scholarly journals Association of Blood Lead Level and Breast Cancer Risk in US Women: A Cross-Sectional Study(NHANES 2003-2014)

Author(s):  
Qin Xu ◽  
Shan Gao ◽  
Qinchuan Yi

Abstract Background: Environmental chemical exposures may play a important role in breast cancer etiology.Higher levels of exposure to environmental chemicals, such as heavy metals, may be associated with a high risk of patients with breast cancer, but the evidence is limited and inconsistent. Thus, In the current study, we used National Health and Nutrition Examination Survey (NHANES) data to to assess the influence of blood lead on risk of patients with breast cancer.Methods:This cross-section study was conducted by extracting data from NHANES database(2003-2014). Logistic regression was applied to explore the association between the blood lead and the incidence of breast cancer. Cox proportional hazards regression models were used to estimate the odds ratio (OR) and 95% confifidence interval (CI) of association between blood lead and the risk of breast cancer adjusted for confounder factors.Results: A total of 10153 participants were included. Elevated blood levels of lead was associated with higher risks of breast cancer ( OR 1.16; 95% CI, 1.09–1.22)in the unadjusted analysis.But after adjustment for established risk factors, we did not observe significant association between blood levels and breast cancer risk ( OR 1.00; 95% CI, 0.95–1.15).Conclusion: We found no significant association between elevated blood lead levels and high risk of breast cancer in our cross-section study.

Author(s):  
Katherine D. Crew

Breast cancer is the most common malignancy among women in the United States, and the primary prevention of this disease is a major public health issue. Because there are relatively few modifiable breast cancer risk factors, pharmacologic interventions with antiestrogens have the potential to significantly affect the primary prevention setting. Breast cancer chemoprevention with selective estrogen receptor modulators (SERMs) tamoxifen and raloxifene, and with aromatase inhibitors (AIs) exemestane and anastrozole, is underutilized despite several randomized controlled trials demonstrating up to a 50% to 65% relative risk reduction in breast cancer incidence among women at high risk. An estimated 10 million women in the United States meet high-risk criteria for breast cancer and are potentially eligible for chemoprevention, but less than 5% of women at high risk who are offered antiestrogens for primary prevention agree to take it. Reasons for low chemoprevention uptake include lack of routine breast cancer risk assessment in primary care, inadequate time for counseling, insufficient knowledge about antiestrogens among patients and providers, and concerns about side effects. Interventions designed to increase chemoprevention uptake, such as decision aids and incorporating breast cancer risk assessment into clinical practice, have met with limited success. Clinicians can help women make informed decisions about chemoprevention by effectively communicating breast cancer risk and enhancing knowledge about the risks and benefits of antiestrogens. Widespread adoption of chemoprevention will require a major paradigm shift in clinical practice for primary care providers (PCPs). However, enhancing uptake and adherence to breast cancer chemoprevention holds promise for reducing the public health burden of this disease.


2014 ◽  
Vol 54 (2) ◽  
pp. S87
Author(s):  
Angela R. Bradbury ◽  
Linda Patrick-Miller ◽  
Brian Egleston ◽  
Lisa Schwartz ◽  
Lisa Tuchman ◽  
...  

2021 ◽  
Vol 13 (578) ◽  
pp. eaba4373 ◽  
Author(s):  
Adam Yala ◽  
Peter G. Mikhael ◽  
Fredrik Strand ◽  
Gigin Lin ◽  
Kevin Smith ◽  
...  

Improved breast cancer risk models enable targeted screening strategies that achieve earlier detection and less screening harm than existing guidelines. To bring deep learning risk models to clinical practice, we need to further refine their accuracy, validate them across diverse populations, and demonstrate their potential to improve clinical workflows. We developed Mirai, a mammography-based deep learning model designed to predict risk at multiple timepoints, leverage potentially missing risk factor information, and produce predictions that are consistent across mammography machines. Mirai was trained on a large dataset from Massachusetts General Hospital (MGH) in the United States and tested on held-out test sets from MGH, Karolinska University Hospital in Sweden, and Chang Gung Memorial Hospital (CGMH) in Taiwan, obtaining C-indices of 0.76 (95% confidence interval, 0.74 to 0.80), 0.81 (0.79 to 0.82), and 0.79 (0.79 to 0.83), respectively. Mirai obtained significantly higher 5-year ROC AUCs than the Tyrer-Cuzick model (P < 0.001) and prior deep learning models Hybrid DL (P < 0.001) and Image-Only DL (P < 0.001), trained on the same dataset. Mirai more accurately identified high-risk patients than prior methods across all datasets. On the MGH test set, 41.5% (34.4 to 48.5) of patients who would develop cancer within 5 years were identified as high risk, compared with 36.1% (29.1 to 42.9) by Hybrid DL (P = 0.02) and 22.9% (15.9 to 29.6) by the Tyrer-Cuzick model (P < 0.001).


Author(s):  
Cheng Peng ◽  
Chi Gao ◽  
Donghao Lu ◽  
Bernard A Rosner ◽  
Oana Zeleznik ◽  
...  

ABSTRACT Background Carotenoids represent 1 of few modifiable factors to reduce breast cancer risk. Elucidation of interactions between circulating carotenoids and genetic predispositions or mammographic density (MD) may help inform more effective primary preventive strategies in high-risk populations. Objectives We tested whether women at high risk for breast cancer due to genetic predispositions or high MD would experience meaningful and greater risk reduction from higher circulating levels of carotenoids in a nested case-control study in the Nurses’ Health Studies (NHS and NHSII). Methods This study included 1919 cases and 1695 controls in a nested case-control study in the NHS and NHSII. We assessed both multiplicative and additive interactions. RR reductions and 95% CIs were calculated using unconditional logistic regressions, adjusting for matching factors and breast cancer risk factors. Absolute risk reductions (ARR) were calculated based on Surveillance, Epidemiology, and End Results incidence rates. Results We showed that compared with women at low genetic risk or low MD, those with higher genetic risk scores or high MD had greater ARRs for breast cancer as circulating carotenoid levels increase (additive P-interaction = 0.05). Among women with a high polygenic risk score, those in the highest quartile of circulating carotenoids had a significant ARR (28.6%; 95% CI, 14.8–42.1%) compared to those in the lowest quartile of carotenoids. For women with a high percentage MD (≥50%), circulating carotenoids were associated with a 37.1% ARR (95% CI, 21.7–52.1%) when comparing the highest to the lowest quartiles of circulating carotenoids. Conclusions The inverse associations between circulating carotenoids and breast cancer risk appeared to be more pronounced in high-risk women, as defined by germline genetic makeup or MD.


2011 ◽  
Vol 29 (15_suppl) ◽  
pp. 1581-1581
Author(s):  
A. R. Bradbury ◽  
L. J. Patrick-Miller ◽  
L. Tuchman ◽  
C. Moore ◽  
P. Rauch ◽  
...  

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 1004-1004
Author(s):  
D. Euhus ◽  
R. Ashfaq ◽  
D. Bu ◽  
A. M. Leitch ◽  
C. Lewis

1004 Background: Tumor suppressor gene (TSG) methylation is frequently detected in benign proliferative breast tissue suggesting that it occurs early in breast carcinogenesis. If it can be screen-detected and is associated with breast cancer risk it could be exploited for breast cancer prevention. Methods: Nipple duct lavage (NDL) samples, obtained from 150 women selected to represent a wide range of breast cancer risk, were evaluated by quantitative methylation-specific real time PCR. High risk breasts were defined as those contralateral to a breast cancer (N = 63) and those of women with a 5-year Gail risk ≥ twice the age- and race-matched general population risk (N = 64). The prevelence of TSG methylation and marked atypia was compared for high risk and lower risk breasts using Chi-square. Data for breasts ipsilateral to a breast cancer are shown for comparison, but not included in the calculations for the high risk category. Results: Samples with adequate cellularity were obtained for 219 breasts (76%). The proportion of healthy breasts with ≥ 1% of the gene copies methylated was 13% for Cyclin D2, 19% for APC, 19% for HIN-1, 16% for RASSF1A, and 9% for RAR-beta. RAR-beta provided the best risk discrimination as 15% of high risk breasts were methylated at a level that exceeded the 95th percentile of the lower risk breasts (0.9% of gene copies methylated, P = 0.05). For the table , methylation fractions for all five genes were summed and the threshold for classifying a breast as positive was set to the 95th percentile of the lower risk breasts (methylation sum = 25.0%). Both methylation and marked atypia provide some discrimination between high and lower risk breasts; the combination, however, provides the best discrimination (24% marker positive for high risk versus 9% for lower risk, P = 0.02). Conclusions: TSG methylation in NDL samples is a marker of breast cancer risk that is complementary to cytology. [Table: see text] [Table: see text]


2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 1508-1508
Author(s):  
D. Euhus ◽  
D. Bu ◽  
S. Milchgrub ◽  
A. M. Leitch ◽  
C. M. Lewis

1508 Background: Tumor suppressor gene (TSG) methylation is identified in nearly all breast cancers, but rarely in histologically normal breast tissue from wonen unaffected with breast cancer. Its occurrence in high risk preneoplasia and in benign breast tissue adjacent to breast cancer suggests that it may represent a high risk field change that could be exploited for cell-based breast cancer risk stratification. Methods: TSG methylation was measured by quantitative methylation-specific real time PCR in 53 breast tumor fine needle aspiration (FNA) biopsies, 84 cellular random periareolar FNAs (RP-FNA) ipsilateral or contralateral to these cancers, 36 cellular RP- FNAs from unaffected women at high risk for breast cancer by the Gail model, and 95 cellular RP-FNAs from unaffected women at lower risk by the Gail model. Results: The breast tumors showed a high frequency of TSG methylation: RASSF1A 80%, HIN-1 65%, Cyclin D2 60%, RAR-β2 53%, and APC 47%. In general, RP-FNA samples from cancer patients and Gail high risk patients showed a greater frequency of methylation than samples from Gail lower risk patients: RASSF1A 43% vs. 21%, P = 0.001, HIN-1 32% vs. 20%, P = 0.05; Cyclin D2 18% vs. 9%, P = 0.10; RAR-β2 21% vs. 18%, P = 0.68; and APC 25% vs. 16%, P = 0.17. Twelve of 215 RP-FNA samples (5%) showed very high levels of methylation (>10% methylation for two or more genes). Only two of these samples were from women classified as lower risk by the Gail model. Methylation frequencies were entirely independent of cell yields but the frequency of RASSF1A methylation increased with increasing Masood scores (P = 0.05). Methylation of RASSF1A in one breast was highly predictive of RASSF1A methylation in the opposite breast (P < 0.0001). Conclusions: TSG methylation appears to be a breast cancer risk-associated field change that can be quantified in RP-FNA samples. RASSF1A methylation occurs frequently in benign breast epithelium, provides reasonable discrimination between high and lower risk breasts (O.R. = 2.0), is related to cytological atypia, and may be an early marker of a methylator phenotype. Quantification of TSG methylation in RP-FNA samples may provide a valuable surrogate endpoint biomarker for Phase II prevention trials. No significant financial relationships to disclose.


2008 ◽  
Vol 26 (15_suppl) ◽  
pp. 1542-1542
Author(s):  
M. E. Rader ◽  
J. Chun ◽  
A. Abraido-Lanza ◽  
A. Komorowski ◽  
J. S. Anker ◽  
...  

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