Current reported incidence rates for ovarian cancer may significantly underestimate the true rate because of the inclusion of women in the calculations who are not at risk for ovarian cancer due to prior benign salpingo-oophorectomy (SO). We have considered prior SO to more realistically estimate risk for ovarian cancer. Kentucky Health Claims Data, International Classification of Disease 9 (ICD-9) codes, Current Procedure Terminology (CPT) codes, and Kentucky Behavioral Risk Factor Surveillance System (BRFSS) Data were used to identify women who have undergone SO in Kentucky, and these women were removed from the at-risk pool in order to re-assess incidence rates to more accurately represent ovarian cancer risk. The protective effect of SO on the population was determined on an annual basis for ages 5–80+ using data from the years 2009–2013. The corrected age-adjusted rates of ovarian cancer that considered SO ranged from 33% to 67% higher than age-adjusted rates from the standard population. Correction of incidence rates for ovarian cancer by accounting for women with prior SO gives a better understanding of risk for this disease faced by women. The rates of ovarian cancer were substantially higher when SO was taken into consideration than estimates from the standard population. Cancer incidence rates are calculated by dividing new primary cancer cases of a disease by the population at risk in the same time period adjusted by the US standard population . This asThere is an inherent problem in the incidence calculation for some malignancies due to the inclusion of patients in the denominator who are not at risk for the disease . In gyneBRCA) mutations who undergo risk-reducing salpingo-oophorectomy and have dramatically lower risk of ovarian malignancy [There is less guidance in the literature concerning the impact of oophorectomy rates on ovarian cancer incidence. Many women have salpingo-oophorectomy (SO) performed alone due to benign ovarian disease, or have SO performed at the time of hysterectomy for benign conditions. There has been new evidence linking the origin of serous ovarian cancer to the fimbriated end of the fallopian tube ,15,16,17lignancy ,19.The current reported incidence of ovarian cancer from 2005–2009 is 12.7 per 100,000 women . OvarianTo calculate the cancer incidence rates, the most recent five-year ovary cancer cases diagnosed in years 2009–2013 from the Kentucky Cancer Registry (KCR) were extracted. Ovary cancer cases were defined as ICD-O-3 site codes C569 excluding ICD-O-3 histology codes 9050–9055, 9140, 9590–9992. Only invasive cancer cases were included for the analysis.The KCR is a population-based registry, and has been awarded the highest level of certification by the North American Association of Central Cancer Registries for an objective evaluation of completeness, accuracy, and timeliness every year since 1997. The KCR is part of both the CDC National Program of Cancer Registries and the NCI Surveillance, Epidemiology, and End Results (SEER) program, which are considered among the most accurate and complete population-based cancer registries in the world. The KCR also links its database annually with the National Death Index (NDI) to capture the most accurate survival information. No new data was collected from subjects specifically for this study and no contact with any patients was required. All data was previously de-identified.In order to correctly calculate the age-adjusted rates for ovary cancer incidence, the underlying risk population needs to be modified to reflect the fact that women who had SO will have minimal risk of having ovarian cancer. To estimate the prevalence of women who had prior SO for years 2009–2013 in Kentucky, the Kentucky health claims data (KHCD) 2000–2014 data sets were acquired from the Office of Health Policy in the Kentucky Cabinet for Health and Family Services (KCHFS).http://lrc.ky.gov/KAR/900/007/030.htm). The data include in-patient and out-patient files containing de-identified individual records. Key elements, such as ICD-9 procedure codes, CPT codes, and demographics are included in the files. Age is presented in the format of age groups. The KHCD data include hospital discharge reports from all Kentucky hospitals, Medicare provider-based entities and ambulatory facilities (https://www.cdc.gov/brfss/). For women aged 18 and older, responses to the question “Have you had a hysterectomy?” are included. The data related to this question was used to estimate the prevalence of prior hysterectomy by age group. Since the hysterectomy question was presented every other year, the Kentucky BRFSS data 2008–2012 was acquired from the KCHFS to match the ovary cancer incidence data 2009–2013.The Behavioral Risk Factor Surveillance System (BRFSS) data is the annual telephone survey that collects state data related to health risk behavior, chronic health conditions, and use of preventive services for all 50 states, the District of Columbia, and three territories in the U.S  was also used to develop programs to calculate the complete prevalence rates from the KHCD data. Statistical tests were two sided with a There was a total of 81,359 SO cases identified from the KHDC data during 2004–2013 . The higUsing the Counting Method, estimates of annual prevalence counts and rates by age group for the years 2009 to 2013 were calculated from the KHCD. Only results from the years 2009 and 2013 based on the assumption that SO incidences prior 2004 are same as the average in 2004–2013 are shown in In A total of 1403 invasive ovary cancer cases for years 2009–2013 were extracted from the KCR database. The age-adjusted rates from the standard Kentucky population show the rates 10.7 per 100,000  10.2–11.3) for all ages . To matcIn the efforts reported here, the rates of ovarian cancer were 33% to 65% higher when prior SO was taken into consideration than estimates from the standard population. Due to the limitation of data availability, the risk-population adjusted prior SO rates have rarely been calculated previously. In the current study, we used the KHCD data and the Counting Method, a modern statistical approach, to estimate the prior SO prevalence rates based on various assumptions and the risk-population adjusted SO rates. We also estimated the SO rates using estimated SO prevalence rates from the BRFSS data. The prevalence rates of prior SO from the Counting Method and the BRFSS data are different because of various assumptions and different data sources, hence leading to the variation of the risk-population adjusted SO rates. The results demonstrate the challenge to correctly estimate the rates because of the data limitations.Compared to previous published studies with only one type of estimate , our stuOvarian cancer remains the deadliest gynecologic malignancy in the United States, being the 5th most common cause of cancer death in women. Over 14,000 deaths from ovarian cancer are expected for the US in 2016 ,29,30. WScreening for malignancy has been highly effective for other common malignancies such as breast and cervix cancers ,34. OvarTaken together, the available data from these four trials suggests screening works to detect disease at an earlier stage, which leads to improved survival. However, one of the most common criticisms of screening and the studies that have evaluated it is the lower positive predictive values, which are likely driven by the lower prevalence of this disease. Statistical calculations for predictive values vary greatly depending on the prevalence of the disease being studied, unlike sensitivity and specificity of a test, which remain constant. Thus, a test with inherently good sensitivity and specificity can be brought to improved predictive ability by narrowing the screening population to a high-risk group for which the prevalence is high.One way to narrow threat risk population for ovarian cancer is by focusing on ages at which incidence is high. This has been commonly applied in previous screening trials and the results of the work reported here confirm the importance of age. In the present study, ovarian cancer incidence is highest for women over age 75, while the rate of hysterectomy peaks at age 65. Age continues to be one of the most important risk factors for ovarian cancer.Given the importance of correct incidence to predictive calculations for screening programs and epidemiologic risk assessment, an accurate calculation of incidence is critical. An accurate assessment of risk is more easily determined in some diseases than others. If all subjects are at risk, then the calculation is a straight forward division of those diagnosed with disease by those at risk. This is not so clear in all diseases, however. For example, surgical interventions for unrelated problems can reduce the at-risk pool for certain disease sites. This has been demonstrated in the literature regarding endometrial cancer ,43. CorrSalpingo-oophorectomy (SO) is even more difficult to quantify than hysterectomy. Many women elect to have their ovaries and tubes removed at the time of a hysterectomy that is performed for a variety of reasons related to primary uterine pathologies. Additionally, many women undergo bilateral SO either separate from a hysterectomy or at some time after a hysterectomy has been performed for a wide variety of primary ovarian or other conditions, many of which are benign. These include endometriosis, non-cancerous ovarian cysts or masses, risk reduction for genetic conditions, and for hormone reduction in breast cancer patients. The overall trend for SO in the US has been on the decline . This deGiven the robust number of women who have undergone SO, risk of ovarian cancer is greatly reduced for these women and importantly alters the epidemiology of risk for malignancy at this site on a population level. There are some important caveats to this reduction. Serous peritoneal cancers behave nearly identically to ovarian cancer and the risk for these cancers is unlikely to be altered by SO . It shouAn additional consideration is that there is a rising trend in bilateral salpingectomy rather than bilateral salpingo-oophorectomy, which allows ovarian preservation while still potentially reducing cancer risks . The degOverall, the SO rate nationally has been reported to be declining. However, it is still common for women to undergo this procedure and greater than 40% of women still undergo bilateral SO at the time of hysterectomy ,51. ThisThere is inherent difficulty in establishing the overall risk associated with SO. Prior studies have shown an overall mortality disadvantage for women who undergo premenopausal oophorectomy—prior to age 50—and never used estrogen therapy . This deThe strengths of this study include the use of population level data and novel statistical evaluation to correct risk assessments in an important way for women with regard to ovarian cancer. A couple of limitations are worth noting. Most notably, this study is limited by the dependence on CPT and ICD codes for diagnosis. Actual operative reports or pathology reports were not available for confirmation of procedure performed. This introduces the possibility of inappropriate coding leading to incorrect inclusion (or exclusion) of patients in the analysis. In addition, newer ICD codes are more specific in that bilateral procedures are noted and were isolated for inclusion. This is important as unilateral procedures would not be expected to confer the same protection against ovarian cancer as the remaining ovary and/or tube could lead to a malignancy. Not all CPT codes separate unilateral from bilateral procedures and thus there is some uncertainty on the extent of adnexal removal with patients coded this way. Also, some hysterectomy codes are nonspecific as to inclusion of adnexal removal or not. Most notably, abdominal hysterectomy CPT codes can include patient “with or without” adnexal removal. Thus, using these codes can contribute uncertainty to the study as the true proportion of patient with adnexal removal with hysterectomy is not indicated by the code. About 30% of the procedures included in this study came from these questionable codes and the estimates for SO prevalence are likely over estimated because of their inclusion. Future studies can address these uncertainties by verifying procedures performed by complete chart review. This technique is time consuming and would limit the number of patients able to be studied, but is important for refining the exact risk estimates. The advancement of electronic medical records and more specific ICD coding will make this kind of confirmation more feasible in the future.Additionally, because the KHCD data prior to 2004 are not available, the SO rates prior to 2004 were based on assumptions. This will certainly generate biases for the estimates. Although this study requires a range of assumptions, it is reasonable to assume the true estimate is captured within the variation of the estimates. There are no survival data available in the KHCD data, hence the 2010 US life table estimates with a 0.5% deduction were used to estimate the alive SO cases for specific prevalence date and age groups. How much bias is introduced and in which direction this bias goes is unknown. With increasing availability and reliability of health claims data, this approach will likely provide more accurate estimates in the future. The BRFSS data are limited by sampling biases, recall biases, and missing data. Overall, although we cannot provide specific rates for the SO estimates, we can conclude that the corrected rates of ovarian cancer were substantially higher when SO was taken into consideration than estimates from the standard population. Finally, this study should be expanded to a broader national population as there may be important differences between the population in Kentucky versus other parts of the nation.In conclusion, this study presents an important concept for correcting the underestimation of ovarian cancer risk for women who retain their ovaries and tubes. This correction has critical implications for the calculation of screening program performance in terms of predictive value. It is also critically important to refine the epidemiological assessment of the distribution of this disease and the populations at risk so that the highest risk groups can be identified, which will improve screening programs ability to reduce mortality from ovarian cancer while reducing harm from unnecessary interventions.


Estrogen-mimicking chemicals, such as cadmium, may be associated with increased susceptibility to hormone-dependent cancers, though supporting data are sparse, particularly for endometrial cancer. The Health and Environmental Exposure Research (HEER) study worked with the Arkansas Central Cancer Registry, Iowa Cancer Registry and Missouri Cancer Registry to obtain names of women diagnosed with endometrial cancer who were willing to be contacted for participation in our case control study. Voter registration lists from Iowa and Missouri were used to randomly select similarly aged women as represented in the case population. Participants were interviewed by telephone to obtain information on known or suspected endometrial risk factors. Urine kits were sent to participants for home collection and returned for analysis. Our case-control study consisted of 631 incident cases of endometrial cancer diagnosed from January 2010 to October 2012 and 879 age-matched population-based controls, ages 18–81 years (mean age 65 years). We quantified cadmium amounts in urine and standardized these values through creatinine adjustment. Using data from all survey completers, we developed a multivariable model for endometrial cancer. Creatinine-adjusted cadmium concentration was added to this model. Odds ratio (OR) and 95% confidence intervals (CIs) for endometrial cancer were calculated. After multivariable adjustment, higher creatinine-adjusted cadmium exposure was associated with a statistically significant increase of endometrial cancer risk . Our results provide evidence that cadmium may increase the risk of endometrial cancer, possibly through estrogenic effects. Cadmium is a toxic, bioaccumulating, non-essential, and highly persistent metal with a variety of adverse health effects. These include renal dysfunction; breast, lung, pancreatic, and endometrial cancer risk; and disturbances in calcium homeostasis –5. IngesBecause the estimated biological half-life of cadmium is from 10 to 30 years , and parEndometrial cancer, the fourth most common cancer for women, occurs primarily in postmenopausal women. In 2016, approximately 60,050 women are expected to be diagnosed with cancer of the body of the uterus , with 10,470 projected deaths . EndometThe Health and Environmental Exposure Research (HEER) study aimed to investigate the association between endometrial cancer risk from cadmium exposure after adjusting for confounders in a population-based case-control study using cases from the Arkansas, Iowa and Missouri state cancer registries and age-matched controls from voter registration lists.Incident cases of endometrial carcinoma diagnosed from January 2010 to October 2012 were obtained from three cancer registries: Arkansas Central Cancer Registry, Iowa Cancer Registry and Missouri Cancer Registry. In situ and non-carcinomas, such as Müllerian and mesodermal malignant tumors, were excluded. Case ascertainment also included local or regional stages, excluding metastatic endometrial cancer cases. Cases were contacted by each registry to obtain written permission to pass their names to our study. Of those that agreed, contact information was sent to HEER study staff.Age-matched controls were randomly selected from the Iowa and Missouri voter registration lists only as the Arkansas voter registration list was not available. The Iowa voter registration list provided date of birth, whereas the Missouri voter registration list provided age. For Missouri controls, the age of the participant was at the last voter registration period and was thus approximately one to three years younger than the current year. We therefore used the listed age plus 2 years for matching. For each case, four to five controls were randomly selected from the voter registration lists with replacements on names for which the telephone number or residential address in Missouri or Iowa was missing. Imperfect age matching occurred due to variable responses by potential controls. The age or year of cancer diagnosis for cases was used as the reference age or year for the matched controls, respectively. Most questions asked for information during the year before diagnosis, i.e. reference age/year minus one.Introductory letters with a study brochure and consent information were sent to all potential participants. For participants who did not contact the study to decline participation and for whom a valid address was obtained, contact information was sent to the University of South Carolina Call Center for telephone interviews.All participants were interviewed by telephone by trained interviewers. The 35-minute interview asked about physical activity, reproductive history, alcohol consumption, height and weight, use of oral contraceptives and hormone replacement therapy, personal and family medical history, demographic factors, a limited set of dietary components, and smoking history.Upon completion of the telephone interview a kit to collect saliva and urine at home and return to the study center was sent to participants. Urine sample containers and urine hats used to collect the sample were exhaustively cleaned using multistep acid leachings before sending to participants . Analyseo Celsius) upon receipt. The University of Missouri Research Reactor (MURR) has a laboratory that is specially designed for trace element analysis and is subject to high-efficiency particulate air filtration. The trace-analysis laboratory at MURR has been has been conducting elemental analyses of biological monitors for 35 years [Urine samples were immediately refrigerated  before the reference date. Women who were taking postmenopausal hormones and still having periods, and women who reported hysterectomy alone were classified as premenopausal if their reference ages were in the first decile of age at natural menopause among the controls (32 years old for current smokers and 36 years old for nonsmokers), and postmenopausal if their reference ages were in the highest decile for age at natural menopause in the control group (56 years old for both current smokers and nonsmokers). For six women in the intermediate age category (second to ninth deciles), menopausal status was considered unknown. Thus, we defined three categories of menopausal status: premenopausal, postmenopausal, and unknown .2 to avoid undue influence of extreme outliers. Variables were selected by both forward stepwise inclusion and backwards elimination, using p = 0.1 for both entry and exit. Both stepwise models retained the same set of variables. Interactions between variables in the model were tested; none were retained.We developed a multivariable conditional logistic model, stratifying on age at diagnosis. Variables that were selected a priori ), menopause at age 56 or older, menopausal status at diagnosis, smoking history, second hand smoke exposure, age at menarche, number of live births, weight gain, history of weight loss attempt, family history of endometrial cancer in a first degree relative, exposure to unopposed estrogen, oral contraceptive use, history of breast or ovarian cancer, history of uterine fibroids, history of diabetes, sleep habits, irregular work schedule, alcohol use, protein shake and whole milk consumption) were tested for association with case-control status in a multivariable conditional logistic regression. Those with p<0.1 were retained for further analysis. BMI, weight in kilograms divided by squared height in meters, was capped at a maximum value of 70 kg/mAfter the “best” case-control model had been achieved, the base-2 logarithm of creatinine-adjusted cadmium was added to the model to test for its association with case-control status after controlling for other risk factors, using data from women who returned a urine sample. Because the women who opted to provide urine samples differed from those who did not, we developed a multivariable logistic model to adjust for a potential non-response bias in the case-control model . The invOf those approached by the cancer registries (n = 2597) for permission to send their names to our study for potential enrollment, 29% of the names (m = 749) were sent to the call center for interviewing (approximately 25% of MO and AR and 44% of IA). Of 711 eligible cases 89% (n = 631) completed an interview. In comparison to those who declined to pass their names to our study from the cancer registries, those who passed their names were more likely to be White (94% versus 90%) or married or living with partner (66% versus 55%) and less likely to be diagnosed with endometrioid carcinoma  (74% versus 78%). The proportion with tumor grade I or II and age at diagnosis were similar between the two groups.For the controls, 4280 age-matched names were randomly selected from the voter registration lists. Of the 4280 controls, 3120 were eligible and 888 completed the survey (28% participation proportion). Of the 2597 cases, 749 women with endometrial cancer were passed to the HEER study and 631 completed the survey (24% participation proportion for cases). Among these 1519 participants, 498 cases (79%) and 545 controls (61%) also returned urine specimens. For the current study, we excluded 9 controls with a history of endometrial cancer, leaving 1510 participants (631 cases and 879 controls). See Controls were slightly older than cases , with less education (high school graduate or less: 43% versus 37%). Both marital status and racial compositions differed between the two groups . The casOf those who actively declined to participate, 17% (9 cases and 194 controls) agreed to answer a few demographic questions. Compared to participants who completed the interview, these helpful refusals were more likely to be high school graduate or less (40% versus 54%) and be married or living with partner (71% versus 82%). The educational attainment of spouse or partner, percent of Hispanic ethnicity, race, income and sexual orientation were similar between these two groups.Of the 165 respondents contacted for a second interview, 2 individuals refused to be interviewed, 29 could not be reached, and 134 were re-interviewed . Mean time between interviews was 13.9 months (range 3.1–20.8 months). Time between interviews was not different between cases and controls.Participation in moderate and vigorous physical activity showed good concordance between interviews (93% and 82% respectively); the kappa for moderate physical activity was 0.54 (lower confidence limit (LCL) 0.27) and vigorous physical activity was 0.64 (LCL 0.51). History of ever using mineral supplements showed good concordance (84.2%) with a low kappa . The kappa for BMI category was .84 (LCL .75) with 89.6% concordance. There was high concordance for ever being diagnosed with polycystic ovarian syndrome (97%); the kappa was 0.48 (LCL 0.05). Ever being diagnosed with diabetes showed high concordance (95.5%); the kappa was 0.85 (LCL .74). Having a biological family member that was ever diagnosed with endometrial cancer had high concordance between interviews (88.1). Correlation between the repeated measures of weight, height, BMI, and age of diabetes diagnosis was high .Creatinine-adjusted cadmium levels ranged from to 0.005 to 0.417 (mean 0.037) μg/g in case participants and from 0.006 to 0.649 (mean 0.041) μg/g in control subjects. A t-test of the creatinine-adjusted cadmium concentration between cases and controls was not statistically significant (p = .101).After multivariable adjustment, a doubling of urine cadmium increased the endometrial cancer risk by 22% . A similIn this population-based case-control study of Midwestern U.S. women, we found a statistically significant positive association between urine cadmium levels and endometrial cancer risk. Specifically, a 22% increased risk of endometrial cancer was associated with doubling cadmium exposure. Our confidence in the findings is strengthened by our large number of cases, evaluation of those who declined to participate at various points in our study, use of population-based controls that were age-matched to cases, inclusion of a reliability study, the use of urine as the biomarker to ascertain lifetime cadmium exposure, additional analyses to account for potential bias, and using an established protocol for urine analysis with an additional correction factor.To our knowledge, this is the first published report on cadmium exposure and endometrial cancer risk using urine as a biomarker for cadmium measurement. Four other cohort studies conducted in Japan, Denmark, Sweden, and the United States have reported on this association; all have estimated cadmium exposure using food frequency questionnaires with mixed results. Eriksen et al reported a null finding for cadmium exposure and risk of endometrial cancer  using a Danish population-based prospective study as did Sawada in the Japan Public Health Center-based Prospective Study  ,34. The After humans ingest or inhale cadmium, the body excretes only a very small fraction and efficiently retains the rest . Among tCadmium levels are related to level of smoking. Heavy smokers may have twice as much cadmium, and moderate smokers’ cadmium burden may increase by approximately sixty percent compared to non-smokers . Former With our extensive survey we were able to consider numerous variables that are known or suspected to increase the risk of endometrial cancer in the logistic regression models. Well documented endometrial cancer risk factors include late menopause, early menarche, nulliparity, and obesity . Among tSeveral limitations should be considered in evaluating our results. One limitation was the low participation proportion of women diagnosed with endometrial cancer. Obtaining consent was a two-stage process. Each cancer registry approached eligible cancer cases and required written consent to pass their names to the HEER study. The second stage involved verbal consent upon contact by telephone by the HEER study team. Only 25%-44% of the women agreed to let their respective cancer registry pass their names onto HEER for enrollment. The second stage participation proportion was 89% of the eligible cases; another 1% who declined to participate answered a few questions to obtain a few details. Those not consenting at stage one were more likely to be married or living with partner while those opting to not enroll at stage two were less likely to be married or living with partner. Differences were also observed in racial composition. Those not consenting to pass their names were more likely to be Black, but no difference was observed in declining to enrollment by race. Proportion of tumor grade I and II and age of diagnoses was similar at both stage one and two among the two groups—those consenting and those declining to participate. Among the controls, our participation proportion was 28%. Although we assessed some characteristics between the two groups at the time of interview (n = 203) in our refusal sub-study, we cannot rule out the possibility of selection bias.As with any case-control study, the risk of recall bias is also a limitation. All data except information about urinary cadmium measurement and tumor characteristics for cases ere self-reported. However, of the few questions we re-asked of participants, the concordance between the two time periods was quite good. Nevertheless, we cannot eliminate the possibility of recall bias.In conclusion, our results provide evidence that cadmium may increase the risk of endometrial cancer, possibly through estrogenic effects. Further studies that employ urinary cadmium as the biomarker are necessary given the weak association with estimated cadmium from dietary sources. A comprehensive list of suspected and known risk factors should also be collected to fully adjust the regression models.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.


Findings from a recent prospective cohort study in California suggested increased risk of breast cancer associated with higher exposure to certain carcinogenic and estrogen-disrupting hazardous air pollutants (HAPs). However, to date, no nationwide studies have evaluated these possible associations. Our objective was to examine the impacts of mammary carcinogen and estrogen disrupting HAPs on risk of invasive breast cancer in a nationwide cohort.We assigned HAPs from the US Environmental Protection Agency’s 2002 National Air Toxics Assessment to 109,239 members of the nationwide, prospective Nurses’ Health Study II (NHSII). Risk of overall invasive, estrogen receptor (ER)-positive (ER+), and ER-negative (ER-) breast cancer with increasing quartiles of exposure were assessed in time-varying multivariable proportional hazards models, adjusted for traditional breast cancer risk factors.A total of 3321 invasive cases occurred  during follow-up 1989–2011. Overall, there was no consistent pattern of elevated risk of the HAPs with risk of breast cancer. Suggestive elevations were only seen with increasing 1,2-dibromo-3-chloropropane exposures .Exposures to HAPs during adulthood were not consistently associated with an increased risk of overall or estrogen-receptor subtypes of invasive breast cancer in this nationwide cohort of women.The online version of this article (10.1186/s12940-018-0372-3) contains supplementary material, which is available to authorized users. To date, known risk factors explain only half of all breast cancer diagnoses, demonstrating a need to identify additional targets for prevention –3. GeogrTwo recent analyses based in the California Teachers Study (CTS) cohort  have focused on Hazardous Air Pollutants (HAPs) as another potential group of environmental risk factors for breast cancer . HAPs arIn a related analysis, Liu et al. examined associations with HAPs that had been previously identified as estrogen disruptors and risk of breast cancer in the CTS . There wThe NHS II is a prospective cohort established in 1989 when 116,430 female nurses aged 25–42 years completed a baseline questionnaire. At enrollment, the women resided in 14 states . However, as of the mid-1990’s, members of the cohort resided in all 50 states and the District of Columbia. Biennial follow-up questionnaires are mailed with response rates over 90% and collect information on incidence of disease and on a variety of lifestyle characteristics. Women were excluded from the current study if they had been diagnosed with cancer (other than non-melanoma skin cancer) before returning the baseline questionnaire. Women were censored during follow-up if they were diagnosed with a cancer other than breast or non-melanoma skin cancer. Participants were excluded if they lived outside of the contiguous US, or if they lived in a Census tract with no information available on exposure. After these exclusions, there were a total of 109,239 women available for analysis. The study was approved by the Institutional Review Board (IRB) of Brigham and Women’s Hospital and participants provided implied consent through the return of questionnaires.Participants self-report newly diagnosed invasive breast cancers on the biennial questionnaires. Deaths are ascertained from family members, the US Postal Service (in response to cohort mailings), or through annual searches of the National Death Index. Medical records were reviewed by study staff without knowledge of exposure status. In this cohort, pathology reports have been shown to confirm 99% of self-reported diagnoses of breast cancer. Therefore, we included self-reported cases where medical record information was not available in our analyses. Tissue block collection and tissue microarray (TMA) construction were described in detail previously  and immuAs part of the questionnaire mailing process, residential address histories are available for each participant from 1989 forward. Each address has been geocoded to obtain latitude and longitude, as well as Census geographies for each decennial Census. Ambient concentrations of HAPs identified as potential mammary carcinogens or estrogen disruptors were assigned to each residential address using information from the US Environmental Protection Agency’s National Air Toxics Assessment (NATA) . This prWe used an approach parallel to that used in the CTS to select HAPs of interest , 27. TheInformation on potential risk factors and effect modifiers is obtained on each biennial follow-up questionnaire . We considered a number of risk factors for invasive breast cancer, or predictors of exposure including: race, height, body mass index (BMI) at age 18 and difference between current BMI and BMI at age 18, alcohol consumption at ages 15 and 18, smoking status, diet quality  as assessed by the Alternative Healthy Eating Index,  physicalP-values for trend were obtained from models using the median value for each quartile of exposure. All models were stratified by current age in months and questionnaire cycle to control for age and temporal effects. We performed sensitivity analyses examining the associations in subgroups of interest including models restricted to ER+ or ER-, analyses among women who were currently pre-menopausal, and models stratified by smoking status (ever vs never).Person-months of follow-up were calculated from June 1989 through the end of follow-up (May 2011), incidence of invasive breast cancer, incidence of other cancer (including in situ breast cancers), death, or loss to follow-up, whichever occurred first. Time-varying Cox proportional hazards models were used to assess the association of incident invasive breast cancer with quartiles of each compound, and hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated comparing each quartile to the lowest. Quartiles of exposure were used after cubic regression splines indicated that several dose-responses statistically significantly deviated from linearity. As noted above, we conducted sensitivity analyses using estimates from the 1996 NATA to determine if the choice of predictions affected our results. We also assessed Pearson and Spearman correlations between the 1996 and 2002 predictions of each HAP.− 7 μg/m3 for benzidine to 1.34 μg/m3 for benzene, with the majority of compounds having their full range of concentrations below 1 μg/m3.Age-standardized characteristics of the cohort throughout the full period of follow-up are presented in Table During 2,203,192 years of follow-up, a total of 3321 invasive cases occurred . Overall, there was little consistent evidence of adverse effects of increasing exposure to any of the mammary carcinogens with increasing risk of invasive breast cancers , hydrazine (decreased risk), and 2-chloroacetophenone (decreased risk), none of the examined HAPs had a statistically significant dose-response with risk of ER+ cancers. Elevations in risk were observed with some exposures  and reductions in risk with others  , and vinyl chloride  . We did The CTS investigators reported elevations in risk of ER+/PR+ breast cancers and exposures to acrylamide, benzidine, carbon tetrachloride, ethylidene dichloride, and vinyl choloride and for risk of ER-/PR- breast cancers and benzene. With the exception of a suggested elevated risk in risk of ER+ breast cancer comparing women with non-zero exposures to carbon tetrachloride to women with a level of zero , we did not observe similar findings in the NHSII.We observed a suggestive increased risk of invasive breast cancer with exposures to two estrogen disruptors, diesel engine emissions  and 4-nitrophenol . The CTS investigators reported that they did not observe an association between any of the examined estrogen disruptors and risk of breast cancer, however their multivariable adjusted HR for the top quintiles of diesel engine emissions  and 4-nitrophenol  were also elevated . Both diIt is important to note that there are a number of differences between the CTS and NHSII cohorts. The cohorts are quite different spatially  and the women in the CTS were less likely to be white (85% in CTS compared to 95% in NHSII) and were much more likely to be postmenopausal . Women iOur study has a number of limitations. The use of the US EPA HAPs data as our source of exposure may have introduced exposure misclassification in a number of ways. Although we used updated address information, the HAPs estimates were chosen from a single year, 2002. This decision was made to increase comparability with the CTS analyses, and based recommendations from EPA not to use multiple NATA assessments due to differences in the methodology. However, this means that we are unable to assess the impacts of concentration changes over time. Another source of exposure error is the use of a Census tract level measure as an estimate of personal exposures for each participant. The use of an area-level proxy likely leads to substantial nondifferential exposure misclassification, as it does not take into account differences within a Census tract, differences in the amount of time each participant spends in the Census tract where she lives, and differences in the amount of time each woman would be exposed to these specific ambient concentrations. We are also unable to account for exposures to any of these HAPs from indoor sources, or from other routes of exposure. Importantly, for this population, who may have been exposed to some of these chemicals as nurses, we are unable to account for exposures to these chemicals at work, as NHSII does not have information on workplace chemical exposures. The potential for substantial exposure error may explain the overall lack of associations in our study.Another limitation of our analysis is the inability to examine exposures early in the life of our participants. Studies have demonstrated that exposures in early childhood and adolescence, and before first birth may be more strongly associated with risk of breast cancer than exposures later on in life , 51. HowLastly, although this is the first nationwide study to be able to examine the impacts of mammary carcinogens and estrogen disruptor HAPs available from the 2002 NATA, our findings may not be generalizable to all populations of women in the US. Participants in the NHSII do not tend to live in neighborhoods with low SES, which may be disproportionally impacted by emissions of these HAPs from industrial sources. If there are interactions between measures of socioeconomic disadvantage and HAPs on risk of breast cancer, then our results would not be generalizable to groups with lower SES.Our study also has a number of strengths. Participants of the NHSII live in all of the 48 contiguous US and the District of Columbia, allowing us to examine the impacts of these HAPs across the US. The restriction of our cohort to a single occupational group and the availability of time-varying information on a wide variety of personal- and area-level potential confounders allowed us to tightly control for potential confounding. Lastly, the large size of the cohort, combined with over 20 years of follow-up, allowed us sufficient power to detect small differences in exposure if they had been present.In this nationwide cohort of women, exposures to HAPs determined to be mammary carcinogens or estrogen disruptors during adulthood were generally not associated with an increased risk of overall or hormone-receptor subtype specific risk of breast cancer. Additional studies are needed to determine if these exposures are associated with risk of breast cancer, especially during key windows of potential susceptibility.Additional file 1:Supplemental tables and figures. (DOCX 284 kb)Additional file 2:Multivariable adjusted associations of increasing quartiles of each mammary carcinogen HAP exposure on risk of incident invasive overall, estrogen-receptor positive (ER+) or estrogen-receptor negative (ER-) breast cancer 1989–2011 among 109,239 members of the Nurses’ Health Study II cohort (DOCX 51 kb)


Chinese women residing in Hong Kong for the past 7 years were recruited by media advertisements . The control participants were recruited by convenience sampling from health workshops held in clinics and communities of 15 districts of Hong Kong. After completing test–retest reliability, all participants were asked to complete diet pattern questionnaires about their food preferences and dietary patterns. The Student’s unpaired hot, neutral, and cold. The participants with breast cancer exhibited a stronger preference for hot food than the control group . A higher frequency of breast cancer was associated with a higher frequency of dining out for breakfast  and lunch . The participants with no history of breast cancer consumed CM supplements and Guangdong soups  more frequently than those with breast cancer.Three major CM dietary patterns were identified: neutral  dietary pattern, and consumed CM supplements and Guangdong soups more frequently.Non-breast cancer participants adopted a The online version of this article (doi:10.1186/s13020-017-0138-9) contains supplementary material, which is available to authorized users. Breast cancer is the most common and one of the most fatal cancers in women worldwide. The number of breast cancer cases among women in Hong Kong increased from 2273 in 2004 to more than 2870 in 2009, and average incidence rate per 100,000 women between 2004 and 2009 was 386.7 . Howeverhot or cold features) may increase the risk of related diseases, such as cancers [The dietary patterns of women in Hong Kong have been changing with western lifestyle in recent years , 7. The  cancers . Althoug cancers –11, the cold, cool, hot, and warm) can be used to classify diets as hot, neutral, or cold; these types of food have different functions in the body. Under this classification, beef, shrimp, chicken, and even ice cream are hot foods  , who were compared with healthy women (controls). The cases were recruited by advertisements in the local media, such as in newspapers, in booklets issued by the University of Hong Kong, on posters, and on the radio. Convenience sampling was used to recruit the control group from health workshops in clinics and communities in 15 Hong Kong districts. All participants had to fulfill the following conditions: (1) female, (2) Chinese women residing in Hong Kong, (3) 28–60 years of age, (4) living in Hong Kong for the past 7 years, (5) able to understand the questionnaire items in Chinese, and (6) provision of an informed consent. Controls were required to fulfill the following conditions: (1) no breast cancer or other gynecologic diseases; (2) no metabolic, nutritional, or other severe diseases; and (3) no serious disease after doctor consultation in the past 3 months. Case inclusion criteria were: (1) new primary breast cancer patients, and (2) a diagnosis of primary breast cancer through biopsy and/or discharge report. Participants were excluded from the study if they had (1) other prior cancer diagnosis, (2) treatments with psychotropic drugs, (3) any medical conditions that limited their physical activity, and (4) not completed the questionnaire.hot foods and an expected odds ratio (OR) of 2.32 according to the formula of OR was found. For the pilot study, we invited 37 people from the oncology outpatient clinic and the general outpatient clinic in the School of Chinese Medicine, University of Hong Kong, to complete the questionnaire: 20 were breast cancer cases and 17 were not. After the pilot study, we analyzed the responses and calculated the consumption of 12 kinds of hot foods including beef, mutton, shrimp, soft drinks, and milk. Of the breast cancer cases, 45.8% consumed hot foods regularly, whereas only 26.7% of the controls did so. We chose 10% as the estimated prevalence of hot food consumption for control group and calculated the estimated prevalence of hot food consumption for breast cancer group as 0.2 the expected OR of 2.32 to estimate the sample size of our research. The estimated sample size was calculated as about 207 participants in each group. Taking into account the nullified questionnaires, 202 participants were eventually included out of the total 207 participants recruited in each group.We conducted a pilot study with 37 participants whose total diet comprised mainly A self-administered FFQ was used to collect the dietary intake information. This questionnaire was adapted from the Fred Hutchinson Cancer Research Center Food Questionnaire, which is based on Western dietary patterns , 14. Twocold, neutral, and hot foods, classified according to CM). A total score for each subscale was obtained by summing the relevant item scores. Higher scores indicated higher consumption frequencies [The questionnaire consists of two sections. The first section assessed sociodemographic information, including age, working status, education level, weight, height, and marital status. The second section contained the CM FFQ items, which measured consumption of very common foods. The FFQ consists of 74 items scored on a 5-point scale, ranging from 1  to 5 (consumed very often). It comprises nine subscales of food consumption frequency for CM-defined foods, and a general preference score for three kinds of food . The results produced a Every potential participant was interviewed by a trained research doctor at the clinic center of the School of Chinese Medicine, University of Hong Kong. Participants completed a self-administered questionnaire and had a face-to-face consultation for the same research doctor to check the questionnaire.Three important steps were taken throughout the process to ensure the quality of investigation. First, to ensure consistency of the survey across sites and over time, the same interviewer used the same procedures and standards to investigate all women. Second, each questionnaire was checked by the same researcher carefully and those questionnaires with missing data on foods or dietary patterns were excluded. Third, to prevent input error, we used a double data entry method by different researchers to enter all data into SPSS.cold, neutral, and hot properties and have different nutritional constituents, each with their own cold, neutral, and hot properties. These food properties are based on the functions the foods perform after consumption. All vegetables, meat, fruits, and grains are classified according to one of these three properties, as summarized in Additional file hot, neutral, or cold was determined by the average score for all the foods listed. For example, if the overall FFQ score for neutral food was the highest, then the overall preference score for food was considered neutral. The hot and cold preference scores were calculated in the same manner.According to CM theory, different foods possess different dim sum/tea drinking) and Western-style food consumption  . Demographic information such as age, marital status, current height and weight (to obtain body mass index), and education level was also collected.Additionally, participants were asked to indicate the frequency of (1) eating out for breakfast, lunch, and dinner ; (2) CM intake, CM supplement intake, and Chinese soup consumption ; and (3) Chinese-style food  was used for data analysis. Descriptive statistics were calculated; continuous variables were presented as mean ± standard deviation, and categorical variables were presented as frequencies. The mean difference between continuous variables was tested using Student’s Regarding the validity and reliability assessment of the questionnaire, we examined test–retest reliability by inviting participants in the pilot study to complete the questionnaire a second time. We compared both test results using ANOVA, which produced a value of 0.915, showing high test–retest reliability. All the pilot study participants were interviewed about the interpretability of the questionnaire, and the interpretability was reported to be good.The case and control groups were similar in terms of body mass index, occupation, education, marital status, age at first menstrual cycle, and family history of cancer Table .Table 1SP < 0.001): participants in the control group were 2.4 years younger. However, when age was categorized according to five different age groups, there was no significant difference between the case and control groups (P = 0.061). Because there was a marginally significant difference in age between the two groups, we considered age a confounder in multivariate analysis of variance (MANOVA).When age was treated as a continuous variable, there was a significant difference between the case and control groups (P < 0.001). We analyzed the between-group differences in food preferences of hot, neutral, and cold using logistic regression with age as a variable. The consumption of hot foods such as roast meat, shrimp, shellfish, soft drinks, popcorn/fries, and candies was significantly different between groups. Participants with a strong preference for roast meat, shrimp, shellfish, and popcorn/fries had 6-, 9.2-, 13.3-, and 7.6-times higher risk, respectively, of having breast cancer compared with participants with an average preference for these foods. Women who preferred soft drinks had a 4.9-times higher risk of having breast cancer compared with women who had an average preference. Higher preferences for nuts and freshwater fish  and tofu and squid (cold) showed a lower OR for breast cancer  and lunch  was positively associated with the risk of breast cancer. In general, participants who dined out for breakfast and lunch (at least four times per week) had at least twice the risk of having breast cancer compared with participants who dined out 0–1 time per week   dietary pattern, along with consumption of CM supplements and Guangdong soups.Non-breast cancer participants adopted a healthy and balanced (Additional file 1. Classification and characteristics of foods according to TCM theory.Additional file 2. Reference food questionnaire.Additional file 3. Minimum standards of reporting checklist.Additional file 4. Questionnaire-N-dietary.Additional file 5. IRB.


African-American women, especially in the southern United States, are underrepresented in cancer genetics research. A study was designed to address this issue by investigating the germline mutation rate in African-American women in Arkansas with a personal and/or family history of breast cancer. Women were tested for these mutations using a large panel of breast cancer susceptibility genes. In this analysis, we discuss the challenges encountered in recruiting African-American women from an existing biorepository to participate in this study.We attempted to contact 965 African-American women with a personal and/or family history of breast cancer who participated in Spit for the Cure (SFTC) between 2007 and 2013 and provided consent to be recontacted. The SFTC participants were invited by telephone and email to participate in the genetic study. Enrollment required completion of a phone interview to obtain a family and medical history and return of a signed consent form.n = 583), declined to participate (n = 57), did not keep phone interview appointments (n = 82), completed the phone interview but never returned a signed consent (n = 54), were deceased (n = 13), or were too confused to consent to participate (n = 2).Among eligible SFTC participants, 39.6% (382/965) were able to be contacted with the phone numbers and email addresses they provided. Of these, 174 (45.5%) completed a phone interview and returned a signed consent form. Others were not able to be contacted (Recruiting African-American women into our breast cancer genetics study proved challenging primarily due to difficulty establishing contact with potential participants. Given their prior participation in breast cancer research, we anticipated that this would be a highly motivated population. Indeed, when we were able to contact SFTC participants, only 14.9% declined to participate in our study. Innovative communication, retention, and recruitment strategies are needed in future studies to address the recruitment challenges we faced. Breast cancer is the most commonly diagnosed cancer in African-American women with an estimated 30,700 new diagnoses in 2016 –6.BRCA1, BRCA2 and other known cancer susceptibility genes [In the era of precision medicine, cancer genetics is being emphasized to better identify factors that underlie breast cancer disparities . Howeverty genes , 11.Women were recruited for the genetic study using the established Spit for the Cure (SFTC) cohort. SFTC created a biorepository at the University of Arkansas for Medical Sciences of over 26,000 women’s saliva samples that were donated for breast cancer research. Women were recruited between 2007 and 2013 at community events across Arkansas, a state that is 43.8% rural according to census data , 13, andSFTC participants completed a questionnaire including demographics and medical information, such as family history of breast cancer in first-degree relatives, personal history of breast cancer, treatment if the participant had breast cancer, reproductive history, BMI, alcohol consumption, and physical activity . ApproxiAfter receiving IRB approval, eligible SFTC participants were identified. Eligibility criteria included self-identification as an African-American woman, a personal and/or family history of breast cancer, and agreement to be recontacted for future research. For purposes of this study, family history of breast cancer was defined as having a first-degree relative with breast cancer. Participants with both a personal and family history of breast cancer were included in the personal history group. If participants were found to be from the same family, they were not excluded from enrolling. A total of 965 African-American SFTC participants, including 296 with a personal history and 669 with a family history of breast cancer, were considered eligible based on these criteria.At least two and up to four attempts were made to contact each SFTC participant using the telephone numbers and/or email addresses they provided to SFTC. If the participant had an automated answering service, a voicemail with our contact information was left. If other members of the household answered, they were asked to give a message with our contact information to the participant. If SFTC participants were interested in our study and available when called, the phone interview was conducted immediately. If SFTC participants responded via email or expressed interest but could not complete the interview immediately, an appointment was made for the interview to be completed at the participant’s convenience. When scheduling appointments, the study team was available from 7 am to 8 pm on weekdays and weekends.After confirming eligibility, the purpose of the research study was explained. This included counseling about the benefits and risks of having genetic testing, including the possible outcomes of genetic testing. Genetic counseling was provided and informed consent was obtained by a certified genetic counselor or a physician. Once a woman verbally consented to participate, a detailed family and medical history was obtained. Although we did not record interview duration, we estimate the average interview lasted 30 min but varied depending on the family size and complexity of their medical history. After the interview, a consent form was mailed with instructions to sign and return it within 2 weeks using a pre-addressed, stamped envelope. Participants were given a tentative timeline of when to expect results. They were informed that further genetic counseling would be available regardless of the results. If an abnormality was detected, confirmatory testing, counseling, and cascade testing would also be provided to participants and their potentially affected family members. Variants of unknown significance were not returned to participants; if these variants were to be reclassified to a pathogenic mutation in the future, every effort would be made to reach those participants Women who completed the phone interview but did not return a signed consent form within 1 month were called with a reminder to return the signed form. An additional consent form was also mailed.When women initially enrolled in SFTC, they provided their education level, location , and age. In the 965 women that we attempted to contact in 2015, the average age of the women at attempted recontact was 55.8 years with a range of 25–91 years. The majority of the women in this subset of SFTC were located in urban areas (79.4%) versus in rural areas (20.6%) [Table p < 0.01).Of the 965 SFTC participants, 364 provided a phone number(s) when they enrolled in SFTC, three provided an email address, and 598 provided both a phone number and email address. Of the 174 SFTC participants who enrolled in our study, 37 provided only their phone number, whereas 137 provided both a phone number and an email address. Overall, significantly fewer of those who provided only a phone number (10.2%) were enrolled into our study compared to those who provided both a phone number and an email address , declining to participate (n = 57), not keeping scheduled appointments for phone interview (n = 82), completing the phone interview but never returning a signed consent form (n = 54), being deceased (n = 13) or being too confused to consent to participate (n = 2) [Fig. Among eligible SFTC participants, 39.6% (382/965) were contacted or accounted for with the phone numbers and email addresses they provided. Of these, 174 (45.5%) completed a phone interview and returned a signed consent form and thus were enrolled in the study. Reasons why other eligible subjects were not enrolled in the study included inability to contact them .Of the 296 African-American SFTC participants with a personal history of breast cancer, we were able to contact or account for 142 of them (48.0%). Of these, 85 completed a phone interview and 65 returned a signed consent form  were contacted or accounted for. Of those, 143 completed a phone interview and 109 returned a signed consent form (Table p < 0.01), but both groups had similar rates of returning signed consent forms after interview completion . Therefore, a higher percentage of women with a personal history of breast cancer were ultimately enrolled into our study. A similar percentage of women with a personal or family history of breast cancer declined to be in the study .A significantly higher percentage of those with a personal history of breast cancer completed an interview compared to those with a family history of breast cancer  of the African-Americans compared to 58.7%  of the non-African Americans, suggesting low rates of successful recontact of African-American SFTC participants as early as 2008 and 2010 . Furthermore, this demonstrates that African-American participants had lower rates of recontact compared to non-African Americans in the SFTC cohort. The 39% recontact rate of African-American women in 2010 mirrors our experience, where we were able to account for 39.6% (382/965) of SFTC participants.When contact was made, we had the most success with enrollment when participants were able to complete the interview immediately. We lost 82 potential enrollees when future scheduled appointments were not kept. The length of the interview therefore negatively impacted enrollment, but we consider that unavoidable as the information gathered in the interview was critical to the goals of the study. Other studies have shown that shortening the data collection portion of enrollment and/or allowing some of this process to be available online positively impacts recruitment . AdditioOnce the interview was completed, 76.3% (174/228) of participants signed and returned a consent form. The failure of 54 potential participants to return the consent form could represent passively declining to participate. Over the phone, some participants might not have fully understood the purpose, risks, and benefits of the study and were hesitant to participate once they saw them written in the consent. However, forgetting to return the consent forms, not understanding the importance of returning a signed consent form, or mailing errors could also account for the lack of signed consent forms. These are all commonly encountered issues in studies involving renewing consent . In the Strategies are needed to overcome the barrier of low recontact rates for future studies. When planning a biorepository where minority participation is emphasized, one strategy to overcome low recontact rates could be to oversample minorities. Although SFTC recruited a proportional number of African Americans compared to Arkansas demographics and oversampled women with a personal and/or family history of breast cancer, specifically oversampling African-American women could have aided our recruitment efforts. Other studies have also suggested oversampling as an effective way to increase the number of individuals who participate . AnotherAnother strategy to increase success of recontact attempts is collecting more detailed contact information when enrolling participants. Multiple modes of contact, including a secondary contact person, have been shown to be effective tools for retention in cohort studies , 22. In Increased frequency of contact and quality of interactions between the researchers and participants is an additional strategy to consider for future studies. We made between two and four attempts to contact each woman. Each additional attempt netted successful contacts, but we did not keep a detailed record of the exact increase in yield. A genetic counseling study with 192 women found that most of the African-American women who were retained in the study were reached after about five call attempts and that calling in the evening netted a higher yield , 22. AgaWe encountered challenges recruiting African-American women with a personal or family history of breast cancer from an existing biorepository to participate in a study evaluating germline genetic mutations, despite the fact that they had previously participated in breast cancer research. When we were able to contact SFTC participants, only 14.9% declined to participate in our study. However, recruiting women into our study was hampered by difficulty contacting the subset of SFTC participants eligible for our study. Invaluable resources like the SFTC biorepository can be maximized for future studies by employing innovative strategies for maintaining participant engagement and up-to-date contact information, as well as allocating adequate resources for intensive contact attempts in subsequent studies.


P = 0.0004, CP= 0.0012), whereas the G allele of rs7003146 was associated with decreased breast cancer risk . Furthermore, the rs1225404 polymorphism positively correlated with negative progesterone receptor status. A positive correlation with positive estrogen receptor (ER) status was observed for the rs7003146 polymorphism. Our results suggest that TCF7L2 polymorphisms rs1225404 and rs7003146, but not rs7903146, may affect breast cancer risk in Northwest Chinese women. Additionally, the tag polymorphisms in TCF7L2 are associated with the clinical features of breast cancer, which may provide us novel insight into the pathogenesis of breast cancer.Genetic variations in transcription factor 7-like 2 (TCF7L2) are associated with cancer risk. This study was conducted to establish the relationship between TCF7L2 polymorphisms  and clinical features and risk of breast cancer in Northwest Chinese Han women. In this study, three polymorphisms of TCF7L2  were genotyped in 458 patients with breast cancer and 500 healthy controls using the Sequenom MassARRAY-iPLEX system. We evaluated the associations between the polymorphisms and breast cancer using odds ratios (ORs) and corresponding 95% confidence intervals (95% CIs). The C allele of rs1225404 was associated with increased breast cancer risk (OR = 1.58,  Breast cancer (BC) accounts for the largest proportion of women cancer globally, and has a significant tendency to onset among younger people . By estiThe transcription factor 7-like 2 (TCF7L2) gene is situated on human chromosome 10q25.3 and 215.9 kb long, with 17 identified exons. It is considered to be the “sweet gene”, and is closely associated with diabetes and obesity . B-catenSingle-nucleotide polymorphisms (SNPs) in the TCF7L2 gene are proposed risk factors for BC development. The rs12255372 (G/T) polymorphic variant increases the susceptibility of familial BCamong German patients,  and the Pervious studies reported the inconsistent conclusions about the association between the three polymorphisms  and breast cancer in different ethnicities, so we performed this study to confirm the effect of the TCF7L2 polymorphisms to BC risk, we genotyped all participants from a Chinese Han population in Northwest China for the three important SNPs, rs1225404, rs7003146, and rs7903146.BC patients and healthy participants were matched according to age and menopausal state Table . The meaIn Table P = 0.08). The rs1225404 CT genotype significantly increased BC susceptibility compared with the rs1225404 TT genotype , even after Bonferroni correction (CP= 0.006). In addition, the frequency of the rs1225404 C allele was significantly higher in BC patients than that in health . The similar trend was also found even after Bonferroni correction was performed (CP= 0.0012).As displayed in Table P = 0.15). The rs7003146 T and G allele frequencies in the two groups were 82.2% and 18.0%, and 87.8% and 12.8%, respectively. We found that the GT genotype of rs7003146, and G allele specifically, had a protective effect on breast cancer risk . But when the Bonferroni correction was conducted, the protective effect of GT on breast cancer risk was lost (CP = 0.09).We compared the GG and TT genotype frequencies of the TCF7L2 rs7003146 polymorphism in the BC group and health group and did not found any meaningful difference . The rs7903146 C and T allele frequencies in the patient and control groups were 93.6% and 6.4%, and 93.2% and 6.8%, respectively. The difference in the two groups was not statistically significant .The TCF7L2 rs7903146 polymorphism has three genotypes: CC, CT, and TT. We compared the frequency of the CC genotype with those of the CT and TT genotypes and found no statistical difference between the patient and control groups . However, there was no difference when we performed the Bonferroni correction (PC = 0.06). A relation between CT/CC genotypes and positive PR status was also observed . GT and GG genotypes at the TCF7L2 rs7003146 locus have a correlation with positive ER status , compared with the TT genotype. LN involvement was significantly higher in patients with TCF7L2 rs7903146 CT and TT genotypes compared with patients with the CC genotype . When the Boferroni correct was conducted, the significance was lost (CP = 0.09). Table Analysis of the TCF7L2 rs1225404 polymorphism revealed a significant difference in tumor size between patients with the CT genotype, or CT and CC genotypes compared with the TT genotype, independent of other clinical and pathological data  in the controls as the reference and compared the other haplotypes with Trs1225404Trs7003146Crs7903146. No significant relationships were observed between all haplotypes and BC risk (P >0.05), which indicated the three polymorphisms may affect the breast cancer risk in separate ways.The three polymorphisms were in LD, and then we conducted the haplotype analysis. As shown in Table P = 0.002). The frequencies of rs1225404 genotypes were positively correlated with positive PR status (P< 0.05), indicating that genetic variations at the locus may influence the prognosis of the breast cancer patients. In contrast, Connor's study surmised that the CC genotype of rs1225404 was inversely related with breast cancer susceptibility [In this study, we found that the C allele and CT genotype of the rs1225404 polymorphism were increased risk factors during breast cancer progression . Therefore, consistent with the research of Wei and Tang, [The TCF7L2 rs7003146 GT genotype frequency in the patients was significantly different from that in the controls  might be associated with breast cancer risk in Northwest Chinese Han populations. Combined with previous studies, our data indicate that such polymorphisms could be a potential candidate for breast cancer susceptibility. Our case-control study helps to understand the possible pathogenesis breast cancer. However, TCF7L2 has multiple polymorphic sites, and their associations with breast cancer are uncertain. Further studies, with larger sample sizes, are needed to validate our conclusionsand the role of these variants in breast carcinogenesis.A case-control study was performed with 458 breast cancer patients randomly selected from the Second Affiliated Hospital of Xi'an Jiaotong University between January 2013 and March 2014. All patients, who were Han Chinese, had undergone modified radical mastectomy or radical mastectomy reservations and had complete clinical pathological data. The tumor diameter range was 1.6–4.5 cm . Pathological analysis of the samples confirmed that patients had not been receiving treatment (neoadjuvant chemotherapy or endocrine therapy) before surgery, and all of the histological classifications were referenced to NCCN 2013 Guidelines. Menopausal status was defined as the date of last menses followed by 12 months without menses. The clinico-pathological variables and prognostic factors, including tumor size, histology, axillary lymph node (LN) involvement, hormone receptor (including estrogen receptor (ER) and progesterone receptor (PR)) status, and human epidermal growth factor receptor (HER2) status, were obtained from medical records. Histological determination, including tumor type and disease stage, was performed according to the World Health Organization criteria and the TNM classification system, respectively.The control group was comprised of 500 unrelated, sex-, age-, and ethnicity-matched healthy individuals from the same hospital. Control subjects had no known medical illnesses or hereditary disorders and were not taking any medications. Written informed consent was obtained from the two groups at the time of recruitment. This study was approved by the Institutional Review Board of Xi'an Jiaotong University .Limosis venous blood (5 ml) was extracted from each subject and stored in EDTA anticoagulant tubes at -80 °C. Whole-blood genomic DNA was extracted using the salting-out technique. DNA concentrations were measured by spectrophotometryhttp://www.hapmap.org) indicted that these tag SNPs capture the majority of the known common variation in TCF7L2 within the Chinese Han population. Sequenom MassARRAY Assay Design 3.0 software was used to design a Multiplexed SNP MassEXTEND assay. SNP genotyping was performed using Sequenom MassARRAY RS1000 according to manufacturer's recommendations. The primers used for each SNP are listed in Table . Three tag SNPs  were selected for analysis. Data from HapMap  at diagnosis between case and control groups, and the results showed they all subjected to normal distribution. Then the student's t-test was used to determine the differences in age and BMI between the two groups. Chi-square (Pearson's χ2) test or Fisher's exact test was used, where appropriate, to calculate the P values and corresponding odds ratios (OR) with 95% confidence intervals (CI), in order to determine the associations between genotypes and breast cancer risk or clinical variables. We conducted the haplotype analysis under the generalized linear models by using the software PHASE.Statistical analyses were performed with SPSS 18.0 for Windows . All tests were two-sided and statistical significance was set at


The environmental contributors to geographic variation in breast density in urban and rural areas are poorly understood. We examined the association between breast density and exposure to ambient air pollutants . Breast density was assessed using the American College of Radiology’s Breast Imaging-Reporting and Data System (BI-RADS) four-category breast density classification. PM2.5 and O3 estimates for grids across the USA (2001–2008) were obtained from the US Environmental Protection Agency Hierarchical Bayesian Model (HBM). For the majority of women (94%), these estimates were available for the year preceding the mammogram date. Association between exposure to air pollutants and density was estimated using polytomous logistic regression, adjusting for potential confounders.Participants included women undergoing mammography screening at imaging facilities within the Breast Cancer Surveillance Consortium (2001–2009). We included women aged ≥40 years with known residential zip codes before the index mammogram (2.5 and lower O3 exposures than women with fatty breasts (8.97 vs. 8.66 ug/m3 and 33.70 vs. 35.82 parts per billion (ppb), respectively). In regression analysis, women with heterogeneously dense vs. scattered fibroglandular breasts were more likely to have higher exposure to PM2.5 (fourth vs. first quartile odds ratio (OR) = 1.19, 95% confidence interval (CI) 1.16 − 1.23). Women with extremely dense vs. scattered fibroglandular breasts were less likely to have higher levels of ozone exposure .Women with extremely dense breasts had higher mean PM2.5 and O3 may in part explain geographical variation in mammographic density. Further studies are warranted to determine the causal nature of these associations.Exposure to PM Mammographic breast density is a well-established independent risk factor for breast cancer . Previou2), fine particles <2.5 μm in diameter (PM2.5), polycyclic aromatic hydrocarbons (PAHs), carbon monoxide, sulfur dioxide, and volatile organic compounds [2.5 and risk of death from breast cancer [2.5, total suspended particles, ozone, or particles 2.5–10 μm in diameter (PM10) [2, acrylamide, benzidine, carbon tetrachloride, ethylidene dichloride, and vinyl chloride, and between ER-negative/PR-negative (ER-/PR-) subtypes and ambient levels of benzene, cadmium, and inorganic arsenic [The evidence on association between air pollution and breast cancer is limited. In some previous studies there have been reports of positive associations between risk of breast cancer and nitrogen dioxide  , 20. In  arsenic , 21, 22.2 was investigated, there was a borderline inverse association between air pollution and the risk of mixed/dense breast density patterns [2.5 and ozone (O3) in a large consortium of population-based mammography registries. We further examined the interactions of PM2.5 and ozone with menopausal status and use of postmenopausal hormones, family history of breast cancer, and BMI.Whether air pollution could contribute to high breast density is unknown. In the only study in which the association between breast density and nitrogen oxides or NOpatterns . To add http://www.bcsc-research.org/), which is funded by the National Cancer Institute, and is a population-based prospective cohort of women undergoing mammographic screening at facilities affiliated with a network of breast imaging registries throughout the USA. Five BCSC registries were included in this analysis: New Hampshire Mammography Network, Vermont Breast Cancer Surveillance System, New Mexico Mammography Project, San Francisco Mammography Registry, and Group Health Cooperative in western Washington State.Women in this study were selected from participants in the Breast Cancer Surveillance Consortium (BCSC)  have received Institutional Review Board approval for either active or passive consenting processes or a waiver of consent to enroll participants, link data, and perform analytic studies. All procedures comply with the Health Insurance Portability and Accountability Act, and all registries and the SCC have received a Federal Certificate of Confidentiality and other protection for the identities of women, physicians, and facilities studied by this research.Categorical mammographic breast density was defined using the American College of Radiology’s Breast Imaging-Reporting and Data System (BI-RADS) breast density classification recorded by the clinician on the woman’s screening mammogram (category 1 (BI-RADS I) - predominantly fat, category 2 (BI-RADS II) - fat with some fibroglandular tissue (reference), category 3 (BI-RADS III) - heterogeneously dense, and category 4 (BI-RADS IV) - extremely dense). In the fourth edition of the BI-RADS manual (2003) , the per2.5 and O3 estimates for grids across the USA from 2001 to 2008 were obtained from the US Environmental Protection Agency Hierarchical Bayesian Model (HBM), which combines monitoring data with numerical output from the Community Multi-scale Air Quality model [2.5 and 8-hour maximum concentrations of O3 were used to calculate annual concentrations for each grid. Yearly mean exposures of PM2.5 and O3 for each subject were calculated using the inverse distance-weighted method based on their zip code centroid and HBM grid centroids. For each zip code centroid, the distance to the centroids of the four closest HBM grids were calculated based on the “Haversine” great circle distance using the latitude and longitude coordinates. The four closest distances were assigned a weight:i is the distance between the zip code centroid and each of the four closest HBM grid centroids. The yearly mean exposure for the zip code was then calculated as the weighted average:i is the pollutant concentration.Exposure data for PMty model . For bot2.5 and O3 exposures for the year preceding the mammogram were retrieved for analysis. Data were unavailable for the preceding year in 6% of the study sample, and thus the exposure estimates were retrieved for the year of the mammogram. The PM2.5 and O3 exposures were modeled as continuous variables and as quartiles based on their distribution in the study population  for O3). In a secondary analysis, we also used the exposure data from the year of the mammogram in all women to examine the association between exposure and breast density.PMInformation on covariates was available from the date of the index mammogram. We included the following potential confounders in the models: age, race/ethnicity, BMI, study site, menopausal status/use of hormone replacement therapy, age at menarche, parity and age at first birth, history of breast biopsy, family history of breast cancer, and median household income for the zip code.2.5 and O3. In these models, women with predominantly fat, heterogeneously dense, or extremely dense breasts were compared to women with scattered fibroglandular density (BI-RADS II). Each air pollution variable was modeled as a continuous variable and as quartiles based on the distribution in the study population. The lowest exposure category was used as the reference in all analyses. A two-sided test for trend was performed, modeling each air pollutant as an ordinal variable and using the median air pollutant level in each category. The risk estimates in all analyses were adjusted for age , race/ethnicity , BMI , study site, menopausal status/use of hormone replacement therapy , age at menarche , parity and age at first birth , history of breast biopsy (yes or no), first-degree family history of breast cancer (yes or no), and median household income for the zip code .We used multivariate polytomous regression to examine the associations between categorical data on breast density and exposure to PM2 vs. ≥30 kg/m2) and both family history of breast cancer and menopausal status/hormone replacement therapy use were modeled as nominal data. Statistical significance in all analyses was assessed at the 0.05 level. Finally, for variables that had significant interactions, we examined the association between exposure and breast density separately across the variable strata. The analyses were performed using SAS software .We next examined the two-way interactions between each of the exposures and BMI, family history of breast cancer, history of breast biopsy, or menopausal status/postmenopausal hormone use. To test these interactions, we implemented different approaches by using both continuous exposure variables and the respective medians within each of the exposure categories, to model the interaction term. The results were similar with both approaches. BMI was modeled as a binary variable  and concentrations of O3 were lower in women with higher density .Characteristics of the 279,967 women included in this study are presented in Table 2.5 (fourth vs. first quartile odds ratio (OR) = 1.19, 95% confidence interval (CI) 1.16–1.23; third vs. first quartile OR = 1.19, 95% CI 1.16–1.22) and women with fatty breasts were less likely to have been exposed to higher levels of PM 2.5   and women with fatty breasts were more likely to have been exposed to higher O3 concentrations   and BMI p for interaction <0.01 and <0.0001 for PM2.5 and O3, respectively)  and nitrogen dioxide (NO2)) was evaluated among 4769 women [3 of NOx and OR 0.89, 95% CI 0.80–0.98 per 10 μg/m3 of NO2) and no interaction between the exposure and menopause, smoking, or obesity [2.5 or O3 levels. Further, we used the BI-RADS density classification in our study with BI-RADS II (scattered fibroglandular breasts) as the reference group, rather than collapsing four density categories into two groups. We also observed significant interactions of both air pollutants with menopausal status and hormone therapy use and BMI. These differences between studies could be potentially explained by the significantly larger sample size in our study, the different classification of density and the approaches used to characterize air pollution.In a recent study in the Danish Diet, Cancer and Health cohort (1993–1997) the association between mammographic breast density (defined as mixed/dense or fatty breasts) and traffic-related air pollution , dibenzofurans (PCDF), and biphenyls (PCB) have also been shown to interfere with normal endocrine function [The chemical composition of PM effects –10. PAHs effects –36. Amonfunction , 11. Finfunction , 13. As 3 to higher levels of oxidative stress and demonstrated that biological properties of O3 can cause many deleterious effects, including cellular death, in distant tissues [3 on breast tissue could result from these inhibitory processes resulting in a lower percentage of fibroglandular structures, and subsequently, lower breast density. However, the causal links, if any, and the underlying biological mechanisms behind the associations of air pollution with breast density need to be elucidated and confirmed.Previous studies have consistently linked higher levels of O tissues . Recent  tissues . It is p2.5 across the strata were small. The findings suggested a stronger association between O3 and breast density in women with BMI ≥30. The patterns of association across categories of menopausal status/hormone use, however, were inconsistent.Previous studies suggest that associations between breast density and risk factors for breast cancer differ by menopausal status, hormone use, family history of breast cancer, and BMI –42. Prem2.5 or O3.Our study utilized an established consortium of population-based mammography registries with information on breast cancer risk factors, demographics, residential history, and breast density. To our knowledge, this is the largest study to date to examine the association between air pollution and breast density and the first study to explore the associations between breast density and PM2.5 for BI-RADS IV were not statistically significant. Even though BI-RADS IV represented the smallest density group, the absolute number of women in this category was sufficiently large to detect significant associations. The biological explanation for an association with BI-RADS III density but not BI-RADS IV density is unclear and confirmation of these findings in other populations is warranted.Our study has a few limitations. We controlled for known determinants of density in our analysis; however, residual confounding cannot be ruled out. The risk estimates in our study were not adjusted for smoking status. However, the findings on associations between smoking and breast density in previous studies have been inconsistent –46. FurtWe used data from Environmental Protection Agency (EPA) air monitoring rather than individual-level exposure data; thus, exposure misclassification cannot be excluded, as our model assumed that the pollutant concentrations were equal throughout each HBM grid. However, this exposure misclassification would likely be non-differential and thus could drive our results towards the null. Using an exposure assessment model with a higher spatial resolution, like land use modeling, was not feasible with only zip code information from the women. Using the community multiscale air quality (CMAQ) model allowed us to assess exposure for locations and days that might be otherwise missing from monitoring stations. Furthermore, the CMAQ model has been shown to be more accurate and precise than interpolation of monitoring data with ordinary kriging regression .2.5 and mammographic breast density and inverse associations between ozone and mammographic breast density. Our findings suggest that previously reported geographic variation in breast density could in part be explained by different air pollution patterns in urban and rural areas. Future studies are warranted to determine the causal nature of these associations and to explore whether breast density mediates the ffect of air pollution on breast cancer risk.In conclusion, in this large population-based sample of cancer-free women, we found positive associations between and PM


IJE, Gao and colleagues explore the causal effect of adiposity on several cancers using two-sample Mendelian randomization (MR), and find some evidence that greater adult body mass index (BMI) causally reduces the risk of breast cancer while increasing ovarian, lung and colorectal cancer.In this volume of theIJE,P-value threshold as shown in journal publications. It is this extensive availability of results that allows Gaoet al.to relate genetic variants used as instrumental variables for adiposity traits to the cancer outcomes that they are interested in; the published GWAS for the cancer outcomes would not have reported on the adiposity variants unless they reached a pre-specified GWAS significance value.Beyond the study findings themselves, this paper is interesting in its use of publicly available genome-wide association study (GWAS) summary data in a two-sample MR approach. Whereas MR has been increasingly used over the past decade since it was first proposed in the,MR is a form of instrumental variable analysis that uses genetic variants as instrumental variables. It has been described as nature’s or your god’s randomized controlled trial (RCT), referring to the random allocation of genetic variants at conception that mean genetic variants are less likely to violate some of the assumptions of instrumental variable analyses than non-genetic instruments.Two-sample MR exploits the fact that it is not necessary to obtain the effect of the instrumental variable-risk factor association (ratio denominator) and instrumental variable-outcome association (ratio numerator) from the same sample of participants. Indeed, there are some advantages to obtaining them from two different sets of participants. In particular ‘winners’ curse’, which can underestimate true causal effects in one-sample MR,MR could be undertaken in one ‘sample’ of participants with genetic instrument and outcome data on all participants, and data on the risk factor in a (random) subsample. For example, UK Biobank will soon release GWAS data on all 500 000 participants and has already amassed large numbers of incident cases of cardiovascular disease and common cancers such as breast cancer.et al.illustrates (see below), one has to use the summary results presented, even when these are not idea, for example because they have been adjusted for co-variables that you would rather they had not been adjusted for or the sample used is not idea for your question.The disadvantages of using summary data in two-sample MR are similar to those of meta-analysing summary data of RCTs or multivariable regression observational results—the quality of the pooled results is dependent on that of the individual studies. Thus, as the paper by GaoWhen using summary GWAS data in what might be considered to be true two-sample MR, it is possible that the two samples overlap because of some cohort studies contributing to both GWAS . If this overlap is large, then some of the advantages of two-sample over one-sample MR are potentially lost, but the disadvantages of using summary data are maintained. Although it seems unlikely that this is an issue in the study undertaken by Gao and colleagues, methods to explore this ought to be included and their results discussed in any two-sample MR paper using summary data.,Gao and colleagues set out to explore ‘the potential causal relationship between obesity across different life stages and risk of multiple cancers’. Gao and colleagues examine the effects of childhood BMI and adult BMI, but they are not really able to determine effects at different life stages because of the correlation between BMI assessed at different ages and because of the nature of MR. Whereas one of the noted advantages of MR is that it generally assesses the cumulative effects of a risk factor over a long period of the life course  without requiring repeat risk factor assessment and with little chance of regression dilution bias or reverse causation ,The authors note that whereas their MR results suggest a protective effect of greater adult BMI on breast cancer, many observational studies have reported a protective effect of greater BMI on premenopausal breast cancer but a detrimental effect on postmenopausal breast cancer. They are unable with the summary data available to test differences in effect between pre- and postmenopausal breast cancer, as GWAS separated by these sub-phenotypes are not presented by the breast cancer consortia. However, one hypothesis regarding the positive association of BMI with postmenopausal breast cancer is that women who are fatter after the menopause are likely to have had a greater lifetime exposure to estrogen; but Gao and colleagues are able to examine effects with estrogen receptor-positive cancers and they find the same inverse association with these as seen for all breast cancer cases combined.IJEauthor recommendations and recent guidance from the American Statistics Society,et al.largely base their conclusions on findings with aP-value equivalent to < 0.05 after multiple testing. Greater adult BMI, but not waist-hip ratio (WHR), is concluded to decrease breast cancer and increase ovarian, lung and colorectal cancer risk. However, some of the point estimates for BMI and WHR are not that dissimilar. Thus, it is concluded BMI reduces breast cancer risk {odds ratio[OR] 0.66 }, but the same is not concluded for WHR [0.73 ]. Similarly, an odds ratio of 1.27  for the effect of adult BMI on all lung cancers is declared as a positive result but the same conclusion is not made for an odds ratio of 1.33  for the MR effect of WHR on squamous lung cancer.Contrary toA related issue is whether the WHR findings could have been biased towards the null more than BMI findings. One disadvantage of using summary data is that you have to take the results as analysed in the original study. The WHR variants used by Gao and colleagues were adjusted for BMI, which the authors do not seem to acknowledge. This adjustment is likely to have biased the effect of genome-wide variants associated with unadjusted WHR (away from the null).2for the genetic instrument-adiposity trait associations. From the original papers it can be seen that the instrument for adult BMI is stronger than for the other traits .,et al.use the most up-to-date BMI GWAS data,A further potential explanation for why most of the emphasized  MR results are seen for adult BMI, rather than any of the other adiposity risk factors, is that the genetic instrument for adult BMI is stronger than for the other traits. Weak instrument bias in one-sample MR results in bias towards the confounded multivariable regression result, but in two-sample MR the bias is towards the null .3Gao et al., this does not seem to be the case. According to data presented by Gao in their Supplementary Table 1, it seems that the association of the genetic instrumental variable with each adiposity trait has been taken from samples that combine females and males, whereas for the association of the genetic instrument with breast and ovarian cancer, females only are included and with prostate cancer males only are included.et al.For two-sample MR to be valid, the two samples have to be from the same underlying population, but for the sex-specific cancers in the paper by Gao,I looked at the four original GWAS papers to explore whether there was any difference in the GWAS of the adiposity traits by sex.What is more surprising is that they seem to have also used sex-combined results for determining effects of WHR adjusted for BMI, despite the fact that it is clear from the title of the original GWAS paper that sex differences were examined and foundThe extent to which bias towards the null as a possible result of weak instrument bias and adjustment of WHR for BMI (discussed above) is balanced by possible exaggeration of the true effect as a result of not using sex-specific data for the genetic instrument-WHR association in the female cancers, is impossible to tell. But this study does illustrate some of the pitfalls of using summary GWAS data and methods that might be used to limit these. Thus, I would suggest the following recommendations for using summary data in two-sample MR.Ensure that the two samples are from the same populations. If this is not the case (as in this paper for the sex-specific cancers), check the original paper publications and/or contact the original authors to see if it is possible to obtain results from the same population (here sex-specific results). If that is not possible, consider possible biases, undertake sensitivity analyses and/or consider whether it is appropriate to undertake the analyses.Report on the extent of any overlap between the two samples. This will require searching of the original publications and/or the consortia website. If overlap is large, then the study should be considered to be more like a one-sample MR and the discussion of strengths and limitations should be directed towards those of one-sample MR.Determine whether any covariables have been adjusted for in the original GWAS and report on this. If there have been adjustments, ensure that presentation and interpretation of results take this into account.Report how risk factors and outcomes were assessed, including whether disease cases were prevalent, incident or a mixture. Consider whether measurement error and/or survivor bias  might have influenced findings.Describe any key additional analyses that would have been important to conduct, such as of sub-phenotypes or interactions, that were not possible because of the summary data.,,et al.comment on the ‘strong’ assumptions of MR, but rarely do we see such statements about the equally strong, and untestable, assumptions of conventional multivariable regression analyses. Now genetic epidemiologists have shown us how to provide complete open-access summary data, and it is likely that over the coming decade important and impactful use will be made of these data.What strikes me in watching (and participating in) the development of GWAS and MR over the past decade is how slow those of us largely working in epidemiology, including in intervention research, have been to do what we all know is good science. Our genetic colleagues have led the way in ensuring replication in large collaborations where ‘team science’ is appreciated and for the large part appropriately rewarded. Those developing MR as a method have from the start been very open about its limitations and have worked at developing methods to test and limit sources of bias.A decade before the first paper proposing the use of MR,