We tested the hypothesis that body mass index (BMI) aged 20 years modifies the association of adult weight gain and breast cancer risk.We recruited women (aged 47–73 years) into the PROCAS  Study. In 47,042 women, we determined BMI at baseline and  at age 20 years, and derived weight changes. We estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for new breast cancer using Cox models and explored relationships between BMI aged 20 years, subsequent weight changes and breast cancer risk.2 (75th percentile), absolute weight gain was associated with breast cancer [HR per SD: 1.31 (95% CIs: 1.21–1.42)], but there were no associations for women with a recall BMI aged 20 years of >23.4 kg/m2 .With median follow-up of 5.6 years, 1142 breast cancers  occurred. Among post-menopausal women at entry, BMI aged 20 years was inversely associated [HR per SD: 0.87 (95% CI: 0.79–0.95)], while absolute weight gain was associated with breast cancer [HR per SD:1.23 (95% CI: 1.14–1.32)]. For post-menopausal women who had a recall BMI aged 20 years <23.4 kg/m2 aged 20 years.Adult weight gain increased post-menopausal breast cancer risk only among women who were <23.4 kg/m Childhood and early adulthood adiposity may mediate effects on subsequent breast cancer risk through later changes in adipose tissue, for example, weight gain, and downstream hormonal changes. This hypothesis will be the focus of this paper. Additionally, it has recently been appreciated that childhood and early adulthood adiposity might mediate effects on mammary carcinogenesis through mammographic density.4Body mass index (BMI), as an approximation of general body fatness, is positively associated with risk of post-menopausal breast cancer, yet inversely associated with pre-menopausal breast cancer.6 of prospective studies concluded that adult weight gain (expressed as absolute weight change) is positively associated with post-menopausal breast cancer risk, but not for pre-menopausal breast cancer. Weight gained in adolescence is mainly a combination of gains in muscle mass and adipose tissue, which in women, is distributed primarily on the hips and thighs (pear shaped).7 Later in adulthood, most weight gain is through adipose accumulation, preferentially around the waist. In turn, this adipose distribution is associated with adverse metabolic phenotypes and insulin resistance, and might be detrimental for later cancer risk.8 Thus, a hypothesis emerges that the absence of excess body fat in late adolescence or early adulthood increases risk of breast cancer, since most of the excess body fatness gained by these women during later adulthood might be metabolically ‘bad’ adipose tissue. This pathophysiology might not apply to women in young adulthood who already have accumulated excess adiposity.Two recent meta-analyses6 and 2015,5 respectively. From these, there were nine cohort studies that reported BMI or weight at ages 18 to 21 years, weight change and breast cancer risk ,12 limited to post-menopausal women, specifically addressed whether the associations of breast cancer risk from weight gain differed by BMI strata at age 18 years, and found stronger associations for lower BMI strata at age 18 years. An updated analysis from the Nurses’ Health Study (2017) reported persistent inverse associations for weight at age 18 years for both pre- and post-menopausal breast cancers, independent of weight gain.18The above meta-analyses covered studies up to 2014Here, we tested the hypothesis that BMI aged 20 years modifies the effect of the association of adult weight gain and breast cancer risk. We stratified new breast cancer a priori by menopausal status.21 Between October 2009 and June 2015, 131,373 women aged 46–73 years were, in 15 screening areas across Greater Manchester, United Kingdom, invited for routine three-yearly mammographic screening, were mailed information, a consent form and a two-page questionnaire to elicit information about family history, hormonal and lifestyle risk factors (https://www.ncbi.nlm.nih.gov/books/NBK379485/?report=reader). A total of 57,902 women agreed to enter PROCAS.The Predicting Risk Of Cancer At Screening (PROCAS) study has been described in detail elsewhere.2), high normal weight (22.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2) and obese (≥30.0 kg/m2).Cohort entry BMI was calculated from separately reported weight and height and categorised as follows: low normal weight  from age 20 years to cohort entry; relative weight gain ; and absolute change per year (kg per year).Participants recalled weight at 20 years of age, and BMI for age 20 years was calculated using the cohort entry reported height. We evaluated the validity of assessing BMI at 20 years of age using recalled weight by comparing mean recall BMI distributions against age- and sex-specific BMI distributions in contemporaneous populations , hormonal replacement therapy (HRT: ever/never user), statin (ever/never user), alcohol use , any exercise (any/no/missing: exercise quantity was not available), number of children, age at first pregnancy, hysterectomy, number of ovaries removed, menopausal status  defined by Phipps et al.Inclusion for the analysis required full data on BMI at study entry, recall BMI at age 20 years and menopausal status.24The primary outcome was diagnosis of a new breast cancer  from entry screen onwards, as identified through the National Health Service Breast Screening Programme system and the Somerset and North West Cancer Intelligence services. We categorised breast cancer as prevalent (i.e. screen detected at entry) if a tumour was diagnosed within 100 days after initial study entry; the remainder as incident . While all women were screened, there might be a detection bias across the BMI range—for example, excess body fatness might mask early-stage breast cancer (even on mammography). Thus, we assessed stage presentation across the BMI distribution by capturing T-stage and N-stage according to AJCC 7th Edition.We tested for trends in baseline characteristics across recall BMI categories at age 20 years using Cuzick’s tests for continuous and Cochrane–Armitage tests for categorical data. We tabulated Pearson’s correlations for the anthropometric parameters.25). To directly address our hypothesis, we explored relationships between BMI at age 20 years (as a strata) and subsequent weight changes, selecting post hoc the 75th percentile of the BMI aged 20 years distribution (23.4 kg/m2) as a cut-off. We justified this cut-off (rather than the median of 21.7 kg/m2) on three grounds: (i) using a method similar to that reported by New et al.,26 a data-driven exploration revealed potential pivots in risk for BMI values between 23.0 and 24.0 kg/m2; (ii) this cut-off was a good predictor for later weight gain (see Results); and (iii) by stratifying the population into a quarter versus three-quarters, there are potential opportunities for targeted public health strategies.For time-to-event analyses, we expressed risk in absolute terms per 1000 person years and then estimated hazard ratios (HRs) and 95% confidence intervals (CIs) using Cox models. The timescale was attained age at cohort entry. To standardise our results, we expressed risk estimates per standard deviation (SD) for each anthropometric distribution , where obese women, who did not develop breast cancer by cohort entry were less susceptible, by testing an interaction term between exposure and age at entry after splitting the timescale into refined age categories. For all analyses, we used STATA .2, both at cohort entry and recall at age 20 years . The following were noted with increasing BMI aged 20 years: decreasing median age at study entry, proportion who were menopausal, and current users of HRT ; any type of oophorectomy (P = 0.002); decreasing height (P < 0.001); higher proportion ever user of statins (P < 0.001); lower use of alcohol (P < 0.001); lower median units of any exercise per week (P < 0.001); lower median age at menarche (P < 0.001) and first pregnancy (P < 0.001); and lower proportion who were parous (P < 0.001). BMI aged 20 years was correlated with BMI at cohort entry, but not correlated with weight changes (Supplementary Table The baseline characteristics according to categories of BMI aged 20 years are shown in Table With a median follow-up of 5.6 [inter-quartile range (IQR): 4.7–6.4] years, there were 1142 new diagnoses of breast cancer [absolute risk: 4.6 (95% CI: 4.3–4.8) per 1000 person years] Table .Table 2HIn the fully adjusted model (model B), BMI aged 20 years was inversely associated with subsequent occurrence of breast cancer [HR per SD: 0.880 (95% CI: 0.817–0.948)]. By contrast, the following were positively associated with breast cancer: height [HR per SD: 1.102 (95% CI: 1.033–1.175)]; BMI at cohort entry [HR per SD: 1.094 (95% CI: 1.026–1.167)]; absolute weight gain from age 20 years to cohort entry [HR per SD:1.185 (95% CI: 1.115–1.260)]; relative weight gain [HR per SD: 1.196 (95% CI: 1.127–1.270)]; and rate of weight gain [HR per SD: 1.184 (95% CI: 1.110–1.262)].The addition of BMI at age 20 years to the adjusted models increased the association with breast cancer for BMI at cohort entry [HR per SD: 1.207 (95% CIs: 1.123–1.298)], but attenuated associations from absolute weight gain, relative weight gain, and rate of weight gain.Of the 1142 breast cancers, there were 829 in women who were post-menopausal at cohort entry [absolute risk: 4.7 (95% CI: 4.3–5.0) per 1000 person years] and 313 in women classified as either pre- or peri-menopausal at cohort entry [absolute risk: 4.3 (95% CI:3.9–4.8) per 1000 person years] . Similar again, the following were positively associated with breast cancer: height [HR per SD:1.080 (95% CIs: 1.002–1.165)]; BMI at cohort entry [HR per SD: 1.265 (95% CIs: 1.164–1.374)]; absolute weight gain [HR per SD: 1.227 (95% CIs: 1.142–1.319)]; relative weight gain [HR per SD: 1.227 (95% CIs: 1.142–1.317)]; and rate of weight gain [HR per SD: 1.234 (95% CIs: 1.145–1.329)].By contrast, among pre- and peri-menopausal women, there was a positive association with height [HR per SD: 1.154 (95% CIs: 1.023–1.301)], but no associations with subsequent occurrence of breast cancer associated with BMI at age 20 years, absolute weight gain, relative weight gain, and rate of weight gain.Pinteraction = 0.036), relative weight gain (Pinteraction = 0.027), and rate of weight gain (Pinteraction = 0.012), but not for BMI at cohort entry.Given the difference in risk association patterns by menopausal status, we tested for interactions. This suggested modest effect modification by menopausal status for absolute weight gain (2).We explored whether BMI aged 20 years modified the relationship between subsequent weight changes to BMI at cohort entry and breast cancers, stratified at the 75th percentile of BMI aged 20 years (23.4 kg/m2 aged 20 years was strongly predictive for being either overweight or obese at cohort entry [odds ratio: 5.687 (95% CIs: 5.415–5.974) (Supplementary Table 2 aged 20 years gained weight (>5 kg) compared with only 24% of women with a BMI ≥23.4 kg/m2 aged 20 years (P < 0.001). Patterns were similar whether women were post- or pre-/peri-menopausal at cohort entry Supplementary  among women with a BMI aged 20 years ≥23.4 kg/m2 ]; absolute weight gain [HR per SD: 1.312 (95% CIs: 1.210–1.424)]; relative weight gain [HR per SD: 1.288 (95% CIs: 1.195–1.390)]; and rate of weight gain [HR per SD: 1.328 (95% CIs: 1.220–1.445)].This led us to test whether this stratification modified the relationships with subsequent occurrence of breast cancer according to weight change. Among women who were post-menopausal at study entry and who had a recall BMI at aged 20 years <23.4 kg/mPinteraction = 0.014), relative weight gain (Pinteraction = 0.050) and rate of weight gain (Pinteraction = 0.028), and BMI at cohort entry (Pinteraction = 0.005).We tested for interactions. This suggested an effect modification by strata of recall BMI aged 20 years for absolute weight gain (We repeated the main models excluding DCIS cases (Supplementary Table 2 (75th percentile of BMI distribution at age 20 years) predicted for subsequent weight gainers and was an effect modifier for the weight gain/breast cancer risk association. The novel finding was that adult weight gain increased post-menopausal breast cancer risk among women who had a BMI <23.4 kg/m2 aged 20 years, but not among women who had a BMI >23.4 kg/m2 aged 20 years.In a large UK mainly urban (mainly Caucasian) population, we found the following. First, we confirmed that young adulthood BMI is inversely associated with breast cancer risk, while adult weight gain is positively associated with breast cancer among women who were post-menopausal at cohort entry. Second, standardised estimates for adult weight gain expressed as absolute, relative or as a rate, were broadly similar. Third, the selection of BMI aged 20 years cut-off of 23.4 kg/m5 and the recent Pre-menopausal Breast Cancer Collaborative Group27 analysis indicate that young adulthood BMI is inversely associated with risk of pre-menopausal as well as post-menopausal breast cancer. Our analysis did not show the former, reflecting the small numbers of pre- and peri-menopausal cancers in our cohortPrevious reviews6 meta-analysis and the WCRF 2018 report.5 The summary estimates from the latter were: post-menopausal breast cancer risk ; pre-menopausal breast cancer risk .For adult weight gain, our study found positive associations between weight gain and breast cancer in post-menopausal but not pre- and peri-menopausal women. This concurs with the Keum et al.2 was an effect modifier for determining subsequent breast cancer risk in women who were post-menopausal at cohort entry. The only other study to perform a similar stratification was reported by Eliassen et al.12 for the Nurses’ Health Study cohort. Using a cut-off of 21 kg/m2, they found stronger associations between weight gain (expressed only as absolute weight change) and breast cancer in women with BMI at age 18 years <21 kg/m2 compared with those with BMI values >21 kg/m2. This effect modification was significant (Pinteraction = 0.04). Their analysis was limited to post-menopausal women.We tested for interactions between BMI aged 20 years, weight changes and breast cancer risk. We found that the selection of BMI aged 20 years cut-off of 23.4 kg/m12 it is important to note that these cut-points are population specific. As adolescent obesity trends evolve with time, these cut-points are likely to differ (most likely upwards) in future studies.In the interpretation of the similar findings from our study and those from Eliassen et al.,28 The equivalent arguments might apply to recall BMIs at age 18–21 years. However, a recent meta-analysis indicates that there is little bias for these recall estimates.29 We tested BMI distributions of our study against a UK population data contemporaneous with our population for the respective age strata and found similar BMI parameters. Second, our findings on associations with BMI aged 20 years may not be generalisable to today’s populations, as BMI distributions for equivalent age groups have shifted upwards.30 Third, the majority of the studied population were Caucasian and thus results might not be generalisable to other ethnic groups. The risk of breast cancer varies across racial groups; and relationships between high BMI and breast cancer risk and the changes in anthropometric measures vary by racial groups.31 Fourth, we included prevalent cancers in our main analysis and there might be confounding on weight change due to the presence of disease . This confounding is likely to be exceptionally small in this asymptomatic screen-detected population, and the exclusion of prevalent cancers in our sensitivity analysis made no material impact on risk estimates. Fifth, there is risk of residual confounding from measured and unmeasured factors. Even among the measured confounders, the classifications of confounders like alcohol consumption and exercise were relatively crude. Finally, the increased risk of weight gain and the development of post-menopausal breast cancer are generally limited to non-HRT users.6 We did not stratify our analyses in post-menopausal breast cancer by HRT status because of diminishing sample sizes.There are potential study limitations. First, the height and weight were self-reported, which may bias BMI–cancer associations. This bias tends to underestimate weight in older individuals and in heavier individuals.32 is associated with reduced uptake for breast cancer screening, hence introducing an ascertainment bias. This has not been found in UK populations.33 Third, even with representative BMI distribution in the screened population, a further bias might occur if excess adiposity influenced cancer stage detection on mammography. We found this not to be the case. Fourth, we addressed changes in weight across adulthood using three parameters and standardised the derived risk estimates. We found risks to be similar for all three parameterisations. Finally, we undertook sensitivity analyses and found our models to be consistent.Strengths of the present study include (first) its prospective design and a contemporaneous population. Second, there are concerns in the literature that obesity (and its associated co-morbidities)34 Thus, in future studies, adult weight gain might replace BMI as a risk factor in breast cancer risk prediction models. Furthermore, weight gain is more easily interpreted by providers and policy makers, and better understood in weight management programmes. The biological mechanisms underlying the paradoxical ‘protective’ association between elevated BMI in young adulthood (relative to women with BMI aged 20 years <23.4 kg/m2) and post-menopausal breast cancer are unclear, but there are a number of potential pathways. Baer et al.35 and Schoemaker et al.27 summarised some of these as follows. First, elevated BMI in adolescent girls is associated with higher basal insulin levels and decreased plasma levels of sex hormone binding globulin, leading to increased free oestrogen and testosterone. Second, high levels of androgens in adolescent obese girls are associated with metabolic features similar to ‘polycystic ovary syndrome, greater frequency of anovulatory cycles, and reduced fertility later in life. Third, Hilakivi-Clarke et al.36 postulated ‘that early oestrogen exposure may reduce breast cancer risk by increasing the expression of tumour suppressor genes such as BRCA1’ resulting in a more stable mammary gland epithelial genome. Fourth, Dowsett and Folkerd37 point out (contrary to common perception) that in the young woman, serum oestrogen levels are not elevated in obesity as this is regulated through a negative hypothalamic feedback. However, progesterone levels are reduced and this better explains the apparent reduced risk of breast cancer in obese pre-menopausal women. Further mechanisms may include altered circulating insulin growth factor-1 (IGF-1) levels in adulthood 38 or mediation through lower adult mammographic density.39 Using mediation analysis, Rice et al.4 estimated that, in pre-menopausal women, the associations between adolescent somatotype and breast cancer risk was substantially mediated by per cent mammographic density (per cent mediated = 73%), but in post-menopausal women, the proportion of the associations with adolescent somatotype that was mediated by per cent mammographic density was less (per cent mediated = 26%).Our results support the existing literature that weight gain more strongly associates with breast cancer risk than a ‘once-only’ measure of BMI and an emerging theme that cancer prevention should be targeted across the life course.40 Ideally the effects of weight change over time on breast cancer risk would be assessed in longitudinal studies with repeated measurements commencing in early adulthood. The statistical handling of these data will be complex and computationally demanding. Finally, it remains to be seen whether preventing weight gain and/or reducing BMI via effective interventions in adult populations reduces breast cancer risk and forms the basis for public health strategies to prevent breast cancer worldwide.Important questions remain regarding how to best quantify the cumulative effects of excess body weight over several decades, the differential effect of key weight change periods during the life course, and interactions with other risk factors. This knowledge is equally required to inform prediction models and risk stratification approaches.Supplemental material


In women under 50 years of age, however, milk consumption was inversely associated with breast cancer risk. In the comparison between highest (≥1 serving/day) and lowest (<1 serving/week) intake categories of milk, the multivariate HR (95% CI) was 0.58 ) among women under 50 years of age. In conclusion, our findings show that milk consumption in Korean women aged 50 or younger is associated with a decreased risk for breast cancer, when compared to those who never or rarely consumed milk. Further studies need to be conducted to assess this relationship and confirm these results.Epidemiologic studies regarding breast cancer risk related to milk consumption remain controversial. The aim of this study was to evaluate the association between milk consumption and the risk for breast cancer. A total of 93,306 participants, aged 40–69 years, were included in the prospective cohort study in the Health Examinees-Gem (HEXA-G) study between 2004 and 2013. Dietary intake was assessed using a validated food frequency questionnaire. Information on cancer diagnosis in the eligible cohort was retrieved from the Korea Central Cancer Registry through 31 December 2014. The Cox proportional hazards model was used to estimate multivariate hazard ratios (HRs) and 95% confidence intervals (CIs). A total of 359 breast cancer cases were observed over a median follow-up period of 6.3 years. Milk consumption was not associated with decreased risk for breast cancer in the total population ( The breast cancer incidence rate varies greatly worldwide from 25.9 per 100,000 women in South Central Asia to 92.6 per 100,000 women in Western Europe , so undeReproductive or menstrual factors, a family history of the disease, and the use of hormones are associated with an increased risk for breast cancer ,5,6,7. RDairy products are an important food group in a diet and have bioactive components that can affect the risk for breast cancer. The consumption of milk and/or dairy products, due to the high saturated fat content and insulin-like growth factor 1 (IGF-1), has been hypothesized to influence the risk for breast cancer ,12. In cSeveral epidemiologic studies have examined the association between consumption of milk and dairy products and the risk for breast cancer ,18,19. HTherefore, we examined the association between milk and dairy consumption and the risk for breast cancer among Korean women in the Health Examinees-Gem (HEXA-G) study.The Health Examinees (HEXA) study recruited participants aged 40–69 years between 2004 and 2013 from 38 general hospitals and health examination centers in eight regions around Korea in accordance with standardized study protocol. Well-trained interviewers used a structured questionnaire to collect information on socio-demographic and lifestyle factors, disease history, and dietary habits. Information on reproductive history was also collected from the women, and trained medical staff performed physical examinations on all participants. Details about the HEXA study are described elsewhere ,24. The Updated from the previously published HEXA studies ,24, the n = 8531) and those who did not provide information on food frequency questionnaire (FFQ) (n = 1165). Missing data on FFQ was treated according to the criteria provided by KoGES, which excluded individuals who: (1) did not answer any of the questions in the FFQ; (2) left more than 12 frequency questions blank; (3) did not answer any questions on rice consumption; or (4) had extremely low  or high  energy consumption [n = 1069; <500 or >3500 kcal/day).Among the HEXA-G cohort, we excluded participants who had no information on the date and cause of cancer from the Korea Central Registry (sumption . Particin = 4000) and women who were diagnosed with breast cancer during the first two years of the follow-up period to minimize the impact of reverse causation (n = 221). As a result, our study included a total of 78,320 participants. We excluded women who were diagnosed with cancer before enrolment  of the Seoul National University Hospital in Seoul, Korea (IRB number 0608-018-179) and the Korea National Institute of Health (IRB number 2014-08-02-3C-A). Written informed consent was obtained from all participants.The usual diet from the previous year was assessed using an interviewer-administered semi-quantitative FFQ with 106 food items ,28. The Dairy products included in the FFQ items included milk , yogurt , and cheese  . The amoTotal energy and nutrient intakes were calculated by multiplying the reported frequency of each food item by the nutrient content in a serving of that food. The nutrient values were based on the food composition table in the Recommended Dietary Allowances for Koreans . The reliability and validity of the FFQ is described in a previous study . The FFQConsumptions of carbohydrates (g/day), protein (g/day), fat (g/day), fruits (g/day), and vegetables (g/day) were total energy intake adjusted using the residual method .Information on cancer diagnoses in the eligible cohort was obtained by linkage to the Korea Central Cancer Registry, using resident registration number, a unique number assigned to every citizen and foreign resident in Korea. Cancer incidence until 31 December 2014 was provided by the National Cancer Center Korea. Breast cancer was defined based on the 10th revision of the International Classification of Disease (ICD-10) code C50. The primary outcome was breast cancer incidence. The first cancer diagnosis was considered, resulting in 359 breast cancer cases identified and a total of 78,320 participants remained in the final analysis .Each participant’s age, educational level, age at first birth, number of live births, age at menarche, oral contraceptive use, exercise stratus, alcohol consumption, smoking status, marital status, and menopausal status were obtained via structured questionnaire.2) was calculated by dividing the body weight in kilograms by the height in meters squared.We obtained information on the participants’ physical examinations including their height (cm), weight (kg), and waist circumference (cm) directly measured by the medical staff using the standardized KoGES protocol of Korea Centers for Disease Control and Prevention. Body mass index (BMI)  or yogurt  was less than those who never or rarely consumed cheese , we categorized these women into three  or four  groups.Participants were classified into three or four categories based on the distribution of their reported daily intake of each targeted milk and dairy food item. As the number of participants who never or rarely consumed (<1 serving/week) milk  and the means and standard deviations for continuous variables and the number and percentages for categorical variables were provided.Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated by Cox proportional hazards models. Since age is one of the most important determinant for cancer outcome, we chose age as the time scale in Cox proportional hazard models. Age at baseline was used as the entry time to reflect the left-truncation time of the data . Age at Person-years of follow-up were calculated from the date the baseline questionnaire was returned to the date of breast cancer diagnosis, date of cancer diagnosis, or end of follow-up (31 December 2014), whichever came first.2, continuous), and energy intake , educational level , parity , age at first birth , age at menarche , oral contraceptive use , regular exercise , alcohol consumption , and family history of breast cancer . To test for linear trends across categories of milk and dairy product intake, participants were assigned the median value of each category, and this variable was entered as a continuous term in the model, the coefficient for which was evaluated by the Wald test. Multivariate HRs were adjusted for BMI . The likelihood ratio test was used to test for significance in interaction models by comparing models with and without cross-product terms.The proportion of participants with missing data for the covariates was generally low (<3%); we thus assigned median values for continuous variables and the most common category for categorical variables to the missing variables in the participant data.To examine the possible presence of a time lag effect, we excluded the first two years of the follow-up period from the analysis.p ≤ 0.05.The statistical software package SAS for Windows version 9.4  was used for all statistical analyses and two-side tests were statistically significant at A total of 78,320 participants, aged 40 to 69 years, were included in the analysis. During an average follow-up period of 6.3 years, 359 women were diagnosed with breast cancer through 31 December 2014. The mean age of the participants was 52.33 years.p for trend = 0.0687). In women under 50 years of age, however, milk consumption was inversely associated with breast cancer risk. In the comparison between the highest (≥1 serving/day) and lowest (<1 serving/week) intake categories for milk, the multivariate HR (95% CI) was 0.58 (0.35–0.97), and increased consumption of milk was associated with a lower risk for breast cancer (p for trend = 0.0195) among women under 50 years of age. However, this interaction was not statistically significant.The multivariate HRs (95% CIs) of breast cancer according to milk consumption are shown in We observed no association between breast cancer risk and yogurt or cheese consumption in either age groups .In this prospective cohort of Koreans, we found that milk consumption in women aged 50 or younger was associated with a decreased risk for breast cancer, when compared to those who never or rarely consumed milk (<1 serving/week). These results are in line with findings from previous studies, which also found association between decreased risk for breast cancer and high consumption of milk ,32. A prPrevious studies reported that dairy products have anti-carcinogenic effects, due to large amounts of calcium, vitamin D, and some bioactive components including conjugated linoleic acid (CLA) ,18,22. ISeveral explanations can be postulated that would explain our inconsistent findings on milk and other dairy product consumption. Dairy products have both pro- and anticarcinogenic effects. Dairy products, which are relatively high in calcium, vitamin D, and CLA, have effects on cell proliferation and differentiation and/or inhibit tumor development ,14,15,16Our findings showed that milk consumption was not associated with a decreased risk in the study population as a whole. When stratified by age group, however, an inverse association, was observed in women less than 50 years of age, whereas among older women the association was null. Since some risk factors for breast cancer vary according to age or menopausal status and the etiologies of pre or postmenopausal breast cancer are different in many aspects, the association between diet and breast cancer risk may be different between younger vs. older women and pre vs. postmenopausal women . ConsistPotential limitations need to be considered in our study. First, measurement errors in dietary assessments based on self-reported FFQs are unavoidable. However, intake assessments of nutrients based on our FFQs have reasonable validity and reproducibility . FFQs arDespite these limitations, the strengths of our study include prospective design, large population, and high accuracy of breast cancer diagnoses, as the data on cancer diagnoses in this cohort were retrieved from the Korea Central Registry. Moreover, information bias by differential recall was minimized because the dietary assessments were performed at baseline survey when the participants were free of cancer and unaware of the research hypothesis. Additionally, we were able to adjust for reproductive or menstrual factors, presence of breast cancer family history, and hormone use associated with breast cancer risk.In conclusion, our results provide evidence that milk consumption in Korean women less than 50 years of age is associated with a decreased risk of breast cancer compared to those who never or rarely consumed milk. To confirm any potential effect of specific dairy products, more prospective studies need to be conducted.


C-peptide is usually considered as a marker of insulin secretion and has no physiological function. This study aimed to assess the association between serum C-peptide level as independent risk factor and breast cancer and explored the possible underlying mechanisms. This was a population-based cohort study. All the data was collected according to a standard protocol. The C-peptide and insulin-like growth factor binding proteins-3(IGFBP-3) concentrations were measured in blood. The breast cancer deaths were confirmed by National Death Index records. Cox proportional hazard regression analysis was conducted to determine the hazard ratio of serum C-peptide level for breast cancer deaths. Analysis of covariance was used to assess the association between serum C-peptide and IGFBP-3 level, and the linear trend was tested by using a linear model. A total of 8,373 women 17 years of age or older were included in the study, and 57 breast cancer deaths were observed over the study period. The result of survival analysis showed that breast cancer deaths increased with increasing levels of serum C-peptide. The hazard ratio was 1.69 . The levels of circulating IGFBP-3 were positively associated with changes in serum C-peptide levels and showed a strong linear trend in the covariance analysis. Serum C-peptide level was associated with increased risk of breast cancer death. Our results suggest that the increased risk of breast cancer death can be via a pathway that serum C-peptide level positive associated with the change in serum IGFBP-3 level. Breast cancer is the most common malignancy and the second leading cause of death among women worldwide. Moreover, the incidence of and mortality related to breast cancer keep rising in most countries despite efforts to prevent cancer in the past 20 years. More than one million women are diagnosed with breast cancer annually, and the economic burden of disease continues to increase globally. Therefore, breast cancer remains a major public health challenge for women worldwide , 2.The exact cause of breast cancer has not been completely elucidated. However, previous studies have identified several risk factors that increase the risk of breast cancer events and deaths in women, including modifiable and non-modifiable risk factors. Non-modifiable risk factors include age, family history of breast cancer, and genetic factors. Modifiable risk factors include lifestyle-related factors, such as smoking, alcohol consumption, physically inactivity, radiation exposure, and drug abuse. Moreover, risk factors that are called potentially modifiable risk factors, such as age of menarche, age of menopause, few pregnancies, and short or no periods of breastfeeding have been identified . Recent Numerous epidemiologic studies have shown that the serum C-peptide level is independently associated with cardiovascular disease, and cancer and total mortality . MoreoveHowever, the etiology of breast cancer is complex and unclear. Studies have reported the association between serum C-peptide level and breast cancer, but several important questions have remained unanswered. First, previous studies did not assess whether the association between serum C-peptide levels and breast cancer is independent of serum insulin level but only as an indicator of insulin secretion . Second,This study was based on data from the National Health and Nutrition Examination Survey III (NHANES III), a national cohort survey that aimed to assess the health and nutritional status in the general population of the United States. Baseline data were collected from 1988 to 1994 using a complex, multistage cluster sampling design. The survey was divided into 2 phases; the first phase was conducted from 1988 to 1991 and surveyed 44 locations, and the second phase was conducted from 1991 to 1994 and surveyed 45 locations. A representative sample comprising 33,994 US residents aged >2 months was registered. The 33,994 US residents were interviewed and 30,818 were examined in the 6-year sample. Breast cancer deaths were ascertained by linking the records to National Death Index records. Participants were included in this study if they met the following selection criteria : women aThe NHANES III survey data consisted of 3 parts: home interview, clinical tests, and physical examinations. The home interview included participants’ self-reported demographics, behavior habits, and general health statuses. Participants were asked whether they had engaged leisure time physical activities and specific exercises in the past month, and the frequency and intensity of each activity were also recorded. Smoking status was categorized as never, former, or current smoker. Tobacco use was assessed by the age of initiation, frequency, and total number of years for which they had or have been smoked. Alcohol intake was measured by recalling the frequency, amount, and type of alcoholic beverages consumed. Participants were defined as drinkers if they had consumed at least 12 drinks during the past 12 months. Total daily energy intake consumed from fat, carbohydrates, and protein were obtained from the 24-hour dietary recall at the MEC.The body measurements involved measuring the weight, height, lengths, skinfolds, circumferences, and breadths. Body Mass Index (BMI) was calculated as the weight in kilograms divided by the square of the height in meters (kg/m2). Waist circumference (WC) was measured by using a metal tape. Waist-to-hip ratio was calculated as waist circumference divided by the circumference buttocks. Skinfold thickness was measured at four different anatomic body sites.Blood and urine specimens were obtained, and several tests and measurements were performed following laboratory procedures used for the NHANES III. Serum C-peptide, serum insulin, and fasting plasma glucose levels were measured in participants who had fasted for 8 to 24 hours during the morning examination session. The serum concentrations of insulin-like growth factor-1(IGF-1) and insulin-like growth factor binding proteins-3 (IGFBP-3) were determined by using IGF-1 enzyme-linked immunosorbent assay and IGFBP-3 immunoradiometric assay.The demographic characteristics of study participants were analyzed by using descriptive statistics. The weighted percentages for general population and breast cancer cases were calculated by categorical variables according to the analytic and reporting guidelines for the NHANES III.Cox proportional hazards model was used to identify the association between serum C-peptide levels and breast cancer death. The hazard ratios (HRs) and 95% confidence intervals (CIs) were computed to obtain serum C-peptide levels, serum insulin levels, and plasma glucose levels. The model 1 adjusted following variables: age, ethnicity, smoking status, alcohol use, education levels, physical activity, BMI, serum creatinine level, and menopausal status. The model 2 included serum C-peptide levels, serum insulin levels, and plasma glucose levels by using the stepwise method.We conducted analysis of covariance (ANCOVA) to evaluate the association among the changes in IGFBP-3, serum C-peptide, and serum insulin levels. The serum IGFBP-3 level was the dependent variable, and serum C-peptide and serum insulin levels were the independent variables. The serum C-peptide levels were divided into four categories based on quartiles. The first quartile was defined as the 25th percentile (0.40 nmol/L), the median value represented the 50th percentile (0.65 nmol/L), and the third quartile was defined as the 75th percentile (1.00 nmol/L). The serum insulin levels were divided into four categories by the 25th percentile (39.12 pmol/L), 50th percentile (56.34 pmol/L), and 75th percentile (86.10 pmol/L) using the same method. Comparisons were made among four categories by using an F test with a significance level of 0.05, and the linear trend was assessed by using a general linear model. The first quartile was used as the reference group. The mean and standard error were calculated for serum IGFBP-3 levels by using the quartiles of serum C-peptide levels and serum insulin levels. The ANCOVA covariates included age, ethnicity, smoking status, alcohol use, education levels, physical activity, and BMI. Normality test and variance homogeneity test were carried out for every dependent variable. Further analyses were performed to evaluate the changes in the subscapular skinfold (SSF) and WC in the quartiles of the serum C-peptide levels after stratifying for insulin levels .We plotted survival functions for two groups with different levels of serum C-peptide  by using the Kaplan-Meier method and made comparisons across breast cancer deaths among groups using the log-rank test. All analyses were performed by using SAS 9.2 for Windows .2.The characteristics of the study participants are shown in The results of time-to-event analysis by Cox proportional hazard regression model are shown in The negative log transformed survival function estimated the hazard ratio of breast cancer in two groups with different serum C-peptide levels over the study period. A higher cumulative incidence of breast cancer deaths was observed in the group with higher serum C-peptide level compared with the group with lower serum C-peptide levels .P < .001); however, no significant association was observed between plasma glucose level and changes in SSF and WC.In The national prospective observational study demonstrated that the serum C-peptide level was associated with increased breast cancer deaths, and further analyses showed that the association was independent of insulin level. The serum C-peptide level was positively associated with the serum IGFBP-3 level, and a significant dose–response association was observed by a linear trend test; however, the association was not obtained with insulin. Moreover, the serum C-peptide levels remained associated with increased SSF and WC after stratified analysis of serum insulin levels.Few studies have shown that serum C-peptide level is associated with increased risk of breast cancer, and C-peptide has only been assessed as a marker for pancreatic insulin secretion. Moreover, in a previous report, a hypothetical mechanism has been described in which C-peptide acts as a marker of insulin resistance and increases breast cancer incidence. However, recent studies have confirmed that C-peptide is independent of insulin and has an important role in fat distribution and lipid metabolism , 17. TheRecent studies have shown that altered cholesterol metabolism, as a comorbidity of obesity, has emerged as an independent risk factor in breast cancer . MultiplIGFBP-3 is known as the main binding partner for circulating insulin-like growth factors and regulates their bioavailability . SeveralIn the present study, we observed that serum C-peptide level was strong positively associated with circulating IGFBP-3 level, and this finding may explain the mechanism by which C-peptide increases breast cancer deaths, at least in part. Overall, the possible mechanisms by which C-peptide affects breast cancer pathogenesis are complex and multifactorial. Our study demonstrates that C-peptide, as an important bioactive peptide, plays a trigger role in the development and growth of breast cancer and it is independent of insulin. Notably, recent update on the Human Gene Database showed that the expression of C-peptide receptor in adipose tissue is much higher than that in other 26 body’s tissues and organs. Recent studies have found that the lack of GPR146 plays an important role in the prevention of hypercholesterolemia. Moreover, the cancer genome database showed that higher expression levels of GPR146 in patients with breast cancer. These findings further support the results of current study –45.The main strength of this study is that it was based upon a nationally prospective follow-up study. The data were collected through a large national surveillance system, and the determination of biomarkers was performed according to standard methodology. We controlled for several important confounding factors by using highly representative biomarker data. Moreover, this is the first study to investigate the association between serum C-peptide level and breast cancer independent of insulin level as a bioactive peptide. The serum insulin level, as an important confounding factor, was controlled using stratified analysis. ANCOVA analyses were conducted to identify the strong linear association between serum C-peptide level and circulating IGFBP-3 level, but serum insulin level did not show significant association with circulating IGFBP-3 level. The ANCOVA result provided strong evidence for some previous etiological assumptions. The study has a major limitation. This is a prospective cohort study and provides new evidence and potential explanations on breast cancer pathogenesis in the population level. If there is a significant association between GPR146 expression levels and breast cancer death or progression, it will strongly support the conclusion that C-peptide itself promote the cancer progression. However, at present, there are no study about the causal link between the GPR146 and breast cancer death. Accordingly, the approaches for evaluating the impact of C peptide on breast cancer pathology and molecular mechanisms are needed for further study. In addition, hyperinsulinemia resulting from insulin resistance is a well-known risk factor for several malignancies, including breast cancer. In this study, no significant association was found between insulin levels and breast cancer mortality after adjusting for confounding factors. Although we used stepwise regression to select the optimal regression equation, the results were obtained on the basis of statistical analysis only. Moreover, C-peptide level is a more sensitive indicator of insulin resistance than insulin level. Therefore, the present result showed that C-peptide is an independent risk factor that increases the risk of breast cancer requires additional supporting evidence.In conclusion, the present study indicated that serum C-peptide level is associated with increased risk of breast cancer death, and it is independent of insulin level. The partial mechanism may be linked to the changes in increased level of circulating IGFBP-3, and the finding is supported by previous studies. However, the association between circulating IGFBP-3 level and serum insulin level was not observed in the study. Our results suggested that serum C-peptide may increase the risk of breast cancer death via a pathway that is associated with increased IGFBP-3 level.


There are increasing concerns about the potential impact of air pollution on chronic brain inflammation and microglia cell activation, but evidence of its carcinogenic effects is limited.x, NO2), kriging interpolation for ozone (O3), carbon monoxide, and particulate matter , and nearest monitoring station measurements for benzene for 103 308 men and women from the Multiethnic Cohort, residing largely in Los Angeles County from recruitment (1993–1996) through 2013. We used Cox proportional hazards models to examine the associations between time-varying pollutants and risk of malignant brain cancer  and meningioma  with adjustment for sex, race and ethnicity, neighborhood socioeconomic status, smoking, occupation, and other covariates. Stratified analyses were conducted by sex and race and ethnicity.We used kriging interpolation and land use regression models to estimate long-term air pollutant exposures of oxides of nitrogen . Stronger associations with PM10 , O3 , and benzene  were observed among Latino men. Air pollution was unrelated to risk of meningioma except that O3 exposure was associated with risk in men . Brain cancer risk in women was unrelated to air pollution exposures.Brain cancer risk in men increased in association with exposure to benzene  and PMConfirmation of these sex differences in air pollution–brain cancer associations and the stronger findings in Latino men in additional diverse populations is warranted. Vsorbance  and in csorbance ,18, but x and nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO), and benzene as well as PM2.5 and PM10 on risk of malignant brain cancer and meningioma within the California (CA) component of the Multiethnic Cohort (MEC) in which Latinos and African Americans represent approximately 75% of the participants. To our knowledge, this is the first prospective study on airborne air pollutants and risk of malignant and benign brain tumors with a substantial number of nonwhites and an assessment of sex-specific associations.Air pollution may adversely affect the central nervous system (CNS) by increasing the risk of stroke and decreasing cerebral blood flow and cognitive function , but resThe MEC enrolled 96 810 men and 118 441 women aged 45–75 years from five racial and ethnic groups  residing in Hawaii (HI) or CA (primarily Los Angeles County [LAC]) in 1993–1996 . At baseParticipants were followed prospectively for incident invasive brain cancer through linkage with the CA and HI statewide cancer registries, a part of the Surveillance, Epidemiology, and End Results Program, and for vital status through linkages to the National Death Index and death certificate files. MEC participants older than 65 years were linked to Centers for Medicare Services claims (1999–2016) . To asceEligible CA participants completed a baseline questionnaire and provided valid addresses that were geocoded at the parcel or street segment level across the study period (n = 105 616). We excluded participants with a brain cancer or meningioma diagnosis before cohort entry and those with questionable address data (n = 2189) and other invalid entry or exit dates (n = 41), leaving 103 308 men and women for linkage with air pollution data. This cohort was followed from the date of entry (1993–1996) to the earliest date of diagnosis of brain cancer or meningioma, death, or December 31, 2013 (study end date), whichever came earlier (mean ± SD follow-up time = 16.4 ± 5.4 years). The study was approved by the institutional review boards at all participating institutions.The MEC actively maintains accurate and up-to-date addresses via periodic mailings of newsletters, follow-up questionnaires, and linkages to administrative data and registries. For the 103 308 CA MEC participants included in this study, 178 714 addresses were recorded during the follow-up period.2 and NOx exposures based on data from spatially dense air-monitoring campaigns , and the California Line Source Dispersion Model (CALINE4) have been published . Krigingampaigns . The CALway data . The US 3 and PM10 overall and by sex.We employed time-dependent approaches to assess air pollutant exposures, capturing the variability over time and the differing duration of exposures across participants, to evaluate their associations on risk. For every participant’s address across the study period, we calculated a set of averages of cumulative exposures for a series of time intervals based on monthly (kriging and LUR) or yearly (CALINE4) exposures at cohort entry and each month and year during the follow-up until the censoring month and year . Averages of cumulative exposures were entered into Cox proportional hazard models as time-dependent variables via the counting process style of input. For the regression calculation, the average exposure starting from entry time until the time of the event was used for risk calculations, using age at cohort entry (5-year age categories) as a stratum variable and adjusted for covariates at baseline and yearly estimates of neighborhood socioeconomic status (nSES) at baseline and at time of event. Hazard ratios (HRs) and 95% confidence intervals (CIs) for a standard unit increase in an air pollutant were calculated; these common fixed units were chosen to be close to the respective interquartile ranges  (x to 2.45 for CO) but not in women. In men, a hazard ratio of 1.80 (95% CI = 1.00 to 3.23) for PM10 was observed. Modelled estimates of LUR NO2 and NOx and CALINE4 NOx did not suggest any increase in risk for malignant brain cancer  . There wP for heterogeneity  = 0.29)  and this was again due to an elevated risk in men  but not in women . The ben = 0.29) . We cond = 0.29) .10 , O3 , and benzene , but this was not observed in Latino women   (The air pollutants measured at the closest monitor (benzene) and modeled by kriging, LUR, and CALINE4 were moderately to strongly correlated (to 3.11) .3 was associated with meningioma risk in men  but not in women . Benzene exposure based on less than 20 km to monitors was not associated with risk in men , but this association became stronger when we restricted to residence less than 10 km of the monitoring stations .Benzene and air pollutants determined by kriging  followed by kriged NO2 (r = 0.824) than with the LUR and CALINE4 modeled NOx and NO2  may be a chance finding, but this result is compatible with our O3–malignant brain cancer finding in men.Our null results for meningioma agree with previous studies ,12,13. T3, PM10, and benzene among men but not in women. Results in women were similar for those who had ever used hormone therapy and never users (data not shown). There are intriguing sex differences in benzene-induced toxicities in animal models that may be related to hormonal differences (PON2), an intracellular antioxidant and antineuroinflammatory enzyme that protects cells in the CNS; estrogens are important modulators of PON2  was implicated in a study of glioma conducted in Mexico  and women (35.6%) lived within 500 m of major roads compared with non-Latino men (28.7%) and women (28.1%), this factor alone is unlikely to explain the stronger association in Latino men that was absent in Latino women. Moreover, distance from major roads was not associated with risk in Latino men or women . Interactions with occupational or genetic risk factors and coexposures with other carcinogens in Latino men may be additional factors. Interestingly, a polymorphism in the h whites .3 inhalation may be associated with elevated circulating proinflammatory mediators that can access the CNS by crossing the blood-brain barrier .2.5 absorbance . Although we used established approaches  to assess long-term air pollution exposures, the different approaches have varying levels of uncertainty pointing to imprecision of our estimates and the potential for nondifferential misclassification of individual exposures. Ambient benzene measurement is challenging. To our knowledge, no cancer studies, including ours, have long-term high spatiotemporal resolution estimates for benzene exposures. Although it is desirable to account for collinearity of pollutants beyond using simple mutual adjustment approaches, it is difficult to implement multipollutant approaches   to examisorbance . Finally3, and PM10 levels experienced a higher risk of malignant brain cancer. Confirmation of these results is needed in additional well-designed studies including minority men and women and using long-term and improved spatiotemporal exposure assessment. This will help to better understand the role that ambient air pollution plays in malignant brain cancer development.In conclusion, this prospective cohort study found that men, especially Latino men, living in areas with high ambient benzene, OThis work was supported by the Health Effects of Air Pollution Foundation, the National Cancer Institute (U01 CA164973), USC Norris Comprehensive Cancer Center Core Support (P30 CA014089), Environmental Exposures, Host Factors, and Human Disease (P30 ES007048021), California Air Resource Board (contract 04–323), National Institute of Environmental Health Sciences (NIEHS R21 ES016379), Health Effect Institute (HEI 4787-RFA09-4110–3), and National Cancer Institute (5-R21-CA094723-03).The funders had no role in the design of the study; the collection, analysis, and interpretation of the data; the writing of the manuscript; and the decision to submit the manuscript for publication.The authors are grateful to the study participants and research team of the MEC. The authors confirm they have no personal or financial conflicts of interest.pkz107_Supplementary_DataClick here for additional data file.


Occupational factors can lead to breast cancer, though the relationship between these variables is not well established. The objective of this study was to search the relevant literature for information on the association between breast cancer and exposure to occupational risk factors. For that purpose, electronic databases were searched using the following keywords: breast cancer and occupational exposures. A total of 40 articles published in the 10-year period from 2009 to 2019 were included in this review. Workers exposed to metals such as cadmium, chemical products, radiation and night work were more susceptible to breast cancer. The findings showed significant evidence to support an association between breast cancer and some chemical products, ionizing radiation and night work. However, most studies have difficulty establishing a causal relationship between these variables, pointing to the need for further investigation of these issues. In the early 18th century, Bernardino Ramazzini found that the prevalence of breast cancer was higher among nuns than in the general population. Ramazzini attributed this association to the celibate lifestyle of the nuns.8 It is estimated that 8 to 16% of cancers are caused by occupational exposures.2 This phenomenon is caused by the presence of carcinogens in the work environment.1 Carcinogenic compounds include, but are not limited to, organic fibers and dust particles , metals , solvents (benzene and trichloroethylene), mineral oils , reactive chemical products .1In recent years, technological developments have allowed for a more thorough study of the association between breast cancer and occupational factors.11In the context of breast cancer, some studies have assessed occupational exposures that may increase the risk of this condition in specific occupations. Sources of exposure include ionizing and non-ionizing radiation, melatonin suppression in night workers, pesticides, solvents, aromatic hydrocarbons, some metals and other chemical substances.International Agency for Research on Cancer (IARC) has stated that there is still a need for further evidence of the association between occupational risk factors and breast cancer in humans.5Despite the long history of research on breast cancer and the fact that some substances are proven carcinogens, the Therefore, the aim of this study was to conduct an integrative literature review on the association between breast cancer and occupational exposures.An integrative literature review method was used. The study was conducted in the following stages: research question, literature search, data collection, data analysis, interpretation of results, discussion and presentation of the findings.This review was guided by the following questions: is breast cancer related to occupational risk factors? And if so, which occupational risk factors contribute to the development of cancer?The PubMed, SciELO and LILACS databases were searched using the following keywords: “breast cancer” and “occupational exposures”. The search was limited to articles published between 2009 and 2019. All original studies published in English, Portuguese or Spanish were eligible for this review. Incomplete articles, abstracts, theses, dissertations and studies of non-occupational risk factors associated with breast cancer were excluded from consideration.After the literature search, the articles were read in order to identify the risk factors analyzed and the strength of their association with breast cancer. The information on the risk factors, authors, publication year, type of study, conclusions regarding significant or non-significant association and population were extracted from each study and compiled into tables. These findings were then discussed based on previous literature on the risk factors associated with breast cancer.46 The risk factor, author, publication date, study type, conclusion and population analyzed in each study were entered into tables. Four occupational risk factors were identified in the studies: metals, chemical agents, radiation and circadian rhythm disruption.The literature search retrieved 409 articles . After limiting the search to the aforementioned 10-year period, 369 articles were left. This was followed by abstract screening, application of selection criteria, and removal of duplicates, which resulted in the inclusion of 40 articles in the review.41 , used especially as non-ionic surfactants, but with a wide range of other applications.19 However, prolonged and continuous exposure to these substances can increase the risk of breast cancer, especially in women with a family history of these malignancies.19Polycyclic aromatic hydrocarbons are a large group of chemical compounds formed in the combustion of organic matter and are common environmental pollutants.16 The carcinogenic properties of mineral oil, which is classified as carcinogenic to humans, are mainly attributed to its polycyclic hydrocarbon content. Diesel exhaust was classified by the IARC as carcinogenic to humans based on evidence of its association with an increased risk of lung cancer.15 However, we did not identify high-quality evidence on diesel and gasoline emissions and their association with breast cancer.Direct exhaust gases and petroleum-derived products are complex mixtures of paraffinic, naphthenic and aromatic products refined from mineral oil.17 Products derived from ethylene oxide include plastics, polyester fibers, detergents and ethylene glycol antifreeze.17 It is also used as an agricultural insecticide and in the sterilization of hospital equipment. The National Toxicology Program has classified ethylene oxide as a carcinogen based on epidemiological data.17 However, we found no evidence to support a correlation between breast cancer and exposure to ethylene oxide.Ethylene oxide is a highly reactive chemical, used primarily as an intermediate in the synthesis of several industrial chemicals.25 The health effects of radiation are influenced by the duration and intensity of exposure. Ionizing radiation has been more thoroughly studied, and findings support its association with the risk for several types of malignancy, including breast cancer.23 Sources of radiation exposure include the particles and rays emitted by radioactive metals, high-tension equipment, nuclear reactions and cosmic phenomena. Examples of this type of radiation are gamma and X-rays, both of which are associated with malignancies such as breast cancer.2 Health care workers such as radiology technicians are especially vulnerable to this type of exposure. Non-ionizing radiation includes electromagnetic frequencies and microwaves, whose emission sources include household appliances.47 The duration of exposure appears to be a significant determinant of the effects of non-ionizing radiation. According to one of the studies reviewed, individuals exposed for at least 30 years had a nearly three-fold increase in the risk of breast cancer relative to individuals with a shorter duration of exposure.47Ionizing and non-ionizing radiation are natural phenomena. Both are considered carcinogenic, but the evidence of the association between breast cancer and ionizing radiation is far more extensive.5 Alterations in the circadian rhythm are thought to increase the risk of cancer by interfering with melatonin synthesis.5 A possible mechanism of action for this effect involves the suppression of nocturnal melatonin production due to exposure to artificial light, which can also influence the patterns of sex hormone production and, in turn, increase the risk of breast cancer.5 Two studies focused specifically on populations susceptible to circadian rhythm disruption due to rotating shift work.33 Another professional category that is vulnerable to circadian misalignment is that of flight attendants. These professionals often travel across time zones and experience the effects of jet lag.50 Flight attendants are also exposed to cosmic radiation. Long flights at high latitudes - that is, closer to the poles - are associated with a higher risk of exposure to cosmic rays.51Most epidemiological studies suggest a positive association between breast cancer and circadian disruption. Night work was classified as a Group 2A carcinogen by the IARC in 2007.The association between occupational exposures and the etiology of breast cancer has been examined by several studies which have supported the relationship between these variables and added to the growing interest in this issue demonstrated by scientists studying cancer and workers’ health issues.Occupational exposure to chemicals, ionizing radiation and circadian disruption have proved to have the greatest association with breast cancer. Nevertheless, many studies have pointed to the difficulty in establishing a causal link between these factors due to the number of variables involved, such as the duration and intensity of exposure to risk factors.There is a need for more studies centered on the relationship between breast cancer and occupational exposures. Such studies can provide a basis for the development of effective protection measures for these workers.


Most studies demonstrating an association between excess adiposity and postmenopausal breast cancer have used anthropometric measures, particularly body mass index (BMI). However, more direct body fat measures may more accurately determine the relationship between body fat distribution and breast cancer risk.Cox proportional hazards regression models were created to examine the associations of dual‐energy x‐ray absorptiometry (DXA) body fat measures (at baseline and during follow‐up) with breast cancer risk among 10 931 postmenopausal women from the Women's Health Initiative cohort. A total of 639 incident invasive breast cancer cases (including 484 estrogen receptor positive (ER+) cases) were ascertained after a median follow‐up of 15.0 years.Excess whole body fat mass and trunk fat mass were positively associated with risk invasive breast cancer risk. These associations persisted even after additional adjustment for standard anthropometric measures. In time‐dependent analyses, we observed that both whole body fat mass and trunk fat mass, in the highest versus lowest category, were associated with a doubling of risk of invasive breast cancer overall  and of ER+ breast cancer . The remaining DXA measures were also positively associated with breast cancer risk in baseline and time‐dependent analyses.These findings suggest that DXA‐derived body fat measures are positively associated with breast cancer risk after adjustment for BMI and other conventional breast cancer risk factors. Our study demonstrated that DXA‐derived body fat measures were positively associated with breast cancer risk after adjustment for BMI and other conventional breast cancer risk factors. These findings suggest that DXA‐derived measures of overall and central adiposity may explain invasive breast cancer risk beyond that of BMI and other risk factors. Thus, in the study reported here, which was conducted in a subset of participants in the WHI and represents an extension of an earlier report,22.1A detailed description of the WHI design and study population can be found elsewhere.2.2More detailed information on the ascertainment of DXA measures has been previously published.Body fat measures included whole body fat (kg), percentage whole body fat, trunk fat (defined by the fat contained in the torso apart from head and limbs), fat mass of the legs, and the ratio of trunk to leg fat mass. In addition, the fat mass index (FMI) and trunk fat mass index (TFMI) were calculated as whole body fat mass and trunk fat mass in kilograms divided by height in meters squared, respectively.2.3Our analytic cohort was restricted to women with available DXA measures (N = 11 393). Among these women, we excluded 108 women who were missing baseline DXA measures, 325 women with a previous history of breast cancer, and 29 women with missing information on follow‐up time. After exclusion, 10 931 women remained for our analyses, of whom 6078 were in the OS group and 4853 were in the CT intervention group   coding system.2.5Cox proportional hazards regression was used to estimate the age and multivariable adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations between the body fat measures and risk of invasive breast cancer  with time to diagnosis of invasive breast cancer as the underlying timescale. Participants were censored (non‐cases) if they died, withdrew from the study before the end of follow‐up, or did not develop invasive breast cancer by the end of follow‐up . Cases contributed person‐time to the study from their date of enrollment until the date of diagnosis of breast cancer, and non‐cases contributed person‐time from their date of enrollment until the date of death, the date of withdrawal from the study, or the end of follow‐up, whichever came first. For the analysis involving ER+ breast cancer, we additionally censored the non‐ ER+ breast cancer cases.P value for non‐linearity was determined by testing whether the coefficient of the second spline transformation was equal to zero (P < .05). In sensitivity analyses, we excluded women with a breast cancer diagnosis within two years of enrollment to assess for possible reverse causation.The body fat measures were analyzed as continuous variables (per standard deviation (SD) increase and per 5 or 2 kg increase) and as octiles. We used octiles in order to evaluate the full range of risk. Covariates were selected based on existing evidence or if they caused a 10% change in the HR estimates obtained from the multivariable models. For our main analyses, all regression models were adjusted for age at enrollment (continuous), education , race/ethnicity , family history of breast cancer , age (years) at menarche , parity , age (years) at menopause , age (years) at first full‐term pregnancy , ever use of combined estrogen and progesterone therapy , ever use of unopposed estrogen therapy , physical activity , alcohol intake ((servings/week), continuous), smoking , and study component . Additional adjustment for diabetes and dietary calories from fat did not alter the estimates. Therefore, we did not adjust for these variables. We also performed similar analyses restricted to women who were not randomized to any of the clinical trials . In three separate models, we additionally adjusted the DXA‐derived measures for BMI, WHR, or WC to assess whether the DXA‐derived measures influence risk of invasive breast cancer beyond that due to the anthropometric measures and other conventional breast cancer risk factors. To assess the nonlinear dose‐response relationship between the continuous body fat measures and risk of ER+ breast cancer, restricted cubic splines with three knots at the 25th, 50th, and 75th percentiles were fitted in Cox proportional hazards regression models.P‐values for trend (P‐trend) were determined by including the ordinal body fat variable as continuous variables in the regression models and using Wald tests to assess statistical significance. The proportional hazards assumption was confirmed using Schoenfeld residuals.We also examined the associations between the DXA‐derived measures and risk of invasive breast cancer by hormone therapy (HT) status  to assess whether HT use at baseline is an effect modifier. For this analysis, the exposures were categorized by quintiles, given the relatively small number of cases in some strata. Also, women in the HT intervention arms  were excluded. To estimate the P‐value for heterogeneity, we included an interaction term in the Cox regression models and used the Wald test to test its coefficient.To account for the influence of changes in the measurements over time on risk of breast cancer, Cox proportional hazards regression models with time‐dependent exposures were also created. We conducted sensitivity analyses whereby physical activity, alcohol consumption, and smoking were also included as time‐varying covariates. However, the estimates were virtually unchanged. Therefore, only the body fat measures were fitted as time‐dependent. These models were adjusted for the aforementioned covariates (except the anthropometric measures).P‐values were two‐sided. P‐values were considered to be statistically significant if <.05.All statistical analyses were performed using Stata 14.1 (StataCorp). 3Table In multivariable analyses , we observed that all DXA‐derived measures were positively associated with risk of invasive breast cancer Table . In partThe multivariable models (excluding adjustment for whole body fat mass or trunk fat mass) also demonstrated positive associations of all of the anthropometric measures  with risk of invasive breast cancer overall and with ER+ breast cancer, but these associations did not persist after further adjustment for whole body fat mass or trunk fat mass Tables . When coTo assess whether the DXA‐derived measures explained risk of breast cancer beyond that of the anthropometric measures and other conventional breast cancer risk factors, we additionally adjusted for the anthropometric measures (separately). In these analyses, the associations for whole body fat mass and trunk fat mass remained after further adjustment (separately) for BMI, WC, and WHR Table . With reTable The associations of the DXA measures with risk of breast cancer  were attenuated, but remained statistically significant after additional adjustment for lean body mass Tables . FurtherPheterogeneity: .031)  Table .The results of our time‐dependent covariate analyses also demonstrated positive associations between all DXA‐derived body fat measures and risk of invasive breast cancer  in postmenopausal women.In studies examining the associations between adiposity and risk of chronic disease, less robust methods than DXA, such as bioelectrical impedance analysis (BIA), have been widely used to determine levels of adiposity.Anthropometric measures, particularly BMI, are also widely used as measures of adiposity.Body fat composition varies with height. However, epidemiological studies have principally evaluated the associations of absolute values or percentages of the DXA‐derived measures, which do not reflect height differences, with risk of breast cancer.Findings from previous studies suggest that HT use modifies the association between adiposity and risk of invasive breast cancer.2 (PGE2) and leptin induce aromatase, the rate‐limiting enzyme for estrogen biosynthesis.Our findings are in accord with biological evidence which suggests that excess adiposity contributes to altered metabolism. Specifically, in an obesogenic environment, adipocytes become enlarged.Lean body mass is thought to exert anti‐carcinogenic benefits by inhibiting cancer‐associated processes such as insulin resistance, chronic inflammation, and dysregulated lipid metabolism.A strength of this study was the availability of body fat measures, which were obtained using DXA. DXA is a quick, non‐invasive, and highly reproducible method for directly measuring body composition. This method, however, lacks the ability to distinguish between subcutaneous and visceral abdominal adipose tissue. However, studies have shown that DXA is almost as accurate as MRI and CT in determining skeletal muscle mass and fat mass compartments.5Our findings suggest that overall and central adiposity may influence the risk of postmenopausal breast cancer  beyond the contributions of anthropometric measures and standard breast cancer risk factors. As this is the first known study to explore the associations between TFMI with risk of breast cancer, further studies are needed to confirm the utility of height‐normalized values for predicting the risk of breast cancer among postmenopausal women. Our findings lend support for the development of behavioral or pharmacological interventions aimed at promoting weight loss among women at high risk for breast cancer due to excess body fat.None declared.Program Office:  Jacques Roscoe, Shari Ludlum, Dale Burden, Joan McGowan, Leslie Ford, and Nancy Geller.Clinical Coordinating Center:  Garnet Anderson, Ross Prentice, Andrea LaCroix, and Charles Kopperberg).Investigators and Academic Centers:  JoAnn E, Manson;  Barbara V Howard;  Marcia L. Stefanick;  Rebecca Jackson;  Cynthia A. Thomson;  Jean Wactawski‐Wende;  Marian Limacher;  Robert Wallace;  Lewis Kuller;  Rowan T. Chlebowski;  Sally Shumaker.Women's Health Initiative Memory Study:  Sally Shumaker.Additional information: A full list of all the investigators who have contributed to Women's Health Initiative science appears at: https://www.whi.org/researchers/Documents%2520%2520Write%2520a%2520Paper/WHI%2520Inv estigator%20Long%20List.p Click here for additional data file.