| Literature DB >> 31429809 |
Mary Beth Terry1, Karin B Michels2, Julia Green Brody3, Celia Byrne4, Shiuan Chen5, D Joseph Jerry6, Kristen M C Malecki7, Mary Beth Martin8, Rachel L Miller9, Susan L Neuhausen10, Kami Silk11, Amy Trentham-Dietz12.
Abstract
BACKGROUND: The long time from exposure to potentially harmful chemicals until breast cancer occurrence poses challenges for designing etiologic studies and for implementing successful prevention programs. Growing evidence from animal and human studies indicates that distinct time periods of heightened susceptibility to endocrine disruptors exist throughout the life course. The influence of environmental chemicals on breast cancer risk may be greater during several windows of susceptibility (WOS) in a woman's life, including prenatal development, puberty, pregnancy, and the menopausal transition. These time windows are considered as specific periods of susceptibility for breast cancer because significant structural and functional changes occur in the mammary gland, as well as alterations in the mammary micro-environment and hormone signaling that may influence risk. Breast cancer research focused on these breast cancer WOS will accelerate understanding of disease etiology and prevention. MAIN TEXT: Despite the plausible heightened mechanistic influences of environmental chemicals on breast cancer risk during time periods of change in the mammary gland's structure and function, most human studies of environmental chemicals are not focused on specific WOS. This article reviews studies conducted over the past few decades that have specifically addressed the effect of environmental chemicals and metals on breast cancer risk during at least one of these WOS. In addition to summarizing the broader evidence-base specific to WOS, we include discussion of the NIH-funded Breast Cancer and the Environment Research Program (BCERP) which included population-based and basic science research focused on specific WOS to evaluate associations between breast cancer risk and particular classes of endocrine-disrupting chemicals-including polycyclic aromatic hydrocarbons, perfluorinated compounds, polybrominated diphenyl ethers, and phenols-and metals. We outline ways in which ongoing transdisciplinary BCERP projects incorporate animal research and human epidemiologic studies in close partnership with community organizations and communication scientists to identify research priorities and effectively translate evidence-based findings to the public and policy makers.Entities:
Keywords: Breast neoplasms; Environment; Menopause; Pregnancy; Puberty
Mesh:
Year: 2019 PMID: 31429809 PMCID: PMC6701090 DOI: 10.1186/s13058-019-1168-2
Source DB: PubMed Journal: Breast Cancer Res ISSN: 1465-5411 Impact factor: 8.408
Fig. 1BCERP framework. A model of transdisciplinary community-engaged research by epidemiologists, basic scientists, communication researchers, and advocates to examine environmental causes of breast cancer, as conducted by the Breast Cancer and the Environment Research Program (BCERP)
Epidemiologic studies investigating environmental exposures during three windows of susceptibility in relation to an intermediate marker of breast cancer risk or breast cancer
| First author (Year) | Exposure | Outcome | Population | Sample size | Risk estimate | 95% CI | Notes |
|---|---|---|---|---|---|---|---|
| Exposure during prenatal window | |||||||
| Bonner (2005) [ | Regional total suspended particulates | Breast cancer | Women 35–79, New York | 1166 cases and 2105 controls | OR 2.42 | 0.97–6.09 | > 140 vs < 84 μg/m3 TSP, postmenopausal women |
| OR 1.78 | 0.62–5.10 | > 140 vs < 84 μg/m3 TSP, premenopausal women | |||||
| Bocskay (2005) [ | Personal airborne PAH; PAH DNA adducts | Chromosomal aberrations from cord blood | Newborns in Northern Manhattan; Bronx | 60 (32 female, 28 male) | Data not shown for PAH adducts | “No strong association” | |
Airborne PAH | Linear regression line slope | ||||||
| Cohn (2015) [ | Maternal o,p’-DDT | Daughter breast cancer | Mothers and adult daughters in Alameda County, CA | 118 cases and 354 controls | OR 3.7 | 1.5–9.0 | Fourth vs first quartile (> 0.78 vs < 0.27 ng/mL) |
| Exposure during puberty window | |||||||
| Tsai (2015) [ | Serum PFOA | log-transformed SHBG | Taiwanese girls aged 12–17 | 65 | 2.96 (SE 0.34) vs 3.50 (SE 0.24) | Mean PFOA levels 90th vs 50th percentile (> 9.80 vs < 3.63 ng/mL) | |
| Data not shown | FSH and testosterone | ||||||
| Wolff (2015) [ | Urinary phenols | Age at breast development | US girls aged 6–8 followed for 7 years | 1239 girls | Enterolactone: HR 0.79 Benzophenone-3: HR 0.80 Triclosan: HR 1.17 2,5-dichlophenol: HR 1.37 | 0.64–0.98 0.65–0.98 0.96–1.43 1.09–1.72 | 5th vs 1st quintiles of biomarkers |
| Wolff (2014) [ | Low and high molecular weight phthalate (MWP) metabolites from urine | Age of breast and pubic hair development | US girls aged 6–8 followed for 7 years | 1239 girls | Pubic hair development age: HR 0.91 Breast development age: HR 0.99 | 0.84–0.99 0.91–1.08 | 5th vs 1st quintiles of high MWP metabolites. Results null for low MWP metabolites. |
| Wolff (2010) [ | Low and high molecular weight phthalate (MWP) metabolites from urine | Stage of breast and pubic hair development | US girls aged 6–8 followed for 1 year | 1151 girls | Pubic hair development: PR 0.94 Breast development: PR 1.03 | 0.88–1.00 0.97–1.10 | 5th vs 1st quintiles of high MWP metabolites. Results attenuated for low MWP metabolites ( |
| Windham (2015) [ | PBDE, PCB, OCP | Tanner stage 2+ vs 1 (breast development) | US girls aged 6–8 followed for 7 years | 645 girls | PBDE: TR 1.05 PCB: TR 1.05 OCP: TR 1.10 | 1.02–1.08 1.01–1.08 1.06–1.14 | 4th vs 1st quartile. Results similar for pubic hair development. |
| Cohn (2007, 2019) [ | p,p’-DDT metabolites in serum taken after giving birth (initial DDT exposure likely before age 14 years) | Breast cancer before age 50 | Women in Child Health and Development Studies cohort | 129 cases and 129 matched controls | OR 5.4 | 1.7–17.1 | Highest vs lowest tertile (> 13.90 vs < 8.09 μg/L) |
| Breast cancer diagnosis during ages 50–54 | 153 cases and 432 matched controls | OR 1.88 | 1.37–2.59 | One-unit change in log2 (p,p’-DDT), approximately equal to a 2-fold increase | |||
| Exposure during pregnancy | |||||||
| Nie (2007) [ | Regional total suspended particulates at time of first birth | Post-menopausal breast cancer | Women 35–79 in Erie and Niagara Counties | 220 cases and 301 controls | OR 2.57 | 1.16–5.69 | Highest vs lowest quartile |
| Bonefeld-Jorgensen (2014) [ | 16 serum PFAS during pregnancy including 10 PFCA, 5 PFSA, and PFOSA | Breast cancer | Danish National Birth Cohort | 250 cases and 233 controls | PFOSA: RR 1.04 PFHxS: RR 0.66 | 0.99–1.08 0.47–0.94 | Continuous per ng/ml. All other PFAS were null. |
| Cohn (2012) [ | Serum PCB during early postpartum | Breast cancer before age 50 | Women in Child Health and Development Studies cohort | 112 cases with matched controls | PCB 167: OR 0.24 PCB 187: OR 0.35 PCB 203: OR 6.34 | 0.07–0.79 0.11–1.14 1.85–21.7 | Highest vs lowest quartile (> 0.30 vs < 0.08 mmol/l) (> 0.66 vs < 0.38 mmol/l) (> 0.42 vs < 0.34 mmol/l) |
Abbreviations: AA African American, BMI body mass index, FSH follicle-stimulating hormone, HR hazard ratio, IRR incidence rate ratio, NHANES National Health and Nutrition Examination Survey, OR odds ratio, PAH polycyclic aromatic hydrocarbons, PFAS perfluoroalkylated substances, PFHxS perfluorohexanesulfonate, PFOA perfluorooctanoic acid, PFOSA perflurooctane-sulfonamide, PR prevalence ratio, RR relative risk, SHBG sex hormone-binding globulin, TR time ratio of median ages across quantile groups
Epidemiologic studies investigating cadmium exposure in relation to breast cancer risk according to the menopause window of susceptibility (WOS)
| First author (year) | Exposure | Population | Sample size | Risk estimate | 95% CI | Notes |
|---|---|---|---|---|---|---|
| Cadmium exposure stratified by menopausal status | ||||||
| McElroy (2006) [ | Urinary cadmium | Women aged 20–69 years | 246 cases and 254 controls | All ages OR 2.29 20–56 years OR 2.34 57–69 years OR 1.36 | 1.3–4.2 1.1–5.0 0.5–3.4 | Highest (≥ 0.58) vs lowest (< 0.263 μg/g) quartile |
| Gallagher (2010) [ | Urinary cadmium | Long Island (LI), NY and NHANES women aged ≥ 30 years | LI 100 cases and 98 controls NHANES 99 cases and 3120 non-cases | All ages OR 2.81 n.s. difference by age All ages OR 2.32 30–54 years OR n.s. ≥ 55 years OR 7.25 | 1.11–7.13 0.92–5.84 n.s. 1.04–50.7 | Highest (≥ 0.60) vs lowest (< 0.22 μg/g creatinine) quartile |
| Itoh (2014) [ | Dietary cadmium | Japanese women aged 20–74 years | 212 cases and 253 controls | All cases OR 1.04 Premeno. OR 1.01 Postmeno. OR 1.06 Post. ER+ OR 1.08 Post. ER− OR 0.99 | 1.00–1.08 0.96–1.07 1.06–1.11 1.03–1.14 0.92–1.06 | Continuous cadmium intake (μg/day) |
| Amadou (2019) [ | Long-term airborne exposure to cadmium | E3N French cohort aged 40–65 years | 4059 cases and 4059 controls | Overall OR 0.98 Premeno OR 0.72 Postmeno. OR 1.06 ER+ OR 1.00 ER− OR 0.63 | 0.84–1.14 0.45–1.15 0.89–1.27 0.82–1.22 0.41–0.95 | Highest (> 5.47) vs lowest (≤ 0.033 mg/m2) quintile |
| Grioni (2019) [ | Dietary cadmium | Italian cohort aged 34–70 years | 8924 total in cohort with 481 cases | Overall HR 1.54 Premeno HR 1.73 Postmeno HR 1.29 ER+ HR 1.64 ER− HR 1.30 | 1.06–2.22 1.10–2.71 0.68–2.44 1.06–2.54 0.60–2.83 | Highest (≥ 8.82) vs lowest (< 6.73 μg/day) quintile |
| O’Brien (2019) [ | Cadmium from toenail clippings | Sister and two-sister studies aged < 50 years | 1217 sister-pairs of cases and controls | OR 1.15 | 0.82–1.60 | Highest (> 0.011) vs lowest (< 0.003 μg/g) quartile |
White (2019) [ | Residential census tract airborne exposure to cadmium at baseline | Sister study aged 35–74 years | 50,884 total in cohort with 2587 cases | Overall HR 1.1 Premeno 1.0 Postmeno 1.1 | 0.96– 1.3 0.78– 1.4 0.96– 1.3 | Highest vs lowest quintile |
| Postmenopausal women only | ||||||
| Julin (2012) [ | Dietary cadmium | Swedish postmenopausal women | 55,987 total in cohort with 2112 cases | All cases RR 1.21 ER+ cases RR 1.19 ER− cases RR 1.33 | 1.07–1.36 1.03–1.36 0.95–1.87 | Highest (> 16) vs lowest (< 13 μg/day) tertile |
| Adams (2012) [ | Dietary cadmium | Postmenopausal women in VITamines And Lifestyle cohort | 30,543 total in cohort with 899 cases | HR 1.00 n.s. difference by ER status ( | 0.72–1.41 | Highest (> 13.3) vs lowest (< 7.48 μg/day) quartile |
| Eriksen (2014) [ | Dietary cadmium | Danish postmenopausal women | 23,815 total in cohort with 1390 breast cancer cases | All cases IRR 0.99 ER+ IRR 1.00 ER− IRR 0.88 | 0.87–1.13 0.85–1.15 0.62–1.22 | Per 10 μg/day increase in intake |
| Adams (2014) [ | Dietary cadmium | Postmenopausal women aged 50–79 years | 155,069 total in cohort with 6658 cases | HR 0.90 n.s. difference by ER status | 0.81–1.00 | Highest (> 14.21) vs lowest (< 7.10 μg/day) quintile |
| Adams (2016) [ | Urinary cadmium | Postmenopausal women ages ≥ 50 years in Women’s Health Initiative | 12,701 total in cohort with 508 cases and 1050 controls | All HR 0.80 ER+ HR 0.98 ER−/PR- HR 0.88 | 0.56–1.14 0.87–1.07 0.70–1.11 | Highest (> 0.748) vs lowest (< 0.325 μg/g creatinine) quartile |
| All ages | ||||||
| Sawada (2012) [ | Dietary cadmium | Japanese women aged 45–74 years | 48,351 females total in cohort with 402 breast cancer cases | HR 0.87 | 0.61–1.23 | Highest (median 32.3) vs lowest (median 19.2 μg/day) tertile |
| Nagata (2013) [ | Urinary cadmium | Japanese women ages ≥ 25 years | 153 cases from one hospital and 431 controls invited for breast cancer screening | OR 6.05 | 2.90–12.62 | Highest (> 2.620) vs lowest (< 1.674 μg/g creatinine) tertile |
| Gaudet (2018) [ | Blood cadmium | Cancer Prevention Study II women 47–85 years of age | 816 cases and 816 controls | All RR 1.01 ER+ RR 0.89 ER− RR 0.96 | 0.76–1.34 0.62–1.27 0.44–2.09 | Continuous per μg/L |
| Italian women aged 35–70 years | 292 cases and 294 controls | RR 0.80 | 0.61–1.03 | Continuous per μg/L | ||
| Swedish women aged 30–61 years | 325 cases and 325 controls | RR 0.73 | 0.54–0.97 | Continuous per μg/L | ||
| Combined 3 nested case-cohort studies | 1433 cases and 1435 controls | RR 0.84 | 0.69–1.01 | Continuous per μg/L | ||
Abbreviations: BCSC Breast Cancer Surveillance Consortium, CI confidence interval, EPA Environmental Protection Agency, ER estrogen receptor, HR hazard ratio, IRR incidence rate ratio, NHANES National Health and Nutrition Examination Survey, n.s. not statistically significant, OR odds ratio, RR relative risk