The Joanne Knight Breast Health Cohort was established to link breast cancer risk factors, mammographic breast density, benign breast biopsies and associated tissue markers, and blood markers in a diverse population of women undergoing routine mammographic screening to study risk factors and validate models for breast cancer risk prediction.Women were recruited from November 2008 to April 2012 through the mammography service at the Joanne Knight Breast Health Center at Washington University in St. Louis, Missouri. Baseline questionnaire risk factors, blood, and screening mammograms were collected from 12,153 women. Of these, 1,672 were excluded for prior history of any cancer (except non-melanoma skin) or diagnosis of breast cancer within 6 months of blood draw/registration for the study, for a total of 10,481 women. Follow-up is through linking to electronic health records, tumor registry, and death register. Routine screening mammograms are collected every 1–2 years and incident benign breast biopsies and cancers are identified through record linkage to pathology and tumor registries. Formal fixed tissue samples are retrieved and stored for analysis. County-level measures of structural inequality were derived from publicly available resources.Cohort Composition: median age at entry was 54.8 years and 26.7% are African American. Through 2020, 74% of participants have had a medical center visit within the past year and 80% within the past 2 years representing an average of 9.7 person-years of follow-up from date of blood draw per participant. 9,997 women are continuing in follow-up. Data collected at baseline include breast cancer risk factors, plasma and white blood cells, and mammograms prior to baseline, at baseline, and during follow-up.This cohort assembled and followed in a routine mammography screening and care setting that serves a diverse population of women in the St. Louis region now provides opportunities to integrate study of questionnaire measures, plasma and DNA markers, benign and malignant tissue markers, and repeated breast image features into prospective evaluation for breast cancer etiology and outcomes. Many of the current cohorts of women for cancer study in the USA are insufficient for examining factors from the biology to the environment that are associated with breast cancer risk among a diverse group of women. Most of these ongoing cohorts of women for the study of cancer have extensive questionnaire risk factor data collected for all participants, yet they are made up predominantly of White women. Smaller subcohorts have blood samples for prospective analysis of hormones, metabolic markers, and DNA . These cDr. Colditz and colleagues established the Joanne Knight Breast Health Cohort at Siteman Cancer Center to collect, store, and ultimately share comprehensive data sets and tissue specimens for future research. The screening mammography service at Siteman and Washington University School of Medicine offered us the potential to recruit a diverse population of women  and to bThe Joanne Knight Breast Health Center at Siteman Cancer Center at Washington University School of Medicine, St. Louis, Missouri provides mammography services for women from varying socioeconomic and racial backgrounds in the St. Louis region, including those with coverage through the Missouri breast and cervical cancer screening programs (Centers for Disease Control and Prevention and state funded), the Komen Fund and Barnard Fund coverage for the uninsured, and regularly insured women with private insurance or Medicare coverage. All women are screened with the same technology (Hologic). The mammography service stores all images and as of 2019 all screening used tomosynthesis (Hologic).Posters describing the study were placed in waiting areas and women attending mammographic breast screening or diagnostic procedures at the Joanne Knight Breast Health Center were approached to participate, all of them completing extended data collection for breast cancer risk estimation. The Joanne Knight Breast Health Center screens approximately 25,000 women and does high risk and diagnostic screening for another 15,000 women per year [The variables needed for the simplified Rosner–Colditz breast cancer risk prediction model  (these iCohort participants consented to (1) retrospective and prospective review of medical records ; (2) one-time 20 ml blood draw; (3) access to tissue not required for clinical care ; and (4) optional future contact for the purposes of long-term follow-up and/or to recruit for other related research projects. Record linkage identifies new mammograms, biopsies, and other visits to BJC Health Care facilities. BJC is a non-profit health care organization serving metro St. Louis, mid-Missouri, and Southern Illinois.Enrollment from November 2008 to April 2012 included 12,153 women who provided blood and risk factor data. A survey of 158 women who opted not to enroll over a two-week period in October 2009 showed most women who did not participate cited a lack of time to give the blood sample (30.4%). The next largest group (19.0%) wanted more time to think about participation. The remaining reasons for not participating included not wanting to give a blood sample (8.9%), not wanting researchers to have access to their medical records (8.9%), and (13.9%) provided no answer. The majority of the women came in for screening mammogram and a subset for diagnostic follow-up . Of these enrolled women, 1,672 had a history of cancer at enrollment leaving 10,481 women free from cancer at baseline.Follow-up of cohort participants as determined by mammography and other clinic visits through December 2020 was 78% seen in 2019 or 2020; a further 4.4% seen most recently in 2018 and a further 2.4% in 2017. All women remain under surveillance for return to follow-up mammography. Follow-up is passive through medical record linkages every 6 months, annual tumor registry searches, and annual mortality searches. This results in over 80% active follow-up for women seen within the last 36 months. The average person-years of follow-up through most recent contact is 9.2 person-years.2 freezers.At enrollment a baseline questionnaire, blood draw, and mammogram were obtained along with address for follow-up and for geocoding for measures of structural inequality. Baseline blood samples were taken and stored in multiple aliquots; DNA extraction  and plasma aliquots of 1 ml (6 per participant) and placed into cryovials and stored at − 80 °C in LNWomen self-reported breast cancer risk factors on entry to the cohort. These are drawn from established and validated measures . The basa = almost entirely fat, b = scattered areas of fibroglandular density, c = heterogeneously dense, d = extremely dense). Routine screening mammograms were obtained using Hologic machines.Mammograms: a screening mammogram 12–24 months prior to baseline, at baseline, and subsequent follow-up screening have been identified and stored. These images are stored along with BI-RADS density report recorded  in SAS 9.4  and theory-driven choices. The data were publicly available and previously compiled by the Health Inequality Project . We alsoThe cohort free from cancer at baseline includes 10,481 women. The distribution by race/ethnicity reflects the racial distribution in our catchment area and is summarized in Table 2 and of note it was higher for Black women (32.8 kg/m2) vs White women (28.0 kg/m2). During follow-up linkages to cancer registry and pathology records have identified 272 incident invasive breast cancers and 116 in situ lesions through October 2020. A total of 623 benign biopsy samples from 6/28/2010 through 12/31/2020 have been identified and are stored for centralized pathology review and classification. Through January 2021, we have confirmed 329 deaths within this cohort.Breast cancer risk factors at baseline are summarized in Table Socioeconomic status varied among participants. 45.6% were living in counties with debt of any kind at or above 30% of the population (Table Plasma samples from the cohort have been evaluated for carotenoid concentrations and risk of proliferative benign breast disease diagnosed from baseline through April 2016 . Among wThis new cohort brings breast images and pathology from routine care in a clinical setting that serves a diverse population into prospective epidemiologic investigations for breast cancer. The integration of blood markers in addition to questionnaire-based risk factors and tissue samples for all breast biopsies, in addition to repeated mammograms on participants, brings unique strengths to this cohort. Furthermore, the diversity of this population that is approximately one-quarter African American fills gaps in both breast cancer etiologies, risk prediction development, and validation of breast cancer risk models in diverse populations.Although repeated visits to the breast health center for screening mammography could facilitate updated or repeated blood measures, the epidemiologic evidence and resources to justify this have not been assembled to date. However, because breast images are the product of the mammography visit, improving approaches to maximize use of the information in these repeated images appears to be the most efficient approach to improve risk stratification as part of routine breast health services.Through IRB approval of deidentified data, plasma or tissue samples can be shared with investigators. Applications submitted to Dr. Colditz are reviewed by an internal Siteman committee, including breast pathology, mammography, and Tissue Procurement Core leadership. Material Transfer agreements are developed once access is approved and data, tissue samples, or blood samples are shipped as agreed. The overall study is approved by the institutional review board at Washington University in St. Louis.


Many breast cancer patients have both non-alcoholic fatty liver disease (NAFLD) and non-alcoholic fatty pancreas disease (NAFPD). Consequently, we hypothesized that NAFPD and NAFLD were associated with breast cancer, and aimed to build a novel risk-stratification scoring system based on it. In this study, a total of 961 patients with breast cancer and 1,006 non-cancer patients were recruited. The clinical characteristics were collected and analyzed using logistic analysis. Risk factors were assessed by a risk rating system. Univariate analysis showed that body mass index, triglyceride, total cholesterol, NAFLD, NAFPD, low-density lipoprotein, and uric acid (UA) were significantly related to breast cancer. Among them, NAFLD, NAFPD, and UA were independent risk factors related to breast cancer identified by multivariate analysis. The risk assessment model was established based on these factors and demonstrated that the odds ratio sharply increased with the rising scores. Compared with the low-risk group, the odds ratio in the intermediate- and high-risk groups were 1.662 (1.380–2.001) and 3.185 (2.145–4.728), respectively. In conclusion, the risk-stratification scoring system combining NAFLD, NAFPD, and UA can accurately predict the occurrence of breast cancer. In addition, more and more studies have shown that NAFLD is a multi-system disease with extrahepatic complications, such as cardiovascular disease, chronic kidney disease, pulmonary insufficiency, and extrahepatic malignancies ,12. BiliNon-alcoholic fatty pancreas disease (NAFPD) is also an important manifestation of metabolic syndrome . PancreaTherefore, in this study, we explored the risk factors for breast cancer and the relationship between breast cancer and NAFLD/NAFPD, and developed a novel risk-stratification scoring system based on the independent risk factors to predict the incidence of breast cancer.22.1This retrospective study was approved by the Ethics Committee of Quanzhou First Hospital Affiliated to Fujian Medical University. Written informed consent was not applicable due to retrospective nature of the study.A total of 961 patients with breast cancer in Quanzhou First Hospital Affiliated to Fujian Medical University from May 2017 to August 2019 were recruited. Exclusion criteria included: (1) drinking > 20 g/day; (2) patients had serious illness at the time of examination, such as cardiovascular disease, malignancies other than breast cancer, etc.; (3) patients with hepatitis A, hepatitis B, and autoimmune hepatitis; (4) history of surgery or chemotherapy. Besides, 1,006 non-breast cancer subjects were included in this study. Exclusion criteria included: (1) drinking > 20 g/day; (2) patients with hepatitis A, hepatitis B, and autoimmune hepatitis; (3) patients with pancreatitis and chronic kidney disease; (4) patients with metabolic disorders caused by drug treatment in the past year. All patients with breast cancer in the study were preoperatively diagnosed by upper endoscopy and preoperatively staged with barium radiography, computed tomography (CT), or endoscopic ultrasonography. All non-breast cancer subjects were confirmed non-breast cancer by the breast ultrasound examination, and some also underwent mammography. Hepatitis A was diagnosed by detection of immunoglobulin M (IgM) antibodies against HAV using colloidal gold strip . Autoimm2.2After fasting overnight (fasting for more than 8 h), all subjects underwent laboratory tests measured by automatic biochemical analyzer , consisting of triglyceride (TG), total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), uric acid (UA), alanine aminotransferase (ALT), and glutamic oxaloacetic transaminase (GOT).2.3All subjects were at least 12 h on an empty stomach and in supine position during examination. The ultrasound examinations were performed by a qualified and experienced radiologist using a high-resolution ultrasound machine equipped with a 5 MHz convex-array probe . The data were evaluated by another experienced radiologist to ensure unbiased evaluation.NAFLD was diagnosed as the presence of at least two of the following findings : diffusely increased echogenitic (“bright”) liver with liver echogenicity greater than kidney or spleen, vascular blurring, and deep attenuation of ultrasound signal .The diagnosis of NAFPD was based on the previous literature criteria : Pancrea2.4t-test. Categorical variables were presented as counts (percentages) and analyzed with χ2 test. Univariate and multivariate logistic regression analysis were performed to assess the risk factors of breast cancer, and the odds ratio (OR) and 95% confidence interval (95% CI) were calculated. Variables with a value of P < 0.05 in the univariate analysis were subsequently included in a multivariate logistic regression analysis. The risk scoring system was used to evaluate the associations between risk predictors and breast cancer incidence. P values <0.05 were considered statistically significant.Data were analyzed by SPSS version 18.0 . Continuous data were expressed as mean values ± SD and analyzed by unpaired two-tailed Student’s Ethics approval: This retrospective study was approved by the Ethics Committee of Quanzhou First Hospital Affiliated to Fujian Medical University. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.33.1P < 0.05), while no significant differences were observed between the two groups in terms of age, diastolic blood pressure, GOT, TG, HDL, and LDL.The demographic and clinical characteristics of subjects are summarized in 3.2P < 0.05), while there were no significant association between breast cancer and the clinical characteristics including age, systolic blood pressure, diastolic blood pressure, and HDL , NAFPD , and UA  . In acco4Early diagnosis and accurate treatment are essential for breast cancer. Currently, several predictive risk models of breast cancer were established, but none of the research included NAFPD and NAFLD ,26. To tP < 0.001). Besides, the univariate and multivariate analysis further revealed that NAFPD was an independent risk factor related to breast cancer.NAFPD is a phenotype of pancreatic steatosis, defined as the pancreatic fatty accumulation associated with obesity and metabolic syndrome ,28, and In recent years, the relationship between NAFLD and breast cancer has become a research hotspot. some studies reported that breast cancer was a common extrahepatic complication of NAFLD . At the In addition to NAFLD and NAFPD, we also found that UA was an independent risk factor for breast cancer, which was consistent with other study . Yue et This study had some limitations which should be considered. First, this study was a retrospective analysis, which still need more data for validation. Second, this was a single-center study, and whether the models are applicable to other patient sets need further external validation. Besides, we did not evaluate known risk factors in this study such as family history of breast cancer, diabetes, breast feeding, smoking, hormone replacement therapy, and history of benign breast disease . Then, since biopsy is not clinical recommended for routine examination , imaging techniques were used to diagnose NAFLD in this study, although liver biopsy is usually the reference standard for NAFLD diagnosis and staging. Finally, we analyzed only the preoperative clinicopathological data of the patients. In the future research, we should include postoperative data for analysis to better reflect the practical value of this model.5NAFLD, NAFPD, and UA were independent risk factors related to breast cancer. The risk-stratification scoring system combining NAFLD, NAFPD, and UA could accurately predict the incidence of breast cancer, which may help clinicians make clinical decisions.


Q4vsQ1 = 2.2, CI: 1.2–4.3). A high intake of total dietary nitrite was inversely associated with gallbladder cancer , driven by an inverse association with plant sources of nitrite . Additionally, small intestine cancer was inversely associated with a high intake of animal nitrite . There were no other dietary associations. Nitrate concentrations in PWS  were not associated with cancer incidence. Our findings for stomach cancer are consistent with prior dietary studies, and we are the first to evaluate nitrate and nitrite ingestion for certain gastrointestinal cancers.Nitrate and nitrite are precursors in the endogenous formation of N-nitroso compounds (NOC) which are potent animal carcinogens for the organs of the digestive system. We evaluated dietary intakes of nitrate and nitrite, as well as nitrate ingestion from drinking water (public drinking water supplies (PWS)), in relation to the incidence (1986–2014) of cancers of the esophagus (n = 36), stomach (n = 84), small intestine (n = 32), liver (n = 31), gallbladder (n = 66), and bile duct (n = 58) in the Iowa Women’s Health Study . Dietary nitrate and nitrite were estimated using a food frequency questionnaire and a database of nitrate and nitrite levels in foods. Historical nitrate measurements from PWS were linked to the enrollment address by duration. We used Cox regression to compute hazard ratios (HR) and 95% confidence intervals (CI) for exposure quartiles (Q), tertiles (T), or medians, depending on the number of cancer cases. In adjusted models, nitrite intake from processed meats was associated with an increased risk of stomach cancer (HR Helicobacter pylori infection and alcohol use for stomach cancer [Cancers of the digestive system include tumors of the pancreas, liver, bile ducts, gallbladder, as well as the gastrointestinal (GI) tract, which includes the mouth, esophagus, stomach, small intestine, large intestine, and anus. The upper gastrointestinal tract includes the mouth, esophagus, and stomach. Four of the five cancer sites with the worst prognosis  are GI cancers . While th cancer ,6; hepath cancer ; and prih cancer ,9. HowevOne potential risk factor for digestive system cancers that has not been widely evaluated is nitrate and nitrite intake. The International Agency for Research on Cancer (IARC) classified nitrate and nitrite as probable human carcinogens when ingested under conditions that result in endogenous nitrosation . EndogenThe primary exposure to nitrate is typically through the ingestion of vegetables containing high levels of nitrate. Processed meats usually contain added nitrite or nitrate, resulting in the formation of carcinogenic NOC . Red andEpidemiologic studies have assessed the relationship between ingested nitrates and nitrites and digestive cancers. A prospective study of nitrate and nitrite, estimated based on dietary intakes in the Netherlands, found that NOCs were positively associated with a risk for esophageal and stomach cancer subtypes . A populThere have been few studies of digestive system cancers and other water contaminants, including disinfection by-products (DBP), some of which are probable or possible human carcinogens ,23. TrihOur objective was to investigate the association between dietary and drinking water nitrate and nitrite ingestion and the risk of specific digestive system cancers  among postmenopausal women in the IWHS. We extended the previous analysis of Weyer et al.  with an As previously described, the Iowa Women’s Health Study is a large prospective cohort study of postmenopausal women in Iowa . In 1986Here, we analyzed specific digestive system cancers diagnosed from between 1986 and 2014 from the State Health Registry of Iowa, which included cancers (ICD-10 code group) of the esophagus (C15.x), stomach (C16.x), small intestine (C17.x), liver (C22.X), intrahepatic bile duct (C22.1), extrahepatic bile duct (C24.0), ampulla of Vater (C24.1), and gallbladder (C23.9). We excluded incident cases of lymphoid cancer, mesothelioma, and Kaposi sarcoma (N = 37) at these sites. Each participant’s vital status was obtained from the State Health Registry of Iowa and the National Death Index. We calculated person–years of follow-up from the enrollment date until the date of (1) incident cancer diagnosis, (2) death, (3) emigration from Iowa, or (4) the midpoint of the last contact date and 31 December 2014.3-N or total trihalomethanes (TTHM) measurements for their PWS (N = 4362). A total of 20,507 participants remained in the drinking water analyses after these exclusions , with 15,577 participants on PWS and 4930 participants on private wells. As previously shown, excluded participants were similar to those retained in the drinking water analysis [Using self-reported data at enrollment, we excluded participants who were premenopausal (N = 569) and who had been diagnosed with prior cancer (except non-melanoma skin cancer) or received cancer chemotherapy (N = 3830). We also excluded participants who reported unrealistic dietary intakes , responded to 30 or fewer dietary questions (N = 2782) on the baseline survey or were missing key covariate information (N = 744). A total of 33,964 participants remained in the dietary analyses after these exclusions. An additional 4300 participants were excluded from the drinking water analysis because they had not participated in the 1989 follow-up survey on which drinking water source was reported. Of the participants who responded to the 1989 follow-up, we further excluded participants who reported using their water supply for ten years or fewer , for an unreported period (N = 67), and those who reported drinking only bottled or other water sources (N = 1013). We excluded participants from municipalities for which we lacked sufficient public drinking water supply (PWS) source information, or had a groundwater aquifer or single surface water source for less than 75% of the study period (N = 2428), to reduce sources of uncertainty in the exposure assessment. We further excluded participants without NOanalysis .We assessed participant dietary intake at baseline using the Harvard food frequency questionnaire (FFQ)  which asBased on the 1989 follow-up questionnaire , we obtained the primary source drinking water at participants’ homes . The majority of participants (76.7%) indicated that they used a public water supply , 18.5% had a private well, and fewer than 5% reported using bottled or other water sources. Participants also reported the duration they had used this water source . Ninety percent had used their drinking water source for longer than ten years.3-N) measured in water samples from municipal water supplies in Iowa across the 33-year historical exposure period (1955–1988) to calculate the annual average NO3-N (mg/L) concentrations for each PWS. We used the historical annual estimated concentration (µg/L) of TTHM, and the sum of the most prevalent DBP class, as a surrogate for the presence of other halogenated DBPs in our drinking water analyses. Experts estimated concentrations before the promulgation of regulations in the 1980s based on the known characteristics of the PWS, including treatment practices and water source measurements [3-N averages on the period of measurements available for each utility, with a maximum of 33 years, and we created two exposure metrics based on the duration of use: (1) long-term average based on years of use, and (2) the number of years in these periods when the annual average NO3-N or TTHM concentrations were greater than one-half of the maximum contaminant level .We assessed contaminants in PWS similarly to previous investigations ,36. Brieurements . Using curements , we estiWe used Cox proportional hazards regression to estimate hazard ratios (HR) and 95% confidence intervals (CI) of the association between dietary nitrate and nitrite, drinking water nitrate, and incident digestive system cancers. We grouped certain cancer types because we had small sample sizes for specific cancers. “Bile duct” cancers combined intrahepatic bile duct and extrahepatic bile duct, and ampulla of Vater and “biliary” cancers combined bile duct cancers and gallbladder cancer. In dietary analyses, we compared the risks in nitrate and nitrite intake quartiles and the 95th percentile to the risks in the lowest exposure quartile (Q1) for cancer groups with a sufficient total number of cases (N ≥ 50), including biliary, gallbladder, bile duct, and stomach cancers. We compared the risks in nitrate and nitrite intake tertiles and the 95th percentile to the risks in the lowest exposure tertile (T1) for esophagus, small intestine, and liver cancers. We tested for linear trends by including nitrate and nitrite intake as continuous variables. The intakes were log-transformed to achieve normality. We adjusted final dietary models for potential confounders assessed in the baseline questionnaire, including age and total calorie intake  as well as categorical smoking status , smoking pack-years, alcohol consumption (grams/day), body mass index (BMI), and categorical rurality  based on the lowest Akaike information criterion (AIC) during a stepwise model selection. We additionally examined the association between nitrite intake from various sources . For analyses of nitrite from animal sources and processed meats, we also adjusted models for saturated fat intake (g/day). We did not assess dietary nitrate from animal and plant sources separately because the majority of dietary nitrate (median proportion = 97%) was derived from plant sources .For the drinking water analysis, we compared the risks in average nitrate and the TTHM exposure quartiles to the risk in the lowest exposure quartile (Q1) for biliary tract cancer (N ≥ 50) and exposure tertiles to the risk in the lowest exposure tertile (T1) for stomach cancer (50 < N ≥ 30). For esophagus, small intestine, gallbladder, bile duct, and liver cancers, we compared the risks in the above-median to the below-median (M1) exposure categories (N < 30). We evaluated participants with any years (>0 years) of exposure > 1/2 MCL compared to participants with no years (0 years) of exposure > 1/2 MCL. We transformed continuous variables using natural logarithms to achieve normality and tested for a linear trend by modeling continuous intakes. Because no nitrate data were available for participants using private wells, we compared them to participants on PWS in Q1  of the average nitrate level. We adjusted final drinking water models for age, as well as smoking status, smoking pack-years, alcohol intake, BMI, and categorical rurality based on the lowest Akaike information criterion (AIC) during an exhaustive model selection. We assessed effect modification by vitamin C (median split) and smoking status (ever/never) with stratified analyses for cancers with a sufficient frequency of cases (biliary and stomach cancers).survival package (Version 3.2-7 [We used R (Version 3.6.3 ) and theon 3.2-7 ) for allAfter exclusions, there were 308 digestive system cancer cases in the dietary analysis and 171 cases in the drinking water analysis. The mean participant age at the baseline was 61.6 years (SD = 4.2 years), and participants were followed up for an average of 21.6 years (SD = 8.1 years). Participants in the highest quartile of dietary nitrate intake were more often former smokers residing in towns with more than 1000 residents who had a lower median total caloric intake but a higher median intake of vitamins C and E than participants in the lowest quartile . ParticiQ4vQ1 = 0.37, CI: 0.16–0.85) and gallbladder , but a nonsignificant inverse association between dietary nitrite and cancer of the bile ducts. We found inverse relationships for both medium and high intakes of dietary nitrite and small intestine cancer, but only a statistically significant relationship for the medium level . We found nonsignificant associations between dietary nitrite and cancers of the stomach and esophagus and a nonsignificant positive association with liver cancer , but no association with any other digestive system cancer . Nitrite intake from processed meats was inversely associated with esophageal cancer , but positively associated with stomach cancer risk .We found an inverse association between nitrite intake from plant sources and gallbladder cancer  compared to the IWHS levels (1.84 mg/L), reported no association between drinking water nitrate and stomach cancer [Weyer et al.  found a h cancer . A populh cancer . Similarh cancer . A smallh cancer . A populh cancer . A small hospital-based case–control study in India of gallbladder cancer, using controls with cholelithiasis, measured the nitrate concentration in bile fluid and found a positive association between biliary nitrate concentration and gallbladder cancer , but theThe main strength of our study is that we assessed exposure to nitrate and nitrite from both drinking water and from dietary sources for these rare digestive system cancers. There have been few studies, especially prospective cohort studies, that have evaluated both sources of these exposures. Other strengths include the availability of historical water quality data, a long period of follow-up, and low rates of residential mobility among the cohort participants .H. pylori infection, an important risk factor for stomach cancer [Our analyses have some limitations. We had a small number of cases for some cancer sites, especially for the drinking water analyses. Small numbers precluded examining effect modification by vitamin C, red and processed meat intake, and smoking for the rarer digestive system cancers. The FFQ used to estimate dietary intake may be a source of exposure misclassification that is likely non-differential; however, a previous validation effort in the IWHS indicated good accuracy and reliability to support its usage to assess macronutrients ,57. We dh cancer .We observed a positive association between a relatively low dietary intake of nitrite from processed meats and stomach cancer risk in postmenopausal women consistent with the majority of prior studies. We found inverse associations between dietary nitrite intake from plant sources and gallbladder cancer and nitrate from animal sources and small intestine cancers. However, these relationships were weak and based on a small number of cases. We found no association between long-term exposure to nitrate or TTHM levels in public water supplies and the risk of these digestive system cancers. This study adds important information on these exposures and digestive system cancer risks among women.


Salazar and colleagues  conducteother implies MBD truly is a “biological pathway.”But this could occur only if MBD is in fact causal for breast cancer. The authors said that “it is worth noting that breast cancer arises from other biological pathways unrelated to, or completely independent of MBD” , and theIt is incontrovertible that MBD causes breast cancers to be missed at mammographic screening because the mammographically dense (white and bright) regions can mask existing tumors from detection. Therefore, higher MBD would cause an increase in the incidence of interval breast cancers . That is, MBD impedes the ability of mammographic screening to identify cancers earlier.In a screening population, reducing MBD would decrease the incidence of interval cancers and increase the incidence of screen-detected cancers. Given the latter is substantially higher than the former, this would result in an increase in a woman’s overall risk of being diagnosed with breast cancer.The authors concluded that trials are needed to identify chemoprevention agents that can “reduce MBD” . They coSo, how is it that MBD is associated with an increased risk of screen-detected breast cancer? There are other aspects of a mammogram that are associated with increased risk of screen-detected breast cancer, based on brightness and texture, and these are positively correlated with MBD . When eaIn summary, finding ways to decrease MBD has the potential to prevent women from dying from breast cancer. But doing so will naturally lead to an increase in breast cancer incidence overall. If conclusions are to be made about whether MBD is a “biological pathway” or a “surrogate marker for preventive interventions,” trials need to study breast cancer as the outcome, not just MBD. They should also study other aspects of mammograms associated with risk.JLH is supported by a NHMRC Senior Principal Research Fellowship. TLN is supported by Cancer Council Victoria. SL is supported by a Victorian Cancer Agency Early Career Research Fellowship (ECRF19020).Role of the funders: The funders had no role in the study design, data collection, and analysis; decision to publish; or preparation of the manuscript.Disclosures: The authors have no conflict of interest to declare.Author contributions: All authors contributed to the conceptualization and writing, both original draft preparation and review and editing.Not applicable.


Mammographic microcalcifications are considered early signs of breast cancer (BC). We examined the association between microcalcification clusters and the risk of overall and subtype-specific BC. Furthermore, we studied how mammographic density (MD) influences the association between microcalcification clusters and BC risk.n = 53,273) of Swedish women with comprehensive information on BC risk factors and mammograms. The total number of microcalcification clusters and MD were measured using a computer-aided detection system and the STRATUS method, respectively. Cox regressions and logistic regressions were used to analyse the data.We used a prospective cohort  compared to women with no clusters. The estimated risk was more pronounced in premenopausal women . For postmenopausal women, microcalcification clusters and MD had a similar influence on BC risk. No interaction was observed between microcalcification clusters and MD. Microcalcification clusters were significantly associated with in situ breast cancer .Microcalcification clusters are an independent risk factor for BC, with a higher estimated risk in premenopausal women. In postmenopausal women, microcalcification clusters have a similar association with BC as baseline MD. Breast microcalcifications are deposits of calcium in the breast tissue and appear as small bright spots on mammograms . MicrocaOnly one risk prediction model for breast cancer has included microcalcifications and masses . All othIn this study, we were able to address some limitations in previous studies. We used the unique prospective Karolinska Mammography Project for Risk of Breast Cancer (KARMA) cohort  to invesn = 34 women who were asked to be removed from the study. The ethical review board of Karolinska Institutet approved the study.KARMA is a population-based prospective screening cohort, which includes 70,874 women who were invited when conducting either screening  or clinical mammography at four hospitals in Sweden, from January 2011 to March 2013 . Not all®, Nashua, USA) [Negative, non-diagnostic, mammograms were used when analysing microcalcification clusters and mammographic density. Digitally processed mammograms were collected and analysed  . All womua, USA)  is a Fooua, USA) . The algua, USA) , 19  . Hereaftua, USA)  and illuua, USA) . We usedua, USA) , 23.2) and average percent density (%) over the left and right breasts were used [2) and , respectively.Using the STRATUS method, mammographic density was measured in each breast and the average dense area , alcohol consumption (gram/day), age at menarche (years), age at first birth (years), parity , breastfeeding duration (months), oral contraceptive use , menopausal hormone therapy use , first-degree family history of breast cancer  and menopausal status . Women reporting no natural menstruation over the past 12 months before study entry or no menstruation due to oophorectomy were considered postmenopausal. Women with missing information on menstruation status or having no menstruation due to gynaecological surgeries other than oophorectomy were considered premenopausal if they were age 50 years or younger and postmenopausal if older than 50 years.Cox proportional hazard regression was used, with age as the underlying timescale, to estimate the association between microcalcification clusters and their asymmetry with the risk of breast cancer. These models were adjusted for BMI (continuous), baseline mammographic density (continuous), smoking status , alcohol consumption (continuous), age at menarche (continuous), age at first birth (continuous), number of children (continuous), breastfeeding duration (continuous), oral contraceptive use , menopausal hormone therapy use  and family history of breast cancer . In addition, we also investigated the association between baseline mammographic density and risk of breast cancer while adjusting for all the above-mentioned potential covariates. Hazard ratios (HRs) and 95% confidence intervals (CIs) were reported. The proportional hazard assumption was tested using the Schoenfeld residual test, and no major model violation was observed. We repeated the analyses, allowing for interaction between microcalcification clusters and mammographic density, to study how these jointly influence breast cancer risk. A global test was used to determine the presence of interaction. Logistic regression was used to investigate the association between the presence of microcalcification clusters and breast cancer tumour characteristics (in situ vs. invasive and oestrogen receptor (ER)-positive vs. ER-negative), for women who developed breast cancer during follow-up, while adjusting for potential confounders. For this analysis, microcalcification clusters were categorised as 0 and ≥1. All statistical tests were two-sided.P values, obtained from two-sided Wald/maximum likelihood ratio tests, of less than 0.05, were considered statistically significant.The Spearman correlation test was used to assess the correlation between the total number of microcalcification clusters and their asymmetry. All statistical analyses were performed with R version 3.6.1. A total of 676 women were diagnosed with breast cancer Table . The meaOverall, each additional microcalcification cluster was associated with 20% increased risk of breast cancer in all women ) (Table 2) had a nearly threefold higher risk of breast cancer  compared to those with the lowest density (<10.0 cm2) after adjustment for potential confounders (Table Women in the highest-density category >41.0 cm had a ne2) were used as the reference when testing for the interaction between microcalcifications and mammographic density (Table Pinteraction = 0.65). Similar results were seen when using mammographic percent density  Table . MicrocaThe correlation between the total number of microcalcification clusters and their asymmetry was ρ = 0.95 indicating a strong correlation between these two variables. The results in Tables Using a large prospective cohort, we found microcalcification clusters to be significantly associated with an increased risk of breast cancer. Based on our categorisation of microcalcification clusters and mammographic dense area, these two entities influenced the risk of breast cancer to the same extent in postmenopausal women. In premenopausal women, mammographic density had a more pronounced influence on breast cancer risk. We found no interaction between microcalcification clusters and mammographic density on the risk of breast cancer. Microcalcification clusters were significantly related to in situ breast cancer but were not associated with ER status.Breast microcalcifications are common, the majority are benign, increase with age and are characterised by their morphology, size and distribution , 31. TheReassuringly, our finding that breast cancer risk increases with the number of microcalcification clusters has been shown before , 9. The The biological mechanism behind calcium deposition in breast tissue is not clearly understood, but given the heterogeneity of microcalcifications, most likely several biological processes are involved , 35. EpiMammographic density is a strong and established risk factor for breast cancer –39. We dOur study had a number of limitations that should be emphasised. Information on breast cancer risk factors was based on a self-reported questionnaire and therefore is prone to information bias. However, a differential misclassification is unlikely since women were not aware of the presence of microcalcification clusters in their breasts at the time of assessment. We used an FDA-approved CAD software for identifying microcalcification clusters with malignant potential. Some of these microcalcifications were likely found in blood vessels and not in the breast tissue. However, given the quite substantial risk of breast cancer seen in women with microcalcification, we believe that the majority of identified calcifications were not breast arterial calcifications. The strengths of our study were the prospective population-based design, the number of women included, detailed information of the established breast cancer risk factors, including pre- and postmenopausal women, small proportion of missing data for the majority of the risk factors, having full access to mammograms for the measurement of mammographic density using the fully automated STRATUS tool and comprehensive reporting of incident breast cancer through nationwide registers.To conclude, we found that microcalcification clusters, with malignant potential, were an independent risk factor for breast cancer with a similar effect as mammographic density, at least in the postmenopausal part of life. Our study is the first comprehensive attempt using a clinically proved method to detect suspicious microcalcification clusters and to shed light on the risk of breast cancer associated with the presence of microcalcification clusters. Very little is known about the aetiology of microcalcifications. It is, therefore, safe to state that more research is needed to identify the predictors of mammographic microcalcifications and thereby possibly the mechanism behind the association between microcalcifications and breast cancer.Supplementary MaterialSTROBE Statement


The burden of breast cancer has been increasing globally. The epidemiology burden and trends need to be updated. This study aimed to update the burden and trends of breast cancer incidences, deaths, and disability-adjusted life-years (DALYs) from 1990 to 2019, using the Global Burden of Disease 2019 study.The data of incidences, deaths, DALYs, and age-standardized rates were extracted. Estimated annual percentage changes were used to quantify the trends of age-standardized rates. Besides, the population attributable fractions of the risk factors of breast cancer were also estimated.Globally, the incidences of breast cancer increased to 2,002,354 in 2019. High social-development index (SDI) quintiles had the highest incidence cases with a declining trend in age-standardized incidence rate. In 2019, the global deaths and DALYs of breast cancer increased to 700,660 and 20,625,313, respectively. From 1990 to 2019, the age-standardized mortality rates and age-standardized DALY rates declined globally, especially in high and high-middle SDI quintiles. Besides, the trends varied from different regions and countries. The proportion of the patients in the 70+ years age group increased globally. Deaths of breast cancer attributable to high fasting plasma glucose and high body mass index increased globally, and high fasting plasma glucose was the greatest contributor to the global breast cancer deaths.The burden of breast cancer in higher SDI quintiles had gone down while the burden was still on the rise in lower SDI quintiles. It is necessary to appeal to the public to decrease the exposure of the risk factors. It has been estimated that breast cancer surpassed lung cancer as the first cause of global cancer incidence in females, with 2,261,419 new cases in 2020 . AlthougThe Global Burden of Disease (GBD) study estimates a variety of metrics of diseases annually since1990, which provides a great opportunity for comparable assessment of the breast cancer burden and trends at the global, regional and national levels. In addition, the GBD study also estimates the population attributable fractions of risk factors related to breast cancer that may be useful for policy makers to identify intervention priorities for public health actions. Researchers have presented the estimation of the global burden of breast cancer based on the GBD study 2017 , 6. Recehttp://ghdx.healthdata.org/gbd-results-tool). Meanwhile, the information about the distributions of age was also acquired. To be consistent with previous articles, socio-Demographic index (SDI), which is a comprehensive measurement of educational level, per capita income, and total fertility, was used to group the countries into five SDI quintiles . Moreover, the world was separated into 21 regions in terms of geography.Data about the breast cancer burden were collected from GBD 2019, which systematically and comprehensively estimates the burden of 359 diseases and injuries and 84 risk factors across ages, sexes, regions and 204 countries and territories from 1990 to 2019 , 8. HereDetailed methodology of GBD study 2019 was described in two previous studies , 8. In bThe GBD 2019 used the comparative risk assessment framework to quantify the associations between the burden of diseases and impairments and 84 environmental, occupational, metabolic, and behavioral risk factors . Populati, where i denotes the ith age class) and the number of persons (or weight) (wi) in the same age subgroup i of the chosen reference standard population, then dividing the sum of the standard population weights, i.e , age-standardized mortality rates (ASMRs), age-standardized DALY rates, were calculated in accordance with the direct method by summing up the products of age-specific rates , and its 95% confidential interval (CI) also could be computed from the linear regression model. The ASRs were considered to be in a decreasing trend when EAPC and the 95%CI were <0. On the contrary, the ASRs were deemed to be on the rise when EAPC and the 95%CI were > 0. In addition, we analyzed the correlation between EAPC and SDI values and ASRs by using the Pearson’s correlation test model. The 95% uncertainty intervals (UIs) were calculated as taking the 2.5th and 97.5th values of 1,000 draw-level estimates for each quantity of interest. All statistical analyses were conducted by R program (Version 3.6.1), with a P value <0.05 considered statistically significant.Moreover, the trends of ASRs provided clues for the dynamic changes of risk factors, as well as surrogates for shifting disease patterns in the population. The estimated annual percentage changes (EAPCs) were used to track the ASRs trends over a specified time interval. A linear regression line was fitted to the natural logarithm of the rates with calendar year as an independent variable i.e., y = α + βx + ε, where x referred to calendar year, and y presented ln(ASR). Then EAPC could be calculated as 100 ×  new cases of breast cancer, with an ASIR of 24.17 per 100,000 persons (95% UI: 22.11–26.24) in 2019 Table 1.Among 21 GBD regions, East Asia  had the highest number of breast cancer incidence cases in 2019, while High-income North America had the highest ASIR  Table 1.Compared to 1990, the global deaths of breast cancer increased by 83.95% (95% UI: 70.07–96.74%), with 700,660  cases in 2019, but the ASMR decreased to 8.62 per 100,000 persons (95% UI: 7.95–9.25), with an EAPC of −0.56 (95% CI: −0.60–0.51) 2. SubgrBreast cancer deaths mostly occurred in South Asia in 2019 , while the largest increase of deaths was detected in Oceania, with a 2.17-fold increase Table 1.Globally, the breast cancer-related DALYs increased to 20,625,313  in 2019 Table 1.For different geographic areas, South Asia , East Asia  and Southeast Asia  had the highest DALY burden in 2019, while Oceania  had a particularly high age-standardized DALY rates Table 1.At the regional-level, a positive association was found between ASIR and the SDI Figure 2Age distribution was a vital parameter of cancer epidemiology. Therefore, patients were divided into three groups  Figure 4Generally, high FPG was the greatest distributor of deaths in the global level, high SDI, low-middle SDI and low SDI quintiles. The contribution ratio of diet high in red meat was relatively weak. Deaths of breast cancer attributable to high FPG and high BMI increased globally and in all the SDI quintiles between 1990 and 2019, while low physical activity showed with a moderate trend. Except the moderate trend of tobacco and alcohol use attributed deaths in the low SDI quintile, the other SDI quintiles showed decreased trend during study period. Diet high in red meat led to 3.21% of global breast cancer deaths in 2019 with a declining trend between 1990 and 2019. On the contrary, deaths associated with diet high in red meat increased in the low SDI, low-middle SDI and the middle SDI quintiles Figure 5Basing on the GBD 2019 results, this research presented the latest comprehensive overview of the global, regional, and national burden of breast cancer and its relevant risk factors. Our results revealed that breast cancer still was a major global public health problem in 2019, and large variations in the trends of incidences, deaths, and DALYs, as well as the percentages of attributable risk factors across regions and countries remained a main epidemiological feature, indicating the importance of a more elaborate delineation of hot spots to the identification of high-risk populations. Although a downtrend of ASIR was detected in High-income North America and some countries, they still reached at a higher than expected level of ASIR during the study period. It is urgent to design a flexible country-tailored approach to effectively decrease the breast cancer burden and counter the imbalance in health care attributable to breast cancer.The estimated numbers of new cases of female breast cancer at the global level were lower than those estimated with the GLOBOCAN 2020 . We estiSince 1990, both the incidence case and ASIR of breast cancer generally increased with increasing SDI. Higher incidence of breast cancer in high SDI quintiles could be caused by the ageing population and the lifestyle choices that increase the exposure to risk factors; some of the risk factors for breast cancer are found to be more prevalent in high SDI quintiles than in low ones . With itWe found that the growth of the number of deaths and DALY decreased with the elevated SDI level. The ASMR and age-standardized DALY rates decreased in the high SDI and the high-middle SDI quintiles from 1990 to 2019, while slightly increased in other three quintiles. For example, although breast cancer along accounted for 30% of new cases for female cancer in United States of America , mortaliGlobally, nearly 49% of breast cancer patients were aged 50–69 years. In well-resourced settings, WHO suggests organized, population-based mammography screening every 2 years for females aged 50 to 69 years . For fem2) who conventionally would be considered to be healthy compared to obese females , Shantou Medical Science and Technology Planning Project (grant number 200622115260639), Undergraduate Innovation and Entrepreneurship Training Program Project of Shantou University (grant number 201910560249), and the Bethune-Ethicon Excellence Surgery Fund Project (grant number HZB-20181119-25).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.


Polygenic risk scores (PRS) could potentially improve breast cancer screening recommendations. Before a PRS can be considered for implementation, it needs rigorous evaluation, using performance measures that can inform about its future clinical value.To evaluate the prognostic performance of a regression model with a previously developed, prevalence-based PRS and age as predictors for breast cancer incidence in women from the Estonian biobank (EstBB) cohort; to compare it to the performance of a model including age only.We analyzed data on 30,312 women from the EstBB cohort. They entered the cohort between 2002 and 2011, were between 20 and 89 years, without a history of breast cancer, and with full 5-year follow-up by 2015. We examined PRS and other potential risk factors as possible predictors in Cox regression models for breast cancer incidence. With 10-fold cross-validation we estimated 3- and 5-year breast cancer incidence predicted by age alone and by PRS plus age, fitting models on 90% of the data. Calibration, discrimination, and reclassification were calculated on the left-out folds to express prognostic performance.A total of 101 (3.33‰) and 185 (6.1‰) incident breast cancers were observed within 3 and 5 years, respectively. For women in a defined screening age of 50–62 years, the ratio of observed vs PRS-age modelled 3-year incidence was 0.86 for women in the 75–85% PRS-group, 1.34 for the 85–95% PRS-group, and 1.41 for the top 5% PRS-group. For 5-year incidence, this was respectively 0.94, 1.15, and 1.08. Yet the number of breast cancer events was relatively low in each PRS-subgroup. For all women, the model’s AUC was 0.720 (95% CI: 0.675–0.765) for 3-year and 0.704 (95% CI: 0.670–0.737) for 5-year follow-up, respectively, just 0.022 and 0.023 higher than for the model with age alone. Using a 1% risk prediction threshold, the 3-year NRI for the PRS-age model was 0.09, and 0.05 for 5 years.The model including PRS had modest incremental performance over one based on age only. A larger, independent study is needed to assess whether and how the PRS can meaningfully contribute to age, for developing more efficient screening strategies.The online version contains supplementary material available at 10.1186/s12885-021-08937-8. Breast cancer screening programs aim to detect tumors before they develop into symptomatic, more advanced cancers, as early detection of localized breast cancer (i.e. cancers that has not yet metastasized) is known to have a better prognosis . CurrentIn this context, age is still the dominant risk marker for identifying the general target group for breast cancer screening programs. However, notwithstanding a significant increase in breast cancer incidence with age , 7, mostBreast cancer is a heterogenic disease for which several different subtypes of genetic mutations and variations have been identified. For example, mutations in the single high-penetrance tumor suppressor gene BRCA1/2 are associated with a high relative risk of developing breast cancer. The high-penetrant genes are, however, rare and although women with a BRCA1/2 mutation have a seven times higher risk of breast cancer , this suIn contrast to rare, high-penetrant genes, the so-called single-nucleotide polymorphism (SNPs) are genetic variants that are more commonly expressed in the general population , 15. Sev− 8 [Although studies have identified more than 100 low penetrance genes as prognostic for breast cancer , no sing− 8 . It is w− 8 –25. Comb− 8 , 26.So far, the validity of a previously developed prevalence based PRS was mostly demonstrated through relative risks . Before Next in our PRS evaluation for breast cancer screening , we estiWe included de-identified data from 30,901 female participants from the EstBB cohort. In brief, the EstBB is managed by the Estonian Genome Center at the University of Tartu (EGCUT) and was established to collect genetic and health information, from a large sample of the Estonian population, to advance public health –31.Eligible participants were 18 years or older volunteers with Estonian nationality. Approximately 10,000 participants were recruited from 2002 to 2004. Recruitment was thereafter paused until 2006, due to financial circumstances, but continued again from 2006 to 2012 from all of Estonia (15 counties), at which the EstBB cohort had included almost 52,000 participants.Participants were initially, non-randomly, recruited through general practitioners. Recruitment was later extended to private practices and hospitals, and special recruitment offices of the EGCUT. Recruitment was completely volunteer-based, since no direct contact with the Estonian population was allowed; participants actively signed up after hearing about the cohort study in private, at their health care institution, or through promotion at special public events and in the media , 30.See Method section in the Included in this analysis were data from women from the EstBB cohort between 20 and 89 years and without a current or previous diagnosis of breast cancer at the time of cohort entry. We excluded women who had entered the cohort after 2011, as their 3- and 5-year follow-up information was not yet available at the time of analysis  was initially collected on the day of recruitment through standardized interviews and questionnaires, blood samples, and from existing medical records. Diseases were classified according to the international Classification of Diseases (ICD-10) , 30.2) used in this analysis [The selection of SNPs and the development of PRS follow advanced and complex methods that have been described in detail elsewhere , 15. In analysis .Follow-up information on incident breast cancers and deaths was collected through biennial linkage to the Estonian Health Insurance Fund, the Estonian Causes of Death Registry, and the Estonian Cancer registry. Every recorded diagnosis of breast cancer was confirmed by an oncologist. The last linkages used in this analysis were performed in December 2015 for breast cancer and in June 2017 for death . Below, Descriptive statistics for our study group are presented in Table Only two covariates were statistically significant and retained in subsequent models: age and PRS. We then considered the model including only age, reflecting the current screening strategy, and another adding PRS to age as predictors. With the latter PRS-age-based model, fitted to the full data set, we estimated breast cancer specific hazard ratios with main effects for age and PRS-groups  of breast cancer events within 3 or 5 years among the total number of women in each PRS-age subgroup, with corresponding 95% CI calculated using the Wilson method Fig. . Per stuReceiver operating characteristic (ROC) curves were constructed and the corresponding area under these curves (AUC) was calculated to express discrimination based on age_mInc and PRSage_mInc, for both the 3- and the 5–year time points. The 95% CI of the AUC was calculated with a non-parametric method (Delong) Fig. .Using a risk threshold of ≥1%, we classified women as high or low risk within 3 and 5 years. Separately for women with and without observed breast cancer we thus constructed reclassification tables for PRSage_mInc and age_mInc Of the 30,901 women from the EstBB cohort between 20 and 89 years, 309 women had prevalent breast cancer at recruitment while 227 women had been recruited after 2011. Hence, a total of 30,365 women were eligible, of which 30,312 had complete data and were included in the analyses  at 3 years and 6.0‰ (185 events) at 5 years  with no significant improvement in model fit for the full model with all evaluated putative predictors .The PRS-age-based model fitted the data significantly better than the age-based model  to 4.6 (95% CI: 2.97–7.14), relative to the lowest 0–25% PRS subgroup  and with the age plus PRS-based model (PRSage_mInc). The PRSage_mInc distribution showed a slightly wider spread than age_mInc, visible in the right-hand tail for both the 3- and 5-year estimated incidence Fig.  - S8.FiWe evaluated the prognostic performance of PRSage_mInc in terms of calibration, discrimination, and reclassification, comparing it to age_mInc whenever appropriate.95% CIs for the observed incidences were wide: 1.55 to 7.35‰ and 3.39 to 11.04‰ at 3 and 5 years, respectively. Calibration varied across the PRS-age subgroups, however, the estimated incidences were still within the 95% CI for all subgroups , the ratio of observed cumulative incidence vs PRSage_mInc was 0.86 for women in the 75–85% PRS-group, 1.34 for the 85–95% PRS-group, and 1.41 for the top 5% PRS-group. For 5-year PRSage_mInc, these were 0.94, 1.15, and 1.08, respectively.n = 265), for which the PRSage_mInc was considerably overestimated. Yet, the number of breast cancer events were low in each of the PRS-age subgroups. More detail on calibration for the 3- and 5-year observed incidence and PRSage_mInc across all PRSage subgroups is shown in Supplementary Table SThe approximation of PRSage_mInc to observed incidence was similar for the top 3 PRS-groups for women below 50 years and also for the age groups above 62 years, with exemption of the groups of women in the top 5% PRS-group and older than 62 years   for 3-years and 0.704 (95% CI: 0.670 to 0.737) for 5-years, respectively, just 0.022 and 0.023 higher than for the age_mInc model. Neither increment was statistically significant .The 3-year risk reclassification showed that 13% of women with breast cancer (13/101) were correctly reclassified into a high risk-group while 1% (1/101) was incorrectly reclassified by PRSage_mInc. This resulted in a net-improvement of 12% correctly reclassified women with breast cancer. Among women without breast cancer , PRSage_mInc reclassified more women incorrectly as high risk (3%) than correctly as low risk (0.4%), resulting in a net-loss of 2.6% of incorrect upward reclassifications (Supplementary Table Sp = 0.17).For the 5-year risk, PRSage_mInc correctly reclassified 15% of women with breast cancer as high-risk (27/185) but also incorrectly reclassified 10% (18/185) downwards, leading to a net improvement of 5% correct reclassifications of women with breast cancer. In women without breast cancer , 6.7% were incorrectly reclassified upwards, while 6.9% were correctly reclassified downwards  showed a HR of 4.2 (95% CI: 2.8–6.2), when women from the Estonian Biobank Cohort (EstBB) in the 95th percentile (the top 5% PRS-group) were compared to women in the lowest 50th percentile PRS-group [Several studies have shown that women with a high PRS are at increased risk of breast cancer –39, 42. RS-group . In agreRS-group –39, 42, In terms of calibration, PRSage_mInc approximated the observed incidence relatively well for each PRS-age subgroup, as only one exception, the top 5% PRS in women older than 62 years, was considerably overestimated at both time points. This overestimation may own to the low sample size of this subgroup. Some under- and overestimation is expected at both tails of the PRSage_mInc. Calibration was more accurate for the highest 75–100% PRS-group than for the lowest 0–25% PRS-group. This too could be explained by the considerably higher observed incidence in the highest 75–100% PRS-group, with similar total sample size.The AUC estimates the percentage of (randomly selected) pairs of women with and without breast cancer where the woman with breast cancer has the higher risk prediction, here age_mInc and PRSage-mInc. Upon adding PRS to age, the increase in AUC was limited to 2–3% more pairs assigning higher risk to the breast cancer case. Although the AUC in our analysis are slightly higher than in other PRS evaluations –39, 42, The 3-year PRSage_mInc showed a modest but statistically significant improvement in overall reclassification over age_mInc, whereas the improvement was smaller and non-significant for the 5-year incidence. Several other studies also showed that incremental improvements by SNPs/PRS were primarily restricted to women with breast cancer, while improvements for women without breast cancer was almost absent , 38, 39.If and to what extent the PRS could meaningfully contribute to the development of more efficient screening strategies could not be fully answered by our data and should be further evaluated. The wave of incoming data from 150.000 new participants in the EstBB cohort –45 will Future evaluations may consider different risk thresholds, use microsimulation models to explore how risk-based screening could bring benefits for the high-risk group, in terms of breast cancer specific- and overall mortality. It may also document corresponding potential harms.To our knowledge, no countries have implemented genetic testing for screening purposes. Yet advanced technology for genotyping is already in place in Estonia , and sucFor a complete biomarker evaluation, a large, pragmatic, randomized trial would bring valid and convincing evidence of clinical effectiveness. Though few such studies are conducted in biomarker research, several larger trials were recently initiated, to evaluate the feasibility and/or effect of alternative risk-based strategies, that utilizes women’s personal risk to guide at what age to start, when to stop, and how often to screen. The women’s personal risk is then calculated using PRS, among other risk factors.ClinicalTrials.gov identifier NCT02620852). This is a prospective, randomized trial, aiming to compare the proportion of breast cancers detected (stage IIB or higher) between a control group, which receives standard of care  screening and an intervention, i.e. risk-based, group of women between 40 to 74 years, for a time horizon of 5 years. Personal risk is calculated using 9 high penetrant genes and nearly 200 SNPs, in addition to other known risk factors including family and medical history, and breast density [ClinicalTrials.gov Identifier NCT03672331) [ClinicalTrials.gov identifier NCT03989258). As a secondary objective, this study compares the number of screen-detected breast cancer in 3 years between two cohorts of women; one that receive standard of care and another that also receives screening recommendation, according to their genetic personal risk.One is the WISDOM study in US ( density . A secon3672331) . This trOur analysis of women from the EstBB showed a low observed cumulative incidence, which varied across PRS percentile groups. Adding the PRS significantly improved the fit of a Cox proportional hazards model based on age only. The derived 3- and 5-year cumulative incidence based on PRS and age approximated the observed incidence relatively well. Through cross validation, the calculated PRS-age based incidence showed modest incremental prognostic performance, compared to the age-based incidence, in terms of discriminatory ability and reclassification. The low number of breast cancer events in the study group was associated with large uncertainty intervals and further research is needed to assess whether and how PRS can contribute to more effective breast cancer screening strategies.Additional file 1: Fig. S1. Schoenfeld’s residuals for the continuous predictor age (top) and the categorized predictor PRS (bottom). Table S1. Demographic and clinical characteristics of PRS groups. Table S2. Demographic and clinical characteristics of age groups. Fig. S2. Age density distribution for PRS groups. Table S3. Breast Cancer specific hazard ratios with main effects for age and PRS-groups. Table S4. Descriptive statistics for 3- and 5-year PRSage-mInc and age-mInc distributions. Table S5. Three-year PRSage-mInc: number (proportions) above 0.5, 1, and 1.5% thresholds. Table S6. Three-year age_mInc: number (proportions) above 0.5, 1, and 1.5% thresholds. Table S7. Five-year PRSage-mInc: number (proportions) above 0.5, 1, and 1.5% thresholds. Table S8. Five-year age_mInc: number (proportions) above 0.5, 1, and 1.5% thresholds. Table S9. Three-year observed cumulative incidence and PRSage_mInc per 1000 women. Table S10. Five-year observed cumulative incidence and PRSage_mInc per 1000 women. Table S11. Three-year observed cumulative incidence and PRSage_mInc per 1000 women for the lowest 0–25%, highest 75–100%, and top 5% PRS groups. Table S12. Five-year observed cumulative incidence and PRSage_mInc per 1000 women for the lowest 0–25%, highest 75–100%, and top 5% PRS groups. Table S13. Reclassification tables of women with 1% 3-year risk threshold. Table S14. Reclassifications in women with 1% 5-year risk threshold.


The rates of smoking among women are rising. Previous studies have shown that smoking is associated with early menopause. However, the association of gynecological cancer, including breast and cervical cancer, with early menopause and smoking, remains unclear. Therefore, this study aimed to determine the association between smoking and early menopause, breast cancer, and cervical cancer. This cross-sectional study used data from the Korean National Health and Nutritional Survey Examination (KHANES) (2016–2018). Early menopause was defined as menopause before 50 years of age. A total of 4,481 participants were included in the analysis. There was no association between early menopause and cervical cancer , but women who had experienced early menopause had a significantly higher risk of breast cancer than women who had experienced normal menopause . Early menopause was not associated with an increased risk of breast cancer in ever-smoker , but was associated with a significantly increased risk of breast cancer in never-smokers . Early menopause was associated with an increased risk of breast cancer in women who had never smoked, but not in women who had ever smoked. Menopause transition, is the time when the ovarian follicle pool falls below the standard threshold. It is a biological process experienced by all women . Natural menopause is described defined as a period of 12 or more months of amenorrhea that is not a result of specific medical procedures such as chemotherapy and surgery or other known causes . It is associated with a decline in female sex hormones, and affects the child-bearing process. While menopause occurs in all women, the age at which it occurs varies. Early onset of menopause is associated with an increased risk of various health conditions, such as certain types of cancer, heart disease, stroke, and osteoporosis, thus becoming, a serious public health issue, affecting women’s fertility rates worldwide . Therefore, there is a need to evaluate the risk factors associated with early menopause in order to be able to inform women of the health risks. Several studies have been conducted regarding the risk factors associated with early menopause. A study by Mikkelsen et al., (2007) found that nulliparous women were at a higher risk of early menopause than parous women. Factors such as higher educational levels, moderate alcohol intake, and higher coffee intake were associated with a lower risk of early menopause. In contrast, they found a greater prevalence of pre-menopause among women who smoked during the perimenopausal age period, indicating that active smoking was a risk factor for early menopause. Further, they established dose-response effect of smoking on early menopause, and that active and passive smoking resulted in early menopause . The association between smoking and early menopause has been described as the decline in the quality and quantity of ovarian follicles caused by the byproducts found in cigarettes. Smoking causes fluctuation in the levels of reproductive hormones during the fertility period, which alters the follicular development patterns and consequently, leads to changes in the follicle-stimulating hormone levels. Further, the impact of smoke exposure on the follicle pool has been found to affect the timing of menopause . Prior studies have established that tobacco smoking has an adverse impact on hormone production in women, given that these toxins affect multiple sites involved in the hormonal synthesis. In this regard, considerable studies affirm the effects of active smoking on the timing of menopause. This aspect is also evident in the study given that a significant proportion of active and former smokers experienced early menopause .Prenatal smoke exposure has been found to lead to early menopause by damaging the follicles and suppressing their formation. A study by Lutterodt et al., (2009) found that prenatal smoke exposure damaged the somatic cells during the process of ovarian development, which could lead to early menopause. The higher risk of early menopause due to smoking makes women vulnerable to developing various health conditions such as gynecological cancers.It is fundamental to note that menopause typically occurs between the ages of 45 to 55 years . For instance, Korean women have a 49.2-year average of menopause. However, this stage can occur earlier in life as a result of multiple factors. While some women dread menopause, others may find it relieving owing to the associated pain and other menstrual complications. In this regard, menopause symptoms vary from one woman to another, and they may be confused for other natural biological processes . The current trend of increasing prevalence of smoking in women and the close connection between smoking and early menopause is a critical public health concern. While previous studies have addressed the association between smoking and early menopause , the association of gynecological cancer, including breast and cervical cancer, with early menopause and smoking, is not clear. Therefore, this study aimed to investigate the association of early menopause, breast cancer, and cervical cancer with smoking. th editions of Korean National Health and Nutritional Survey Examination (KHANES), conducted from 2016 to 2018 by Korea Centers for Disease Control and Prevention, Ministry of Health and Welfare. The KNHANES is an annual cross-sectional survey, and its target population comprises nationally representative non-institutionalized South Korean citizens. The study population was screened for eligibility by KNHANES . The gross population sample for this study, comprising 24,269 participants was selected between 2016 and 2018. Since this study was women-centric, 11,071 men were excluded from this study population leaving 13,198 women. Women aged < 40 years, and those who were pregnant or menstruating were excluded. Additionally, women who failed to answer the study questions regarding menopause, and those who provided responses such as ‘do not know,’ or had missing responses, were excluded. This resulted in the exclusion of an additional 8,717 participants from the study population. Women aged 40–80 years who were comfortable with answering questions on their menopause status were included. Finally, a total of 4,481 women were included in this analysis. This cross-sectional study used data from the 7The target population was first assessed for breast and cervical cancers. The variables considered were as follows: age , age at menopause , educational level (high school and college), household income , smoking status , drinking status , body mass index (BMI) , and exercise . An analysis was conducted on the percentage of participants who experienced menopause at the various ages. Two models were used to calculate the odds ratio (OR) for breast cancer and cervical cancer based on the menopausal status. The first model was a crude model, which included the OR and 95% CIs for gynecological cancers with respect to the occurrence of menopause. The second model was an adjusted model, in which the ORs and 95% CIs for gynecological cancers with respect to the menopause status were adjusted for the variables in the study. The variables included in the second model were educational level, smoking status, drinking status, household income, BMI, and exercise. OR was used to evaluate the possibility of occurrence of breast and cervical cancer in women who had early menopause. It compares the odds of an event occurring after exposing the participant to specific risk factors with the odds of the same event in a controlled situation, where the participant is not exposed to any risk factor. In this study, OR enabled the understanding of the likelihood of developing cervical and breast cancers in women who experienced early menopause, and the possibility of cancer occurring in women who experienced menopause at the average age. Further, the OR enabled us to determine the relationship of early menopause, breast cancer, and cervical cancer with tobacco smoking in Korean women.Of the 4,841 study participants, 3,037 (67.78%), 1,358 (30.31%), and 86 (1.92%) were ≥ 60, 50-59, and 40-49 years old, respectively. Menopause was experienced by women across all age groups. As shown in Of the participants, 86.89% had a high school education and 13.11% had a college education. Most of the participants were from low- (33.91%) and mid-low-income (25.75%) families. Regarding the smoking status, 4,149 (92.59%) had never smoked, 127 (2.83%) were current smokers, 157 (3.50%) were former smokers, 29 (0.65%) were occasional smokers and 19 (0.42%) reported their smoking status as ‘do not know.’ Regarding alcohol consumption, 1,208 (26.96%) were current drinkers, 3,259 (72.72%) were non-drinkers, and 14 did not report their drinking status. For the BMI, 1,728 (38.8%) were underweight, 1,088 (24.43%) were of normal weight, and 1,638 (36.78%) were overweight. Additionally, a majority of the participants exercised . The distribution of gynecological cancers  among the participants categorized according to the identified variables are summarized in Model 1, the crude model, revealed an increased likelihood of breast cancer and cervical cancer in women who experienced early menopause. ORs for breast and cervical cancers were 1.613 (95% CI: 1.048-2.481) and 1.615 (95% CI: 0.828-3.149), respectively, using normal menopause as the reference group. In contrast, there was a reduced possibility of cancer in women who experienced normal menopause. In Model 2, the adjusted model, the ORs were adjusted for educational levels, household income, exercise, BMI, and smoking and drinking habits. The adjusted ORs for breast and cervical cancer with respect to early menopause were 1.683 and 1.435, respectively. Additionally, the adjusted ORs for breast and cervical cancer among never-smokers were 1.828 (95% CI: 1.171–2.852) and 1.336 (95% CI: 0.653–2.734), respectively  and 3.The variables considered in this research aimed to classify the study population according to their respective characteristics. This study focused on identifying how the selected variables affected the possibility of developing gynecological cancers  in women experiencing early menopause. The ORs of breast cancer and cervical cancer according to the age at menopause were analyzed. The two models revealed a higher OR for breast cancer and cervical cancer according to the age at menopause. The first model indicated that early menopause increased the potential of breast cancer and cervical cancer in women. The second model suggested that early menopause was attributable to the study variables, namely household income, BMI, exercise, smoking and drinking habits, and education levels, which increased the possibility of women developing gynecological cancer. These findings were supported by Rosenberg et al., (2013) who found a close association between early menopause and the incidence of breast cancer. The study also showed that negative symptoms such as weight gain, fatigue, sleep challenges, and depression and anxiety, may characterize early menopause associated with the increased risk of breast cancer. According to Shuster et al., (2010), 25% of breast cancer cases involve premenopausal women and 75% involve menopausal women. Therefore, early menopause can be considered a risk factor for breast cancer. A study by Taneri et al., (2016) also found that natural menopause was associated with a reduced risk of breast, ovarian, and endometrial cancers. This indicated a decreased occurrence of breast cancer and cervical cancer in patients who experienced menopause at the normal age. The incidence rates of early menopause in women raise a serious public health concern  . Thus, larger numbers of women are at risk of breast cancer and cervical cancer due to early menopause. Studies have proposed that coping with this challenge requires addressing the behavioral aspects associated with early menopause. Smoking is a modifiable and independent risk factor for early menopause. A study by Yang et al., (2015) on Korean women revealed that the menopausal age of current smokers was considerably lower than that of never-smokers. Therefore, addressing the challenge of smoking in women could significantly reduce the incidence of gynecological cancer. Moreover, according to Whitcomb et al., (2018), modifiable risk factors can have a significant impact on the aging of ovaries, and further, that smoking is also a lifestyle factor that can affect the timing of menopause significantly. In this study, the never-smoker status was significant, as the number of participants who had not smoked in their lifetime was low. The findings of this study have created a new area for future research which, when using more extensive data sets, should investigate smoking as an effect modifier. The major strength of this research was its large sample size that helps reduce statistical noise due to variations in participants’ characteristics . This implies that the results of our research were authentic and reliable. Further, the large sample used in this research ensured that the study population is more representative of the entire Korean women population. Thus, the generalizations developed from this research can be used for policy formulation and further research. Furthermore, the study findings were consistent with those of past studies; hence, the findings have external validity. Our study also had limitations. First, it was not possible to verify the participants’ responses in the study. Therefore, the results may be affected by potential biases due to the possibility of incorrect answers. Further, this study did not extensively address the scope of smoking, particularly the association of passive smoking and early menopause with the occurrence of gynecological cancer. Passive smoking should have been considered a significant concern in this research since it affects many women who do not actively engage in smoking behavior. The non-inclusion of passive smoking in this research limits the scope of the study. More robust mathematical analyses using regression and correlation models should be applied to enhance the reliability of the research results. To conclude, the findings of this study suggested that smoking is positively correlated with a reduction in the menopausal age of Korean women. Moreover, this study identified an association between the occurrence of early menopause and gynecological cancers. Those who experienced menopause before the age of 50 years had  of developing breast cancer than those who experienced menopause after the age of 50 years which was statistically significant. Additionally, early menopause and breast cancer were not associated in patients of the ever-smokers group, while those in the never-smokers group had  of developing breast cancer. Thus, the association between early menopause and breast cancer is relevant, especially in non-smokers.Joyce M. Kim, Yeun Soo Yang, Su Hyun Lee, and Sun Ha Jee contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript.


Background/Significance: The presence of a breast cancer (BRCA) gene mutation increases a woman’s lifetime risk of developing breast cancer. Bilateral risk-reducing mastectomy is a proactive treatment option which lowers that risk. However, breast removal can create a change in physical appearance. It is unclear if BRCA-positive women undergoing this surgery in young adulthood, a life stage where intimate relationships, families, and careers are being established, have the same experience with body image as women in later stages of life.Purpose: The aim of this literature review is to assess how bilateral risk-reducing mastectomy impacts body image in young BRCA-positive women less than 40 years of age, with no history of breast cancer.Methods: Database searches were performed, yielding 402 results. Studies were considered if participants had an increased lifetime breast cancer risk/BRCA-positive diagnosis and history of bilateral risk-reducing mastectomy, body image was evaluated, and mean age was less than 40 years. A total of three qualitative studies and three quantitative studies were identified as relevant for this review.Results: A dearth of information exists on body image in young women following bilateral risk-reducing mastectomy. It was found in this review that some women experienced a decline in body image following surgery, while in others body image was maintained or improved.Conclusion: Understanding factors that impact body image following this risk-reducing surgery will allow clinicians to support this unique population. Open and informative discussion should be encouraged with young women considering, or who have undergone, bilateral risk-reducing mastectomy. The presence of a breast cancer (BRCA) gene mutation is predicted to increase a woman’s lifetime risk of developing breast cancer to 72%, compared to the 12% lifetime risk of a woman without this gene mutation . ApproxiBilateral risk-reducing mastectomy (BRRM), a surgical procedure in which healthy breasts are removed to prevent breast carcinoma, is the most effective proactive treatment option available for BRCA-positive women. Lifetime breast cancer risk is reduced by 90% following BRRM . This prYounger women who undergo BRRM note having a close family member diagnosed with breast cancer and the reduction of breast cancer-related worry as common reasons for seeking this surgery , despiteSearches were performed 06/16/2021, and 08/22/2021 utilizing a combination of the key terms of “risk-reducing mastectomy,” “BRCA,” “body image,” and “young.” CINAHL, PubMed, and Scopus databases were utilized in the review of literature. No limits were placed on geographic location or length of time for follow up. Studies were limited to those published in peer reviewed journals from 2011 and onward, and in the English language. This timeframe for article selection was chosen as the use of prophylactic breast surgery to reduce lifetime breast cancer risk in young women is a relatively new concept.Studies selected for this review included those in which participants had previously undergone BRRM, were BRCA-positive or had an increased familial risk of breast cancer, and those in which body image was addressed within the study. Articles were included if body image broadly met the definition provided in this review. Due to the paucity of studies comprised solely of participants less than 40 years of age, studies were considered for inclusion if the mean age of study participants was less than 40 years. Studies were also included if data on women who had undergone BRRM were reported separately from women undergoing other risk-reducing surgery or who were breast cancer survivors. Two qualitative articles used data collected from semi-structured interviews in the same sample of participants. From this study, both articles were included as each pursued different research questions and aims . IneligiSearches yielded 402 results, with 316 articles remaining after duplicates were removed. Titles and abstracts of these results were then screened for relevance, with 34 articles identified for full text review. The articles were then read in their entirety and were excluded for the following reasons: three contained participants that were only breast cancer survivors; 17 had a mean age greater than 40 years; three did not provide a mean age for study participants; two did not address body image; and two did not differentiate participants with and without breast cancer. Seven articles were identified as meeting inclusion criteria for this literature review see .n = 3), Australia and New Zealand (n = 2), and the United States (n = 1). In all studies participants underwent some form of reconstruction following mastectomy, though details on reporting reconstruction type(s) varied widely across studies. Three quantitative articles were included in this review. All are observational, with participants completing surveys. Of the three qualitative studies included in this review, all conducted semi-structured interviews to obtain data. In analyzing this data, two articles, which used the same sample population, utilized interpretative phenomenological analysis theory  underwent nipple-sparing BRRM before the age of 30 (n = 241) had higher BREAST-Q scores than the group with breast cancer (n = 56). On the satisfaction with breasts scale the BRRM group scored 62 compared to a score of 56 in the cancer group, and on the psychosocial well-being scale the BRRM group score 73, compared to a score of 60 in the cancer group.The BREAST-Q Reconstruction Module was used to evaluate body image in two studies in this review . This inge of 30 . Gandhi In the third study, body image was measured over time with a 31 item study-specific body image scale. It found that satisfaction with breast appearance at the preoperative baseline level was 82.9%. A decline in body image occurred 6 months after BRRM, with only 45.7% of participants reporting satisfaction with breast appearance. At the 21 month follow up body image had improved but remained lower than baseline with 71.4% of women satisfied with breast appearance .n = 12), with a higher preoperative cancer distress score (−0.33) associated with decreased body image at 6 months (Cohen’s d = −0.63), and a higher preoperative cancer distress score (0.10) associated with decreased body image at 21 months (Cohen’s d = −0.83) (Women with a BRCA-positive diagnosis have been found to experience breast cancer- related worry and uncertainty surrounding if and when a breast cancer diagnosis might occur . Increas= −0.83) .The importance of knowledge and understanding to the change in physical appearance following BRRM appeared to be a key factor in participants forming realistic expectations of breast appearance following surgery . One stuTwo studies demonstrated that reconstruction following BRRM did not negatively impact body image. In one study, women who were unhappy with their breast appearance preoperatively experienced an improvement in body image due to change in breast shape and size following reconstruction . In a seThis literature review aimed to evaluate how body image was impacted following BRRM in young BRCA-positive women. Findings from both quantitative and qualitative studies indicate that body image can be affected both positively and negatively following this risk-reducing surgery. In quantitative studies, body image was measured through both validated and study-specific instruments. A decline in body image was experienced by some women due to breast cancer-related worry, and persisted for many months . HoweverQualitative studies in this review found that some participants noted body image decline following BRRM due to insufficient education from their medical team . InformaAs a weakness in this review, it should be noted that only three articles included samples comprised solely of women less than 40 years of age at the time of BRRM . All othFurther investigation is needed following BRRM in BRCA-positive young women to further elucidate factors effecting body image. Such efforts are needed to ultimately develop interventions that would improve a negative body image following BRRM in this population. A strength of this review is the discovery of a gap in the literature, with few studies offering the perspective of body image following this risk-reducing surgery in BRCA-positive women less than 40 years of age.While a dearth of information exists that focuses exclusively on how young BRCA-positive women perceive their physical appearance after undergoing BRRM, this literature review has identified that younger women experience both positive and negative impacts to body image following this risk-reducing surgery. This echoes findings from studies of body image after BRRM comprised of women across the lifespan . For cliThe author confirms being the sole contributor of this work and has approved it for publication.The author declares 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.