Background: Metabolic acidosis promotes cancer metastasis. No prospective studies have examined the association between dietary acid load and breast cancer recurrence among breast cancer survivors, who are susceptible to metabolic acidosis. Hyperglycemia promotes cancer progression and acid formation; however, researchers have not examined whether hyperglycemia can modify the association between dietary acid load and breast cancer recurrence. Methods: We studied 3081 early-stage breast cancer survivors enrolled in the Women’s Healthy Eating and Living study who provided dietary information through 24-h recalls at baseline and during follow-up and had measurements of hemoglobin A1c (HbA1c) at baseline. We assessed dietary acid load using two common dietary acid load scores, potential renal acid load (PRAL) score and net endogenous acid production (NEAP) score. Results: After an average of 7.3 years of follow-up, dietary acid load was positively associated with recurrence when baseline HbA1c levels were ≥ 5.6% (median level) and ≥5.7% (pre-diabetic cut-point). In the stratum with HbA1c ≥ 5.6%, comparing the highest to the lowest quartile of dietary acid load, the multivariable-adjusted hazard ratio was 2.15  for PRAL and was 2.31 (95% CI 1.42-3.74) for NEAP. No associations were observed in the stratum with HbA1c levels were <5.6%. P-values for interactions were 0.01 for PRAL and 0.05 for NEAP. Conclusions: Our study demonstrated for the first time that even at or above normal to high HbA1c levels, dietary acid load was associated with increased risk of breast cancer recurrence among breast cancer survivors. Impacts: Our study provides strong evidence for developing specific dietary acid load guidelines based on HbA1c levels. Breast cancer survivors are susceptible to recurrence. Analyses based on Surveillance, Epidemiology, and End Results (SEER) reported that approximately 36.8% of breast cancer survivors had recurrence within 10 years, with most of these recurrences (81.9%) occurring within 5 years after diagnosis . ResultsAlthough metabolic acidosis can promote cancer metastasis ,10,11,12We will leverage a large cohort, the Women’s Healthy Eating and Living (WHEL) study, to conduct the current study. Because this cohort was originally a dietary trial of increasing vegetable and fruit intakes and reducing fat intakes, the range of dietary acid load is wider in this cohort than the typical American diet, which enabled us to better examine the dose–response relationship of dietary acid load with breast cancer recurrence.This study aimed to determine whether dietary acid load is a risk factor of breast cancer recurrence among early-stage breast cancer survivors and whether hemoglobin A1c (HbA1c), a biomarker reflecting glucose status in the past three months, can modify this association. We hypothesized that dietary acid load would be positively associated with the incidence of breast cancer recurrence and the association would be stronger in women with high levels of HbA1c.This study is a prospective cohort, the Women’s Healthy Eating and Living (WHEL) study, comprising mainly early-stage  breast cancer survivors. Between 1995 and 2000, the WHEL study enrolled 3088 women within 4 years of diagnosis. The WHEL study was initially a multi-site randomized trial designed to test whether a diet low in fat and rich in vegetables, fruit, and fiber reduced the risk of invasive breast cancer recurrence and death from any cause in women. Briefly, inclusion criteria were as follows: women who had stage I (≥1 cm), II, or IIIA breast cancer diagnosed within the previous 4 years, had no evidence of cancer recurrence, had completed primary therapy, were 18–70 years old at diagnosis, did not have life-threatening comorbidities, and were able to communicate dietary data via 24-h food recall. Exclusions included insulin dependence and the diagnosis of a comorbidity requiring a specific diet or the use of a medication that contraindicated a high-fiber diet and insulin dependence. Women with diagnoses after age 70 years and those with stage 1 tumors smaller than 1 cm were also excluded. Extensive details can be found in previous publications from the WHEL study . BrieflyThe Institutional Review Board at the University of California at San Diego approved the original study. All subjects provided written informed consent. The de-identified data were provided by the principal investigator of the WHEL study. The current study was an ancillary study using the de-identified data from the WHEL study, thus the exempt IRB was approved by the San Diego State University IRB committee (protocol number: Temp-1286).At baseline, Year 1, and Year 4, dietary intake was assessed by four prescheduled, 24-h dietary recalls collected by telephone on random days over a 3-week period: two on the weekends and two during weekdays. Dietary assessors used the multi-pass software-driven recall protocol of the Nutritional Data System software .In terms of the assessment of dietary acid load, two commonly used scores were used to estimate dietary acid load in epidemiological studies: the potential renal acid load (PRAL) score and the net endogenous acid production (NEAP) score. The PRAL score considers the intestinal absorption rates for contributing nutrient ionic balances for protein, potassium, calcium, and magnesium and the dissociation of phosphate at pH 7.4 . FrassetPRAL (mEq/day) = (0.49 × protein [g/day]) + (0.037 × phosphorus [mg/day]) − (0.021 × potassium [mg/day]) − (0.026 × magnesium [mg/day]) − NEAP (mEq/day) = (54.5 × protein [g/day]/potassium [mEq/day]) − 10.2This study used both scores for dietary acid assessment because they reflect slightly different nutritional intakes and biological mechanisms. A Western diet rich in animal products is an acid-producing diet whereas a diet rich in fruit and vegetables is an alkaline-producing diet. A negative PRAL value reflects an alkaline-forming potential; a positive value reflects an acid-forming potential . For NEAThe primary outcome of this study is breast cancer recurrence. Information on outcomes were obtained through health status questionnaires completed during each of five clinic visits scheduled during the 6 years as well as during semiannual telephone interviews. Any reported event/death led to a medical record/death certificate review by two independent study physicians. In addition, participants out of study contact were searched for end points using the National Death Index (NDI). Breast cancer recurrence was defined as the combination outcome of invasive breast cancer recurrence  or new primary breast cancer. Carcinoma in situ was not counted as a study outcome. Approximately 4% of study participants were lost to follow-up, and these were censored at date of last contact. At the close of the study in June 2006, vital status was known for 96% of the participants. Mortality data were updated through September 2009 using the Social Security Death Index. Median follow-up time was 7.3 years for breast cancer event-free survival. For participants without an event, follow-up time was censored at the time of the participant’s death, at the last documented staff contact date, or at the study completion date .HbA1c was measured using ion exchange high-performance liquid chromatography  in washed red blood cells collected at the baseline visit. The coefficients of variation were 1.5% and 1.6%, respectively, for high- and low-quality controls.Demographic characteristics were self-reported. Variables abstracted from patient records included initial cancer diagnosis and treatment. Specific variables abstracted included tumor stage, size, hormone receptor status, and use of radiation, chemotherapy, and/or post treatment anti-estrogens use. Physical activity levels were assessed using an adapted validated questionnaire from the Women’s Health Initiative . PhysicaWe used Cox proportional hazard models to assess the association between dietary acid load and incidence of breast cancer recurrence. Time was calculated from the study entry to the time when participants were diagnosed with the incidence of recurrent breast cancer, died, were lost to follow-up, or censored at the end of follow-up period, whichever came first. As previously introduced, dietary acid load was characterized by PRAL and NEAP scores. Repeated measures of PRAL and NEAP at Year 0, Year 1, and Year 4 were analyzed by modeling them as time-varying covariates using the Cox proportional hazard regression. PRAL and NEAP scores were classified into quartiles or tertiles using the average intakes at years 0, 1, and 4 to set up the cut-point for each quartile or tertile. The multivariable Cox regression models were used to estimate hazard ratios. Death was considered as a competing risk. We controlled the following covariates based on a priori assumption: age at diagnosis, race/ethnicity, education level, intervention group, menopausal status at baseline, total calorie intake, alcohol intake, smoking status, pack-years, physical activity, body mass index (BMI), education level, tumor stage, tumor size, estrogen and progesterone receptor status, type of anti-estrogens, radiotherapy, and chemotherapy. Among these covariates, time-varying covariates included BMI, physical activity, smoking status, pack-years, total calorie intake, alcohol intake, and types of anti-estrogen therapy. We controlled these covariates in all the multivariable models, except for analyses conducted in four HbA1c strata , by median (<5.6 and ≥5.6%) and by pre-diabetic range (<5.7% and ≥5.7%). If interactions were significant, meaning the effect of dietary acid load was different among hemoglobin A1c groups, then the stratified analysis were performed. Cox models were used to determine these associations in stratified analyses. To assess whether a significant interaction occurred between dietary acid load and HbA1c, we used the Wald P-value for the interaction term in a model that also included the main effects. The proportional hazards assumption was examined and satisfied in all Cox proportional hazards regression models by testing the significance of the product terms for our variable of interest and log time. All analyses were conducted using SAS version 9.4 .The correlation between PRAL and NEAP is 0.96; therefore, we only presented baseline characteristics according to PRAL. As shown in We used the median of HbA1c at 5.6% to classify two groups: low-HbA1c group (<5.6%) and high-HbA1c group (≥5.6%). As shown in p values for the trends were > 0.2 for both PRAL and NEAL. When we considered only distant recurrent cases, the results were similar, and no significant associations were found between dietary acid load and distant recurrence for two acid load scores.During the average of 7.3 years of follow-up, 517 validated recurrent cases in total were confirmed. These included 357 distant recurrent cases. Neither PRAL nor NEAP was associated with an increased risk of incident breast cancer recurrence in age- or multivariable-adjusted models. As shown in P values for interactions were ≤0.05 for both PRAL and NEAP. Thus, we further collapsed the two strata with HbA1c < 5.6% into one stratum and two strata with HbA1c ≥ 5.6% into one stratum. No associations were found in stratum with HbA1c < 5.6% but positive associations were found in stratum with HbA1c ≥ 5.6%. As shown in p for trend = 0.0005 for PRAL and p for trend = 0.0004 for NEAP). Women with PRAL at quartile 4 (PRAL ≥ 7 mEq/day) showed 2.15 (95% CI 1.34-3.48) times the risk of breast cancer recurrence than women with PRAL at quartile 1 (PRAL < -19.5 mEq/day). A greater magnitude was observed for the NEAP score. The HR between extreme quartiles of NEAP was 2.31 . No associations were observed for strata with HbA1c < 5.6%. Further, we removed local and regional cases and analyzed the association of acid load with distant recurrent cases in the stratum with HbA1c <5.6% and the stratum with HbA1c ≥ 5.6%. The associations were similar, with the exception of NEAP, where the association was stronger in the HbA1c ≥ 5.6% stratum. The HR comparing the highest to the lowest quartile of NEAP was 2.48 .To determine whether HbA1c levels can modify the association between dietary acid load and breast cancer recurrence, we first created four strata using the cut-point of the quartile range of HbA1c. As shown in p values for trends were 0.02 for both.Moreover, we conducted additional analyses in the stratum with HbA1c ≥ 5.6%. We simultaneously analyzed the PRAL with acid-contributing foods, including fresh red meat, processed meat, cruciferous vegetables, allium vegetables, legumes, soy legumes, and other vegetables Table A1Similar patterns were found when we used the pre-diabetic range to create two strata, one stratum with HbA1c < 5.7% and one stratum with HbA1c ≥ 5.7%. Positive associations were observed in stratum with HbA1c ≥ 5.7% but no associations in stratum with HbA1c < 5.7% (data not shown).In this prospective cohort of early-stage breast cancer women in the USA, a diet with a high acid load, characterized by a high PRAL or high NEAP score, was associated with an increased risk of incidence of breast cancer recurrence among women at or above normal to high levels of HbA1c (HbA1c ≥ 5.6%). Among this group, a high-acid load diet can increase breast cancer recurrence by at least 2 times compared to a low-acid load diet. No associations were found between dietary acid load and breast cancer recurrence in the low HbA1c group (<5.6%). Our findings provide important messages for clinicians and clinical dieticians that we should pay attention to both dietary acid load and HbA1c levels; the cut-point for HbA1c should be lower than the pre-diabetic cut-point (5.7%) when considering the joint impact of dietary acid load and HbA1c.It is not surprising that we found positive and significant associations among women with higher HbA1c levels but a null association between dietary acid load and breast cancer recurrence in women with low HbA1c levels. We postulate that the following reasons may explain our findings. Dietary acid load and hyperglycemia may have synergistic impacts on breast cancer progression. Dietary acid load can promote metabolic acidosis if acid–base balance cannot be appropriately adjusted. Cancer patients have a reduced capacity to adjust acid–base balance ,14. MetaOur study also demonstrated that dietary acid load can better capture the acid–base influences than some acid-contributing  or alkaline-contributing foods  and was associated with breast cancer recurrence independent of the acid- or alkaline-contributing foods, as shown in This study has several strengths. This is the first large prospective cohort study investigating the associations of dietary acid load with breast cancer recurrence among breast cancer survivors. Four 24-h recalls during each visit were the unique advantages of this cohort, but were rarely conducted in other cohorts. The repeated measurements of dietary assessment at baseline, Year 1, and Year 4 enabled us to assess the longitudinal impact of dietary acid on breast cancer recurrence. As this study was originally a trial of high-vegetable, high-fruit, and low-fat intake interventions, we observed a wider range of dietary acid load than other cohorts and an immediate shift of dietary acid load to lower levels from baseline to year 1. This finding enabled us to better assess a longitudinal dose–response relationship of dietary acid load with the outcome. This study was also the first large prospective study among breast cancer survivors to assess the associations between dietary acid load and breast cancer recurrence; the large sample size provided us with sufficient power and enabled us to adjust for multiple covariates.This study also faced several limitations. First, the original randomization was not based on dietary acid load, thus we analyzed the data as observational data. Although causal-effect conclusions cannot be obtained, cohort analyses provide the strongest evidence for making causal suggestions among different types of observational studies and provide strong evidence for conducting future clinical trials. Using HbA1c levels to predict group responses to dietary acid load in a clinical trial will likely be a new emerging type of clinical trial—namely, an adaptive clinical trial targeting individualized nutrition. Furthermore, the average follow-up was 7.3 years, not 20 years; thus, the long-term impact of dietary acid load on breast cancer recurrence could not be determined in this study.Our study further highlights that it is important to reduce a high-acid load diet for breast cancer survivors who had normal to high (≥5.6%) or moderate to high level of abnormal range of HbA1c (≥5.7%). To our knowledge, this is the first study to show that dietary acid load is a risk factor for breast cancer recurrence among women who have moderately increased long-term glucose levels, as measured by HbA1c. Our findings suggest a significant interaction between dietary acid load and HbA1c. When considering dietary acid load together with HbA1c, the cut-point for increased risk should be lower than the pre-diabetic cut-point of 5.6%. Our study provided strong evidence for making tailored dietary guidelines in terms of dietary acid based on HbA1c status among breast cancer survivors.


Although preclinical studies suggest that fermented soy foods may have a protective effect against breast cancer, no prospective cohort studies have examined this association.Our study examined the association between fermented and nonfermented soy food intake and breast cancer risk using a population‐based prospective cohort study in Japan.We included a total of 47,614 women aged 45–74 years in an analysis of the Japan Public Health Center‐based Prospective Study (JPHC Study). A validated food frequency questionnaire (FFQ) was used for the assessment of dietary intake. Breast cancer incidence was analyzed by multivariate Cox proportional hazards regression models.p for trend = 0.305 and 0.393). Unlike nonfermented soy, higher intake of fermented soy foods was associated with a significant decrease in the risk of nonlocalized breast cancer. The HR and 95% CI in the highest compared to lowest intake category of fermented soy foods was 0.53  versus nonfermented soy foods 0.85  (p for trend = 0.026 and 0.797).During an average of 15.5 years of follow‐up, 825 breast cancer cases were newly identified. We found no association of intake of soy foods with breast cancer risk, regardless of fermentation, with multivariate hazard ratios (HRs) and 95% confidence intervals (CIs) for the highest quartiles of fermented and nonfermented soy food intake of 0.94  and 1.15  compared with the lowest quartile (Our analyses showed that fermented soy foods had no association with overall breast cancer but may be associated with decreased risk of nonlocalized breast cancer. We investigated the association between fermented and non‐fermented soy food intake and breast cancer risk using a population‐based prospective cohort study in Japan. Our analyses showed that fermented soy foods had no association with overall breast cancer but may be associated with decreased risk of non‐localized breast cancer. However the evidence is inconsistent. Two meta‐analysesTo assess these different effects, the association of fermented and nonfermented soy foods should be evaluated separately. In this study, we examined the association between fermented and nonfermented soy intake and the risk of breast cancer incidence in a prospective cohort study in Japan.22.1This study was a prospective cohort study conducted based on the Japan Public Health Center‐based Prospective Study (JPHC study), which targeted 140,420 residents  to investigate the associations between lifestyle and illness such as cardiovascular disease and cancer. The JPHC study is being conducted in 11 public health centers (PHCs) across Japan. Cohort I was established in 1990, and enrolled residents aged 40–59 years who had registered their addresses with the Akita (Yokote), Iwate (Ninohe), Nagano (Saku), Tokyo (Katsushika), and Okinawa (Ishikawa) local governments at the time of baseline collection. Cohort II was established in 1993, and enrolled residents aged 40–69 and who had registered their addresses with the Niigata (Kashiwazaki), Ibaraki (Mito), Osaka (Suita), Kochi (Chuohigashi), Nagasaki (Kamigoto), and Okinawa (Miyako) local areas at the time of baseline collection. The JPHC Study been approved by the Institutional Review Board of the National Cancer Center . Completion of the questionnaire after receiving a briefing about the study purposes and methods was considered informed consent. Details of the JPHC Study have been previously published.2.2Dietary intake was assessed with a validated self‐administered food frequency questionnaire (FFQ) administered at the time of the 5‐year follow‐up survey. Intake frequency of 138 foods and beverages during the previous one year using standard portion sizes was collected.The validity of food item intake assessed with the FFQ was evaluated for subsamples of 498 subjects using 14‐ or 28‐day dietary records and the FFQ used in the 5‐year follow‐up survey.2.3Breast cancer incidence was defined as the primary outcome of this study. Cases were identified by active patient notification with data coming from major local hospitals and linked with data from population‐based cancer registries. Use of data was approved by local governments responsible for the registries. Breast cancer cases were classified using codes C500‐C509 defined in the Third Edition of the International Classification of Diseases for Oncology. We categorized breast cancer with metastasis to regional lymph nodes, or adjacent or distant organs as nonlocalized cancer. We did not include cancer in situ as cases. Death certificates provided supplementary information. A total of 879 breast cancer cases were identified. Among these, 19 cases depended on death certificate notification (DCN = 2.2%), for which diagnostic information was unavailable for 18 cases (DCO = 2.1%). For 805 (96.3%) cases, diagnosis was microscopically verified. Only the first incidence of breast cancer was considered in this analysis in women with more than one diagnosis.2.4Follow‐up started with the 5‐year follow‐up survey administration date. Person years of follow‐up were accumulated until the date of cancer diagnosis, death, migration out of a study area, or end of follow‐up , whichever occurred first. Residential registers of each municipality confirmed residence status and survival once a year in the study areas and the municipal office of the area the subjects moved to. Dates of death were verified by linkage with death registrations under the Ministry of Health, Labor, and Welfare.2.5For the primary outcome, we examined the association between soy food intake and breast cancer incidence. Intake of soy foods and isoflavone was energy‐adjusted using the residual approach.Further, we analyzed the association by cancer extent  to evaluate the effect of fermented soy foods. Finally, we also looked for interactions with menopausal status  and BMI category . In the analyses of cancer extent and menopausal status, we conducted log‐rank tests to compare the results of two groups.p < 0.05. All statistical analyses were conducted with Stata MP 14.All tests were two‐sided and the threshold for significance was 3During an average of 15.5 years (interquartile range: 15.1–18.8) of follow‐up, 47,614 women contributed 738,504 person years, and 825 cases of breast cancer were newly identified. Mean age at diagnosis was 65.0 years (standard deviation = 8.7). Table Table 2 = 15.1, p = 0.0001). There was no association between either fermented and nonfermented soy foods and isoflavone intake and localized breast cancer. In contrast, nonlocalized breast cancer risk significantly decreased in the higher quartiles of fermented soy foods consumption compared with nonfermented. We reanalyzed the degree of cancer extent by menopausal status, but the results showed no meaningful change.Information on cancer extent was available for 732 cases . Table 2 = 4.56, p = 0.033). We did not observe a statistically significant tendency of breast cancer risk reduction by higher intake of fermented soy foods in either premenopausal or postmenopausal women.Table In subgroup analysis by BMI category, each category and individual food item showed no consistent association with breast cancer risk (data not shown).4This study found no association between soy food consumption and the risk of total breast cancer, regardless of fermentation status. In subgroup analyses, women with the highest intake of fermented soy foods had half the risk of nonlocalized breast cancer than women in the lowest intake category. No association was evident between fermented soy foods and breast cancer risk in premenopausal or postmenopausal women.As expected, even though the number of cases analyzed was small and 95% CIs were wide, the highest quartile of fermented soy food intake showed a decreased risk of nonlocalized breast cancer. Unexpectedly, however, the same inverse association was not observed for overall or localized breast cancer. Some studies have reported that components of soybeans like isoflavone have anticancer activity.Most previous cohort studies showed no association between soy food intake and breast cancer risk. Our previous reportThe effect of soy foods by menopausal status is also inconsistent among previous studies. One studySeveral limitations of this study should be acknowledged. First, there could be a measurement error in assessment of the dietary data. The FFQ used in this study was not originally designed to test the effect of soy foods alone and other soy products such as soy sauce were not included in this analysis. There also may have been changes in dietary habits during the study period. However, the FFQ is a validated instrument for measuring dietary intake and can be used to rank individuals according to intake.Despite these limitations, to our knowledge this is the first epidemiological study to examine the association between soy foods, with division by fermentation and the risk of breast cancer. Because Japanese consume a relatively high amount of soy foods, this study was also characterized by a wide range of soy intake. The study design, collecting exposure data before diagnosis, made recall and selection bias minimal. A very small number of participants were lost to follow‐up in this cohort (0.7%), and the high response rate to this study (83.5%) was also a major strength in minimizing selection bias. Breast cancer incidence was confirmed by the cancer registry not self‐report, reducing the potential for misclassification of the outcome. The participants of this study were enrolled from the general population and the incidence of breast cancer identified in this study did not differ from the national data of Japan. Therefore, the results of this study may be generalized to the general Japanese population, or to populations with a similar background.In conclusion, we found an increase in fermented soy food intake was associated with a decrease in nonlocalized breast cancer risk. The reason why fermented soy foods would have an association with nonlocalized breast cancer but not localized breast cancer is yet to be clarified. Our findings warrant the need to consider the effects of fermented soy and total soy in the etiology of breast cancer and its prevention separately.All authors declare no conflicts of interest.M. Inoue, NS and ST designed and conducted research; RS analyzed data; RS, ES, SKA, and M. Inoue wrote the paper; NS, JI, RT, TS, TY, AG, M. Iwasaki, and ST made substantial contributions to strengthening the Subjects and Methods and Discussion sections. M. Inoue holds primary responsibility for final content. All authors read and approved the final manuscript.


Non-European populations are under-represented in genetics studies, hindering clinical implementation of breast cancer polygenic risk scores (PRSs). We aimed to develop PRSs using the largest available studies of Asian ancestry and to assess the transferability of PRS across ethnic subgroups.The development data set comprised 138,309 women from 17 case-control studies. PRSs were generated using a clumping and thresholding method, lasso penalized regression, an Empirical Bayes approach, a Bayesian polygenic prediction approach, or linear combinations of multiple PRSs. These PRSs were evaluated in 89,898 women from 3 prospective studies (1592 incident cases).The best performing PRS (genome-wide set of single-nucleotide variations [formerly single-nucleotide polymorphism]) had a hazard ratio per unit SD of 1.62 (95% CI = 1.46-1.80) and an area under the receiver operating curve of 0.635 (95% CI = 0.622-0.649). Combined Asian and European PRSs (333 single-nucleotide variations) had a hazard ratio per SD of 1.53 (95% CI = 1.37-1.71) and an area under the receiver operating curve of 0.621 (95% CI = 0.608-0.635). The distribution of the latter PRS was different across ethnic subgroups, confirming the importance of population-specific calibration for valid estimation of breast cancer risk.PRSs developed in this study, from association data from multiple ancestries, can enhance risk stratification for women of Asian ancestry. Rare pathogenic variants in several susceptibility genes, including BRCA1, BRCA2 and PALB2, confer increased risks of breast cancer; however, a majority of the genetic variations in risk is polygenic owing to the fact that a large number of genetic variants combine, in which each genetic variant confers a small increase in risk. The effects of these variants can be summarized as polygenic risk scores (PRSs). Mavaddat et al developed and validated a 313 variant breast cancer PRS (PRS-313), using data from European-ancestry women in the Breast Cancer Association Consortium (BCAC). The lifetime risk of breast cancer was estimated to be 2.6% for women in the lowest 1% of the PRS-313 distribution and approximately 32% for women in the highest 1%; the latter group would be classified as at high-risk of developing breast cancer according to the National Institute for Health and Care Excellence and other clinical management guidelines. This shows the potential of PRS to improve quantification of risk and consequently optimize breast cancer screening and prevention strategies.Genetic inheritance is an important risk factor for breast cancer. and exacerbate health disparities. This is important for ethnic minorities in high income countries, where clinical evaluation of the European PRS-313 is already underway but perhaps more so in low- and middleincome countries, where there is an urgent need to develop breast cancer screening strategies to address rapidly rising breast cancer incidence and high breast cancer mortality.Non-European populations are under-represented in genetic studies, and this could limit PRS adoption and applicability but make up only 15% of participants in the breast cancer genome-wide association studies (GWAS). Efforts to develop breast cancer PRS specifically for Asian populations have so far been limited. In our previous work, we showed that PRS-313, developed for Europeans, was predictive of breast cancer risk in Asian populations, although the effect size was somewhat smaller than that reported in European populations. However, an important outstanding question is whether a more predictive PRS using Asian data can be developed. Thus far, the largest study to attempt this involved 23,372 women of Asian ancestry. This study evaluated previously published breast cancer risk single-nucleotide variations (SNVs) (formerly singlenucleotide polymorphisms) and took forward SNVs that were significantly associated with breast cancer risk in Asians (P < .05) for PRS derivation, resulting in a 44-SNV PRS. Although predictive, we have shown in our previous work that the discriminatory power of 44-SNV PRS (area under the receiver operating curve [AUC] = 0.586) was much lower than PRS-313 (AUC = 0.617), derived from European ancestry women, for predicting breast cancer risk in Asian women.Asians constitute more than half of the world’s population and are facing a dramatic increase in breast cancer incidenceIn this study, our objectives were twofold: (1) to develop improved breast cancer PRSs using data from Asian populations and to validate their performance in prospective cohorts using the largest available breast cancer genetic study of Asian ancestry and (2) to assess the transferability of PRSs across Asian ethnic subgroups.P < .0001 were available); (2) set 2, which comprised 16,680 invasive cases and 83,414 controls of East Asian ancestry from studies participating in BCAC together with BioBank Japan (where GWAS summary statistics were available); and (3) set 3, which comprised 122,977 invasive cases and 105,974 controls of European ancestry participating in BCAC(where GWAS summary statistics were available). The validation data set comprised (1) 6392 invasive cases and 6638 controls of Chinese or Malay ancestry and (2) 585 invasive cases and 1018 controls of Indian ancestry participating in 2 multiethnic case-control studies: the Malaysian Breast Cancer Genetics study and the Singapore Breast Cancer Cohort study. The testing data set comprised 89,898 women (1595 incident cases) from 3 prospective cohorts of East Asian ancestry: the Singapore Chinese Health Study, the China Kadoorie Biobank, and the Korean Cancer Prevention Study Biobank. Ancestry informative principal components (PCs) were available for Asian ancestry samples in the BCAC and validation data sets generated using methods as previously described. See The study population was divided into training, validation, and testing data sets. The training data sets included (1) set 1, which comprised 22,013 invasive cases and 22,114 controls of East Asian ancestry from studies participating in BCAC and Asia Breast Cancer Consortium  for association between the standardized PRSs and breast cancer risk in the validation set. The studies in the validation set were genotyped in 2 batches and hence treated as different strata for the purposes of adjustment. Cox proportional hazard model, adjusted for the first 2 PCs for Singapore Chinese Health Study (SCHS) and Korean Cancer Prevention Study-II and the first 12 PCs for China Kadoorie Biobank, was used to estimate hazard ratios per SD (HRperSD) for the association between the PRS and breast cancer risk in the test set. The discrimination of PRS was assessed using AUC. The HRperSD and AUC were obtained individually for each study and combined using a fixed-effect meta-analysis. Test of heterogeneity between studies were obtained using rma command in the metafor package in R version 3.6.1.PRSs were given by the following equation: The approaches for SNVs selection to be included in PRS and the corresponding weights are described in subsequent sections. r > 0.9); the SNV with the lowest P value for association in the correlated pairs was retained, resulting in 3050 SNVs. SNVs were further clumped within prespecified clumping window sizes and threshold of a correlation r. PRSs were then computed using the subset of SNVs that were significant at a prespecified P value threshold (set at 5 × 10−8 and then increased in steps of 10−10 up to 10−3). The PRS with the highest AUC in the validation data set was selected as the best PRS. The clumping and derivation of PRSs were performed using PRSice v2.11, whereas the AUCs for PRSs were generated using the pROC package in R version 3.6.1.Training data set 1 was used in these analyses. SNVs clumping (within 1 megabase pair windows) was conducted to remove highly correlated SNVs  in the validation data set was selected.All 3050 SNVs described in clumping and thresholding (C + T) section were included in these analyses, together with genotype data from Asian controls in BCAC OncoArray studies for calculating linkage disequilibrium among SNVs. The analyses were run using the package  only 287 SNVs with imputation info score > 0.9 in validation data set were retained for subsequent analyses. Reported weights were used to derive the European PRS (hereafter denoted as PRS287_EUR). Asian PRSs generated from C + T or lasso penalized regression were linearly combined with PRS287_EUR. The relative contribution of each PRS were estimated by logistic regression using the validation data set.Of the 313 SNVs included in PRS developed for European women,287_ASN), and (2) weights based on a combination of the Asian and European weights using an Empirical Bayes (EB) approach (hereafter denoted as PRS287_EB), where the optimal weight is given by the following equation: We considered 2 sets of weights for PRS derivation using the 287 SNVs: (1) Asian weights estimated from the training data set 1, taking into account the correlation between SNVs using jA, EBβ is the estimated posterior effect sizes in Asians given the data and jp is the allele frequency for SNV j . together with Asians and Europeans in the 1000 Genomes Phase 3 project as linkage disequilibrium reference panels. PRSs generated using European- (hereafter denoted as PRSGW_EUR) and Asian-specific posterior weights (hereafter denoted as PRSGW_ASN) were linearly combined (hereafter denoted as PRSGW_EUR + PRSGW_ASN) in the validation data set. The analyses were repeated across a range of global shrinkage parameter (φ), and the φ that gave the linear combination of PRSs with the highest AUC in the validation data set was selected as the optimal φ. Analyses were run using the published Python code-based tool in Github.Training sets 2 and 3 were used as training data sets for PRS-CSxperSD). Given the much smaller sample size of Indian-ancestry women, we did not attempt to generate a South Asian–specific PRS, but we considered estimating the weights in the linear combinations of multiple PRSs using the South Asian validation data set.The predictive performance of PRSs developed for East Asian–ancestry women in Indian-ancestry women were assessed using AUC and OR per SD  (perSD (95% CI) of 1.35   , with OR= 0.586) . Other crovement .2985) (perSD (95% CI) of 1.41 , slightly more predictive than the PRS46  = 0.014 and shrinkage parameter (s) = 0.9, resulting in a PRS that included 2985 SNVs (PRS2985) , with ORhe PRS46 .perSD [95% CI] = 1.54 [1.49-1.60]; AUC = 0.623) yielded markedly higher predictive accuracy in East Asian–ancestry women than that achieved using the Asian-specific PRSs alone , but relative contribution of PRS46 to the linear combination model was approximately 30%  was slightly more predictive than PRS287_ASN  and PRS287_ EUR  and markedly more predictive than PRS46 and PRS2985  of 1.62 (1.52-1.68) and AUC of 0.636 for PRSGW_EUR + PRSGW_ASN, markedly better than all the PRSs described thus far . The ORperSD (95% CI) and AUC for PRSGW_ASN alone was 1.44 (1.39-1.49) and 0.601, respectively, only slightly better than PRS46 , larger for PRS based on the European PRS (HRperSD [95% CI] = 1.50 [1.35-1.65] for PRS287_EB), and still larger for PRS based on combining the Asian and European PRS (HRperSD [95% CI] = 1.53 [1.37-1.71] for PRS46 + PRS287_EB). As in the validation data set, PRS generated using PRS-CSx showed the strongest association with breast cancer risk (HRperSD [95% CI] = 1.62 [1.46-1.80]) and highest AUC (0.635). There was no evidence of heterogeneity in the hazard ratios among studies for any PRS  for women in the lowest 1% and highest 1% of the PRS distribution were 0.53 (0.33-0.82) and 3.01 (2.25-4.06), respectively, compared with middle quintile. The estimated ORs did not differ from those predicted under a theoretical polygenic model in which the log OR increases linearly with the PRS   . The PRS = .983) . Figure  = .983) .46 + PRS287_EB were observed between the Indian-ancestry women and Japanese/Korean women (with Indians being the biggest outlier).The patterns of PRS distribution by population  are mirrPersonalized risk stratification for prevention and early detection of breast cancer has gained increasing interest; however, it is important to recognize the need to study women representing diverse ancestries to lessen health disparities. Our study provides essential information about the utility of PRSs for breast cancer risk prediction in women of Asian ancestry. We developed and validated different PRSs for East Asian–ancestry women. The key observations were (1) PRSs generated by integrating information from European ancestry and Asian ancestry GWAS data sets performed better than PRSs based purely on weights derived from single-ancestry GWAS data, and (2) there were substantial differences in PRS distributions across ethnic groups. (PRSGW_EUR + PRSGW_ASN). This PRS had a notably larger effect size than the European PRS (PRS287_EUR) that we had previously shown to be the best breast cancer PRS for women of Asian ancestry . However, despite the rapid drop in cost associated with next-generation sequencing, implementation of PRS comprising approximately 1 million SNVs can be practically more challenging than the implementation of the European PRS that included only 313 variants.Based on the largest available breast cancer GWAS data sets, the best PRS for East Asian–ancestry women was based on PRS-CSx approach46) improved predictive power. This approach of linearly combining PRSs may reduce the gap in prediction accuracy between European and non-European populations as described previously. Second, incorporating Asian weights further improved predictive power (PRS46 + PRS287_EB) but to a lesser extent. The 313-SNV PRS is being used in several clinical studies in European populations, including the MyPeBs  and WISDOM (Women Informed to Screen Depending On Measures of risk) trials, and the PRS46 + PRS287_EB PRS would be relatively easy to implement in clinical settings.We showed that adaptions based on the European 313-SNV PRS can improve risk prediction in women of East Asian ancestry. First, incorporating SNVs identified in the Asian populations  was applied to an Asian population without adjustment, the 60th percentile in Chinese-ancestry and Malay-ancestry women and 80th percentile in Indian-ancestry women correspond, approximately, to the 90th percentile in the European population, resulting in overestimation of risk in these women  emphasize the need for ethnic-specific calibration before translating PRS into practice for diverse Asian populations.SuppMaterialTableS1TableS2TableS3TableS4


There has been substantial interest in developing methods to predict the risk of breast cancer. The Gail model is one the first model have been widely used to identify women at higher risk of breast cancer. This study aimed to determine the 5-year and the general life-time risk of breast cancer and also to determine breast cancer predictors in women using the Gail model. We used the Gail model to estimate the risk of breast cancer in female Turkish outpatients aged above 35 years in this cross-sectional study. Age, life-style habits, breast-feeding duration, family history of breast cancer, and body mass index were compared between high and low-risk subjects. We have performed the Patient Health Questionnaire 9-item (PHQ-9) and the Generalized Anxiety Disorder 7-item (GAD-7) tools on patients regarding depression and anxiety. We also assessed the association of these covariates with the estimated risk of breast cancer in multivariate linear regression analysis. We enrolled 1065 subjects with a mean age of 52.9 ± 8.4 years. The mean of the five-year risk for breast cancer was 1.33%±0.6. Meanwhile, the mean of lifetime risks for breast cancer was 10.15%±3.18, respectively. Nearly one-third of the participants had one child, 55.9% had breast-fed their children more than six months. Meanwhile, 18.5% of the subjects had a high depression score, 15.2% had a high anxiety score. Higher age, age at first birth, and parity; lower age at menarche; presence of menopause and family history of breast cancer were higher in the high-risk group. Higher age, and age at first birth; lower age at menarche; family history of breast cancer, presence of menopause, and parity were independently associated with higher breast cancer risk. We identified certain risk factors for breast cancer in our study population and Gail model is a reliable and useful breast cancer risk prediction model for clinical decision-making. This study contributes to the body of evidence in order to facilitate early detection and better plan for possible malignancies in Turkish population. Breast cancer is the most commonly diagnosed cancers in women worldwide and its account for 29% of all new cancers in women at 2015  with high morbidity and mortality rates. Risk assessment tools estimating the individual’s absolute risk for developing breast cancer and identifying the women at high level of risk are crucial for decision-making about prevention and screening . Among Turkish women, breast cancer is the most common cancer and the most common cause of cancer related death . Investigators have suggested various models to estimate the risk of breast cancer using several risk factors identified in epidemiological studies . Among these tools, the Gail model, which utilizes the risk factors identified in the Breast Cancer Detection Demonstration Project (BCDD) in 1989, is the most commonly used one  and has been modified . About 5% to 10% of breast cancers are thought to be hereditary, caused by abnormal genes passed from parent to child . Genetic factors play an important role in the pathogenesis of breast cancer and family history of breast cancer is a well-known risk factor . Given the benefits of early diagnosis and prevention of breast cancer, implementation of breast cancer screening is crucial, especially for “high-risk” women . The Gail model incorporates six breast cancer risk factors, namely: age, age at menarche, age at first live birth, number of breast biopsies, history of atypical hyperplasia, and number of first-degree relatives with breast cancer .One of the advantages of Gail model  is the extensive validation in various female populations for more than two decades. We aimed to estimate 5-year and life-time risk of breast cancer and determine risk factors associated with higher breast cancer risk in Turkish population.This cross sectional study was conducted among the outpatients patients registered in internal medicine clinics, surgery, ophthalmology of outpatient clinics, Obstetrics - Gynecology - Medipol International Health Centers during the study period from June 2016 to March 2018 among Turkish citizens above 35 years of age. IRB ethical approval for this study was provided by the Istanbul Medipol University and conducted in accordance with the Declaration of Helsinki. All participants gave consent and approved prior to inclusion in the study. The sample size calculation was based on previous studies that determined the prevalence of Breast cancer in Turkey  to be between 25%-27%, with the 99% confidence interval and with 2.5% error of estimation. The minimum sample size for the current study was 1,500. Subjects were recruited by the systematic 1-in-2 sampling procedure. During the study period, a representative sample of 1,500 women aged 35 years and older was selected, 1,065 (71.0%) subjects gave consent. Meanwhile, 435 women either refused or were not available to take part in the study due to personal reasons and lack of time.We have calculated the five-year and life-time risk for breast cancer using Breast Cancer Risk Assessment Tool  which is based on the Gail model . According to the Gail model, women with the breast cancer risk of >1.66% were considered as high-risk according to the estimated 5-year breast cancer- risk assessment. The Gail Risk Assessments Tool is useful to guess the approximate number of women with a lifetime risk of ≥15% in the general population .. BCRAT is an interactive tool which estimates 5-year and lifetime risk for breast cancer. Stratification of risk level is one of the main targets of risk assessment tools which facilitates screening decision and clinical management. According to the Gail model, while a five-year breast cancer risk of less than 1.66% indicates low risk, an estimated risk of 1.66% or higher suggests high risk . We determined the mean 5-year and lifetime risks of our study population and classified the subjects as low- or high-risk if they had a lower or higher risk than the mean of the study population respectively. In this context, a five-year breast cancer risk of 1.33% or higher and a life-time breast cancer risk of 10.15% or higher indicated high-risk for this study.We calculated the five-year and life-time risk for breast cancer using Breast Cancer Risk Assessment Tool (BCRAT) which is based on the Gail model Patient health questionnaire 9-item (PHQ-9)Understanding psychological factors relevant to women’s participation in breast cancer screening is essential to ensuring that clinical and public health policies and interventions improve breast cancer morbidity and mortality. The PHQ-9 contains nine items referring to criteria for depression  and the answers refer to the past two weeks. The PHQ-9 has been shown to have good reliability and validity . Overall, the PHQ-9 items showed good internal  reliability. A cut-off score of 10 was established for the PHQ-9 , correctly classifying 86% of patients with current depression. Generalized anxiety disorder 7-item (GAD-7)The GAD-7  tool conducted on subjects are asked how often, bothered by each of the seven core symptoms of generalized anxiety disorder and cut-off point of 10 to describe clinical symptoms of anxiety in the sample. The GAD-7 has been shown to have good reliability and validity . Cronbach’s alpha in the current study was 0.87.Statistical analysis and comparisons are based on Independent Student-t test, Chi-square and Fisher’s exact test. Multiple linear stepwise regression analysis performed to predict the 5-year and lifetime breast cancer risk. The level p<0.05 was considered as the cut-off value for significance. We assessed content validity, face validity and reliability of the questionnaire in 100 subjects. These tests demonstrated a high level of validity and high degree of reproducibility (kappa = 0.87). We tested the validation of the pilot survey instruments in 100 subjects. We demonstrated adequate scale reliability with Cronbach’s alpha coefficient values higher than 0.70. The Cronbach’s alpha for overall internal reliability was 0.87.The mean of the five-year risk for breast cancer was 1.33%±0.6. Meanwhile, the mean of lifetime risks for breast cancer was 10.15%±3.18, respectively. While age, age of first birth, family history, menopausal status, and parity were positively related with five-year breast cancer risk, age at menarche was inversely related with five-year breast cancer risk according to multivariate linear regression analysis . VariablThe Gail model .is most commonly used in estimation of risk of breast cancer. It is important to accurately assess breast cancer risk to take preventive measures and make decision on screening procedures. Breast cancer is the most common type of cancer and accounts for the greatest number of cancer-related death among women in Turkey . The Gail model might overestimate breast cancer risk for Asian and Middle-Eastern women. Thus, it is needed to revalidate the Gail model in these populations. In this study, we aimed to revalidate the Gail model using data of Turkish women, and subsequently update and revalidate the Turkish version of the Gail model using our data. The results of our study indicate that Turkish version of the Gail model is valid and provides clinically relevant information. Age, age of first birth, age at menarche, family history, menopausal status, and parity seems to be independently associated with estimated breast cancer risk.In Iran study , the mean five-year risk of breast cancer for all participants was 1.608±0.729% (range 0.2±13.8%).The mean lifetime risk of breast cancer was 11.705±3.91% (range 0.6 54.7%).Similarly, in Turkey, analysis revealed that the mean of the five-year risk for breast cancer was 1.33%±0.6. and meanwhile, the mean of lifetime risks for breast cancer was 10.15%±3.18, which is consistent with the Iranian results.The best-known statistical model available for predicting an individual woman’s chance of developing breast cancer is that derived using information from regularly screened Caucasian women from the United States participating in the Breast Cancer screening and detection project . The Gail model might overestimate the risk of and development of breast cancer in other populations in which other races predominate . However, utilization of the Gail model for Turkish population and other populations with white women predominance might provide more accurate estimations . Accordingly, the five-year and life-time estimated risk for breast cancer was not higher in our study than different studies . Unfortunately, the Gail model for breast cancer risk prediction of consanguineous marriages for fist degree and second-degree relatives is not included as a risk factor. On the other hand, the Claus model  takes presence of both first and second-degree relatives with breast cancer and their age at diagnosis into account as risk factors. BRCAPRO model utilizes Bayesian statistics and Mendelian approaches and considers family history of bilateral breast cancer and ovarian cancer as risk factors . The Tyrer-Cuzick  estimated 10-year risk model which also provides information about the possibility of a breast cancer susceptibility gene mutation . Estimation of breast cancer risk is essential in the management of screening and prevention of this cancer. Closer screening may be beneficial in high-risk individuals. Screening with mammography has provided an important decrease in breast cancer mortality in women over 50 years old in Western countries . However, the advantage of screening is less clear for younger women. It would be better to individualize breast cancer screening for women under 50 years old. In this context, risk estimation tools such as the Gail model may prove beneficial., Although, applying risk assessment models can help healthcare providers to calculate a women’s risk of developing breast cancer. It has been recommended that women should have annual mammograms starting at age 40 . In fact, women with a higher risk of developing breast cancer should get extra screening procedures; as well as they might also get beginning screening at an earlier age with more repeated periods .To our knowledge, the Gail model has not been validated in a population with as large sample size as our study in Turkish women. Nevertheless, we examined the effect of factors that did not take place in the Gail model . In line with the findings of several studies , although our study suggested that the breast cancer risk was high in older age, presence of menopause, lower age at menarche, higher age at first birth, women with family history of breast cancer.Furthermore, PHQ -9 and GAD-7 appears to be a reliable and valid instrument that can be used to diagnose major depression (18.5%) and anxiety (15.2%) among Turkish women. This is consistent the with prevalence rate of major depression and anxiety observed rates 15.4% and 14.4% in United States among women , also similarly study reported for the depression 14.7%, and anxiety 10.1% conducted in Latvia women .In conclusion, breast cancer is major public health issue in Turkey. For preventing and screening of breast cancer, estimation of risk of breast cancer in Turkish population is crucial., Our study indicates that Gail model is a reliable and useful breast cancer risk prediction model for clinical decision-making. The Gail et al., model fits very well in this sample in terms of predicting numbers of breast cancer cases in specific risk factor strata but had modest discriminatory accuracy at the individual level. Our study may prove beneficial in the management of breast cancer counseling provided that the results of our study validated in prospective studies.AB, CCB and YÖ are designed and supervised the study and were involved in data collection, statistical analysis the writing of the paper. AA was involved in interpretation of data and writing manuscript. All authors approved the final version of this manuscript.Ethics committee approval was received for this study.Informed consent was obtained for this study.The authors declared that this study has received no financial support.We have no financial interest to declare.


Our results confirm the protective association between physical activity and mortality after breast cancer diagnosis, and demonstrate that this association significantly differs by age and cancer stage. Larger studies are warranted to substantiate our findings.Physical activity is recommended for most cancer patients as a nonpharmacological therapy to improve prognosis. Few studies have investigated the association between physical activity and breast cancer prognosis by ethnicity, biological, and modifiable risk factors for mortality. We investigated the association between physical activity and long-term survival among breast cancer survivors. A total of 397 survivors (96 Hispanic and 301 non-Hispanic White (NHW)) from the New Mexico HEAL study contributed baseline and biological data approximately 6 months after diagnosis. Study outcomes included all-cause, breast cancer-specific, and non-breast cancer mortality. The exposure was self-reported physical activity within the past month. Multivariable hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox Proportional Hazards regression. A total of 133 deaths (53 breast cancer-specific deaths) were observed after a median follow-up time of 13 years. Engaging in >6.9 metabolic equivalent hours/week (MET-h/week) of moderate to vigorous physical activity (active) was inversely associated with all-cause mortality among all women  and NHWs . Active NHW women also had a reduced risk of non-breast cancer mortality , compared to inactive women (0 MET-h/week). In subgroups, we observed the inverse associations with all-cause mortality among women >58 years old ( In fact, breast cancer is the leading cause of death among U.S. Hispanic women, surpassing heart disease over a decade ago2.Breast cancer is the most commonly diagnosed cancer in women in the United States and worldwide. Though breast cancer survivorship has been significantly prolonged with advanced screening and treatment technologies, breast cancer remains among the top common causes of death from cancer across races and ethnicities in the US3. According to guidelines for cancer patients and survivors from American College of Sports Medicine, cancer survivors are suggested to complete 150 min/week of aerobic exercise and to perform strength training activities twice/week at moderate to vigorous intensity in order to ease cancer-related symptoms, improve general health and quality of life4. Studies have suggested that being physically active has a positive effect on physical and psychological levels: the underlying mechanisms might include regulation of sex hormones, metabolic hormones, adipokines, oxidative stress, and immune function5. Among women with breast cancer, there is accumulating epidemiological evidence indicating that survivors participating in physical activity of moderate to vigorous intensity have reduced all-cause mortality, breast cancer-specific mortality, risk of cancer-related symptoms, and improved physical health-related quality of life after their breast cancer diagnosis7. A systematic review by Lahart and colleagues8 found significant risk reductions for all-cause and breast cancer-related death for higher level of lifetime prediagnosis recreational physical activity (hazard ratio (HR) 0.82, 95% confidence interval (CI) 0.70–0.96, and HR 0.73, 95% CI 0.54–0.98, respectively), higher level of more recent prediagnosis physical activity , and higher level of postdiagnosis physical activity .Physical activity is recommended for most cancer patients as a nonpharmacological therapy to improve prognosis11. We previously reported that fructosamine, a biomarker reflecting individual blood glucose level and closely related to diabetes, is negatively associated with breast cancer survival12. Furthermore, several recent studies have found that Hispanic ethnicity may modify the association of obesity, diabetes, and fructosamine level with mortality among breast cancer patients14, but the interaction between physical activity and ethnicity is not well established.While most epidemiological studies including breast cancer patients have focused on the impact of specific physical activities, few have investigated the interaction of physical activities and other demographic or modifiable risk factors of death among women diagnosed with breast cancer. Factors of interest that could be important to the relationship between physical activity and survival among breast cancer patients due to their biological roles in energy balance are obesity, history of type 2 diabetes, and glucose level. Studies have found that breast cancer patients with diabetes experience a higher risk of breast cancer-specific and all-cause mortality, and being obese increases the risk of breast cancer-specific mortality by 45–57%15. The present study provides an update to this previous analysis with longer follow-up time and examines a different timing of physical activity at approximately 5 months postdiagnosis. We hypothesize that this time window within months of diagnosis and the completion of treatment could serve as a critical timepoint for intervention to reduce risk of poor outcomes long-term. We also assessed the potential interactions between physical activity and other risk factors associated with all-cause mortality, breast cancer-specific mortality and non-breast cancer mortality, and our analysis is specific to the New Mexico HEAL site.A previous study from the Health, Eating, Activity, and Lifestyle (HEAL) study reported that higher level of postdiagnosis moderate-intensity sports/recreational physical activity was associated with reduced risk of all-cause and breast cancer-specific mortality among breast cancer survivors, and that risk differed among different subgroups for age at diagnosis, ethnicity, body mass index (BMI), cancer stage, treatment, and ER status2 or were overweight. The mean BMI of Hispanic women was 27.21 ± 5.83 kg/m2, which was significantly higher than the mean BMI of 25.68 ± 5.36 kg/m2 for non-Hispanic White women. In addition, more Hispanic women received chemotherapy, while more non-Hispanic White received surgery and radiation. Regarding physical activity level, Hispanic and non-Hispanic White women reported a similar level of total physical activity (52.24 ± 38.18 MET-h/week for Hispanics and 46.05 ± 36.97 MET-h/week for non-Hispanic whites), moderate to vigorous physical activity (7.02 ± 12.24 MET-h/week for Hispanics and 9.20 ± 16.06 MET-h/week for non-Hispanic whites) and recreational activity (7.34 ± 12.36 MET-h/week for Hispanics and 9.59 ± 16.45 MET-h/week for non-Hispanic whites). However, Hispanic women tended to have higher household physical activity of 42.03 MET-h/week compared to non-Hispanic White women (33.90 MET-h/week).Table p-trend 0.04) than people who participated in no moderate to vigorous physical activity. The protective association was much stronger for non-Hispanic White women  than Hispanic women . A non-significant trend based on HRs of reduced risk was also observed for breast cancer-specific mortality  and non-breast cancer-specific mortality .Table p-trend 0.08) than the non-Hispanic White women .The protective associations between physical activity level and mortality for total, vigorous only and recreational physical activity by ethnicity were similar to those for moderate to vigorous physical activity (data not shown), while household physical activity showed a different pattern. As presented in Table Figure We also observed larger benefits of being physically active in women treated with surgery only comparing to women with other treatment, and in women with fructosamine levels higher than 233 μmol/L compared to women with lower fructosamine levels, although interaction effects were not statistically significant. Our study lacked sufficient statistical power to evaluate differences by history of diabetes since only 10% of women reported having a history of diabetes. We also evaluated the interaction between tamoxifen use and physical activity, but no significant differences were observed (data not shown). Lastly, we evaluated the interaction effects between these variables and physical activity levels with risk of breast cancer-specific and non-breast cancer mortality. Similar trends of stronger protective associations were observed among older women, those with localized disease, and overweight women for both breast cancer-specific and non-breast cancer mortality (data not shown). However, these models were limited in statistical power due to the small number of deaths for each stratified group.Our study results suggest that breast cancer survivors who participated in higher physical activity levels within several months of diagnosis have a reduced risk of all-cause mortality, even after 13 years of follow-up. Furthermore, this protective association differs by physical activity type. The protective association between higher moderate to vigorous physical activity level with all-cause mortality was observed in the non-Hispanic White population, but was not apparent among the Hispanic population. Though most of the associations with household physical activity were not statistically significant, Hispanic women with the highest level of household activity had more of a reduced risk of breast cancer-specific mortality than non-Hispanic White women. Additionally, biological and clinical characteristics including age at diagnosis and disease stage were found to significantly alter the effect of physical activity on the risk of survival after breast cancer.15. The previous study focused on prediagnosis (1 year before) and postdiagnosis (3 years after) physical activity, while we examined physical activity around the time of diagnosis (approximately 5 months after). In the HEAL study population, participants’ physical activity level was found to be reduced significantly after breast cancer diagnosis16. Our study results provide insight from the perspective of how being physically active shortly after diagnosis affects long-term mortality among breast cancer survivors. Compared to the previous study, we had a smaller study population as we only included the New Mexico site of the HEAL study (397 vs. 688), but we had a longer median follow-up time (13.5 years from baseline vs. 6 years from baseline) and more cases of death (133 deaths vs. 53 deaths), which allowed us to better evaluate the long-term effect of physical activity. While our overall results were similar to the prior HEAL report, their stratified analyses were not statistically different by age, BMI, disease stage, treatment, or ER status. We however observed significantly stronger protective associations in older breast cancer survivors and those with localized cancer.Several aspects of our findings were similar to the previous HEAL report results regarding the protective association of higher physical activity level on survival, although the time of assessment of physical activity level was different18. Although this association among breast cancer survivors has not been well established, a few preclinical studies have suggested physical exercise can reduce doxorubicin-related cardiotoxicity introduced during breast cancer treatment and thus improve cardiovascular function in animal models21. There are also studies showing that cardiovascular risk factors including overweight, hypertension and hyperlipidemia can be controlled after the implementation of exercise intervention among breast cancer survivors 24.Additionally, we analyzed whether physical activity levels influenced non-breast cancer mortality. We found an inverse trend between physical activity and non-breast cancer deaths, and the association was significant among non-Hispanic White women. Since a large proportion of the non-breast cancer deaths in our study population were deaths due to cardiovascular disease (CVD), our results are in agreement with other studies that reported the benefit of physical training on CVD mortality among the general population7, where our colleagues suggested a significant reduction of all-cause mortality in Hispanic breast cancer survivors only. Differences in assessing and categorizing physical activity and the case–control design of the NMWHS may account for the differences in findings. However, although the magnitude and significance levels were different, the reduction trends were similar. Another possible reason is we did not observe this significant reduction in risk among Hispanic women might be due to the small study population and limited statistical power to detect a difference in mortality within this ethnic group.To our knowledge, few studies have evaluated the association of physical activity and mortality in the U.S. Hispanic population. Our findings are different from those of the New Mexico Women’s Health Study (NMWHS)7. Hispanic women are also considered to have a higher prevalence of overweight and obesity compared to non-Hispanic Whites in the general population25.The differences observed for the magnitude of associations by ethnicity in our study might also be partially explained by the difference in physical activity patterns and other characteristics across ethnicity. In our study population, though Hispanics and non-Hispanic White women reported similar total physical activity patterns, the Hispanic population had slightly lower moderate to vigorous recreational physical activity levels and significantly higher household activity levels. Hispanic women were also younger and more likely to be overweight or obese than non-Hispanic White women in our study. The characteristics of Hispanic women in our study were similar to the New Mexico breast cancer population in other studies. NMWHS reported that household activities took up 62.2% of total activity in Hispanic women and 52.9% in non-Hispanic White women26. We observed a stronger protective effect among the older women compared to younger women and among overweight women compared to the normal-weight women. In a Nurses’ Health Study analysis conducted by Holmes et al.27, postmenopausal women who were physically active  had a significant reduction in risk of breast cancer mortality  while the reduction was not significant among the premenopausal women . Regarding different BMI groups, studies by Cleveland et al.28 and Holick et al.29 both showed nonsignificant lower breast cancer-specific mortality in active normal-weight patients (BMI < 25) and significantly lower risk in active overweight (BMI ≥ 25) patients.Although the effects of physical activity on certain subgroups of breast cancer survivors has not been well established, some studies have suggested that there is a reduction in the risk of mortality outcomes among active older patients/survivors and among women who are physically active and overweight26 and Hispanic women31 generally have a worse prognosis after breast cancer diagnosis and treatment, these study results may add to the development of personalized physical activity recommendations following a breast cancer diagnosis.A major strength of this study is that we evaluated the difference in response to physical activity among ethnically diverse breast cancer survivors by specific subgroups, including women over the age of 58 years at breast cancer diagnosis, women who were overweight, and women diagnosed with localized disease; all of whom might benefit more from physical activity interventions after their breast cancer diagnosis. While older and overweight patients7, we had a smaller study population. Our smaller study may have limited our power to observe and interpret the true association. However, we had an average follow-up time of 13.5 years, which was comparable to the 14.7 years follow-up in NMWHS. The small sample size also limited our power to conduct stratified analysis for breast cancer-specific death and non-breast cancer death. An additional limitation of our study design was that we were unable to assess to changes in physical activity levels throughout follow-up. Other limitations are recall bias and information bias introduced by self-reported physical activity. Studies have shown, in relation to social desirability bias and other possible theories, study participants have been known to over-report physical activity behavior, and this overestimation was found to be greater in the older population and higher BMI groups35. Self-reported information was not assessed using other methods in the HEAL study to address the reporting bias. However, we did not observe significantly higher overall physical activity patterns in the older group or the overweight group.Some limitations should be considered when interpreting the study results. The primary limitation is the small sample size. Compared to a similar study evaluating the association between physical activity and survival among breast cancer survivors by ethnicity, NMWHS (722 participants)In conclusion, being physically active at the time of a breast cancer diagnosis can improve prognosis by reducing the risk of all-cause mortality. Furthermore, specific subgroups with certain biological and clinical characteristics might benefit even more from physical activity. Although the present study had limited power in making precise interpretations for other causes of death, our study findings can still inform the public health implications of physical activity interventions or specific recommendations for breast cancer survivors, particularly for certain subgroups who might be at higher risk of poor outcomes.37. Briefly, a total of 999 women with primary breast cancer, aged 18 years or older, diagnosed with in situ to Stage IIIA primary breast cancer between July 1996 and March 1999 living Bernalillo, Santa Fe, Sandoval, Valencia, or Taos counties were ascertained through the Surveillance, Epidemiology, and End Results (SEER) registry in New Mexico. Of these, 615 women participated. Participants were asked to complete three self-reported health and medical assessments within 2 years after diagnosis and a voluntary 50 mL blood draw.The New Mexico HEAL study is part of a multicenter, multiethnic prospective cohort study designed to examine the associations of various modifiable and nonmodifiable factors with prognosis among women diagnosed with in situ to Stage IIIA breast cancer. Detailed study objectives, eligibility, recruitment, data collection and quality control procedures are described elsewhereWritten informed consent was obtained from all study participants. The study was approved by the Human Research Protections Office at the University of New Mexico, in addition to the Institutional Review Board for Human Subjects at the University of Louisville.n = 99) and the lack of a stored plasma sample collected at baseline interview (n = 117). We further excluded women who had no information on date of interview (n = 2). The final analyses include a total of 397 invasive breast cancer survivors, among which 96 were Hispanic and 301 were non-Hispanic White.Data for the present study was derived from the in-person interview of the New Mexico HEAL study at baseline (5.7 ± 1.7 months postdiagnosis) and the New Mexico Tumor Registry (NMTR) records. Among the 615 participants, 216 women were excluded because of having in situ breast cancer  and vigorous only activities (>6 METs)40. Weekly average energy expenditure for each physical activity was measured by MET-hours per week (MET-h/wek), which was computed by MET value times weekly frequency and duration.A metabolic equivalent of task (MET) value was assigned to each activity based on the revised Compendium of Physical Activity12. Demographic information and other covariates, including age at breast cancer diagnosis, education, ethnicity (nonHispanic White and Hispanic), tamoxifen use and self-reported diabetes history, were assessed at during an interviewer-administered questionnaire.Stage of disease  or unknown) and breast cancer treatment  were obtained from medical record abstraction and NMTR registry records. Height and weight were measured and BMI was computed as weight (kg) divided by the square of height (m). Waist and hip circumferences were measured at the clinic visit to compute a waist-hip ratio. Aliquots from stored plasma samples were used for fructosamine measurement. Plasma fructosamine level was measured at the Rifai Laboratory of Boston Children’s hospital on the Roche P Modular system using product from Roche41. All deaths with a cause of death other than breast cancer were considered to be non-breast cancer specific.Participants’ vital status information was obtained through linkage with NMTR from the National Death Index records. Participants were followed by NMTR till death or December 2013, whichever occurred first, with a median follow-up time of 13.5 years. Survival time was defined as from the date of interview to death or December 2013. Breast cancer-specific mortality was calculated using breast-cancer specific death, which was classified as died from breast cancer based on the International Classification of Diseases, 10th revision (ICD-10) code C50p-values were calculated by chi-square tests and t-tests to compare across ethnic groups. Cox proportional hazards models were applied to compute hazard ratios (HRs) for death classified as all cause, breast cancer-specific, and non-breast cancer-specific mortality by ethnicity. Total physical activity, moderate to vigorous physical activity, household physical activity, and recreational physical activity were evaluated in tertiles based on distribution within each stratum. Vigorous only activities were evaluated as binary (no vigorous activity vs. some vigorous activity) due to the fact that only a few participants (n = 72) had a MET-h/week greater than zero for vigorour activity. The group with the lowest active time was considered to be the reference group in each stratum. Aside from household activities, results were similar for other types of physical activity. Thus, only results for household activities and moderate to vigorous activities are presented.Means and standard deviations (SDs) were calculated to describe the demographic and clinical characteristics of all participants by ethnicity. 29. Waist-hip ratio was highly correlated with BMI, so we included BMI only. The final models included one age-adjusted model and a multivariable model adjusting for age at diagnosis, BMI, breast cancer treatment, stage of disease, self-reported diabetes, and plasma fructosamine. The proportional hazard assumption was examined by interaction between physical activity and other covariates with time. None of the interactions were significant, therefore the assumption was not violated. Linear trends were tested using the Wald test by treating exposure tertiles as ordinal.No covariates other than age altered the HRs by 10%, thus covariates were retained if considered to be biologically meaningful based on previous literaturep-values of interaction terms of risk factors and physical activity measures were presented. All analyses were performed using Stata/SE Version 15.0  and SAS version 9.4  with significance level of 0.05.To assess the interaction between physical activity and other risk factors of death among participants, we also applied multivariable cox proportional hazard models stratified by each risk factor of interest adjusting for remaining covariates. Further information on experimental design is available in the Reporting Summary


BRCA2, ATM, PALB2, and NBN, no defined phenotype is currently associated with biallelic pathogenic variants (PVs) in CHEK2. This study compared the prevalence of breast and other cancers in women with monoallelic and biallelic CHEK2 PVs.Compared to breast cancer risk genes such as CHEK2 PV carriers were identified through commercial hereditary cancer panel testing (09/2013–07/2019). We compared cancer histories of 6473 monoallelic carriers to 31 biallelic carriers. Breast cancer risks were estimated using multivariate logistic regression and are reported as odds ratios (OR) with 95% confidence intervals (CI).CHEK2 PV carriers  than monoallelic carriers . Biallelic carriers were more likely to be diagnosed at or before age 50  and to have a second breast cancer diagnosis  compared to monoallelic carriers . Proportionally more biallelic carriers also had any cancer diagnosis and > 1 primary diagnosis. Compared to women with no PVs, biallelic PV carriers had a higher risk of developing ductal invasive breast cancer  and ductal carcinoma in situ  than monoallelic carriers .Breast cancer frequency was higher among biallelic CHEK2 PV carriers have a higher risk for breast cancer, are more likely to be diagnosed younger, and to have multiple primary breast cancers compared to monoallelic carriers. Biallelic carriers also appear to have a higher risk of cancer overall. Therefore, more aggressive management may be appropriate for women with biallelic PVs in CHEK2 compared with current recommendations for monoallelic carriers.These data suggest that biallelic  CHEK2 is considered a moderate risk breast cancer gene, with estimates of the relative risk for women carrying a single pathogenic variant (PV) ranging from 2.0 to 4.8 for a first breast cancer. The risk for a second primary breast cancer following an initial diagnosis is estimated to be increased 2.8- to 3.5-fold over individuals with breast cancer without pathogenic variants (PVs) in breast cancer risk genes [CHEK2 PV carriers, however the evidence for this association is not well established [CHEK2 PV carriers, phenotypic differences between monoallelic and biallelic carriers are not yet understood. Biallelic carriers of PVs in other, dominant breast cancer susceptibility genes such as BRCA2, ATM, PALB2, and NBN are known to have more severe cancer phenotypes than monoallelic carriers [CHEK2 PV carriers have yet to be adequately established.sk genes –4. An inablished . More reablished , and letablished . Despitecarriers –13. SimiCHEK2 founder mutation, c.1100del, is present in 1.1% of individuals of Northern and Eastern European ancestry [CHEK2 PVs are included. Homozygous CHEK2 c.1100del has, in fact, been reported in 14 female breast cancer cases in the Dutch population. In one study of 8 female homozygous carriers, the risk of breast cancer was estimated to be twice the risk in heterozygous carriers [A single ancestry . Assumincarriers . Becausecarriers .CHEK2 PVs, current NCCN guidelines recommend annual mammography and consideration of breast MRI beginning at age 40 [CHEK2 c.1100del or are biallelic carriers of any CHEK2 PV have higher breast cancer risks than monoallelic carriers, since this could impact management recommendations. In order to better assess the cancer risks in rare female carriers of biallelic CHEK2 PVs, we identified CHEK2 PV carriers in a large series of patients undergoing hereditary cancer panel testing. We then compared the prevalence of breast cancer and other cancers in women with biallelic and monoallelic CHEK2 PVs.Based on the evidence of increased breast cancer risk in women with monoallelic t age 40 . It is iCHEK2 PVs were identified through clinical pan-hereditary cancer panel testing at a Clinical Laboratory Improvement Amendments and College of American Pathologists-approved commercial laboratory  between September 2013 and July 2019. The panel test included at least 25 genes  or up to 35 genes . All patients provided informed consent for clinical testing. De-identified clinical information was obtained from provider-completed test request forms (TRFs). Data was included only for women residing in states with no legal prohibitions on the use of de-identified data for research purposes.Monoallelic and biallelic female carriers of CHEK2 founder mutations c.470C > T (p.Ile157Thr) and c.1283C > T (p.Ser428Phe) were not included in this analysis, as these were classified as variants of uncertain significance by the laboratory during the study time period and this analysis was restricted to PVs [CHEK2 PV carriers who had a PV in another gene were also excluded. Phase in biallelic carriers was determined by allelic haplotypes or by confirming carrier status in relatives. Biallelic carriers with first-degree relatives confirmed to have only one PV were considered confirmed to carry PVs in trans. Fisher’s exact tests for difference in proportions were used to determine differences between monoallelic and biallelic CHEK2 carriers. p-values < 0.05 were considered significant. Adjustments for multiple comparisons were not performed as this analysis was hypothesis-driven.Variants were classified according to the American College of Medical Genetics and Genomics Guidelines supplemented with additional statistical algorithms , 18. Thed to PVs . CHEK2 PCHEK2 PV carrier status  as a dependent variable. Independent variables included personal (binary flags) and family (numeric counts weighted by degree of relative) cancer histories of breast , ovarian, colorectal, melanoma, gastric, pancreatic, prostate, endometrial, and colon (polyps) as well as age at testing, sex and ancestry. 95% confidence intervals (CIs) were calculated using Wald statistics.A previously-published multivariable logistic regression model was used to estimate breast cancer risks . A separCHEK2 PVs. Of those 42 biallelic CHEK2 PV carriers, phase was confirmed to be in trans for 31 cases, 16 of which were homozygous for CHEK2 c.1100del. In this cohort, the majority of CHEK2 PV carriers were of White/Non-Hispanic origin, both in monoallelic and biallelic carriers  of monoallelic CHEK2 PV carriers  higher in biallelic carriers  than in monoallelic carriers . The increased prevalence of breast cancer, breast cancer at age 50 or younger, and second primary breast cancer was also observed when the comparisons were restricted to monoallelic and homozygous carriers of CHEK2 c.1100del  as compared to monoallelic and CHEK2 c.1100del monoallelic carriers .Only 9.7% 3/31) of biallelic  of biallCHEK2 PV carriers was also compared. Biallelic CHEK2 PV carriers were significantly more likely to be affected with at least one primary cancer as compared to monoallelic PV carriers . These differences were also significant for carriers of homozygous CHEK2 c.1100del PVs as compared to monoallelic CHEK2 c.1100del carriers. The converse comparison of unaffected monoallelic and biallelic CHEK2 PV carriers revealed that biallelic carriers were significantly less likely to be unaffected with cancer as compared to monoallelic carriers (p < 0.0001). Similarly, 50.4% of CHEK2 c.1100del monoallelic carriers were unaffected while only 12.5% of CHEK2 c.1100del homozygous carriers were unaffected by any cancer (p = 0.0022).The prevalence of multiple cancers among monoallelic and biallelic CHEK2 PV carriers for any cancer, and for breast cancer specifically. A trend toward younger median age at cancer diagnosis was observed in biallelic CHEK2 PV carriers as compared to monoallelic carriers for all evaluated categories , though this trend was generally not statistically significant . Similarly, homozygous carriers of CHEK2 c.1100del with at least two cancers were significantly younger than monoallelic carriers in this category .The median age of first cancer diagnosis was compared in monoallelic and biallelic CHEK2 PV carriers and monoallelic carriers. As there were no cases of lobular invasive breast cancer among the 31 biallelic CHEK2 PV carriers, it was not possible to determine an overall odds ratio (OR) for developing any type of breast cancer . Therefore, ORs were determined separately for the breast cancer types for which there were sufficient numbers in both biallelic and monoallelic CHEK2 PV carriers: ductal invasive and DCIS  for ductal invasive breast cancer compared to 2.02 for monoallelic CHEK2 PV carriers (95% CI 1.90–2.15). Similarly, biallelic CHEK2 PV carriers had a higher risk of developing DCIS than did monoallelic carriers, with biallelic carriers having an OR of 4.98 (95% CI 2.00–12.35) and monoallelic carriers having an OR of 1.82 (95% CI 1.66–2.00). While the CI for biallelic CHEK2 PV carriers are wide for both ductal invasive breast cancer and DCIS, the lower end of the CI range for both show at least a twofold higher risk of cancer compared to non-carriers.Multivariate logistic regression analysis was used to quantify the risk of breast cancer in biallelic CHEK2 to monoallelic carriers identified by hereditary cancer panel testing. Using a similar cohort of patients who underwent clinical hereditary cancer testing, our laboratory has previously shown that women with any PV in CHEK2 have approximately twofold greater risk of developing any breast cancer than PV-negative women [CHEK2 PV carriers had a significantly higher risk for breast cancer, were more likely to be diagnosed at or before age 50, and were more likely to have multiple primary breast cancers compared to monoallelic CHEK2 PV carriers. Biallelic CHEK2 PV carriers also appeared to have a higher risk of cancer overall, although we did not observe a statistically significant excess of any individual cancer other than breast. The ORs calculated for women with biallelic PVs (8.69 for invasive breast cancer and 4.98 for DCIS) suggest that biallelic CHEK2 PV carriers could have breast cancer risks higher than those associated with PVs in BRCA1 and BRCA2. It may therefore be reasonable to consider biallelic findings in CHEK2 as high, rather than moderate, penetrance for the purposes of management.This study compared the cancer phenotypes of women with biallelic PVs in ve women . This isBRCA2 and PALB2, Ataxia Telangiectasia for ATM, and Nijmegen Breakage Syndrome for NBN [CHEK2 is distinguished by not having a defined recessive phenotype [CHEK2 PVs. Additionally, clinical information unrelated to cancers and precancerous findings were not documented on the TRF, and this study could not identify an association between biallelic CHEK2 PVs and other phenotypes.Other breast cancer susceptibility genes have recessive phenotypes associated with biallelic PVs, including Fanconi Anemia for  for NBN , 20–22. henotype . AscertaCHEK2 PV carriers also limits this study, as certain analyses would be underpowered and could therefore not be performed. While this does not weaken the conclusions drawn from the statistically-powered analyses presented, it limits the types of analyses that could be performed.A possible limitation of the current study is that cancer histories were extracted from provider-completed TRFs and the reported diagnoses were not verified. Studies evaluating the accuracy of reporting of family cancer histories have shown that accuracy decreases with increasing distance in degree of relationship to the proband , 25. HowCHEK2 PVs include breast mammography with consideration of tomosynthesis and consideration of breast MRI starting by age 40 [CHEK PVs have a considerably higher breast cancer risk than monoallelic carriers, and therefore may benefit from more aggressive management, possibly beginning at younger ages. The observed high risk for more than one primary breast cancer diagnosis in women with biallelic PVs may also have significant implications for treatment of an initial malignancy. The findings of this study add evidence to support germline testing in affected women for informing future cancer risk.Current breast cancer screening guidelines for women who have inherited y age 40 . The fin