Nutrient-Wide Association Study of 92 Foods and Nutrients and Breast Cancer Risk

Alicia K. Heath; David C. Muller; Piet A. van den Brandt; Nikos Papadimitriou; Elena Critselis; Marc Gunter; Paolo Vineis; Elisabete Weiderpass; Guy Fagherazzi; Heiner Boeing; Pietro Ferrari; Anja Olsen; Anne Tjønneland; Patrick Arveux; Marie-Christine Boutron-Ruault; Francesca Romana Mancini; Tilman Kühn; Renée Turzanski-Fortner; Matthias B. Schulze; Anna Karakatsani; Paschalis Thriskos; Antonia Trichopoulou; Giovanna Masala; Paolo Contiero; Fulvio Ricceri; Salvatore Panico; Bas Bueno-de-Mesquita; Marije F. Bakker; Carla H. van Gils; Karina Standahl Olsen; Guri Skeie; Cristina Lasheras; Antonio Agudo; Miguel Rodríguez-Barranco; Maria-José Sánchez; Pilar Amiano; María-Dolores Chirlaque; Aurelio Barricarte; Isabel Drake; Ulrika Ericson; Ingegerd Johansson; Anna Winkvist; Tim Key; Heinz Freisling; Mathilde His; Inge Huybrechts; Sofia Christakoudi; Merete Ellingjord-Dale; Elio Riboli; Konstantinos K. Tsilidis; Ioanna Tzoulaki

Disclosures

Breast Cancer Res. 2020;22(5) 

In This Article

Results

Of the 343,985 eligible women in the EPIC study without a pre-baseline diagnosis of cancer, we excluded 3343 participants who did not complete dietary or lifestyle questionnaires. A further 68,544 were excluded because they had missing values for relevant covariates, leaving 272,098 women available for analysis. In these women, 10,979 incident invasive breast cancers were identified during a median follow-up time of 15 years. After 20.3 years of follow-up, 3339 incident invasive breast cancer cases were identified in the NLCS. Women with incomplete or inconsistent dietary data (520 cases, 411 subcohort members) and those with missing data on confounders (451 cases and 326 subcohort members) were excluded, leaving 2368 invasive breast cancer cases (including 93 cases who were subcohort members) and 1608 non-case subcohort members in this analysis. Women in the NLCS subcohort were older than women in the EPIC study (mean age 61 years versus 50 years, respectively) (Table 1 and Table 2). Among participants in the EPIC study, the distribution of baseline demographic characteristics did not differ substantially between breast cancer cases and non-cases (Table 1).

The mean (SD) intakes of the 92 foods and nutrients that were evaluated in the EPIC study are presented in Additional file 1. Of these foods and nutrients, six were associated with risk of breast cancer when controlling the FDR at 0.05 (Figure 1). Higher intakes of alcohol, beer/cider, and wine were associated with a higher risk of breast cancer (HRs for a 1 SD increment in intake = 1.05, 95% CI 1.03–1.07, 1.05, 95% CI 1.03–1.06, and 1.04, 95% CI 1.02–1.06, respectively), whereas higher fibre, apple/pear, and carbohydrate intakes were associated with a lower risk of breast cancer (HRs per 1 SD increment in intake = 0.96, 95% CI 0.94–0.98; 0.96, 95% CI 0.94–0.99; and 0.96, 95% CI 0.95–0.98, respectively). Model estimates for the 92 dietary factors are provided in Additional file 2. In a model with mutual adjustment for intakes of fibre, apple/pear, and carbohydrates, the associations were slightly weaker: HRs per 1 SD increment in intake were 0.98, 95% CI 0.95–1.00 for fibre; 0.98, 95% CI 0.96–1.00 for apple/pear; and 0.98, 95% CI 0.96–1.00 for carbohydrate).

Figure 1.

Volcano plot showing results from the nutrient-wide association study method to evaluate the association between dietary intake of 92 foods and nutrients and breast cancer risk in the EPIC study. The y-axis shows the negative log10 transformation of the estimated q values from the multivariable adjusted Cox proportional hazards regression coefficients, and the x-axis is the estimated log hazard ratio for a one standard deviation increment in intake in relation to risk of breast cancer. The q values represent the adjusted p values using the false discovery rate method, and the horizontal line indicates the false discovery rate threshold of 0.05. Each dietary factor was analysed one at a time, and ordered left to right according to the lowest to highest HR. Models were stratified by age at recruitment and study centre and adjusted for energy intake, history of diabetes, educational attainment, smoking status, BMI, physical activity, menopausal status at baseline, menopausal status by BMI interaction, age at menarche, and the interaction of parous (yes/no) and age at first pregnancy. The six dietary factors that were selected for confirmation in the NLCS are labelled

In separate analyses by menopausal status, alcohol, beer/cider, and wine intakes were associated with a greater risk, and fibre intake was associated with a lower risk of breast cancer among postmenopausal women (N = 5738 cases) but not among premenopausal women (N = 5241 cases) (Figure 2). We also found intakes of spirits and molluscs (both associated with higher risk) met the FDR threshold among postmenopausal women. None of the other foods or nutrients met the FDR threshold among either postmenopausal or premenopausal women. There were no substantial differences in the magnitude of the associations of alcohol, beer/cider, wine, fibre, apple/pear, and carbohydrates with breast cancer risk by hormone receptor status of tumours, with the possible exception of apple/pear and carbohydrate intake, which may have no association with risk of ER/PR negative tumours (Figure 3).

Figure 2.

Volcano plot of estimates and q values for 92 foods and nutrients in relation to breast cancer risk, for the nutrient-wide association study run separately by menopausal status at baseline in the EPIC study. The y-axis is the negative log10 transformation of the estimated q value, and the x axis is the estimated log hazard ratio for a one standard deviation increment in intake. The horizontal line indicates the false discovery rate threshold of 0.05. Estimates are from Cox regression models stratified by age at recruitment and study centre and adjusted for energy intake, history of diabetes, educational attainment, smoking status, BMI, physical activity, age at menarche, and the interaction of parous (yes/no) and age at first pregnancy. Variables that met the FDR threshold are labelled

Figure 3.

Estimated hazard ratios and 95% confidence intervals for six foods and nutrients in relation to breast cancer risk by hormone receptor status in the EPIC study. Estimates are from Cox regression models stratified by age at recruitment and study centre and adjusted for energy intake, history of diabetes, educational attainment, smoking status, BMI, physical activity, menopausal status at baseline, menopausal status by BMI interaction, age at menarche, and the interaction of parous (yes/no) and age at first pregnancy. There was an insufficient number of ER−/PR+ cases to allow separate estimation

In the NLCS, we evaluated the six dietary factors that were identified in the EPIC study overall. The magnitude and direction of the association observed in the NLCS was similar to that in EPIC for each of the factors, with the exception of beer/cider intake, which was not associated with risk of breast cancer in the NLCS (Figure 4). The NLCS results did not vary appreciably by ER/PR status (data not shown).

Figure 4.

Estimated hazard ratios and 95% confidence intervals for six foods and nutrients in relation to risk of breast cancer from the EPIC analysis (yellow) and the replication in the NLCS (green). Estimates are from Cox regression models stratified by age at recruitment and study centre (EPIC only) and adjusted for energy intake, history of diabetes, educational attainment, smoking status, BMI, physical activity, menopausal status at baseline (EPIC only), menopausal status by BMI interaction (EPIC only), age at menopause (NLCS only), age at menarche, the interaction of parous (yes/no) and age at first pregnancy, and family history of breast cancer in mother or sister/s (NLCS only)

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