Skip to main content
Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Association between the Adherence to the International Guidelines for Cancer Prevention and Mammographic Density

  • Adela Castelló ,

    acastello@isciii.es

    Affiliations Cancer Epidemiology Unit, National Center for Epidemiology, Instituto de Salud Carlos III, Madrid, Spain, Consortium for Biomedical Research in Epidemiology & Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain, Cancer Epidemiology Research Group, Oncology and Hematology Area, IIS Puerta de Hierro (IDIPHIM), Madrid, Spain

  • Leandro Prieto,

    Affiliation Cancer Epidemiology Unit, National Center for Epidemiology, Instituto de Salud Carlos III, Madrid, Spain

  • María Ederra,

    Affiliation Navarre Breast Cancer Screening Program, Public Health Institute, Pamplona, Spain

  • Dolores Salas-Trejo,

    Affiliation Valencian Breast Cancer Screening Program, General Directorate of Public Health, Valencia, Spain

  • Carmen Vidal,

    Affiliation Cancer Prevention and Control Unit, Catalonian Institute of Oncology (ICO. Barcelona, Spain

  • Carmen Sánchez-Contador,

    Affiliation Balearic Islands Breast Cancer Screening Program, Regional Authority for Health & Consumer Affairs, Palma de Mallorca, Islas Baleares

  • Carmen Santamariña,

    Affiliation Galician Breast Cancer Screening Program, Galician Regional Health Authority, A Coruña, Spain

  • Carmen Pedraz,

    Affiliation Castile-León Breast Cancer Screening Program, General Directorate of Public Health, Burgos, Spain

  • Pilar Moreo,

    Affiliation Aragón Breast Cancer Screening Program, Aragon Health Service, Zaragoza, Spain

  • Nuria Aragonés,

    Affiliations Cancer Epidemiology Unit, National Center for Epidemiology, Instituto de Salud Carlos III, Madrid, Spain, Consortium for Biomedical Research in Epidemiology & Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain, Cancer Epidemiology Research Group, Oncology and Hematology Area, IIS Puerta de Hierro (IDIPHIM), Madrid, Spain

  • Beatriz Pérez-Gómez,

    Affiliations Cancer Epidemiology Unit, National Center for Epidemiology, Instituto de Salud Carlos III, Madrid, Spain, Consortium for Biomedical Research in Epidemiology & Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain, Cancer Epidemiology Research Group, Oncology and Hematology Area, IIS Puerta de Hierro (IDIPHIM), Madrid, Spain

  • Virginia Lope,

    Affiliations Cancer Epidemiology Unit, National Center for Epidemiology, Instituto de Salud Carlos III, Madrid, Spain, Consortium for Biomedical Research in Epidemiology & Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain, Cancer Epidemiology Research Group, Oncology and Hematology Area, IIS Puerta de Hierro (IDIPHIM), Madrid, Spain

  • Jesús Vioque,

    Affiliations Consortium for Biomedical Research in Epidemiology & Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain, Universidad Miguel Hernandez, Sant Joan D'Alacant, Spain

  • Marina Pollán,

    Affiliations Cancer Epidemiology Unit, National Center for Epidemiology, Instituto de Salud Carlos III, Madrid, Spain, Consortium for Biomedical Research in Epidemiology & Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain, Cancer Epidemiology Research Group, Oncology and Hematology Area, IIS Puerta de Hierro (IDIPHIM), Madrid, Spain

  • DDM-Spain research group

    Membership of the DDM-Spain research group is provided in the acknowledgments section.

Abstract

Introduction

Mammographic density (MD) is considered a strong predictor of Breast Cancer (BC). The objective of the present study is to explore the association between MD and the compliance with the World Cancer Research Fund and the American Institute for Cancer Research (WCRF/AICR) recommendations for cancer prevention.

Methods

Data of 3584 women attending screening from a population-based multicenter cross-sectional study (DDM-Spain) collected from October 7, 2007 through July 14, 2008, was used to calculate a score that measures the level of compliance with the WCRF/AICR recommendations: R1)Maintain adequate body weight; R2)Be physically active; 3R)Limit the intake of high density foods; R4)Eat mostly plant foods; R5)Limit the intake of animal foods; R6)Limit alcohol intake; R7)Limit salt and salt preserved food intake; R8)Meet nutritional needs through diet. The association between the score and MD (assessed by a single radiologist using a semi-quantitative scale) was evaluated using ordinal logistic models with random center-specific intercepts adjusted for the main determinants of MD. Stratified analyses by menopausal status and smoking status were also carried out.

Results

A higher compliance with the WCRF/AICR recommendations was associated with lower MD (OR1-unit increase = 0.93 95%CI:0.86;0.99). The association was stronger in postmenopausal women (OR = 0.91 95%CI:0.84;0.99) and nonsmokers (OR = 0.87;95%CI:0.80;0.96 for nonsmokers, OR = 1.01 95%CI:0.91;1.12 for smokers, P-interaction = 0.042). Among nonsmokers, maintaining adequate body weight (OR = 0.81 95%CI:0.65;1.01), practicing physical activity (OR = 0.68 95%CI:0.48;0.96) and moderating the intake of high-density foods (OR = 0.58 95%CI:0.40;0.86) and alcoholic beverages (OR = 0.76 95%CI:0.55;1.05) were the recommendations showing the strongest associations with MD.

Conclusions

postmenopausal women and non-smokers with greater compliance with the WCRF/AICR guidelines have lower MD. These results may provide guidance to design specific recommendations for screening attendants with high MD and therefore at higher risk of developing BC.

Introduction

The World Cancer Research Fund and the American Institute of Cancer Research (WCRF/AICR) estimate that around one fourth of cancer cases from high and medium income countries are preventable by adopting healthier lifestyles concerning diet, physical activity and body fatness. Regarding Breast Cancer (BC), published evidence indicates that between 20% and 42% of cases could be prevented in countries such as the USA, the UK, Brazil and China [1]. Taking into account that breast tumours are the most common cancer among women and one of the main causes of adult female mortality in developed countries [2], preventive strategies are of special importance. The WCRF/AICR issued in 2007, 8 general and 2 special recommendations on diet, physical activity and weight management for cancer prevention based on the available evidence [3, 4]. Five studies have already explored the association between compliance with such recommendations and BC risk, showing a beneficial effect [59].

A high mammographic density (MD), i.e. a high percentage of dense breast tissue, is considered a strong risk factor for BC [10, 11]. MD has also been associated with some of the WCRF/AICR recommendations such as body fatness [12, 13], diet[14, 15] and other obstetric and gynecological factors [16, 17], although, to our knowledge, no studies have investigated the relationship between adherence to the WCRF/AICR guidelines and MD. In this paper, the association between compliance with these recommendations and MD was explored in a group of Spanish women attending population-based BC screening programs, globally and separately according to their menopausal status. Finally, given the antiestrogenic effect of tobacco [18, 19], we hypothesized that this association might be different in smokers and nonsmokers. Therefore, we also explored the relationship between these recommendations and MD by smoking status.

Methods

Study population and data collection

The DDM-Spain study (Determinantes de la Densidad Mamográfica en España- Determinants of Mammographic Density in Spain) is a cross-sectional multicenter study including seven specific screening centers within the Spanish Breast Cancer Screening network located throughout the Spanish territory [13, 16]. All women aged 50–69 (45–69 in some regions), regardless of nationality or legal status, are screened under these government-sponsored programs every 2 years. Considering an initial prevalence of 25% of women with high MD, sample size was estimated as 3500 women (at least 500 women per center). This sample size allows identifying effects equal or greater than 1.25 for exposures over 40% with a statistical power of 80%. Women were randomly selected among screening attendants and invited to participate on a daily basis, until reaching the minimum sample size fixed for each center (500 women). A total of 3,584 women were recruited, with an average participation rate of 74.5% (range 64.7–84.0% across centers).Women were interviewed at the screening center by trained interviewers that collected demographic, anthropometric, physical activity, gynecologic, obstetric and occupational data, as well as family and personal history (including weight and height at age 18). Smoking information included current status and months since quitting for ex-smokers. Current smokers were defined as those women who smoke at the time of mammography or quit less than 6 months before. Dietary intake during the preceding year was collected using a validated 95-items food frequency questionnaire (FFQ) [20, 21]. Post-menopausal status was defined as self-reported absence of menstruation in the last 12 months. Interviewers measured weight, height, waist and hip circumferences twice using the same protocol and identical balance scales, stadiometers and measuring tapes. A third measure was taken when the first two were not similar. MD was assessed by a single radiologist from the craniocaudal mammogram of the left breast using a visual semiquantitative score with six categories proposed by Boyd [22], namely, A (0%), B (0–10%), C (10–25%), D (25–50%), E (50–75%) and F (>75%). Given the small percentage of women in category A (4%) the two lowest categories were grouped together, creating the definitive outcome variable categorized as: <10%, 10–25%, 25–50%, 50–75% and >75%.

After excluding 10 women who developed breast cancer within 6 months of mammography, 16 women without MD assessment and 8 women with a daily kcal intake under 750 or above 4500, information on 3,550 women was considered in the analyses. In the remaining sample, the mean calorie intake was 2054 (Min-Max: 835–4246).

Ethics statement

The DDM-Spain study protocol was formally approved by the bioethics and animal welfare committee at the Carlos III Institute of Health and all participants signed a consent form, including permission to publish the results from the current research.

WCRF/AICR score

Based on the WCRF/AICR guidelines [3, 4] and following the methodology described in previous studies [59]. A score was constructed considering the 8 general recommendations (Table 1): R1) body fatness, R2) physical activity, R3) foods and drinks that promote weight gain, R4) plant foods, R5) animal foods, R6) alcoholic drinks, R7) preservation, processing and preparation of foods and R8) dietary supplements. The breast is a fat storage area, therefore MD heavily depends on body mass index (BMI)[23]. For this reason, BMI at the time of mammography was considered as a confounder and was not included as part of the score. However, the association between MD and body fatness during adolescence is still not clear [24] and several studies report a positive association between adult weight gain and MD [13, 25, 26]. Accordingly, we decided to include BMI at age 18 and weight gained during adulthood to calculate the subscore for R1. Special recommendation S1 was not included because breastfeeding seem to be positively associated with MD [16, 17], therefore the protective effect of breastfeeding on BC is not likely to act through its association with MD. Finally, special recommendation for cancer survivors S2), was not applicable to this population.

thumbnail
Table 1. Operationalization of the WCRF/AICR recommendations for cancer prevention in a score (0–8) using DDM-Spain study data.

https://doi.org/10.1371/journal.pone.0132684.t001

For each item considered under each recommendation a maximum score of 1 was assigned when the item was fully met, an intermediate score of 0.5 when the item was not far from being met and 0 points otherwise. This decision was taken based on the cutoffs provided in WCRF/AICR report [3] or on the distribution of the data when the cutoff was not specified. The score for recommendations including several items was calculated as the average of their marks. Each recommendation was considered to contribute equally to the final index that was calculated as the sum of the individual scores. Therefore, the WCRF/AICR score ranged 0–8 and represented the minimum number of recommendations fully met. This score was grouped into 4 categories as follows: 0 to <4, 4 to <5, 5 to <6, 6 to 8. The cut points were selected ensuring a sufficient number of women in each category and following the methodology proposed by previous research [59].

Statistical methods

The association between MD and the WCRF/AICR score was evaluated using ordinal logistic models with random center-specific intercepts. These models were adjusted for a set of potential confounders including age, BMI, parity, family history of breast cancer, use of hormonal replacement therapy (HRT), menopausal status and smoking habit. The categorical and continuous associations with the index were evaluated. Separate analyses were performed according to menopausal status (pre- and post-menopausal) and tobacco consumption (smokers and nonsmokers). Heterogeneity of effects was tested including in the model an interaction term between the score and menopausal or smoking status. In all instances, non-linear associations were explored using natural splines, with 5 knots located in Harrell’s recommended percentiles, namely, 5, 27.5, 50, 72.5 and 95% confidence intervals (CI) [27].

Same analyses were carried out to evaluate the association between MD and each specific recommendation. These models were adjusted for the set of variables described above plus the effect of the rest of the recommendations. To do so, the sum of the scores for all recommendations excluding the one under study was calculated and used as a potential confounder.

All analyses were performed using Stata statistical software (version MP 12.1; Stata Corp LP, College Station, TX). The last statistical analyses were conducted in August 2014.

Results

Table 2 shows the distribution of the overall WCRF/AIRC score and individual recommendations as well as some baseline characteristics of the study sample stratifying by menopausal and smoking status. Postmenopausal women showed a higher adherence to the WCRF/AIRC guidelines, particularly to those related to body fatness, consumption of high density, plant, animal and salty foods. Nonsmokers also seemed to have better lifestyle habits than smokers concerning the intake of high density, plant and animal foods, as well as alcohol consumption. Regarding baseline characteristics, as expected, postmenopausal women showed lower MD, higher age, BMI, number of deliveries and use of HRT than their premenopausal counterparts. MD was lower among nonsmokers who were also older, with higher BMI and had greater number of deliveries than smokers. For interested readers, S1 Table summarizes the distribution (mean and standard error) of the WCRF/AIRC score and of each specific recommendation per categories of MD, adjusted by age, BMI and center.

thumbnail
Table 2. Description of all women’s characteristics and by menopausal status and by smoking habit.

https://doi.org/10.1371/journal.pone.0132684.t002

The relationship between adherence to the WCRF/AICR guidelines and MD for all women and stratifying by menopausal and smoking status is summarized in Table 3. Overall, a clear inverse association between the score and MD was observed and the linear trend was found to be statistically significant (Odds ratio (OR) = 0.93; 95%CI = 0.86;0.99; p-trend = 0.019). The interaction between the score and menopausal status was not significant (p-int = 0.446), but a clear association was only observed in postmenopausal women (OR = 0.91; 95%CI = 0.84;0.99; p-trend = 0.017). On the other hand, the relationship between MD and the score was significantly different among smokers and non-smokers (p-int = 0.042). Thus, while the relationship between the WCRF/AICR score and MD was statistically significant in non-smokers (OR = 0.87; 95%CI = 0.80; 0.96; p- trend = 0.002), no association was observed in smokers (OR = 1.01; 95%CI = 0.91; 1.12; p- trend = 0.965).

thumbnail
Table 3. Association between the WCRF/AICR score and MD in all women, by menopausal status and by smoking habit.

https://doi.org/10.1371/journal.pone.0132684.t003

Analysis of the dose-response shape is presented in Fig 1. A clear trend was only seen in the postmenopausal group and in those who were nonsmokers (overall trend p-values of 0.004 and 0.023 respectively). Regarding postmenopausal women, there was a statistically significant departure from linearity (p-value = 0.013), meaning that, a downward trend of MD with the WCRF/AICR score was only seen in women who met at least 5 recommendations. On the other hand, the dose-response curve was linear for non-smokers.

thumbnail
Fig 1. Dose-response shape (natural splines) of the association between the WCRF/AICR score and MD stratifying by menopausal status and smoking habit.

The first quintile of the score has been taken as reference.

https://doi.org/10.1371/journal.pone.0132684.g001

The analysis of the association between individual recommendations and MD (Table 4) revealed that an adequate body fatness throughout life (OR = 0.84; 95%CI = 0.70;1.00) and a moderate consumption of high density foods (OR = 0.75; 95%CI = 0.56;1.01) seem to be marginally associated with a reduced MD, especially in postmenopausal women (OR = 0.84; 95%CI = 0.69;1.03 and OR = 0.71; 95%CI = 0.51;0.99 respectively) for which avoiding excessive alcohol consumption also appeared to be an important factor (OR = 0.80; 95%CI = 0.61;1.04). Among nonsmokers, a reduced MD was observed in women with an adequate body fatness throughout life (OR = 0.81; 95%CI = 0.65;1.01),those with moderate consumption of high density foods (OR = 0.58; 95%CI = 0.40;0.86) and those avoiding excessive alcohol consumption (OR = 0.76; 95%CI = 0.55;1.05). Additionally, physical activity was also inversely associated with MD in this subgroup (OR = 0.68; 95%CI = 0.48;0.96). Interestingly, the interaction term with smoking status was significant for physical activity and for intake of high density foods, being the inverse association only observed among the nonsmokers’ group.

thumbnail
Table 4. Association between individual recommendations and MD in all women, by menopausal status and by smoking habit.

https://doi.org/10.1371/journal.pone.0132684.t004

Discussion

Our study suggests that a higher compliance with the WCRF/AICR recommendations is associated with a lower MD in postmenopausal women. Regarding smoking, no associations were observed among women who smoked. Among nonsmokers, these recommendations and specifically those focused on maintaining adequate body fatness throughout life, practicing physical activity, avoiding consumption of high density foods and limiting alcohol consumption were associated with decreased MD.

To our knowledge, this is the first study exploring the association between MD and the adherence to the WCRF/AICR guidelines. However, our results are in agreement with the few studies that have assessed the relationship between these recommendations and BC risk [59]. All of them report a significant downward trend in BC risk as the number of recommendations met increases. Two of these studies found the strongest associations with the recommendations related to body fatness and alcohol intake [5, 8] and another two with energy dense foods [8, 9], findings that are in agreement with ours. Concerning the association between individual recommendations and MD, while current BMI is negatively correlated with MD, adult weight gain (Rec 1) seems to be positively associated as we have previously reported [13]. Two other studies reported similar results [25, 26], whereas a third suggested otherwise [28]. This inconsistency may be explained, at least in part, by the use of different tools to assess MD [13]. Regarding physical activity (Rec2) a recent review fails to identify a clear effect on breast density [29], something confirmed by recent works [30, 31]. In our study, a clear heterogeneous effect among smokers and nonsmokers was found that must be corroborated by others. Similarly, we found a negative association between lower consumption of energy-dense foods and sugary drinks and MD (Rec 3) among nonsmokers, that agrees with other published research [3235]. Some studies suggest a possible inverse association between plant foods (Rec 4) and MD, mostly regarding vegetable consumption [7, 3537], while others found no effect or a positive association between fruit intake and MD [7, 38]. The combination of these two items in a single category might explain the lack of any effect. In fact, in our study, the relationship between MD and vegetables and fruits and MD seem to go in different directions (being negative for vegetables and positive for fruits), but neither of them reached statistical significance (data not shown). The influence of red/processed meat consumption (Rec 5) on MD is not clear with the few existing studies suggesting either a positive [14, 35] or a null [33] relationship. As seen in our results, avoiding excessive intake of alcohol (Rec 6) might be associated with lower MD [34, 36], especially among postmenopausal women [34]. Regarding salty foods (Rec7), to our knowledge no previous studies have explored their association with MD, but breast cancer is not among the tumors that seem to benefit from this recommendation [3, 4]. Finally, among the three previous studies that explored the effect of dietary supplements on MD (Rec8), one of them reported an inverse relationship [37], while the other two suggested otherwise [34, 39]. Concerning the analysis by menopausal status, it is important to highlight that, even though the number of premenopausal women was insufficient to detect statistically significant differences between pre and postmenopausal screening attendants, a clear dose-response effect of the WCRF/AICR score was only seen in the last group. It is widely known that obesity increases the risk of BC only in menopausal women [3, 4]. It is possible that, as it is the case of body fatness and alcohol, these recommendations exert their effect influencing the levels of circulating estrogens and other hormones, such as insulin and insulin-like growth factor 1 [3, 4, 40]. Their impact on MD would be particularly important after menopause, when these variations of the hormonal milieu may influence the natural process of mammary involution [41].

Unfortunately, none of the above mentioned studies on WCRF/AICR recommendations and BC stratified by smoking status. In our study, the negative association of MD with the adherence to these guidelines was only seen in women who did not smoke. In this sense, we have previously described a relationship between alcohol consumption and MD that was only observed among nonsmokers [42] and other authors have found an interaction between the effect of smoking and obesity on breast cancer risk [43]. Estrogen level is an important mediator of the mechanism by which some of these risk factors exert their action [3, 4, 40]. The antiestrogenic effect of tobacco [18, 19] may explain the lack of association between the score and MD among smokers. On the other hand, cigarette smoke is known to contain over 7,000 chemicals, 69 of which are established carcinogens [44], including over 20 that are established mammary carcinogens [45]. Therefore, it is possible that the harmful effect of smoking counteracts the potential benefits of these recommendations.

DDM-Spain is the biggest study published up to date on risk factors and MD that contains complete dietary information and is the first exploring the effect of WCRF/AICR recommendations on MD. Nevertheless, our study also has some limitations. As mentioned before, in spite of the differences seen in the pre and postmenopausal subgroups, the number of premenopausal women was insufficient to reach conclusions for this particular subgroup. This limitation results from the age-groups targeted by Spanish screening programs that, with few exceptions, initiate screening at age 50 [46]. Secondly, even though screening participation rates are high [46], it is well known that screening attendants tend to be more concerned about their health than non-attendants, which may imply an underrepresentation of less compliant women in our study. However, our study included women from all socioeconomic levels, and the prevalence of different lifestyle factors, such as smoking, physical activity and use of hormonal treatment was very similar to that reported by the Spanish National Health Survey for women in the same age range [47]. Regarding data collection, the use of different mammographic devices and interviewers in different centers might introduce some heterogeneity. Random center-specific intercepts were used in order to account for these unmeasured sources of variability. Breast density was visually assessed by a single radiologist using a semi-quantitative scale on analog and digital mammograms, which may imply a degree of subjectivity. However, the evaluation of the intra-agreement of MD measurements was excellent [48], and we have confirmed that this visual scale is a risk predictor of subsequent BC development [11]. Finally, some methodological issues should be taken into account when interpreting the results. On the one hand, the cross-sectional design of the current study precludes the establishment of causal relationships between the adherence to the WCRF/AICR guidelines and MD. On the other hand, multiple testing is a concern in situations where a great number of tests are carried out. While this is not a problem in the analysis of our main objective, namely the association between global compliance with the WCRF/AICR guidelines and MD, it can be an issue in secondary analyses of individual recommendations. However, taking into account that we adjusted 24 models, an alpha error of 5% implies that chance would explain only 1 of the statistically significant results presented here.

Conclusion

A high compliance with the WCRF/AICR guidelines, particularly maintaining an adequate weight throughout adult life, practicing physical activity, and limiting the consumption of alcohol, high density foods, and sugary drinks, is associated with a lower MD. More studies are needed to investigate the potential of these recommendations to reduce MD, one of the strongest risk factors for BC.

Supporting Information

S1 Table. Distribution of the total and individual scores for the WCRF/AICR recommendations for cancer prevention in women participating in DDM-Spain study by Boyd categories of mammographic density adjusted by age, bmi and center.

https://doi.org/10.1371/journal.pone.0132684.s001

(DOCX)

Acknowledgments

The authors wish to thank the DDM-Spain study participants for their contribution to breast cancer research and to all collaborator researchers: Mª Pilar, Mª Soledad Abad, Francisca Collado, Francisco Casanova, Jose Antonio Vazquez, Nieves Ascunce, Milagros García, Manuela Alcaraz,Mª Soledad Laso, Josefa Miranda and Francisco Ruiz Perales.

Author Contributions

Conceived and designed the experiments: ME DS-T CV CS-C CS CP PM NA BP-G VL JV MP. Performed the experiments: ME DS-T CV CS-C CS CP PM NA BP-G VL JV MP. Analyzed the data: AC LP VL JV MP. Contributed reagents/materials/analysis tools: AC LP ME DS-T CV CS-C CS CP PM NA BP-G VL JV MP. Wrote the paper: AC MP. Read and approved the final version of the manuscript: AC LP ME DS-T CV CS-C CS CP PM NA BP-G VL JV MP.

References

  1. 1. WCRF/AICR. World Cancer Research Fund / American Institute for Cancer Research. Policy and Action for Cancer Prevention. Food, Nutrition, and Physical Activity: a Global Perspective. Washington DC: AICR. 2009.
  2. 2. WHO. Global health risks: mortality and burden of disease attributable to selected major risks. Geneva, World Health Organization. 2009.
  3. 3. WCRF/AICR. World Cancer Research Fund / American Institute for Cancer Research. Food, Nutrition, Physical Activity, and the Prevention of Cancer: a Global Perspective. Washington DC: AICR. 2007.
  4. 4. WCRF/AICR. World Cancer Research Fund / American Institute for Cancer Research. Continuous Update Project Report. Food, Nutrition, Physical Activity, and the Prevention of Breast Cancer. 2010.
  5. 5. Hastert TA, Beresford SA, Patterson RE, Kristal AR, White E. Adherence to WCRF/AICR cancer prevention recommendations and risk of postmenopausal breast cancer. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2013;22(9):1498–508. pmid:23780838; PubMed Central PMCID: PMC3774119.
  6. 6. Romaguera D, Vergnaud AC, Peeters PH, van Gils CH, Chan DS, Ferrari P, et al. Is concordance with World Cancer Research Fund/American Institute for Cancer Research guidelines for cancer prevention related to subsequent risk of cancer? Results from the EPIC study. The American journal of clinical nutrition. 2012;96(1):150–63. Epub 2012/05/18. pmid:22592101.
  7. 7. Thomson CA, McCullough ML, Wertheim BC, Chlebowski RT, Martinez ME, Stefanick ML, et al. Nutrition and Physical Activity Cancer Prevention Guidelines, Cancer Risk, and Mortality in the Women's Health Initiative. Cancer prevention research. 2014;7(1):42–53. pmid:24403289.
  8. 8. Catsburg C, Miller AB, Rohan TE. Adherence to cancer prevention guidelines and risk of breast cancer. International journal of cancer Journal international du cancer. 2014. pmid:24723234.
  9. 9. Castello A, Martin M, Ruiz A, Casas AM, Baena-Canada JM, Lope V, et al. Lower breast cancer risk among women following the World Cancer Research Found and American Institute for Cancer Research lifestyle recommendations: EpiGEICAM case-control study. PloS one. 2015;In press.
  10. 10. Boyd NF, Rommens JM, Vogt K, Lee V, Hopper JL, Yaffe MJ, et al. Mammographic breast density as an intermediate phenotype for breast cancer. The lancet oncology. 2005;6(10):798–808. Epub 2005/10/04. pmid:16198986.
  11. 11. Pollan M, Ascunce N, Ederra M, Murillo A, Erdozain N, Ales-Martinez JE, et al. Mammographic density and risk of breast cancer according to tumor characteristics and mode of detection: a Spanish population-based case-control study. Breast cancer research: BCR. 2013;15(1):R9. Epub 2013/01/31. pmid:23360535; PubMed Central PMCID: PMCPmc3672793.
  12. 12. Harris HR, Tamimi RM, Willett WC, Hankinson SE, Michels KB. Body size across the life course, mammographic density, and risk of breast cancer. American journal of epidemiology. 2011;174(8):909–18. pmid:21911827; PubMed Central PMCID: PMC3218634.
  13. 13. Pollan M, Lope V, Miranda-Garcia J, Garcia M, Casanova F, Sanchez-Contador C, et al. Adult weight gain, fat distribution and mammographic density in Spanish pre- and post-menopausal women (DDM-Spain). Breast cancer research and treatment. 2012;134(2):823–38. Epub 2012/06/13. pmid:22689088; PubMed Central PMCID: PMCPmc3401511.
  14. 14. Tseng M, Byrne C, Evers KA, Daly MB. Dietary intake and breast density in high-risk women: a cross-sectional study. Breast cancer research: BCR. 2007;9(5):R72. Epub 2007/10/24. pmid:17949495; PubMed Central PMCID: PMCPmc2242670.
  15. 15. Garcia-Arenzana N, Navarrete-Munoz EM, Lope V, Moreo P, Vidal C, Laso-Pablos S, et al. Calorie intake, olive oil consumption and mammographic density among Spanish women. International journal of cancer Journal international du cancer. 2014;134(8):1916–25. pmid:24254818.
  16. 16. Lope V, Perez-Gomez B, Sanchez-Contador C, Santamarina MC, Moreo P, Vidal C, et al. Obstetric history and mammographic density: a population-based cross-sectional study in Spain (DDM-Spain). Breast cancer research and treatment. 2012;132(3):1137–46. pmid:22215386; PubMed Central PMCID: PMC3332340.
  17. 17. Sung J, Song YM, Stone J, Lee K, Lee D. Reproductive factors associated with mammographic density: a Korean co-twin control study. Breast cancer research and treatment. 2011;128(2):567–72. pmid:21442196.
  18. 18. Michnovicz JJ, Hershcopf RJ, Naganuma H, Bradlow HL, Fishman J. Increased 2-hydroxylation of estradiol as a possible mechanism for the anti-estrogenic effect of cigarette smoking. The New England journal of medicine. 1986;315(21):1305–9. Epub 1986/11/20. pmid:3773953.
  19. 19. Gu F, Caporaso NE, Schairer C, Fortner RT, Xu X, Hankinson SE, et al. Urinary concentrations of estrogens and estrogen metabolites and smoking in caucasian women. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2013;22(1):58–68. Epub 2012/10/30. pmid:23104668; PubMed Central PMCID: PMCPmc3643002.
  20. 20. Vioque J, Navarrete-Munoz EM, Gimenez-Monzo D, Garcia-de-la-Hera M, Granado F, Young IS, et al. Reproducibility and validity of a food frequency questionnaire among pregnant women in a Mediterranean area. Nutrition journal. 2013;12:26. pmid:23421854; PubMed Central PMCID: PMC3584829.
  21. 21. Willett WC, Sampson L, Stampfer MJ, Rosner B, Bain C, Witschi J, et al. Reproducibility and validity of a semiquantitative food frequency questionnaire. American journal of epidemiology. 1985;122(1):51–65. pmid:4014201.
  22. 22. Boyd NF, Byng JW, Jong RA, Fishell EK, Little LE, Miller AB, et al. Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian National Breast Screening Study. Journal of the National Cancer Institute. 1995;87(9):670–5. pmid:7752271.
  23. 23. Assi V, Warwick J, Cuzick J, Duffy SW. Clinical and epidemiological issues in mammographic density. Nature reviews Clinical oncology. 2012;9(1):33–40. pmid:22143145.
  24. 24. Yochum L, Tamimi RM, Hankinson SE. Birthweight, early life body size and adult mammographic density: a review of epidemiologic studies. Cancer causes & control: CCC. 2014;25(10):1247–59.
  25. 25. Rauh C, Faschingbauer F, Haeberle L, Jud SM, Heusinger K, Fasching PA, et al. Factors influencing breast changes after pregnancy. European journal of cancer prevention: the official journal of the European Cancer Prevention Organisation (ECP). 2013;22(3):259–61. Epub 2012/10/02. pmid:23022745.
  26. 26. Tseng M, Byrne C. Adiposity, adult weight gain and mammographic breast density in US Chinese women. International journal of cancer Journal international du cancer. 2011;128(2):418–25. Epub 2010/03/24. pmid:20309943; PubMed Central PMCID: PMCPmc2913167.
  27. 27. Harrell FE Jr (2001). General aspects of fitting regression models. Relaxing linearity assumption for continuous predictors. In: Harrell FE Jr (ed) Regression modeling strategies with applications to linear models, logistic regression, and survival analysis. Springer, New York, pp 16–26.
  28. 28. Samimi G, Colditz GA, Baer HJ, Tamimi RM. Measures of energy balance and mammographic density in the Nurses' Health Study. Breast cancer research and treatment. 2008;109(1):113–22. Epub 2007/06/27. pmid:17592770.
  29. 29. Yaghjyan L, Colditz GA, Wolin K. Physical activity and mammographic breast density: a systematic review. Breast cancer research and treatment. 2012;135(2):367–80. Epub 2012/07/21. pmid:22814722; PubMed Central PMCID: PMCPmc3641148.
  30. 30. Brand JS, Czene K, Eriksson L, Trinh T, Bhoo-Pathy N, Hall P, et al. Influence of lifestyle factors on mammographic density in postmenopausal women. PloS one. 2013;8(12):e81876. Epub 2013/12/19. pmid:24349146; PubMed Central PMCID: PMCPmc3857226.
  31. 31. Pettee Gabriel K, Klifa C, Perez A, Kriska AM, High RR, Snetselaar L, et al. Adolescent and young adult exposure to physical activity and breast density. Medicine and science in sports and exercise. 2013;45(8):1515–23. Epub 2013/02/05. pmid:23377838; PubMed Central PMCID: PMCPmc3897238.
  32. 32. Martin LJ, Greenberg CV, Kriukov V, Minkin S, Jenkins DJ, Yaffe M, et al. Effect of a low-fat, high-carbohydrate dietary intervention on change in mammographic density over menopause. Breast cancer research and treatment. 2009;113(1):163–72. Epub 2008/01/25. pmid:18214671.
  33. 33. Mishra GD, dos Santos Silva I, McNaughton SA, Stephen A, Kuh D. Energy intake and dietary patterns in childhood and throughout adulthood and mammographic density: results from a British prospective cohort. Cancer causes & control: CCC. 2011;22(2):227–35. Epub 2010/11/30. pmid:21113794; PubMed Central PMCID: PMCPmc3399075.
  34. 34. Vachon CM, Kushi LH, Cerhan JR, Kuni CC, Sellers TA. Association of diet and mammographic breast density in the Minnesota breast cancer family cohort. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2000;9(2):151–60. Epub 2000/03/04. pmid:10698475.
  35. 35. Takata Y, Maskarinec G, Park SY, Murphy SP, Wilkens LR, Kolonel LN. Mammographic density and dietary patterns: the multiethnic cohort. European journal of cancer prevention: the official journal of the European Cancer Prevention Organisation (ECP). 2007;16(5):409–14. pmid:17923811.
  36. 36. Masala G, Ambrogetti D, Assedi M, Giorgi D, Del Turco MR, Palli D. Dietary and lifestyle determinants of mammographic breast density. A longitudinal study in a Mediterranean population. International journal of cancer Journal international du cancer. 2006;118(7):1782–9. Epub 2005/10/19. pmid:16231317.
  37. 37. Voevodina O, Billich C, Arand B, Nagel G. Association of Mediterranean diet, dietary supplements and alcohol consumption with breast density among women in South Germany: a cross-sectional study. BMC public health. 2013;13:203. Epub 2013/03/19. pmid:23497280; PubMed Central PMCID: PMCPmc3599490.
  38. 38. Voon NS, Chelliah KK. Is there an influence of dietary habits on breast density as seen on digital mammograms? Asian Pacific journal of cancer prevention: APJCP. 2011;12(8):1969–72. Epub 2012/02/02. pmid:22292635.
  39. 39. Berube S, Diorio C, Brisson J. Multivitamin-multimineral supplement use and mammographic breast density. The American journal of clinical nutrition. 2008;87(5):1400–4. Epub 2008/05/13. pmid:18469264.
  40. 40. Hvidtfeldt UA, Gunter MJ, Lange T, Chlebowski RT, Lane D, Farhat GN, et al. Quantifying mediating effects of endogenous estrogen and insulin in the relation between obesity, alcohol consumption, and breast cancer. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2012;21(7):1203–12. Epub 2012/05/09. pmid:22564867; PubMed Central PMCID: PMCPmc3858186.
  41. 41. Martin LJ, Boyd NF. Mammographic density. Potential mechanisms of breast cancer risk associated with mammographic density: hypotheses based on epidemiological evidence. Breast cancer research: BCR. 2008;10(1):201. Epub 2008/01/30. pmid:18226174; PubMed Central PMCID: PMCPmc2374950.
  42. 42. Cabanes A, Pastor-Barriuso R, Garcia-Lopez M, Pedraz-Pingarron C, Sanchez-Contador C, Vazquez Carrete JA, et al. Alcohol, tobacco, and mammographic density: a population-based study. Breast cancer research and treatment. 2011;129(1):135–47. Epub 2011/03/05. pmid:21373874.
  43. 43. Luo J, Horn K, Ockene JK, Simon MS, Stefanick ML, Tong E, et al. Interaction between smoking and obesity and the risk of developing breast cancer among postmenopausal women: the Women's Health Initiative Observational Study. American journal of epidemiology. 2011;174(8):919–28. Epub 2011/09/01. pmid:21878422; PubMed Central PMCID: PMCPmc3218630.
  44. 44. Centers for Disease C, Prevention, National Center for Chronic Disease P, Health P, Office on S, Health. Publications and Reports of the Surgeon General. How Tobacco Smoke Causes Disease: The Biology and Behavioral Basis for Smoking-Attributable Disease: A Report of the Surgeon General. Atlanta (GA): Centers for Disease Control and Prevention (US); 2010.
  45. 45. Johnson KC, Miller AB, Collishaw NE, Palmer JR, Hammond SK, Salmon AG, et al. Active smoking and secondhand smoke increase breast cancer risk: the report of the Canadian Expert Panel on Tobacco Smoke and Breast Cancer Risk (2009). Tobacco control. 2011;20(1):e2. Epub 2010/12/15. pmid:21148114.
  46. 46. Castells X, Sala M, Ascunce N, Salas D, Zubizarreta R, Casamitjana M. Descripción del cribado del cáncer en España. Proyecto DESCRIC. Madrid: Plan de Calidad para el Sistema Nacional de Salud. Ministerio de Sanidad y Consumo. Agència d’Avaluació de Tecnologia i Recerca Mèdiques de Cataluña; 2007. Informes de Evaluación de Tecnologías Sanitarias, AATRM núm. 2006/01.
  47. 47. Garcia-Arenzana N, Navarrete-Munoz EM, Peris M, Salas D, Ascunce N, Gonzalez I, et al. Diet quality and related factors among Spanish female participants in breast cancer screening programs. Menopause. 2012;19(10):1121–9. pmid:22760085.
  48. 48. Garrido-Estepa M, Ruiz-Perales F, Miranda J, Ascunce N, Gonzalez-Roman I, Sanchez-Contador C, et al. Evaluation of mammographic density patterns: reproducibility and concordance among scales. BMC cancer. 2010;10:485. pmid:20836850; PubMed Central PMCID: PMC2946309.