Introduction: Medium-chain fatty acids contain 6–12 carbon atoms and are absorbed directly into the blood vessels, proceeding to the portal vein and, finally, to the liver, where they are immediately utilized for energy. We aimed to determine the medium-chain fatty acid levels in women with and without breast cancer. Materials and Methods: A total of 200 women (100 breast cancer subjects and 100 control subjects) were recruited for the study as per the inclusion and exclusion criteria. Blood samples were collected for biochemical estimations. Fatty acid methyl esters were isolated, and medium-chain fatty acid levels in plasma were analyzed using gas chromatography (GC-FID). Statistical analysis was performed using SPSS 20.0 software; p ≤ 0.05 was considered statistically significant. Results: The fatty acid analysis revealed a significant decrease in the levels of caprylic acid (C:8) and lauric acid (C:12) and a significant increase in the level of capric acid (C:10) in the breast cancer subjects when compared to the control group. The level of caproic acid (C:6) was not significantly increased in the breast cancer subjects. In particular, the HER2- and ER-positive breast cancer subjects showed a decrease in their caprylic acid and lauric acid levels compared to other receptors. Conclusions: The results of the current study imply that lower levels of caprylic and lauric acid may be associated with a higher risk of breast cancer. The relevance of medium-chain fatty acids for preventive and therapeutic interventions will be amplified by further research on the possibility that alteration in a patient’s medium-chain fatty acid composition may mechanistically contribute to disease progression or breast cancer risk. Breast cancer is the major cause of cancer-related death in women. In India, in the year 2018, 162,468 women were newly diagnosed with breast cancer and 87,090 women died of breast cancer [Medium-chain fatty acids (MCFAs) are saturated fatty acids with 6 to 12 carbon atoms. Coconut oil (CO) and palm kernels are some natural sources. Increased levels of long-chain fatty acids (LCFA) are also related to an increased risk of breast cancer . Contrar+, K+, Cl−, total bilirubin, albumin, globulins, A/G (Albumin/globulin) ratio, and total protein were noted. Clinical characteristics such as TNM (Tumor Node Metastases) stage, tumor size, receptor status, grade, and histological type were noted in breast cancer subjects. A total of 3 mL of blood was collected for from each subjects.The study was approved by the Central Ethics Committee of Nitte (Deemed to be a University). A total of 100 histopathologically proven breast cancer subjects and 100 control subjects attending the OPD for general health check-ups between the age of 25–60 were recruited for the study after obtaining informed consent. Demographic data such as age, BMI (Body Mass Index), diet, menopausal status, and first-degree family history with biochemical parameters like Hb (Hemoglobin), RBS (Random blood sugar), platelet count, blood urea, creatinine concentration, ALP , AST (Aspartate aminotransferase), ALT (Alanine transaminase), NaThe study group included 100 women as per the inclusion and exclusion criteria. Subjects diagnosed with breast cancer proven through pathology reports without any treatment were included in the study. Subjects who had breast implants or any malignancies were excluded from the study. We recruited 100 age-matched control women who attended the oncology OPD and not undergone breast-conserving surgery/not reported breast cancer at the time of enrollment. Control subjects, pregnant women, and any other benign proliferations were excluded from the study. A total of 3 mL of the blood was collected from the recruited subjects, plasma samples and stored at −20 °C and until further analysis.TC , TG (Triglycerides), and HDL-C (High-density lipoprotein cholesterol) levels were analyzed using commercially available kits (Liqui CHEKTM AGAPPE). Friedewald formula was used for LDL-C (Low-density lipoprotein cholesterol) and VLDL-C (Very low-density lipoprotein cholesterol) calculation . The forLipid transesterification of stored plasma to fatty acid methyl esters (FAMEs) was performed according to the modified protocol of Metcalfe et al. . Fatty at-test was used to compare the two groups. The Mann–Whitney U test was used for non-parametric variables. To correlate the non-parametric variables with each other for all subjects, the Spearman’s correlation coefficient test was used. All statistical tests were two-sided, and p < 0.05 was considered significant.Statistical analysis of the obtained results was performed using GraphPad Prism, Version 8.0.2 . Categorical variables were analysed using a chi-square test. Student’s A total of 200 participants were included in this study. Of them, 100 were control subjects and 100 were breast cancer subjects. The demographic characteristics are summarized in p < 0.027). The clinicopathological characteristics are summarized in Among the 100 breast cancer subjects, 63 had invasive ductal carcinoma and 37 had invasive lobular carcinoma. Concerning the clinicopathological differences among the individuals in the two types of breast cancer groups, there was no significant difference observed with TNM stage, tumor size, lymph node status, and receptor status of breast cancer, but a significant difference was observed concerning grade  and SGPT (Serum glutamic-pyruvic transaminase) suggest that liver and kidney function impairment might be triggered by tumor invasion . This stThe traditional sources of fatty acids include diet, circulation from adipose tissue, and surplus carbohydrates the liver that turn into fat. Most human diets contain various saturated fatty acids of different carbon chain lengths. MCFAs are saturated fatty acids with 6–12 carbon atoms that are more quickly taken from the intestine to the liver via the portal vein and immediately used for energy. Naturally, medium-chain fatty acids are found in coconut oil, palm kernel oil, and in milk fat ,22,23. TAmong the saturated medium-chain fatty acids, the caprylic acid concentration was higher in the control group and lower in the breast cancer subjects. Caprylic acid is enriched in coconut and goat’s milk. Narayanan et al.  reportedIn this study, we found that the level of lauric acid in the breast cancer subjects was significantly lower than in the control group. The major source of lauric acid is coconut oil and palm kernel oil. Lauric acid promotes cell death, which is assisted through the activation of EGFR and the Rho-associated kinase pathway, according to Lappano et al. . AccordiAccording to numerous studies, postmenopausal older women in industrialized countries are more likely to acquire breast cancer than younger premenopausal women. In our study, the age-wise distribution of caprylic and lauric acids was significantly lower in the breast cancer subjects than in the control group . The youIn this study, In the present study, we found that the caprylic and lauric acid levels were significantly lower in all breast cancer subjects receptor subtypes compared to control subjects . Based oThe caproic acid levels showed a weakly positive link with total cholesterol and a weakly negative correlation with ALT, total bilirubin, and the A/G ratio in the breast cancer individuals. Between total cholesterol, AST, and globulin, there was a weakly negative association with the caprylic acid levels. While the lauric acid levels showed a weakly negative association with triglycerides, LDL-C, VLDL-C, AST, and globulin, capric acid levels showed a weakly negative correlation with AST and ALT .There is a knowledge gap concerning the mechanism of action of medium-chain fatty acids on the oncogenic signal transduction pathway, and also clinical studies are limited. Identifying how medium-chain fatty acids are associated with the oncogenic signal transduction regulation and clinical presentation of breast cancer will be key in elucidating the mechanism behind this disease. Our study is vital in demonstrating the importance of medium chain fatty acid in cancer management and prevention.There are considerable barriers o improving the prognosis of breast cancer subjects due to the lack of diagnostic technologies that are characterized by substantial patient compliance and good clinical applicability. Here, we have described the levels of different MCFAs in human samples. Among the medium-chain fatty acids investigated, the levels of caprylic and lauric acid were decreased in the subjects with breast cancer. Our findings imply that increasing the intake of caprylic and lauric acid while lowering the intake of caproic and capric acids may be an effective strategy for preventing breast cancer. To fully comprehend the impact of these medium-chain fatty acids on the development of breast cancer, more in vitro and in vivo investigations are required. The possible mechanisms of MCFA’s effects must be determined, to further understand and enhance the use of MCFAs as a complementary breast cancer treatment and their effectiveness across breast cancer receptor and pathological subtypes. Further investigation would increase the knowledge and understanding of fatty acids and breast cancer, including the impact of MCFAs on the overexpressing receptor subtypes. Future research in this field should focus on the effect of early MCFA exposure on long-term breast cancer risk.


However, the risk of breast cancer among women with (multi-)morbidity remains unclear. In this study, using data of 239,436 female participants aged 40–69 of the UK Biobank cohort, we identified five chronic disease patterns: no-predominant morbidity, psychiatric morbidities, respiratory/immunological morbidities, cardiovascular/metabolic morbidities, and unspecific morbidities. After a median follow-up of 7 years, 5326 women developed breast cancer. We found no association between breast cancer risk and either the number of chronic diseases or chronic disease patterns, apart from an increased risk among women aged younger than 50 with a psychiatric pattern. Women with any multi-morbidity were more likely to die or to be diagnosed with other cancers. Our findings suggest that multi-morbidity may not be a key factor to help identify patients at an increased risk of breast cancer.(Multi-)Morbidity shares common biological mechanisms or risk factors with breast cancer. This study aimed to investigate the association between the number of morbidities and patterns of morbidity and the risk of female breast cancer. Among 239,436 women (40–69 years) enrolled in the UK Biobank cohort who had no cancer history at baseline, we identified 35 self-reported chronic diseases at baseline. We assigned individuals into morbidity patterns using agglomerative hierarchical clustering analysis. We fitted Cox models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for breast cancer risk. In total, 58.4% of women had at least one morbidity, and the prevalence of multi-morbidity was 25.8%. During a median 7-year follow-up, there was no association between breast cancer risk (5326 cases) and either the number of morbidities or the identified clinically relevant morbidity patterns: no-predominant morbidity (reference), psychiatric morbidities , respiratory/immunological morbidities , cardiovascular/metabolic morbidities , and unspecific morbidities , overall. Among women younger than 50 years of age only, however, there was a significant association with psychiatric morbidity patterns compared to the no-predominant morbidity pattern . The other associations did not vary when stratifying by age at baseline and adherence to mammography recommendations. In conclusion, multi-morbidity was not a key factor to help identify patients at an increased risk of breast cancer. Breast cancer is the most common female cancer, with 2,088,849 new cases worldwide in 2018, accounting for 11.6% of incident cancer cases ,4, leadiIn developed countries, most breast cancer cases are diagnosed among women of middle age or older , coincidAs people get older, they often develop two or more chronic diseases. With an aging population, the number of people experiencing several multi-morbidities is rising globally ,19,20,21However, to date, there is no epidemiological evidence as to whether and to what extent breast cancer risk varies according to different patterns of morbidity. In this context, our study aimed to investigate the association between the number of morbidities and patterns of morbidity and the risk of female breast cancer.The UK Biobank is a prospective population-based cohort that recruited 273,375 women, aged 40 to 69 years, from March 2006 to July 2010 . IndividWe excluded women with any cancer diagnosis prior to baseline except non-melanoma skin cancer , women who underwent a mastectomy prior to baseline (n = 2457), and women with less than one year of follow-up (n = 2150), leaving 239,436 women in the final analysis .Based on an established list of morbidities, which was originally designed by Barnett et al.  to measuWe defined breast cancer as a diagnosis of invasive or in situ breast cancer, using the international classification of diseases (ICD) versions 9 and 10 . We considered only breast cancer cases that were the first cancer diagnosed.p-value < 0.05) as confounders if their inclusion in the age-adjusted Cox models changed the hazard ratio by 5% or more [All confounding factors , ethnicity, Townsend score, level of physical activity, alcohol consumption) were measured/collected at baseline. We selected well-established breast cancer risk factors based on previous studies ,31. We a or more . See AppAmong 35 baseline self-report morbidities, we included only morbidities with a prevalence of more than 1%  to obtaiUsing the numerical outputs of the MCA, we performed agglomerative hierarchical clustering (AHC) preceded by K-means clustering with 2000 initial cluster seeds , throughWithin each cluster, we computed the observed/expected ratios (“O/E-ratios”) for each single morbidity, i.e., the ratio between the prevalence of a given condition in a cluster and its prevalence in the overall study population. Similarly, we computed the exclusivity of each single morbidity, i.e., the number of individuals that had a given morbidity in a cluster over the number of individuals with the same morbidity in the whole study population. A morbidity was considered part of a given morbidity cluster when its O/E-ratio was ≥2 and its exclusivity was ≥25% ,43. We nThe follow-up time started at the date of first registration at a UK Biobank center and ended at the date of the first cancer diagnosis  or mastectomy, death, loss to follow-up, or 31 March 2016, whichever came first. We fitted Cox proportional hazard models to estimate hazard ratios and 95% confidence intervals (95%CIs) of breast cancer risk associated with each single pre-existing baseline morbidity included in the cluster analysis, the number of morbidities, and the morbidity patterns. The timescale was the follow-up time.We graphically assessed the proportional hazards assumption using scaled Schoenfeld residuals plots and log linearity assumption (for quantitative covariates) using Martingale residuals plots and deviance residuals plots. The final multivariable Cox models were adjusted for age at baseline, age at menarche, age at menopause, menopausal hormone therapy use, oral contraception use, parity and age at first birth, BMI, ethnicity, the Townsend score, level of physical activity, and alcohol consumption.We tested the modifying effects of age at baseline, the adherence to the recommendations for breast cancer screening, the BMI, the socioeconomic status, the physical activity level, and the menopause status at baseline with the likelihood ratio test. We conducted several sensitivity analyses: (i) we restricted analyses to menopausal women; (ii) we considered only invasive breast cancer as the outcome; (iii) we used the attained age as the timescale; (iv) we considered death and diagnosis of non-breast cancer as competing risks, using sub-distribution hazards models ; (v) we All statistical analyses were performed using R version 4.1.0.In the study population, the median age at baseline was 57.7 years . At least one morbidity was present in 58.4% of women at baseline, and the prevalence of multi-morbidity was 25.8%. Hypertension was the most prevalent morbidity (23.1%), followed by painful conditions (17.2%) and asthma (12.3%). The prevalence of obesity was 23.5%, and 23.5% of women had menopause after the age of 51 at baseline. Most women were postmenopausal (73.5%) and were adherent to breast cancer screening recommendations (66.6%) at baseline, as assessed at recruitment . During We considered the first five MCA dimensions see , which eThe median age at baseline was 57.4 years , and the median follow-up time was 7.1 years . There was no morbidity with an O/E ratio ≥ 2. The main features of this pattern were the low rate of multi-morbidity (6.9%) and the high rate of the absence of morbidity (62.6%).The median age at baseline was 55.7 years , and the median follow-up time was 7.0 years . Women with this pattern were predominantly diagnosed with anxiety and depression disorders.The median age at baseline was 56.7 years , and the median follow-up time was 7.1 years . Women with this pattern were predominantly diagnosed with psoriasis/eczema, COPD, and asthma.The median age at baseline was 62.6 years , and the median follow-up time was 7.0 years . Women with this pattern were predominantly diagnosed with diabetes, stroke, and coronary–heart disease. The main features of this pattern were the high proportions of elderly (about 65% were 65 years or older at baseline), multi-morbidity (96.6%), and deprived people (37.1% of women with this pattern were in the quintile with the highest levels of deprivation).The median age at baseline was 59.2 years , and the median follow-up time was 7.1 years . Women with this pattern were predominantly diagnosed with migraine, diverticular intestine disease, inflammatory bowel disease, rheumatoid disease, and threated dyspepsia.p-value interaction = 0.43 and 0.07, for the analyses on the number of morbidities and morbidity patterns, respectively) and adherence to recommendations for breast cancer screening among women aged 50 and older , although we found an increased risk among women aged of up to 50 years in the psychiatric morbidities pattern   . The res02–1.52) , when co02–1.52)  and in o02–1.52) .Among female participants in the UK Biobank cohort, 58.4% had at least one chronic disease, while 25.8% had two or more simultaneous morbidities. Hypertension was the most prevalent disease (23.1%) at baseline. We found five morbidity patterns: no-predominant morbidity, psychiatric morbidities, respiratory/immunological morbidities, cardiovascular/metabolic morbidities, and unspecific morbidities. There was a 1.12-fold increased risk among women who self-reported depression and a 25% increased risk of breast cancer associated with a psychiatric morbidity pattern compared to that with the no-predominant morbidity pattern, among women younger than 50 only. We did not observe other significant associations between either the number of morbidities or any morbidity pattern and the risk of breast cancer, which did not vary according to adherence to breast cancer screening recommendations, socioeconomic status, BMI, physical activity level, or menopausal status.Despite heterogeneous findings in previous studies on morbidities across different populations and settings, several morbidity patterns often emerge in the literature, which were also observed in our study ,23,46,47Indeed, to our knowledge, our study is the first to investigate the association between morbidity patterns and breast cancer risk. We found no association between either the number of morbidities or morbidity patterns and breast cancer risk, regardless of the women’s age at baseline, and socioeconomic characteristics, apart from an increased risk among women aged less than 50 having multiple psychiatric diseases. Analyses stratified based on adherence to breast cancer screening recommendations did not modify our main results, suggesting that surveillance bias is not an important modifying factor in the association between breast cancer risk and morbidities. Previously, there was only a case-control study reporting results on the association between multi-morbidity and breast cancer risk. The findings indicated that an increasing number of morbidities measured with the Charlson comorbidity index (CCI) was associated with an increasing breast cancer risk  after a 10-year follow-up of women aged 45–85, but no association was found for individual morbidities . HoweverThere are several hypotheses to explain the null results. First, women with morbidity could experience other serious long-term outcomes before a breast cancer diagnosis. Indeed, when accounting for death and malignancies other than breast cancer as competing risks, we found that compared to that in women with no predominant morbidity, women with other patterns were more likely to die and/or to be diagnosed with other cancers. This is particularly pronounced among women with cardiovascular/metabolic and respiratory/immunological morbidities. Second, given the different biologic characteristics of divergent breast cancer subtypes  and the Strength and limitations: The UK Biobank cohort is a large population-based cohort with a high follow-up rate and important number of breast cancer cases. The cohort includes a wide range of information on personal medical history, reproductive factors, lifestyle factors, socioeconomic status, and family medical history, with low levels of missing data. Nevertheless, there are several limitations that must be acknowledged. Assuming that the prevalence of having at least one morbidity in women in the UK Biobank cohort is slightly lower than what has been found  in previous studies of Barnett and Gondek, respectively , this suggests the occurrence of “healthy” volunteer bias  ,60. HoweFemale participants in the UK Biobank cohort can be classified into five morbidity patterns: no-predominant morbidity, psychiatric morbidities, respiratory/immunological morbidities, cardiovascular/metabolic morbidities, and unspecific morbidities. We found a significant increased risk among women aged younger than 50 with a psychiatric diseases pattern, but there was no other significant association among the number of morbidities, the morbidity patterns, and the risk of breast cancer in this population. Our findings suggest that multimorbidity is not a decisive factor to help identify patients at increased risk of breast cancer.


Identifying breast cancer risk factors is a critical component of preventative strategies for this disease. This study aims to identify modifiable and non-modifiable risk factors of breast cancer in Iranian women.We used international databases  and national databases  to retrieve relevant studies until November 13, 2022. The odds ratio (OR) with a 95% confidence interval using the random-effect model was used to estimate the pooled effect. The publication bias was assessed by the Egger and Begg test. A sensitivity analysis was conducted to evaluate the effect of each included study on the final measurement.I2 = 85.7) followed by age at marriage (25–29 vs < 18 years old) , second-hand smoking , smoking , abortion history , oral contraceptive use , age at marriage (18–24 vs < 18 years old) . Of non-modifiable risk factors, history of radiation exposure , family history of breast cancer , and age at menarche (12–13 vs ≥ 14 years old)  significantly increased the risk of breast cancer.Of the 30,351 retrieved articles, 24 matched case–control records were included with 12,460 participants (5675 newly diagnosed cases of breast cancer and 6785 control). This meta-analysis showed that of the known modifiable risk factors for breast cancer, obesity  had the highest risk (OR = 2.17, 95% CI 1.47 to 3.21; Since most risk factors related to breast cancer incidence are modifiable, promoting healthy lifestyles can play an influential role in preventing breast cancer. In women with younger menarche age, a family history of breast cancer, or a history of radiation exposure, screening at short intervals is recommended.The online version contains supplementary material available at 10.1186/s40001-022-00952-0. Women's breast cancer is the leading cause of cancer incidence in 2020, with about 2.3 million new cases, accounting for 11.7% of all cancer cases (1 in 4 cancer cases). It is the fifth leading cause of cancer mortality worldwide, with 685,000 deaths (1 in 6 cancer deaths) ; however, this association was not statistically significant Fig. A. The reIn seven evaluated studies, there was no association between illiteracy and the odds of breast cancer [OR = 1.00 (95% CI 0.59 to 1.69)]. The overall estimate changed to 0.84 and 1.32 excluding the studies of Vahid  and Ghiap = 0.09) ]; however, this association was not statistically significant Fig. C. In senThe association between residential place and the odds of breast cancer was assessed in 4 studies. No significant association was found between living in urban and the odds of breast cancer [OR = 1.46 (95% CI 0.96 to 2.21)] ]. Also, the age group of 25–29 vs < 18 years was significantly associated with odds of two times for developing breast cancer [OR = 2 (95% CI 1.53 to 2.61). This effect was greater in the age group of ≥ 30 vs < 18 years [OR = 2.02 ] ]; however, this association was not statistically significant. According to sensitivity analysis, the overall estimate changed to 0.56 and 1.05 excluding the studies of Fararouei  and DianThe overall effect of six evaluated studies showed that physical activity of occasionally and never versus actively was associated with increased odds of breast cancer by 37% [OR = 1.37 (95% CI 82 to 2.30)] and 54% [OR = 1.54 (95% CI 0.93 to 2.54)], respectively. However, these associations were not significant ] ] Fig. C. SensitIn two evaluated studies, there was no association between alcohol use and the odds of breast cancer [OR = 0.59 (95% CI 0.15 to 2.29)] Fig. D. AccordBased on five studies, the overall effect measure showed that supplement intake was associated with decreased odds of breast cancer by 39% [OR = 0.61 (95% CI 0.35 to 1.07)]. However, this association was not significant Fig. .According to five evaluated studies, the overall effect measure showed no association between parity and odds of breast cancer [OR = 0.94 (95% CI 0.71 to 1.24)] Fig. F. The seIn five studies, the overall effect measure showed that abortion was significantly associated with increased odds of breast cancer by 44% [OR = 1.44 (95% CI 1.02 to 2.05)] ] Fig. B. AccordIn five evaluated studies, the overall effect measure showed that HRT history was associated with increased odds of breast cancer by 3% [OR = 1.03 (95% CI 0.50 to 2.14)]. However, this association was not significant ]. However, this association was not significant ]. However, this association was not significant ]. Also, the age group < 12 vs ≥ 14 years was associated with odds of 2.72 times for developing breast cancer [OR = 2.72 (95% CI 0.93 to 7.99)]  ]. However, this association was not significant ]. However, this association was not significant Fig. .Based on 11 studies, the overall effect measure showed that family history of breast cancer in the first-degree and second-degree relatives significantly was associated with odds of 2.47 times for developing breast cancer [OR = 2.47 ] ]. However, this association was not significant ] . This dose–response study confirmed a significant linear relationship between BMI and breast cancer risk. In the analysis of premenopausal and postmenopausal subgroups, BMI in the premenopausal group played a protective role in developing breast cancer  [According to the results of this study, obesity vs normal weight significantly increased the risk of breast cancer. In a meta-analysis study, Liu et al. showed that every 5 units increase in BMI could lead to a 2% increased risk of breast cancer P < 0.00. This do = 1.04) . The rel = 1.04) , 29, andGiven that sexual relations are often formed in the context of marriage in Iranian culture, Iranian studies emphasize the age of marriage as a risk factor for breast cancer. In the present study, the age at marriage of 18–24 years vs < 18 years increased the risk of breast cancer by 22%, which was also observed in the age of 25–29 vs < 18 years and was significant in both groups. Reports from the Statistical Center of Iran show that the mean age at marriage increased from 25.6 to 27.4 years in men and from 22.4 to 23 years in women during 1996–2016 . Other sThe results of the present study confirmed that smoking and second-hand smoke were associated with increased odds of breast cancer by 83% and 86%, respectively. In particular, childhood exposure has been associated with an increased risk of premenopausal cancer . AlthougThe results of 5 studies showed that having a history of abortion increased the risk of breast cancer by 44%. A similar meta-analysis of 403,000 women showed an increased risk of developing breast cancer in women with ≥ 3 abortions  . DespiteThirteen examined studies have shown that the risk of developing breast cancer is significantly increased by 35% by taking oral contraceptive pills. The American Cancer Society states that the recent use of birth control pills is associated with about 20% higher risk of breast cancer, especially before the first pregnancy . OCPs arIn nine articles, the risk of developing breast cancer in employed women was 37% higher than that in housewives, although it was not significant at an error level of 5%. The lack of uniform definition and job changes in different periods is an important limitation in examining the causal relationship between occupation and breast cancer. In one meta-analysis, increasing the number of years of jobs with night shift work increased the risk of developing breast cancer by about 1.1 times [In this study, urbanization was associated with an increased risk of developing breast cancer . In a meta-analysis of 31 studies, Akinyemiju et al. showed a positive relationship between an increased risk of breast cancer and urbanization compared to rural life  . The desPhysical activity plays a protective role in developing cancer with some hormonal regulation mechanisms such as lowering insulin levels. It is also effective in weight loss, which is indirectly associated with the reduction of breast cancer incidence by lowering insulin levels , 40. DatData from 47 epidemiological studies in 30 countries showed a 7% reduction in the relative risk of breast cancer with each delivery after adjusting for breastfeeding . The preIn the present study, the risk of developing breast cancer with HRT after menopause increased by 3%. Other studies confirm this relationship, too . Since tAccording to the present meta-analysis, breastfeeding reduced the risk of breast cancer by up to 8%. Also, breastfeeding for less than 2 years compared to 2 years and more increased the risk of breast cancer by 47%. None of these estimates were significant at an error level of 5%. In examining the relationship between breastfeeding and breast cancer, it is necessary to define this exposure more accurately in terms of duration, continuity of time, and quality of breastfeeding.In this study, the earlier age of menarche increased the risk of breast cancer. Yi-Sheng et al. showed that the risk of breast cancer decreased by 5–10% for every 1-year delay in menarche . A meta-Results showed that the family history of breast cancer in first-degree and second-degree relatives increased the risk of developing breast cancer by 2.47 times, although this estimate did not consider distinguishing between family relationships and the number of people involved. A review study on 113000 women in the UK showed family history in one first-degree relative increased OR by 1.75 times and in the case of two or three people involved, this ratio increased to 2.5 times . AccordiIn two studies, a history of radiation exposure significantly increased the risk of breast cancer by 3.48 times, but this odds ratio is biased because of considering any kind of radiation exposure such as radiography as a risk factor. There was no clear definition for this variable without determining the dose and time of exposure. The results of a systematic review study also showed that a history of radiation exposure to the chest area linearly increased the risk of breast cancer in young women, with a standardized incidence ratio of 13.3 to 55.5 . Our resMenopausal age less than 49 years increased the risk of breast cancer by 2.03 times compared to older ages, which was not statistically significant. This contradictory result, in addition to the limited number of studies, could be due to the induced menopause of young patients. Due to the young population composition of Iran, the age of breast cancer incidence is about a decade lower than that in other countries  and in mHaving a family history of cancer increased the risk of breast cancer by 2.57 times. According to the American Cancer Society, a family history of ovarian, pancreatic, and prostate cancer is associated with an increased risk of breast cancer . HoweverSome of the variables mentioned could not be pooled because they were only in one of the articles. These variables included socioeconomic status, infertility treatment, night bra use, hair coloring, past life stress, prenatal age, hysterectomy, cosmetic use, day sleep duration, parity number, diabetes, irregular menstruation, and ovarian cyst that were not significantly associated with breast cancer, while variables such as day bra use, sunlight exposure, stress, high-fat diet, migration, history of > 20 kg weight gain after 18 years old were introduced as risk factors and regular bedtime, quality sleep, diet containing sufficient fruit and vegetables, were as protective factors for breast cancer.One of the issues which make distinguish this meta-analysis from the previous ones is the clarity of the reference group, use of incident cases of breast cancer, lack of combination of different study designs, and conducting sensitivity analysis. This is the first research that has tried to estimate more accurately the attributable risk factors of breast cancer in Iran by considering the methodological limitations of the published studies.In this study, there are several limitations. Despite the inclusion of studies with newly diagnosed patients, there is still a recall bias publication bias tests were not significant in a small sample size, so the absence of publication bias is not ruled out. In risk of bias assessment, most records had moderate quality due to not selecting the control group from the community and not mentioning ascertainment of exposure and non-response rate. Due to the small number of studies, it was impossible to do metaregression for finding the cause of heterogeneity. Although there were the same risk factors in many studies, due to the lack of similar reference groups, we cannot use all of these studies. There was no complete geographical distribution of breast cancer risk factors. Due to the absence of cohort studies, it is not possible to conclude with certainty the causal relationship of the obtained risk factors. The obtained odds ratios may be overestimated because of not achieving the conditional logistic regression in the primary studies.In general, in the present study, age at marriage of 18–29 years, obesity, smoking, second-hand smoking, abortion history, OCP use, age at menarche of 12–13 years, family history of breast cancer, and history of radiation exposure were introduced as risk factors for breast cancer. Given that most of the above are modifiable risk factors, lifestyle changes can play an influential role in the primordial prevention of breast cancer. In women whose risk factors are non-modifiable , screening at short intervals can play an effective role in the secondary prevention of breast cancer.Additional file 1: Appendix S1. The search strategy of breast cancer risk factors in Iran.


For the implementation of personalised surveillance, it is important to create more awareness among HCPs with regard to the risk for locoregional recurrences (LRRs). The aim of this study is to evaluate the current awareness and estimations of individual risks for LRRs after completion of primary treatment for breast cancer among health care professionals (HCPs) in the Netherlands, without using any prediction tools.A cross-sectional survey was performed among 60 HCPs working in breast cancer care in seven Dutch hospitals and 25 general practitioners (GPs). The survey consisted of eleven realistic surgically treated breast cancer cases. HCPs were asked to estimate the 5-year risk for LRRs for each case, which was compared to the estimations by the INFLUENCE-nomogram using one-sample Wilcoxon tests. Differences in estimations between HCPs with different specialities were determined using Kruskal–Wallis tests and Dunn tests.HCPs tended to structurally overestimate the 5-year risk for LRR on each case. Average overestimations ranged from 4.8 to 26.1%. Groups of HCPs with varying specialities differed significantly in risk estimations. GPs tended to overestimate the risk for LRRs on average the most (15.0%) and medical oncologists had the lowest average overestimation (2.7%).It is important to create more awareness of the risk for LRRs, which is a pre-requisite for the implementation of personalised surveillance after breast cancer. Besides education for HCPs, the use of prediction models such as the INFLUENCE-nomogram can support in estimating an objective estimate of each individual patient’s risk. Aftercare focusses on informing on, monitoring and addressing of complaints, symptoms and (late) physical or psychosocial effects of the disease and treatment [surveillance is early detection of locoregional recurrences (LRRs) or secondary primary breast tumours (SPs) [In the Netherlands, the incidence of breast cancer has been rising until 2019 and survival rates have improved [rs (SPs) , 4. UnliIn general, patients treated for breast cancer have a low risk for LRRs. About 2.6% of Dutch women diagnosed with breast cancer between 2003 and 2006 developed a LRR in the first 5 years following primary treatment [To identify patients with a low or high risk for LRRs who might benefit from less or more intensive surveillance, risk prediction models can be used. The INFLUENCE-nomogram  is an exTo support implementation of the INFLUENCE-nomogram and risk-based surveillance, it is important to evaluate to which extent health care professionals are currently aware of patients’ risk for LRR. The more the estimates of the health care professionals (HCPs) deviate from the objective risk estimate calculated using the INFLUENCE-nomogram, the more the nomogram can add value to decision-making regarding personalised surveillance. Moreover, existing and systematic deviations might form a barrier for the uptake of the INFLUENCE-nomogram. An objective risk estimate could also prevent HCPs from misinforming patients causing excessive fear of recurrence.Therefore, the aim of this study is to evaluate the current awareness of and estimations of individual risks for LRR after finalising primary treatment for breast cancer among HCPs in the Netherlands and to compare these values with the risks estimated using the INFLUENCE-nomogram.In this study, a cross-sectional survey was performed among Health Care Professionals (HCPs) working in breast cancer care in seven hospitals  in the western part of the Netherlands, which together form the “regional oncology network West” (RO-West). Moreover, GPs working within the same region were invited to participate.n = 35) from the network of one of the researchers (AZ) were invited by e-mail. Participating GPs received the survey via e-mail.Per hospital, the chair of the Multidisciplinary Tumour Board (MDT), in most cases a breast cancer surgeon, was approached by one of the researchers involved in this study (PS) and was asked to participate in the study. Subsequently, this HCP made sure that the researcher could attend one of the MDT meetings. MDTs in the Netherlands consist of HCPs from all disciplines involved in breast cancer care, i.e. radiologists, pathologists, (plastic) surgical oncologists, medical oncologists, radiation oncologists, nurse practitioners and breast cancer nurses. At the start of the MDT meeting, the researcher explained the aim of the study, after which the attending HCPs in that particular meeting completed the survey individually. HCPs were not allowed to discuss anything while completing the survey. This approach was chosen, so that HCPs would be unprepared when answering the question. GPs were approached using a convenience sampling strategy. GPs (https://www.evidencio.com/models/show/721). The cases that were presented to the HCPs are displayed in Table The survey was developed by the research team and was pre-tested with a sample of four HCPs in one hospital and was then modified based on the obtained feedback. The survey consisted of eleven realistic surgically treated breast cancer cases, each with different patient, tumour, and treatment characteristics. These characteristics included the following: the patient’s age; tumour characteristics (post-operative T-stage (pT); post-operative lymph node status (pN); receptor status (ER/PR); grade (Bloom -Richardson); and multifocality); and type of post-surgery treatment . Participants were asked to estimate the risk of LRR per case. Participants were not asked to estimate the risks for second primary tumours (SPs) because these could not be estimated by the INFLUENCE-nomogram version 1.0 and estimations could therefore not be compared to the nomogram’s estimation. Furthermore, of all cases, the risk of LRR was estimated using the INFLUENCE-nomogram (version 1.0) by filling in the variables in the nomogram on the Evidencio platform . All statistical analyses were performed in R version 4.1.0 .Frequencies and percentages were used to display responses to individual questions. One-sample Wilcoxon tests were used to test whether the medians of the risks estimated by the HCPs were significantly different from the risk estimated using the INFLUENCE-nomogram. One-sample Wilcoxon tests were used instead of one-sample N = 13, 15.3%), nurse practitioners , or surgical oncologists   were hospital HCPs and 25 (29.4%) were GPs. Most of the hospital HCPs were residents at the surgery department (p-levels were < 0.001).In Table Average overestimations ranged from 4.84% on case 1a to 26.08% on case 5b  and medical oncologists had the lowest average overestimation (2.7%).Variations in the risk estimations were smallest on the case of a 53-year-old women with a hormone positive small tumour (< 1 cm) without lymph node involvement and treated with radiation and anti-hormonal therapy (case 1a). Case 1a can be seen as a common case in terms of tumour characteristics. Variations were largest on case 5b, that of a 50-year-old woman with a ER positive, PR negative big tumour (> 5 cm) with lymph node involvement and multifocality which was treated with radio- and anti-hormonal therapy, but without chemotherapy. Case 5b is obviously harder to predict for HCPs. An explanation for this could be that in earlier years of research, tumour size in combination with the extent of lymph node involvement have always been the most important prognostic factors for breast cancer. However, from more recent research, we know that tumour biology (molecular signature and grading) is more decisive. Moreover, targeted therapies may even decrease the risk of LRRs in initially advanced staged breast cancer when a pathologic complete response is achieved .That medical oncologists have more knowledge about the effect of targeted therapies on the risk of LRRs, is clearly demonstrated by our study. There was a significant difference in risk estimates between groups of HCPs with differing specialties and medical oncologists tended to estimate the risk for LRRs the most accurately. Another explanation for the more accurate estimates could be the fact that medical oncologists have more experience with the use of risk information and prediction models (such as Predict ) in deciThe large overestimation by GPs could be due to a knowledge-gap on the effects of different therapies, experience with estimating risks for LRR due to a small number of patients with breast cancer in their practice, and the use of prediction models. GPs are currently hardly actively involved in surveillance after breast cancer . HoweverIn this study, we see that the risks for LRRs estimated by HCPs deviate substantially from the objective risk estimates, calculated using the INFLUENCE-nomogram. In this context, the added value of the nomogram exists in clinical practice, especially in decision-making regarding personalised post-treatment surveillance. A potential explanation for the higher estimates could also be that some HCPs included the risk for second primary tumours (SPs) or distant metastasis (DM) in their estimates instead of only the risk for LRRs. This pitfall is often observed in patients as well. Whereas the risk for SPs can impact personalisation of surveillance, DM is not actively looked for during surveillance, as early detection of DMs does not improve survival . These rAn update of the first INFLUENCE-nomogram has recently been performed leading to the CE certified INFLUENCE 2.0 model, which uses random forest models . The 2.0Recent studies have shown that shared decision-making about personalised surveillance is desirable , 12. GenTo our knowledge, this the first study to examine HCPs perceptions of risks for LRRs after breast cancer. This study also knows limitations. First, the limited number of respondent, and uneven spread of respondents over de groups of HCPs  might limit the generalisability of the results. This study was performed in six teaching hospitals and one academic hospital which is a strength due to different approaches to follow-up but which also limits generalisability to other types of institutions such as general, or specialised hospitals. Second, we did not ask about risks for metastasis, due to the fact that early detection of DM is not an aim of surveillance, because it does not influence prognosis. Although this was stated on the survey/mentioned in the explanation, we cannot completely rule out that some HCPs might have perceived these elements as part of the risk for recurrences when making estimations. Thirdly, GPs were invited from one of the researchers’ networks. However, we do not feel that this has biased the results. Fourthly, we have no insight in the numbers of breast cancer patients that GPs see in their clinical practice. These numbers could vary and might have had an effect on the estimations of the GPs. Furthermore, we did not add the type of surgery (e.g. lumpectomy or mastectomy) and the HER-2 status in the patient cases. These factors are known to influence the risk for LRR, but could not be included in the cases because they were not yet included in the INFLUENCE-nomogram version 1.0. In future research, these factors can be included. Lastly, we currently have no insight in what influenced HCPs to estimate a certain risk for LRRs. For future studies, it would be interesting to also qualitatively explore which factors influence HCPs estimations of the risk for LRRs.Risks for LRRs estimated by HCPs deviate substantially from the objective risk estimates as calculated using the INFLUENCE-nomogram. Creating more awareness of the risk for LRRs is an important pre-requisite for the implementation of personalised surveillance after breast cancer. Besides education of HCPs, the use of prediction models such as the INFLUENCE-nomogram can support in estimating an objective estimate of each individual patient’s risk. The risk estimate calculated by the INFLUENCE-nomogram can also be combined with patient information, e.g. in a decision-aid, to support decision-making about personalised post-treatment surveillance.


We aimed to evaluate the association between three previously defined pro-vegetarian (PVG) food patterns and the cancers of the oesophagus, stomach, and pancreas in a multi case–control study. We analyzed data from a multi-case hospital-based study carried out in two Mediterranean provinces in Spain. A total of 1233 participants were included in the analyses: 778 incident cancer cases, histologically confirmed  and 455 controls. A dietary assessment was performed using a validated food frequency questionnaire (FFQ). Three PVG food patterns  were estimated using 12 food groups for the general PVG (gPVG), scoring positive plant-based foods and negative animal-based foods, and 18 food groups, for the healthful (hPVG) and unhealthful (uPVG) food patterns. Multinomial logistic regression was used to estimate relative risk ratios (RRR) and confidence intervals (95% CI) for quintiles of adherence to PVG patterns and as a continuous variable. The RRR (95% CI) for the highest vs. the lowest quintile of gPVG were, RRR = 0.37  for the oesophagus, RRR = 0.34  for the stomach, and RRR = 0.43  for pancreas cancer. For the hPVG, the RRR were RRR = 0.72  for the oesophagus, RRR = 0.42  for the stomach, and RRR = 0.74  for pancreas cancer. The uPVG was associated with a higher risk of stomach cancer RRR = 1.76 . Higher adherence to gPVG and hPVG food patterns is associated with a lower risk of oesophageal, stomach, and pancreas cancers, while a higher adherence to a uPVG food pattern is associated with a higher risk of stomach cancer. Cancer remains one of the world’s leading causes of death. The International Agency for Research on Cancer (IARC) estimates that in 2020 there were approximately 20 million new cases and 9.9 million cancer deaths worldwide. After lung and breast cancer, digestive cancers are the major cause of morbidity and mortality by cancer, especially in men . The canAlcohol and tobacco consumption have been associated with an increased risk of these cancers although the results are not fully consistent ,4,5. TheOne difficulty in studying the role of specific nutrients and foods is that people do not consume foods in isolation, but rather a combination of them. Thus, the study of food patterns has been proposed as a more complex and realistic alternative for assessing the combined effect of diet. One of the most studied food patterns has been the Mediterranean diet (MD). A recently published systematic review  has showThus, we aimed to investigate the association between three a priori defined PVG food patterns  and cancers of the oesophagus, stomach, and pancreas, in the PANESOES multi case–control study carried out in Spain.The PANESOES study is a hospital-based, multi case–control study aimed to explore the effect of major lifestyles, including diet, on the risk of three types of gastrointestinal cancer: oesophageal, stomach, and pancreatic. More details of the study have been described previously ,25,26,27After excluding those participants with missing information for the variables of interest and/or without the necessary information to construct the PVG food patterns, 1233 participants (840 men and 393 women), corresponding to 199 cases for oesophageal cancer, 414 cases for stomach cancer, 165 cases for pancreatic cancer, and 455 controls, were included in the present analysis, for which there was cyto-histological confirmation and/or ample clinical evidence. A total of 455 controls were selected from the same hospitals and with the same characteristics as the cases matched by frequencies of sex, age , and province . The diagnoses of the controls were a priori unrelated to the exposure of interest .All participants, prior to data collection, were informed of the aims of the study and agreed by informed consent to complete the interview.Information on usual dietary intake was collected by trained interviewers using a semi-quantitative food frequency questionnaire (FFQ), based on the Harvard questionnaire , which wThe PVG food patterns were created following, in the case of the gPVG, the methodology proposed by Martínez-González , and in The following information about different socio-demographic and lifestyle characteristics was also collected from participants: age (in years), sex , province , educational level , tobacco consumption , alcohol consumption , and energy intake .We used multinomial logistic regression to estimate relative risk ratios (RRR) and 95% confidence intervals (95% CI) to explore the association between adherence to PVG food patterns by quintile of adherence and oesophagus, stomach, and pancreas cancer. We also explored this association per 1 additional point of adherence to each PVG food pattern.Two models were adjusted, a first model included the matching variables age (years), sex , and province , and a second model additionally adjusted for variables previously described in the literature to be associated with these cancers, and also for variables that changed the association with exposure by 10% or more, once we excluded them from the model: educational level , tobacco consumption , alcohol consumption  and energy intake .p-values are from two-sided tests. All analyses were performed with R version 4.0.3 .Tests for the trend in the RRR across exposure strata were calculated for ordinal variables by using models that included categorical terms as continuous variables in a model with all the potential confounders. For trend tests, we used the likelihood ratio test statistic with one degree of freedom. Statistical significance was set at 0.05. All reported p-trend = 0.01), a 66% lower risk of stomach cancer, RRR = 0.34 , and a 57% lower risk of pancreas cancer, RRR = 0.43 . When the association with adherence to the gPVG was evaluated as a continuous term, a 5 to 6% lower risk was observed for the three cancers for each additional unit of adherence.Compared with the lowest quintile of adherence to the gPVG, the highest adherence showed a 63% lower risk of oesophageal cancer, RRR = 0.37 . Each unit of additional adherence to the uPVG food pattern was associated with a 3% increased risk of stomach cancer risk, RRR = 1.03 . No associations were observed between uPVG food pattern and oesophagus and pancreas cancer.In contrast to the inverse association observed for the other patterns, the unhealthful PVG was associated with a higher risk of stomach cancer . The higThis study suggests that a higher adherence to the general and healthful PVG food patterns is associated with a lower risk of oesophageal, stomach, and pancreatic cancers, while a higher adherence to the unhealthful PVG food pattern is associated with a higher risk of stomach cancer.The PVG food patterns are a novel dietary model with potential future public health implications, although they are still understudied. To our knowledge, this is the first study that has evaluated the association between three PVG patterns, including hPVG and uPVG versions, and the risk of these three gastrointestinal cancers. Some prospective cohort studies in the US ,31 and KThere are several mechanisms that could explain the protective association observed for gPVG and hPVG food patterns and cancers of the oesophagus, stomach, and pancreas in our study. Plant-based foods included in these patterns are the main source of several key nutrients . Dietary fiber is an important component of whole grains, fruits, and vegetables, and has been associated with a lower risk for several gastrointestinal cancers . Fiber cOn the other side, the association between the uPVG food pattern and an increased risk of stomach cancer that we found in our study may be related to the combined effect of high-processed plant-based foods included in this pattern. Refined grains, potato chips, sugar-sweetened beverages, and sweets belong to the ultra-processed category and have been associated with an increase in overall cancer . These fThis study has some limitations. Firstly, the sample was limited, especially for oesophageal and pancreatic cancers, which may have reduced the statistical power to detect some associations. Nevertheless, dose–response and statistically significant associations were found for these cancers. The case–control study design is more susceptible to some biases, such as selection bias, although the participation rate was very similar in cases and controls (98%). The fact that diet was assessed five years before the interview might have caused misclassification bias, although the response was similar in cases and controls, and the 5-year reproducibility and validity of the FFQ we observed was satisfactory. In addition, although several confounding factors were considered in multivariable analyses, there may be other potential confounding factors that could influence the risk of developing the cancers of interest.Our study also presents some strengths. Firstly, the association found between higher adherence to the three PVG food patterns and cancer risk after adjusting for several well-known exposure factors such as tobacco and alcohol consumption. Secondly, the use of the same protocol for the three cancers and the use of a well-structured questionnaire with a validated FFQ allowed us to construct the PVG food patterns that reinforced our findings. Finally, the strength of the associations and the existence of a dose–response effect also lent robustness to the results obtained.In conclusion, this multi case–control study suggests that greater adherence to gPVG and hPVG food patterns rich in fruits and vegetables is associated with a lower risk of oesophageal, stomach, and pancreatic cancers. In contrast, a PVG food pattern that prioritizes less healthy foods like high-processed plant-based foods such as refined grains, potato chips, sugar-sweetened beverages, and sweets may be associated with a higher risk of stomach cancer and their consumption should be restricted. If confirmed by other studies, these findings could be a good alternative for making more precise public health recommendations based on healthy and unhealthy plant-based foods.


The past decade has witnessed an improvement in survival rates for breast cancer, with significant inroads achieved in diagnosis and treatment approaches. Even though chemotherapy is effective for this patient population, cardiotoxicity remains a major challenge, especially in older people. It has been established that cardiovascular events are a major cause of death in older female primary breast cancer patients that underwent chemotherapy. In the present study, the independent prognostic factors were identified to develop a novel nomogram for predicting long-term heart disease-specific survival (HDSS) and improving patient management.Older female primary breast cancer patients that underwent chemotherapy from 2010 to 2015 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and randomly assigned to a training cohort and a validation cohort at a ratio of 7:3. HDSS was the primary endpoint of this study. Univariate and multivariate Cox regression analyses were conducted on the training cohort to identify independent prognostic factors of HDSS and construct a nomogram to predict the 5- and 8-year HDSS. The performance of the constructed nomogram was evaluated by calibration curve, receiver operating characteristic (ROC) curve, and decision curve analyses. Finally, a risk classification system was constructed to assist in patient management.A total of 16,340 patients were included in this study. Multivariate Cox regression analysis identified six independent prognostic factors: age, race, tumor stage, marital status, surgery, and radiotherapy. A nomogram based on these six factors yielded excellent performance, with areas under the curve of the ROC for 5- and 8-year HDSS of 0.759 and 0.727 in the training cohort and 0.718 and 0.747 in the validation cohort. Moreover, the established risk classification system could effectively identify patients at low-, middle-, and high- risk of heart disease-associated death and achieve targeted management.Independent prognostic factors of HDSS in older female primary breast cancer patients that underwent chemotherapy were determined in this study. A novel nomogram for predicting 5- and 8-year HDSS in this patient population was also established and validated to help physicians during clinical decision-making and screen high-risk patients to improve outcomes. Cancer is the second most common cause of death in the US, behind heart disease . AccordiIncreasing age is a natural driver of cardiovascular morbidity and mortality in the general population; cardiovascular diseases have been documented to be a significant risk factor for mortality in older females with breast cancer. Abdel-Qadir et al. showed that among breast cancer women aged 66 years or older with no cardiovascular disease, the 10-year risk of breast cancer- and cardiovascular disease-associated death were 11.9 and 7.6%, respectively. Interestingly, among patients with pre-existing cardiovascular disease, the risk of death from cardiovascular disease and breast cancer was comparable for the first 5 years. However, the risk of death from cardiovascular disease exceeded breast cancer over time, with a 10-year cumulative mortality rate of 16.9 and 14.6%, respectively . Over thAlthough risk factors associated with HDSS in breast cancer have been identified, there is currently no universally accepted scoring system to predict long-term HDSS in this subpopulation. Given that different clinical-pathological variables can affect the patient prognosis, a new approach that integrates key prognostic predictors is warranted to help during treatment selection and improve patient quality of life. Nomograms are nowadays widely accepted as a simple multivariate visualization tool for predicting individual patient survival outcomes, especially in oncology , 7. Comphttps://seer.cancer.gov/seerstat/) collects data from 18 separate cancer registries covering ~30% of the US population. It was used in this retrospective cohort study  and validation cohorts  according to a ratio of 7:3. The former was used to identify HDSS-related independent prognostic factors and establish a prognostic nomogram and risk classification system for this subpopulation. The latter was used to verify the constructed nomogram and risk classification system.The inclusion criteria consisted of (i) patients with the following site-specific codes for cancer that originated in the breast: C50.0 (Nipple), C50.1 , C50.2 (Upper-inner quadrant of the breast), C50.3 (Lower-inner quadrant of the breast), C50.4 (Upper-outer quadrant of the breast), C50.5 (Lower-outer quadrant of the breast), C50.6 (Axillary tail of the breast), C50.8 (Overlapping lesion of the breast), and C50.9  ; (ii) olPatient demographic characteristics , tumor factors , disease characteristics  metastasis, and treatment information (surgery and radiotherapy) were analyzed in this study. The optimal cut-off values for age and tumor size in the training and validation cohorts determined by the X-tile software were 71 and 76 years old and 22 and 36 mm, respectively  15). Pa. Pa15). p-value <0.05 was statistically significant. First, values were assigned to each variable included in this study. The statistical difference between the enrolled variables was identified using the Kaplan-Meier method and univariate Cox regression analysis. Then, variables with a p-value <0.05 were incorporated into a multivariate Cox regression analysis to eliminate confounding effects and identify HDSS-related independent prognostic factors in this subpopulation. HDSS-related independent prognostic factors were then used to construct a nomogram to predict 5- and 8-year HDSS. The corresponding scores of the independent prognostic factors in the HDSS nomogram were obtained. Then, the bootstrap-corrected concordance index (C-index) and calibration curves were constructed to verify the prediction and discrimination performance of the nomogram, and a decision curve analysis (DCA) was constructed to demonstrate the clinical utility value of the nomogram. The discriminative power of the nomogram was assessed by constructing the receiver operating characteristic (ROC) curves for the 5- and 8-year HDSS based on the area under the curve (AUC) values of the corresponding variables. In addition, the total score was calculated as the sum of the scores corresponding to the HDSS-related independent prognostic factors, and the optimal cut-off value for the total score was obtained using the X-tile software. Then, a risk classification system was established to stratify the cardiovascular mortality risk of this subpopulation into low-, middle-, and high-risk subgroups. Finally, Kaplan-Meier method was used to identify the differences between the three risk subgroups.All data were analyzed using SPSS (version 22.0) and R (version 4.0.3) software. A n = 11,440, 70%) and validation  cohorts. The majority of patients were aged between 65 and 70 years old , white , and married . No significant difference was found between low-grade (grade I–II) and high-grade (grade III–IV) tumors. Moreover, low-stage (stage I–II) tumors occupied a higher proportion (78.34%) of cases than high-stage (stage III–IV) tumors. The size of most tumors was <22 mm, while C50.4, C50.8, and C50.2 represented the top three primary sites, accounting for 69.38%. The incidence of distant metastases was relatively low. Besides, luminal A was the most common molecular subtype, accounting for 51.51%. Most patients were classified as ER-positive , PR-positive , and HER2-negative . As for the treatment, 97.3 and 62.93% of patients underwent surgery and radiotherapy, respectively . In contrast, no significant difference in ER status, HER2 status, and brain metastasis were found  metastasis were significantly associated with HDSS , and the 1·3·5 Project for Disciplines of Excellence, West China Hospital, Sichuan University (Grant Number: ZYJC18039).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


In recent decades, “environmental xenobiotic-mediated endocrine disruption”, especially by xeno-estrogens, has gained a lot of interest from toxicologists and environmental researchers. These estrogen-mimicking chemicals are known to cause various human disorders. Pesticides are the most heavily used harmful xenobiotic chemicals around the world. The estrogen-mimicking potential of the most widely used organochlorine pesticides is well established. However, their effect is not as clearly understood among the plethora of effects these persistent xenobiotics are known to pose on our physiological system. Estrogens are one of the principal risk modifiers of various disorders, including cancer, not only in women but in men as well. Despite the ban on these xenobiotics in some parts of the world, humans are still at apparent risk of exposure to these harmful chemicals as they are still widely persistent and likely to stay in our environment for a long time owing to their high chemical stability. The present work intends to understand how these harmful chemicals may affect the risk of the development of estrogen-mediated human cancer. Xenobiotics  are chemicals of synthetic origin that are foreign to living systems. These chemicals are not part of the normal metabolic activity of living organisms and therefore may interfere with the functioning of the physiological system. These unwanted chemicals inside the body are known to have harmful effects on functioning and are responsible for diseases in humans. One such detrimental effect caused by some of these chemicals is xeno-estrogenicity .‘Xeno-estrogenicity’  denotes the property of foreign chemicals being able to produce responses in physiological systems comparable to estrogens produced by the body by different mechanisms. Xeno-estrogens are a group of chemicals that disrupt the endocrine system by mimicking the activity of natural estrogens or producing a similar response in the body by other mechanisms. Majorly known persistent organochlorine pesticides (OCPs) and other classes of chemicals present in the environment are known to bind estrogen receptors and produce a physiological response equivalent to the hormone in various organs and tissues . TherefoXenobiotics, including from pesticide exposure, are significant causes of cancer incidence worldwide ,12. ThesOther natural and man-made compounds are known to exist with xeno-estrogenic potential in the environment, such as butylated hydroxyanisole, phthalate esters, nonylphenol, benzyl butyl phthalate, and bisphenol-A, etc. . HoweverPesticides are the most well-known carcinogenic chemicals to humans. Studies from both humans and animals have unambiguously confirmed the development of pesticide exposure-mediated human cancer . All knoThe International Agency for Research on Cancer (IARC) has listed OCPs as potent carcinogens in animal studies ,32. CancIndividual susceptibility to environmental chemical exposure leading to diseases, including cancer, is modified by genetic susceptibility, and polymorphism in such gene families is a major risk. Genetic variations in phase I and/or phase II genes may alter the activity of the corresponding enzyme responsible for the bioactivation of xenobiotics; individuals with genetically impaired xenobiotic elimination functions will be at higher risk of disease susceptibility over their lifetime. Identification of the genes responsible for the elimination of xenobiotics and their variability is key for the identification of individuals at higher risk ,33,34,35Since the advent of modern agriculture, pesticide use has become a primary means to enhance crop productivity. Amongst all the pesticides, OCPs became the first choice of farmers around the world after their introduction, attributed to their low cost and versatile effectiveness against a wide range of agricultural pests. The hormone-like activity of these man-made chemicals was realized long after they were released into our environment. Krishnan et al. (1993) accidentally found unknown chemicals to be estrogenic because they disrupted experiments conducted in laboratories while studying the effects of natural estrogens. Later experiments confirmed that the disruption of the experiments was due to the plastic tubes used in the experiments possessing estrogenic activity . In humaThese chemicals are known to stay in the environment for a long period of time, which could be from a few years to several decades in different media; for instance, the most well-known pesticide, DDT, has a half-life of up to 30 years in soil and 3 to 6 years in the human body (up to 150 years in water) . TherefoThese xenobiotics contaminate water resources and animals living in pesticide-contaminated water, leading to their accumulation in the animals’ bodies. The quantity of OCPs in their bodies can be several folds higher than that in the surrounding water. If these animals are consumed by other organisms, the OCPs will enter and accumulate in those animals’ bodies (bioaccumulation) . StudiesData from the observation of occupational exposure were not sufficient to understand the adverse effects of these OCPs. Nevertheless, animal- and cell-line-based studies unambiguously confirmed the xeno-estrogenic potential of heavily used and well-known pesticides ,55,56.Animal-based experiments first confirmed the biological response to DDT comparable to that for estrogens, such as increased uterine weight and vaginal epithelial cornification in lab animals, known as uterotrophic assay . Later, p,p′-DDT (more than 85% of w/w), was found to be estrogenic in animal and cell lines experiments [The highest isoform of dichlorodiphenyltrichloroethane, the most well-known pesticide in the world, eriments . Additioeriments .Dieldrin was reported to cause various forms of cancer in at least seven strains of mice upon oral administration . In one Chemicals produced by humans and nature possess estrogenic activity. However, the molecular details or structure rendering a chemical/compound its xeno-estrogenic activity are not well known. However, the majority of environmental xeno-estrogens share a common structural motif either of phenol or structurally similar to phenol as observed in all the OCPs .Mimicking the effect of endogenous steroidal hormones (androgens and estrogens).Antagonizing steroidal hormones.Altering the synthesis and metabolism of endogenous steroidal hormones.Modifying hormone receptor expression in different tissues.The understanding of mechanisms of endocrine disruptors exerted by such chemicals is growing with time. Scientists proposed that by following these mechanisms, these environmental chemicals (including OCPs) are reported to lead to endocrine disruption (including xeno-estrogenicity) . Figure Through human studies, the role of estrogens in female as well as male cancers has been well established. Breast and endometrial cancers in females are primarily estrogen-mediated . In maleTherefore, the risk of estrogen-mediated cancer has been analyzed in relation to exposure to OCPs by researchers. Risk of breast, endometrium, and ovary cancers in females and risk of prostate and testis cancers in males are estrogen-dependent to some extent . StudiesThe role of these xeno-estrogenic pesticides in elevated breast cancer risk has been most extensively studied compared to any other cancer in humans ,81,82,83Incidence of breast carcinoma is the world’s second highest, and it has the fifth highest mortality rate, while 36.8% of all incidences directly link to lifestyle and/or environmental factors in females over the age of 30 years. A significant association was found between the breast cancer epidemic and environmental contaminants exposure—especially DDT ,75. In oIt is not clear how the estrogenic mechanism increases breast cancer risk. In a toxicoproteomic study amongst breast cancer patients, these OCPs were found to downregulate the expression of ER (a common event in a large number of breast cancer cases) by disrupting the relevant pathways . In vitrStudies analyzing the role of xeno-estrogenic pesticides in endometrial cancer risk are few and rather inconclusive. Previous studies did not find any correlation between xeno-estrogenic OCP levels and endometrial cancer risk ,88. MoreThe role of xeno-estrogens in modifying the risk of male cancer and other related disorders is heavily debated and not well understood. In utero and early postnatal estrogen exposure is a significant contributory factor for testicular cancer risk in young men ,90. StudEndocrine-disrupting chemicals were found to contribute to increased testicular cancer risk in some studies ,92,93. LDue to the hormonal basis of prostate development, researchers have valid reasons to understand the potential relationship of xeno-estrogenic pesticides with prostate cancer risk . The humThe association of pesticides, including OCPs, with cancer risk is well established. However, some of the mechanisms, such as xeno-estrogenicity, that increase the risk of carcinogenic progression are not clearly understood. Accumulating evidence is consolidating the role of xeno-estrogenic OCPs in the risk of not only cancers but also a plethora of other human disorders. These chemicals, despite their apparent adverse health effects, are still used in some countries. Although the risk of some cancers such as breast cancer is clearly modified with exposure to the discussed chemicals, the link with the risk of other estrogen-mediated cancers, especially in males, is not clearly understood and needs to be studied more.


We performed a meataanalysis of BRCA1 or BRCA2 germline pathogenic or likely pathogenic variant (gBRCA) in 108,699 unselected breast cancer patients and in 238,972 unaffected individuals. The metanalysis shows that 3.4% unselected breast cancer patients have a gBRCA. In unselected breast cancer patients with gBRCA, more than half of tumors are estrogen receptor-positive. 0.5% of unaffected individuals of the studied populations are gBRCA carriers. The frequency of gBRCA2 and gBRCA1 heterozygosity is estimated at 1/288 and 1/434, respectively. In unselected breast cancer and in unfafected individuals gBRCA2 is more frequent than gBRCA1.BRCA2 gene was involved in 54% of breast cancer cases in unselected patients with gBRCA  and 60% of unaffected individuals  as compared with 38% of the largest gBRCA family cohort . The meta-analysis showed that 1.66% (95% CI 1.08–2.54) and 1.71% (95% CI 1.33–2.2) of unselected breast cancer patients carried gBRCA1 and gBRCA2, respectively. In a population of unaffected individuals, the frequency of heterozygosity for gBRCA1 and gBRCA2 was estimated at 1/434 and 1/288, respectively. Nearly 0.5% of unaffected individuals in the studied populations carried a gBRCA. Carriage of a gBRCA was 2.5% for patients with ER+ tumors (95% CI 1.5–4.1) and 5.7% (95% CI 5.1–6.2) for those with ER- tumors. Overall, 58% of breast tumors occurring in women carrying a gBRCA were ER+ . Conclusions: This meta-analysis showed that gBRCA2 carriage is predominant in unselected breast cancer patients and unaffected individuals. ER+ tumors among women with gBRCA-related breast cancer are predominant and have been underestimated. Because PARPi agents improve progression-free survival with ER+ gBRCA breast cancer in most clinical trials, breast cancer should be considered, regardless of ER status, for BRCA1/2 screening for therapeutic purposes. Background: Poly(ADP-ribose) polymerase 1 inhibitor (PARPi) agents can improve progression-free survival of patients with breast cancer who carry a germline BRCA1 or BRCA2 pathogenic or likely pathogenic variant (gBRCA) in both the metastatic and adjuvant setting. Therefore, we need to reassess the frequency of gBRCA1 and gBRCA2 in order to redefine the criteria for women and tumor phenotype that should be tested. Objective: We studied the relative distribution of gBRCA1 and gBRCA2 in unselected populations of women with breast cancer and in unaffected individuals. We also analyzed the proportion of estrogen receptor (ER)-positive (ER+) tumors in unselected breast cancer patients with gBRCA. Design: We performed a meta-analysis of studies of unselected breast cancer that analyzed the relative contribution of gBRCA1 versus gBRCA2 among unselected breast cancer cases in gBRCA carriers. We then performed a meta-analysis of gBRCA carriage in unaffected individuals from genome-wide population studies, the gnomAD databank, and case–control studies. Results: The  BRCA1 and BRCA2 account for the most important genes identified for risk of the disease )) AND ((breast cancerg[MeSH]) OR (breast tumor[MeSH]) OR (estrogen receptor[MeSH]) OR (case control[MeSH]) OR  OR (unselected[Tw]) OR  OR (unaffected[Tw]) AND ((pathogenic variant[MeSH]) OR (likely pathogenic variant[MeSH]) OR (germline mutation [MeSH]) OR (tumor mutation[Tw]) OR (somatic mutation[Tw])) AND (english[Language]) AND (“1995/01/01”[Date—Publication]: “2020/06/20”[Date—Publication]) NOT (case reports[Publication Type]) NOT (case reports[Tiab]) NOT (mice[Tw]). The literature search used variations and Boolean connectors of key terms. The results of the database searches were supplemented by searching bibliographies of seminal articles or reviews and contributions from expert panel members’ references.The PubMed database was searched for English-language studies from January 2000 to August 2021 by using the following query of terms related to gBRCA and ER status frequency in patients with breast cancer:  OR  OR (BRCA 1 gene[MeSH]) OR (BRCA 2[MeSH]) OR (BRCA1 2[MeSH]) OR (breast cancer 1 gene[Tw]) OR (breast cancer 2 gene[tw]) OR . Studies of somatic mutation were retained only if gBRCA data were available. The Quoron flowchart is given in 2. I2 is the proportion of total variation contributed by between-study variation. If heterogeneity was present (Q statistic significant at 5%), we took it into account by using a random-effects model. Publication bias was visually estimated with funnel plots and quantified with the Egger test and the “trim-and-fill” method of Duval and Tweedie. Details of the meta-analyses are given in We used Comprehensive Meta-Analysis software to estimate the pooled values of the parameters as well as their 95% confidence intervals (CIs). We estimated heterogeneity by using the Cochran Q test with the point estimate IFor the meta-analysis of gBRCA in unselected breast cancer, the following studies were retained ,22,23,24Carriage of gBRCA was explored in the exome studies ,27, in cBRCA1, 16 allele counts with 14 variants in BRCA2).Only likely pathogenic and pathogenic variants from the ClinVar database were considered carrier variants. This decision was made because of premature truncating codons in gnomAD classified as variants of uncertain significance, conflicting, likely benign, or benign in ClinVar, and their prevalence is high . Premature truncating codons variants not available in ClinVar were not retained because their American College of Medical Genetics and Genomics classification is still uncertain and their prevalence in the database is low .A total of 10 studies of unselected breast cancer series were retained on the basis of the selection criteria described in the Quoron flowchart . Meta-anp = 0.92 and p =0.99 for gBRCA1 and gBRCA2, respectively).gBRCA1 and gBRCA2 status accounted for 1.66% (95% CI 1.08–2.54) and 1.71% (95% CI 1.33–2.2) of unselected breast cancer cases . There wAmong breast cancer patients with gBRCA, 46% (n = 1496) carried a gBRCA1 and 54% (n = 1776) a gBRCA2 .p = 0.46).The meta-analysis of 7 studies including a total of 86,870 patients showed that 2.5% of patients with ER+ tumors .In total, 5.7% of patients with ER- tumors  had ER+ tumors, whereas among gBRCA2 carriers, 75% (1085/1453) had ER+ tumors . OverallWe performed a meta-analysis of available data from genome-wide population studies ,27, the p = 0.23).Carriage of a gBRCA1 or BRCA2 pathogenic variant in the population of 238,972 individuals was 0.5% (95% CI 0.4–0.6) . There wThe frequency of gBRCA1 and gBRCA2 was 0.18% (95% CI 0.12–0.25) and 0.32% (95% CI 0.27–0.38), respectively , S3, S4.BRCA2 gene accounted for 60% of all gBRCA carriers in the general population  in the absence of a BRCA second event ,39. MaxwIn the adjuvant setting, the positive outcomes of the OlympiA trial in terms of progression-free survival but also overall survival  suggest Because of the clinical benefits of PARPi therapy in BRCA-related cancer, the lack of timely identification of a BRCA mutation represents a lost opportunity for patients. From 30% to 50% of patients with breast but also ovarian, prostate, or pancreatic cancer do not fulfill personal or family criteria for usual preventive BRCA testing ; thus, fThe higher incidence of gBRCA1 found in the largest international cohort of identified gBRCA carriers  could beBRCA1/2 breast cancer cases are missed when genetic testing criteria are used [BRCA2, are being missed when testing strategies focus on NCCN or other hereditary genetic guidelines. Thus, testing criteria in women with newly diagnosed breast cancer should be expanded. Clear genetic information concerning individual and family consequences of identifying a gBRCA, including preventive end screening strategy and information to siblings, should be given and informed consent obtained from all patients for whom a genetic test is proposed. Specialized genetic counselling should be proposed with a strong family history or if additional information is required. Our data strongly suggest that the frequency of gBRCA2 carriage and ER+ tumors among women with breast cancer is underestimated because classical genetic testing criteria are often missing in gBRCA2 families. In a previous review of the literature, we found that 50% of overall are used . Accordiare used  showed tare used , which eThis study shows that the frequency of gBRCA2 carriage is higher than expected in both unselected breast cancer patients and in the general population. It also shows that more than half of breast cancer cases occurring in women with a gBRCA are in fact ER+. Because of the important benefit of identifying gBRCA carriage for PARPi treatment and prevention, ER+ breast cancer should be fully considered for BRCA screening.