How do previously developed breast cancer polygenic risk scores (PRSs) perform in a clinical setting for women of different ancestries?In this multicenter cohort study linking electronic medical records to genotyping data that including 39 591 women, PRSs were significantly associated with breast cancer risk in women of all ancestries, although the effect sizes were smaller in women with African ancestry.Previously developed PRS models for breast cancer risk performed well for women with European and Latinx ancestries in different clinical settings; these results suggest that larger studies are needed to develop and validate PRSs for women with African ancestry. This cohort study of multiple US medical centers with electronic medical records linked to genotype data examines the association of the ancestry of populations used to model polygenic risk scores for breast cancer with their ability to estimate cancer risk for women with different racial/ethnic ancestries. Multiple polygenic risk scores (PRSs) for breast cancer have been developed from large research consortia; however, their generalizability to diverse clinical settings is unknown.To examine the performance of previously developed breast cancer PRSs in a clinical setting for women of European, African, and Latinx ancestry.This cohort study using the Electronic Medical Records and Genomics (eMERGE) network data set included 39 591 women from 9 contributing medical centers in the US that had electronic medical records (EMR) linked to genotype data. Breast cancer cases and controls were identified through a validated EMR phenotyping algorithm.Multivariable logistic regression was used to assess the association between breast cancer risk and 7 previously developed PRSs, adjusting for age, study site, breast cancer family history, and first 3 ancestry informative principal components.This study included 39 591 women: 33 594 with European, 3801 with African, and 2196 with Latinx ancestry. The mean (SD) age at breast cancer diagnosis was 60.7 (13.0), 58.8 (12.5), and 60.1 (13.0) years for women with European, African, and Latinx ancestry, respectively. PRSs derived from women with European ancestry were associated with breast cancer risk in women with European ancestry , women with Latinx ancestry , and women with African ancestry . For women with European ancestry, this association with breast cancer risk was largest in the extremes of the PRS distribution, with ORs ranging from 2.19  to 2.48  for the 3 different PRSs examined for those in the highest 1% of the PRS compared with those in the middle quantile. Among women with Latinx and African ancestries at the extremes of the PRS distribution, there were no statistically significant associations.This cohort study found that PRS models derived from women with European ancestry for breast cancer risk generalized well for women with European, Latinx, and African ancestries across different clinical settings, although the effect sizes for women with African ancestry were smaller, likely because of differences in risk allele frequencies and linkage disequilibrium patterns. These results highlight the need to improve representation of diverse population groups, particularly women with African ancestry, in genomic research cohorts. For example, using large consortia of women with European ancestry, a PRS developed in the Breast Cancer Association Consortium (BCAC), reported approximately 2-fold and 4-fold increases in breast cancer risk for women in the top 10% and 1% of the PRS respectively; compared with women in the middle quantiles of risk (40% to 60%).2 This association has been replicated in validation studies using large cohorts of women with European ancestry.4Polygenic risk scores (PRSs) have consistently shown the ability to stratify the risk of breast cancer among women with European ancestry,5 With few exceptions,6 studies have not yet evaluated the performance of breast cancer PRSs using clinical data extracted from the EMR.Understanding the performance of these PRSs in diverse populations is crucial as we move toward clinical implementation of the PRS. In order to incorporate PRSs into clinical practice, models will need to be integrated with other clinical covariates like family history in the electronical medical records (EMR).7 By using the rich resources of the eMERGE network, including the extensive breast cancer phenotyping algorithm and a diverse population assembled across the network’s federated sites, this study aims to provide a systematic evaluation of the generalizability of previously developed breast cancer PRSs for women of European, African, and Latinx ancestry.The Electronic Medical Records and Genomics (eMERGE) network is a federated network of academic medical centers in the US and has compiled EMRs and genotype data for genomic research.8 Note that for Latinx women, we only used self-report because of the diversity of admixture genetic background. We did not include Asian, American Indian/Native American, Native Hawaiian/Pacific Islander, and other ancestry groups in this study given the corresponding small number of breast cancer cases.The participants involved in this cohort study were women enrolled through the eMERGE network from 9 contributing US medical centers with EMRs linked to genotype data. We identified breast cancer cases and controls through a validated phenotyping algorithm. We established ancestry by requiring the observed/self-reported ancestry to match the genetic ancestry inferred by principal component analysis-based k-means group, as previously described.9 The institutional review board of each contributing institution approved the eMERGE study, and the Columbia University Health Sciences institutional review board approved this study because analysis was conducted using deidentified data. All participants provided written informed consent prior to study inclusion. A specific discussion of the ethical considerations across the eMERGE III study is described elsewhere.10 This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.This study was conducted in accordance with the principles of the Declaration of Helsinki.16 to calculate each PRS (more details in the eMethods in the 2 and 1 from UKBiobank data [UKBB]11), which included 313, 3820, and 5218 variants, respectively. We also included 2 PRS models developed in or adapted to Latinx women , as well as 2 PRS models developed in women with African ancestry   . For wom8 For this study, variants that match the following 3 criteria were retained for PRS calculation: (1) a mean 2R imputation quality greater than 0.3 across genotype array-batches; (2) P value greater than 1 × 10−6 in ancestry-specific Hardy Weinberg Equilibrium tests; and (3) minor allele frequency (MAF) greater than 0.005. Principal component analysis was performed in both the combined data set and ancestry-specific data set after the MAF filtering and linkage disequilibrium (LD) pruning.Details of the eMERGE genotyping, imputation, and quality control procedures have been previously described.International Classification of Diseases, Ninth Revision (ICD-9) and ICD-10 diagnostic codes  that incorporated information from 8 age, breast cancer family history, and study site. We defined age as the time period between the year the phenotyping algorithm was executed and the year of birth. In addition, we examined the association of breast cancer by percentiles of PRS, compared with the middle quantile (40% to 60%) or with the remainder of the population.To evaluate the performance of each model, we standardized the PRSs to have a risk score unit expressed as an SD of the control distribution. The association of the standardized PRSs and breast cancer risk was evaluated by logistic regression adjusted for the first 3 ancestry-specific principal components,2R calculated for the full model inclusive of the PRS plus the covariates minus 2R for the covariates alone. We also chose the PRS showing the largest effect size within each ancestry to estimate the cumulative risk of breast cancer for high PRS risk (top tertile), moderate PRS risk (middle tertile), and low risk (bottom tertile) individuals in each ancestry using iCARE17 , with only the PRS used as a predictor. To estimate the percentage of the total variance in breast cancer risk explained by PRS, we used Nagelkerke’s pseudo 2 1.23,13 and 1.5812 for women with European, African, and Latinx ancestry, respectively. Our power analysis shows we have 100%, 58%, and 99% power to detect an association with the above assumed ORs for women with European, African, and Latinx ancestries, respectively. When we assumed a moderate PRS effect size  for women with Latinx ancestry as reported in Allman et al,13 we observed 79% power to detect an association in Latinx women. However, if we assumed the same OR estimated for women with European ancestry in non-European women , we should have 100% and 99% power to detect an association for breast cancer in women with African and Latinx ancestry, respectively. All analyses were carried out in R version 3.0.2 . All statistical tests were 2-sided, and P values < .05 were considered significant.We also assessed statistical power for testing associations of PRSs with breast cancer given sample size for each ancestry. Based on ancestry-specific empirical effect sizes of the PRS obtained from the literature, we assumed odds ratios (ORs) of 1.61,Our study included 39 591 women, including 33 594 women with European ancestry , 3801 with African ancestry , and 2196 with Latinx ancestry  . The totOur primary analysis examined the association of BCAC-S, BCAC-L, and UKBB in 3960 breast cancer cases and 29 634 control women with European ancestry and is shown in 11)  and 2 developed in women of African ancestry (ROOT and WHI-AA) in 3801 women with African ancestry (including 274 cases). We found statistically significant associations for the 3 PRS models based on women with European ancestry and breast cancer risk with average ORs per SD of the PRS ranging from 1.15  to 1.19 , but not for PRSs based on women with African ancestry . CompareWe examined the association of 5 PRSs , 2 of which were developed in or adapted to women with Latinx ancestry (WHI-LA and LATINAS) in 2196 Latinx women (including 147 cases). For Latinx women, we observed a statistically significant association for overall breast cancer risk for 3 of the PRSs examined , with ORs per SD ranging from 1.20  to 1.31  . CompareAs shown in 2 This smaller magnitude might be explained by the reduced variant set, caused by the genotype platform discrepancy between the eMERGE network and published studies.For PRSs developed in cohorts of women with European ancestry , we replicated associations for increased breast cancer risk in women with European ancestry, although the ORs we observed in our study were smaller in magnitude than the original studies  and Native American ancestry .It is important to note that our study had limited sample size of women with non-European ancestries, despite using a large resource like eMERGE. Similar to other studies investigating the generalizability of PRSs in cohorts of women with European and non-European ancestry, we found that European ancestry–based PRS models generalized well in women with Latinx and African ancestry, but with attenuated associations observed in women with African ancestry as reported in a recent study.23 and a large study examining European ancestry–based PRS in over 19 000 women with African ancestry, including over 9000 cases of breast cancer.18 While our power analysis suggests we have limited power (58%) to recover the signal detected by the original African ancestry–based PRS , we did have 100% power to detect an association in women with African ancestry if the European ancestry–based PRS can generalize as well in women with African ancestry . The flip-flop phenomenon, in which a variant is a risk factor in 1 population but protective in another, has been observed among approximately 30% to 40% of variants across studies.14 Although the ROOT model used in our study only consisted of variants with the effect size in the same direction among women with European and African ancestries, it did not generalize well in the women with African ancestry in the eMERGE network. The poor generalizability may also be partly explained by differences in risk allele frequencies and LD patterns among diverse ancestries.23Given the Eurocentricity of genomic studies, the smaller effect sizes for European ancestry–based PRSs with breast cancer risk in women with African ancestry in our study is not surprising and is consistent with PRS performance in non-European cohorts for other diseases24 account for both invasive and noninvasive breast cancer. Future PRS development work may consider including DCIS in the training sample.Among the European ancestry–based PRSs , UKBB achieved the largest effect size in the eMERGE cohort among women with European ancestry. Although UKBB used the same genome-wide association study (GWAS) summary statistics provided in the BCAC study, it developed and validated the PRS based on an independent larger sample size collected through UK Biobank, which can contribute to its stronger generalizability. Another possible explanation is that UKBB’s similar phenotype definition and data was collected in the clinical setting utilizing EMRs. However, our phenotype algorithm included women with ductal carcinoma in situ (DCIS) who have stage 0 or noninvasive breast cancer. Our sensitivity analysis suggested that defining cases excluding DCIS achieved a slightly higher OR (eTable 6 in the 26 We found that while 61 of 179 variants (34.1%) included in LATINAS were also included in UKBB model, only 12 of 71 (16.9%) included in WHI-LA were included in the UKBB model. Because the PRSs developed in studies using populations of non-European ancestry are often based on much smaller GWAS cohorts, the uncertainty of the effect sizes used in those PRSs is larger, making their predictive power lower for populations with non-European ancestry.14 In addition, the PRSs based on individuals with non-European ancestry included fewer variants passing the statistical threshold because of the smaller sample size in the discovery GWAS cohort, which would possibly contribute further to their weaker generalizability. Of note is the limited sample size for women with Latinx or African ancestry in our study, so future studies with adequate power are warranted to evaluate PRS performance for these groups. Furthermore, even with an adequate sample size for populations with non-European ancestry, limitations inherent to the genotyping platforms used in GWAS27 can make this subpopulation optimization theoretically insufficient to reduce the bias if the subpopulation risk allele is not captured by the genotype platform, which is possible because many array designs are based on samples of populations with European ancestry. Moreover, previous findings that women with African ancestry have a 40% higher mortality rate,28 which is often attributed to later stage of diagnosis and related preventative health care barriers, underscores the urgent need to increase diversity in genomic studies so that future clinical applications of the PRS do not exacerbate existing health disparities.For PRSs developed in non-European ancestry study populations  or adapted to non-European ancestry populations (LATINAS), we did not replicate the previously reported associations in the eMERGE cohort for women with Latinx or African ancestry, except for the LATINAS in Latinx women. LATINAS is a multiethnic PRS that utilized effect sizes obtained from populations with European ancestry and further developed the PRS in a cohort of Latinx women, suggesting that combining training data from samples from individuals with European ancestry could improve the observed associations in non-European ancestry populations.29 and the All of Us Research Program30 are 2 programs actively involved in increasing recruitment of diverse patients to help address the gap. These EMR-derived cohorts provide a scalable approach to independently validate previously developed PRSs for different phenotypes across multiple clinical operation sites.33 We found similar magnitudes of PRS association in women with European ancestry across all study sites, except for Vanderbilt University  significantly reduced the number of variants in the genotype data set, our results were largely unchanged , low imputation quality, or allele mismatch. Theoretically, expected PRSs can be calculated for the full variant set in the original published PRS by taking the imputed probabilities for mismatched genotypes into consideration. However, given the low imputation quality for those mismatched genotypes we excluded in this study, the expected PRS for the full variant set could have a large variance, and as such, we did not conduct the calculation in our study. Our sensitivity analysis found that while using a more conservative imputation quality threshold (ie, imputation In summary, we found PRS models based on populations with European ancestry were significantly associated with breast cancer risk in women with European ancestry in the eMERGE network. We also found that these PRSs generalized well to women with European and Latinx ancestry, and to a lesser degree to women with African ancestry, although further studies with larger sample size of women with African ancestry are needed. Additionally, we found that PRS developed in small GWAS studies of populations with non-European ancestry did not generalize well in the respective ancestry group. Our results highlight the need to increase the inclusion of racially and ethnically diverse individuals, particularly individuals with African ancestry, in large-scale genomic studies. Until well-developed and validated PRSs for women with non-European ancestry become available, the current PRSs developed based on cohorts with European ancestry could be adapted for Latinx women, but not women with African ancestry, in clinical settings until additional data sets become available in this important and high-risk group.


An increased risk of breast cancer was observed for pictogram scores 3–4  and for the highest level of self-reported body size, pictograms ≥ 5 . Conclusions: The risk of breast cancer is associated with an increased BMI and visual overweigh and obesity. The results suggest that a self-reporting alternative tool can be used to assess the prevalence of overweight/obesity, particularly in situations where no other tools are available.Background: The use of self-reported body size as an alternative tool to estimate body weight for health risk assessment is not widely reported, especially in relation to breast cancer. Therefore, we examined the association between breast cancer and body-mass index (BMI) and the usefulness of pictograms. Methods: The case–control study was conducted among postmenopausal women from 2015 to 2019. The study involved 151 women with breast cancer and 67 control subjects. Data were collected by a self-reported detailed questionnaire. Results: An increased, 4.13-fold risk of breast cancer  was observed for women with BMI 25.0–29.9 kg/m Overweight and obesity besides factors such as social history, childhood experiences, mental health, socioeconomic status are mainly caused by modifiable risk factors that can be reduced or controlled. Despite this, the number of overweight and obese people is growing all the time. Obesity is considered a top public health concern and the number of people with obese has almost tripled since 1975, worldwide . According to a recently published report by the International Agency for Research on Cancer (IARC) Working Group, there are significant associations between BMI and risk of colon, rectum, gastric cardia, liver, gallbladder, pancreas, and kidney cancer and for esophageal adenocarcinoma. The IARC report also found that absence of body fatness lowers breast cancer risk in postmenopausal women, particularly for estrogen-receptor-positive tumors . The WorThe biological mechanisms by which obesity may increase the risk of breast cancer are not fully understood. Hypothetical biological pathways include increased levels of endogenous hormones and inflammatory factors . ObesityIn health-related research, self-reported body size and pictorial representations ranging from very lean to overweight is sometimes used to describe the characteristics of the human body and provides an alternative approach to estimate body weight and height. It is especially useful when studies do not have precise measuring instruments or do not have data on anthropometric measures in the past. Results from the analysis of pictogram body-size score, as an alternative measure of excess body weight, showed that the body image pictogram is a good semi-quantitative tool for BMI estimation . The useThe aim of this study was to examine the relationship between breast cancer and BMI and the usefulness of pictograms in the risk assessment of breast cancer in postmenopausal women. The case–control study was conducted among postmenopausal women in the Lodz Province during the period from 2015 to 2019. The case group included 151 women diagnosed with breast cancer. The women were patients of the oncological surgery department and clinic. The criterion for including women in the case group were histologically confirmed breast cancer and no history of other cancers.The control group included 67 women without a diagnosis of breast cancer. Controls were community-based. The criterion for including women in the control group was no history of any cancers.A detailed questionnaire was used to determine demographic variables, anthropometric data, lifestyle and reproductive history characteristics , family history of cancer, and employment history.2). BMI was analysed in three variables: 18.5–24.9 kg/m2 , 25.0–29.9 kg/m2 overweight) and ≥30 kg/m2 (obese). Detailed information was collected on body size. Subjects were asked to report their weight (kg) and height (m). Then, based on these data, we calculated body-mass index (BMI) with the following formula: weight (kg) divided by height squared  were used to indicate body sizes (from very lean to obese). The analysis covered two time points (assessed by women 15 years before the interview and the period when the data were collected) . We usedThe study was approved by the ethics boards of all participating institutions and participants provided written informed consent. The study design received a positive opinion from the Bioethics Committee at the Medical University of Lodz (RNN/236/15/EC on 22 September 2015).p level was at <0.05. The program used was Statistica 13.3, licenced by the Medical University of Lodz, Poland.The results were analysed with the use of statistical methods, including some multidimensional tests. The Shapiro–Wilk test was used to assess normal distribution. The studied characteristics of non-normal distribution as well as qualitative and quantitative data were analysed with the use of non-parametric tests, including, Pearson’s chi-squared and Mann–Whitney U tests. General descriptive statistics methods were also used. A logistic regression model was used for multivariate analysis, where applicable. Analysis of covariance (ANCOVA) was used to control for the effect of some continuous variables that were not of primary interest when evaluating the effect of categorical independent variables on the dependent one. Goodness of statistical fit of the models was assessed with Hoshmer–Lemeshow test and ROC curves. The statistically significant A total of 151 breast cancer cases and 67 controls were included in the analysis. The characteristics of the examined women are presented in 2, while in the case group it was 26 kg/m2. The proportion of controls who had normal weight was 70% compared to 40% of controls. In the case group there was a significantly higher percentage of people with overweight (35% vs. 16% among controls) and obesity (25% vs. 13% among controls). Among cases, 36% showed pictograms score < 3, 41% pictograms 3–4 and 23% pictograms ≥5. In the control group, most women (81%) had a score of 3 or less. The relative frequency of pictogram score 5 or more was substantially higher in cases (23%) than in controls (10%), while scores 1–2 were more common among controls (81%) than among cases (36%).Cases generally had a higher body-mass index than non-cases and the difference was statistically significant. The average BMI value in the control group was 24 kg/m2). Among obese patients there was a significant positive association with an OR of 3.17   . p > 0.05). Multivariate analysis using all variables which was found to have a significant risk on univariate analysis and showed that the probability of breast cancer increased from 313%  in overweight women to 75%  in obese women, whereas the association in the case of obese women was not statistically significant. The association between higher BMI and the risk of breast cancer was stronger among overweight women, than the number of patients categorized as obese decreased, and the association became not significant.Three variables were found to be significant on the univariate analysis, which included smoking, family history of breast cancer and earlier age of first childbirth. Age of patients, place of residence, education level, number of children, time of breast feeding, date of menarche as well as age of menopause were not found to be statistically significant confounders  and risk of breast cancer in postmenopausal women. The analysis covered two time points (assessed by women 15 years before the interview and the period when the data were collected). No positive association was observed for body size assessed by women, therefore the analysis was presented for the self-reported body sizes 15 years ago. The results obtained from multiple logistic regression analysis are shown in When we restricted the analysis to self-reported body size as a continuous variable (Model 2) we observed a 38% increased risk  of breast cancer per unit increase in body-size score assessed 15 years before the interview. The other parameters taking into account variables that were found to have a significant risk were smoking, family history of breast cancer and earlier age of first childbirth, which showed a similar level of risk in the analysis using pictograms as categorical variable (Model 1) .2 had more than a 2-fold higher risk of death compared to women of normal weight  ) . In the , 3.19]) . In a Ko, 3.19]) .Chlebowski et al. presented a different approach to the problem of the relationship between BMI and breast cancer risk. A study among postmenopausal women found that women who lost weight had a significantly lower breast cancer risk compared with women whose weight remained stable  . In an aPrevious studies have shown that pictograms are a good semi-quantitative tool for BMI estimation. Peterson et al. examined body perception among 215 students. The results provided data that using pictograms may help health programs track and measure body image perception data among the population . In currPostmenopausal women with overweigh and obesity are at increased risk of breast cancer. Our findings showed that self-reported body size may be used as a quick and effective alternative to measured BMI, which is essential for the detection of this modifiable risk factor for breast cancer. The results suggest that a self-reporting alternative tool can be used to assess the prevalence of overweight/obesity, particularly in situations where no other tools are available.Healthcare professionals have an easy tool to assess the risk of health problems that can be linked to the risk of breast cancer, which has important implications in preventive actions. As our results may not be generalized to other populations, there is a need for more studies comparing BMI measurements and self-reported body size.


Preclinical and epidemiological studies indicate a potential chemopreventive role of low-density lipoprotein cholesterol (LDL-C) -lowering drugs in the risks of breast cancer and prostate cancer, but the causality remains unclear. We aimed to evaluate the association of genetically proxied inhibition of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase, Niemann-Pick C1-Like 1 (NPC1L1), and proprotein convertase subtilisin/kexin type 9 (PCSK9) with risks of breast cancer and prostate cancer using a two-sample Mendelian randomization (MR) method.HMGCR, NPC1L1, and PCSK9 associated with LDL-C in a genome-wide association study (GWAS) meta-analysis from the Global Lipids Genetics Consortium  were used to proxy inhibition of HMG-CoA reductase, NPC1L1, and PCSK9. Summary statistics with outcomes were obtained from a GWAS meta-analysis of the Breast Cancer Association Consortium  and a Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome  consortium. SNPs were combined into multiallelic models and MR estimates representing lifelong inhibition of targets were generated using the inverse-variance weighted method.Single-nucleotide polymorphisms (SNPs) in P = 0.005) and NPC1L1  equivalent to a 1-mmol/L (38.7 mg/dL) reduction in LDL-C was associated with reduced breast cancer risk. There were no significant associations of genetically proxied inhibition of PCSK9 with breast cancer. In contrast, genetically proxied inhibition of PCSK9  but not HMG-CoA reductase and NPC1L1 was negatively associated with prostate cancer. In the secondary analysis, genetically proxied inhibition of HMG-CoA reductase  and NPC1L1  was associated with estrogen receptor-positive breast cancer, whereas there was no association of HMG-CoA reductase and NPC1L1 with estrogen receptor-negative breast cancer.Genetically proxied inhibition of HMG-CoA reductase (OR: 0.84; 95% CI 0.74–0.95; Genetically proxied inhibition of HMG-CoA reductase and NPC1L1 was significantly associated with lower odds of breast cancer, while genetically proxied inhibition of PCSK9 was associated with reduced risk of prostate cancer. Further randomized controlled trials are needed to confirm the respective roles of these LDL-C-lowering drugs in breast cancer and prostate cancer.The online version contains supplementary material available at 10.1186/s13058-022-01508-0. There were 19.3 million new cancer cases and almost 10.0 million cancer deaths worldwide in 2020, which caused an enormous economic burden for patients and society , primaryHMG-CoA; target of statins) reductase, Niemann-Pick C1-Like 1  and proprotein convertase subtilisin/kexin type 9  are common targets of low-density lipoprotein cholesterol (LDL-C)-lowering drugs for the prevention of cardiovascular disease [3-hydroxy-3-methylglutaryl coenzyme A ( disease . Previou disease –8. For e disease . In addi disease . However disease . Both brMendelian randomization (MR) method may investigate the potential causal effect of an exposure of interest on the risk of disease by utilizing genetic variants as a proxy , 13. PotAs illustrated in Fig. To generate genetic instruments to proxy HMG-CoA reductase, NPC1L1, and PCSK9, summary genetic association data were obtained from a GWAS meta-analysis of LDL-C levels in the Global Lipids Genetics Consortium (GLGC) . The GLGP < 5.0 × 10−8) and within ± 100 kb windows of the gene region encoding HMG-CoA reductase were obtained. Similarly, 3 LDL-C related SNPs within ± 100 kb from NPC1L1 and 11 LDL-C related SNPs within ± 100 kb from PCSK9 were used to proxy NPC1L1 and PCSK9, respectively. To maximize instrument strength, SNPs used as proxies for each drug target were permitted to be in low linkage disequilibrium (r2 < 0.20) with each other to increase the proportion of variance in each respective drug target explained by the instrument. The characteristics of genetic variants genetically proxied HMG-CoA reductase, NPC1L1 and PCSK9 are shown in Table To proxy HMG-CoA reductase, 5 SNPs associated with LDL-C at the genome-wide significance level . The BCAC is an international collaboration initiated in 2005 to study genetic susceptibility to breast cancer, including 122,977 female cases and 105,974 female controls of European ancestry . In BCACF = (N − K − 1) × R2/K × (1 − R2), where R2, calculated by using the package gtx in R , was the proportion of variation in HMG-CoA reductase, NPC1L1, or PCSK9 explained by the SNPs, N was the sample size, and K was the number of SNPs in genetically proxied inhibition of HMG-CoA reductase, NPC1L1, or PCSK9. Conventionally, an F statistic of at least 10 was indicative of evidence against weak instrument bias [https://shiny.cnsgenomics.com/mRnd/) was used to calculate the statistical power in this MR study [The strength of the genetic instruments of LDL-C-lowering drug targets was evaluated using the F-statistic , calculaent bias . In addiMR study .In the main analysis, we used the inverse-variance weighted (IVW) MR method to estimate the effect of genetically proxied HMG-CoA reductase, NPC1L1, and PCSK9 on breast cancer and prostate cancer . The hetP < 0.008 . For the secondary outcome (ER-positive breast cancer and ER-negative breast cancer), an observed 2-sided P < 0.05 was considered to be statistically significant because these analyses were only exploratory analyses. All analyses were conducted with packages named TwoSampleMR, MendelianRandomization, and gtx in R software .All MR estimates were presented as odds ratios (ORs) and were scaled up from individual SNP-level effects on LDL-C levels to reflect the equivalent of a 1-mmol/L (38.7-mg/dL) reduction in LDL-C levels. For the two primary outcomes (breast cancer and prostate cancer), all statistical tests were 2-sided, and a significance threshold was set at The strength of genetic instruments for LDL-C-lowering drug targets  and the statistical power of this MR analysis are presented in Additional file 1: Table S1. The F statistics for the association of genetic instruments with HMG-CoA reductase, NPC1L1, and PCSK9 ranged from 71.63 to 195.81, suggesting that there is little instrument bias in the present MR study.P = 0.005) and NPC1L1  equivalent to a 1-mmol/L (38.7 mg/dL) reduction in LDL-C was significantly associated with a reduced risk of breast cancer, respectively. However, the results showed no significant relationship between genetically determined inhibition of PCSK9 and the risk of breast cancer, although there was a trend of reduced risk of breast cancer . In addition, further analysis showed that genetically proxied inhibition of HMG-CoA reductase and NPC1L1 were mainly associated with lower risk of ER-positive breast cancer  but not ER-negative breast cancer .In light of Cochran’s Q test, the fixed-effect IVW models were used in the main analysis of breast cancer due to the lack of heterogeneity . As shown in Table P = 0.77), NPC1L1 , and PCSK9  and breast cancer . The results of leave-one-out sensitivity analyses showed that no individual SNP substantially drove the associations of HMG-CoA reductase and NPC1L1 with breast cancer .In the sensitivity analyses, MR-Egger showed no evidence of directional pleiotropy for the associations between inhibition of HMG-CoA reductase . In contrast, genetically proxied inhibition of HMG-CoA reductase  or NPC1L1  was not significantly associated with prostate cancer.In the main analysis of prostate cancer, fixed-effect IVW models were used for HMG-CoA reductase and NPC1L1 because of no heterogeneity, while random effect IVW model was also used for PCSK9 . As shown in Table P = 0.52), NPC1L1 , and PCSK9  and the risk of prostate cancer . In the sensitivity analysis applying the weighted median method, the association of genetically proxied PCSK9 inhibition with prostate cancer  remained significant. Leave-one-out sensitivity analysis showed that the association between PCSK9 inhibition and prostate cancer was not substantially driven by any individual SNP .In light of the MR-Egger regression findings Table , we founP = 0.007). For prostate cancer, significant associations were observed for genetically proxied PCSK9 inhibition and low odds of prostate cancer .The results of multivariable MR analysis on the associations of LDL-C-lowering drug targets with breast cancer and prostate cancer are presented in Table S8. In terms of breast cancer, multivariable MR analyses adjusting for body mass index and age at menarche suggested that genetically proxied inhibition of HMG-CoA reductase was associated with a reduced risk of breast cancer  on the risks of breast cancer and prostate cancer. We found that genetically proxied inhibition of HMG-CoA reductase and NPC1L1 but not PCSK9 were significantly associated with reduced risk of breast cancer (mainly ER-positive breast cancer), while only genetically determined PCSK9 inhibition was associated with low odds of prostate cancer. These findings suggested that there were possible mechanism-specific protective effects of statins (targeting HMG-CoA reductase) and ezetimibe (targeting NPC1L1) on breast cancer, whereas PCSK9 inhibitors might have protective effect on prostate cancer.In recent decades, observational studies investigating the effects of statins and alternate LDL-C-lowering drugs (ezetimibe and PCSK9 inhibitors) on the risks of breast cancer and prostate cancer have yielded inconsistent results , 32. In With regard to the inherent bias of residual confounding and reverse causation in observational studies, MR is an increasingly used method to draw definitive conclusions regarding the causality of the association between exposures and diseases via considering genetic variants as instrumental variables –14. MR lThere exists numerous important public health significances and clinical implications. From our findings, the problems that lipid lowering drugs maybe prevent breast cancer and prostate cancer in population deserve to be further studied, and even statins and ezetimibe may be recommended for the prevention of breast cancer in the population, especially ER-positive breast cancer, among females with hyperlipidemia. In contrast, PCSK9 inhibitors may be the preferred LDL-C-lowering drug among males with hyperlipidemia to prevent prostate cancer. Some randomized controlled trials for preventing cardiovascular disease have suggested that statins have provocative and unexpected benefits for reducing colorectal cancer and melanoma –36. HoweSeveral biological mechanisms may underlie the beneficial effect of LDL-C-lowering drugs on breast cancer and prostate cancer. HMG-CoA reductase inhibition may reduce intratumoural autocrine hormone production by lowering intracellular cholesterol, promote apoptosis breast cancer cells through inducing nitric oxide synthase expression , 38. As This study had a number of important strengths. First, to the best of our knowledge, this is the first MR study to detect the causal effects of lipid-lowering drugs on breast cancer, breast cancer subtypes, and prostate cancer. Second, the present study used genetic variants within genes that encoded drug targets to proxy the potential effect of commonly prescribed LDL-C-lowering therapies, which could reflect the impact of life-long exposure on breast cancer or prostate cancer and avoid the drawback of limited exposure time and follow-up time in clinical trials or observational studies. Third, the summary statistics of lipid-lowering drugs, breast cancer and prostate cancer were collected from well-designed GWAS with large sample size, which enabled us to make causal inferences with high statistical power. Fourth, our study harnessed the homogeneity of European populations with respect to genetic background and external sociocultural determinants, decreasing spurious associations due to population stratification and other confounding factors . CertainOur study also had several limitations. First, MR estimates represented the long-term modulation of drug targets on disease risk, which might suggest larger risk reductions per unit change in drug target compared with those obtained from drug administration over a relatively shorter duration. Therefore, if lipid-lowering drug treatment could lower the risk of breast cancer or prostate cancer, the magnitude of risk lowering achieved through taking lipid-lowering drugs might not correspond to the effect size observed in this MR study. Second, although we attempted to control for pleiotropy in the MR study, pleiotropy still represented a major challenge to decipher the roles of specific lipid-based pathways. In the present study, MR-Egger regression analysis showed no pleiotropic effects, indicating that the possibility of directional pleiotropy bias may be minimal. Third, MR analysis might be biased by potential violations of standard instrumental variable assumptions . HoweverGenetically proxied inhibition of HMG-CoA reductase and NPC1L1 was significantly associated with lower odds of breast cancer , while genetically proxied inhibition of PCSK9 was associated with reduced risk of prostate cancer. Further randomized controlled trials are needed to confirm the respective roles of these LDL-C-lowering drugs in breast cancer and prostate cancer.Additional file 1. Supplementary Online Content.


Objective: Laboratory findings indicated that vitamin D might have a potent protective effect on breast cancer, but epidemiology studies reported conflicting results. The aim of the study was to conduct a systematic review and meta-analysis to clarify the efficacy of vitamin D supplementation on risk of breast cancer.Methods: MEDLINE, EMBASE, The Cochrane Central Register of Controlled Trials, ClinicalTrials.gov, and abstracts of three major conferences were searched . Parallel randomized controlled trials (RCTs) examining the efficacy of vitamin D supplementation on risk of breast cancer or change of mammography compared with placebo in females were included. Data were meta-analyzed using a random-effects model. Bayesian meta-analysis was conducted to synthesize the results using data from observational studies as priors.Results: Seven RCTs were identified for effect of vitamin D on risk of breast cancer, with 19,137 females included for meta-analysis. No statistically significant effect of vitamin D on risk of breast cancer was found in classical random-effects meta-analysis . When Bayesian meta-analyses were conducted, results remained non-significant. There was no statistically significant effect of vitamin D on mammography density observed: mean difference = 0.46, 95% confidence interval: −2.06 to 2.98, p = 0.72.Conclusion: There is insufficient evidence to support the efficacy of vitamin D supplementation in breast cancer risk and change of mammography density. The protective effect of vitamin D on risk of breast cancer from previous observational studies may be overestimated.Systematic Review Registration: PROSPERO, identifier CRD42019138718. Breast cancer is one of the most commonly occurring cancers and is among the leading causes of death in women worldwide . AlthougVitamin D is the precursor to the hormone calcitriol , which regulates numerous actions in many tissues of the human body . VitaminMammographic breast density [mammography density (MD)] is one of the strongest indicators of breast cancer –29. It iGiven the inconsistent findings and more recent research published, we conducted a systematic review and meta-analysis to summarize the most up-to-date evidence from RCTs, aiming to clarify the effect of vitamin D supplementation on risk of breast cancer. Our outcomes included risk of breast cancer and MD.The systematic review was conducted according to the Cochrane Handbook for Systematic Reviews of Interventions . We summMEDLINE, EMBASE, The Cochrane Central Register of Controlled Trials (CENTRAL), and ClinicalTrials.gov were searched by two reviewers . Moreover, an updated search was performed up to December 8, 2020. We used descriptors that include synonyms of vitamin D, breast cancer, MD, and RCT in different combinations (descriptors can be found in https://www.oncoconferences.ch/Breast-Cancer-Conferences), American Society of Clinical Oncology Annual Meeting (https://meetings.asco.org/am/attend), and San Antonio Breast Cancer Symposium (https://www.sabcs.org/). Missing data were sought from the original authors or from secondary publications of the same study.Unpublished studies were identified by searching the abstract of top three conferences in the area of breast cancer: St. Gallen International Breast Cancer Conference  independently screened and selected studies for possible inclusion in the study. Any disagreement was resolved by consensus, and a third reviewer (GL) was consulted if disagreement persisted. Agreement between authors was quantified using the kappa statistic.For studies that fulfilled our inclusion criteria, two reviewers (ZL and LW) independently extracted data using a especially developed data extract form, which was designed and piloted prior to its use. The following information was extracted: (1) participant characteristics: age, status of menopause, baseline levels of serum 25(OH)D, baseline MD (for secondary outcome), total sample size, number of female participants, and study settings including countries and latitude; (2) intervention: number of arms, sample size of each arm, randomization and allocation concealment method, blinding, dose and type of supplementation (D2 or D3), frequency and duration of intervention, cointervention in each arm, and withdrawals and drop-offs; and (3) outcome measures: description of measures used, incidence of breast cancer, and measures of MD. Any disagreement was resolved by discussion and consensus among all the review authors.2 statistic, with I2 > 50% or p < 0.1 taken as implying significant heterogeneity.According to our protocol, a random-effects model was performed to synthesize the data by pooling the results of the included studies. We analyzed the data using Review Manager (RevMan) V5.2 for Windows . We calculated the pooled RR for dichotomous data (breast cancer risk) and the weighted mean difference (WMD) for continuous data (MD) measured on the same scale. Heterogeneity between included studies was assessed using the Ip = 0.026)  . By comp= 0.026) . Posteri= 0.026) . Converg= 0.026) . The autFor primary outcome, the heterogeneity was examined by carrying out the following subgroup analysis: (1) different vitamin D dosages (less than median dose vs. no less than median); (2) different durations of intervention (less than median duration vs. no less than median); (3) different menopausal status of the participants (post- vs. pre-); (4) different latitudes where the studies were carried out (lower than median dose vs. no less than median); and 5) different serum 25(OH)D levels of the participants at baseline (less than median vs. no less than median).We conducted three sensitivity analyses using random-effects models for risk of breast cancer by (1) excluding studies that were rated as at high risk of bias; (2) excluding studies that involved non-study cointerventions; and (3) using the data from the shortest study period in each study.The risk of bias for each included studies was examined by Cochrane Collaboration “risk of bias” assessment tool , which iPotential risk of publication bias was examined by the construction of funnel plots, Begg's rank correlation test, and Egger's regression test.We identified 3,157 citations from the literature search and reference lists, from which 688 duplicates were removed, leaving 2,469 citations that remained for title and abstract screening. Forty-five citations were retrieved for full-text screening. There were eight discrepancies resolved by discussion between reviewers . No further studies were identified from unpublished literature. Ten studies [nine full texts , 52–58 aCharacteristics of included studies are shown in Regarding risk of bias assessment, three studies were graded as high risk of bias , 57, 58,p = 0.71). The heterogeneity among studies was not significant .The pooled results from the individual RCTs including 19,137 females are displayed in When the Bayesian approach was applied using γ distribution as non-informative prior distribution, the findings were similar to classical analysis results . With thI2 = 0%). The three sensitivity analyses also produced similar findings to the main results . The heterogeneity among studies was substantial .Results for the effect of vitamin D on MD including 584 females are shown in The quality of a body of evidence obtained from all the included trials for risk of breast cancer was graded as moderate, because of risk of incomplete outcome data in the included trials . The quap-values > 0.05).For both risk of breast cancer and MD, the symmetric funnel plot suggested no evidence of publication bias . When data from observational studies were introduced in informative prior distribution, the point estimation suggested a protective effect of vitamin D supplementation on breast cancer (RR = 0.96) with a posterior probability of a protective effect of vitamin D supplementation of 0.78, although without statistical significance . These rExperimental studies suggested that the protective effect of vitamin D on breast cancer could be stronger in postmenopausal women –63, 68, In a previous review by Bjelakovic et al.  that synA priori and post-hoc subgroup analyses and sensitivity analyses were carried out to better combine the available evidence. The particular strength of the current review was the use of the Bayesian approach, which allowed us to utilize external information from previous observational studies for our meta-analysis of RCTs and to explore the robustness of our results under different assumptions .Our current study is the most up-to-date review investigating the effect of vitamin D supplementation on breast cancer risk synthesizing RCTs, with a recent published large trial included. We performed a comprehensive and exhaustive search to retrieve all relevant studies and extracted and managed data in duplicate with a good level of consensus. Our study included a large and representative sample, therefore enhancing the generalizability of our findings. I2 = 0%). Furthermore, we used the trim-and-fill technique as a sensitivity analysis by excluding either Avenell's  or Manson's  studies at one time. Results consistent to the global analysis . However, the number of included RCTs was small (n = 3), which precluded our further exploration on their heterogeneity. Three out of 10 included studies were graded as high risk of bias, mainly because of inconsistency due to no clear reason of loss of follow-up. Small sample size, substantial heterogeneity or the secondary outcome, and high risk of bias in some of the studies downgraded the quality of the analysis.There are several limitations to this systematic review and meta-analysis. First, vitamin D is proved to inhibit both ER-positive and ER-negative breast cancer cells, while the effect on ER-positive cancer cells is stronger . Unfortuanalysis  were fouThe current evidence does not support the effect of vitamin D supplementation on decreased breast cancer risk. Although the observational studies found a significant protective effect of vitamin D on breast cancer, the Bayesian analysis implicated that such results from observational studies could be overestimated. However, some evidence has suggested the protective role of vitamin D in the progression and prognosis of breast cancer. For example, vitamin D was associated with increased effectiveness of anticancer drugs, reduced risks of metastasis and recurrence, and superior survival in patients , 71. BesIn conclusion, our systematic review and meta-analysis found that there was a lack of evidence to support the efficacy of vitamin D supplementation in breast cancer risk and change of MD. The protective effect of vitamin D on risk of breast cancer from previous observational studies may be overestimated. Further high-quality RCTs with large sample size and longer study duration are needed to clarify the efficacy the efficacy of vitamin D in breast cancer.The original contributions presented in the study are included in the article/Conceptualization: ZL, LW, GL, and LT. Data collection and analyses: ZL, LW, and GL. Writing—original draft preparation: ZL. Writing—review and editing. ZL, XH, JZ, GL, and LT. GL had primary responsibility for final content. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


There is a rising concern about treatment-associated cardiotoxicities in breast cancer patients. This study aimed to determine the time- and treatment-specific incidence of arrhythmia, heart failure, and ischemic heart disease in women diagnosed with breast cancer.A register-based matched cohort study was conducted including 8015 breast cancer patients diagnosed from 2001 to 2008 in the Stockholm-Gotland region and followed up until 2017. Time-dependent risks of arrhythmia, heart failure, and ischemic heart disease in breast cancer patients were assessed using flexible parametric models as compared to matched controls from general population. Treatment-specific effects were estimated in breast cancer patients using Cox model.Time-dependent analyses revealed long-term increased risks of arrhythmia and heart failure following breast cancer diagnosis. Hazard ratios (HRs) within the first year of diagnosis were 2.14 (95% CI = 1.63–2.81) for arrhythmia and 2.71 (95% CI = 1.70–4.33) for heart failure. HR more than 10 years following diagnosis was 1.42 (95% CI = 1.21–1.67) for arrhythmia and 1.28 (95% CI = 1.03–1.59) for heart failure. The risk for ischemic heart disease was significantly increased only during the first year after diagnosis . Trastuzumab and anthracyclines were associated with increased risk of heart failure. Aromatase inhibitors, but not tamoxifen, were associated with risk of ischemic heart disease. No increased risk of heart disease was identified following locoregional radiotherapy.Administration of systemic adjuvant therapies appears to be associated with increased risks of heart disease. The risk estimates observed in this study may aid adjuvant therapy decision-making and patient counseling in oncology practices.This work was supported by the Swedish Research Council (grant no: 2018-02547); Swedish Cancer Society (grant no: CAN-19-0266); and FORTE (grant no: 2016-00081). The use of adjuvant systemic therapies at least halves the risk of dying from breast cancer . NowadayAlthough the benefits of radiotherapy far outweigh the risk of heart diseases, some studies have found increased incidence of and mortality due to heart disease in women subjected to radiotherapy techniques . KnowledRisk assessment of immediate and later occurring heart disease events following breast cancer is important for the planning of cardiac surveillance programs and possible prophylactic pharmacotherapy. Here, we report the risks of heart diseases in a cohort representative of the general breast cancer population with long-term follow-up. We specifically aimed to assess risks of heart diseases by time since diagnosis, and according to adjuvant treatments.This study took advantage of the Stockholm-Gotland Breast Cancer Register comprising all women diagnosed with primary invasive breast cancer between 2001 and 2008 in the Stockholm-Gotland region. The Stockholm-Gotland Breast Cancer Register has about 99% completeness and provides detailed information on tumor and treatment characteristics, as well as routine follow-up on locoregional recurrences and distant metastases . DetaileThe matched cohort was linked through the unique personal identity number to the Swedish Cancer Register, Patient Register, Cause of Death Register and Migration Register, and follow-up started from the index date until the date of heart disease diagnosis, emigration, death, breast cancer relapse, or end of follow-up , whichever occurred first. We also performed linkage with the Prescribed Drug Register which contains data on all drugs dispensed from Swedish pharmacies from July 2005 onward to validate the use of hormone therapy.The study was approved by the Regional Ethical Review Board in Stockholm.We identified the following heart diseases according to relevant ICD  codes in the Swedish Patient Register and the Cause of Death Register : heart fWe extracted the treatment data from the Stockholm-Gotland Breast Cancer Register. As ~90% of HER-2 positive cancers were treated with trastuzumab between 2005 and 2008 in the Stockholm-Gotland region and the Swedish Prescribed Drug Register does not cover data on treatment with trastuzumab, HER-2 positivity was used as a proxy when no registry data on trastuzumab was available during this time period (30% of the HER-2 positive patients had missing information on trastuzumab). Data on adjuvant endocrine therapy was verified against the Prescribed Drug Register and categorized into tamoxifen and/or aromatase inhibitor use. Since radiotherapy to the left breast has, in particular, been implicated in heart complications, radiotherapy was categorized according to tumor laterality (left vs. right). Bilateral tumors were coded separately in this analysis. Chemotherapy was coded as anthracycline-based, anthracycline plus taxane-based, cyclophosphamide, methotrexate, and fluorouracil (CMF)-based regimens.The Stockholm Breast Cancer Register contains data on date of diagnosis, menopausal status at diagnosis, and type of surgery (breast conserving surgery vs. mastectomy). Tumor characteristics were also retrieved from this register, including tumor size (T), regional lymph node involvement (N), and presence of metastases (M), all from pathology records and summarized in TNM stage as defined according to the We compared the risk of heart diseases in breast cancer patients with that observed in the matched cohort, using flexible parametric model (FPM) with time since index date as underlying time scale. The FPM is similar to the Cox proportional hazards model in that it provides a hazard ratio (HR) as measure of association. In our analysis, a restricted cubic spline with four internal and two boundary knots (five degrees of freedom) placed at quintiles of the event times was used in the FPM for the baseline hazard. The key advantage of FPM is that non-proportional hazards can easily be fitted by adding a second spline for the interaction with time. Considering the correlation within the matched clusters, a shared frailty term (as random effects) was incorporated into the model and the maximum  marginal likelihood method was used to estimate the regression coefficients and the variance for the frailty. Aalen-Johansen estimation was used to assess the cumulative incidences of heart diseases in breast cancer patients and matched reference individuals, while other causes of death were considered as competing events.Next, we studied the association of adjuvant breast cancer therapy with heart disease risk in breast cancer patients using Cox proportional hazards models. We adjusted these analyses for age and year of diagnosis (model 1), and additionally for menopausal status at diagnosis, cancer stage, type of surgery, CCI score, hypertension, chronic pulmonary disease, and tobacco abuse. All treatment-specific models were mutually adjusted for adjuvant therapies. Considering the possible selection bias in the administration of radiotherapy , the anaAll statistical analyses were performed using STATA version 15.1.Descriptive characteristics of the study population are described in Over a median follow-up of 10.8 years (interquartile range = 6.5 years), arrhythmias were the most frequently reported heart disease (n = 570), followed by ischemic heart disease (n = 307) and heart failure (n = 243). The cumulative incidence of arrhythmias, ischemic heart disease, and heart failure in the breast cancer patients cohort was 11.0%, 5.7%, and 4.8% after 15 years of follow-up, while the cumulative incidence was 8.2%, 5.9%, and 3.8% in the matched cohort, respectively .In a population-based setting, we demonstrated that the incidence of heart disease in breast cancer patients was significantly higher than the incidence observed in matched reference individuals from the general population. The risks of arrhythmia and heart failure were increased even beyond 10 years after diagnosis. Receipt of trastuzumab, as well as administration of anthracycline±taxane-based regimens, was independently associated with an increased risk of heart failure, while receipt of aromatase inhibitor therapy was associated with an increased risk of ischemic heart disease.We found an increased risk of arrhythmia and heart failure in breast cancer patients as compared with the matched reference individuals from the general population, which is similar to the risk of heart failure reported by a previous Dutch study , indicatAnalysis by time since diagnosis revealed long-term increased risks of arrhythmia and heart failure following breast cancer diagnosis, suggesting that a longitudinal cardiac monitoring schedule might be helpful to improve cardiac health in breast cancer patients. As the long-term risk was observed for heart failure but not ischemic heart disease, the cardiotoxic effect of chemotherapy might be mainly on the myocardium mediated by the effect of DNA double-strand breaks through topoisomerase (Top) 2β, but not the cardiac vessels . The finThe findings that administration of anthracycline±taxanes, as well as trastuzumab, is associated with increased risks of heart failure are in agreement with previous observational studies  and clinPopulation-based estimates of incidence of ischemic heart disease following treatment with aromatase inhibitors have not been extensively reported in the literature . Abdel-QWhile several previous studies have shown that radiotherapy for breast cancer increase the risk of ischemic heart disease and heart failure , some stA major strength of our study is that we were able to investigate risks of heart disease by time since diagnosis and adjuvant treatment. The risk estimates presented in this study therefore provide an insight into the short-term and long-term effects of adjuvant breast cancer therapy in routine clinical practice. Notably, all breast cancer patients were followed-up for a relatively long period. The population-based nature of the study and linkage to registry-based data further minimized the risk of selection and information biases.We acknowledge several limitations. The Swedish Patient Register has high validity for heart failure, arrhythmia, and ischemic heart disease  , by analThrough this study, we demonstrate that compared to the general population, women with breast cancer have increased risks of heart disease including heart failure and arrhythmia. The short-term risk of ischemic heart disease diminished after 1 year post diagnosis. The increased risk of arrhythmia and heart failure however appears to persist long-term, beyond the first decade after diagnosis. Administration of systemic adjuvant therapies is associated with risks of heart disease. The risk estimates observed in this study may serve as reference to aid adjuvant therapy decision-making and patient counselling in oncology practices. We feel that your work will be of interest to breast medical oncologists, cardiologists, and primary care providers who treat patients with breast cancer. We commend you for this study, which achieves its goal of identifying the incidence and hazard ratio of cardio-toxicity associated with breast cancer treatment within a general breast cancer population. The international nature of your collaborative study along with its large patient cohort size and long horizontal follow up are quite attractive features in solidifying previous findings and discovering future areas of exploration. public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers and the evaluation has been overseen by Eduardo Franco as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Philip Boonstra (Reviewer #3).Thank you very much for submitting your work to As you will see in the attached reviews, while the Referees generally recognized the merits of your draft, several technical points of concern in your methodology and findings were also highlighted. Thus, we remain interested potentially publishing an extensively revised version of your manuscript. However, we would like you to specifically address the following perceived weaknesses of your work, in particular the biostatistical methodology, as follows:Essential revisions:1. The title of the draft suggests the risk of heart disease is associated with breast cancer itself, but the content of the manuscript and conclusions emphasize the risk based upon treatment effects. Please, modify the title to reflect the risk of breast cancer therapy.2. For Table 1: In addition to Charlson Comorbidity Index (CCI score), please include CVD risk factors  to help readers understand the baseline cardiovascular risk of your patients. This criticism assumes importance as these risk factors were used in the risk models, and independent of cancer treatment are known to be associated with risk of arrhythmias, ischemic heart disease, and congestive heart failure.3. For Table 2: Please add the word "Ratio" i.e. "Hazard ratio for heart diseases" in the title.4. The finding of no significant increased risk of ischemic heart disease after left breast radiation is quite interesting. This provocative result would become more powerful (and better supported) if estimates of mean heart dose of radiation or even total cumulative radiation dose administered were included. If those may not be available from the registry data, please discuss this potentially counterintuitive finding.5. Regarding hypotheses made in the Discussion section: The authors appropriately provide citations regarding early increased risk of ischemic heart disease due to emotional distress, however there are several other factors that could potentially increase this risk that warrant consideration, namely: surgery for breast cancer (98.9% of patients in this cohort) typically takes place within the first year of diagnosis and may increase risk of arrhythmia, ischemic event; patients with cancer are at increased risk of arterial thromboembolism (ATE) which includes myocardial infarction 150 days prior to cancer diagnosis and this risk appears to attenuate 1 year after diagnosis. . "Risk of Arterial Thromboembolism in Patients with Cancer." J Am Coll Cardiol 70(8): 926-938) . "Arterial thromboembolic events preceding the diagnosis of cancer in older persons." Blood 133(8): 781-789.)6. Please discuss the fact that while the results are generally interesting and hypothesis-generating, the patient population is overall young and healthy ; thus, one should be cautious to extrapolate results to guide individual therapy decisions in clinical practice.7. It is also unclear whether there was any protocol in place for cardiac monitoring for patients receiving cardiotoxic chemotherapy or Anti Her2neu agents. Please clarify it in the revised draft, either way.8. With regard to the matched analysis of time to heart disease diagnosis: For the breast cancer cohort, were patients with a diagnosis of heart disease prior to cancer diagnosis included in the analysis? If so, how was the event (which precedes time = 0) incorporated into the analysis? If not, please make sure to make note of this important restriction. Please, keep in mind that Referee 3 clearly favors the latter approach.9. Moreover, for the matched cohort: What is time = 0 for these persons? i.e. how does one interpret "Time since diagnosis" on Figure 1 for a patient who has not been diagnosed with breast cancer?10. Finally, for the matched cohort: How was the matching incorporated into the FPM? Presumably there should be a frailty term of some sort to indicate the matched groups, within which there is expected to be correlation.11. Kaplan-Meier curves were used to estimate the cumulative incidence of heart disease. How was death of the patient prior to diagnosis of heart disease handled? Of note, Referee #3 argues that Kaplan-Meier is not the best analytical approach here because Kaplan-Meier tends to overestimate the event rate when competing events are counted as censoring. In this setting, Referee #3 favors an Aaalen-Johansen-type estimator, which treats death as a competing event. For instance, please see: https://pubmed.ncbi.nlm.nih.gov/10204198/12. Please address and correct: The sentence "Missing indicators were included for the analysis of these covariates in the model" and the results in Table 3 suggest that some missing values were analyzed "as is", meaning that "missingness" was used as a category itself. This, of course, is not desirable and there exists methodology+software for more appropriately handling these data, e.g. multiple imputation with chained equations. For example, how does one interpret that "unknown chemotherapy" status is positively associated with heart failure but less so than anthracycline-based chemotherapy.13. Please address and correct: The reported HRs (at the top of p. 10) seem incongruous with the FPM model demonstrated in Figure 1, since there is clearly a non-linear relationship between the hazard and the outcome.14. Please address and correct: It seems unlikely that breast cancer diagnosis could ever be "protective" for ischemic heart disease. A more constrained model that does not allow for the possibility of HR < 1 could provide a more sensible estimate of this time-dependent HR.15. Please address and correct: As an alternative to a four-category radiotherapy variable, which (as the authors note) requires assuming that bilateral radiotherapy is equivalent to left-sided radiotherapy, it would seem sensible to create two separate binary variables .16. Please double-check rows two and three of the first column of Table 2. One would expect the HRs for disease history  to fall on either side of the overall HR (1.27), but they don't: Is this Simpson's paradox or a mistake or something else? Please verify and clarify.Reviewer #1 (Recommendations for the authors):Thank you for the opportunity to review this manuscript.– The title of the paper suggests the risk of heart disease is associated with breast cancer itself, but the content of the manuscript and conclusions emphasize the risk based upon treatment effects. I would consider changing the title to reflect the risk of breast cancer therapy.– For Table 1: In addition to Charlson Comorbidity Index (CCI score), would also include CVD risk factors  to better understand patients' baseline cardiovascular risk, particularly as these risk factors were used in the risk models, and independent of cancer treatment are known to be associated with risk of arrhythmias, ischemic heart disease, and congestive heart failure.– For Table 2: Would add the word "Ratio" ie "Hazard ratio for heart diseases" in the title.– The finding of no significant increased risk of ischemic heart disease after left breast radiation is quite interesting. This finding would be more powerful and better supported if estimates of mean heart dose of radiation or even total cumulative radiation dose administered was included (which may not be possible from the available registry data).– Regarding hypotheses made in the Discussion section: the authors appropriately provide citations regarding early increased risk of ischemic heart disease due to emotional distress, however there are several other factors that could potentially increase this risk that warrant consideration: surgery for breast cancer (98.9% of patients in this cohort) typically takes place within the first year of diagnosis and may increase risk of arrhythmia, ischemic event; patients with cancer are at increased risk of arterial thromboembolism (ATE) which includes myocardial infarction 150 days prior to cancer diagnosis and this risk appears to attenuate 1 year after diagnosis. . "Risk of Arterial Thromboembolism in Patients With Cancer." J Am Coll Cardiol 70(8): 926-938) . "Arterial thromboembolic events preceding the diagnosis of cancer in older persons." Blood 133(8): 781-789.)– While the results are interesting and hypothesis generating, the patient population is overall young and healthy  therefore would be cautious to recommend extrapolating results to guide individual therapy decisions in clinical practice.Reviewer #3 (Recommendations for the authors):As an alternative to a four-category radiotherapy variable, which as the authors note requires assuming that bilateral radiotherapy is equivalent to leftsided radiotherapy, it would seem sensible to create two separate binary variables .Can the authors double check rows two and three of the first column of Table 2? Intuitively, I would expect the HRs for disease history  to fall on either side of the overall HR (1.27), but they don't. Is this Simpsons paradox or a mistake or something else? Essential revisions:1. The title of the draft suggests the risk of heart disease is associated with breast cancer itself, but the content of the manuscript and conclusions emphasize the risk based upon treatment effects. Please, modify the title to reflect the risk of breast cancer therapy.As suggested by the reviewer, we have revised the title of the paper to “Risk of heart disease following treatment for breast cancer: results from a population-based cohort study”.2. For Table 1: In addition to Charlson Comorbidity Index (CCI score), please include CVD risk factors  to help readers understand the baseline cardiovascular risk of your patients. This criticism assumes importance as these risk factors were used in the risk models, and independent of cancer treatment are known to be associated with risk of arrhythmias, ischemic heart disease, and congestive heart failure.In Table 1, we have now added the numbers for the risk factors used in the risk models . Diabetes was already included in the Charlson Comorbidity Index, so we did not list diabetes separately.3. For Table 2: Please add the word "Ratio" i.e. "Hazard ratio for heart diseases" in the title.We have revised the title for Table 2 to “Hazard ratios for heart diseases in breast cancer patients compared to the matched cohort.”4. The finding of no significant increased risk of ischemic heart disease after left breast radiation is quite interesting. This provocative result would become more powerful (and better supported) if estimates of mean heart dose of radiation or even total cumulative radiation dose administered were included. If those may not be available from the registry data, please discuss this potentially counterintuitive finding.Unfortunately, information on the mean heart dose of radiation or total cumulative radiation dose administered was not recorded in the Stockholm-Gotland Breast Cancer Register. We therefore cannot analyze this. We have now discussed this finding in more detail in the Discussion section.In discussion, page 14-15, line 308-320“Indeed, the time trends of heart dose improvement and the use of modern heart dose sparing techniques, together with individualized doses of therapy, may result in lower doses of radiation to the heart . Another reason for inconsistent results in the literature could be that patients with a left sided breast cancer who are susceptible to cardiovascular disease are less likely to be selected for radiotherapy . Therefore, in our study, we compared risk of ischemic heart disease in patients treated with radiotherapy for left-sided to those treated for right-sided breast cancer. We found slightly increased non-significant risk of ischemic heart disease, this risk appeared (as expected) more pronounced in the follow-up period after 10 years. Overall, our results indicate only small risk of heart disease due to radiotherapy in women treated in Sweden after year 2000. Further studies with detailed information on the mean heart dose of radiation or total cumulative radiation dose administered are therefore needed to confirm and provide more context to this finding.”5. Regarding hypotheses made in the Discussion section: The authors appropriately provide citations regarding early increased risk of ischemic heart disease due to emotional distress, however there are several other factors that could potentially increase this risk that warrant consideration, namely: surgery for breast cancer (98.9% of patients in this cohort) typically takes place within the first year of diagnosis and may increase risk of arrhythmia, ischemic event; patients with cancer are at increased risk of arterial thromboembolism (ATE) which includes myocardial infarction 150 days prior to cancer diagnosis and this risk appears to attenuate 1 year after diagnosis. . "Risk of Arterial Thromboembolism in Patients with Cancer." J Am Coll Cardiol 70(8): 926-938) . "Arterial thromboembolic events preceding the diagnosis of cancer in older persons." Blood 133(8): 781-789.)We thank the reviewer for providing this potential alternative explanation and have now added this in our Discussion.In discussion, page 12, line 264-271“The finding that risk of ischemic heart disease in breast cancer patients was only transiently elevated after diagnosis is not unexpected, considering the emotional distress of dealing with a new cancer diagnosis in the patients, which may lead to higher short-term rates of ischemic heart disease . In addition, surgery after breast cancer diagnosis might increase the risk of arterial thromboembolism , which includes myocardial infarction, and the effect appears to attenuate one year after diagnosis. ”6. Please discuss the fact that while the results are generally interesting and hypothesis-generating, the patient population is overall young and healthy ; thus, one should be cautious to extrapolate results to guide individual therapy decisions in clinical practice.Patients involved in this analysis were obtained from the Stockholm-Gotland Breast Cancer Register, covering more than 99% of the breast cancer patients in the Stockholm-Gotland region. As the reviewer noticed, the median age at diagnosis was 59, and the majority of them had no comorbidities. Therefore, caution is needed to generalize the findings in this study to very old patients with multiple comorbidities. We have now added this in the discussion.In discussion, page 12, line 246-251“We found an increased risk of arrhythmia and heart failure in breast cancer patients as compared with the matched reference individuals from the general population, which is similar to the risk of heart failure reported by a previous Dutch study , indicating the generalizability of our findings to European countries. However, as patients in our cohort were aged between 25 and 75 years, caution is needed when generalizing these findings to older patients, who may have more comorbidities.”7. It is also unclear whether there was any protocol in place for cardiac monitoring for patients receiving cardiotoxic chemotherapy or Anti Her2neu agents. Please clarify it in the revised draft, either way.In Sweden, there is no cardiac monitoring for chemotherapy in routine clinical practice. For HER2-therapy, cardiac monitoring with a thorough cardiac assessment prior to treatment, including history, physical examination, and determination of left ventricular ejection fraction before, during and right after treatment has been mandatory since introduction in clinical routine. We have now added this information to the discussion.In discussion, page 13, line 288-291“As there is no cardiac monitoring for chemotherapy in routine clinical practice and cardiac assessment is only performed prior to and during the treatment period for HER-2 positive patients in Sweden, a longer-term cardiac monitoring program might be helpful for these patients.”8. With regard to the matched analysis of time to heart disease diagnosis: For the breast cancer cohort, were patients with a diagnosis of heart disease prior to cancer diagnosis included in the analysis? If so, how was the event (which precedes time = 0) incorporated into the analysis? If not, please make sure to make note of this important restriction. Please, keep in mind that Referee 3 clearly favors the latter approach.As suggested by Referee 3, we have now excluded those patients with a diagnosis of heart disease prior to cancer diagnosis. We have updated the results and the methods section accordingly.In Materials and methods, page 6, line 106-108“We included all patients diagnosed with non-metastatic breast cancer (stages I-III) and without prior diagnosis of heart disease at age 25 to 75 years (N = 8015).”9. Moreover, for the matched cohort: What is time = 0 for these persons? i.e. how does one interpret "Time since diagnosis" on Figure 1 for a patient who has not been diagnosed with breast cancer?We apologize for this misunderstanding and have revised it to “Time since index date  ” in Figure 1.10. Finally, for the matched cohort: How was the matching incorporated into the FPM? Presumably there should be a frailty term of some sort to indicate the matched groups, within which there is expected to be correlation.In the flexible parametric survival model for matched cohort data, a shared frailty term was incorporated into the model to indicate the matched cluster. The maximum  marginal likelihood method is used to estimate the regression coefficients and the variance for the frailty. We have added this explanation in the methods part.In Materials and methods, page 9, line 167-170“Considering the correlation within the matched clusters, a shared frailty term (as random effects) was incorporated into the model and the maximum  marginal likelihood method was used to estimate the regression coefficients and the variance for the frailty.”11. Kaplan-Meier curves were used to estimate the cumulative incidence of heart disease. How was death of the patient prior to diagnosis of heart disease handled? Of note, Referee #3 argues that Kaplan-Meier is not the best analytical approach here because Kaplan-Meier tends to overestimate the event rate when competing events are counted as censoring. In this setting, Referee #3 favors an Aaalen-Johansen-type estimator, which treats death as a competing event. For instance, please see: https://pubmed.ncbi.nlm.nih.gov/10204198/As suggested by the reviewer, we have now used the Aalen-Johansen method to estimate the cumulative incidence of heart disease and revised the text in the Methods, as well as the tables and figures in the supplement.In Materials and methods, page 9, line 170-172“Aalen-Johansen estimation was used to assess the cumulative incidences of heart diseases in breast cancer patients and matched reference individuals, while other causes of death were considered as competing events.”12. Please address and correct: The sentence "Missing indicators were included for the analysis of these covariates in the model" and the results in Table 3 suggest that some missing values were analyzed "as is", meaning that "missingness" was used as a category itself. This, of course, is not desirable and there exists methodology+software for more appropriately handling these data, e.g. multiple imputation with chained equations. For example, how does one interpret that "unknown chemotherapy" status is positively associated with heart failure but less so than anthracycline-based chemotherapy.Missingness of the type of adjuvant treatment was considered as a category in the previous version of our manuscript. To address potential biases resulting from missing data, we have now used multiple imputation with chained equations and revised the methods and Table 3 accordingly.In Materials and methods, page 9, line 183-185“Multiple imputation with chained equations was used to deal with the treatment categories with missing information. We replaced the missing data with 10 rounds of imputations and all the covariates were included in the imputation model.”13. Please address and correct: The reported HRs (at the top of p. 10) seem incongruous with the FPM model demonstrated in Figure 1, since there is clearly a non-linear relationship between the hazard and the outcome.As shown in the FPM model in Figure 1, HRs were not constant according to time since index date. Therefore, in the revised version, we only showed the HRs separately in <1, 1-2, 2-5, 5-10 and 10-17 years after diagnosis. We have revised the abstract, methods, and Table 2.In Abstract, page 3, line 52-58:“Time-dependent analyses revealed long-term increased risks of arrhythmia and heart failure following breast cancer diagnosis. Hazard ratios (HRs) within the first year of diagnosis were 2.14 (95% CI = 1.63-2.81) for arrhythmia and 2.71 (95% CI = 1.70-4.33) for heart failure. HR more than 10 years following diagnosis was 1.42 (95% CI = 1.21-1.67) for arrhythmia and 1.28 (95% CI = 1.03-1.59) for heart failure. The risk for ischemic heart disease was significantly increased only during the first year after diagnosis .”In Materials and methods, page 8, line 159-161:“We compared the risk of heart diseases in breast cancer patients with that observed in the matched cohort, using flexible parametric model (FPM) with time since index date as underlying time scale.”In Results, page 10, line 213-216:“A short-term increase in risks of arrhythmia and heart failure was found in breast cancer patients .”14. Please address and correct: It seems unlikely that breast cancer diagnosis could ever be "protective" for ischemic heart disease. A more constrained model that does not allow for the possibility of HR < 1 could provide a more sensible estimate of this time-dependent HR.To the best of our knowledge, the inverse association between breast cancer and the long-term risk of ischemic heart disease is possible considering that some of the reproductive risk factors for breast cancer have protective effect on the risk of ischemic heart disease. We have now discussed about this in Discussion.In Discussion, page 13, line 271-276“The long term lower risk of ischemic heart disease in breast cancer patients compared to age-matched women might be explained by the opposite role of reproductive factors in breast cancer and ischemic heart disease. Women with younger age at menarche and older age at menopause were associated with increased risk of breast cancer, while decreased risk of ischemic heart disease were found among these women .”15. Please address and correct: As an alternative to a four-category radiotherapy variable, which (as the authors note) requires assuming that bilateral radiotherapy is equivalent to left-sided radiotherapy, it would seem sensible to create two separate binary variables .As we have explained in the Methods section, there could be selection bias in the administration of radiotherapy to the patients. Therefore, the analysis for radiotherapy only included patients receiving radiotherapy. To show the effect of bilateral radiotherapy, we categorized these patients into a separate group, and revised the Methods section and Table 3.In Materials and methods, page 7, line 139-141“Since radiotherapy to the left breast has, in particular, been implicated in heart complications, radiotherapy was categorized according to tumor laterality (left vs. right). Bilateral tumors were coded separately in this analysis.”In Materials and methods, page 9, line 180-183“Considering the possible selection bias in the administration of radiotherapy , the analysis for radiotherapy only included patients receiving radiotherapy, making a comparison between left-sided, right-sided and both-sided breast cancer.”16. Please double-check rows two and three of the first column of Table 2. One would expect the HRs for disease history  to fall on either side of the overall HR (1.27), but they don't: Is this Simpson's paradox or a mistake or something else? Please verify and clarify.There was a typing error in the previous version. In the revised version of manuscript, we do not present the overall HRs as suggested by the reviewers and editor.Reviewer #1 (Recommendations for the authors):Thank you for the opportunity to review this manuscript.– The title of the paper suggests the risk of heart disease is associated with breast cancer itself, but the content of the manuscript and conclusions emphasize the risk based upon treatment effects. I would consider changing the title to reflect the risk of breast cancer therapy.As suggested by the reviewer, we have revised the title of the paper to “Risk of heart disease following treatment for breast cancer: results from a population-based cohort study”.– For Table 1: In addition to Charlson Comorbidity Index (CCI score), would also include CVD risk factors  to better understand patients' baseline cardiovascular risk, particularly as these risk factors were used in the risk models, and independent of cancer treatment are known to be associated with risk of arrhythmias, ischemic heart disease, and congestive heart failure.In Table 1, we have now added the numbers for the risk factors used in the risk models . Diabetes was already included in the Charlson Comorbidity Index, so we did not list diabetes separately.– For Table 2: Would add the word "Ratio" ie "Hazard ratio for heart diseases" in the title.We have revised the title for Table 2 to “Hazard ratios for heart diseases in breast cancer patients compared to the matched cohort.”– The finding of no significant increased risk of ischemic heart disease after left breast radiation is quite interesting. This finding would be more powerful and better supported if estimates of mean heart dose of radiation or even total cumulative radiation dose administered was included (which may not be possible from the available registry data).Unfortunately, information on the mean heart dose of radiation or total cumulative radiation dose administered was not recorded in the Stockholm-Gotland Breast Cancer Register. We therefore cannot analyze this. We have now discussed this finding in more detail in the discussion section.In discussion, page 14-15, line 308-320“Indeed, the time trends of heart dose improvement and the use of modern heart dose sparing techniques, together with individualized doses of therapy, may result in lower doses of radiation to the heart . Another reason for inconsistent results in the literature could be that patients with a left sided breast cancer who are susceptible to cardiovascular disease are less likely to be selected for radiotherapy . Therefore, in our study, we compared risk of ischemic heart disease in patients treated with radiotherapy for left-sided to those treated for right-sided breast cancer. We found slightly increased non-significant risk of ischemic heart disease, this risk appeared (as expected) more pronounced in the follow-up period after 10 years. Overall, our results indicate only small risk of heart disease due to radiotherapy in women treated in Sweden after year 2000. Further studies with detailed information on the mean heart dose of radiation or total cumulative radiation dose administered are therefore needed to confirm and provide more context to this finding. ”– Regarding hypotheses made in the Discussion section: the authors appropriately provide citations regarding early increased risk of ischemic heart disease due to emotional distress, however there are several other factors that could potentially increase this risk that warrant consideration: surgery for breast cancer (98.9% of patients in this cohort) typically takes place within the first year of diagnosis and may increase risk of arrhythmia, ischemic event; patients with cancer are at increased risk of arterial thromboembolism (ATE) which includes myocardial infarction 150 days prior to cancer diagnosis and this risk appears to attenuate 1 year after diagnosis. . "Risk of Arterial Thromboembolism in Patients With Cancer." J Am Coll Cardiol 70(8): 926-938) . "Arterial thromboembolic events preceding the diagnosis of cancer in older persons." Blood 133(8): 781-789.)We thank the reviewer for providing this potential alternative explanation and have now added this in our Discussion.In discussion, page 12, line 264-271“The finding that risk of ischemic heart disease in breast cancer patients was only transiently elevated after diagnosis is not unexpected, considering the emotional distress of dealing with a new cancer diagnosis in the patients, which may lead to higher short-term rates of ischemic heart disease . In addition, surgery after breast cancer diagnosis might increase the risk of arterial thromboembolism , which includes myocardial infarction, and the effect appears to attenuate one year after diagnosis. ”– While the results are interesting and hypothesis generating, the patient population is overall young and healthy  therefore would be cautious to recommend extrapolating results to guide individual therapy decisions in clinical practice.Patients involved in this analysis were obtained from the Stockholm-Gotland Breast Cancer Register, covering more than 99% of the breast cancer patients in the Stockholm-Gotland region. As the reviewer noticed, the median age at diagnosis was 59, and the majority of them had no comorbidities. Therefore, caution is needed to generalize the findings in this study to very old patients with multiple comorbidities. We have now added this in the discussion.In discussion, page 12, line 246-251“We found an increased risk of arrhythmia and heart failure in breast cancer patients as compared with the matched reference individuals from the general population, which is similar to the risk of heart failure reported by a previous Dutch study , indicating the generalizability of our findings to European countries. . However, as patients in our cohort were aged between 25 and 75 years, caution is needed when generalizing these findings to older patients, who may have more comorbidities.”Reviewer #3 (Recommendations for the authors):As an alternative to a four-category radiotherapy variable, which as the authors note requires assuming that bilateral radiotherapy is equivalent to leftsided radiotherapy, it would seem sensible to create two separate binary variables .As we have explained in the Methods section, there could be selection bias in the administration of radiotherapy to the patients. Therefore, the analysis for radiotherapy only included patients receiving radiotherapy. To show the effect of bilateral radiotherapy, we categorized these patients into a separate group, and revised the Methods section and Table 3.In Materials and Methods, page 7, line 139-141“Since radiotherapy to the left breast has, in particular, been implicated in heart complications, radiotherapy was categorized according to tumor laterality (left vs. right). Bilateral tumors were coded separately in this analysis.”In Materials and Methods, page 9, line 180-183“Considering the possible selection bias in the administration of radiotherapy , the analysis for radiotherapy only included patients receiving radiotherapy, making a comparison between left-sided, right-sided and both-sided breast cancer.”Can the authors double check rows two and three of the first column of Table 2? Intuitively, I would expect the HRs for disease history  to fall on either side of the overall HR (1.27), but they don't. Is this Simpsons paradox or a mistake or something else?There was a typing error in the previous version. In the revised version of manuscript, we do not present the overall HRs as suggested by the reviewers and editor.