Women in the highest combined risk group (high polygenic risk score and WID™-qtBC) had a 9.6-fold increased risk for BC [95% CI: 4.7–21] compared to the low-risk group and tended to present at more advanced stages. Importantly, the WID™-qtBC is influenced by non-genetic BC risk factors, including age and body mass index, and can be modified by a preventive pharmacological intervention, indicating an interaction between genome and environment recorded at the level of the epigenome. Our findings indicate that methylation levels at mQTLs in relevant surrogate tissues could enable integration of heritable and non-heritable factors for improved disease risk stratification.To individualise breast cancer (BC) prevention, markers to follow a person’s changing environment and health extending beyond static genetic risk scores are required. Here, we analysed cervical and breast DNA methylation ( Since 20073, genome-wide association studies (GWAS) have identified hundreds of single nucleotide polymorphisms (SNPs) associated with breast cancer risk. A combination of 313 SNPs  has previously been reported to achieve an area under the receiver-operator curve (AUC) of 0.630 (95%CI: 0.628–0.651) overall, and 0.641 and 0.601 for estrogen receptor (ER) positive (ER+) and negative (ER-) cancers, respectively4. This PRS313 improves discrimination over age and clinical risk factors in initial analyses5, and is being implemented in primary and secondary breast cancer preventive strategies7. While PRSs have been suggested to offer promise in improving clinical risk stratification, they suffer from several shortcomings. First, PRSs often show varying performance in non-European-ancestry populations9 due to being derived from GWAS in populations of largely European descent, although limited portability can be overcome by including more diverse populations and improved technology, deriving arrays to cover diverse variants, and efforts are underway to recalibrate PRSs for all ancestries10. Second, a major limitation of PRSs is that they only reflect static genetic risk and do not account for environmental, socioeconomic, or lifestyle risk factors, or their interplay with genetic risk factors, which constitute important modifiable factors and targets for disease prevention11. For instance, childhood postcode as an indicator of socioeconomic inequalities has been found to perform equally well as a predictor of risk for most common diseases as most polygenic risk scores12. The PRS can subsequently also not be used to monitor changes in risk over time11. Even the most predictive PRSs have been suggested to explain only a low proportion of the overall variance for a trait in each population13, and a recent study raised concern about the potential applicability of PRSs14. Biomarkers and risk scores capable of capturing and integrating both genetic and modifiable risk factors, for instance DNA methylation (DNAme), may therefore be suitable to extend risk prediction beyond the limitations of PRSs.Reliable tools to risk-stratify women for primary and secondary breast cancer preventive measures are urgently needed15. This ‘epigenetic field defect’ may be elicited by both genetic and non-genetic factors including lifestyle, reproductive, and environmental exposures contributing to breast cancer development16, and could be exploited for risk stratification, yet breast samples require invasive biopsies and are therefore not feasible for routine clinical screening. Several proof of principle studies, almost exclusively performed in blood, have demonstrated that certain DNAme changes in a non-invasively collected ‘surrogate’ tissue could also be associated with breast cancer predisposition22. Steroid hormones play an essential role in breast cancer formation, as prolonged23 or higher than average24 exposure to progesterone are strongly associated with the formation of poor prognostic breast cancer28, and the progesterone receptor antagonist mifepristone effectively prevents breast cancer formation in mice29. To record steroid hormone exposure and possible other factors contributing to breast cancer formation, the surrogate sample in which DNAme is measured for risk detection should ideally resemble the cell of origin for the cancer, i.e., a hormone-sensitive epithelial cell. It has recently been shown that trait-linked methylation quantitative trait loci (mQTLs) exhibit molecular regulatory pleiotropy and enrichment in trait-relevant tissues30, and in previous work we demonstrated that DNAme patterns in cervical samples can indicate current or future women’s cancers, including anatomically distant breast and ovarian cancers32. Cervical samples are possibly suitable surrogate samples for women’s cancer detection as they contain hormone-sensitive epithelial cells similar to the tissue at risk , and can be obtained without a tissue biopsy, but it has not been systematically assessed whether cervical samples are better suited for breast/ovarian cancer risk prediction than other promising non-invasive surrogate samples such as buccal samples or blood.DNAme changes have been observed in histologically normal breast tissue adjacent to breast cancers33.Here, we compare the suitability of three surrogate sample types - cervical, buccal, and blood - to reflect breast DNAme variability, develop a new mQTL-based predictor for breast cancer risk, and investigate the interplay of genetic and non-genetic factors using CpG sites whose DNAme levels are strongly associated with breast cancer SNPs (mQTLs)n = 113 BRCA1/2 wild type and n = 109 BRCA1/2 mutant), assessed which surrogate tissue best represents the breast tissue DNAme variability, accounting for cell type heterogeneity (see Methods section)  Fig. . The topon) Fig. , indicatles Fig. . Variabiles Fig. .Fig. 1Idn = 442, for n = 423 SNP data were available) that had at least one poor prognostic feature  and validated in an independent validation dataset consisting of cervical samples from 225 controls and 113 breast cancer cases . The WID™-qtBC obtained an AUC of 0.71  , the WID™-qtBC AUC in the external validation set is higher than expected by chance.In the validation dataset, we observed a significantly higher WID™-qtBC in women with breast cancer compared to controls  Fig. . To asse6]) Fig. , demonstWe were next interested whether the cervical classifier would be able to distinguish normal breast tissue from breast tissue adjacent to a cancer. The WID™-qtBC was elevated in normal tissue surrounding breast cancer compared to normal tissue from unaffected women Fig.  and coul313, and the mQTL-based classifier. Surprisingly, although mQTLs were identified based on genetic variants in the PRS313, the WID™-qtBC exhibited limited correlation with PRS313   Fig. , indicat313 or WID™-qtBC, the higher risk groups exhibited odds ratios of 2.7 (95% CI: 1.7–4.4)  were associated with higher tumour stages and grades . Of note, the proportion of patients with a low PRS and a low WID™-qtBC appeared higher in the ER- than in the ER+ group, which would not clinically be expected. In a logistic regression model, either accounting or not accounting for additional receptor status and stage/grade, ER status was associated with an increase in risk group, but this was not significant. We therefore believe this to reflect the relatively small sample size rather than a clinically significant effect. Due to the case/control design our study population is not entirely reflective of the general population, and a prospective collection of cohort samples will be required to further evaluate the use of the WID™-qtBC.The highest risk scores . Interestingly, the WID™-qtBC also was significantly positively associated with age, body mass index, and age at menopause (the latter two only in breast cancer cases)  Fig.  in cervi36, and that MRI screening is preferred in high-risk women37. Hence, taking the distribution of controls in the validation set as a proxy for the general population, offering the 13.8% of women within the high PRS313/high WID™-qtBC group screening will result in identification of 50.4% of all breast cancers, and these will likely be cancers which might benefit most from earlier detection, e.g., using MRI or Digital Breast Tomosynthesis.Our study demonstrates that a combination of 104 mQTL CpGs can identify women with breast cancer using hormone-sensitive surrogate samples  and ER- breast cancers .Our results indicate that mQTL sites may be sensitive to environmental impacts and cancer driving mechanisms in a cell-type specific manner Figs.  and 4, a41. A side-by-side comparison of WID™-qtBC with the PRS313 and study of their benefits – individually or in a combined score – across different risk grouping strategies in prospectively collected samples from diverse ethnic populations will be required to motivate the use of the WID™-qtBC in a clinical setting. Moreover, samples predating diagnosis will be required to assess how long in advance of breast cancer onset the WID™-qtBC index could identify at-risk women and whether it would be suitable for implementation into primary preventive strategies – both as a risk indicator and a tool to monitor the efficacy of primary preventive measures due to its dynamic nature. Further studies will also need to evaluate the real-world sensitivity and specificity to avoid overdiagnosis, a common shortfall of PRS screening that can cause negative emotions for patients and substantial financial burden for healthcare providers11.Additional studies will be required to assess whether convenience and acceptability of obtaining a WID™-qtBC index can be further increased by utilising a self-collected cervicovaginal sample, a strategy well-accepted for cervical screeningThe significance of our findings should also encourage other groups to assess mQTLs for other diseases in surrogate tissues which are histologically and biologically more closely connected to the organ at risk.N = 50 breast samples (“breast variability set”) from healthy volunteers (n = 14), BRCA1/2 mutation carriers undergoing risk-reducing surgery (n = 14) or normal tissue adjacent to triple negative cancers (n = 22), and evaluated them in n = 666 samples from 222 women for which matched cervical, buccal, and blood samples were collected (“matched surrogate samples”). To derive a new classifier index based on mQTL loci, samples from the FORECEE case-control study31 were utilised: “discovery set” samples were collected with the intent of index development, while the “validation set” was only used for index validation. The WID™-qtBC was evaluated in n = 40 normal breast tissue samples before and after vitamin or mifepristone treatment (“breast treatment set”), as well as normal breast samples (n = 14) and normal breast samples adjacent to triple negative cancers (n = 22) (subset of the “breast variability set”). Of note, the “breast variability set” did not influence the risk score development or training, it was solely used to highlight differential variability of CpGs in different tissues.An overview of samples used in this study is shown in Supplementary Table BRCA1 (n = 10) or a BRCA2 (n = 4) mutation, and 14 normal samples from women who underwent surgery for triple-negative breast cancer . All samples were collected fresh from theatre and processed within 1 h of surgical excision. Fresh samples were rapidly frozen in Liquid Nitrogen and stored at −80° C. Ethical approval was obtained from the NRES Committee East of England (reference number 15/EE/0192). The second set contained an additional n = 8 normal-adjacent samples  from Gene Expression Omnibus dataset GSE133985.Breast variability set samples were derived from two sources. The first set contained a total of 42 breast samples from premenopausal women aged 19–54 years: normal breast tissue from 14 women who underwent cosmetic breast operations, normal breast tissue from women who underwent prophylactic mastectomies due to a n = 14) and normal tissue adjacent to a triple-negative breast cancer (n = 14) from the breast variability set plus an additional n = 8 normal-adjacent samples  from Gene Expression Omnibus dataset GSE133985.The breast validation set was a subset of the breast variability set and comprised normal (n = 22 samples at two timepoints) or anti-progestine Mifepristone ) from the trial “Mifepristone treatment prior to insertion of a levonorgestrel releasing intrauterine system for improved bleeding control – a randomised controlled trial” (EudraCT number: 2009-009014-40) were obtained as previously described in ref. 35.Normal breast tissue samples before and after two months of Vitamin . The FORECEE programme had ethical approval from the UK Health Research Authority (REC 14/LO/1633) and all other contributing centres42. Participants were aged >18 years and <86 years. After signing an informed consent, participants completed a pre- and post-enrolment questionnaire.Matched cervical, buccal, and blood samples from 222 healthy volunteers were part of the FORECEE programme Cervical liquid-based cytology samples were collected at appropriate clinical venues by trained staff using the ThinPrep system . Cervical cells were sampled from the cervix using a cervix brush , which was rotated 5 times through 360 degrees whilst in contact with the cervix to maximise cell sampling. The brush was removed from the vagina and immersed in a ThinPrep vial containing Preserve-cyt fluid and then pushed against the bottom of the vial 10 times to facilitate release of the cells from the brush into the solution. The sample vial was sealed and stored locally at room temperature. Buccal cells were collected using two Copan 4N6FLOQ Buccal Swabs  by firmly brushing the swab head 5-6 times against the buccal mucosa of each cheek. The swabs were re-capped and left to dry out at room temperature within the sampling tube which contains a drying desiccant. 2.5 ml of venous whole blood was collected in PAX gene blood DNA tubes (BD Biosciences #761165) and stored locally at 4 °C. All samples were shipped to University College London (UCL) at ambient temperature. Biological samples were given an anonymous Participant ID Number, which was assigned to the person’s name in a securely stored link file.Samples from the case-control study , and recruitment centre/country. However, due to an imbalance in recruitment of cases and controls at some centres, a number of cases were matched on age and menopausal status alone. Cancer histological data was collected post-recruitment either by clinicians directly involved in the diagnosis/treatment of the cancer cases or by a nominated data manager with access to the in-house hospital systems.When preparing for sample storage in the laboratory, cervical samples were poured into 50 ml Falcon tubes and left to sediment at room temperature for 2 h. 1 mL wide bore tips were then used to transfer the enriched cellular sediment into a 2 mL vial. The cervical sediments were washed twice with PBS, lysed, and stored temporarily at −20 °C ahead of extraction. The Copan 4N6FLOQ Buccal Swabs were cut and lysed sequentially in the same aliquot of lysis buffer prior to temporary storage at −20 °C ahead of extraction. Whole blood samples were simply held transiently at −20 °C until DNA extraction. DNA was extracted from whole blood, cervical, and buccal tissue lysates on a Hamilton Star liquid handling platform using the Nucleo-Mag Blood 200 µl kit  with prior modifications for optimal lysis of cervical cell pellets and paired buccal swabs. For breast tissues, DNA was extracted from up to 40 mg of tissue using the Lipid Tissue kit from Macherey Nagel (cat # 740471.50), and the manufacturer’s instructions were followed. DNA concentration and quality absorbance ratios were measured using Nanodrop-8000, Thermoscientific Inc. Extracted DNA was stored at −80 °C until further analysis.Cervical, buccal, and breast tissue DNA were normalised to 25 ng/µL and 500 ng total DNA was bisulfite modified using the EZ-96 DNA Methylation-Lightning kit  on the Hamilton Star Liquid handling platform. 8 µL of modified DNA was subjected to methylation analysis on the Illumina InfiniumMethylation EPIC BeadChip  at UCL Genomics according to the manufacturer’s standard protocol.43 , and chrY probes were removed from the dataset. An additional 6102 previously identified probes that followed a trimodal methylation pattern characteristic of an underlying SNP were removed.All methylation microarray data were processed through the same standardised pipeline running in R version 4.0.2. Raw data were loaded using the R package minfi, version 1.36.0. Any samples with median methylated and unmethylated intensities < 9.5 were removed. Any probes with a detection p-value > 0.01 were regarded as having failed. Any samples with >10% failed probes, and any probes with >10% failure rate were removed from the dataset. Beta values from failed probes (approximately 0.001% of the dataset) were imputed using the impute.knn function as part of the impute R package, version 1.62.0. Non-CpG probes (2932), SNP-related probes as identified by Zhou et al.Background intensity correction and dye bias correction were performed using the minfi single sample preprocessNoob function. Probe bias correction was performed using the beta mixture quantile normalisation (BMIQ) algorithm of the ChAMP package, version 2.18.3.The fraction of immune cell contamination, and the relative proportions of different immune cell subtypes in each sample, were estimated using the EpiDISH algorithm utilising the epithelial, fibroblast and immune cell reference dataset, version 2.6.1. The top 1000 most variable probes (ranked by standard deviation) were used in a principal component analysis. Statistical tests were performed in order to identify any anomalous associations between plate, sentrix position, date of array processing, date of DNA creation, study centre, immune contamination fraction, age, type (case versus control), and the top ten principal components. Finally, two-thirds of the discovery dataset was randomly selected for use as the training dataset and the remaining third was allocated to the internal validation dataset.33. The 822 probes at loci associated with breast cancer risk were extracted, of which 704 unique CpGs were present in the EPIC array datasets after QC and filtering. The Discovery set was randomly split 70–30% into a training and internal validation set. The R package glmnet, version 4.1.3, was used to train L1 or L2 regularised classifiers (lasso or ridge regression) based on these 704 mQTL-associated CpGs in the training set. Ten-fold cross-validation was used in the training set via the cv.glmnet function in order to determine the optimal value of the regularisation parameter lambda. The AUC in the internal validation set, computed using the pROC package, version 1.18.0, was used as a metric of classifier performance. The final classifier was selected based on the highest AUC obtained in the internal validation dataset. The training and internal validation datasets were then combined, and the classifier was refitted using the entire discovery dataset using lasso regularisation. This finalised classifier was then applied to the external validation dataset and the corresponding AUC was computed.mQTL data was obtained from Supplementary Table μ and σ are defined as the mean and standard deviation of the quantity Denoting the 104 selected mQTL CpGs as 313 analyses .Genotyping of 318 breast cancer cases and 850 controls from the discovery set and 113 breast cancer cases and 225 controls in the validation set was conducted using the Illumina 650k Infinium Global Screening Array. Whole blood DNA was normalised to 75 ng/µL and Global Screening Array (GSA). Whole blood DNA was normalised to 75 ng/µL and a total of 300 ng applied to the Infinium Global Screening Array – 24 V2  at UCL Genomics according to the manufacturer’s standard protocol. Genotype calling was performed using GenomeStudio, with genetic variants found to be clustering poorly being removed from further analyses. One control subject from the Discovery set failed to genotype. For duplicate genetic variant pairs, the variant within each pair with the lowest calling and clustering score was excluded. Autosomal SNPs were used in subsequent QC and PRS44. In the Discovery set, three breast cancer cases and eight controls with a call rate less than 95% were excluded, and one breast cancer and three controls were further removed due to genetically inferred sex not being female. No subjects failed QC in the Validation set. Genetic variants with missing genotype rate greater than 5%, minor allele frequency (MAF) less than 1%, or a significant departure from Hardy-Weinberg equilibrium (p < 5 × 10−6) were excluded. KING45, a relatedness inference algorithm, was next used to identify duplicate/monozygotic twin or first-degree relative pairs. In the Discovery set, one control subject pair was identified as being a duplicate/monozygotic twin pair, and nine control pairs were inferred to be first-degree relatives. In the Validation set, one control pair was inferred to be first-degree relatives. The subject in each related pair with the lower call rate was excluded. In the Discovery set, 314 breast cancer subjects and 816 controls, and 479,105 variants were retained after SNP QC, of which 809 and 312 overlapped with samples that passed QC for methylation. In the Validation set, 113 breast cancer cases and 224 controls, and 501,209 variants passed QC, of which 210 and 111 overlapped with samples that passed QC for methylation.General subject and single nucleotide polymorphism (SNP) quality control (QC) was performed using PLINK version 1.9http://zzz.bwh.harvard.edu/plink/res.shtml. Subjects not found to cluster around HapMap European samples were excluded from further analysis. After excluding non-European subjects, 312 breast cancer cases and 809 controls were retained in the Discovery set, while 111 breast cancer cases and 210 controls were retained in the Validation set.Non-European subjects were identified by plotting the top two principal components, generated using GTCA version 1.93.2, for the Discovery or Validation set, respectively, and 270 HapMap phase II release 23 samples , downloaded in PLINK-formatted binary files from 46 and the 1000 Genomes Phase 3 reference panel, the SNP discovery dataset went through further QC before being phased (Eagle2) and imputed. Variants were strand, allele, genetic position, or allele frequencies were not concordant with the 1000 Genomes Phase 3 reference panel were removed before phasing and imputation using Strand Tools. After imputation, exclusion of variants with imputation R2 < 0.5 and removal of variants with 3 or more alleles, both the Discovery and Validation set contained 303 of the 313 SNPs used by Mavaddat et al.4 to develop a 313 SNP breast cancer polygenic risk score (PRS313) were imputed. Of note, only 293 SNPs of the 303 SNPs in each set were overlapping between the Discovery and Validation set. We constructed the breast cancer PRS313 for each subject as follows  in the publication by Mavaddat et al. and xij is the number of copies of the effect allele present in each discovery cohort subject. Scores were generated using PLINK version 1.9.Using the Michigan Imputation Serversed Eagle and impu313 or WID™-qtBC values was conducted by computing the median PRS313 or WID™-qtBC across all samples in a given set. High PRS313 or WID™-qtBC scores were defined as above the median, while low PRS313 or WID™-qtBC scores were equal to or below the median. For risk stratification using both PRS313 and WID™-qtBC, the groupings (high/low) as described above were combined.Stratification into high and low PRSFurther information on research design is available in the Supplementary InformationSupplementary Table 3.Reporting Summary


Associations of body shape with breast cancer risk, independent of body size, are unclear because waist and hip circumferences are correlated strongly positively with body mass index (BMI).We evaluated body shape with the allometric “a body shape index” (ABSI) and hip index (HI), which compare waist and hip circumferences, correspondingly, among individuals with the same weight and height. We examined associations of ABSI, HI, and BMI (per one standard deviation increment) with breast cancer overall, and according to menopausal status at baseline, age at diagnosis, and oestrogen and progesterone receptor status (ER+/-PR+/-) in multivariable Cox proportional hazards models using data from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort.During a mean follow-up of 14.0 years, 9011 incident breast cancers were diagnosed among 218,276 women. Although there was little evidence for association of ABSI with breast cancer overall , we found borderline inverse associations for post-menopausal women  and breast cancers diagnosed at age ≥ 55 years  and clear inverse associations for ER + PR- subtypes  and ER-PR- subtypes . There were no material associations with HI. BMI was associated strongly positively with breast cancer overall , for post-menopausal women , for cancers diagnosed at age ≥ 55 years , and for ER + PR + subtypes , but not for PR- subtypes.In the EPIC cohort, abdominal obesity evaluated with ABSI was not associated with breast cancer risk overall but was associated inversely with the risk of post-menopausal PR- breast cancer. Our findings require validation in other cohorts and with a larger number of PR- breast cancer cases.The online version contains supplementary material available at 10.1186/s12885-023-11056-1. Associations of adult body size with breast cancer risk are reasonably well established. Adult body mass index (BMI) is associated positively with post-menopausal breast cancer but inversely with pre-menopausal breast cancer –3. AssocThe allometric “a body shape index” (ABSI) and hip index (HI) , 6 were We have previously examined associations of ABSI and HI with breast cancer risk in UK Biobank, but found no material evidence for association . This waTherefore, we used data from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort to examine prospectively the associations of ABSI and HI with the risk of breast cancer overall, and according to menopausal status at the anthropometric assessment, age at breast cancer diagnosis, and hormone receptor status of the cancer.EPIC is a multicentre cohort including participants from ten European countries (recruited in 23 centres from 1991 to 1999). Details regarding the recruitment and the collection of information on socio-demographic, lifestyle, reproductive, and dietary factors has previously been described . In accoAnthropometric measurements were obtained within one year of cohort recruitment, except for France, where all available measurements were obtained on average 3.8 years after recruitment (standard deviation 1.3 years), and the UK (Oxford cohort), for which predictive equations based on available measurements were applied to self-reported values . To adju− 2/3 (kg) ∗ height 5/6 (m) [− 0.482 (kg) ∗ height 0.310 (cm) [− 2 (m). For the main analyses, we calculated study-specific z-scores, as value minus mean  divided by standard deviation . To explore non-linearity, we derived study-specific quintile categories (Q1-Q5).We calculated ABSI as waist circumference  ∗ weight Women completed detailed questionnaires on lifestyle, menstrual and reproductive history, use of exogenous hormones, and diet at cohort recruitment , and thiIncident cancer cases were identified by record linkage to cancer registries in Denmark, Italy, the Netherlands, Spain, Sweden, and the UK, and using a combination of active follow-up of study participants, cancer and pathology registries, and health insurance records in France and Germany . First pOestrogen receptor (ER) and progesterone receptor (PR) status were available for 52.1% of breast cancers included in this study and human epidermal receptor 2 (HER2) status was available for 32.8%, but not for women from Denmark. Positive hormone receptor status was determined using one of the following standardised thresholds: ≥10% of cells stained, any “plus system”, ≥ 20 fmol/mg, an Allred score of ≥ 3, an immunoreactive score (IRS) ≥ 2, or an H-score ≥ 10, as in a previous EPIC study examining associations with breast cancer subtypes . In the In the main models, we examined ABSI, HI, and BMI on a continuous scale . To explore non-linearity, we examined in secondary analyses ABSI, HI, and BMI quintile categories (Q1-reference). We estimated hazard ratios (HRs) and 95% confidence intervals (CIs) with delayed-entry Cox proportional hazards models, which are conditional on surviving cancer-free to the start of cancer follow-up and account for left-truncation. The timescale was age in years and women were considered at risk from the date of birth, which defined the origin of the timescale. Cancer follow-up started at the date of the anthropometric assessment, which was considered as baseline in this study, with the following exception. For breast cancers diagnosed at age ≥ 55 years in women younger than 55 years at the anthropometric assessment, cancer follow-up started at age 55 years, as women would have to survive cancer-free to age 55 years to qualify for diagnosis at age ≥ 55 years. Women with follow-up ending before age 55 years were excluded from this analysis . Cancer follow-up ended at the earliest of the date of diagnosis of the first incident breast cancer (censoring cancers outside the breast and breast cancers with rare morphology at the date of diagnosis), or the date of death, or the date of the last complete follow-up of the corresponding centre, or the date at age 55 years . Women at age ≥ 55 years at the anthropometric assessment  were excluded from the analyses of breast cancer diagnosed at age < 55 years.All models were stratified by age at the anthropometric assessment (5-year categories), country, and categories of menopausal status, further dividing post-menopausal women according to age at menopause as follows: pre-menopausal, peri-menopausal, post-menopausal (menopause at age < 46 years), post-menopausal (menopause at age 46 to < 52 years), post-menopausal (menopause at age ≥ 52 years), post-menopausal (menopause at unknown age). All models were adjusted for height, smoking status and intensity, alcohol consumption, physical activity, attained education, HRT use, oral contraceptives use, age at the first period, parity with age at the first live birth, breastfeeding with duration, and log-transformed energy intake assessed at cohort recruitment. Values missing for more than 5% of women were defined as separate categories and the rest were imputed with the median category for the corresponding country and menopausal status (see categories and details in Supplementary Table heterogeneity). To test for non-linearity, we compared with a likelihood ratio test the fully adjusted main models, including ABSI, HI, and BMI on a linear untransformed scale, with models including restricted cubic splines, individually for each anthropometric index, with knots at the corresponding quintile boundaries (pnon−linearity).In subgroup analyses, we examined separately pre-menopausal and post-menopausal women, breast cancers diagnosed at age < 55 years and at age ≥ 55 years, and breast cancer subtypes according to hormone receptor status. To test for heterogeneity, we used the data augmentation method of Lunn and McNeil , individTo compare traditional and allometric anthropometric indices, we first calculated partial Pearson correlation coefficients (r), with adjustment for age at the anthropometric assessment (continuous), country, and categories of menopausal status and age at menopause. We considered as traditional indices waist and hip circumferences and two sets of residuals, derived individually for each of waist and hip circumferences from linear regression either on BMI (WCadjBMI and HCadjBMI), or on weight and height (WCadjWtHt and HCadjWtHt). We then repeated the main analyses, examining individually waist and hip circumferences and BMI, with adjustment for height and covariates, and combining WCadjWtHt, HCadjWtHt, and BMI, with adjustment for height, and covariates.To examine the influence of covariates, we omitted all covariates and retained only the stratification and the adjustment for height. To examine possible reverse causality, we excluded women with less than two years of follow-up. To examine the influence of hormone receptor status availability, we repeated the main analyses censoring breast cancers with unknown ERPR status at the date of diagnosis.We used R version 4.1.3 , for dat2 between the mean BMI of Q5 vs. Q1, compared to 1.8 kg/m2 for ABSI quintiles  or for ER+/-PR- subtypes (pheterogeneity=0.680). HI was not associated with breast cancer overall  or within subgroups by menopausal status at the anthropometric assessment or by age at diagnosis, or with any individual hormone receptor subtypes. BMI was associated strongly positively with breast cancer overall , specifically for post-menopausal women , for cancers diagnosed at age ≥ 55 years , and for ER + PR + subtypes , but not for ER + PR- or ER-PR- subtypes (pheterogeneity<0.001 for ER+/-PR- vs. ER + PR+), with no evidence for heterogeneity according to HER2 status for ER + PR + subtypes (pheterogeneity=0.449) or for ER+/-PR- subtypes (pheterogeneity=0.693)  but there were suggestive weak inverse associations with ABSI in post-menopausal women  and for breast cancers diagnosed at age ≥ 55 years , although with no nominal significance for heterogeneity (Table non−linearity=0.020), with the lowest HRs for Q4 vs. Q1 for both ER + PR- and ER-PR- subtypes, but with no evidence for association of Q2 vs. Q1  but only weakly positively with height  . As the stronger positive correlations with height of residuals adjusted only for BMI resulted from the transformation and were thus artificial, we did not examine further their associations with breast cancer risk. The residuals of waist and hip circumferences adjusted for weight and height were corelated weakly not only with BMI but also with height  and were each correlated very strongly positively with the corresponding allometric index, ABSI or HI (r ≥ 0.97). ABSI and HI were correlated minimally with BMI and height (r within +/-0.06)  (Supplementary Table In our study, ABSI was associated inversely with post-menopausal PR- breast cancer subtypes, irrespective of ER and HER2 status, while HI showed no material associations. BMI was associated strongly positively with ER + PR + but not with PR- subtypes. Waist and hip circumferences resembled the associations of BMI, while residuals of waist and hip circumferences adjusted for weight and height were identical to the corresponding associations of ABSI and HI with breast cancer risk.To our knowledge, this is the first large prospective study to report an inverse association of ABSI with post-menopausal PR- breast cancer subtypes, which clearly differs from the positive associations of ABSI with other obesity-related cancers . ABSI waThe inverse association of ABSI with post-menopausal breast cancer risk is unusual and unexpected. This is, however, plausible because large abdominal size is a hall-mark not only of metabolic dysfunction , but alsIt is likely, therefore, that the net outcome for breast cancer development depends on the balance between the opposing actions of insulin, oestradiol, progesterone, glucocorticoids, and other alternative pathways. A different balance of ABSI-related factors within different populations and datasets may explain why the inverse association with ABSI was more prominent for the subset of cancers with available ERPR status in our study and why other studies have either failed to find evidence for association, or have reported a positive association with ABSI.A major strength of our study is the availability of waist and hip size measurements from a large multicenter cohort. Our study included women with a variety of lifestyle, reproductive, and dietary patterns from eight European countries and a sizeable number of incident breast cancer cases. A major limitation of our study is the unavailability of hormone receptor status for part of the cases, resulting in a relatively smaller sample size for PR- subtypes and an even smaller sample size for HER2 status, because the routine determination of HER2 status is more recent and was missing for a large proportion of women. A further limitation is the lack of information for menopausal status at the time of cancer diagnosis, which confined us to classifying cancers by age at diagnosis, although women post-menopausal at the anthropometric assessment were clearly post-menopausal at cancer diagnosis. Menopausal status in women from France may also have changed from cohort recruitment to the anthropometric assessment, some three years later, potentially introducing misclassification bias, but we did correct menopausal status for women from France according to age at the anthropometric assessment. In addition, similarly to all prospective studies using exposures assessed at a single time-point, we were limited to assuming that body shape and body size assessed near cohort recruitment had remained the same throughout the follow-up period.In the EPIC cohort, ABSI was not associated with breast cancer risk overall, but was associated inversely with post-menopausal breast cancer and PR- subtypes. Our findings require validation in other cohorts and with a larger number of PR- breast cancer cases but suggest that a competition between ABSI-related factors such as glucocorticoids, sex steroids, and insulin resistance determines the net outcome for the risk of individual breast cancer subtypes.Below is the link to the electronic supplementary material.Supplementary Material 1


Chemotherapy-induced peripheral neuropathy (CIPN) can lead to chemotherapy dose reduction, delay, and discontinuation, and has limited effective prevention strategies. Our study aimed to identify patient characteristics associated with CIPN severity during weekly paclitaxel chemotherapy in people with early-stage breast cancer.We retrospectively collected baseline data including participants’ age, gender, race, body mass index (BMI), hemoglobin (regular and A1C), thyroid stimulating hormone, Vitamins , anxiety, and depression up to 4 months prior to their first paclitaxel treatment. We also collected CIPN severity by Common Terminology Criteria for Adverse Events (CTCAE) after chemotherapy, chemotherapy relative dose density (RDI), disease recurrence, and mortality rate at the time of the analysis. Logistic regression was used for statistical analysis.P = .024). No significant correlations were observed in other covariates. At median follow-up (61 months), there were 12 (9.5%) breast cancer recurrences and six (5.7%) breast cancer-related deaths. Higher chemotherapy RDI was associated with improved disease-free survival .We extracted 105 participants’ baseline characteristics from electronic medical records. Baseline BMI was associated with CIPN severity  is a common side effect of chemotherapy. This study aimed to identify patient characteristics associated with CIPN severity during weekly paclitaxel chemotherapy in people with early-stage breast cancer. N = 105) was conducted to identify patient characteristics associated with chemotherapy-induced peripheral neuropathy (CIPN) severity during weekly paclitaxel chemotherapy in people with early-stage breast cancer. Results showed that baseline body mass index may be a risk factor for CIPN and that suboptimal chemotherapy delivery due to CIPN may negatively impact disease-free survival in patients with breast cancer. This association between weight and CIPN offers clinicians a targetable characteristic to optimize and lessen the often life-long and treatment-limiting effects of CIPN.This retrospective analysis  causes a length-dependent neuropathy that can progress proximally. Common CIPN symptoms include numbness, tingling, altered touch, and paresthesia often triggered by cool or warmth.2 Although CIPN is predominantly a sensory neuropathy, motor, and autonomic changes can occur in severe CIPN, including shooting and/or burning pains, muscle weakness, orthostatic hypotension, constipation, and altered sexual or urinary function.2 CIPN symptoms can be long-lasting, persisting after chemotherapy completion and leading to increased psychological distress and fall risks.7 Studies have found that up to half of women with CIPN continued to report symptoms at six years post-treatment with resultant compromise in fall risk, function, and quality of life.6-8Chemotherapy-induced peripheral neuropathy (CIPN) is a common and potentially debilitating side effect of chemotherapy agents such as platinum agents, taxanes, vinca alkaloids, and bortezomib.9 However, the consequence of chemotherapy dose reduction and discontinuation is suboptimal total drug delivery, which may worsen cancer-specific outcomes. In addition, currently, for patients with chronic painful CIPN after chemotherapy completion, duloxetine is the only agent that has been recommended for modest pain reduction.10Despite the high incidence and debilitating symptoms of CIPN, the American Society of Clinical Oncology (ASCO) 2020 CIPN guideline recommends that, during chemotherapy, the main intervention is chemotherapy dose reduction or discontinuation due to a lack of evidence of any effective CIPN prevention approaches.11Additional treatment to reduce CIPN symptoms is needed and further emphasize the importance of CIPN prevention. Our group conducted a phase IIA trial that showed weekly acupuncture may be effective in preventing the progression of CIPN severity. In this retrospective study of this trial, we analyzed baseline characteristics associated with CIPN onset and progression during weekly paclitaxel in women with early-stage breast cancer to identify potentially modifiable risk factors that increase the risk of CIPN during chemotherapy, and to examine the effects of chemotherapy dose reduction on breast cancer-specific outcome.11 Eligible participants were age 21 or older, diagnosed with stages I–-III breast cancer, and planned to receive 12 weekly sessions of neoadjuvant or adjuvant paclitaxel dosed 80 mg/m2, with an Eastern Cooperative Oncology Group (ECOG) performance status of 0-2 at recruitment. Concomitant trastuzumab and carboplatin treatments were allowed in this study.We consented to participants from May 2015 to March 2017 as part of screening in a phase IIA trial of weekly acupuncture intervention in women with early-stage breast cancer.11We excluded participants with known metastatic disease; pre-existing peripheral neuropathy within 28 days of screening consent; or current use of anti-neuropathic pain agents including gabapentin, pregabalin, duloxetine, or glutamine. Informed consent was obtained from all participants prior to enrollment. The study was approved by the Memorial Sloan Kettering Cancer Center Institutional Review Board  and we previously reported the primary outcome.Baseline data including age, body mass index (BMI), gender, race, CIPN severity, hemoglobin, hemoglobin A1c, thyroid stimulating hormone (TSH), Vitamin B6, B12, and D levels, as well as the presence of anxiety or depression, were retrospectively extracted from the electronic medical record (EMR) on the first day of treatment or up to 4 months prior to chemotherapy initiation.12 The CIPN severity was assessed by a primary physician and treating nurse at each week of chemotherapy. In this study, we used CIPN severity at the completion of paclitaxel for analysis.Severity of CIPN was defined according to the NCI-CTCAE v4.0 guidelines: grade 1 CIPN or paresthesia; grade 2 CIPN or moderate symptoms limiting instrumental activities of daily living; grade 3 CIPN or severe symptoms limiting self-care activities of daily living; and grade 4 CIPN, or life-threatening consequences for which urgent intervention is indicated.Chemotherapy dosing was also extracted from each participant at the conclusion of the twelve-week treatment period in the EMR. Chemotherapy cumulative relative dose intensity was calculated by dividing the total chemotherapy dose received by the total chemotherapy dose planned. Per protocol, chemotherapy dosage adjustments were at the discretion of the treating provider.Breast cancer recurrence and breast cancer-free survival were determined at the average follow-up period of 61 months (± 6 months) and confirmed at the time of retrospective chart review.P < .05 indicating significance.We performed statistical analysis using STATA software . Descriptive statistics of the study participants’ demographic and clinical variables were summarized using numbers and percentages. Univariate logistic regression was used to explore factors potentially associated with CIPN severity and disease-free status. All analyses were two-sided with a A total of 105 participants, who were enrolled from May 2015 to March 2017 in the screening phase of the pilot study, were included in the retrospective analysis . The parP = .024, Of the baseline measures, there was a statistically significant correlation between BMI and CIPN severity . Age, race, and HbA1C were not significantly associated with the risk of CIPN  in various malignancies.13-18 Our work adds to the evidence in patients with breast cancer exclusively, pulling together a more disease-specific population.19Taxanes are cornerstones of chemotherapy regimens for all stages of breast cancer. However, taxane-related CIPN remains a highly prevalent side effect that can jeopardize the optimal delivery of chemotherapy. Currently there is no effective strategy to prevent CIPN development during chemotherapy.20 Evidence about whether diabetes is a risk factor for CIPN is mixed, leaning against an association. Of the 13 studies addressing this question, 4 found an association and 9 did not.21 Our study had a very limited sample size of Hb A1c and diabetes; we were not able to detect any statistical significance in its relationship to CIPN. We only have HbA1c values on 10% (n = 11) of our cohort, although 57% (n = 60) were overweight according to BMI (>25) which would indicate HbA1c screening. However, current literature on potential associations between CIPN and HbA1c largely fails to find an association.22-25 Additionally, an association in normoglycemic overweight patients with peripheral neuropathy is noted, indicating additional contributions between weight distribution and nerve injury.16 Another theory is that the increased chronic inflammation known to be associated with obesity may worsen the neuroinflammation that is thought to drive CIPN via increasing proinflammatory cytokines and immune signaling pathways.26-28The pathophysiology of this connection is not fully understood, but it is likely multifactorial. There is a suspected underdiagnosed prevalence of diabetes or prediabetes in the overweight population which in and of itself can influence nerve conduction.29 In our cohort, 20% of patients were African American. Among them, 41% developed grade 2 CIPN compared with 22% of white patients who developed grade 2 CIPN. Similarly, we observed that 38% of patients with Hispanic ethnicity had grade 2 CIPN compared with 27% of non-Hispanic patients. The difference was not statistically significant, most likely due to the small sample size. However, it is noteworthy that race and education level might indicate different perceptions of pain, in addition to genetic risks and health disparity factors, in CIPN detection and diagnosis.Those of African American race has a higher risk of CIPN and poor breast cancer outcomes.14 Age as a risk factor has had mixed results, with some studies finding a strong correlation and others, specifically in the breast cancer population, finding no such effect,15 consistent with this study. Pre-existing peripheral neuropathy due to other etiologies were excluded from our cohort.Baseline anemia was previously found to be correlated with CIPN severity, though in our cohort, there was no association between hemoglobin level and CIPN.P = .028). This is consistent with a retrospective review of 874 breast cancer patients that showed a negative impact on overall survival (OS) with chemotherapy dose reductions.30 In the metastatic setting, a recent study found a significant correlation between OS with RDI < 85% in patients with a hormonal positive breast cancer and < 75% in triple-negative breast cancer patients.31 In early-stage breast cancer, RDI was found to be significant regarding OS when looking at neoadjuvant chemotherapy regimens, with a difference in 5-year OS between 91.2% and 76.3% when RDI was 85% and < 85%, respectively (P = .015).32 These data are consistent with other literature supporting the association of RDI with increased progression free survival (PFS) and decreased disease recurrence.33-35Another major finding of our study was the association between weekly paclitaxel chemotherapy dose intensity and disease recurrence, as higher chemotherapy RDI was associated with improved disease-free survival (OR 1.025; 95% CI, 1.00-1.05; Some weaknesses of our study include a small cohort size, and that it included all women with early-stage breast cancer and overall mild to moderate CIPN. All baseline laboratory values were inconsistent due to the nature of a retrospective analysis, in particular, baseline thyroid function tests, vitamin levels, and hemoglobin A1c. This limits our conclusions regarding thyroid function and diabetes status as risk factors for CIPN status.Despite these limitations, our study further supports that decreasing the chemotherapy regimen is associated with increased odds of disease progression, an effect not explicitly studied in recent work. Additionally, this study highlights an increased BMI as a risk factor for worsened CIPN. Better understanding and validation of these risks provide opportunities to identify those patients at risk for CIPN and interventions to mitigate the modifiable risks associated with this condition. CIPN remains a highly relevant clinical challenge and further research in risk characterization and optimization is needed.


Aromatase inhibitors (AIs) are recommended as adjuvant treatment for estrogen-receptor positive breast carcinoma in postmenopausal women. Studies demonstrate mixed results as to the impact of AIs on cardiovascular (CV) events and overall survival. With the increasing number of pre- and postmenopausal women on AIs for five to ten years, understanding the long-term impact of AIs on blood vessels and CV risk in cancer survivors is vital.A single arm longitudinal study of 14 postmenopausal women with ER+ breast cancer prescribed adjuvant AIs at the University of Minnesota. Subjects with a history of tobacco use, hypertension, or hyperlipidemia were excluded. Participants underwent routine labs, blood pressure assessments, and vascular testing at baseline (prior to starting AIs) and at six months. Vascular assessment was performed using the EndoPAT 2000 and HDI/PulseWave CR-2000 Cardiovascular Pro ling System and pulse contour analysis on two occasions as previously described. Vascular measurements were conducted by one trained vascular technician. Assessments were performed in triplicate, and the mean indices were used for analyses. All subjects were on an AI at the follow-up visit. The protocol was approved by the UMN Institutional Review Board and all participants were provided written informed consent. Baseline and follow-up characteristics were compared using Wilcoxon signed-rank tests. Analyses were performed using R version 3.6.1 .® ratio declined to a median 1.12  and median estradiol levels decreased to 2 pg/mL . There was no evidence of association between change in EndoPAT® and change in estradiol level (p=0.91). There were no statistically significant changes in small or large arterial elasticity.After six months of AI treatment, EndoPATEndovascular dysfunction is an early sign for atherosclerosis and vascular impairment. This study suggests that postmenopausal breast cancer survivors on aromatase inhibitor therapy develop endothelial dysfunction as early as six months which is a predictor of adverse CV disease. We hypothesize that long-term use of AIs can lead to persistent endothelial dysfunction. It is unclear if these changes are reversible once AI use is discontinued and further investigation is necessary. Aromatase inhibitors (AIs), enzymes that result in reduction of estrogen, are recommended as adjuvant treatment for estrogen-receptor positive (ER+) breast carcinoma in postmenopausal women, which includes approximately two-thirds of all women with breast cancer.2 Estrogen is known for its cardiovascular (CV) protective properties through a variety of mechanisms including vasodilation of blood vessels and inhibition of vascular injury resulting in the prevention of atherosclerosis.3 In clinical trials and prospective cohorts, the long-term use of AIs can increase the risk for hypertension and hyperlipidemia. Studies demonstrate mixed results as to the impact of AIs on actual CV events and overall survival.5Breast cancer is estimated to comprise about 290,560 new cases in 20226 With the rising number of pre- and postmenopausal women on AIs for five to ten years, understanding the long-term impact of AIs on blood vessels and CV risk in cancer survivors is vital.We hypothesized that the use of AIs and the associated reduction in estrogen would result in endothelial dysfunction, a predictor of early CV disease in women with breast cancer. Endothelial dysfunction, identified by reactive hyperemia using Endo-PAT, a non-invasive device that measures arterial vasoreactivity by assessing the peripheral arterial tone (Zoll Itamar), has been associated with an increased risk of CV events, independent of the Framingham risk score.7 Vascular measurements were conducted by one trained vascular technician (NF). Assessments were performed in triplicate, and the mean indices were used for analyses. Biomarkers were obtained using a fasting blood draw to evaluate lipids, total cholesterol (TC), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), triglycerides (TG), high sensitivity CRP (hsCRP), serum glucose, 17-betaestradiol (estradiol), von Willebrand factor, tissue plasminogen activator, and plasminogen activator inhibitor-1. All subjects were on an AI at the follow-up visit. The protocol was approved by the UMN Institutional Review Board and all participants were provided written informed consent. Baseline and follow-up characteristics were compared using Wilcoxon signed-rank tests. Analyses were performed using R version 3.6.1 .We conducted a single arm longitudinal study of 14 postmenopausal women with ER + breast cancer prescribed adjuvant AI at the University of Minnesota (UMN). Subjects with a history of known tobacco use, hypertension, or hyperlipidemia were excluded to eliminate potential confounding factors. Participants underwent routine labs, blood pressure assessments, and vascular testing at baseline (prior to starting AIs) and at six months. Vascular assessment was performed using the EndoPAT 2000 and HDI/PulseWave CR-2000 Cardiovascular Pro ling System  and pulse contour analysis on two occasions as previously described.Nine (64.3%) of the 14 participants had stage I breast cancer, four (28.6%) had stage II disease, and one(7.1%) had stage III disease. All fourteen received neoadjuvant or adjuvant chemotherapy. Ten (71.4%) participants received radiation therapy . Anastrozole was the most commonly used AI  followed by letrozole  and exemestane . None of the participants were on tamoxifen.2 . Median systolic and diastolic blood pressure was 120  and 70  mm/Hg, respectively, and total cholesterol 228 mg/dL . Median baseline ultrasensitive estradiol level was 7 pg/mL  and hsCRP was 2.45 mg/L . Prior to AI therapy, EndoPAT ratio was 2.18 . Median baseline small arterial elasticity and large arterial elasticity was 4.1 mL/mmHg x100  and 12.4 mL/mmHg x10 , respectively.Patient characteristics and outcomes are outlined in ® ratio declined to a median 1.12 . There were no statistically significant changes in small arterial elasticity, which at 6 months was 4.3 mL/mmHg x100  or large arterial elasticity, which at 6 months was 14.1 mL/mmHg × 10 . There were no statistically significant differences in serum glucose, TC, LDL, HDL, nor hsCRP at 6 months compared to baseline. hsCRP remained elevated at median 2.98 mg/L. Biomarkers of von Willebrand factor, tissue plasminogen activator, and plasminogen activator inhibitor-1 did not change over timeAfter six months of AI treatment, EndoPAT7 Our EndoPAT pilot study suggests that postmenopausal breast cancer survivors on AIs therapy develop endothelial dysfunction, a predictor of adverse CV disease. These changes develop while on AIs, correlating with a decline in estradiol levels.Endovascular dysfunction is an early sign for atherosclerosis and vascular impairment; therefore, measuring endovascular dysfunction using flow-mediated dilation or EndoPAT helps identify patients who may be at risk for CV events.8. The Long Island Breast Cancer study demonstrated increased CV deaths and decreased survival after seven years of treatment with AI;8 this study, however, did not differentiate whether findings were related to secondary causes such as the development of hypertension, hypercholesterolemia, or directly related to AI therapy. A more recent retrospective cohort of 15,815 breast cancer patients diagnosed 2006–2012 demonstrated an increase in heart failure in those treated over the age of 75 years when treated with an AI compared to tamoxifen. The risk of ischemic heart disease increased in those who took AIs for at least four years (hazard ratio (HR): 2.12; 95% CI: 1.40–3.25) compared to those who took no or had short term exposure to AI.5 In the study cohort of the UK Clinical Practice Research Datalink of 17,922 breast cancer patients treated with AI, there was an increase in heart failure risk  and CV mortality  in those treated with AI compared to tamoxifen.4 Contrarily, a randomized double blinded study compared anastrozole to placebo did not show an increase risk for CV events.9Large clinical studies report higher rates of hypertension, hypercholesterolemia, and ischemic CV disease in postmenopausal breast cancer survivors receiving AIs11 In this study, treatment of postmenopausal breast cancer with AI was associated with increased endothelial dysfunction, which coincided within six months of starting the medication and declines in estrogen. This finding is significantly lower than the 1.67 EndoPAT level which was previously linked to higher CV events by Shechter et al.12 Our prior work also demonstrated higher rates of impaired endothelial function compared with healthy postmenopausal controls7. There was a suggestion endothelial changes were associated with a decline in estrogen levels; however, this did not meet statistical significance7. Markers of inflammation (hsCRP) remained persistent; other biomarker work did not suggest the etiology of these changes7.CV disease development is multifactorial due to risk factors such as aging, hypertension, hyperlipidemia and tobacco use. This development often begins with endothelial dysfunction and which ultimately leads to atherosclerosis and ischemic events. Inflammation, fibrosis, and estrogen depletion can lead to changes in the endothelium.9 Given our work, and prior published work, we hypothesize that long-term use of AI can lead to persistent endothelial dysfunction, and further investigation is necessary. It is not clear if these changes are reversible once the AI use is discontinued.Given the high prevalence of breast cancer and recommendations for extended use of AIs in postmenopausal women, it is important to investigate further the risk of CV disease development due to AI use as prior studies have been inconclusive. Additionally, with further reductions in estrogen in premenopausal women, where ovarian suppression plus AI is often recommended, understanding the long-term implications of this treatment regimen on overall cardiac health is imperative. In the current literature, few studies have shown the correlation between estrogen levels and endothelial dysfunction. Luca et al. reported study results of ten premenopausal women who showed that estradiol serum levels were inversely proportional to endothelial dysfunction and subsequent CV events by measuring flow-mediated dilation.This study has a few limitations. It is a pilot study, and as such, has a small sample size. Additionally, the population was predominantly Caucasian, limiting the generalizability. This study was designed to detect AI-specific risk because subjects with a known major risk factors such as tobacco use, hypertension, or hyperlipidemia were excluded. These findings set the stage for a larger study to more conclusively determine the association between AI exposure and cardiovascular outcomes. Further studies should evaluate for multivariate associations with modifiable risk factors for CV disease.Postmenopausal breast cancer survivors prescribed AIs develop endothelial dysfunction, a predictor of adverse CV disease. Understanding the exact mechanisms through further detailed biomarker research long-term, along with potential interventions, such as modifications of CV risk factors, to mitigate these effects is necessary and warrants further investigation.


Breastfeeding has been extensively studied in relation to breast cancer risk. The results of the reviewed studies consistently show a decreased risk of breast cancer associated with breastfeeding, especially for 12 months or longer. This protective effect is attributed to hormonal, immunological, and physiological changes during lactation. Breastfeeding also appears to have a greater impact on reducing breast cancer risk in premenopausal women and specific breast cancer subtypes. Encouraging breastfeeding has dual benefits: benefiting infants and reducing breast cancer risk long-term. Healthcare professionals should provide evidence-based guidance on breastfeeding initiation, duration, and exclusivity, while public health policies should support breastfeeding by creating enabling environments. This review examines the existing literature and analyzes the correlation between breastfeeding and breast cancer risk. Breast cancer is the most frequent form of cancer among women . In 2020Numerous risk factors for breast cancer have been studied, and the most prevalent ones are early menarche, late menopause, brief or lifetime breastfeeding duration, late age of first full-term pregnancy, low parity, genetics, and nulliparity .Mortality and incidence of breastfeeding differ across races and countries, reflecting variations in risk factors, healthcare access and systems, genetics, lifestyle and cultural influences, attitudes toward screening and treatment, and other socioeconomic factors. Developed countries, such as the United States, Canada, Australia, and Western European nations, generally have higher breast cancer incidence rates and better survival outcomes due to advanced healthcare systems and robust screening programs . In deveBreastfeeding is the natural process of feeding an infant with breast milk produced by the mother’s mammary glands. It is recommended by healthcare professionals globally as the optimal source of nutrition for infants, especially during the first six months of life. Breast milk provides all the necessary nutrients, antibodies, and immune factors that support the newborn’s growth, development, and overall health. It promotes healthy weight gain, boosts the immune system, helps prevent malnutrition and childhood obesity, and gives protection against various illnesses, including allergies, ear infections, respiratory infections, and gastrointestinal tract infections. Breast milk also contains prebiotics that contribute to the development of healthy gut bacteria and is easily digestible. It, hence, plays a crucial role in reducing the chances of digestive issues such as constipation, diarrhea, and gastroenteritis and improves the infant’s overall metabolic and immune health. The essential fatty acids, growth factors, and hormones in breast milk support brain development and neurodevelopmental outcomes. Breastfeeding has been associated with improved cognitive development and higher IQ scores in children. Breastfeeding has been shown to reduce the risk of breast cancer in mothers. The longer a woman breastfeeds over her lifetime, the greater the reduction in breast cancer risk. This protective effect is attributed to the hormonal changes that occur during lactation, which lead to the differentiation of breast cells and reduction in the number of breast cell divisions, thereby decreasing the likelihood of genetic mutations that may lead to cancer . AdditioDiscussionBreast MilkHuman milk's abundant components confer enduring benefits . PredomiBreast CancerBreast cancer is a prevalent disease that develops and spreads through complex genetic and clinical mechanisms. Age and family history, particularly having a first-degree relative with the disease, increase the risk of breast cancer . There aIn addition to high-risk genes, moderate-risk genes are associated with breast cancer. These include heterozygous mutations in the ataxia-telangiectasia  gene, soThe cancer stem cells (CSCs) hypothesis suggests that a small subset of cells possesses unique characteristics and contributes to cancer initiation, resistance, and relapse. Targeting CSCs is an active area of research, with ongoing clinical trials investigating therapies specifically designed to eliminate CSCs while sparing normal stem cells . EffectiThe Correlation Between Breastfeeding and Breast Cancer Risk: Current EvidenceThe study on breastfeeding and its association with breast cancer risk yielded noteworthy findings. The results demonstrated a significant inverse relationship between the risk of breast cancer and the duration of breastfeeding in premenopausal women. Statistical analysis indicated a consistent decrease in risk with longer breastfeeding duration, with a significant linear trend observed  . in partIn a comparative study, researchers examined two groups of women: those who exclusively breastfed for over 18 months and those who breastfed but never exclusively. The study indicated a potential protective effect associated with extended exclusive breastfeeding. Although the results did not achieve statistical significance, the findings revealed that women who exclusively breastfed had a lower adjusted risk of developing premenopausal breast cancer . FurtherHER2: Regarding HER2+ breast cancer, conflicting findings have been reported in studies investigating the association between pregnancy and HER2+ breast cancer risk. However, consistent evidence indicates that breastfeeding is associated with a lower risk of HER2+ breast cancer ,37. The HR+: Regarding breast cancer subtypes, cohort studies generally indicate no significant links between breastfeeding and ER+/PR+ or ER+ and/or PR+ breast cancers. Nonetheless, in a subset of these studies, one or two out of four and seven studies, respectively, revealed an inverse correlation . In the BRCA mutations: For women with BRCA1 (n=685) mutations, breastfeeding for more than one year was associated with a 22-50% reduced risk of breast cancer compared to those women who never breastfed  . The risThese findings highlight the potential protective effect of breastfeeding in decreasing the risk of breast cancer, particularly in specific subtypes and populations. Further research is necessary to understand the underlying mechanisms better and establish comprehensive recommendations regarding breastfeeding practices for breast cancer prevention.How Breastfeeding May Lower Breast Cancer Risk: Mechanisms and TheoriesBy the end of pregnancy, the mammary glands have an outstanding share of well-differentiated secretory units than those of a nullipara. Epithelial cells of these secretory units have a longer cell cycle and possess a more efficient DNA excision repair capacity rendering it unscathed by the effects of genetic mutations . MoreoveLactation also causes long-term endogenous hormonal changes, possibly by reducing estrogen and increasing prolactin production, which may decrease a woman’s cumulative exposure to estrogen, thereby inhibiting the initiation or growth of breast cancer cells ,44. The One of the primary components of human milk postulated to affect cancer risk is alpha-lactalbumin. HAMLET  can induce tumor cell death. HAMLET induces apoptosis only in tumor cells, while normal differentiated cells resist its effects. Therefore, HAMLET may provide safe and effective protection against the development of breast cancer .Calcium and the presence of breast fluid help reduce the number of cells on the inner surface of the breast by promoting cell growth and differentiation. During breastfeeding, calcium and breast fluid remove breast CSCs .There is also evidence from human cell culture systems that BRCA1 suppresses estrogen-mediated breast cell proliferation . IndividBreastfeeding could drive terminal differentiation of breast ductal cells via increased production of the GATA family of transcription factors, specifically GATA-3, which has been shown to actively maintain the differentiated luminal epithelium actively; thus, conferring protection from aberrant transcription changes, GATA-3 may play a causal role in the loss of tumor differentiation and malignant conversion in breast cancer. The loss of GATA-3 in the mammary gland causes luminal cell proliferation and basement-membrane detachment .Inhibition of exosomes containing miR-29s in primary bovine mammary epithelial cells results in global DNA hypermethylation (gene silencing) and increases promoter methylation of several lactation-related genes, including Elf5 . Since mA meta-analysis showed that there is a protective effect of breastfeeding against hormone receptor-negative breast cancers, which are more common in younger women and generally have a poorer prognosis than other breast cancer subtypes . BreastfEpigenetic reprogramming could occur by lack of breastfeeding and result in aberrant DNA methylation of FOXA1 and other genes in breast luminal progenitor cells . If termThe Role of Duration and Frequency of Breastfeeding in Breast Cancer PreventionLncRNAs usually have regulatory roles in multiple processes, including cell differentiation, proliferation, migration, and the cell cycle. A recent study identified upstream Eleanor (u-Eleanor), a novel lncRNA with key functions in breast cancer -59.LncRNAs, u-Eleanor, and HOTAIR are associated with hormone-dependent reproductive risk factors in breast neoplasms . A previThe terminally differentiated cells would be killed by the inflammatory environment associated with the involution of the breast tissue to a pre-pregnancy stage, typically induced by prolonged lactation periods. In contrast, forced involution by a lack of or brief duration of lactation may result in an inflammatory environment that promotes the survival and growth of less differentiated cells and the formation of aggressive TNBCs and PACS -63.Another study by the Journal of Human Lactation revealed that the risk for breast neoplasm is reduced in women who exclusively breastfeed; the breast cancer risk is reduced further if lactation is continued for at least one year . Any durBreastfeeding and Breast Cancer OutcomesBreast cancer treatment encompasses a range of therapeutic approaches, including surgical intervention, radiotherapy, and adjuvant drug therapy. Surgical options such as breast-conserving surgery have demonstrated efficacy in removing tumors while preserving the integrity of the breast . HoweverThe timing of breastfeeding during a breast cancer diagnosis impacts the survival rate. Breastfeeding near diagnosis is linked to lower survival, while breastfeeding improves survival outcomes after diagnosis. Whiteman et al. discovered heightened mortality in women aged 20-45 who gave birth within 12 months prior but no significant association between breastfeeding and prognosis . SimilarBreastfeeding and Breast Cancer Risk in Specific Populations: Age, Ethnicity, and GeneticsMany factors influence infant feeding practices around the world. These include but are not limited to racial and ethnic differences, the mother’s age, maternal knowledge, genetic factors, socioeconomic status, and efficacy in infant feeding . Other rAmong U.S. women, breast cancer is the most diagnosed cancer. It is typical for women in developed countries to breastfeed for a shorter lifetime duration, contributing to the high incidence of breast cancer in these countries . There aCompared to White women, Black women in the United States have lower rates of breastfeeding and nearly two times the rates of more aggressive forms of breast cancer. In general, there is a lack of social and cultural acceptance in their communities, and the support from the healthcare community is also low. These factors and unsupportive work environments magnify their challenges . These wSimilarly, a study done in Tunisia examining the association between breastfeeding and the risk of breast cancer reported an inverse association between the two. The mean duration of breastfeeding per child was significantly associated with a reduced risk of breast cancer for women who breastfed for >24 months per child. The OR was 0.46 ) when compared to those who breastfed for <6 months .Mexico has one of the lowest rates of breastfeeding worldwide. A study done in Mexico concluded that there is a high burden of breast cancer due to poor breastfeeding practices. This burden includes morbidity, premature mortality, and economic costs to the health sector and society .Compared to the more developed Western countries, fewer studies have been done in developing Asian countries. Developed Asian countries show similar results as most developed countries in the Western world. A case-control study of breast cancer risk factors in 7,663 women in Selangor, Malaysia, between October 2022 and December 2016 identified longer breastfeeding duration, soy intake, and physical activity as modifiable risk factors for the development of breast cancer .In Asia, a case-control study was conducted among Sri Lankan women aged 30-64, which included 100 cases of histologically confirmed breast cancer and 203 age and parity-matched controls. It showed that women who breastfed >/=24 months during their lifetime had a significantly lower risk of developing breast cancer than those who breastfed for <24 months. Compared to women who breastfed for 0-11 months in their lifetime, there was a 66.3% reduction in breast cancer risk in women who breastfed for 12-23 months, 87.4% reduction in 24-35 months and 94% reduction in 36-47 months categories. This study concluded that prolonged breastfeeding significantly reduces the risk of breast cancer and offers a protective effect .Among Israeli women, investigators noted that breast cancer was more common in nulliparous women and women who did not have a history of breastfeeding. The short duration of lifetime breastfeeding, late age at first breastfeeding, and experience of insufficient milk were found to increase breast cancer risk . InvestiWhile studies across a majority of the countries concluded that breastfeeding contributes to reducing the risk of breast cancer, some studies found no association or a negative association. A cohort study among Icelandic women indicated a negative association between breastfeeding and breast cancer. However, only two cohort studies have been done there to check the correlation, and the results of these two studies are inconsistent . AnotherChallenges and limitationsBreast cancer represents a significant global challenge and an urgent global priority as the burden of the disease is increasing globally. The incidence and mortality are seen more in underserved populations due to late diagnosis ,91. EvenBreastfeeding is particularly interesting for breast cancer prevention because it is a modifiable risk factor. Breastfeeding reduces breast cancer risk and provides other health benefits to the mother, including reduced risk for endometrial (uterine) cancer, ovarian cancer, hypertension, and diabetes .Breastfeeding problems are common and lead to early cessation . The chaBreastfeeding promotionBreastfeeding has known protective effects against breast cancer, but it is unclear how aware women are of this benefit . The WHOEffective support techniques for breastfeeding include assistance from healthcare professionals like nurses, physicians, and midwives, along with regularly scheduled visits. Targeted support directed toward specific groups of people can also be beneficial . The disWorldwide efforts are also being made to promote breastfeeding. The Baby-Friendly Hospital Initiative, supported by WHO and UNICEF, encourages hospitals to follow the “Ten Steps to Successful Breastfeeding” and implement the International Code of Marketing of Breast-milk Substitutes. These measures promote breastfeeding and provide support for new mothers. World Breastfeeding Week, an international initiative, also aims to promote exclusive breastfeeding of newborns for the first six months of life. WHO and UNICEF recommend initiating breastfeeding to infants within the first hour of birth and exclusively breastfeeding for the six months of a baby’s life without any other foods or liquids. It provides essential nutrients and health benefits for the baby .Breast cancer is a significant health concern affecting women worldwide. Breastfeeding, a modifiable risk factor for breast cancer, is inversely related to breast cancer in premenopausal women. The protective effect is increased with increased duration and frequency of breastfeeding. Its protective effect is attributed to hormonal, immunological, and physiological changes during lactation. There is cellular differentiation of breast tissue and a reduction in the lifetime number of ovulatory cycles, thus reducing exposure to estrogen hormone. Therefore, breastfeeding is associated with a decreased risk of breast cancer, and encouraging mothers to breastfeed has dual benefits: benefiting infants and reducing breast cancer risk long-term to mothers.


Additionally, each 10 μg/dL of total carotenoids, α-carotene, β-carotene, and β-cryptoxanthin was associated with 2%, 22%, 4%, and 10% lower risk of breast cancer, respectively. This relationship was stronger at lower levels of total carotenoids and β-cryptoxanthin. The certainty of evidence was rated from very low to low. Most studies were performed among Western nations, which should be acknowledged for extrapolation of findings. Total circulating carotenoids, α-carotene, β-carotene, β-cryptoxanthin, lycopene, and lutein seem to be related to a decreased risk of breast cancer. Our findings could have practical importance for public health.Carotenoids appear to have anticancer effects. Prospective evidence for the relation between serum carotenoids and breast cancer is controversial. The present systematic review and meta-analysis aimed to investigate the link between circulating carotenoids and the risk of breast cancer. We performed a systematic search of PubMed, Scopus, and Web of Science up to 30 November, 2022. Prospective studies on adults aged ≥18 y that have reported risk estimates for the association between circulating carotenoids and breast cancer risk were considered. Study quality was assessed using the Newcastle–Ottawa Scale. A random-effects model was used for combining studies’ risk estimates. Dose–response relations were explored through a 1-stage random-effects model. Fifteen publications (17 nested case–control studies and 1 cohort study) with 20,188 participants and 7608 cases were included. We observed an inverse association between the highest level of circulating total carotenoids (relative risk [RR]: 0.76; 95% confidence interval [CI]: 0.62, 0.93; This study was registered at PROSPERO as CRD42023434983. According to previous meta-analyses, carotenoids have been found to have anticancer effects. This systematic review and meta-analysis provides a comprehensive review of the association between circulating carotenoids and the risk of breast cancer by considering the most recent prospective studies with large sample sizes and follow-up duration.Carotenoids are organic pigments made by several plants and bacteria . Of the There are high amounts of carotenoids in fruits and vegetables, especially in yellow/orange fruits and vegetables and green leafy vegetables. Many observational studies have explored the relationship between fruit and vegetable intake and the risk of breast cancer, indicating inconsistent and weak associations . A meta-So far, 2 meta-analyses have investigated the relationship between carotenoids and breast cancer ,17. Aunehttp://www.crd.york.ac.uk/Prospero (registration no. CRD42023434983).A systematic search was conducted by 2 independent researchers (MKD and SE) using PubMed, Scopus, and Web of Science up to 30 November, 2022. The detailed search strategy is provided in 1) prospective cohort, case–cohort or nested case–control design, 2) investigated the relationship between total and different types of carotenoids level and breast cancer risk, 3) reported relative risk (RR) estimates , 4) carried out on the general population. Letters, comments, reviews, meta-analyses, ecologic studies, Mendelian randomization, and studies that were on populations with a previous history of breast cancer were excluded. For publications that were from the same cohort studies, we included those with the longest follow-up period or those with the largest number of cases.Papers were eligible if they had the following inclusion criteria: Two independent reviewers (MKD and KL) extracted the following data from eligible articles: first author’s last name, year of publication, country, mean age or age range of participants, follow-up period, study design, sample size, incident cases, exposure (type of carotenoid), laboratory assessment of blood carotenoids, comparison, fully adjusted risk estimates with the 95% confidence intervals (CIs), and studies’ covariate adjustments. Any discrepancy was resolved by consensus.We used the Newcastle–Ottawa quality assessment scale to estimate risk of bias among eligible papers. This quality assessment tool contains 8 items in 3 domains of selection (4 points), comparability (2 points), and assessment of outcome (3 points). Each paper could receive a score ranging from 0 to 9. In the present study, a score ≥7 was considered to be high quality.Certainty of evidence was examined through Grading of Recommendations, Assessment, Development and Evaluations (GRADE) . Four leI2 estimates were applied to estimate between-study heterogeneity and were indicated as significant if PQ-test <0.05 /or I2 >50%. Subgroup analyses were performed to identify sources of heterogeneity. Subgroup analyses were conducted when ≥10 studies were included based on: age (<55 y/≥55 y), country (United States/non-United States), adjustment for BMI (yes/no), alcohol intake (yes/no), smoking (yes/no), physical activity (yes/no), dietary variables (yes/no), age at menarche (yes/no), hormone therapy (yes/no), oral contraceptive [OC] use (yes/no), age at first birth (yes/no), age at menopause (yes/no), family history of breast cancer (yes/no), and history of benign breast disease (yes/no). If a study reported risk estimates stratified by menopausal status or other variables, we pooled the risk estimates using a fixed-effects model and then included the pooled risk estimate in the main analysis. Visual inspection of funnel plot asymmetry and Egger’s test were used to examine publication bias. We also conducted sensitivity analysis using a random-effects model to assess the influence of each study on the overall risk estimate.The natural logarithm of RRs and 95% CIs were calculated for the highest compared with the lowest categories of total and specific types of carotenoids. To consider between-study heterogeneity, we used a random-effects model to calculate overall effect size. Cochrane’s Q-test and We conducted a linear dose–response analysis per 10 μg/dL of circulating carotenoids using the method developed by Crippa et al. . AccordiP values of <0.05 were considered significant.We also investigated a potential nonlinear dose–response relationship using restricted cubic splines with 3 knots at percentiles of 10%, 50%, and 90% of the distribution . The corn = 1820) or not meeting inclusion criteria, 41 papers remained for full-text evaluation. Nineteen studies were excluded because they investigated dietary carotenoids [In total, 6091 papers were found in the primary search. After exclusion due to being duplicate , dietary variables (n = 8), age (n = 9), alcohol (n = 6), age at menarche (n = 6), and age at first birth (n = 8). Four studies were on postmenopausal women [The present systematic review and meta-analysis includes 17 nested case–control studies and 1 cohort study . These sal women ,57,58, aal women ,59. Accoal women ,54, otheI2 = 45.6%; P = 0.075). According to the sensitivity analysis, no study affected the overall RR. According to Egger’s regression test, publication bias was not significant (P = 0.38). Also, no asymmetry was evident according to funnel plot inspection.We included 8 effect sizes from 7 publications with 10,863 participants and 5425 breast cancer cases in the analysis of total circulating carotenoids and risk of breast cancer ,54,56,57P-nonlinearity = 0.04). A steady drop in the risk of breast cancer was observed for total carotenoid concentrations <1200 μg/dL followed by a plateau  (P = 0.58). No asymmetry was evident according to funnel plot as well.We included 13 effect sizes from 11 publications with 18,851 participants and 6630 breast cancer cases in the analysis of circulating α-carotene and risk of breast cancer . The hig = 0.48) . The ass = 0.48) . HoweverP-nonlinearity = 0.49)  . The lev = 0.49) .I2 = 56.5%; P = 0.004). According to the subgroup analysis, the source of heterogeneity could be adjustment of covariates including BMI, smoking, and physical activity (P = 0.87). Also, no asymmetry was observed in funnel plot.We included 15 effect sizes from 13 publications with 19,161 participants and 6736 breast cancer cases in the analysis of circulating β-carotene and risk of breast cancer . We founactivity . The assP-nonlinearity = 0.46)  . The lev = 0.46) .I2 = 0.0%; P = 0.80) (P = 0.43). No asymmetry was detected through funnel plot.We included 11 effect sizes from 9 publications with 17,347 participants and 5979 breast cancer cases in the analysis of circulating β-cryptoxanthin and risk of breast cancer ,57,58. T = 0.80) . Accordi = 0.80) . Also, tP-nonlinearity = 0.008) in which there is a decreasing slope up to the dose of 22 μg/dL followed by a plateau . According to subgroup analyses, the inverse association remained significant among studies that had adjusted for history of benign breast disease and those without adjustment for BMI, alcohol intake, physical activity, hormone therapy, and OC use. Also, the relation was inversely associated among studies from the United States (P = 0.43). No asymmetry was observed in funnel plot.We included 13 effect sizes from 11 publications with 18,854 participants and 6630 breast cancer cases in the analysis of circulating lycopene and risk of breast cancer . The pood States . AccordiP-nonlinearity = 0.81)  . The lev = 0.81) .I2 = 17.1%; P = 0.30). According to the sensitivity analysis, no study affected the overall result. According to Egger’s regression test, publication bias was not detected (P = 0.70). No asymmetry was observed in funnel plot as well.We included 6 effect sizes from 4 publications with 5244 participants and 2533 breast cancer cases in the analysis of total circulating lutein and risk of breast cancer . The sumP-nonlinearity = 0.12)  . The lev = 0.12) .I2 = 0.0%; P = 0.56). According to the sensitivity analysis, overall RR did not depend on a single study. According to Egger’s regression test, publication bias was not evident (P = 0.99). Funnel plot did not show any evidence of asymmetry as well.We included 4 effect sizes from 3 publications with 4526 participants and 2174 breast cancer cases in the analysis of total circulating zeaxanthin and risk of breast cancer ,52,53. WP-nonlinearity = 0.83)  . The lev = 0.83) .I2 = 0.0%; P = 0.84). According to the sensitivity analysis, no study affected the overall risk estimate. According to Egger’s regression test, publication bias was not detected (P = 0.99). No asymmetry was evident according to funnel plot.We included 5 effect sizes from 5 publications with 12,063 participants and 3446 breast cancer cases in the analysis of total circulating lutein/zeaxanthin and risk of breast cancer ,51,55,58P-nonlinearity = 0.17)  . The lev = 0.17) .According to this systematic review and meta-analysis, there is a significant negative relationship between the risk of breast cancer and the highest levels of circulating total carotenoids, α-carotene, β-carotene, β-cryptoxanthin, lutein, and lycopene. According to the subgroup analysis, these associations remained inverse for all but were not significant for several subgroups. According to linear dose–response analysis findings, breast cancer risk decreased by 2%, 22%, 4%, and 10% for every 10 g/dL of total carotenoids for α-carotene, β-carotene, and β-cryptoxanthin, respectively. With a steady drop at lower concentrations, a nonlinear relation was found for total carotenoids and β-cryptoxanthin with breast cancer.Compared with a previous meta-analysis, we showed that the risk of breast cancer is inversely associated with both total carotenoids and β-carotene . The higVarious fruit and vegetables contain different amounts of carotenoids. Intake of fruit and vegetables has been demonstrated to be connected with greater amounts of circulating carotenoids. . HoweverP for interaction was statistically significant for some subgroups including adjustment for smoking status, BMI, and physical activity. The negative association of β-carotene and breast cancer risk did not remain significant among studies that had adjusted for smoking status. The concentration of carotenoids in blood can become affected and lowered because of oxidative stress caused by smoking [n = 3). Hence, according to the abovementioned results from subgroup analyses, the negative correlation between β-carotene and breast cancer risk might be due to lack of adjustments for such potential confounders.Stratified analysis of several factors was performed. Interactions were nonsignificant for most factors. According to our results, heterogeneity was nonsignificant for all carotenoids except β-carotene. There are some ideas for explaining part of this heterogeneity. According to subgroup analyses,  smoking ,65. Stud smoking . Additio smoking . Women w smoking . The samThere are several underlying mechanisms by which carotenoids can prevent cancer cell formation. One of them is via their antioxidant activity . β-carotThe present meta-analysis has several strengths. We minimized selection bias by including only prospective studies with large number of total participants and incident breast cancer cases. To assess a quantitative association, we performed a linear dose–response analysis. By conducting a nonlinear dose–response analysis, we could predict the shape of associations. Moreover, unlike the traditional 2-stage method used in the previous analysis, we employed a 1-stage random-effects model that estimates study-specific slope lines and combines them to derive a single, more accurate, flexible, and efficient total average slope. We also assessed the level of evidence by performing GRADE analysis. Additionally, several subgroup analyses were performed to compare the associations between different populations and also to assess the interaction of carotenoids with different covariates, including history of benign breast disease. Despite the mentioned strengths, some limitations should be considered. First, because carotenoids are fat soluble, their blood level might be affected by the amount and type of fat intake. However, some studies did not adjust for this factor. Due to the limited number of studies considering fat intake in their analyses, we were not able to do a subgroup analysis. Second, we did not report the results stratified by tumor type including estrogen and progesterone receptor because there were few studies reporting results classified by tumor type distinctly. Tumor type might be an important factor because hormonal factors might affect the antioxidant effect of carotenoids . AccordiIn summary, according to this systematic review and meta-analysis, there is a significant negative relationship between the risk of breast cancer and the level of circulating total carotenoids, α-carotene, β-carotene, β-cryptoxanthin, lutein, and lycopene. Additionally, every 10 g/dL of total carotenoids, α-carotene, β-carotene, and β-cryptoxanthin was associated with 2%, 22%, 4%, and 10% lower risk of breast cancer, respectively. More prospective studies in various populations with adjustments for potential confounders need to be conducted.The authors’ responsibilities were as follows – LA: conception and design; MKD, KL, SE: literature search; MKD, SE: screening; MKD, KL: data extraction; KL, LA: statistical analyses; KL, LA: interpretation of data; MKD, KL SE, LA: drafting of the manuscript; LA: supervised the study; and all authors: read and approved the final manuscript.The authors report no conflicts of interest.Tehran University of Medical Sciences, Tehran, Iran (no. 63955).Financial support for conception, design, data analysis, and manuscript drafting comes from The data that support the findings of this study are available from the corresponding author upon reasonable request.