Women born in 1935–1975, registered in the Swedish Total Population Register (1991–2015), were eligible for inclusion. Survivors had a first breast cancer diagnosis in 1991–2005 and survived ≥5 years after initial diagnosis. Death date was determined by linkage to the National Cause of Death Registry (through 2015). Cancer survivorship was weakly associated with frailty (subdistribution hazard ratio (SHR) = 1.04, 95% confidence interval (CI): 1.00, 1.07). In age-stratified models, those diagnosed at younger ages (<50 years) had higher risk of frailty  than those diagnosed at ages 50–65  or >65  years. Additionally, there was increased risk of frailty for diagnoses in 2000 or later  compared with before 2000 . This supports work from smaller samples showing that breast cancer survivors have increased frailty risk, particularly when diagnosed at younger ages.Incidence and survival of breast cancer, the most common cancer among women, have been increasing, leaving survivors at risk of aging-related health conditions. In this matched cohort study, we examined frailty risk with the Hospital Frailty Risk Score among breast cancer survivors ( CIconfidence intervalHFRSHospital Frailty Risk ScoreIRincidence rateIRRincidence rate ratioSHRsubdistribution hazard ratioBreast cancer is the most common form of cancer among women, and while mortality has declined substantially in the past 4 decades, incidence has increased , 2. HoweCompared with those without a history of cancer, observational evidence has suggested that cancer survivors are at increased risk of cognitive decline, frailty, functional decline, and chronic age-related conditions, even before the age of 65 . FrailtyOverall, prevalence and risk of frailty among survivors of adult cancers is not well-defined. Understanding the long-term health consequences in aging cancer survivors is critical to ensuring high quality of life among this growing population , 14. Furhttps://doi.org/10.1093/aje/kwad048). Comparison subjects were randomly selected from the cohort members who were at risk at the time of the survivors’ diagnoses of breast cancer. Comparisons were initially defined as women with no diagnosis of breast cancer (or who had not yet received a breast cancer diagnosis at the date of matching). We then additionally excluded women with history of any other type of cancer from the analyses. Initially, we identified 447,800 comparison subjects matched to survivors on age at diagnosis. Initially, comparisons were matched to survivors on a 10:1 basis; however, in the final sample, all survivors had at least 6 comparisons, and 30,010 survivors had 10 comparisons after censorship. Of the comparison subjects, 60,290 were censored within the first 5 years, along with their matched cases, and 9,975 died or were censored  in the first 5 years after case diagnosis. The final cohort for analysis included 38,751 survivors and 377,535 comparison subjects, 4.6% of whom developed breast cancer during follow-up and were thus censored at that point.This study was designed as a matched cohort study where we examined risk of developing frailty among adult breast cancer survivors. Women born in 1935–1975 who were registered in the Swedish Total Population Register (TPR) between January 1, 1991, and December 31, 2015, were eligible to be in the study. Subjects were followed from their first record in TPR or their last immigration before their first TPR record (1991 or later) until their first emigration after their last TPR record, death, or end of follow-up. Breast cancer survivors were defined as women with a first breast cancer diagnosis registered in the National Cancer Register between 1991 and 2005, who survived for 5 years after their initial diagnosis. There were 44,780 women with a first breast cancer diagnosis between 1991 and 2005; 6,029 of these women died or were censored within 5 years of the diagnosis and were thus not included in the final analyses  and summed to create a frailty score . Inpatient records were used from 1995–2015 and outpatient from 2001–2015; duplicate codes at the 3-character level were removed. Frailty was assessed using the diagnostic codes from all hospitalizations and outpatient care visits during an entire year for each subject; thus frailty was ascertained on a yearly basis, consistent with previous work , a cumulative deficit measure that uses 109 ty score . The HFRty score , 19 and ty score . Hospitaty score , which hous work . If an ious work . Death aous work .Participant baseline characteristics are presented in We then used subdistribution hazard ratio models to examine risk of frailty over time, specifying death as a competing risk. Competing risk models are a type of survival analysis that accounts for a competing event  that is likely to occur to the participants during follow-up. Unlike traditional survival analysis methods, these models are less likely to overestimate the risk of the outcome. Competing risks are events that hamper the observation of the event of interest or modify the chance that the event of interest occurs. We chose to use competing risk models, because although mortality risk in cancer patients has decreased in recent years, they are still at higher risk of death compared with individuals without cancer . Model 1The mean age at breast cancer diagnosis was 57 years . The meaFive years after diagnosis, the overall incidence of frailty was higher in breast cancer survivors : 10.18, 10.87) compared with comparison subjects . The relative difference was 17% higher incidence  . StratifIn the age-adjusted subdistribution hazard model taking competing risk of death into account, we found breast cancer survivors, compared with those without history of cancer, had a small but elevated risk of developing frailty (subdistribution hazard ratio (SHR) = 1.04, 95% CI: 1.00, 1.07) . AdjustiIn sensitivity analyses, we repeated the analyses including comparison of subjects with a history of another type of cancer besides breast cancer. In these analyses, the association between breast cancer survivorship and frailty was less robust . Further, we excluded those who had an HFRS score of >5 the year prior to cancer diagnosis and found that the association did not change . We additionally adjusted for HFRS score 1 year prior to diagnosis and again did not find any differences in the results . Moreover, we examined risk of frailty from 1 year after diagnosis, instead of 5 years, and found that the association trended towards the null , although many individuals who had been diagnosed with cancer died within this period. We then stratified by follow-up time to understand whether the differences in age and year at diagnosis were attributable to differences in follow-up time but found that this did not explain the differences (results not shown). In post hoc analyses, we examined which diagnoses were most common in the breast cancer survivor and comparison subject groups, to determine which conditions were driving the HFRS. We found that unspecified conditions were most common .In this study, we examined the risk of developing frailty among breast cancer survivors compared with age-matched women without a previous cancer diagnosis. Five years past their initial diagnosis, breast cancer survivors had a small but elevated risk of developing frailty at any age compared with women who had no history of cancer at the time of diagnosis of the matched breast cancer patient. However, the association was seen primarily among breast cancer survivors diagnosed at earlier ages. The risk of death was high among breast cancer survivors, particularly in sensitivity analyses that included the first 5 years immediately after diagnosis, which is consistent with our earlier findings . FurtherOur findings echo those from other studies. Frailty in adult cancer survivors ranges from 9% to nearly 60%—double to quadruple the rates seen in age- and sex-matched populations . A studyThe present study was conducted among women breast cancer survivors, who may be at increased risk of frailty due to several factors. Evidence from childhood cancer survivors suggests that female survivors are at greater risk of developing frailty than male cancer survivors , 28, perAlthough some of these negative effects may be caused by the cancer itself, it seems that treatment does most of the long-term damage , 31, mimImportantly, inflammation is a link between cancer and aging. Inflammation generally increases even in healthy aging , 38, andAnother possible explanation for the functional decline observed in cancer survivors over the long term may instead relate to long-term chemotherapy-induced peripheral neuropathy, which is present in 40% of breast cancer survivors 2 years after treatment initiation . PeripheThis study has multiple strengths, including the large population-based sample, the over 20 years of follow-up, and the lack of attrition, although the findings must also be considered within the study’s limitations. First, inclusion in this study hinged on surviving cancer, because the goal of the study was to examine frailty among breast cancer survivors. Survivorship was defined as living 5 years after the initial diagnosis, which is standard in clinical practice. However, these survivors may be healthier than those who die prior to the 5-year timepoint, thus introducing a survivor bias to the “true” effect of frailty risk from cancer or cancer treatment. We examined frailty prior to diagnosis and during the 5-year period between diagnosis and the start of follow-up for frailty, to ascertain the differences between survivors and comparisons, to the extent possible. We found that, at all diagnosis ages, the mean frailty scores prior to diagnosis and start of follow-up were similar in breast cancer survivors and comparisons. We additionally examined risk of frailty including the first 5 years from diagnosis, and found the association attenuated towards the null. This period is murkier in terms of survivorship, as many of the women diagnosed with cancer died in this period and, clinically, they were not as likely to be “cancer-free.” Further, frailty typically does not have an acute onset; it may take more time to develop in survivors. Therefore, the specific aim of this study was to examine the long-term outcomes in survivors who had lived for 5 years beyond their diagnosis. Second, past studies have shown that among breast cancer survivors, there are racial differences in risk of developing cardiovascular disease . BecauseAs cancer survivorship increases and survivors cope with aging-related conditions , understWeb_Material_kwad048Click here for additional data file.


Body fatness is a dynamic exposure throughout life. To provide more insight into the association between body mass index (BMI) and postmenopausal breast cancer, we aimed to examine the age at onset, duration, intensity, and trajectories of body fatness in adulthood in relation to risk of breast cancer subtypes.Based on self-reported anthropometry in the prospective Norwegian Women and Cancer Study, we calculated the age at onset, duration, and intensity of overweight and obesity using linear mixed-effects models. BMI trajectories in adulthood were modeled using group-based trajectory modeling. We used Cox proportional hazards models to calculate hazard ratios (HRs) with 95% confidence intervals (CIs) for the associations between BMI exposures and breast cancer subtypes in 148,866 postmenopausal women.pheterogeneity 0.03). Compared with women who remained at normal weight throughout adulthood, women with a descending BMI trajectory had a reduced risk of luminal A-like breast cancer , whereas women with ascending BMI trajectories were at increased risk . Overweight duration and weighted cumulative years of overweight and obesity were inversely associated with luminal B-like breast cancer.A total of 7223 incident invasive postmenopausal breast cancer cases occurred during follow-up. Increased overweight duration and age at the onset of overweight or obesity were associated with luminal A-like breast cancer. Significant heterogeneity was observed in the association between age at overweight and overweight duration and the intrinsic-like subtypes (In this exploratory analysis, decreasing body fatness from obesity in adulthood was inversely associated with overall, hormone receptor-positive and luminal A-like breast cancer in postmenopausal women. This study highlights the potential health benefits of reducing weight in adulthood and the health risks associated with increasing weight throughout adult life. Moreover, our data provide evidence of intrinsic-like tumor heterogeneity with regard to age at onset and duration of overweight.The online version contains supplementary material available at 10.1186/s13058-023-01729-x. Breast cancer is a heterogeneous disease consisting of at least five distinct molecular subtypes with different etiological pathways and prognosis –6. OwingThe risk factors for breast cancer include amongst others exposure to endogenous and exogenous female sex hormones. Hormonal risk factors are associated with hormone receptor-positive and luminal A-like subtypes –10. LessBody fatness is not a static measure but varies over a lifetime, and every woman follows her unique exposure trajectory throughout life. These dynamic aspects are likely relevant for disease development, and such a trajectory approach may provide more insight into the relationship between lifetime exposure intensity, duration and onset, and cancer risk than studying only one or a few measures of exposure. Recent studies have suggested a clear dose–response association between the intensity and duration of body fatness and risk of postmenopausal breast cancer , 27. HowThus, we aimed to explore whether the intensity, timing, duration, and trajectories of body fatness throughout adult life were associated with breast cancer in postmenopausal women, and whether associations varied according to subtypes.The Norwegian Women and Cancer (NOWAC) study is a nationally representative prospective cohort study initiated in 1991 to investigate cancer etiology among women in Norway. Women aged 30–70 years were randomly sampled from the National Population Register and invited to participate in the study. A total of 172,472 women were enrolled between 1991 and 2007 and completed up to three follow-up questionnaires (1998–2017) distributed 5–10 years apart. The unique national identification number assigned to every resident in Norway allows for complete follow-up through linkages to national registries . The NOWn = 8156 excluded). As physical activity and tobacco smoking affect weight change and hence fluctuations in BMI, we excluded 6697 women without information on these covariates in any of the questionnaires. Excluded women with less than two BMI measurements or missing information on physical activity and tobacco smoking on all time points were slightly older, had higher BMI and lower education than included women , women who had died or emigrated before start of follow-up (n = 457), and women who reported implausible values for age at menarche , age at menopause , or age at first birth . For the complete-case analyses, women with missing covariates were also excluded (n = 6095). Thus, the final analytical study sample consisted of 148,866 women, of which 142,771 were included in the complete-case analyses.In this study, 164,316 women with at least two self-reported height and weight measurements were eligible for inclusion  and height at wave 1 were used to calculate BMI at up to four time points. As weight loss can follow a cancer diagnosis, weight measurements were not considered valid in women who were diagnosed with cancer up to 1 year before returning the questionnaire. BMI was calculated as weight in kilograms divided by the square of height in meters. Body fatness was defined according to the World Health Organization’s definition .Relevant covariates were extracted from the wave 1 questionnaires. We used a directed acyclic graph to visualize the assumed causal relationships among the exposure, outcome, and covariates, thereby identifying confounding factors to be included in the multivariable regression analysis . Information on death and emigration was obtained through linkage to the Cause of Death Registry and the Central Population Register, respectively.The Cancer Registry of Norway provides information on ER, PR, and HER2 status, assessed using immunohistochemistry (IHC) techniques by pathological departments nationwide. ER negativity was defined as < 10% reactivity before January 2012. From February 2012 onward, the threshold for ER-negative tumors was changed to < 1% reactivity due to changes in the national treatment guidelines. These official thresholds were used in this study. PR negativity was defined as < 10% reactivity. The HER2 expression status was determined using IHC and/or in situ hybridization (ISH). Tumors with no or weak immunostaining were defined as HER2−, while moderate or strong immunostaining was considered HER2 + . ISH was generally used to confirm moderate staining. Breast cancer subtypes were defined by IHC surrogates for molecular subtypes according to the St. Gallen 2013 criteria without using the proliferation marker Ki67 in the subtype definition: luminal A-like (ER + PR + HER2−), luminal B-like (ER + PR− HER2− or ER + PR− HER2 + or ER + PR + HER2 +), HER2-enriched (ER− PR− HER2 +), and TNBC (ER− PR− HER2−)  0.02). Changes in BMI were modeled over a range of 3–58 years, with a mean modeling duration of 36 years (SD 8.74).Five distinct BMI trajectories were identified Fig. : 43.5% oThe individual trajectories for each group are depicted in Additional file Compared with the “Normal-stable” T1) group, the groups with increasing BMI (T2–T4) were less likely to have used exogenous hormones and to be physically active at wave 1  group were more likely to be postmenopausal, never users of oral contraceptives, current users of MHT, current smokers, and less educated compared with the other trajectory groups. They also had higher physical activity levels than the trajectory groups who experienced weight gain (T2–T4).Of the 7223 incident invasive breast cancer cases, 5674 ER + (86.8%), 866 ER− (13.2%), 4379 PR + (67.4%), 2114 PR− (32.6%), 719 HER2 + (12.5%), and 5032 HER2− (87.5%) cases were identified (Table The number of missing cases were 683 (9.5%) for ER status, 730 (10.1%) for PR status, and 1472 (20.4%) for HER2 status. Missing values comprised a higher proportion in the T5 trajectory group but otherwise did not differ considerably across trajectory groups.p trend < 0.01) and belonging to the “Obesity-decrease” trajectory group decreased risk of breast cancer  and 1.13 (95% CI 1.00–1.28), respectively (p linear trend < 0.01). Increasing overweight duration increased the risk of luminal A-like cancer  and decreased the risk of luminal B-like cancer . Significant heterogeneity was observed across the subtypes with regard to overweight duration and age at overweight (pheterogeneity 0.03). The HRs were similar to those of overweight/obesity duration when modeling weighted cumulative years of overweight/obesity for luminal A-like cancer. However, for luminal B-like cancer, HRs of 0.85 (95% CI 0.91–0.99) and 0.61 (95% CI 0.38–0.99) were observed for weighted cumulative years of overweight and obesity, respectively. Compared with the “Normal-stable” trajectory, women with constantly increasing BMI during adult life experienced an increased risk for luminal A-like cancer , whereas those with decreasing weight experienced a nearly 50% reduced risk . With borderline-significance, the “Overweight-obesity” trajectory was associated with decreased risk of luminal B-like breast cancer . No significant associations were observed for HER2-enriched or TNBC subtypes. Results of the age-adjusted analyses are provided in Additional file Increased age at overweight and obesity onset was associated with an increased risk of luminal A-like cancer . Women in the “Obesity-decrease” trajectory had a 43% reduced risk of ER/PR-positive breast cancer . Age-adjusted analyses yielded similar results.The ER/PR-positive breast cancer results were similar to those for the luminal A-like subtype. Body fatness was positively associated with ER/PR-positive breast cancer, whereas we observed no significant association with ER/PR-negative cancer. Specifically, increased age at overweight and obesity onset was associated with ER/PR-positive breast cancer (ptrend < 0.01). Overweight duration per 10 years and weighted cumulative overweight years per 100 unit increase were associated with ER/PR-positive breast cancer . Ascending trajectories from normal BMI were associated with ER/PR-positive breast cancer, where the “Normal-obesity” trajectory increased risk by 34% (95% CI 1.18–1.52). Women belonging to the descending trajectory appeared to be at 59% decreased risk . Age at overweight onset (pheterogeneity 0.04), overweight duration (pheterogeneity 0.04) and the “Normal-overweight” (pheterogeneity 0.01) and “Normal-obesity” (pheterogeneity 0.01) trajectory were differentially associated with ER/PR-positive and ER/PR-negative breast cancer among never MHT users.Stratified analyses of MHT use suggested some extent of effect modification by MHT on the association between body fatness and ER/PR-positive breast cancer Table . SignifiIn this exploratory study, we assessed the relationship between BMI trajectories in adult life, duration, intensity, and onset of body fatness and subtypes of postmenopausal breast cancer in a large national cohort of Norwegian women. We observed that obese women who decreased their weight had a reduced risk of hormone receptor-positive or luminal A-like breast cancer compared with women who remained at normal weight throughout their adult life. Adult overweight duration, increased age at onset of overweight or obesity, and ascending BMI trajectories throughout adulthood were associated with an increased risk of hormone receptor-positive and luminal A-like breast cancer. Similar findings were observed in postmenopausal breast cancer overall, likely because the luminal A-like subtype constitutes the largest proportion of breast cancer cases in postmenopausal women. Significant associations between body fatness and hormone receptor-positive breast cancer were predominantly evident in never users of MHT. The findings regarding BMI trajectories are novel and highlight the potential health benefits of weight reduction among adult women with obesity and the health risks associated with consistent weight gain.Our study aligns with the existing literature revealing that body fatness in adulthood is associated with hormone receptor-positive or luminal A-like tumors , 14–22. While many studies have addressed weight change in relation to the risk of subtypes of breast cancer , 54–57, The mechanisms underlying the association between body fatness and hormone receptor-positive breast cancer in postmenopausal women involve hormonal pathways. Increased circulating levels of bioavailable estrogen are observed with increasing body fatness in postmenopausal women because adipose tissue remains the major site of aromatase activity after menopause, together with reduced production of sex hormone-binding globulin and alterations in androgen metabolism –61. IndeOur study is consistent with previous reports illustrating that the risk of postmenopausal breast cancer related to body fatness is modified by MHT use , 66–69. This study had several limitations. Despite the large sample size, we were restricted by lack of power in the subgroup analyses of the less common subtypes. Under the assumption that receptor status data were missing at random, we chose not to perform multiple imputation in order to maintain transparency and simplicity . MoreoveOur exploratory study suggests that decreasing body fatness from obesity in adulthood is inversely associated with overall, hormone receptor-positive and luminal A-like breast cancer in postmenopausal women. Positive associations were observed with increasing body fatness from normal BMI during adulthood. Furthermore, we demonstrated a dose–response relationship between overweight duration and these subtypes, with significant heterogeneity between the intrinsic-like subtypes. As breast cancer is the most frequently diagnosed malignancy in women, and the prevalence of body fatness is increasing, preventive measures, such as weight loss, could contribute to halting an undue increase in breast cancer incidence following the obesity epidemic.Additional file 1. Table 1. Comparison of selected characteristics at wave 1 of excluded participants vs. study sample. Table 2. Age-adjusted hazard ratios for the association between body fatness and ER/PR/HER2-defined subtypes of postmenopausal breast cancer. Table 3. Age-adjusted and multivariable-adjusted hazard ratios for the association between body fatness and ER/PR-defined subtypes of postmenopausal breast cancer. Figure 1. Directed acyclic graph on the assumed relations between BMI development in adulthood, postmenopausal breast cancer and covariates. Figure 2. Twoway scatterplots of individual BMI trajectories by trajectory group.


BRCA1 or BRCA2 mutation has not been investigated. Thus, we evaluated the association between vitamin D and/or calcium supplement use and breast cancer in this high-risk population. In this case–control study, we identified 134 women diagnosed with breast cancer and 276 women without breast cancer. Women with a BRCA mutation who consumed vitamin D-containing supplements had 46% lower odds of having breast cancer compared to those who did not take any supplements. Increasing vitamin D and calcium supplement intake was inversely associated with the odds of having breast cancer. Higher vitamin D and/or calcium supplement intake may be associated with lower breast cancer risk in this high-risk population.The relationship between vitamin D and calcium supplement use and breast cancer among women in the general population is not clear. Furthermore, whether such an association exists among women at high risk due to a BRCA1 or BRCA2. BRCA mutation carriers enrolled in a longitudinal study were invited to complete a supplemental questionnaire on lifetime supplement use. Cases included women with a prevalent diagnosis of invasive breast cancer, and controls had no history of breast cancer. Vitamin D and calcium use were categorized as never/ever use, and as tertiles of supplement intake . Unconditional logistic regression was used to estimate the odds ratio (OR) and 95% confidence intervals (CIs) of breast cancer. This study included 134 breast cancer cases and 276 controls. Women who used vitamin D-containing supplements had 46% lower odds of having breast cancer compared to those who never used supplements . Increasing vitamin D and calcium supplement intake was inversely associated with the odds of having breast cancer (p-trend = 0.04). Findings were suggestively stronger among BRCA1 mutation carriers; however, analyses were limited by small strata. These findings suggest a potential inverse association between vitamin D and calcium supplementation and BRCA breast cancer. Additional studies are warranted to confirm these findings and accurately inform clinical care guidelines.The role of vitamin D and calcium use in the development of breast cancer among women in the general population is not clear. Furthermore, whether vitamin D and calcium supplement use are associated with breast cancer in high-risk populations has not been evaluated. Thus, we evaluated the association between vitamin D and/or calcium supplement use and breast cancer among women with a pathogenic variant (mutation) in  BRCA1 or BRCA2 have an elevated risk estimated at 70% by age 80 [BRCA1 or BRCA2 mutation carriers is not known.The lifetime risk for developing breast cancer among Canadian women is 12% [y age 80 . Vitaminy age 80 , but then = 22 studies), Hossain et al. reported that supplemental vitamin D was inversely associated with the risk of developing breast cancer and that the protective effect was stronger at doses greater than 10 mcg/day [Findings from studies investigating the role of vitamin D use on breast cancer risk among women in the general population have been inconsistent. In a recent meta-analysis of observational data  and drinking habits (never/ever). Between September 2014 and September 2016, participants from the 10 Canadian centers were invited to complete a supplemental questionnaire, by mail or over the phone, which collected detailed information on past and current supplement use from the age of 18. Women were asked to recall the type of supplements used, their brand names, frequency of use per week, dosage, and duration of use. Information on supplements taken during pregnancy was also collected.All women completed a vitamin D-containing supplement use, categorized as never/ever using any supplements that contained vitamin D alone and in combination with other nutrients, such as in multivitamins or prenatal supplements, and (2) total average daily vitamin D supplement use (mcg/day), calculated from all supplements that contained vitamin D. For example, total average daily vitamin D supplement use was calculated from vitamin D-specific supplements, multivitamins, and prenatal supplements that contained vitamin D as follows:The current study focused on vitamin D and calcium use. Supplement use was assessed in two different ways: (1) mg/day).The same approach was used to determine calcium-containing supplement use (never/ever) and total average daily calcium supplement use (n = 65), default doses were assigned based on the formulation of the Jamieson brand . Multivitamins with missing brand names and doses (n = 97) were also assigned to reflect the formulation of the Jamieson brand . Prenatal supplements with missing doses and brand names (n = 33) had doses assigned from the formulation of the prenatal supplement brand Materna . Jamieson and Materna were used as they are the most popular brands in Canada. Observations were removed if the information on the years or frequency of use was missing . When compared with manufacturers’ labels, the validity of self-reported supplement use and dose had previously been shown to be good for multivitamins, vitamin D, and calcium (kappa 0.58–0.78) [If the brand names of the supplements were given, but doses of vitamin D or calcium were missing, doses were assigned based on the published formulation for the specific brand. If both brand names and doses were missing (58–0.78) . DetaileThe disease was self-reported by the participants in the study questionnaire. Case subjects were women with a prevalent diagnosis of invasive breast cancer. Control subjects were women who never had a history of breast cancer. Medical records were requested for all women who reported having breast cancer and pathology was confirmed in 50% of cases.n = 10), a history of cancer before their breast cancer diagnosis (n = 75), were missing supplement information (n = 9), and mutation type (n = 8). After exclusions, there were 410 women included in the study, including 134 cases and 276 controls.A case–control analysis was used to evaluate the association between vitamin D or calcium supplement use and breast cancer. Among the cases, all exposures and covariates were censored one year prior to the diagnosis of breast cancer. We stratified vitamin D and calcium supplement use as never or ever use and divided the total average daily vitamin D and calcium supplement use into tertiles  based on the distribution of supplement intake of the entire cohort.t-test and a chi-square test to evaluate the differences between the baseline continuous and categorical variables between the cases and controls, respectively. Unconditional logistic regression was used to estimate the odds ratio (OR) and 95% confidence intervals (CIs) for breast cancer associated with supplement use. We constructed a basic model adjusting for age (continuous) and BRCA1 or BRCA2 mutation type. The multivariable model included additional covariates, such as menopausal status , body mass index (BMI) (continuous), parity , alcohol consumption (never/ever), and smoking status (never/ever). Subjects who had a missing BMI (n = 7) were assigned the median BMI of the entire sample. Those with missing smoking (n = 2) or alcohol (n = 2) values were assigned the mode. One subject with a mutation in both genes was classified as a BRCA1 mutation carrier. The P for trend was estimated by modeling the trend per unit increase in vitamin D (mcg) and calcium (mg) to assess dose–response relationships. Analyses were further stratified by BRCA1 or BRCA2 mutation type to assess the impact of supplement use among each population.We used Student’s In a secondary analysis, we performed a joint effects analysis to assess the multiplicative interaction between vitamin D and calcium supplement use and breast cancer. The aim of the joint effects analysis was to observe if the combined effects of vitamin D and calcium are larger than the sum of their individual effects.A quantitative bias analysis was conducted to quantify the level of bias in our study that may be attributed to possible exposure misclassification of vitamin intake  and/or the presence of unmeasured confounding. Multidimensional bias analysis tested a range of sensitivity and specificity values for exposure misclassification ranging from 0.1 to 0.9, which quantifies the level of misclassification needed to attenuate the study estimate to null . Furtherp-values were two-sided and considered statistically significant if p ≤ 0.05.All statistical analyses were conducted using SAS OnDemand for Academics . All 2 vs. 24.9 kg/m2; p = 0.01) and were less likely to be nulliparous compared to controls . A higher proportion of cases than controls were premenopausal . Cases were less likely to have used vitamin D-specific supplements than controls . Case subjects also had lower intakes of total average daily vitamin D and calcium supplements than controls .BRCA1 and BRCA2 mutation carriers is presented in p = 0.02). Women with higher total daily vitamin D supplement use (>7.50 mcg/day) had 56% lower odds of breast cancer compared to those with the lowest tertile of intake . In the analyses stratified by BRCA mutations, these associations were stronger among women with a BRCA1 mutation, although the number of women in the BRCA2 subgroup was few (The association between vitamin D-containing supplement use and breast cancer among  was few .p = 0.07); however, increasing calcium supplement intake was associated with lower odds of having breast cancer   . In the ubgroups .p = 0.07).We also evaluated the joint effects of both calcium and vitamin D supplements . CompareThe multidimensional bias analysis tested a range of values for the sensitivity and specificity of vitamin D supplement use to quantify the level of measurement bias. Assuming a non-differential misclassification of the exposure due to recall bias, the corrected OR only exceeded 1 when the sensitivity was <0.6 and specificity was <0.3. Assuming a differential misclassification of the exposure due to recall bias between cases and controls, the corrected OR only exceeded 1 when the sensitivity and specificity were <0.6, or when the sensitivity and specificity were >0.6 and higher in the controls compared to the cases. In all situations, it is unlikely that exposure misclassification would have a large enough effect to reverse the direction of our findings, as previous studies demonstrated the validity of self-reported supplement use is good, and we would expect higher sensitivity/specificity of exposure measurement among cases compared to controls in differential recall bias .The E-value calculated for the primary outcome was 2.06. Therefore, the observed association between vitamin D-containing supplement use and breast cancer could only be explained away by an unmeasured confounder with a minimum association of an OR of 2.06.BRCA1 or BRCA2 mutation who used vitamin D supplements had 46% lower odds of having breast cancer. Increasing vitamin D supplement intake was also inversely associated with the odds of having breast cancer. Although there was a suggestive, albeit not statistically significant, inverse association between calcium supplement use and breast cancer, we observed a linear dose–response relationship between calcium supplement intake and breast cancer. The effects were stronger for women with a BRCA1 mutation; however, this was based on small strata. A joint effects analysis did not suggest multiplicative combined effects of vitamin D and calcium supplement use and breast cancer in our study. While recall bias and unmeasured confounding is possible in this retrospective study, quantitative bias analysis suggests these biases are unlikely to reverse the direction of our observed effects. Despite the relatively small sample size, these results provide preliminary evidence of an inverse association between high vitamin D and calcium supplement use and breast cancer among women with a BRCA mutation.In this study, we found that women at high risk of developing breast cancer due to an inherited We observed a linear dose–response trend between vitamin D supplement intake and breast cancer. Our findings are in alignment with observational studies conducted among women in the general population ,6. Estébp = 0.97) [p = 0.02) [Although increasing calcium supplement intake was associated with lower odds of having breast cancer in our study, the main source of calcium was from multivitamins and not calcium-specific supplements alone. Since most multivitamins also contain vitamin D, it is possible this association between calcium supplements and breast cancer may be attributed to either vitamin D or the combined use of both supplements. Although limited by a small sample size, this was supported by our joint analysis that showed a stronger inverse association for those who used vitamin D supplements only and combined vitamin D and calcium than for those who used calcium supplements only. In a meta-analysis of ten randomized controlled trials, including seven studies on breast cancer, ≥500 mg/day of calcium supplements without the co-administration of vitamin D did not affect breast cancer risk (relative risk (RR) 1.01; 95% CI 0.64, 1.59,  = 0.97) . In cont = 0.02) . Since c = 0.02) . This waBRCA-associated breast cancer is unclear. Vitamin D first binds to and activates the vitamin D receptor (VDR) expressed in mammary glands. This activation leads to the inhibition of cell proliferation by suppressing growth signals and activating growth-inhibitory signals [BRCA1 mutation, therefore increasing plasma vitamin D levels and inhibiting cell proliferation in BRCA1 mutation carriers [BRCA1-mutated breast cancers compared to sporadic breast cancers [BRCA2 mutations are lacking. Multiple other mechanisms linked to apoptosis have been suggested. One study suggests apoptosis is triggered by BCL2 family proteins, known to regulate cell death [Emerging evidence suggests vitamin D and calcium may protect against breast cancer by inducing anti-proliferative and pro-apoptotic effects; however, their effect on the development of  signals ,23,24,25carriers . A recen cancers . Further cancers . Howeverll death . Peterlill death . Even thll death . This rill death .There were various limitations with the current study, notably the small sample size, particularly in the stratified analyses. Only 56% of women responded to the supplemental questionnaire, which could have contributed to selection bias. The inclusion of prevalent cases may contribute to survival bias, which is difficult to account for. Given the retrospective nature, data collection from women following a breast cancer diagnosis may have introduced recall bias in some cases. Nevertheless, our multidimensional bias analysis demonstrated that the non-differential and differential recall bias of supplement intake is unlikely to reverse the direction of the study estimates. There also remains the possibility of unmeasured confounding in the study; however, the E-value of 2.06 demonstrates that unmeasured confounding is unlikely to explain the study estimates away to null, and the analysis accounted for the most important confounders. Finally, we were not able to disentangle the effects of vitamin D and calcium alone since supplement use mostly consisted of multivitamins rather than individual supplements; however, we were able to evaluate the total average daily intake of each supplement and breast cancer risk. Lastly, we only analyzed supplemental vitamin D and calcium use and not total intake , which likely underestimated the participants’ vitamin D and calcium intake.BRCA1 mutation carriers. Despite this protective effect, women with a BRCA1 or BRCA2 mutation should not consume more than the recommended daily intake of these supplements. It will be important to continue to clarify these associations, especially in prospective analyses using plasma vitamin D and calcium levels, biomarkers that reflect total intake from both dietary sources and supplement use.To our knowledge, this represents the first evaluation of vitamin D and calcium supplement use and breast cancer risk specifically in this high-risk population. Our findings provide preliminary evidence of an inverse association between both exposures and the risk of breast cancer, particularly among


Mammography is the method of choice for breast cancer screening and the only one that demonstrates a reduction in mortality in the population at usual risk.The frequency of performing and the age at which mammogram screening begins are a controversial topic in the literature. Data in our country point to a significant portion of breast cancer in women under 50 years of age.The Brazilian Federation of Gynecology and Obstetrics Associations (Febrasgo), the Brazilian Society of Mastology (SBM) and the Brazilian College of Radiology and Diagnostic Imaging (CBR) agree that mammogram screening should be performed annually by all women from 40 years old.In Brazil, there is an unequal distribution of mammography devices in different regions. Screening policies must consider this inequality.The vast majority of services in Brazil perform opportunistic screening for breast cancer. The implementation of screening organized by age group and risk stratification can optimize the costs of the public health system.High-risk patients need to be screened differently from usual-risk patients. These patients need to have access to breast magnetic resonance imaging (MRI) and start screening at an earlier age.The abbreviated MRI protocol for breast cancer screening of high-risk patients may improve their adherence and access to the screening program.Breast ultrasound is not a screening method in isolation. However, it plays a role as a complementary method to mammography and MRI in specific scenarios, and replaces MRI in patients with contraindications to the use of this method.Dense breasts have low sensitivity for screening mammography.Mammography should be performed as the preferred method of breast cancer screening for women at usual risk.The subgroup of women between 40 and 50 years of age at usual risk should preferably be evaluated with annual mammography, given the prevalence of breast cancer in this age group in Brazil.Ultrasonound should not be used as an isolated method in the screening scenario, but always complementary to mammography or MRI of the breasts. Ultrasound can also be used in patients with contraindications to MRI .Screening of high-risk patients should be done with annual MRI and mammography. When it is not possible to access the MRI exam, ultrasound can be used with reservations related to the examiner’s experience in the breast ultrasound exam.The abbreviated protocol for breast cancer screening of high-risk patients should be considered in services that perform breast MRI, as it saves time and has shown to be equally effective in published series. Furthermore, this protocol can increase patients’ adherence to an annual screening program.The implementation of organized screening should be encouraged in breast cancer screening services, since this measure optimizes program costs.The subgroup of patients with dense breasts should be evaluated with caution, considering the low sensitivity of mammography. In these patients, complementary ultrasound can be performed, as well as MRI screening for patients at normal risk and dense breasts (considering the availability of this resource in the various regions of the country).Patients at intermediate risk for breast cancer should be evaluated individually for the proposed screening. Some of these patients, especially those with dense breasts or lesions that increase the risk, may be candidates for annual MRI in addition to mammography.Breast cancer is the most prevalent in Brazil and the leading cause of cancer death among women. As the chance of cure is higher than 95% if diagnosed early, breast cancer screening measures are fundamental. Technological advances in diagnostic methods, such as digital mammography, tomosynthesis and MRI associated with the evolution in drug treatment for breast cancer are identified as the cause of the drop in breast cancer mortality in developed countries. However, breast cancer mortality curves continue to rise in most regions in Brazil, as a reflection of the lack of access to diagnosis and treatment. These data should make us reflect on the challenges of screening, which go beyond the correct use of available methods and include health policies and public management, allowing the early detection of a lesion, its diagnosis and treatment, so that the screening results in reducing mortality from breast cancer. At least 70% of the target population must be involved for an effective cancer screening program, and these numbers reach a maximum of 35% of women in Brazil. Several factors contribute to this scenario, such as: difficult access to the exam ; fear of performing the exam ; and, above all, the fact that screening is done opportunistically in Brazil, depending on whether the patient seeks the physician, without active tracking of patients. There are many challenges to the success of a screening program. The first step is to ensure the quality of the image and the mammogram report, which can be achieved through mammogram quality programs. The second step is to ensure quick access to the diagnosis of the suspicious mammographic finding through biopsy. And, finally, provide adequate treatment, avoiding delays and providing access to the most effective drugs. Investments in a more effective screening program to address these issues are high, but are also cost-effective. The diagnosis of initial lesions allows treatment to be de-escalated, whether surgical (more conservative surgeries) or adjuvant (less use of chemotherapy and radiotherapy), consequently increasing the chances of cure for patients.According to CBR, SBM and Febrasgo recommendations published in 2012 and updated in 2017, breast cancer imaging screening by age group should occur as follows:Women under 40 years old – usual risk; in general, mammography is not recommended in this age group;Women between 40 and 74 years old – all women in this age group should have a mammogram on an annual basis, preferably using the digital technique (category A); in places where breast tomosynthesis is available, it should preferably be used;Women over 75 years old – screening, preferably with the digital technique, is recommended on an individual basis in this age group; women with a life expectancy greater than seven years and who may undergo cancer treatment, considering their comorbidities, should continue mammographic screening (category D).According to the CBR, SBM and Febrasgo recommendations published in 2012 and updated in 2017, guidelines for the subgroup of high-risk patients are:Women with a mutation in BRCA1 or BRCA2 genes or first-degree relatives with a proven mutation should undergo annual mammogram screening from the age of 30 (category B);Women with a lifetime risk ≥ 20% calculated by one of the mathematical models based on family history should start 10 years before the age of diagnosis of the youngest relative (not earlier than 30 years of age) (category B);Women with a history of having undergone chest irradiation between ages of 10 and 30 years should undergo annual mammogram screening from the eighth year after radiotherapy treatment (not earlier than 30 years of age) (category C);Women diagnosed with genetic syndromes that increase the risk of breast cancer  or affected first-degree relatives should undergo annual mammogram screening after diagnosis (not earlier than 30 years of age) (category D).Note that personalized mammogram screening is being discussed more and more nowadays. Before starting this screening, it is important that the patient has her risk assessment carried out by the professional assisting her. If it is a high-risk patient, the screening program should be intensified. Clearly, the future is to adjust screening for these populations.Category A – Recommendation based on strong scientific evidence, with uniform consensus between CBR, SBM and Febrasgo in strong support of this recommendation.Category B – Recommendation based on reasonable scientific evidence, with uniform consensus between the CBR, SBM and Febrasgo in strong support of this recommendation.Category C – Recommendation based on little scientific evidence, but with consensus between the CBR, SBM and Febrasgo in strong support of this recommendation.Category D – Recommendation based on consensus of experts from CBR, SBM and Febrasgo in support of this recommendation.Breast echography or ultrasound has the challenges of its quality and the expertise of the examiner. This method is not used alone in breast cancer screening neither for usual-risk nor high-risk patients. It can be used in addition to mammography and MRI and to guide biopsies in case of suspicious lesions.High-risk patients are usually a group of younger patients and consequently, with denser breasts. In this specific subgroup, the use of mammography alone has low sensitivity. Since breast MRI is a functional method and not purely morphological, it does not depend on breast density for its effectiveness. Therefore, the use of MRI in high-risk patients is more effective, alone or in combination with mammography.An optimized screening program is essential to reduce breast cancer mortality in Brazil, plus it is cost-effective. All women should have their breast cancer risk assessment at age 30 to ensure they do not belong to a minority classified as high risk. Every asymptomatic woman at usual risk should undergo annual mammography/tomosynthesis starting at 40 years of age, as studies indicate a reduction in mortality from breast cancer due to early diagnosis, which also offers better surgical treatment options and more effective systemic treatment. Regarding the age to stop screening, the patient’s clinical conditions, comorbidities and life expectancy should be considered. Particularly in patients aged 75 years or older who will undergo breast cancer screening. We should discuss the possibility of recall for repeat exams or even to perform additional exams, the risk of undergoing unnecessary (benign) biopsies, the risk of an overdiagnosis , and finally, the anxiety generated by screening. The risks and benefits of screening for each woman should not be discussed in general, but on an individual basis. For high-risk patients and in some special conditions, another complementary diagnostic method (MRI or ultrasound) should be considered.National Specialized Commission on Breast Imaging of the Brazilian Federation of Gynecology and Obstetrics AssociationsPresident:Eduardo Carvalho PessoaVice-President:Henrique Lima CoutoSecretary:Clecio Enio Murta de LucenaMembers:Alexandre Vicente de AndradeDanielle ChambôFabio Postiglione MansaniFelipe Eduardo Martins de AndradeGiuliano Tavares ToselloHélio Sebastião Amâncio de Camargo JúniorHenrique Alberto Portella PasqualetteJorge Villanova BiazúsJosé Luis Esteves FranciscoRodrigo Pepe CostaSandra Regina Campos TeixeiraThais Paiva Moraes


P = 0.03), and baseline CRS were positively associated with OPS change (P < 0.001) but not vice versa. After fully adjustment of confounders, we found a 0.058  units increase of CRS and a 1.021  units increase of OPS as BMI increased 1 kg/m2 per year in postmenopausal women. OPS increased 0.784  units as CRS increased 1 unit per year. However, among premenopausal women, BMI only significantly affected CRS . No significant change of OPS with time-varying CRS was found. Higher increase rates of BMI were associated with worse trajectories of biomarker-based risk of cardiometabolic and breast cancer. The longitudinal impact of CRS on OPS is unidirectional. Recommendations such as weight control for the reduction of cardiometabolic risk factors may benefit breast cancer prevention, especially in postmenopausal women.Prospective inter-relationships among biomarkers were unexplored, which may provide mechanistic insights into diseases. We investigated the longitudinal associations of BMI change with trajectories of biomarkers related to cardiometabolic or breast cancer risk. A longitudinal study was conducted among 444 healthy women between 2019 to 2021. Cross‑lagged path analysis was used to examine the temporal relationships among BMI, cardiometabolic risk score (CRS), and obesity‑related proteins score (OPS) of breast cancer. Linear mixed-effect models were applied to investigate associations of time-varying BMI with biomarker-based risk score trajectories. Baseline BMI was associated with subsequent change of breast cancer predictors ( Higher body mass index (BMI) is a risk factor for outcomes associated with lower quality of life and functional impairment, including cardiometabolic diseases  and breast cancer, the leading cause of morbidity and mortality in women4 and largely mediate the link between obesity and coronary artery disease5. The changes in metabolic factors translate into cardiometabolic stress and enhanced myocardial load, including an increase in fatty acid oxidation and a decrease in glucose oxidation, and a subsequent reduction in cardiac energy, with deleterious hemodynamic consequences9. Based on this, glucolipid metabolism , blood pressure, and central obesity (waist circumference) were combined to form the cardiometabolic risk score. Several studies show that a stronger association is obvious between increased BMI and higher breast cancer incidence, particularly in postmenopausal women13. Abnormal regulations in the blood levels of proteins caused by adipose tissue contribute to breast cancer initiation and progression through the activation of multiple signaling pathways14, such as overexpression of pro-inflammatory cytokines, insulin resistance, hyperactivation of insulin-like growth factors (IGFs), adipocyte-derived adipokines, hypercholesterolemia and excessive oxidative stress16. Obesity-related proteins therefore constituted an important potential predictor of breast cancer risk in addition to a history of estrogen exposure.Through diverse mechanisms, obesity contributes to worsened cardiometabolic health and increases rates of cardiovascular events. Several obesity-related downstream metabolic factors, including elevated blood pressure, insulin resistance, inflammation, dyslipidemia, and hyperglycemia, have been linked to atherosclerotic disease17, hyperinsulinemia18, type 2 diabetes20, high blood pressure22, and high cholesterol24 are associated with an increased risk of breast cancer in women. Cardiometabolic abnormalities may contribute to cancer by enhancing cell proliferation and survival, as well as angiogenesis25. On the other hand, breast cancer survivors are at significantly higher risk of developing cardiovascular disease, partly due to the common pathway and the known associations with aging, obesity, and insulin resistance26. These results highlight the importance of prospective research focusing on relationships among BMI-related adverse health effects in women.There are also intrinsic links between distal obesity-related health outcomes. On the one hand, several studies have provided evidence suggesting that cardiometabolic abnormalities including abdominal obesity29 and breast cancer risk30. Therefore, in the study reported here, we examined the temporal relations and longitudinal associations of (i) BMI and cardiometabolic risk scores (CRS) calculated by insulin, glucose, triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), waist circumference (WC), and blood pressure; (ii) BMI and obesity‑related proteins scores (OPS) calculated by adiponectin (ADP), soluble leptin receptor (sOB-R), resistin (RETN), IGF-binding protein-3 (IGFBP-3) and C-reactive protein (CRP); (iii) CRS and OPS among healthy women in a breast cancer screening cohort.However, there is a paucity of epidemiologic data on the temporal associations of cardiometabolic markers and obesity‑related protein markers of breast cancer risk in healthy individuals. In addition, there were combinations of various obesity-related indicators reported as potential predictors of cardiometabolic riskThis longitudinal study was based on the breast screening cohort at Chengdu Shuangliu Maternal and Child Health Care Hospital, involving female participants aged 35–64 years who lived locally for at least 3 years. We excluded participants with cardiovascular diseases and malignant tumors, or who rejected to participate in the subsequent follow-up. Investigations and blood samples from eligible 444 participants were collected between May and June 2019 (as baseline). Among them, the same examinations were repeated in 335 (1-year follow-up) and 376 participants (2-year follow-up) between 2020 and 2021. Subjects who changed from pre-menopausal to post-menopausal status during follow-up (n = 44) didn’t remain in the analysis stratified by menopausal status (n = 400), but in the sensitivity analysis (n = 444), as shown in Fig. 31 during face-to-face interviews. Written informed consents were obtained prior to questionnaires.Anthropometric data on height and weight were measured using standard procedures by trained staff on site, and body mass index (BMI) was calculated as body weight divided by body height squared. Information on sociodemographic, menstrual, reproductive characteristics, lifestyle covariates, and medical history were collected using a structured questionnaire described previously32. The Z-score of HDL cholesterol was multiplied by − 1, because HDL cholesterol is inversely associated with cardiometabolic. Written informed consents were obtained prior to blood samples donation.Waist circumference was measured after expiration at the midpoint between the bottom of the rib cage and the top of the iliac crest. Blood pressure was measured on the right arm at least twice using a HEM-8613 digital monitor. The measurement protocol included, after a rest of ten minutes, three measurements in the sitting position at 2-min intervals. The mean of all three values was used as the systolic and diastolic blood pressure. Fasting venous blood samples were collected, some of which were immediately tested for blood glucose, insulin, triglycerides (TG), and HDL-C at the Laboratory of Clinical Chemistry of the site hospital, while others were centrifuged and stored at – 80 °C. A continuous cardiometabolic risk score (CRS) variable was calculated as the sum of Z-scores of waist circumference, insulin, glucose, triglycerides, HDL cholesterol, and the mean of systolic and diastolic blood pressure that are specific for the study population33. The concentrations in plasma were determined by standard enzymatic methods , and absorbance was measured by the enzyme labeling instrument . Origin 9.0 software was used to obtain the standard curve and calculate specific concentrations. The pre- and post-menopausal obesity‑related protein scores (OPS) were calculated with linear‑weighted summation and the detailed formulas have been published elsewhere30.Five obesity-related biomarkers including ADP, RETN, CRP, IGFBP-3, and sOB-R were measured at the Public Health and Preventive Medicine Provincial Experiment Teaching Center at Sichuan University, and the selection procedure has been described in detail elsewherevs CRS, BMI vs OPS, and CRS vs OPS. Cross-lagged path analysis that examines reciprocal, longitudinal relationships among a set of inter-correlated variables was used34. Taking BMI vs CRS as an example, this method tested the effect of baseline BMI on subsequent CRS and the effect of baseline CRS on subsequent BMI simultaneously, adjusted for autoregressive effects. The cross-lag path coefficients shown in Fig. Characteristics at baseline were compared by analysis of variance (ANOVA)  and Chi-square tests (or Fisher’s exact test) across baseline and follow-up subgroups. To normalize skewed distributions, a natural logarithmic (ln) transformation was performed for blood glucose, insulin, TG, HDL-C, and these obesity-related proteins. We used a cross-lagged panel design to investigate the bidirectional relationship between BMI Further, to describe the relationships between two risk scores and the continuous BMI over time, we used the linear mixed-effect models for repeated measures (baseline and the two follow-up time points) with random intercepts. According to the menopausal status, a stratified analysis of the associations between time-varying BMI and risk scores was conducted to explore the associations in premenopausal and postmenopausal women. The details are as follows.vs OPS and CRS vs OPS models were fitted in the same way and the sequence of independent and response variables was exchanged to assess the inverse association . We used three models to minimize the role of confounding. Model 1 was adjusted only for age (continuous variable). Model 2 was additionally adjusted for education, occupation, drinking, and smoking status . Model 3 was further adjusted for menarche age and number of live births . The results of Model 3 were considered as our primary findings, complemented by the results of Model 1 and Model 2. Finally, the relationship between the combined risk score and individual biomarkers was further explored based on Model 3 and the effect values were presented in a forest plot. All analyses were conducted using R version 4.2.0 (R Core Team (2022), Vienna, Austria.). A 2-sided P < 0.05 is considered statistically significant.An interaction term between continuous BMI and time was included to capture the associations of time-varying BMI and CRS. Besides the interaction term, baseline BMI and time, the models were further adjusted for age, menopausal status, education, occupation, passive smoking, and health status at baseline to control for variation at the beginning of the study. The other BMI All methods were performed in accordance with the relevant guidelines and regulations and were in accordance with the Declaration of Helsinki. The study protocol was approved by the ethics committee of West China School of Public Health and West China Fourth Hospital, Sichuan University. All subjects participated in the study voluntarily and signed informed consent forms. Confirms informed consent was obtained to publish the information/image(s) in an online open access publication.vs 172 (52%) at the first follow-up and 164 (44%) vs 212 (56%) at the last follow-up  of 24.31(2.89) kg/m2, and WC ranged from 65 to 109 cm with a mean (SD) of 83.62 (8.26) cm. Both BMI (P = 0.201) and WC (P = 0.189) were not statistically different from baseline to follow-up (Table P < 0.05), while blood pressure (SBP and DBP) and other blood-based biomarkers  fluctuated across the three-time points .The mean (SD) age of 444 participants was 49.47 (6.36) years at baseline Table . Among 2β = 0.06, P = 0.06), but not in the reverse direction from baseline CRS to follow-up BMI  , but not in the reverse direction  , but CRS did not change with OPS   were included in the cross-lagged path analysis. We found a positive association for the path coefficients from baseline BMI to follow-up CRS  units increase of CRS and 1.021  units increase of OPS as BMI increased 1 kg/m2 per year in premenopausal women. OPS increased 0.784  units as CRS increased 1 unit per year. Among premenopausal women, after adjusting for demographic characteristics, lifestyle, and reproductive factors, only the significance of BMI → CRS remained . However, associations  were observed significantly in the fully adjusted model in postmenopausal women. No significant change of OPS with time-varying CRS (OPS → CRS) was found in both main and adjusted models.To determine how the two risk scores would be influenced by BMI change and by each other change, we further examined the associations between a time-varying exposure and outcome trajectories (exposure → outcome) Table . We foun95% CI 0.001 to 0.015) ln-units and sOB-R increased by 0.023 (95% CI 0.009 to 0.037) ln-units in postmenopausal women. With a ln-unit per year decrease of HDL-C or increase of TG, OPS increased by 19.983 (95% CI 8.358 to 31.582) and 4.356 (95% CI 0.489 to 8.218) units, respectively, in premenopausal women. In postmenopausal women, an increase of 1 ln-unit insulin and 1 unit blood pressure per year was associated with an increase in OPS of 2.16 (95% CI 0.844 to 3.478) and 0.083 (95% CI 0.042 to 0.124) units, respectively.Since the temporal relationship (CRS → OPS) was initially established, we further assessed the longitudinal effects on individual biomarkers Fig. . With anSensitivity analysis based on the age-adjusted model showed consistent effects among participants, with no exclusion of those who had changed menopausal status during follow-up .36. Unlike metabolic diseases, there are no specific biomarkers or anthropometrics for directly characterizing breast cancer risk. We therefore use a protein score, composed of biomarkers involved in mechanisms of breast carcinogenesis including subclinical chronic low-grade inflammation and oxidative stress, anti-proliferative processes, sex hormones bio-synthesis pathway, and abnormal system and signaling of insulin resistance30. The score was previously established through evidence-based and experimental steps and has achieved robust predictive performance in breast cancer risk. On the other hand, there is an interplay between cardiometabolic disease and breast cancer risk, as shown in patients38. However, few studies prospectively explored the temporal relationship of these two risks in a cohort of healthy people based on respective biomarkers. Our study, to the best of our knowledge, presented the first repeatedly measured cardiometabolic factors and obesity-related proteins in Chinese women, which could help reveal the dynamic change of risk of cardiometabolic and breast cancer over time-varying BMI.The associations between adiposity and the risk of cardiometabolic disease and breast cancer were explored over the past few years, and most of these studies focused on the impact of BMI on the development of health outcome events. This study suggested that short-term changes in BMI trajectories have a significant association with risk scores of cardiometabolic and breast cancer, compensating for the inadequate capture of biomarker fluctuations at long follow-up. We used a validated continuous CRS because it is a more sensitive way to describe cardiometabolic risk than dichotomous definitions for metabolic syndromeWe found that baseline CRS was associated with follow-up OPS, and further confirmed in another analysis that the trajectory of OPS was associated with time-varying CRS. Interestingly, however, no significant association was found after exchanging the dependence of the risk scores. The temporal relationship between these two robust risk scores indicated that women at high cardiometabolic risk should pay more attention to the favorable change of obesity-related factors with the purpose of breast cancer prevention. In addition, cardiometabolic factors, as common clinical indicators, has better accessibility to identify high-risk women of breast cancer risk.41. As expected, a negative correlation between adiponectin and CRS was observed in our study. Although high sOB-R downregulates the bioavailability of leptin with pro-inflammatory features43, we observed increased levels of sOB-R associated with higher CRS in postmenopausal women, seemly inconsistent with the hypothesis that this adipokine may have a favorable role in improving metabolism45. However, this negative association between sOB-R and certain cardiometabolic risk factors is so far only supported by evidence from cross-sectional studies of predominantly non-menopausal women, and more prospective studies stratified by menopausal status are needed to elucidate the mechanisms.It has been reported that adiponectin possesses anti-diabetic, anti-inflammatory, and anti-atherogenic properties46. In the present study, the associations between these two biomarkers and OPS trajectory in the same direction as previous studies were observed only in premenopausal women, highlighting the importance of implementing strategies to improve lipid levels (increase HDL-C and reduce TG) before menopause. Previous studies have found a positive correlation between insulin levels and breast cancer risk in postmenopausal women, suggesting that hyperinsulinemia is an independent risk factor for breast cancer47. A meta-analysis based on 18 case–control studies found an increased risk of breast cancer in women with hypertension, but only for postmenopausal women48. The result was consistent with the positive correlation between blood pressure and OPS found in postmenopausal women in this study.A meta-analysis of 26 prospective studies involving 1,628,871 women showed that TG was significantly related to breast cancer development, and HDL-C was inversely related to breast cancer risk49. The results in the present study should be further validated in other large cohorts with repeated measurement or experimental studies in the future.The strength of this study is the repeated measurements of adiposity-associated biomarkers and numerous validated covariates that enabled us to evaluate the short-term prospective relationships between cardiometabolic risk and adipokine-based breast cancer risk. Leveraging the cohort study design and two types of analyses in parallel, we were able to identify robust temporal relationships. However, several limitations should be noted. First, although we have adjusted several important confounding factors, the possibility of residual confounding and measurement errors cannot be excluded. Second, our results can only explain as an association instead of a causal relationship due to the observational nature. Third, generalizability may be limited because participants in our study were predominantly screening women without cardiovascular diseases or breast cancer. However, this characteristic can also be an advantage because high compliance with screening ensures the collection of high-quality information on risk factors. When evaluating the association of adiposity with cardiometabolic risk, more studies are needed to discriminate between fat mass and lean mass components of BMI using dual X-ray energy absorptiometrySupplementary Tables.


Genome-wide studies of gene–environment interactions (G×E) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide G×E analysis of ~ 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer.Analyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene–environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs.–5 prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability < 15%, we identified two independent SNP-risk factor pairs: rs80018847(9p13)-LINGO2 and adult height in association with overall breast cancer risk , and rs4770552(13q12)-SPATA13 and age at menarche for ER + breast cancer risk .Assuming a 1 × 10Overall, the contribution of G×E interactions to the heritability of breast cancer is very small. At the population level, multiplicative G×E interactions do not make an important contribution to risk prediction in breast cancer.The online version contains supplementary material available at 10.1186/s13058-023-01691-8. Breast cancer is a complex disease involving interplay between lifestyle/environmental and genetic risk factors. Risk factors such as parity, breastfeeding, age at menarche, age at first full-term pregnancy, body mass index (BMI), height, mammographic density, exogenous hormonal use, and alcohol consumption are well-established –7. ThrouTraditional genome-wide association study (GWAS) analyses assess the marginal effects of variants and might miss variants which only show an effect within certain strata in the population. These potential gene–environment interactions where SNPs are associated with disease risk in conjunction with lifestyle/environmental risk factors can be investigated through genome-wide gene-environment interaction studies (GEWIS) –15.Very few genome-wide studies of gene-environment (G×E) interactions in breast cancer have been conducted to date, and three focused on the use of menopausal hormonal therapy as the single environmental risk factor –18. An eAnalyses were conducted using data from 46 studies  participating in the BCAC. We excluded participants if they were genotypically male, of non-European descent, or had a breast tumor of unknown invasiveness or in-situ breast cancer. Women with prevalent breast cancer at the time of recruitment or with unknown reference age (defined as age at diagnosis for cases and age at interview for controls) were also excluded from the analyses. Further, studies with fewer than 150 cases and 150 controls for the risk factor under evaluation were excluded from those analyses. Each participating study obtained informed consent from the participants and was approved by their local ethics committee.2) was conducted among postmenopausal women only. Menopausal status was either self-reported or assigned as postmenopausal if the reference age was greater than 54 years.Risk factor data from individual studies was checked for quality using a multi-step harmonization process based on a common data dictionary. Time-dependent risk factor variables were derived with respect to the reference date defined as date of diagnosis for cases and date of interview for controls. Analyses were conducted with the following risk factors among all women: age at menarche (per 2 years), parity (per 1 birth), adult height (per 5 cm), ever use of oral contraceptives (yes/no), and current smoking (yes/no). The analysis of age at first full-term pregnancy (per 5 years) was conducted among parous women only, and that of body mass index  as the covariate under a log-additive model. Interactions between genetic variants and risk factors were tested by comparing the fit of logistic regression models with and without an interaction term using likelihood ratio tests. All models were adjusted for reference age, study, and ten ancestry-informative principal components. To account for potential differential main effects of risk factors by study design, all models included an interaction term between risk factor and an indicator variable for study design (population-based vs. non-population based). Analyses with current smoking were further adjusted for former smoking.–5 prior probability of a true association for each SNP-risk factor pair. Overall, G×E interactions with BFDP < 15% were considered noteworthy [Analyses were performed separately for overall and ER + breast cancer risk, and also separately by genotyping array. Array-specific results were combined using METAL . Quantilteworthy . For nothf2 = hobs2 × var(X)/P(1-P), where hobs2 is the observed heritability given by LD score regression, var(X) is the variance of the risk factor under evaluation, and P is the proportion of cases in the sample. The proportion of the familial relative risk (FRR) of breast cancer due to G×E interactions is then given by hf2/2log(λ) where λ is the familial relative risk to first degree relatives of cases (assumed to be 2) [We estimated the overall genome-wide contribution of G×E associations for each risk factor to the familial relative risk of breast cancer using LD score regression . The anato be 2) .Studies included in the analysis are summarized in Additional file The genome-wide analysis of interactions with seven environmental risk factors yielded two SNP-risk factor pairs at BFDP < 15%, one for risk of overall breast cancer and one for ER + breast cancer risk  without an apparent marginal effect of the rs80018847 variant . By categories of adult height defined a priori, the estimated per allele ORmeta of rs80018847-G varied from 1.03  for women shorter than 158 cm, 1.13 (1.02–1.25) for women 158–162 cm in height, to ORmeta of 1.01  for women who were 168 cm or taller risk  but not for ER negative (ER-) breast cancer risk . The regional plot for overall breast cancer shows another SNP (rs1360506) at this locus in high linkage disequilibrium (LD) (r2 = 0.81) with rs80018847 . There was weak evidence for a marginal association between the rs4770552-T allele and ER + breast cancer . The per allele ORmeta appeared to decrease with increasing age at menarche, from 1.07  for age at menarche less than 13 years to 0.92  for age at menarche greater than 15 years , but no interaction for ER- breast cancer risk , Pint = 0.66). At this locus, we found suggestive evidence of interactions between further 13 SNPs and age at menarche for ER + breast cancer risk. However, these 13 SNPs are in high LD (r2 = 0.8–1.0) with SNP rs4770552  [LINGO2) on the short arm of chromosome 9 (9p13). This gene encodes a transmembrane protein belonging to the LINGO/LERN protein family [LINGO2 specifically in the central nervous system [Many observational studies have shown an association between increasing adult height and increased breast cancer risk, in both premenopausal and postmenopausal women , 29, 30.IGF1) . The funn family . Studiess system , but it SPATA13) at 13q12. SPATA13 encodes a guanine nucleotide exchange factor (GEF) for RhoA, Rac1 and CDC42 GTPases [MKL1), which in turn has been observed to alter the transcriptional activity of ERα, known to play a critical role in breast tumors [SPATA13 and affect the breast tumorigenesis process via activation of RHoA GTPases.Early age at menarche is known to be associated with elevated risk of breast cancer. There is an approximate 5% decrease in risk with each year delay in the initiation of menstruation . It has  GTPases , 38. Alt GTPases . Activatt tumors . TherefoGiven that the marginal effects of the common genetic variants are small and the associations of environmental risk factors with breast cancer are modest, interactions are also expected to be weak  can be compared with corresponding heritability for the SNP main effects (for which heritability is about 0.47) or the overall heritability based on the familial risk (~ 1.4) , 41. TheIn conclusion, we identified two novel genome-wide gene–environment interactions for overall and ER + breast cancer risk for women of European ancestry. These results contribute to our global body of knowledge on genetic susceptibility for breast cancer by generating plausible biological hypotheses, but they require replication and further functional studies.Additional file 1. A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry. Supplementary Table 1: Participating studies with number of total cases and controls per study. Supplementary Table 2: Detailed information of the characteristics of the study population by study design and case-control status. Supplementary Table 3: Associations of epidemiological risk factors for overall and ER-specific subtype breast cancer risk in population-based and cohort studies. Supplementary Table 4: Stratified analysis results for genome-wide significant interaction results by categories of risk factors. Supplementary Figure 1: Quantile-Quantile (Q-Q) plots of genome-wide interaction of A) Adult height on overall breast cancer risk and B) Age at menarche on ER+ breast cancer risk. Supplementary Figure 2: Frailty-scale heritability explained by GxE interaction on overall and estrogen receptor positive breast cancer risk. Supplementary Figure 3: Regional association plot for the interaction analyses between SNP rs80018847 and adult height for overall breast cancer risk. Supplementary Figure 4: Regional association plot for the interaction analyses between SNP rs4770552 and age at menarche for ER+ breast cancer risk. Supplementary Figure 5: Power (x-axis) to detect gene-environment interaction odds ratio (y-axis) at different minor allele frequencies (0.01 to 0.5: legend below) for 1:1 unmatched case-control study for different sample sizes . Power calculation was performed by Quanto 1.2.4, assuming a log additive model with SNP marginal effect estimate as 1.10, marginal effect estimate of the environmental risk factor as 1.20, and a two-side alpha of 5 x 10-08. We also assumed a 15% prevalence of the environmental risk factor and 1% prevalence of the disease.