Women's Health Team,Villejuif,France.
12
25Cancer Epidemiology Centre,Cancer Council of Victoria,Melbourne,Australia.
13
27Hellenic Health Foundation,Athens,Greece.
14
29Department of Hygiene,Epidemiology and Medical Statistics,University of Athens Medical School,Athens,Greece.
15
30Azienda Ospedaliera Universitaria (AOU) Federico II,Naples,Italy.
16
31Molecular and Nutritional Epidemiology Unit,Cancer Research and Prevention Institute - ISPO,Florence,Italy.
17
32Epidemiology and Prevention Unit,Fondazione IRCCS,Istituto Nazionale dei Tumori,Milan,Italy.
18
33Unit of Cancer Epidemiology - CERMS,Department of Medical Sciences,University of Turin and Città della Salute e della Scienza Hospital,Turin,Italy.
19
34Cancer Registry and Histopathology Unit,'Civile M.P. Arezzo' Hospital,Ragusa,Italy.
20
35Department for Determinants of Chronic Diseases (DCD),National Institute for Public Health and the Environment (RIVM),Bilthoven,The Netherlands.
21
38Department of Epidemiology,Julius Center for Health Sciences and Primary Care,University Medical Center Utrecht,Utrecht,The Netherlands.
22
39Department of Internal Medicine and Clinical Nutrition,The Sahlgrenska Academy,Göteborg,Sweden.
23
40Department of Odontology,Umeå University,Umeå,Sweden.
24
41Department of Public Health and Primary Care,University of Cambridge School of Clinical Medicine,Cambridge,UK.
25
42MRC Epidemiology Unit,University of Cambridge School of Clinical Medicine,Cambridge,UK.
26
43Cancer Epidemiology Unit,Nuffield Department of Population Health,University of Oxford,Oxford,UK.
27
37Department of Epidemiology and Biostatistics,The School of Public Health,Imperial College London,London,UK.

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

PMID:
25702596
DOI:
10.1017/S1368980015000294
[Indexed for MEDLINE]
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