Univariate analysis revealed that multiple surgeries , axillary lymph nodes excision (>10) , being overweight (≥25 kg/m2) , received fewer post-surgery rehabilitation treatment  and hypertension  were associated with an increased risk of BCRL. Meanwhile, multivariate analysis showed that multiple surgeries remained significant and elevated the likelihood of BCRL . Arm swelling was more prominent in the forearm area demonstrated by the highest difference of arm circumference measurement when compared to the upper arm . The total of skinfold thickness of the affected forearm was also significantly higher than the unaffected arms (p < 0.05) as evidenced by the ultrasound examination. The continuous search for risk factors in specific populations may facilitate the development of a standardized method to reduce the occurrence of BCRL and provide better management for breast cancer patients.Breast cancer has been reported to have the highest survival rate among various cancers. However, breast cancer survivors face several challenges following breast cancer treatment including breast cancer-related lymphedema (BCRL), sexual dysfunction, and psychological distress. This study aimed to investigate the potential risk factors of BCRL in long term breast cancer survivors. A total of 160 female breast cancer subjects were recruited on a voluntary basis and arm lymphedema was assessed through self-reporting of diagnosis, arm circumference measurement, and ultrasound examination. A total of 33/160 or 20.5% of the women developed BCRL with significantly higher scores for upper extremity disability (37.14 ± 18.90 vs. 20.08 ± 15.29,  Breast cancer is the most common cancer diagnosed worldwide with an estimated 2.3 million new cases in 2020 and it is more prevalent in less developed countries, such as Middle Africa and Eastern Asia compared to European countries . In MalaThe number of breast cancer survivors has improved over time due to early detection, improved treatments, and multi-disciplinary rehabilitation methods. However, the improved treatments also come with various late side effects such as arm or breast swelling (lymphedema), menopausal symptoms, sexual dysfunction, and psychological distress ,5,6. AppBCRL often arises following the loss, obstruction, or blockage of lymphatic vessels due to cancer treatment including removal of lymph nodes or regional radiotherapy. It is characterized by abnormal lymphatic fluid retention and tissue swelling in one or both arms accompanied by discomfort, heaviness, functional dysfunction, and pain of the upper extremities; subsequently leading to decreased quality of life (QoL). BCRL has been reported to develop in 10–50% of women who undergo axillary lymph node dissection (ALND) and 5–20% of those who have sentinel lymph node biopsy (SLNB) ,9,10. Thpeau d’orange (skin texture that resembles orange peel) that becomes bigger and is no longer relieved by lifting or external pressure [At present, lymphedema that occurs at the upper quadrant extremities or hands is diagnosed by performing a series of tests that include physical examination, clinical history, measurement of arm volume, and examination of lymphatic structures through high-end imaging techniques such as computed topography. Physical examination such as arm circumference measurement, water displacement, bioimpedance analysis, and ultrasound examination are often carried out to incorporate differences between affected limbs from normal baseline . Besidespressure . The conpressure ,24.To our knowledge, studies on BCRL in the Asian region are lacking and this study is the first to address BCRL in Malaysian breast cancer survivors. Factors associated with BCRL vary among individuals, depending on the patient factors including age, BMI, stage of breast cancer, types of treatment, or comorbidities ,25,26. WThis matched case–control study was conducted in Malaysia from September 2018 until December 2019. The inclusion criteria for participants were as follows: (a) female aged 18 years or older; (b) Malaysian citizen; (c) diagnosed with breast cancer; (d) underwent unilateral breast cancer surgery at least three months prior to recruitment; (e) completed all chemotherapy and radiotherapy treatment; (f) no current evidence of cancer; and (g) able to provide informed consent. Meanwhile, the exclusion criteria involved (a) patients with other serious systemic illnesses ; (b) history of corticosteroid use on the body for any reason; (c) abnormality or vascular disease in the upper extremities and (d) inability to provide informed consent.All participants gave informed consent. A set of questionnaires comprising of demographic and medical history, a QoL assessment, and upper quadrant function were self-administered by study participants. Participants were given the questionnaire verbally by the investigators, in cases where they had difficulties reading. Physical and anthropometry measurements  were performed prior to measurement of arm circumference and ultrasound examination of the arms. Participants were provided with a minimal cash incentive for volunteering in the study. A total of 173 breast cancer survivors were initially recruited from eight different states in Peninsular Malaysia and 160 of them were eligible for the study .2) and the categories were defined as normal (<25.0 kg/m2) and overweight (≥25.0 kg/m2). The WHR was measured by dividing the waist circumference  with hip circumference (cm) and classified as lower risk (<0.90) and higher risk (≥0.90) of abdominal obesity [Anthropometric measurements including blood pressure, pulse rate, weight, height, body mass index (BMI), waist to hip ratio (WHR) and fat percentage were recorded for each study participant. Blood pressure and pulse rate were recorded using blood pressure monitor . A body composition monitor  was used to obtain weight and fat percentage values. BMI was calculated as weight  divided by height , eight items on emotional (EWB), six items on social (SWB), and seven items on functional (FWB) well-being and 10 additional items on breast cancer scale (BCS) metrics. The scoring system of FACT-B is a Likert scale, which ranges from 0 to 4; 0—not at all, 1—a little bit, 2—somewhat, 3—quite a bit and 4—very much. The total score was calculated by adding all scores based on instructions in the manual . Higher scores indicate better QoL of an individual.The validated English  and MalaThe DASH  questionnaire is a commonly used instrument to evaluate the upper extremity function of a patient when performing activities related to the upper limbs. It consists of 30 items with responses ranging from 1 to 5, out of which the individual rates their ability to perform specific activities in 25 items . The remaining items in the instrument ask questions relating to the severity of symptoms, sleep interference, and feelings of confidence and usefulness . In the present study, the validated Malay version of DASH was employed for all participants [The definition of arm lymphedema was first explained to the participants during the briefing, to avoid confusion with temporary swelling that occurs within three months after breast cancer surgery. Lymphedema occurring less than 12 months following breast cancer surgery was referred to as early-onset lymphedema, whereas swelling occurring more than one year after the surgery was considered late-onset lymphedema .Lymphedema cases that are diagnosed by a medical practitioner were reported within the questionnaire to classify the participants into the lymphedema or non-lymphedema sub-group. Additional self-reported symptoms and physical assessments were applied to characterize the participants’ arm morbidity. The self-reported soft tissue sensation symptoms of lymphedema included heaviness or tightness (yes/no), difficulty in finding shirts that fit (yes/no), pain at the arm area (yes/no) and swelling (yes/no).A circumference measurement was performed by the same researcher (throughout the study) to identify the circumference difference (CD) between affected and unaffected arms of the participants. Measurements were taken on bilateral arms at four points; metacarpophalangeal joint, wrist, 10 cm distal to antecubital fossa of the elbow, and 15 cm proximal from the antecubital fossa to the lateral epicondyle of humerus, as visualized in A trained researcher performed ultrasound imaging examinations using a linear probe with a 7.5 MHz transducer  for image recording) at two sites of both arms; the anterior forearm at a point of 10 cm distal to the antecubital fossa and at 15 cm proximal from the antecubital fossa . The twot-test was performed to compare the mean between lymphedema and non-lymphedema groups, whereas a paired t-test was applied to compare the difference between affected and unaffected arms of lymphedema group participants. Non-parametric tests, such as the Mann–Whitney U and Wilcoxon signed rank test, were performed for non-normally distributed data. Spearman correlation test was performed to observe correlation exists between FACT-B and DASH scores, with lymphedema-related symptoms. Descriptive statistics are expressed as the mean ± standard deviation (SD) for continuous variables and cross tabulation tables were applied for categorical variables. For normally distributed data, an independent p < 0.05 as statistically significant unless stated otherwise to find the odd ratios (OR). OR were calculated to illustrate the association between variables and lymphedema. A binary logistic regression was applied to identify factors associated with BCRL, using the enter stepwise method. All the significant and relevant variables from the bivariate analysis were classified as independent variables or predictors and lymphedema was set as a dependent variable. Predictors were revised for multicollinearity using a variance inflation factor (<3). For the assessment of lymphedema-related symptoms and risk factors, bivariate analysis  was applied for individual factors denoting p < 0.05 as statistically significant. Graphpad PRISM 9.0 and Biorender.com software were used to generate graphs and diagrams of the findings.The difference of arm circumference measurement was analyzed between unaffected and affected arms of participants in the lymphedema group. Finally, skinfold thickness measurements, including dermis and subcutaneous layer, were performed to compare differences between the affected and unaffected arms. All statistical analyses were performed using SPSS version 25 applying n = 7 having had secondary breast cancer). The mean age of study participants during recruitment was 51.04 ± 8.63 years, with a mean age following breast cancer diagnosis of 5.64 ± 4.34 years and a mean BMI of 27.88 ± 5.51. The study population consisted of three ethnic groups: Malay , followed by Chinese , and Indian/other . The details of the demographic data are presented in Of the 173 participants, 160 were eligible for inclusion in the study (with n = 33). Meanwhile, those with a CD of <1.5 cm at all points with less than two arm-lymphedema symptoms were classified as the non-lymphedema group (n = 127). There was a statistically significant difference between women with and without lymphedema for BMI, WHR values, number of children, FACT-B, and DASH scores between the two groups  (p < 0.01), ranging from a weak to moderate correlation. Apart from swelling of the arm, all arm symptoms were significantly correlated with FACT-B scores (p < 0.05). The details of the correlation of DASH, FACT-B, and the four arm-lymphedema symptoms are presented in The presence of arm lymphedema was assessed in all 160 participants. Based on the self-reported symptoms and/or diagnosis, an individual with a CD of ≥1.5 cm between arms at any two points of measurement was classified into the lymphedema group (o groups . An exte< 0.001) . All foun = 24) of women in the lymphedema group have fewer children as compared to those in the non-lymphedema group . Women in the lymphedema group had significantly higher BMI (78.8% of the women who developed lymphedema had a BMI ≥25 kg/m2) when compared to those who did not develop lymphedema (59.1% of the non-lymphedema group) . A higher WHR (≥0.90) was associated with an increased risk of lymphedema . The medical and treatment-related findings demonstrated that menopausal status (p = 0.256), age during breast cancer diagnosis (p = 0.449), breast biopsy (p = 0.298), side of breast affected (p = 0.730), stage of breast cancer diagnosis (p = 0.875), breast cancer receptors (p = 0.554), chemotherapy (p = 0.364), radiotherapy (p = 0.881) and anti-hormone therapy such as the estrogen receptor modulator (Tamoxifen) or aromatase inhibitor (Letrozole and Anastrazole) (p = 0.399) had no significant association with lymphedema. Additionally, family history of cancer (p = 0.982) and supplement intake (p = 0.890) were also not related to lymphedema.Univariate analysis was performed by extracting the medical and breast cancer history of the study participants to identify individual factors associated with lymphedema . Approxip < 0.001) compared to those who had either mastectomy  or lumpectomy . Although there were missing data for the total number of lymph nodes removed , excision of more than 10 lymph nodes was significantly more common in the lymphedema group . Women who received none or only had one type of post-surgery rehabilitation treatment were at increased risk of lymphedema . Hypertension showed a significant association with BCRL . There was no significant association between the two groups for other comorbid medical conditions including cardiovascular disease, lung or kidney disease, or infection (p > 0.05). Interestingly, there was a significant association with increased risk of lymphedema in women who had extensive surgeries (both mastectomy and lumpectomy) on the same side of breast  and swelling of the arms . No significant difference was found for heaviness or tightness of the chest (p = 0.916) and pain in the arm area (p = 0.089)  .n = 5, non-lymphedema, n = 35) and WHR as the cut-off value for higher risk of metabolic syndrome exceeded normal value (≥0.90) in both groups. The analysis showed that higher BMI, hypertension, and post-surgery rehabilitation treatment were slightly attenuated (p > 0.05), whilst extensive surgeries (lumpectomy and mastectomy) maintained its statistical significance in the model  of the women developed lymphedema at early-onset whereas 51.5% (n = 17) had late-onset lymphedema. The characteristics of women who developed lymphedema with early or late-onset are shown in p = 0.040). No significant difference was found between any of the treatment-related factors, with the exception of the number of lymph nodes removed. Study participants who developed early-onset lymphedema had more lymph nodes removed  as compared to those who developed late-onset lymphedema . Additionally, the odds of having early-onset lymphedema were nine times higher in diabetic patients when compared to those who were not diabetic .Among the 33 participants in the lymphedema group, approximately 48.5% (p < 0.05). Details on the measurement are presented in The arm circumference measurement was taken at four points and compared between unaffected and affected arms of women in the lymphedema group. It was found that the CD of the metacarpo-phalangeal, wrist, forearm and upper arm were significantly higher on the affected arm  and total thickness (p = 0.027) of the forearm areas (p = 0.030) (p > 0.05) (As shown in rm areas a and der= 0.030) b. No sig= 0.030) a or in t > 0.05) b. The ultrasound images of the affected and unaffected arms of individuals with different stages of lymphedema display different patterns of tissue structures mainly within the subcutaneous areas . Figure BCRL is a progressive condition that affects the upper extremity function and that can cause detrimental effects on the quality of life of an individual. It is unclear why some breast cancer patients develop lymphedema, while those who received identical treatment do not. Based on the literature, the risk factors associated with BCRL are divided into treatment-related  and modifiable risk factors .In this study, one in five breast cancer survivors 21.5%) who have undergone unilateral breast cancer surgery developed BCRL. Note that this is the first study to address the occurrence of BCRL in the Malaysian population, and the percentage of BCRL is in line with most reported studies ,21, desp.5% who hBCRL is considered one of the most distressing side effects from breast cancer treatment to some patients as it can cause physical and functional dysfunction, psychological disturbance, as well as an alteration of body image. The assessment of QoL is important to provide information on the long term-impact of cancer treatment and to provide better management for affected individuals . The pre2) as a factor associated with a two-fold increased risk of developing BCRL, as shown in univariate and only slightly attenuated in multivariate analysis. According to the WHO classification for the Asian population, a BMI of ≥25 kg/m2 is considered overweight, while ≥30 kg/m2 is obese I [2), while the lymphedema group was classified as obese (30.0 kg/m2). Higher BMI and obesity have been well documented as novel and independent risk factors of developing BCRL in many studies [The present study also revealed higher BMI , compression garments, physiotherapy, remedial exercise or fitness programs, massages, skin care routines, and surgical treatment for advanced stage lymphedema. Based on our findings from the univariate analysis, participants who had combined physical therapy or rehabilitation  had a 2.4-fold reduced rate of BCRL. It is well documented that combined physical therapy is more effective in reducing the severity of lymphedema as it can improve the lymph circulation through repeated contraction and muscle relaxation . A one-yThe present study combined self-reported questionnaires  and arm circumference measurements to discriminate women who may have lymphedema. Self-reported questionnaires are useful to observe early lymphedema-related signs and provide the capacity to identify those likely to be disease-free . MoreoveArm circumference measurement using tape has been widely used to monitor the difference between arms and is inexpensive and easy to use. In fact, self-monitoring using tape measurement helps make the individual aware of any difference related to their limbs ,70. Our The most common criterion to diagnose lymphedema from circumference measurements is to have equal or greater than 2 cm inter-limb difference at any single location as a cut-off value . HoweverSkin ultrasound examination is a standard diagnostic method to evaluate edema and tissue changes. The application of ultrasound is effective to evaluate severity, composition of epifascial, subfascial, fibrosis, and fluid accumulation over time . The comThese findings are supported by van der Veen et al. who reported hypoechoic dermis of the affected arms . The armThere are several limitations to our study. Given that we applied a case–control study design, the number of BCRL women in our study did not reflect the prevalence of BCRL in our population. Therefore, we could not determine the cause-effect relationship. Reporting of variables such as pitting edema, the number of excised axillary lymph nodes, type of breast cancer receptors, radiotherapy and chemotherapy drugs by participants in this study were limited. Due to this, our results may be skewed because of the missing data for these variables. Although there may be missing cases of subclinical lymphedema, self-reporting symptoms were supported by arm circumference measurements and ultrasonography data. We did not factor in hand dominance of the participants, which may affect the use of the upper extremities and the ability of the participants to perform certain physical activities, assessed in DASH and FACT-B questionnaires. Furthermore, applying a low frequency transducer for ultrasonography may facilitate the evaluation of tissue changes in the skin and subcutaneous tissue, but staging of lymphedema can only be assessed using a high frequency probe (15–20 MHz).The findings of this study suggest that BCRL incidence is affected by both treatment  and patient-related factors . The combined application of screening and assessment methods such as self-reported symptoms and arm circumference measurement facilitated the classification of breast cancer survivors into the lymphedema group. Continuous and extensive studies on BCRL factors are important for the development of a standardized method to reduce BCRL incidence, providing better management and treatment of this complication for breast cancer survivors.


Research on psychosocial stress and risk of breast cancer has produced conflicting results. Few studies have assessed this relation by breast cancer subtype or specifically among Black women, who experience unique chronic stressors.We used prospective data from the Black Women’s Health Study, an ongoing cohort study of 59,000 US Black women, to assess neighborhood- and individual-level psychosocial factors in relation to risk of breast cancer. We used factor analysis to derive two neighborhood score variables after linking participant addresses to US Census data (2000 and 2010) on education, employment, income and poverty, female-headed households, and Black race for all households in each residential block group. We used Cox proportional hazards regression to estimate hazard ratios (HR) and 95% confidence intervals (CI) adjusted for established breast cancer risk factors.During follow-up from 1995 to 2017, there were 2167 incident invasive breast cancer cases (1259 estrogen receptor positive (ER +); 687 ER negative (ER−)). For ER− breast cancer, HRs were 1.26 (95% CI 1.00–1.58) for women living in the highest quartile of neighborhood disadvantage relative to women in the lowest quartile, and 1.24 (95% CI 0.98–1.57) for lowest versus highest quartile of neighborhood socioeconomic status (SES). For ER+ breast cancer, living in the lowest quartile of neighborhood SES was associated with a reduced risk of ER+ breast cancer . With respect to individual-level factors, childhood sexual abuse  and marital status  were associated with higher risk of ER+, but not ER− breast cancer.Neighborhood disadvantage and lower neighborhood SES were associated with an approximately 25% increased risk of ER− breast cancer in this large cohort of Black women, even after control for multiple behaviors and lifestyle factors. Further research is need to understand the underlying reasons for these associations. Possible contributing factors are biologic responses to the chronic stress/distress experienced by individuals who reside in neighborhoods characterized by high levels of noise, crime and unemployment or the direct effects of environmental toxins.The online version contains supplementary material available at 10.1186/s13058-021-01483-y. While recent overall breast cancer incidence rates in the US are similar for Black women and White women, US Black women are more likely to be diagnosed with estrogen receptor negative (ER−) breast cancer, which has fewer treatment options and a poorer prognosis . ReasonsThere have been numerous studies of psychosocial stress in relation to overall breast cancer risk. Some have reported associations of traumatic or stressful life events, including divorce/separation , death oWhile differences in both study design and measurement of psychosocial stress may contribute to the inconsistent findings, failure to assess associations by ER status may also play a role. In studies that considered ER status separately, stressful experience, such as marital separation/divorce  and low We assessed the relation of individual-level and neighborhood-level psychosocial stress factors with risk of ER+, ER− and triple negative breast cancer in over 20 years of follow-up data from Black women participating in a prospective cohort study.In 1995, 59,000 women aged 21 to 69 years enrolled in the Black Women’s Health Study (BWHS), a prospective cohort study of women from across the US who self-identified as Black , 29. At On the 2017 questionnaire, participants were asked whether they had experienced loss of a parent or guardian  before the age of 18. They were also asked if, during their childhood or adolescence, a member of their household served time in prison. The 2013 questionnaire ascertained data on childhood financial hardship. Participants were classified as having severe financial hardship in childhood if their household did not have enough money for food/housing and received public assistance/welfare; as having moderate hardship if their answer was yes to one question, and as having no hardship if their answer was no to both questions. Experiences of sexual and physical abuse in childhood and adolescence were reported in 2005. For each time period (childhood or adolescence), exposure was classified as no abuse, someone exposed their genitals to the participant but no sexual assault, sexual assault 1–3 times, sexual assault ≥ 4 times or physical abuse only. We repeated the analyses using a reference group of no to both childhood and adolescent abuse. Since results were similar to the primary analyses, we do not present results from analyses using the modified reference group.Marital status, reported in 1995 and on two follow-up questionnaires, was categorized as single, married/living together, separated/divorced or widowed. Educational attainment was reported in 1995 and 2003, and grouped as ≤ 12, 13–15, ≥ 16 years of education. Ascertainment of perceived experiences of daily and institutional racism have been described previously . In 1997Depression was measured in 1999 and 2005 using the 20-item Center for Epidemiological Studies Depression (CESD) scale , which aAssessment of neighborhood SES has been described elsewhere . In brieTo determine neighborhood concentrated disadvantage, we linked participants’ addresses from 1995 to 2003 questionnaires to year 2000 Census data, addresses from 2005 to 2009 questionnaires to years 2005–2009 American Community Survey (ACS) data and addresses from 2011 to 2015 questionnaires to years 2007–2011 ACS data at the block group level. We conducted a factor analysis of six variables as described in prior literature : percentWe evaluated the joint effect of neighborhood SES and concentrated disadvantage by creating a combination variable with the following categories: highest SES and least disadvantaged, intermediate SES and intermediate disadvantage, lowest SES and most disadvantaged. In addition, percentage of Black residents, which can be a measure of social cohesion, was assessed in quartiles with data obtained through linkage to Census and ACS data.Incident invasive breast cancer was the outcome of interest. Breast cancer cases were identified through self-report on follow-up questionnaires, linkage to the National Death Index, and linkage to the 24 state cancer registries in which 95% of the participants lived. Trained study personnel blinded to exposure status reviewed pathology reports and registry data to confirm diagnoses and collect information on diagnosis date and tumor characteristics. ER, progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) status were available for 90% of breast cancer cases.Cox proportional hazards regression was used to calculate hazard ratios and 95% confidence intervals (CI) for the association of each individual- and neighborhood-level stressor with risk of breast cancer, according to ER status and for triple negative breast cancer. We stratified regression models by questionnaire cycle and age in one-year increments, such that age was the underlying timescale. Participants accrued person-time beginning at the time of exposure assessment and ending at breast cancer diagnosis, death or end of the study period in 2017, whichever occurred first. Follow-up was considered to begin in 1995 for most exposures, in 1997 for daily and institutional racism, in 1999 for CESD score and in 2005 for perceived stress.Because study participants lived in almost all 50 states across the US, there was very little clustering within neighborhood block groups. 57% percent of block groups had only one BWHS participant, 19% had only two and 9% had only three. Nevertheless, to account for clustering within neighborhoods, we performed a sensitivity analysis using generalized estimating equations to assess associations between neighborhood-level factors and breast cancer risk. The estimates of relative risk from this analysis were nearly identical to those from the primary analysis and therefore we present results from the standard Cox proportional hazards models.2), parity , age at first birth , breastfeeding , menopausal status , age at menopause , oral contraceptive use , first-degree family history of breast cancer , geographic region  and vigorous physical activity . Cigarette smoking, waist-hip ratio, Alternative Healthy Eating Index, and type 2 diabetes were also considered, but were not included in the final regression models because their inclusion did not appreciably change the HRs and they are not established breast cancer risk factors. Individual-level stressors were considered as confounders in analyses of neighborhood-level factors, but their inclusion did not change effect estimates. Missing indicator terms were used to account for missing covariate data. Time-varying covariates were updated at each questionnaire cycle.The following factors were included in multivariable regression models: age at menarche , BMI at age 18  and education (≤ 12 vs. > 12 years). In education-stratified analyses, we collapsed quartiles 3 and 4 of neighborhood SES and quartiles 1 and 2 of neighborhood concentrated disadvantage because of small numbers in some of the categories.p values are two-sided with a 0.05 level of significance. Analyses were performed using SAS 9.4 .Linear trend was assessed in analyses of neighborhood-level exposures by treating the measures as ordinal variables. Reported During the study period from 1995 to 2017, there were 2167 incident invasive breast cancer cases; 1259 were classified as ER+, 687 as ER− and 310 as triple negative.The distribution of individual- and neighborhood-level factors at baseline is displayed in Table None of the individual-level factors were associated with risk of ER− or triple negative breast cancer Table . HoweverPtrend = 0.05). The HR for highest versus lowest quartile of percentage of Black residents was 1.21 . However, with additional control for neighborhood SES, the HR was reduced to 1.14 (95% CI 0.87–1.48) and was also similar when additional control was made for neighborhood disadvantage instead of neighborhood SES. Hazard ratios for triple negative breast cancer were similar to those for ER− cancer, but less precise. In contrast to results for ER− breast cancer, women who lived in the lowest quartile of neighborhood SES were estimated to have a reduced risk of ER+ breast cancer . Neighborhood concentrated disadvantage was not associated with ER+ disease.Women who lived in the lowest quartile of neighborhood SES were estimated to have a 24%  increased risk of ER− breast cancer compared to those living in the highest quartile Table . SimilarAs shown in Table In this large prospective cohort of US Black women, residence in disadvantaged or low SES neighborhoods was associated with an approximately 25% increased risk of ER− breast cancer. Similar results were observed for triple negative breast cancer, based on smaller numbers. Our findings are consistent with prior studies, most of which were case-only or registry-based , 37. ThoThe association between neighborhood disadvantage and ER− breast cancer was apparent only among women with a high school education or less. It may be that higher educational attainment  acts as a buffer against effects of an adverse neighborhood environment. A similar interaction was reported in a case-only study .Contrary to the results for ER− breast cancer, low neighborhood SES was associated with a reduced risk of ER+breast cancer rather than an increased risk. Two previous studies have reported similar associations with ER+ or hormoWe also examined percentage of Black residents in a neighborhood, as it has been a marker of social cohesion. Two case-only studies of neighborhood racial composition suggested an association between percentage of Black residents in a neighborhood and breast cancer subtypes, independent of other neighborhood-level factors , 27. In Our findings suggest that where an individual lives matters with respect to development of ER− breast cancer. Furthermore, the fact that the associations were unchanged by adjustment for multiple factors, including diet, exercise, BMI and type 2 diabetes, indicates that the impact of neighborhood characteristics is not simply due to individual characteristics and behaviors shared by residents of certain neighborhoods. Associations with increased risk of ER− breast cancer may be driven by unmeasured aspects of the neighborhood environment such as environmental toxins, noise and lack of safety. Neighborhoods of lower SES are more often characterized by social , physical  and environmental  disadvantage , 43. TheResearch on the biological impacts of chronic stress suggests that stress may influence breast cancer through multiple pathways. Stress activates the sympathetic nervous system and hypothalamic–pituitary–adrenal axis, which in turn release hormones that trigger the fight or flight response to combat the perceived threat . This prDespite the biologic plausibility of a relation between chronic psychosocial stress and increased risk of breast cancer, or specifically of ER− breast cancer, we found little or no evidence of association with individual-level factors. The only individual-level finding supporting a stress/breast cancer risk hypothesis was the increased risk of ER+ breast cancer (but not ER−) for women who had experienced sexual assault during childhood. One prior study found that women who reported sexual abuse any time during the lifetime had a twofold increased risk of breast cancer . BiologiThe major limitation with regard to analyses of individual-level factors was a lack of information on how much these ostensible stressors actually caused distress in each individual. Experiences of encountering stressors  combined with how the experiences affect the individual person will give a measure of psychosocial “distress”, which is probably more relevant biologically. Individuals respond differently to stressful situations and similar situations can take a very different toll. We did not have a measure of distress, such as would be obtained from responses to a PTSD assessment tool. Misclassification of the actual distress resulting from various factors will have reduced our power to detect associations with individual factors even if chronic psychosocial distress plays a role in the etiology of breast cancer.Other limitations of the study include small numbers for analyses stratified by educational level and for overall analyses of triple negative breast cancer. There is not a gold standard for measuring neighborhood SES or neighborhood concentrated disadvantage, but the composite score variables we used were similar if not identical to composite variables used by other investigators. Although we controlled for many individual-level confounders, we did not have detailed data on additional neighborhood characteristics such as exposure to environmental toxins and thus could not evaluate whether or to what extent those exposures explained the observed association with neighborhood disadvantage.A major strength of the study was the cohort study design, which enabled estimation of relative risk of ER− breast cancer rather than a comparison of ER− with ER+ breast cancer, as in previous case-only studies. Detailed data on breast cancer risk factors, including a complete reproductive history, permitted control for these factors, something that was not possible in registry-based studies. The sample size was large, enabling assessment of associations by ER status and triple negative breast cancer. Importantly, we had collected data on participant address every two years and had geocoded the addresses, allowing us to create time-varying neighborhood-level factors for disadvantage, SES, and segregation.Black women living in disadvantaged neighborhoods were estimated to have an increased risk of ER− breast cancer compared to those living in more advantaged neighborhoods. Given the high prevalence of living in disadvantaged neighborhoods among Black women, it is possible that the disproportionately high incidence of ER− breast cancer in US Black women may be partially explained by neighborhood environment.Additional file 1: Table S1. Distributions of the factors that contribute to neighborhood SES and neighborhood concentrated disadvantage scores, at baseline in 1995.


The trend of women suffering from early-onset breast cancer is increasing in Taiwan. The association of early-onset breast cancer with body mass index (BMI), menarche, and menopausal status has focused interest on the field of cancer epidemiology; however, few studies have explored the interaction of these factors on early-onset risk. This study aimed to estimate the interaction effects of BMI, menarche, and menopausal status on 40-year-old early-onset breast cancer.Breast cancer patients were recruited from Kaohsiung Medical University Chung-Ho Memorial Hospital from 2013 to 2020. Multivariable logistic regression was used to estimate odds ratios (ORs) for early-onset breast cancer risk associated with menarcheal age stratified by sociodemographic factors and for the interaction between BMI and menopausal status on early-onset risk.OR = 4.62, Pinteraction = 0.057). Compared to both BMI ≥ 24 and peri-/postmenopausal status, both the status of BMI < 24 and premenopause were associated with early-onset breast cancer .A total of 775 participants were divided into 131 early-onset cases (≤ 40 years) and 644 late-onset cases (> 40 years). Compared to the age of 13 years at menarche, the age ≤ 11 years was significantly positively associated  and ≥ 16 years was negatively associated  with 40-year-old early-onset breast cancer respectively. In an adjusted model, the status of BMI < 24 and premenopause had 1.76- and 4.59-fold risk of early-onset breast cancer respectively. Especially in BMI < 24 status, premenopause also had a 6.47-fold early-onset risk and the early-onset risk increased by a significant amount per one year younger at menarche . There was also a positive interaction effect on an additive scale between BMI and menopausal status on early-onset breast cancer (RERIThis study suggests that the status of BMI < 24 and premenopause were associated with an increased risk of early-onset breast cancer and there was a positive interaction on an additive scale. Understanding how obesity and menopausal status affect early-onset breast cancer is important for drafting preventive measures for early-onset breast cancer in Taiwan. Breast cancer is the most frequently diagnosed cancer at all ages among women, accounting for 24.5% of female cancers, and the burden of breast cancer continues to increase worldwide . In mostBreast cancer is a multifactorial, heterogeneous, and complex etiology disease. Adiposity, often represented by body mass index (BMI), is an important factor for breast cancer and appears to have opposite effects before and after menopause . MultiplThe interval between menarche and menopause is generally considered as the female reproductive period, because menarche and menopause refer to the onset and cessation of menstruation separately . In manyThis study aimed to estimate the association of 40-year-old early-onset breast cancer with BMI, menarche, menopausal status, and sociodemographic risk factors and the interaction effect between BMI and menopausal status on the risk of early-onset breast cancer in Taiwan and whether this could be used to improve early-onset breast cancer risk by some predictive measures during adolescence.n = 1), were of foreign ethnicity (n = 1), and had no clinical or questionnaire data (n = 48)  Chung-Ho Memorial Hospital, a medical center in southern Taiwan. Participants were excluded if they had suffered from any other cancers , cell grade, tumor size, cell invasiveness, the status of hormone receptors, treatment, recurrence, metastasis, and deaths.t-test, Chi-square test, or Fisher's exact test respectively. Multivariable logistic regression was performed to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between the risk of 40-year-old early-onset breast cancer and sociodemographic risk factors as well as women’s age at menarche and to assess the interaction effect of BMI and menopausal status on early-onset risk. According to the standard classification of the MOHW in Taiwan, BMI was categorized as non-obesity (BMI < 24 kg/m2) and overweight or obesity (BMI ≥ 24 kg/m2). The measurement for interaction effect as additive scale was relative excess risk due to interaction (RERIOR = eβ1+β2+β3—eβ1—eβ2 + 1) [All participants in this study were divided into early-onset (≤ 40-year-old) and late-onset (> 40-year-old) groups to analyze the differences of continuous and categorical variables by using independent sample eβ2 + 1) .p-value multiplied by twenty-five risk factors) to reduce potential problems  raised from multiple comparisons. We also performed G*Power to compute statistical power analyses for the multivariable logistic regression [P-values less than 0.05 were statistically significant.All models were also stratified by the sociodemographic characteristics to assess the risk of 40-year-old early-onset breast cancer in relation to being per one year younger at menarche in women. The adjusted ORs were estimated by adjusting the risk factors that were significantly associated with early-onset breast cancer, including BMI, education, marriage, physical activity, menopausal status, and parity. Because the associations between early-onset breast cancer and twenty-five sociodemographic and clinical characteristics were evaluated at the same time, we used Bonferroni correction  and the age ≥ 16 years was significantly associated with decreased risk of 40-year-old early-onset breast cancer . As the age at menarche increased, the risk of early-onset breast cancer decreased ; especially in the status of BMI < 24, the risk of early-onset breast cancer significantly increased by being per one year younger at menarche . In the stratification of the sociodemographic characteristics, including the status of BMI < 24, higher education level, peri-/postmenopause, parous, and breastfeeding, by being per one year younger at menarche, the aORs of 40-year-old early-onset breast cancer were 1.26 to 1.38 with statistical significance. Premenopausal status was associated with 40-year-old early-onset breast cancer  compared with peri-/postmenopausal status; especially in the status of BMI < 24, the association between premenopausal status and early-onset breast cancer was also significantly positive .The adjusted models in Table OR = 4.62, Pinteraction = 0.057), shown in Fig. p-value < 0.001). Furthermore, in the statistical power analyses for the two important variables of BMI and menopausal status, the statistical power was 87.78% and 100% respectively.There was also a positive interaction effect on an additive scale between BMI and menopausal status on 40-year-old early-onset breast cancer  produced by the liver. The levels of free circulating sex hormones decrease and lose bioavailability by binding SHBG . StudiesInsulin-like growth factor 1 (IGF-1) is important for the development and function of many tissues, including promoting cell proliferation and inhibiting apoptosis , 44. EarObesity has adverse effects on health , althougThere are some limitations in our study. Firstly, this study was a cross-sectional study, so the causation of BMI status for early-onset breast cancer was difficult to clarify; however, the results of age at menarche were credible. Age at menarche occurs before breast cancer occurrence. Secondly, no environmental exposure issues were included in the questionnaire. These factors could be also related to the occurrence of breast cancer and might have interfered with the results in the study. Thirdly, Type I error was raised from multiple comparisons between early-onset breast cancer and several sociodemographic and clinical factors. Bonferroni correction was used to reduce potential problems and most sociodemographic characteristics remained statistically significant results. Furthermore, after adjusting for these confounding factors, there was a positive interaction effect on an additive scale between BMI and menopausal status on early-onset breast cancer.These findings suggested that younger age at menarche, BMI < 24 status, and premenopausal status were related to 40-year-old early-onset breast cancer and there was a positive interaction effect on an additive scale between the status of BMI < 24 and premenopause on early-onset breast cancer. Therefore, maintaining proper weight before menopause and keeping good dietary habits to prevent precocious puberty could both be beneficial to decrease the risk of early-onset breast cancer. Understanding the mechanism of the inverse association between BMI status and the risk of premenopausal early-onset breast cancer could possible modify the pathways. The results of this study require larger sample size to differentiate incident and prevalent patients with early-onset breast cancer to explore cause-and-effect relationships and perform a prospective study design to elucidate this biochemical mechanism.


N = 84,969) and a 1:10 sample of age-matched non-breast cancer controls . Incident breast cancer (time-varying exposure) was the exposure and development of any type of NHL, including diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), mature T/NK-cell lymphomas, anaplastic large cell lymphoma (ALCL), and unspecified types of NHL, was the outcome. During follow-up, 1564 incident cases of NHL occurred. The fully adjusted Hazard Ratio (HR) for NHL associated with the development of breast cancer was 1.64 (95% CI = 1.34–2.00) after adjusting for body mass index, alcohol intake, physical activity, smoking, income, and comorbidity. The adjusted HR for NHL was much higher in participants who were aged <50 years and who received hormone therapy (either tamoxifen or aromatase inhibitors) than in those ≥50 years or who did not receive hormone therapy, respectively. The development of breast cancer was associated with a significantly increased risk of NHL, particularly follicular lymphoma and mature T/NK-cell lymphoma. In particular, the risk of NHL was higher in patients receiving hormone therapy and in younger patients.Several studies have suggested that estrogens have a protective function against lymphomagenesis. The treatment of breast cancer is driven by subtype classification, and the assessment of hormone receptor status is important for treatment selection. Thus, we evaluated the association between breast cancer and the incidence of NHL. We conducted a retrospective cohort study using a population-based nationwide registry in South Korea. We selected all women with newly diagnosed breast cancer between January 1st, 2002 and December 31st, 2016 who received curative treatment ( Non-Hodgkin lymphoma (NHL) is the most common hematologic malignancy worldwide , 2. The An intriguing discovery in hemato-oncology is the lower incidence of lymphomas in women than that in men . DiverseThe treatment of breast cancer is driven by subtype classification, and the assessment of hormone receptor status is important for treatment selection. Although some reports have suggested an association between breast cancer and NHL incidence, these results were based on small numbers of NHL cases , 11. JapWe conducted a retrospective cohort study using a population-based national registry. Korea has a universal single-payer national health insurance system; the National Health Insurance Service (NHIS) maintains the national records of all covered inpatient and outpatient visits, procedures, and prescriptions. The NHIS also includes information on risk factor levels for common chronic conditions collected during routine health screening examinations supported by the national health system.th Revision [ICD-10] C code in any prior claim) as of 2002, we selected those with incident breast cancer (ICD-10 code C50) between January 1, 2003, and December 31, 2016 . We then identified a 1:10 age- and region-matched sample of women who did not develop breast cancer during the study period . From among breast cancer cases and non-breast cancer controls, we selected participants who underwent at least one health screening exam during the study period  and excluded 203,323 participants who presented with any cancer (ICD-10 code C) before the health screening examination, as the health screening examination constituted the baseline for the current study. The final sample size was 1,142,643 . The Institutional Review Board of Samsung Medical Center approved this study (IRB number: 2017-08-147) and waived the requirement for informed consent, as we used only de-identified data.Among all Korean women aged ≥20 years between 2002 and 2016 without a history of cancer . Breast cancer was confirmed based on the presence of a C50 code in more than three claims within 1 year or in an inpatient hospitalization claim in the medical treatment database. Treatments for breast cancer were identified from claims filed within 1 year of breast cancer diagnosis and used to classify patients as those treated with only surgery or surgery plus chemotherapy, radiation therapy, or hormone treatment.The study outcome was the development of NHL . NHL was confirmed based on the presence of C82–C85 or C96 codes in more than three claims within 1 year or in an inpatient. We further categorized NHL into five groups: Diffuse large B-cell lymphoma (DLBCL) , follicular lymphoma , mature T/NK-cell lymphomas , ALCL , and others (C96). The NHIS routinely audits the claims; these data are considered reliable and have been used in numerous peer-reviewed publications . AccordiPatient age and income levels were determined from the insurance eligibility database. Data on smoking habits, drinking habits, physical activity, height, and weight were obtained from the health screening examination database. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. We used data on the age at menarche and age at menopause from the national cancer screening data and generated a variable for menopausal status at the first health screening examination.The participants were included in the study on the date of their first health screening examination (baseline) and were followed up until the development of a study endpoint, death, or the end of the study period . The study endpoint was the development of NHL.The study exposure was breast cancer development, which was considered a time-varying variable. Thus, unexposed person-time was contributed by women who never developed breast cancer, as well as by women who developed breast cancer before developing it. For each outcome, cases of breast cancer occurring after developing a study endpoint (outcome) were not included in the analysis as participant follow-up ended with the development of a study outcome. Since cancer patients receive various exams prior to cancer treatment, to reduce the potential impact of surveillance bias we considered that outcomes occurring in the first 31 days after a diagnosis of breast cancer corresponded to unexposed person-time. Cumulative incidences were estimated using the Kaplan–Meier method. We calculated hazard ratios (HRs) with 95% CIs for developing NHL using a proportional hazards regression model with age as the time scale.th, 31st–70th, >70th percentile), and presence of comorbid conditions at the time of the health screening examination.To account for potential confounding factors at the health screening examination (baseline), we adjusted for BMI categories , alcohol intake , moderate–vigorous physical activity , smoking status , income percentile , we used non-exclusive categories in the analysis of treatment modalities . In addition, to account for competing risks due to mortality, we fitted a proportional subdistribution hazards regression model with death as the competing event . We examN = 84,969; 424,669 person-years of follow-up), non-breast cancer controls  had similar characteristics at the time of the baseline health screening examination (Table Compared to participants who developed breast cancer (P for interaction < .01). Age-related effects were observed for all subtypes  than in older participants  ; radiotherapy ; and radiotherapy, chemotherapy, and hormone therapy  , and the median time from the last breast implantation to an ALCL diagnosis was 8 years range 0–34 years) . In a re years . Our study has several limitations. First, data on exposure and outcomes were extracted from claims data. However, the identification of cancer cases based on claims is considered reliable in Korea, as cancer codes are reviewed by the NHIS and have implications for additional patient benefits. While these data are subject to errors, claims data for the clinical outcomes evaluated in this study are highly specific and have been widely used in population studies. Second, the NHIS claims data do not contain information on laboratory, imaging, pathology, family history, or reproductive profile; we did not have information on cancer stage, type of hormone, and germline mutations in the breast tumors. Further studies with detailed clinical data are necessary to confirm the study findings. Third, there are services and treatments that are not covered by national health insurance, including preventative surgery, over-the-counter drugs, and cosmetic surgery. Thus, we could not determine whether the participants had received breast implants. Since 2015, the national health insurance in Korea has covered breast reconstruction; thus, studies are needed after the accumulation of sufficient follow-up data to observe cancer outcomes. In addition, we were not able to adjust for the potential confounding effects of breast implants or birth control pill use. However, when we used data from the baseline health screening exam, the proportion of women who took birth control pills among those without and with breast cancer was 5.5 and 4.7%. In addition, the proportion of hormone replacement therapy at the baseline health screening exam was similar among women without (10%) and with (9.7%) breast cancer. It is thus unlikely that the use of estrogen-based contraceptives or hormone replacement therapy can explain the differences between the groups. Fifths, patients who develop breast cancer have more frequent contact with the health care system, which may induce surveillance bias. Finally, although we had sufficient statistical power to observe the incidence of NHL in breast cancer patients, a longer follow-up duration may be needed given the incidence of various subtypes of NHL. However, this study’s strength was its nationwide cohort study design that included a relatively large number of NHL cases and consideration of the types of cancer treatments and lifestyle variables that could affect the occurrence of a second malignancy. Finally, the association of breast cancer with overall NHL may depend on the breast cancer treatment or the mix of subtypes of NHL, which may be different in different populations . One of In conclusion, breast cancer development was associated with an increased risk of NHL development. In particular, the risk of NHL was higher in patients receiving hormone therapy and in younger patients. In addition, the excess risk of NHL was higher within 3 years after breast cancer diagnosis. Surveillance guidelines should be developed to screen for NHL in patients with breast cancer, especially those diagnosed at a younger age and who received hormone therapy.Reproducibility checklistUpSet plot of treatment combinations in patients with breast cancerFactors associated with the incidence of Non-Hodgkin lymphoma


Observational studies indicate that phospholipid fatty acids (FAs) have an impact on the etiology in cancers, but the results are conflicting. We aimed to investigate the causal association of phospholipid FAs with breast cancer and prostate cancer.Fourteen single nucleotide polymorphisms (SNPs) were selected as instrumental variables to predict the level of 10 phospholipid FAs from Genome-wide association studies (GWAS). We obtained the summary statistics for the latest and largest GWAS datasets for breast cancer  and prostate cancer  from the Breast Cancer Association Consortium (BCAC) and Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium. Two-sample Mendelian randomization analysis was applied.The results demonstrate that the 10 individual plasma phospholipid FAs are not significantly associated with breast cancer risk and prostate cancer risk.The evidence is insufficient to support the causal association of the 10 individual plasma phospholipid FAs with breast cancer and prostate cancer. Fatty acids (FAs) are involved in various physiological processes, including maintaining cell membrane stability, forming raft lipid to regulate signal transduction, the process of inflammation, and even hormone synthesis . The twon = 25,871) with follow-up for an average of 5.3 years concluded that ω-3 PUFA supplementation did not reduce major cardiovascular events and overall cancer incidence  , a positncidence , while tTo overcome the aforementioned problems, Mendelian randomization (MR) analysis is widely applied to investigate the causality between the exposure and outcome. MR analysis uses single nucleotide polymorphisms (SNPs) for predicting the level of exposure to determine its causal effects on the outcome . AccordiP < 5 × 10–8) of the association with the phospholipid FAs were subsequently searched on the PhenoScanner V2 website to determine whether the SNPs were related to the potential confounders between plasma phospholipid FAs and the two types of cancer. Furthermore, a primary assumption in MR analysis is that the IVs influence the outcome only via the exposure, while other pathways involved between the IVs and outcome can cause a biased result. Given the above, the two SNPs with high pleiotropy are associated with body mass index and alcohol consumption were excluded (rs780093 and rs780094). To ensure the selected SNPs were independently (r2<0.005) associated with the corresponding FAs, we performed a linkage disequilibrium test on the LD-link website (population: CEU)1     were built on the largest genomewide association studies (GWAS) on circulating phospholipid FAs from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium, in which the participants are of European origin . The IVsn: CEU)1 , 2. Finan: CEU)1  to predi= 8,866) , ω-6 PUF= 8,631) , and MUF= 8,961) . The cha2  approach to estimate each IV’s combined causal effects. The IVW approach evaluates the combined effect by calculating the Wald ratio of each SNPs and then using the corresponding inverse variance as weights for meta-analysis . There iAll statistical analyses were conducted by R version 4.0.2 and R package “MendelianRandomization.”We used IVW method as primary analysis, the results of which show that the 10 individual FAs examined are not significantly associated with breast cancer: ALA , EPA , DPA , DHA , LA , AA , POA , OA , PA , SA  .The results of IVW analysis demonstrate that the FAs are not causally associated with prostate cancer: ALA , EPA , DPA , DHA , LA , AA , POA , OA , PA , SA  .WM and MR-Egger analysis could not be performed to test the pleiotropy of EPA, DPA, DHA, LA, AA, OA, and PA because the IVs were less than three SNPs. The sensitivity analysis was conducted only to LA, POA, and SA; the estimated effects of IVW analyses were similar to the results calculated by WM, MR-Egger, and MR-Egger intercept methods , 5. The We used the MR approach to investigate the causal association of the 10 individual plasma phospholipid FAs with breast cancer and prostate cancer, including MUFA, SFA, ω-3, and ω-6 PUFA. Our MR analysis indicates the 10 individual FAs we examined are not significantly associated with the breast cancer risk and prostate cancer risk.case = Ncontrol = 2,982) nested within the European Prospective Investigation into Cancer and Nutrition study, the researchers found no significant association between plasma phospholipid ω-3 PUFA between breast cancer risk (case = Ncontrol = 794) study in Nurses’ Health Study II demonstrated that erythrocyte membrane FAs, which reflected dietary intake for several months were not associated with breast cancer overall, while the subgroup analysis suggested a protective effect of DPA and EPA but not DHA among overweight women , which suggests that higher intake of ALA is not associated with the lethal prostate cancer risk after the introduction of PSA screening (Whether FAs play a role in prostate carcinogenesis is similarly controversial. Meta-analyses have attempted to clarify the association between FAs and prostate cancer risk; a meta-analysis demonstrates that dietary intake of ALA reduces the risk , while screening . Howevercreening , whereascreening , 2014 ancreening  are margcreening . Our MR Researchers have applied the MR approach to investigate the causal association between plasma phospholipid FAs and several diseases, including atrial fibrillation , orthopeIn conclusion, our MR analysis indicates that little evidence supports the causal association of the 10 plasma phospholipid FAs with breast cancer and prostate cancer.The original contributions presented in the study are included in the article/Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.ZY, JL, YS, YLu, and XZ conceived the study design and drafted the manuscript. ZY, JL, YS, and ZQ participated in data extraction and data analysis. YLi, LZ, QH, XJ, and MA did the data checking and analysis. All authors critically reviewed the manuscript.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.


Studies investigating breast cancer polygenic risk score (PRS) in Chinese women are scarce. The objectives of this study were to develop and validate PRSs that could be used to stratify risk for overall and subtype-specific breast cancer in Chinese women, and to evaluate the performance of a newly proposed Artificial Neural Network (ANN) based approach for PRS construction.The PRSs were constructed using the dataset from a genome-wide association study (GWAS) and validated in an independent case-control study. Three approaches, including repeated logistic regression (RLR), logistic ridge regression (LRR) and ANN based approach, were used to build the PRSs for overall and subtype-specific breast cancer based on 24 selected single nucleotide polymorphisms (SNPs). Predictive performance and calibration of the PRSs were evaluated unadjusted and adjusted for Gail-2 model 5-year risk or classical breast cancer risk factors.ANN and PRSLRR both showed modest predictive ability for overall breast cancer  and remained to be predictive after adjustment. Although estrogen receptor negative (ER−) breast cancer was poorly predicted by the primary PRSs, the ER− PRSs trained solely on ER− breast cancer cases saw a substantial improvement in predictions of ER− breast cancer.The primary PRSThe 24 SNPs based PRSs can provide additional risk information to help breast cancer risk stratification in the general population of China. The newly proposed ANN approach for PRS construction has potential to replace the traditional approaches, but more studies are needed to validate and investigate its performance.The online version contains supplementary material available at 10.1186/s12885-022-09425-3. Breast cancer is the most common type of malignant neoplasm and the second leading cause of cancer deaths in women worldwide . The GloBRCA1 and BRCA2 genes that confer high relative risks of breast cancer have been identified [BRCA1 and BRCA2 mutations is lower than that in women of European ancestry [The etiology of breast cancer is multifactorial, with both non-genetic risk factors  and inherited genetic risk factors playing important roles –8. Multientified . Howeverentified , 11, espancestry . In addiancestry . Each ofancestry .−) breast cancer [+) breast cancer. Better prediction of ER-specific breast cancer could enable selection of high-risk women who might benefit from prevention with endocrine therapies.Breast cancer PRSs have been shown to have sufficient predictive power to aid risk stratification, and some have already been implemented in clinical practice , 16. Howt cancer , 23, whiThe primary aim of this study was to develop and validate PRSs for use in stratification of the risk of breast cancer and subtype-specific breast cancer in Chinese women. To that end, we used a GWAS dataset to develop PRSs and validated them in an independent test set from a case-control study. We also aimed to compare different approaches for calculating PRSs, including a newly proposed artificial neural network (ANN)-based approach.https://www.ncbi.nlm.nih.gov/gap). The quality control (QC) procedures applied to the SBCGS dataset are described in Fig. P < 10− 6 and P < 10− 10 for controls and cases, respectively, and samples with KING-robust kinship coefficients > 0.0884  [The dataset used for PRS development was obtained from the Shanghai Breast Cancer Genetics Study (SBCGS) . The SBCsamples) . QC and samples) , 31. AftThe independent test set used for PRS validation was obtained from the Sichuan Breast Cancer Case-Control Study (SBCCS) conducted in Chengdu, Sichuan Province. The study design has been described in detail elsewhere . In brieR2 > 0.9 determined using the LDLink tool [R2 > 0.8) with both rs2162540 and rs2981575  for overall breast cancer in the SBCGS dataset formed the second set of SNPs (Table SR2 < 0.8). Therefore, a total of 34 SNPs were selected and genotyped in the SBCCS  plus 30 blind duplicate samples sent for genotyping. Genotyping of the 34 SNPs was carried out blindly by Bio Miao Biological Company Limited. Time-of-flight mass spectrometry was used for genotyping in strict accordance with a standard protocol.P < 0.05 (rs6730484), and SNPs that were monomorphic  for the kth SNP, and βk is the corresponding effect size, calculated as per-allele log OR for breast cancer associated with the kth SNP. The first approach is known as the repeated logistic regression (RLR) approach. In this approach, βk was estimated in the SBCGS dataset using univariate logistic regression for each SNP individually. The RLR approach is the typical method used to calculate PRSs, since βk estimated from RLR is a summary statistic and can be easily obtained without access to individual-level genotype data. In the second approach, βk was estimated in the SBCGS dataset using multivariate logistic ridge regression, where all 24 SNPs were included in the model simultaneously. The model was also adjusted for age and population structure . The second approach is known as the logistic ridge regression (LRR) approach. The optimal penalty parameter lambda in the ridge regression model was chosen by conducting 10-fold cross-validation on the SBCGS dataset  using corresponding subtype-specific breast cancer cases in the SBCGS dataset.The primary PRSs for overall breast cancer were constructed using all breast cancer cases in the SBCGS dataset. We also constructed the PRSs for subtype-specific breast cancer ; learning rate (0.01), activation function of the hidden layers (Leaky ReLU); activation function of the output layer (sigmoid); loss function (sigmoid cross entropy) and optimizer (Adam optimizer). SBCGS: Shanghai Breast Cancer Genetics Study.4th) of the PRSs in controls with those in the first quartile (Q1st). Calibration was assessed by inspecting the observed OR to the expected OR in each PRS decile and were further estimated using coefficients from log scale linear regression as described by Brentnall et al. [R2 < 0.3).The performance of the PRSs was assessed from two perspectives: predictive ability and calibration. For predictive ability, we used the odds ratio (OR) per interquartile range (IQR) increase (IQ-OR) in the PRSs in the controls as the primary outcome. Discrimination was also used as a metric for the evaluation of predictive ability. Discrimination was assessed by the area under the receiver operator characteristic curve (AUC) with confidence intervals estimated using the Hanley and McNeil’s method . To indil et al. . In addihttps://dceg.cancer.gov/tools/risk-assessment/bcrasasmacro) in SAS . All the statistical analyses were performed using scikit-learn (version 0.21.2), TensorFlow (version 1.13.1) and SciPy (version 1.1.0) in Python 3.6.The Gail-2 model 5-year absolute risks were calculated using SAS Macro (version 4 downloaded from + breast cancer patients and 510 cases were ER− breast cancer patients.The age and ER status profile of the participants in SBCGS are shown in Supplementary Table SP > 0.05). Furthermore, there were no significant differences in 5-year absolute risks of breast cancer predicted by the Gail-2 model between case and control participants (P = 0.07). Comparison of the basic characteristics of the participants in the SBCCS who were included in the current study with those of the participants not included due to unavailability of DNA samples showed that there were no significant differences between these two groups of participants  had very weak correlation with other breast cancer risk factors. Associations between the PRSs and Gail-2 model 5-year risk were also very weak , suggesting that the PRSs were independent of absolute risk predicted by the Gail-2 model.Basic characteristics of the included 427 case and 374 control participants in the SBCCS are shown in Table LRR and PRSANN were comparable  and superior to PRSRLR . As shown in Fig. RLR, PRSLRR and PRSANN, respectively. The primary PRSs showed slightly better predictive ability for ER+ breast cancer but significantly poorer predictive ability for ER− breast cancer. For ER+ breast cancer, PRSANN  outperformed PRSRLR and PRSLRR in terms of predictive ability. Calibration of the PRSs for ER+ breast cancer remained similar. For ER− breast cancer, the primary PRSs had similarly poor IQ-OR (1.27–1.32) and AUC (0.550–0.555). PRSANN was poorly calibrated to ER- breast cancer risks . Adjustment for the Gail-2 model absolute risks had almost no effect on the performance of the primary PRSs  than the primary PRSs constructed using RLR  and LRR . Compared with the primary PRSs, all ER− PRSs showed substantial improvement in the prediction of ER− breast cancer. Among the three ER− PRSs, ER− PRSLRR achieved the highest predictive ability  but still underestimated the ER− breast cancer risk to some extent (O/E OR 1.13 95% CI 0.04–2.21). Adjustment for the Gail-2 model absolute risks and breast cancer risk factors had limited effect on the performance of ER+/ER− PRSRLR and PRSLRR, which was similar to that observed in the primary PRSs. However, adjustment for breast cancer risk factors substantially reduced the predictive and discriminative abilities of the ER+/ER− PRSANN.The performance of the subtype-specific PRSs is shown in Table R2 threshold of 0.8 when selecting the SNPs in order to include more informative SNPs. However, this threshold may have influenced the performance of the PRSRLR. A sensitivity analysis was conducted by incorporating a more rigorous R2 threshold, which led to the removal of seven additional SNPs . The performance of the PRSRLR constructed using SNP-17 was slightly improved but did not exceed the performance of the primary PRSLRR and PRSANN  based on 24 SNPs showed modest predictive ability  and calibration  for overall breast cancer. More importantly, the study results showed that the PRSANN was largely independent of Gail-2 model 5-year risk and other non-genetic risk factors. The PRSANN remained predictive of overall breast cancer after adjustment for classical breast cancer risk factors , although the calibration index seemed to be slightly altered for PRSANN (O/E OR 1.17). These results indicated that our PRSs can provide additional risk information and are therefore suitable for use in conjunction with breast cancer risk prediction models based on non-genetic risk factors to stratify women into different risk groups. Since the Gail-2 model is the only publicly available model that can be implemented in our dataset, we also investigated the combination of the PRSANN and PRSLRR with the Gail-2 model. Although there was a substantial increase in AUC when the PRSs were combined with the Gail-2 model (increased from 0.531 to approximately 0.58), the combined models had lower predictive ability than that using the PRSs alone. This was largely due to the poor performance of the Gail-2 model in the SBCCS dataset, which was consistent with a recent meta-analysis reporting a pooled AUC of 0.55 (95% CI 0.52–0.58) for the Gail-2 model in Asian females [In the current study, the PRSs for the prediction of overall breast cancer and subtype-specific breast cancer in Chinese women were developed using a GWAS dataset and validated in an external case-control dataset. The best PRSs .4th vs. Q1st ORs for PRSANN and PRSLRR were 2.51 and 2.47, respectively, meaning women in the fourth quartiles of the PRSs had an approximately 2.5-fold greater risk of having breast cancer than those in the lowest quartiles. This represents a substantial improvement in predictive ability compared with previous PRSs developed for Chinese women . Through the use of the non-linear activation function and multiple hidden layers, the ANN model is able to fit high-order interactions between variables [ANN showed better predictive ability than the primary PRSLRR in predicting overall and ER+ breast cancer, which suggests the existence of interactions between the included SNPs. To explore possible SNP-SNP interactions, we conducted logistic regression analyses to identify pairwise interactions in the SBCGS dataset. A total of 13 pairs of SNPs with possible SNP-SNP interactions (P < 0.05) were identified, but none of them reached a Bonferroni corrected level of statistical significance (P < 1.8 × 10− 4). Further post-hoc analysis revealed that the interaction between rs10789190 and rs7799039 was statistically significant in both datasets (P < 0.05). The SNPs rs10789190 and rs7799039 are located in the leptin (LEP) and leptin receptor (LEPR) genes, respectively, making their interaction biologically plausible. Adding this interaction term to the PRSLRR slightly improved its predictive ability , indicating the differences between the primary PRSLRR and PRSLRR can be partially attributed to this interaction term. In other words, the ANN approach automatically captures the potential interactions between SNPs, which are likely to be omitted in the traditional approaches. Nevertheless, the ANN approach is more sophisticated and less flexible than the LRR and RLR approaches. Whether ANN can be considered the optimal approach to PRS construction remains to be investigated.We compared three different approaches to PRS construction, consisting of the traditional RLR approach using summary statistics, as well as LRR approach and the newly proposed ANN-based approach using individual-level genotype data. Compared with the traditional summary statistics-based RLR approach, the LRR and ANN approaches can be used to address the issues of overfitting, collinearity and confounding by using individual-level genotype data, thus providing a more accurate estimate of the weighting parameters. Therefore, it is expected that the primary PRSs constructed using the ANN and LRR approaches both achieved better predictive performance than PRSariables . Therefoariables –41, and − breast cancer prediction, which has not been previously conducted in Chinese women. Future studies may consider both ER and human epidermal growth factor receptor 2 (HER2) status when optimizing the PRS for prediction of subtype breast cancer. Third, all the SNPs in the SBCGS and SBCCS were genotyped directly. Imputation was conducted only for sporadic missing genotypes. However, the study also has some limitations. First, the overall performance of the PRSs is not ideal, especially compared with the performance of PRS in women of European ancestry. Due to budget constraints, the search for candidate SNPs was limited to those that are well-validated in Chinese population, hence some newly identified SNPs and SNPs that remain to be validated in Chinese population were omitted. Therefore, the results of our study should be interpreted with caution. Future studies should include more SNPs associated with breast cancer susceptibility, especially those identified in recent GWASs. High-quality genetic studies are also needed to identify and validate more breast cancer susceptibility SNPs in the Chinese population. Second, assessment of the performance of the PRSs in combination with classical breast cancer risk factors was not sufficient, since there is no suitable breast cancer risk prediction model for Chinese women. Further studies are warranted to investigate the performance of the PRSs when incorporated into more accurate risk prediction models for Chinese women. Third, BRCA status information is unavailable for either SBCGS or SBCCS, we are therefore not able to conduct further stratified analyses or make comparisons. Besides, since the SBCGS spanned a long period of time , we cannot rule out the possibility that changes in recommendations for determining ER status may have influenced the results of our study. Finally, our PRSs and study results are limited to Han Chinese women and may not be generalizable to Chinese women in other ethnic groups, although they only account for around 9% of the total population.The current study has several strengths. First, the PRSs were validated in an external dataset, and thus avoided the concern of overfitting. Nevertheless, further validation with an expanded sample, preferably from multiple locations in China, is still needed. Second, we examined the performance of the PRSs by ER status and further optimized the PRSs for ER− breast cancer prediction, the overall performance is still poor and improvements are still needed before it can be implemented to identify women at high risk of ER− breast cancer. Our newly proposed ANN-based PRS construction approach automatically captures the potential interactions between SNPs and showed better performance than the traditional approaches, although additional studies are needed to further validate and investigate this approach.In summary, the SNP-24-based breast cancer PRSs showed significantly better predictive ability than previous PRSs developed for Chinese women. Our SNP-24-based PRSs were largely independent of classical breast cancer risk factors and thus have great potential to improve clinical practice and future risk-based breast cancer screening programs by providing additional risk information for the general population. Nevertheless, the predictive performance of the current PRSs is not ideal and can be improved by incorporating more SNPs that are associated with breast cancer risk in Chinese women. Although the subtype-specific PRSs showed substantial improvement for ERAdditional file 1: Table S1. Basic information of the first set of SNPs along with the SNP selection results. Table S2. Basic information of the second set of SNPs along with SNP selection results. Table S3. Basic information of the 24 SNPs included for PRSs construction. Table S4. Characteristics of the participants in the SBCGS. Table S5 Comparison between the participants in the SBCCS who were included in the current study with the participants excluded due to unavailability of DNA samples or number of failed SNPs ≥3. Table S6. Predictive performance of the primary PRSs for overall and ER+/ER- breast cancer. Table S7. Sensitivity analysis 1 - predictive performance of the primary PRSs for overall and ER+/ER- breast cancer when samples with missing genotypes were all excluded. Table S8 Sensitivity analysis 2 - predictive performance of the SNP-17 PRSRLR for overall and ER+/ER- breast cancer. Table S9. Indirect comparison with previous PRSs for Chinese women. Fig. S1. Linkage disequilibrium matrix of the first set of SNPs. Fig. S2. Linkage disequilibrium matrix of the second set of SNPs. Fig. S3. Hyperparameters tuning results of the logistic ridge regression model. Fig. S4. The structure of the ANN model used in the current study.


Amidst the rising breast cancer burden in Asia, we aim to predict the future mortality risk due to breast cancer and identify the risk-attributable deaths for breast cancer among East and South Asian countries.We used country-level data to predict the trends in the next decade relating to female breast cancer mortality by employing data from 1990 to 2019 from the Global Burden of Disease 2019 study. We used the stochastic mortality modeling and prediction techniques to forecast the age-specific and risk-attributable breast cancer mortality trends at the regional and national levels of East and South Asia.The number of deaths caused by the breast cancer is predicted to increase in East and South Asian countries in the next decade (2020–2030). Age-standardized death rate (ASDR) of breast cancer is predicted to increase by 7.0% from 9.20/100,000 (95% CI: 6.04–12.12) in 1990 to 9.88/100,000 (95% CI: 7.12–11.4) in 2030 in East Asia, and about 35% increase from 13.4/100,000 (95% CI: 9.21–16.02) in 1990 to 18.1/100,000 (95% CI: 13.23–21.10) in 2030 in South Asia. At the national level, the highest percent change in ASDR between 1990 and 2030 was reported in Pakistan (a 62% increase) and Nepal (a 47% increase). The highest percent change in breast cancer mortality between 2020 and 2030 for females of age group 80–84 years was observed in Pakistan , followed by Afghanistan [13.3 (4.0–80.8)], and Nepal [36.6 (11.1–125.7)] as compared to the other countries. In the females of aged 50–80 years, the predicted death rates were associated with high body mass index, high-fasting plasma glucose, and diet high in red meat, across the majority of countries under study. Furthermore, reductions in percent change in mortality rates occurred in several countries with increases in sociodemographic index (SDI), notably across high SDI countries.Breast cancer mortality risk varies substantially across East and South Asian countries with higher mortality risk in low/middle SDI countries. Early detection using screening, awareness among females and health workers, and cost-effective and timely treatment of patients with breast cancer is vital in stemming the tide of breast cancer in the next decade. Developing countries and countries undergoing a westernized lifestyle in Asia may be facing a breast cancer epidemic in the near future due to rising breast cancer cases and deaths , 2. BreaGlobally, an increase in breast cancer has been reported partly due to aging and population expansion , 7. SeveOverall, Asia has forty-eight countries with almost 32% of the global female population and most of them are middle- and low-income countries with poor diagnostic and treatment facilities , 11. TheIn this study, we examine the geographical disparities in breast cancer mortality at the regional and national levels of East and South Asia, from 1990 to 2019 for women aged 20–84 years. We predicted the breast cancer mortality trends in these regions and countries for the period 2020–2030 using Lee–Carter (LC) model, which accounts for spatial variability and predicts future trends in each country and also allows for mortality risk, longitudinal age trends, and age-adjusted trends over time. To assess the risk-attributable breast cancer mortality in each country under study, we also predicted the mortality trends by risk factors. We conclude by discussing possible underlying risk factors for breast cancer mortality and identifying specific areas of public health concern. To the best of our knowledge, this is the first study using advanced statistical methods to predict breast cancer-related mortality trends by age and risk factors for the next decade at the regional and national levels of East and South Asia.This study used publicly available data from the Global Health Data Exchange (GHDx) . As we oThe GHDx is a health-related database available at the Institute for Health Metrics Evaluation (IHME) and IHME produces estimates of the burden of diseases, including breast cancer. The estimates in this database are created from a repository of input data, which includes data directly obtained from cancer registries, surveillance data, administrative records, and vital registration. It provides a systematic assessment of 369 injuries and diseases, 87 risk factors, and 284 reported reasons of death among 204 countries, twenty-one regions, and seven super-regions. This study covered the female population diagnosed with breast cancer as the per International Classification of Diseases (ICD) version 10 code C50 . CountriThe GHDx database produced as a result of the global burden of disease 2019 study presents the latest estimates, which are produced via innovative estimation methodologies that have been detailed elsewhere , 14, 15.The SDI is a composite indicator of a country's lag-distributed income per capita, average years of schooling among persons above 15 years of age, and total fertility rate (TFR) in females under the age of 25 years . The comThe comparative risk assessment method has already been integrated into the Global Burden of Disease (GBD) study  to measuIn this analysis, death rates  of female breast cancer, corresponding to age groups 20–84 years with 5-yearx, t and exposure to death Ex, t for a person of age x last birthday during year t. The central mortality rate mtx,  is calculated using the formula age intervals, and successive thirty calendar years from 1990 to 2019, were considered for modeling analysis. LC model  was applth rates .mx, t is the central mortality rate at age x and year t, and ax is the average (over time) log-mortality at age x, and bx measures the response at age x to changes in the overall level of mortality over time, and kt represents the overall level of mortality in year t, and εtx,  is the residual term. The Singular Value Decomposition (SVD) algorithm was used to forecast the general mortality index for the period 2020 to 2030, for each country separately.The LC-based modeling frameworks are viewed in the current literature as among the most efficient and transparent methods of modeling and projecting mortality improvements. Previously, scholars have used this approach to forecast all-cause and cause-specific mortality in different countries –26. The Based on age-standardized mortality estimates of 2019, we ranked the risk factors of breast cancer for East and South Asian countries. The four risk factors that were ranked first were selected for further prediction analysis . The spaThe mortality modeling methodology was implemented using publically available R programming functions with package ilc , which fThis study adopted the following steps of analysis. First, we predicted the age-specific mortality rates for 11 years from 2020 to 2030 and then the change between 2019 and 2030 in estimated age-specific mortality rates was expressed as a percentage . Age-standardized mortality rates were computed using the direct method based on the WHO World Standard Population . The dirWe also predicted risk attributable mortality rates by potential breast cancer risk factors among females of aged 50–84 years. Finally, we projected the SDI value for the same prediction period and assessed the association between SDI and mortality changes using the spearman correlation coefficient test during the entire period from 1990 to 2030. Additionally, to test the sensitivity of the method, we evaluated the performance of the LC model in comparison with an alternative stochastic mortality model called Booth–Maindonald–Smith (BMS) model  and predx and βx, and the time-dependent parameter kt are reported for each country. Applying SVD to the matrix Mx, t, we obtained an explained variance of 83.2, 82.8, 81.7, 90.9, 94, 73.9, 85.5, 74.5, 96.8, 94.5, 89, and 76% by fitted LC model for East Asia, China, Japan, North Korea, South Korea, Mongolia, South Asia, India, Pakistan, Afghanistan, Nepal, and Bangladesh, respectively. These results indicate that the LC model fitted breast cancer-related mortality data reasonably well. Further, the forecasted breast cancer death rates from 2020 to 2030 had low values of mean squared error (MSE), Akaike's Information Criteria (AIC), and Bayesian Information Criteria (BIC).The results of estimation of parameters in the LC model, for twelve locations (2 regions and 10 countries) are described in The trends in observed and predicted age-standardized death rates (ASDRs)  of female breast cancer at regional and national levels from 1990 to 2030 are depicted in The age-specific breast cancer mortality rates  in East and South Asian regions from 1990 to 2030 are shown in The high BMI- and high FPG-related mortality rates  are increasing in both the regions, with the highest increase observed in Pakistan and Afghanistan during 2020–2030. The alcohol association to increase mortality risk is expected in Japan, Mongolia, South Korea, and Nepal during the next decade than other countries. Six out of ten countries  are predicted to have high mortality attributable to a diet high in red meat during 2020–2030 . The ageAdditionally, we also reported life expectancy trends of patients with breast cancer of age 50–84 years  . We obseThe country- and region-wise trends in SDI for 1990 to 2030 are given in r ≥ 0.83, p ≤ 0.001) was observed between SDI and mortality rate across all the countries in both the prediction and forecasted periods . As a sensitivity check, we estimated the mortality change using both models , progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2), breast cancer which is negative in ER, PR, and HER2 is termed as triple-negative breast cancer (TNBC) . TNBC isThe mortality trend from breast cancer is predicted to increase in the next decade compared with the past decades in both East and South Asian countries. Japan officially recommended in 2004 that women should start mammographic screening at the age of 40 years, and once biannually after that . HoweverThe South Asian region demonstrated increasing mortality trends for all its countries with Pakistan, India, Nepal, and Afghanistan demonstrating a steady upward trend in mortality in the next decade. The population aging is a non-modifiable risk factor for developing breast cancer leading to mortality in Asian women –52. CorrMost of the developed and developing countries of Asia are increasingly adopting a Westernized lifestyle, such as a diet high in fat and processed meat, low in fruits and vegetables, and lack of (or low) physical activity leading to obesity, which is linked with breast cancer apart from several other health risks of obesity . On averAmong 10 countries, six countries  have shown a high-predicted mortality rate attributable to a diet high in red meat. Another study has shown that higher red meat consumption, especially processed red meat, is associated with an increased risk of breast cancer-related mortality, which has a relationship between red meat consumption and mortality risk . A meta-This study predicted life expectancy trends among patients with breast cancer of age 50–84 years. We observed that the life expectancy of a female patient with breast cancer in East Asia, China, and South Korea, might increase while for other countries, it is expected to decrease or remain constant during the prediction period 2020–2030. Increases in life expectancy might be due to changing lifestyles, provision of better hospitalization, and fulfillment of required nutritious standards in these countries . In addiOur research results are estimated by integrating data from multiple sources in the GBD network using a constantly updated modeling process; there are, however, few limitations in this study. First, although GBD estimates of the breast cancer burden are based on multiple sources of data, due to lack of quality PBCRs in low-resource countries of East and South Asia, the estimates are produced using spatiotemporal modeling, because of which, these estimates may be biased. Moreover, the uncertainty intervals may be too wide for a few countries. Second, we have described only the overall trends for each country, but did not describe the subadministrative regions of each country. Third, the estimates may also be biased because of the lack of diagnostic facilities, or healthcare workers expertise in proper stage and grade detection of tumors. Therefore, these estimates cannot be considered a substitute for data collection using quality PBCRs. Moreover, we predicted the trends by modeling the age-period pattern in breast cancer mortality, which can be further confirmed by incorporating the cohort effects using other stochastic mortality methods. Though statistical findings in this study may differ from the actual situation, it provides a rough estimate of disease diagnosis and possible detrimental health effects. These estimates call for policymakers and governments' attention across these regions and countries to take appropriate measures to combat the disease burden.This study predicted the trends of breast cancer-related mortality in East and South Asian countries over 40 years from 1990 to 2030, showing that breast cancer-related mortality has been increasing and is expected to remain a significant public health challenge in most of these Asian countries in the next decade. Several breast cancer deaths are attributable to multiple modifiable risk factors, mainly dietary and metabolic risk factors. The increase in breast cancer-related mortality in countries with weaker public health systems and poor awareness levels may result in the reduction of life expectancy among patients with breast cancer. Therefore, improving early detection, increasing breast cancer awareness and cost-effective treatments are warranted to reduce breast cancer mortality in these Asian countries. Moreover, systematic and accurate epidemiological data collection through cancer registries is obligatory to observe the trends and assess the efficacy of respective interventions, which require collaborations between national and international organizations. Cancer awareness and community health workers education can significantly boost early detection, control, and prevention in low- and middle-income Asian countries.http://ghdx.healthdata.org/gbd-results-tool.Publicly available datasets were analyzed in this study. This data can be found at: Institute for Health Metrics and Evaluation (IHME): SM: conceptualization, data curation, formal analysis, methodology, software, validation, visualization, writing—original draft, and writing—review and editing. RS: software, validation, visualization, and writing—review and editing. SRH: software, validation, visualization, and investigation. MI and XL: data curation, validation, visualization, and investigation. N: methodology, validation, visualization, and investigation. CY: investigation, resources, validation, funding acquisition, project administration, and supervision. All authors contributed to the article and approved the submitted version.This study was funded by the National Key Research and Development Program of China (Grant No. 2018YFC1315302) and the National Natural Science Foundation of China (Grant Nos. 82173626 and 81773552).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.