Physical Activity, Body Mass Index, and Mammographic Density in Postmenopausal Breast Cancer Survivors

Yale University, New Haven, Connecticut, United States
Journal of Clinical Oncology (Impact Factor: 18.43). 04/2007; 25(9):1061-6. DOI: 10.1200/JCO.2006.07.3965
Source: PubMed

ABSTRACT To investigate the association between physical activity, body mass index (BMI), and mammographic density in a racially/ethnically diverse population-based sample of 522 postmenopausal women diagnosed with stage 0-IIIA breast cancer and enrolled in the Health, Eating, Activity, and Lifestyle Study.
We collected information on BMI and physical activity during a clinic visit 2 to 3 years after diagnosis. Weight and height were measured in a standard manner. Using an interview-administered questionnaire, participants recalled the type, duration, and frequency of physical activities they had performed in the last year. We estimated dense area and percentage density as a continuous measure using a computer-assisted software program from mammograms imaged approximately 1 to 2 years after diagnosis. Analysis of covariance methods were used to obtain mean density across WHO BMI categories and physical activity tertiles adjusted for confounders.
We observed a statistically significant decline in percentage density (P for trend = .0001), and mammographic dense area (P for trend = .0052), with increasing level of BMI adjusted for potential covariates. We observed a statistically significant decline in mammographic dense area (P for trend = .036) with increasing level of sports/recreational physical activity in women with a BMI of at least 30 kg/m2. Conversely, in women with a BMI less than 25 kg/m2, we observed a non-statistically significant increase in mammographic dense area and percentage density with increasing level of sports/recreational physical activity.
Increasing physical activity among obese postmenopausal breast cancer survivors may be a reasonable intervention approach to reduce mammographic density.

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    • "The index mammogram was required to have FMMP codes used to define the density phenotype, be a screening (93% of the mammograms) or diagnostic (7% of the mammograms) study, and be a part of FMMP medical examinations. The woman was required to be 40–80 years old at the time of the index mammogram and to have a BMIo35 at the time of the mammogram, to exclude a few morbidly obese women, as BMIX35 has been consistently reported to be associated with lower density (Sala et al, 1999; Gapstur et al, 2003; Titus-Ernstoff et al, 2006; Irwin et al, 2007). As the result, we excluded 24 women with high density and 79 women with low density. "
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    ABSTRACT: We investigated associations of known breast cancer risk factors with breast density, a well-established and very strong predictor of breast cancer risk. This nested case-control study included breast cancer-free women, 265 with high and 860 with low breast density. Women were required to be 40-80 years old and should have a body mass index (BMI) <35 at the time of the index mammogram. Information on covariates was obtained from annual questionnaires. In the overall analysis, breast density was inversely associated with BMI at mammogram (P for trend<0.001), and parity (P for trend=0.02) and positively associated with alcohol consumption (ever vs never: odds ratio 2.0, 95% confidence interval 1.4-2.8). Alcohol consumption was positively associated with density, and the association was stronger in women with a family history of breast cancer (P<0.001) and in women with hormone replacement therapy (HRT) history (P<0.001). Parity was inversely associated with density in all subsets, except premenopausal women and women without a family history. The association of parity with density was stronger in women with HRT history (P<0.001). The associations of alcohol and parity with breast density appear to be in reverse direction, but stronger in women with a family history of breast cancer and women who ever used HRT.
    British Journal of Cancer 02/2012; 106(5):996-1003. DOI:10.1038/bjc.2012.1 · 4.84 Impact Factor
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    • "Overweight and obesity are common problems in the United States, and little evidence indicates that prevalence is less in breast cancer survivors than in the population at large, which is estimated to be more than 60% [14-16]. Thus, given that the majority of breast-cancer survivors have excess weight as a risk factor, the population at risk is large. "
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    ABSTRACT: Healthy body weight is an important factor for prevention of breast cancer recurrence. Yet, weight loss and weight gain are not currently included in clinical-practice guidelines for posttreatment of breast cancer. The work reported addresses one of the questions that must be considered in recommending weight loss to patients: does it matter what diet plan is used, a question of particular importance because breast cancer treatment can increase risk for cardiovascular disease. Women who completed treatment for breast cancer were enrolled in a nonrandomized, controlled study investigating effects of weight loss achieved by using two dietary patterns at the extremes of macronutrient composition, although both diet arms were equivalent in protein: high fat, low carbohydrate versus low fat, high carbohydrate. A nonintervention group served as the control arm; women were assigned to intervention arms based on dietary preferences. During the 6-month weight-loss program, which was menu and recipe defined, participants had monthly clinical visits at which anthropometric data were collected and fasting blood was obtained for safety monitoring for plasma lipid profiles and fasting glucose. Results from 142 participants are reported. Adverse effects on fasting blood lipids or glucose were not observed in either dietary arm. A decrease in fasting glucose was observed with progressive weight loss and was greater in participants who lost more weight, but the effect was not statistically significant, even though it was observed across both diet groups (P = 0.21). Beneficial effects of weight loss on cholesterol (4.7%; P = 0.001), triglycerides (21.8%; P = 0.01), and low-density lipoprotein (LDL) cholesterol (5.8%; P = 0.06) were observed in both groups. For cholesterol (P = 0.07) and LDL cholesterol (P = 0.13), greater reduction trends were seen on the low-fat diet pattern; whereas, for triglycerides (P = 0.01) and high-density lipoprotein (HDL) cholesterol (P = 0.08), a decrease or increase, respectively, was greater on the low-carbohydrate diet pattern. Because an individual's dietary preferences can affect dietary adherence and weight-loss success, the lack of evidence of a negative effect of dietary pattern on biomarkers associated with cardiovascular risk is an important consideration in the development of breast cancer practice guidelines for physicians who recommend that their patients lose weight. Whether dietary pattern affects biomarkers that predict long-term survival is a primary question in this ongoing clinical trial.
    Breast cancer research: BCR 01/2012; 14(1):R1. DOI:10.1186/bcr3082 · 5.49 Impact Factor
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    • "Most of the image processing techniques are implemented on the whole mammogram without taking into consideration that mammograms have different density patterns and that anatomical regions are used by radiologists in the interpretation [16]. The medical community has realized breast tissue density as an important risk indicator for the growth of breast cancer [17] [18] [19] [20] [21]. Wolfe has noticed that the risk for breast cancer growth is determined by mammography parenchymal patterns [22], and it has also been confirmed by other researchers, such as Boyd et al. [23], van Gils et al. [24] and Karssemeijer [25]. "
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    ABSTRACT: The focus of this paper is to review approaches for segmentation of breast regions in mammograms according to breast density. Studies based on density have been undertaken because of the relationship between breast cancer and density. Breast cancer usually occurs in the fibroglandular area of breast tissue, which appears bright on mammograms and is described as breast density. Most of the studies are focused on the classification methods for glandular tissue detection. Others highlighted on the segmentation methods for fibroglandular tissue, while few researchers performed segmentation of the breast anatomical regions based on density. There have also been works on the segmentation of other specific parts of breast regions such as either detection of nipple position, skin-air interface or pectoral muscles. The problems on the evaluation performance of the segmentation results in relation to ground truth are also discussed in this paper.
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