DNA methylation is a potential biomarker for early detection of breast cancer. However, robust evidence of a prospective relationship between DNA methylation patterns and breast cancer risk is still lacking. The objective of this study is to provide a systematic analysis of the findings of epigenome-wide DNA methylation studies on breast cancer risk, in light of their methodological strengths and weaknesses.We searched major databases  from inception up to 30th June 2019, for observational or intervention studies investigating the association between epigenome-wide DNA methylation (using the HM450k or EPIC BeadChip), measured in any type of human sample, and breast cancer risk. A pre-established protocol was drawn up following the Cochrane Reviews rigorous methodology. Study selection, data abstraction, and risk of bias assessment were performed by at least two investigators. A qualitative synthesis and systematic comparison of the strengths and weaknesses of studies was performed.Overall, 20 studies using the HM450k BeadChip were included, 17 of which had measured blood-derived DNA methylation. There was a consistent trend toward an association of global blood-derived DNA hypomethylation and higher epigenetic age with higher risk of breast cancer. The strength of associations was modest for global hypomethylation and relatively weak for most of epigenetic age algorithms. Differences in length of follow-up periods may have influenced the ability to detect associations, as studies reporting follow-up periods shorter than 10 years were more likely to observe an association with global DNA methylation. Probe-wise differential methylation analyses identified between one and 806 differentially methylated CpGs positions in 10 studies. None of the identified differentially methylated sites overlapped between studies. Three studies used breast tissue DNA and suffered major methodological issues that precludes any conclusion. Overall risk of bias was critical mainly because of incomplete control of confounding. Important issues relative to data preprocessing could have limited the consistency of results.Global DNA methylation may be a short-term predictor of breast cancer risk. Further studies with rigorous methodology are needed to determine spatial distribution of DNA hypomethylation and identify differentially methylated sites associated with risk of breast cancer.CRD42020147244Supplementary information accompanies this paper at 10.1186/s12885-020-07543-4. Alterations of DNA methylation patterns are the most common epigenetic aberrations in cancer and occur in cells during early breast cancer development and progression . DNA metAs DNA methylation status of large subset of sites are known to be strongly correlated with each other, approaches that capture the dynamics of several sites simultaneously across the entire genome (epigenome-wide studies) are less prone to bias than candidate gene methylation studies . NumerouDNA methylation studies are aimed at identifying high-risk methylation patterns that may have an application in breast cancer early diagnosis and in identifying high-risk women for targeted interventions . HoweverA systematic review was conducted following a pre-established protocol and the general methods for Cochrane reviews  and repoAn electronic search was conducted in MEDLINE (via PubMed), EMBASE, Web of Science and CENTRAL  databases, from inception to June 30, 2019. Search strategies were developed for each of these databases with text words and index terms referring to breast cancer, methylation and risk Table . No langAny observational or intervention study that evaluated the association between DNA methylation and breast cancer risk, whatever the study design, was eligible for inclusion. No restrictions were applied regarding language or publication type .Women included in the studies before or after breast cancer diagnosis, regardless of age, stage, treatment regimen and menopausal status, were eligible. No participants were excluded based on ethnicity. A special attention was paid to identifying overlapping populations between studies, by comparing study population source, date of start and end of study recruitment, inclusion criteria, follow-up duration and population characteristics. When overlapping populations between studies was encountered, the study with the largest sample size was considered as the reference, and information was supplemented by the other publications as required.Only studies that measured DNA methylation in human samples , on a genome-wide scale (epigenome-wide studies) using the HM450k or EPIC BeadChip were eligible. Measures of global DNA methylation across all included probes or a predefined set of probes  as well as probe-wise differential methylation analysis were considered appropriate exposure estimations.Breast cancer risk, measured as breast cancer incidence, prevalence or breast mammographic density (a recognized breast cancer risk factor), or as defined by authors of included studies, was the primary outcome. Comparisons between matched normal and tumor tissue from the same patient were not considered a measure of breast cancer risk and were not included.The references identified by the search strategy were reviewed independently by two authors (KEI and CD) in a 2-step process. First, the title and abstract of each study were screened to exclude obviously non-eligible studies. Then, the full text of retained articles was examined and subjected to evaluation using the predefined eligibility criteria. Whenever required, a third review author (FD) was consulted. When required, further information was sought from the authors by email.Data extraction was performed using an exhaustive standardized form designed for this review. Information about study design , participants and tumors characteristics at diagnosis  status), exposure assessment , measured outcome and reported results  were collected. For observational studies, special attention was paid to distinguishing between adjusted and unadjusted results, and to the variable selection method used in multivariate analyses. The study’s definition of each retained characteristic or variable was recorded. In the case of multiple publications related to the same study, and to avoid the overlap across studies populations, the publication reporting the outcomes of interest to the present review or the one with the longest follow-up of these outcomes or with the largest sample size was considered as the reference, and information was supplemented by secondary publications as required. Abstracts with insufficient information and data to permit inclusion were excluded from the qualitative synthesis. Data were extracted independently by two review authors  to ensure their consistency.Assessment of risk of bias was performed for each study and for the overall risk of bias across studies. Based on the “STrengthening the Reporting of OBservational studies in Epidemiology” (STROBE) statements , and thep-values were considered not appropriate as these methods are prone to introduce selection bias. A method based on change of estimate of odds ratio was considered inappropriate because of the non-collapsibility of such measures . Once validated, specific CpGs methylation status, and expression value of related genes, could be used in prospective study designs to generate comprehensive predictive models, integrating clinical characteristics and environmental risk factors that would accurately predict breast cancer risk for each woman.Since the launch of the high-throughput HM450k BeadChip for epigenome-wide interrogation of DNA methylation, many epigenome-wide studies have tried to identify high-risk methylation patterns associated with breast cancer risk. Despite methodological differences between studies, we observed a trend toward an association of global blood-derived DNA hypomethylation and higher epigenetic age with breast cancer risk in women. Further epigenome-wide studies should use more conventional epidemiological approaches, including an ethnically homogeneous and representative sampling of breast cancer patients, proper selection of controls and proper correction of potential confounding, in addition to considering the impact of time to diagnosis for cases and length of follow-up in controls and choosing proper data preprocessing methods.Additional file 1 : Table S1. Search strategy for Medline via PubMed. Table S2. Studies of blood-derived methylation and breast cancer risk. Table S3. Studies of breast tissue methylation and breast cancer risk.


In stratified analyses, we found a significant inverse association between intermediate low-fat dairy product consumption and premenopausal BC and between medium total dairy product consumption and postmenopausal BC. Thus, dairy products, especially low-fat dairy products, may be considered within overall prudent dietary patterns.Dairy products might influence breast cancer (BC) risk. However, evidence is inconsistent. We sought to examine the association between dairy product consumption—and their subtypes—and incident BC in a Mediterranean cohort. The SUN (“Seguimiento Universidad de Navarra”) Project is a Spanish dynamic ongoing cohort of university graduates. Dairy product consumption was estimated through a previously validated 136-item food frequency questionnaire (FFQ). Incident BC was reported in biennial follow-up questionnaires and confirmed with revision of medical records and consultation of the National Death Index. Hazard ratios (HR) and 95% confidence intervals (CI) were estimated with Cox regression models. Among 123,297 women-years of follow-up , we confirmed 119 incident BC cases. We found a nonlinear association between total dairy product consumption and BC incidence ( Breast cancer is the leading cancer in women worldwide. There were 2.08 million new cases in 2018, accounting for 24.2% of all new cases of cancer in women. It is the fifth leading cause of death from cancer in women  and seveOne dietary factor for which evidence is still inconclusive in terms of breast cancer prevention is dairy product consumption. These inconsistencies may be due to the fact that dairy products are a complex and varied food group, which can be further classified according to their fat content or to being fermented or not. Dairy products have been suggested to be beneficial for breast cancer prevention mainly due to their content of calcium, vitamin D, and conjugated linoleic acids (CLA) ,15,16,17A meta-analysis published in 2015  concludewww.proyectosun.es, accessed on 20 January 2021) Project is an ongoing, multipurpose cohort of Spanish university graduates. The methodology was widely described elsewhere [The “Seguimiento Universidad de Navarra” (SUN)  . The repFor the present analysis, the endpoint was incident breast cancer. During the follow-up, participants were inquired about any incident cases of breast cancer, and medical records were requested to confirm the diagnosis. Incident breast cancer cases were defined as women who developed breast cancer during the follow-up and were free of the disease at the beginning of the study . They were also inquired about the date of diagnosis. Self-reported breast cancer cases were considered as probable cases. These participants were asked for a copy of their medical records. Then, a trained oncologist confirmed the cases on the basis of the medical records. These trained oncologists were blinded with respect to dietary exposures. Moreover, fatal breast cancer cases were reported to the research team by the subject’s next of kin, work associates, or postal authorities, along with the cause of death. For participants lost during the follow-up or with unidentified causes of death, the National Death Index was consulted. Finally, our analysis included 10,930 women among whom 119 developed breast cancer during the follow-up period (median follow-up = 12.1 years). At baseline, we also collected information about participants’ sociodemographic characteristics, medical history, health-related habits, lifestyles, anthropometric data, physical activity, and family history of breast cancer ,38. Age For the main analysis, participants were divided into quartiles according to their reported consumption of total dairy, whole-fat, low-fat, and fermented product consumption. Baseline characteristics of participants were described as the means and standard deviations for quantitative traits or as percentages for qualitative ones according to quartiles of total dairy product consumption. Dairy product consumption and nutrient intakes were adjusted for total energy intake using the residual method .2), age of menarche , age at menopause , use of hormone replacement therapy (yes/no) and its duration (years) (continuous) only for postmenopausal women, history of pregnancy , lifetime breastfeeding (months), energy intake , tertiles of calcium, vitamin D, and saturated fat from non-dairy products, intake of coffee consumption , sugar-sweetened beverage consumption , and exposure to oral contraceptives (OC) (yes/no). The 2-year questionnaire included a nonspecific question about habitual medication use during the previous 2 years. Those participants who reported a habitual use of either oral combined contraceptives or oral progestin-only contraceptives were considered as OC users. We defined baseline exposure as having reported the habitual use of OC in the 2-year questionnaire, and model 2 was additionally adjusted for the Mediterranean diet adherence score proposed by Trichopoulou et al. [To evaluate the association among baseline total, whole-fat, low-fat, and fermented dairy product consumption and the consequential risk of breast cancer, we used Cox regression models with age as the underlying time variable. Models were stratified for age (decades) and recruitment period. Comparisons had the lowest quartile of consumption as the reference category. We defined follow-up for each participant from the time of entry defined as the date of completion of the baseline questionnaire to the date of breast cancer diagnosis or breast cancer death for incident cases, or date of last follow-up or date of death for a cause other than breast cancer for non-cases. Probable incident cases were censored at the date of self-reported diagnosis. Hazard ratios (HR) and their 95% confidence interval (CI) were estimated. In an attempt to control for potential confounding factors, we used successive models of adjustment according to previous studies. Model 1 was adjusted for height, years at university, family history of breast cancer , smoking status , lifetime tobacco exposure (pack-years), physical activity -h/week), television (TV)-watching (h/day), alcohol intake , body mass index (BMI) . We used the cumulative average method for this analysis for those women with data information in the FFQ after 10 years. n = 67) as the outcome, women who reported being menopausal before baseline assessment were excluded, and we censored follow-up time at the age of 52 years or at the self-reported age of menopause, whichever occurred first. When assessing postmenopausal breast cancer (n = 43), women were considered at risk only after having turned 52 years old or after their self-reported age of menopause, whichever occurred last. For the analyses with postmenopausal breast cancer as outcome, the multivariable models were additionally adjusted for time since recruitment until the beginning of the time at risk. This latter figure was 0 for women who were already postmenopausal at baseline. For initially premenopausal women who turned postmenopausal during follow-up, we calculated time since recruitment as the difference between the self-reported date of menopause and the date of completion of the baseline assessment. Subsequently, we stratified analysis according to menopausal status at breast cancer diagnosis. For the assessment of premenopausal breast cancer  were used. Each model was controlled for all the covariates abovementioned. Assuming an alpha error of 0.05, an overall incidence of 1.1%, a Cox regression coefficient of −0.69—equivalent to an HR of 0.5—and a standard deviation of 0.5, our analyses would have a statistical power of 83%.R2 change. Furthermore, we estimated their contribution related to total dairy product as the grams consumed from each food group divided by the total dairy products (%). We also calculated the main sources of total dairy products and calcium from non-dairy products, as well as the main sources of variability in total dairy products in this Mediterranean cohort for both individual foods adjusted for the residual method. To calculate the contribution of each food item (or group of them) to the between-person variability dairy product consumption, we conducted nested regression analyses after a stepwise selection algorithm. The contribution of each food group is shown in the cumulative p-values and the statistical significance threshold was set a priori at 0.05.Analyses were performed using STATA/SE version 12.0 ; we used two-sided 2 (SD 10.7). Women in the highest category of total dairy product consumption were more physically active and more likely to be never smokers. They also consumed less alcohol per day but more coffee per day than women with lower dairy product consumption. The main baseline characteristics of the 10,930 women included in our analyses according to the quartiles of baseline total dairy product consumption are shown in p for nonlinearity = 0.048; Q2 vs. Q1 0.49; 95% CI 0.28–0.84; p = 0.009 and HRQ3 vs. Q1 0.49; 95% CI 0.29–0.84; p = 0.009) . We observed no significant associations when we addressed the consumption of whole-fat or fermented dairy product consumption and total breast cancer. No significant interactions with adherence to the Mediterranean diet were observed.We confirmed 119 new-onset cases of breast cancer among 123,297 person-years of follow-up (median follow-up 12.1 years). We found a significant nonlinear association between total dairy product consumption and the incidence of breast cancer (= 0.009) . These a= 0.009) . We alson = 67)   . Results= 0.027) . Non sigQ2 vs. Q1 0.28; 95% CI 0.10–0.76; p = 0.012 and adjusted HRQ3 vs. Q1 0.42; 95% CI 0.18–0.96; p = 0.040)  showed a significant inverse association with postmenopausal breast cancer (adjusted HR= 0.040) . Results= 0.040) . No signSkimmed milk contributed the most to the between-person variability when we adjusted for the residual method. On the other hand, whole-fat yogurt contributed the most to the between-person variability without adjusting for the residual method. In terms of quantity, semi-skimmed and skimmed milk contributed most to the total dairy product consumption.We found in this Mediterranean cohort that a moderate consumption of total dairy products (2–4 servings/day) was inversely associated with overall and postmenopausal incidence of breast cancer. Moreover, we found that moderate consumption of low-fat dairy products (1–2 servings/day) was significantly and inversely associated with total and premenopausal breast cancer. No significant associations were observed for whole-fat or fermented dairy products and breast cancer risk. The observed inverse association between all dairy products and overall breast cancer was generally consistent with previous studies. In a meta-analysis including 22 prospective studies, modest and high consumption of total dairy products were associated with a 6% reduced risk of breast cancer . The assDairy products are a heterogeneous food group which can be further classified according to their fat content or their fermentation. As for the first distinction, low-fat dairy products differ from high-fat dairy products in terms of the fatty acid content, and they are produced by filtering full-fat dairy to remove most saturated fatty acids, while maintaining unsaturated fatty acids . In the An interesting aspect to consider is the timing of consumption, which might be important in terms of breast cancer prevention. In subgroup analyses, Zhang et al. observed an inverse association between total dairy product intake and breast cancer incidence only for premenopausal breast cancer but not for postmenopausal breast cancer or among studies which addressed childhood consumption and subsequent breast cancer risk . In lineRegarding biological mechanisms, dairy products contain components which have been proposed to be both potentially beneficial and potentially detrimental in terms of breast cancer prevention. On the one hand, dairy products, especially whole-fat dairy products, are a source of high saturated fat, which is widely associated with breast cancer risk ,48. On tOur study has various potential limitations to take into consideration. First, our statistical power could be limited, because of the observed number of incident cases of breast cancer, especially when considering postmenopausal breast cancer. We acknowledge that, due to the multiplicity of the analysis and comparisons considered, results must be interpreted cautiously. Our cohort mostly represent young Mediterranean women, among whom the incidence of breast cancer is lower. Second, information on breast cancer incidence was self-reported. We are aware that we might have lost some breast cancer cases. Nevertheless, the identified age-adjusted incidence was consistent with the reported incidence of breast cancer in the Spanish population . In addiOur results suggest an inverse association between intermediate total dairy product consumption and overall and postmenopausal breast cancer and between intermediate low-fat dairy product consumption and total and premenopausal breast cancer. Thus, dairy products, especially low-fat dairy products, may be incorporated within overall prudent dietary patterns. Future insight into the role of dairy product consumption in breast cancer may be obtained by separately assessing the associations of high- and low-fat dairy products.


Because birth size appears to be positively associated with breast cancer risk, we have studied whether this risk may differ according to molecular breast cancer subtypes.A cohort of 22,931 women born 1920–1966 were followed up for breast cancer occurrence from 1961 to 2012, and 870 were diagnosed during follow-up. Archival diagnostic material from 537 patients was available to determine molecular breast cancer subtype, specified as Luminal A, Luminal B -), Luminal B (HER2+), HER2 type, and Triple negative (TN) breast cancer. Information on the women’s birth weight, birth length and head circumference at birth was used to estimate hazard ratios (HR) with 95% confidence intervals (CI) for each molecular subtype, applying Cox regression, and stratified by maternal height.Birth length (per 2 cm increments) was positively associated with Luminal A , Luminal B (HER2+) , and TN breast cancer . No clear association was found for birth weight and head circumference. The positive associations of birth length were restricted to women whose mothers were relatively tall (above population median).We found a positive association of birth length with risk of Luminal A, Luminal B (HER2+) and TN breast cancer that appears to be restricted to women whose mothers were relatively tall. This may support the hypothesis that breast cancer risk is influenced by determinants of longitudinal growth and that this finding deserves further scrutiny. It has been demonstrated that breast cancer can be classified into different molecular subtypes using gene expression analysis . HoweverTherefore, it is plausible that underlying risk factors may also differ between subtypes –9, indicUntil recently, factors associated with birth size, i.e. birth weight, birth length and head circumference, have not been comprehensively studied in relation to risk of molecular subtypes of breast cancer. However, the association of birth size has been assessed in relation to hormone receptor status of the tumours; thus, there is some evidence for a positive association of birth weight with risk for ER positive breast cancer, but not for ER negative tumours .The hypothesis that breast cancer may originate in utero is based on the fact that the mammary gland, which is not fully developed at birth, is exposed to high levels of intrauterine mammotropic hormones , 12. ThiIn this follow-up study, we used perinatal data from a cohort of Norwegian women born at one particular hospital between 1920 and 1966. During follow-up from 1961 to 2012, we studied women who were diagnosed with breast cancer at St. Olavs Hospital in Trondheim, Norway, and used surrogate markers applied to tissue microarrays of archival tumour tissue to determine the following molecular subtypes: Luminal A, Luminal B (HER2-), Luminal B (HER2+), and the non-luminal subtypes, the HER2 type and TN breast cancer. Our aim was to assess whether indicators of birth size are differentially associated with the risk for luminal and non-luminal subtypes.A unique ID number was assigned to all Norwegian residents who were alive in 1960, and is assigned to all new residents from 1961 onwards. Each resident’s record is continuously updated on vital status and residential and childbearing history through the National Registry. By combining the mother’s name and the daughter’s unique 11-digit number, we could identify women who were born at St. Olavs hospital, Trondheim University Hospital, between 1920 and 1966. After excluding 524 twins, 111 triplets, 32 women with missing information on plurality, and 12 women whose identity could not be determined with certainty, 22,931 women born between 1920 and 1966 were eligible for breast cancer follow-up until the end of 2012.The reporting of cancer to the Cancer Registry of Norway is mandatory, and breast cancer is registered according to the international classification of diseases . For women born before 1941, breast cancer follow-up started on January 1st, 1961, and for women born after 1940, follow-up started at their 20th birthday. Follow-up ended when a cancer (at any site) was diagnosed, at emigration or at death (from any cause), or on December 31st, 2012, whichever occurred first. During follow-up, a total of 870 women were diagnosed with incident breast cancer. Among them, 598 were diagnosed at St Olav’s Hospital; archival diagnostic tissue was available for all these 598 patients, and molecular subtyping proved to be successful for 537 of these cases.From the birth record of each of the 22,931 women, we extracted information on birth weight (g), birth length (cm), head circumference (cm), gestational age (in weeks or months), and birth order. We also extracted maternal information, including maternal age at childbearing, height, marital status, and socioeconomic status (occupation).  of the population.Prior to molecular subtyping, each case was re-examined by two independent pathologists and classified into histological type and grade according to current guidelines. Sections from representative paraffin tumour blocks were stained with hematoxylin–erythrosine–saffron (HES), and classified into histopathologic type and grade. Tumour size was correlated to information in the pathology report. Tissue microarrays (TMA) were constructed as previously described and referenced in detail by Engstrøm et al. (4) and Valla et al. (3).Briefly, immuno-staining was performed for ER, PR, HER2, the proliferation marker Ki67, and Basal markers and EGFR. Negative controls were included in all staining runs. ER and PR were positive when ≥1% of tumor nuclei showed positive staining and Ki67 was counted in 500 tumor cells (hotspots), and considered high when ≥15% of nuclei were positive, both irrespective of staining intensity. Membranous staining for HER2 was scored from 0 to + 3, and HER2 amplification was defined as a gene to chromosome ratio ≥ 2.For CK5 and EGFR, a staining index was calculated by multiplying the proportion of positive staining cells  by staining pattern/intensity. Staining intensity for CK5 was defined as 0 (no staining); 1 (weak); 2 (moderate), and 3 (strong). For EGFR, membranous staining was 0 (no staining); 1 ; 2 ; 3 , according to Dako PharmDX Kit guidelines. A staining index of 0–1 was classified as negative, and 2–9 as positive.Breast cancers were then reclassified into molecular subtypes by immunohistochemistry and fluorescence in situ hybridization, using the surrogate markers for oestrogen receptor (ER), progesterone receptor (PR), and the proliferation markers Ki67 and HER2. In addition, Cytokeratin 5 and Epidermal growth factor receptor 1 were used to differentiate between the Basal phenotype and the Five negative phenotype, however, we combined these subtypes into one, notified as TN.ER was positive when ≥1% of tumour cells showed positive nuclear staining, and ≥ 10% for PR to be positive . The molWe categorized the continuous or discrete birth size variables  into approximately equally sized categories. We also examined the measures as quantitative increments of approximately one standard deviation (SD), i.e., 0.5 kg for birth weight, 2 cm for birth length, and 1.5 cm for head circumference.p value for heterogeneity from this test would indicate that the associations of birth size with breast cancer risk differs between tumour subtypes. Departure from the proportional hazards assumption was evaluated by Schönfelds residuals and by inspection of the log-log plots, and there were no clear violations to the assumption of proportionality. Attained age was used as the time scale in all analyses. In the multivariable analysis, we adjusted for gestational age, birth year in 5-year categories, and maternal birth order, age at birth, and socioeconomic status. In a subset of women (73%), we considered whether adjustment for maternal height and the adult risk factors age at first birth and parity influenced the findings.We used Cox proportional hazards regression to study the association of the birth size indicators with risk of breast cancer subtypes, and precision of hazard ratios was estimated using 95% confidence intervals (CI). Breast cancer cases that could not be subtyped were censored at the date of diagnosis. To examine if the associations between birth size and breast cancer risk differed by breast cancer subtype, we used the data augmentation method described by Lunn and McNeil . A smallIn a separate analysis, we assessed whether the association of birth size with risk of luminal and non-luminal subtypes differed by maternal height. In that analysis, maternal height was dichotomized at the population median (163 cm). We used the likelihood-ratio (LR) test to assess this possible effect modification.The statistical analyses were conducted using STATA version 13.1 (StataCorp).Baseline characteristics of the cohort members according to birth size can be viewed in Table Table The multivariable results did not substantially differ from those only adjusting for attained age. The results restricted to the subset of women with information on maternal height and the adult risk factors age at first birth and parity (results not tabulated) were similar to the main results, however, for some categories, the number of participants was low in the subset analysis.p = 0.4). For luminal breast cancer ), there was a similar, but weaker pattern: thus, there was a weak positive association if mothers were 163 cm or taller  and no clear association  if maternal height was less than 163 cm (LR-test p = 0.7).In a separate analysis Table , we asseIn this long-term follow-up for breast cancer, we found that birth length was positively associated with risk both for Luminal A, Luminal B (HER2+), and TN breast cancer, and the results suggest that these associations displayed a homogeneous pattern, based on the test for heterogeneity. We also found evidence that the positive association of birth length was present in women whose mothers were relatively tall .To understand these findings, the literature does not provide any direct help, because risk of molecular breast cancer has not been previously studied in the context of birth size. However, one study has assessed birth weight with risk for breast cancer according to hormone receptor status., and fouHowever, the positive association that we found for birth length was also present for the HER+ subtype and for triple negative (TN) breast cancer. Moreover, the strength of the positive associations was rather similar for the different subtypes.The major strength of the present study is the large cohort of women born between 1920 and 1966, and the long-term breast cancer follow-up provided by record linkage with the Cancer Registry of Norway. The notification of cancer to the Cancer Registry of Norway is mandatory and regulated by law, and the registration is virtually complete . BaselinLimitations of the study include lack of longitudinal information on childhood and adolescent growth, including height and weight gain, which could be intermediate factors, and have been shown to be associated with breast cancer risk in previous studies , 25. ForThe main finding of this study suggests that birth length is positively associated with risk of Luminal A and Luminal B (HER2+) breast cancer, both of which are ER positive tumours, and with TN breast cancer, which is ER negative. Interestingly, associations of birth length were not consistent across ER and HER2 status, as indicated by the lack of association for the HER2 type, which is ER negative, and the Luminal B (HER2-) type, which is ER positive. This suggests that the association of birth length with breast cancer risk is still unresolved and may involve other factors than ER and HER2. Despite the relatively large study cohort, the number of incident cases was still moderate, and the results of a larger study, or from other cohort studies, would most likely contribute with useful insights that would nuance our findings.The fact that birth length was the only measure of birth size that was associated with any of the subtypes, may point to longitudinal growth as particularly important, regardless of ER status. It is also compelling that the positive association of birth length was present only among women whose mothers were relatively tall. This may further support the possibility that determinants of longitudinal growth may be at the core of the intrauterine roots of breast cancer , 28.


Studying entire dietary patterns is a promising alternative approach to overcome limitations of the single food or nutrient approach. We evaluated the relationship between the scores of 4 established Dietary Approaches to Stop Hypertension (DASH) diet indexes and breast cancer risk among Iranian women.This case-control study was carried out on 408 eligible women . A validated 168 item semi-quantitative food frequency questionnaire was used for assessing usual dietary intakes. DASH index scores were generated based on predefined algorithms for each of the 4 previously described indexes . Unconditional logistic regression analysis was performed to estimate odds ratio (OR) and 95% confidence intervals (CIs) for score categories or quintiles of DASH diet indexes and breast cancer risk in multivariate adjusted models.Women in the highest categories of the Mellen’s and Günther’s scores had lower odds of breast cancer than those in the lowest quintiles . However, no significant associations were found between Dixon’s and Fung’s DASH score and breast cancer risk. Modification by menopausal status revealed that breast cancer risk was only reduced in postmenopausal women with higher scores on Mellen’s index .A greater adherence to 2 of the 4 DASH indexes (Mellen’s and Günther’s indexes) was associated with decreased risk of breast cancer. Breast cancer, the most prevalent cancer in women, is a major public health problem worldwide . Breast Among environmental risk factors of breast cancer, diet has been considered as an important modifiable exposure . HoweverThis hospital-based case-control study was carried out on women aged ≥30 years who were admitted to Shohadaye Tajrish and Imam Hossein hospitals in Tehran, Iran from September 2015 to February 2016. Only patients with histopathologically proven breast cancer (and no history of other cancers) were designated as breast cancer patients. Eligible cases were all incident cases of breast cancer in past 6 months who did not undergo any cancer treatments at the time of interview. Exclusion criteria for cases were history of hormone replacement therapy, being pregnant or lactating and having special dietary habits such as vegetarian.The control group was then selected randomly among women referred to the same hospitals for a broad spectrum of non-neoplastic diseases not related to known or suspected risk factors for breast cancer and their eating habits. The exclusion criteria for controls were history of physician-diagnosed cancer in any site, HRT and benign breast disease, pregnancy or breast-feeding, and having special dietary habits. Two controls were enrolled for each case and matched for diagnosis hospital, menopausal status and age (±5 years). The participation rate was 95% for cases and 89% for control and 92% for all of them. Of the 408 eligible subjects participated in this study, a total of 401 subjects (134 cases and 267 controls) were included in the final analysis. Five controls and 2 cases were excluded from study because their daily energy intakes were either > 3 or < 3 SD from the mean. The ethics committee of the National Nutrition and Food Technology Research Institute of Shahid Beheshti University of Medical Science approved the study protocol and a written informed consent was obtained from all volunteers before enrolment in the study.Questionnaire data regarding socio-demographic variables, history of cancer and other diseases, family history of cancer, reproductive history, HRT and vitamin D supplement use, and current or past smoking behavior were collected from participants at baseline. Information on the subject’s activity level was gathered using a valid physical activity questionnaire  and was We used a validated 168 item semi-quantitative food frequency questionnaire (FFQ) with multiple choice frequency response options for assessing usual dietary intakes of all participants. Reproducibility and relative validity of this FFQ in evaluating major dietary patterns and food and nutrient intake among Iranian adults have already been demonstrated . SubjectComputation of the DASH scores has previously been described in detail . DASH inMellen et al.  designedFung’s index is the traditional DASH diet scoring system and includes 8 components highlighted or minimized in the DASH diet: high intakes of whole grains, fruits (includes fruit juice), vegetables (excludes potatoes), low-fat dairy products, and nuts and legumes, as well as low intakes red and processed meats, sweetened beverages, and sodium. The scoring system is based on quintile categories of eating the mentioned food items. For recommended components, those in the lowest quintile of intake receive 1 point and those in the highest quintile receive 5 points. In contrast, for components for which lower intakes are favorable, those in the highest and the lowest quintile of intake receives1 and 5 points, respectively. Component scores were then summed up to construct an overall adherence to DASH score, ranging from 8 to 40.Six components on a 10-point scale, which include: (i) fruits and fruit juice, (ii) vegetables and potato, (iii) meat, poultry, fish and eggs, (iv) nuts, seeds and legumes, (v) fats and oils, and (vi) sweets.Four components with a 5-point scale, including: (i) total dairy, (ii) low-fat dairy, (iii) whole grains, and (iv) high-fiber grains.A more complex food-based DASH diet index has been defined by Günter et al. . This inRecommendations for 4 various energy intakes including 1600, 2000, 2300, and 3100 kcal/d are the basis of target intakes for each component that accounts for activity level, sex, and age defined by Dietary Reference Intakes (25). The ultimate DASH index is calculated by adding up the acquired points, and it yields a value in the range of 0 to 80.p-value ≤0.05). In order to conduct statistical analyses on all the above-mentioned DASH scores  in this study, they were expressed as distribution-based indexes and the lowest quintile was considered as the referent category. Given the fact that Dixon’s DASH index is a whole numbers 9-point scale with a limited range of values, score categories ≤1 (referent category), 2, 3, and ≥ 4 were selected. Unconditional logistic regression analysis was performed to estimate odds ratio (OR) and their corresponding 95% confidence intervals for score categories or quintiles of DASH diet indexes and breast cancer risk in multivariate adjusted models. The possibilities of effect modification by menopausal status were considered by additional models. Moreover, complementary analyses were performed to examine whether individual components of DASH index are independently associated with the risk of breast cancer. All multivariable models were adjusted for the following covariates: age (y), BMI (in kg/m2), physical activity, smoking, total energy intake , vitamin D supplement use, age at first live birth, and family history of cancer. Furthermore, in order to compare total scores on the 4 indexes, Spearman’s correlation coefficients were calculated.The statistical analyses were carried out in the SPSS commercial package, version 18 . All the significance tests were performed with the confidence interval of, at least, 95% (corresponding to a Table r = 0.69), while the weakest correlation was between Gunther’s and Mellen’s DASH indexes (r = 0.07).Table The ORs and their 95% CI for total DASH scores and breast cancer risk are shown in Table Further stratification by menopausal status revealed that Dixon’s DASH diet index, Fung’s DASH diet index, and Günther’s DASH diet index were not significantly associated with breast cancer risk between premenopausal and postmenopausal women. However, risk estimates were significantly reduced for breast cancer in postmenopausal women with higher scores on Mellen’s DASH index .Table To the best of our knowledge, our study is the first observational study that epidemiologically examined the association between 4 DASH diet indexes and breast cancer risk. In present case-control study, we found that a greater adherence to 2 of the 4 DASH indexes (Mellen’s and Günther’s indexes) was associated with markedly decreased risk of breast cancer in Iranian women. After stratified by menopausal status, only Mellen’s DASH index was linked to reduced risk of breast cancer, among postmenopausal women. The available evidence linking the DASH diet to breast cancer is limited. Fung et al. reported that the whole DASH was significantly associated with a lower risk of ER-breast cancer in postmenopausal women . RecentlThe results of current study suggest that the main effective components of the DASH diet, which can protect against breast cancer, may exist in almost all indexes. On the other hand, the lack of an association between Dixon’s and Fung’s DASH scores and breast cancer in our research demonstrates that differing in composition of the indexes  and the scoring procedures may affect the results. Dixon and Günther indexes use certain cutoffs and Fung index applies rankings of intakes whereas Mellen’s index includes a density-based method . Furthermore, Dixonʾs and Mellen’s indexes directly assess the saturated fat intake while both indexes developed by Fung and Günther estimate the intake of saturated fat indirectly through consumption of saturated fat rich foods , 24–26. In a cohort study by Miller et al., men with the highest scores on all 4 of the indexes and women with the highest scores on Mellen’s, Fung’s, and Günther’s indexes had significant reduced risk of colorectal cancer . The FunIn previous studies on dietary patterns and breast cancer risk in Iranian women, a ‘healthy’ dietary pattern characterized by the consumption of vegetables, fruits, low-fat dairy products, legumes, olive and vegetable oils, fish, condiments, organ meat, poultry, soya and whole grains was inversely related to breast cancer risk . MoreoveComparing 4 DASH diet indexes in the same study for the same outcome is the main strength of our study. In addition, only recently diagnosed women with breast cancer (within the past 6 months) were enrolled in this study. Therefore, alteration in the diet by cases due to cancer diagnosis is less possible. The high participation rate of the study subjects and adjusting the analysis for confounding variables were the other strength of the study. Where economic resources are severely limited, food consumption is strongly correlated to income so that even little income differences are directly reflected in diet. Thus, studies in developing countries can offer unique opportunities to investigate the association between diet and cancer (due to large between person variation) . MoreoveOn the other hand, our study has some limitations. First, recall bias is possible due to the hospital-based case-control design of study. In case-control studies, cases may recall their previous diet differently in the context of their cancer diagnosis and this can affect the associations (overestimation). Second, the possibility of selection bias cannot be avoided in retrospective case-control studies. In the present study, the probability of selection bias was minimized by high participation rates and by selecting hospital controls from patients whose admission diagnosis was unrelated to alcohol intake, tobacco smoking, and diet-related diseases (limiting share exposure). Lack of precision of results due to small sample size is another limitation of our study. Moreover, although we used a validated food-frequency questionnaire for assessing the dietary intake, measurement errors that might led to underestimation or even over estimation of associations were inevitable . FinallyOverall, we demonstrated that 2 of 4 DASH diet indexes (Mellen’s and Günther’s) reduced breast cancer risk in women. Nevertheless, minor differences in scoring method of these indexes can affect the results which should be taken into consideration in future research.


In recent years, the identification of genetic and phenotypic biomarkers of cancer for prevention, early diagnosis and patient stratification has been a main objective of research in the field. Different multivariable models that use biomarkers have been proposed for the evaluation of individual risk of developing breast cancer.This is a case control study based on a population-based cohort. We describe and evaluate a multivariable model that incorporates 92 Single-nucleotide polymorphisms (SNPs) (Supplementary Table S1) and five different phenotypic variables and which was employed in a Spanish population of 642 healthy women and 455 breast cancer patients.p-value of 3.43E-07. The first decile presented an inverse proportion: 1% of cases and 9% of controls, with an OR of 0.097 and a p-value of 1.86E-08.Our model allowed us to stratify two groups: high and low risk of developing breast cancer. The 9th decile included 1% of controls vs 9% of cases, with an odds ratio (OR) of 12.9 and a These results indicate the capacity of our multivariable model to stratify women according to their risk of developing breast cancer. The major limitation of our analysis is the small cohort size. However, despite the limitations, the results of our analysis provide proof of concept in a poorly studied population, and opens up the possibility of using this method in the routine screening of the Spanish population.The online version contains supplementary material available at 10.1186/s12885-020-07584-9.The online version contains supplementary material available at 10.1186/s12885-020-07584-9. The prevention and early diagnosis of breast cancer is one of the main objectives of cancer research. There are different models to estimate cancer risk based on genetic or non-genetic factors; that is, a high or moderate predisposition , 2. In rAlthough there are previous studies in Caucasian populations, this is the first to combine a PRS of 92 SNPs with other risk factors, such as mammographic density (MD), reproductive factors, and family history, in a Spanish population of 1097 women. The main objective was to analyze the usefulness of this approach in our population using a multivariable logistic method based on the combination of these variables.The present study was submitted to and approved by the Clinical Research Ethics Committee (CEIC) of the Hospital Clínico Universitario de Valencia (Spain) - September 29th, 2016 (2016/169) and July 13th, 2018 (2018/139) - and was conducted in compliance with the Helsinki Declaration.This is case control study compiling full genotyping and phenotypic data for a cohort recruited between January 2017 and December 2018 from two sources: Hospital Clínico Universitario de Valencia and Valencian Community Screening Programme , both in the Autonomous Community of Valencia (on the Mediterranean Coast). A total of 867 healthy women and 640 breast cancer patients were recruited, with ages in the range of 30–70. Patients had developed breast cancer in a maximum period of 5 years prior to data collection, while controls were women who had not developed breast cancer during the same period. Those that presented incomplete phenotypic data or genotyping failure were excluded from the cohort, which left 1097 participants consisting of 642 healthy women and 455 breast cancer cases.The patient cohort was composed of 45% Luminal A, 20% Luminal B, 20% Her-2 positive and 15% Triple Negative tumors (approximate percentages).Clinical information was collected for all subjects at recruitment: family history of breast cancer, date of birth, age, age at menarche, age at menopause, age at first pregnancy, and mammographic density (MD). Breast density was assessed from craniocaudal and mediolateral oblique mammographic projections by an experienced radiologist with more than 10 years of experience. The radiologist used the image viewer system , classifying MD according to Boyd’s semiquantitative scale .< OR < 1.05) and another 3 SNPs with platform genotyping failure were removed. In this way, a total of 92 SNPs [As in our previous PRS risk analysis , we init 92 SNPs –16 were The genotyping method has been described previously . In shorp-value threshold of 0.05.Sample size was calculated with a 95% confidence level (two-tailed test), 80% statistical power, control-case ratio of 1.3 and initial prevalence of breast cancer of 12%; the total number of women necessary for results to be statistically significant was 1138, similar to our case control cohort (1097). In an initial exploratory univariable process, the case/control ratio of each risk factor was compared. During this step, the Wilcoxon-test was used with a two-sided xk is the number of risk alleles  based on the ploidy of each SNP. The βk weights are the ORs of the risk alleles associated with breast cancer described in Online Resource The PRS was based on a combined effect of 92 SNPs statistically associated with breast cancer. This strategy considers an independent effect of each SNP, ignoring departures from a multiplicative model . The PRSmentclass2pt{minimPollan et al. study [Pharoah et al. study [In the phenotypic analysis, the phenotypic categories were transformed into quantitative variables using the ORs described in the l. study , except l. study . In addil. study , which ap-value threshold of 0.05.For the univariable analysis, logistic regression was applied to each risk factor, which has been adjusted for age and centre. The coefficients of the model were standardized using the reghelper library of R . AdditioTo confirm the independence of the PRS and other phenotypic risk factors, pairwise Spearman correlations of unaffected controls were evaluated.For the multivariable study, we performed a logistic regression analysis that incorporated the statistically significant variables obtained in the previous steps, including the interaction terms. Family history and age at menarche were also included in the analyses, even though they were not significant, since they are well-known risk factors. The significance of the final model was evaluated using the Wald Test . To asseTo evaluate improvement in risk prediction for the different models and risk factors, the area under the curve (AUC) was evaluated  as a meaFinally, women were stratified into deciles based on their final individual risk factor, obtained from the multivariable model. The ORs of extreme deciles were evaluated using logistic regression with a reference range of 40–60%.Based on the characteristics of our cohort, the final individual risk factor proposed in this study describes the relative risk of women in the Spanish population of suffering breast cancer in a maximum period of 5 years.p-value of 0.27). The median age of our cohort was 51 years old, with a range of 30 and 70 years in the extreme deciles  and a p-value of 1.64E-7. The main differences between controls and cases were concentrated in the extremes, with respective proportions of 15% versus 11% in the first category (MD 0–10%) and 10% versus 22% in the last category (MD > 75%).p-values of 0.03 and 0.061, respectively. Age at first delivery was associated with an OR of 1.15 (95% CI: 1.02–1.31), and the most marked differences were seen with advanced maternal ages (over 34 years), with a proportion of 11% versus 8% among cases and controls, respectively. In terms of age at menarche, the OR was 0.89 (95% CI: 0.78–1.04). Another reproductive factor we have considered in this study was menopause status, which was associated with an OR of 1.96 (95% CI: 1.72–2.24) and a p-value <2E-16. The greatest difference between cases and controls was observed in the premenopausal category, with values of 46 and 13%, respectively. Regarding family history, cases showed a slightly stronger trend towards more breast cancer antecedents in first- and second-degree family members than controls; however, the logistic regression based on this quantitative variable was not statistically significant, with a p-value of 0.34.The interaction terms identified in our analysis as statistically significant and included in the multivariable model were age with mammary density and age with menopause status, with p-values of 0.004 and 2E-16, respectively. Indeed, the relation between both phenotypes and age has been the subject of study in the field of breast cancer for some time [In our cohort, a higher age at first delivery was associated with an increased risk of development of breast cancer, while age at menarche did not have a statistically significant effect, with ome time –29.The discriminative power of each phenotypic risk factor was compared using ROC curve analysis generated by 10-fold cross-validation Table . The resp-value of 6.30 × 10− 8. For women in the lowest quintile (5%), the PRS distribution presented an OR of 0.38  with respect to women in the middle quintile (40–60%). On the other hand, the highest quintile (95%) of PRS distribution exhibited an OR of 1.87   of 1.41, with a 95% CI of 1.24–1.61 and a All statistically significant univariable risk factors and interaction terms were included in the final multivariable model. Age at menarche and family history were also incorporated into the model based on the scientific literature. The Spearman method did not reveal significant correlations for any other variable (data not shown).p-value of 5.375E-09. These values are slightly higher than those observed in other previously published methods [We evaluated the discriminative accuracy of the multivariable model with and without interaction terms Fig. . The med methods , 31.Figp = 8.12E-07). At the other extreme, in the last decile, OR was 12.9 , and the proportion of cases and control was inversed, with 9% of cases and 1% of controls. These results indicate the capacity of the multivariable model to stratify women according to the risk they run of suffering breast cancer.We investigated how individual risk for cases and controls differed when the final multivariable model was used. Figure In recent years, there have been various proposals for multivariable models that stratify women who might suffer breast cancer according to their individual risk. Different biomarkers have been analyzed as possible predictors, including phenotypic and non-phenotypic markers, and environmental and genetic factors.One approach to measuring genetic variables is the polygenic risk score (PRS). This strategy is based on variable numbers of statistically significant low penetrance variants obtained from large GWAS analyses , 32.Our study was based on a relatively small cohort of women adjusted for center of origin in our univariable and multi-variable models.Employing a specific PRS based on 92 SNPs we obtained an OR of 1.41 (1.24–1.61) that was consistent with the results of other published studies of Caucasian populations using different numbers of SNPs (from 18 to 313) , 32–34.The AUC-ROC was 0.62, with a 95% CI of 0.56–0.66, which is also in line with the literature and assigns a range of 0.58 to 0.65 to European populations and one of 0.53 to 0.64 to non-European populations .p-value of 0.03 and an OR of 1.15 were identified for the former risk factor, while the latter was not found to be statistically significant .Regarding univariable phenotypic risk factor analysis, the most statistically significant results in terms of discriminant variables were obtained for menopause status and mammographic density, which once again is consistent with previous studies , 36, 37.The ORs of the risk factors obtained in our cohort present differences with respect to those previously reported. The most evident concern the lack of a statistical significance of family history and age of menarche. However, the direction (positive or negative) of these well-established effects and our results are concordant. On the other hand, the magnitude of OR of mammographic density was lower than that reported in the literature. These differences may be due to the low number of women in our cohort; however, the concordance of the effect, direction and magnitude of the different ORs of our population corroborates the validity of our study as a first proof of concept in a Spanish population.Additionally, the joint association of our PRS92 with transformed continuous phenotypic variables, such as MD, reproductive factors and family history, was examined in our Spanish population. We did not find any significant correlation between genotypic and phenotypic variables; a multiplicative model would possibly describe this in greater depth and help to improve breast cancer risk estimation.The precision of the multivariable model increased when we added two statistically significant interaction terms associated with women’s age: menopause and mammographic density. Such interactions have previously been observed, and we detected an increase of AUC-ROC from 0.74 (95% CI: 0.71–0.77) to 0.80 (95% CI: 0.77–0.83) Table , a rise p-value 3.43E-07. In contrast, the first decile presented an inverse proportion (1% of cases and 9% of controls); in this case, the OR was 0.097, with a p-value of 1.86E-08. These results indicate the capacity of the multivariable model to stratify women according to risk of developing breast cancer.We were able to stratify the control group within our model Fig. , in whicIn summary, our results indicate that using the multivariable logistic model and a combination of genetic, phenotypic and interaction variables is an effective approach for stratifying women in the Spanish population according to individual risk of suffering breast cancer within a 5-year period, with a capacity similar to that observed in other studies in European and non-European populations. Due to the nature of our study, different biases could have affected the precision of the results; for example, there may have been selection and length biases. Additionally, the small size of our cohort could have led to overfitting of the model in terms of risk estimation or the over/under representation of a specific tumor type. However, in spite of these limitations, our analysis provides proof of concept in a population that has not been studied until now. Larger series are necessary in order to confirm our data and initiate the use of this type of screening method in the Spanish population.Our results endorse the capacity of the multivariable model to stratify women according to their risk of developing breast cancer. Some bias could be present in the study and could have affected the precision of our results; however, the analysis provides proof of concept in a poorly studied population and opens up the possibility of its use in the routine screening of the Spanish population.Additional file 1.


Although innovative and impactful interventions are necessary for the primary prevention of breast cancer, the factors influencing program adoption, implementation, and sustainment are key, yet remain poorly understood. Insufficient attention has been paid to the primary prevention of breast cancer in state and national cancer plans, limiting the impact of evidence-based interventions on population health. This commentary highlights the state of primary prevention of breast cancer and gaps in the current literature. As a way to enhance the reach and adoption of cancer prevention policies and programs, the utility of dissemination and implementation (D&I) science is highlighted. Examples of how D&I could be applied to study policies and programs for chronic disease prevention are described, in addition to needs for future research. Through application of D&I science and a strong focus on health equity, a clearer understanding of contextual factors influencing the success of prevention programs will be achieved, ultimately impacting population health. Primary prevention of breast cancer is a significant public health concern . Breast Primary prevention of breast cancer has not often been a focus of state cancer control plans, although there is considerable overlap of risk factors with other cancer sites , highligIn this commentary, we present a brief summary of the literature regarding primary prevention of breast cancer, identifying gaps in the literature and avenues for innovation. Further, we introduce potential opportunities for dissemination and implementation (D&I) science as a means to enhance our understanding of the linkages (and gaps) between policy, research, and practice, providing a pragmatic lens through which to frame current and future intervention work.Much of the breast cancer prevention research has focused on identifying factors associated with higher or lower rates of breast cancer diagnosis, followed by interventions designed to impact the factors most modifiable. With regard to environmental risk factors, psychological factors, such as stress, anxiety, and depression have been linked to breast cancer incidence in systematic review research, indicating that individuals who report greater prevalence of these symptoms are also at a greater risk for diagnosis ,27. HoweFurther examination of socio-environmental exposures and their impact on breast cancer incidence has identified specific windows of susceptibility (WOS): biological landmarks when a woman’s breast tissue changes including the prenatal, pubertal, pregnancy, and menopausal periods ,32. IndeOther environmental and social determinants impact breast cancer incidence and mortality, with socioeconomic status and educational opportunity found to be driving factors of both . EducatiBrownson and colleagues  summarizGrounded in addressing social determinants of health, a key emphasis is on stakeholder  engagement through finding ways to reach at-risk individuals within community settings, enhancing quality of care. For example, Hardy and Bugella implemented a community-based intervention that targeted college students and peri- and post-menopausal women in Alabama through places of worship, highlighting the meaningful intersection of race, gender, and faith as key factors impacting breast cancer , particuIn addition to gathering stakeholder input, researchers should expand their inquiry to explore the multiple levels of influence impacting breast cancer prevention , with enAs a response to the growing inequities contributing to high cancer incidence in the US, the National Institutes of Health (NIH) sponsored several centers for population health and health disparities across the country with a key goal of enhancing stakeholder-driven research and combining basic and social sciences to address key issues in population health. Such an approach highlights the need to focus on how distal factors, such as social conditions and policies, interact with those more proximal, such as biological/genetic pathways and individual risk behaviors, contributing to cancer risk . ThroughImplementation of these health equity frameworks for cancer prevention would require dedicated resources  and stakeholder engagement from diverse groups within multiple ecological layers of influence, such as clinicians, community-based program officers, researchers, and patients . FurtherTo understand how policies and practices for the primary prevention of breast cancer can have the most potent effect, it is crucial to assess the factors leading to successful adoption and implementation of effective interventions. Dissemination and implementation (D&I) science is the study of the adoption of evidence-based practices when introduced to a new setting or population to minimize the research-to-practice gap ,55,56,57As a cross-disciplinary group of academics, policy makers, and providers, the CBCRP, in collaboration with Breast Cancer Prevention Partners, is working to develop a comprehensive breast cancer primary prevention plan to serve as a roadmap for prevention research and practice . The plaAs previously identified, there is a lack of clear evidence pointing to effective population-level programs and/or policies for primary prevention of breast cancer when examining a wide range of risk factors . Given tBased on extant research, a potential policy intervention at the state and county level could be to examine how county public health departments/and community health centers can enhance awareness and accessibility of DUFP for eligible participants. Counties may be tasked with (a) increasing awareness through a multicomponent communication strategy using social media, email reminders, and/or solicitation at community centers and clinics; and (b) increasing access by facilitating the use of public transport or supporting placement of DUFP partners in food deserts and/or fast food “swamps.” Following initial development and pilot implementation, county public health officials could then report to state public health departments on strategies that were effective and feasible to implement, thus generating a list of potential implementation strategies ,60,61 baFrom these initial best practices and pilot research, leadership and policy support from the state-level public health office could partner with community stakeholders, such as the community-based organizations affiliated with CBCRP. Such collaborative relationship would facilitate implementation of systems-level health incentive programs, such as DUFP, and empower counties to refine implementation strategies based on the nuanced needs and challenges of their communities. Data sharing practices could also be applied whereby county-level officials report the rates of participation in incentive programs and the particular strategies used for policy implementation, so that information can be gleaned about implementation effectiveness , therebyOur brief commentary summarized the current evidence surrounding primary prevention of breast cancer and identified opportunities for D&I science to enhance the uptake of evidence to practice and policy. Efforts are already underway to integrate community-based participatory research and D&I into evidence-informed interventions across California as a means to integrate stakeholder involvement in rapid-cycle research . Such woThis commentary summarizes the current status and further needs pertaining to primary prevention of breast cancer, and the potential utility for dissemination and implementation (D&I) science in such prevention efforts. Given the social determinants of health which disproportionately affect those identified as racial/ethnic minority and with low-income, policy and systems-level innovations are necessary. Although many interventions and policies can be enacted to address social determinants of health, it is crucial to evaluate such initiatives effectively. We provide pragmatic ways that D&I can be integrated to study how prevention policies are adopted, implemented, and sustained, with a key emphasis on stakeholder input. Through such an implementation-focused lens, we hope that evidence-based policies and interventions will have a greater impact on population health and prevention of cancer and chronic disease.