Salomon Minkin

Mammographic density and breast cancer risk: current understanding and future prospects

Variations in percent mammographic density (PMD) reflect variations in the amounts of collagen and number of epithelial and non-epithelial cells in the breast. Extensive PMD is associated with a markedly increased risk of invasive breast cancer. The PMD phenotype is important in the context of breast cancer prevention because extensive PMD is common in the population, is strongly associated with risk of the disease, and, unlike most breast cancer risk factors, can be changed. Work now in progress makes it likely that measurement of PMD will be improved in the near future and that understanding of the genetics and biological basis of the association of PMD with breast cancer risk will also improve. Future prospects for the application of PMD include mammographic screening, risk prediction in individuals, breast cancer prevention research, and clinical decision making.

Dietary fat and breast cancer risk revisited: a meta-analysis of the published literature.

Animal experiments and human ecological studies suggest that dietary fat intake is associated with a risk of breast cancer, but individual-based studies have given contradictory results. We have carried out a meta-analysis of this association to include all papers published up to July 2003. Case–control and cohort studies that examined the association of dietary fat, or fat-containing foods, with risk of breast cancer were identified. A total of 45 risk estimates for total fat intake were obtained. Descriptive data from each study were extracted with an estimate of relative risk and its associated 95% confidence interval (CI), and were analysed using the random effects model of DerSimonian and Laird. The summary relative risk, comparing the highest and lowest levels of intake of total fat, was 1.13 (95% CI: 1.03–1.25). Cohort studies (N=14) had a summary relative risk of 1.11 (95% CI: 0.99–1.25) and case–control studies (N=31) had a relative risk of 1.14 (95% CI 0.99–1.32). Significant summary relative risks were also found for saturated fat (RR, 1.19; 95% CI: 1.06–1.35) and meat intake (RR, 1.17; 95% CI 1.06–1.29). Combined estimates of risk for total and saturated fat intake, and for meat intake, all indicate an association between higher intakes and an increased risk of breast cancer. Case–control and cohort studies gave similar results.

The association of breast mitogens with mammographic densities

Radiologically dense breast tissue (mammographic density) is strongly associated with risk of breast cancer, but the biological basis for this association is unknown. In this study we have examined the association of circulating levels of hormones and growth factors with mammographic density. A total of 382 subjects, 193 premenopausal and 189 postmenopausal, without previous breast cancer or current hormone use, were selected in each of five categories of breast density from mammography units. Risk factor information, anthropometric measures, and blood samples were obtained, and oestradiol, progesterone, sex hormone binding globulin, growth hormone, insulin-like growth factor-I and its principal binding protein, and prolactin measured. Mammograms were digitised and measured using a computer-assisted method. After adjustment for other risk factors, we found in premenopausal women that serum insulin-like growth factor-I levels, and in postmenopausal women, serum levels of prolactin, were both significantly and positively associated with per cent density. Total oestradiol and progesterone levels were unrelated to per cent density in both groups. In postmenopausal women, free oestradiol (negatively), and sex hormone binding globulin (positively), were significantly related to per cent density. These data show an association between blood levels of breast mitogens and mammographic density, and suggest a biological basis for the associated risk of breast cancer.

Body Size, Mammographic Density, and Breast Cancer Risk

Background: Greater weight and body mass index (BMI) are negatively correlated with mammographic density, a strong risk factor for breast cancer, and are associated with an increased risk of breast cancer in postmenopausal women, but with a reduced risk in premenopausal women. We have examined the associations of body size and mammographic density on breast cancer risk. Method: We examined the associations of body size and the percentage of mammographic density at baseline with subsequent risk of breast cancer among 1,114 matched case-control pairs identified from three screening programs. The effect of each factor on risk of breast cancer was examined before and after adjustment for the other, using logistic regression. Results: In all subjects, before adjustment for mammographic density, breast cancer risk in the highest quintile of BMI, compared with the lowest, was 1.04 [95% confidence interval (CI), 0.8-1.4]. BMI was associated positively with breast cancer risk in postmenopausal women, and negatively in premenopausal women. After adjustment for density, the risk associated with BMI in all subjects increased to 1.60 (95% CI, 1.2-2.2), and was positive in both menopausal groups. Adjustment for BMI increased breast cancer risk in women with 75% or greater density, compared with 0%, increased from 4.25 (95% CI, 1.6-11.1) to 5.86 (95% CI, 2.2-15.6). Conclusion: BMI and mammographic density are independent risk factors for breast cancer, and likely to operate through different pathways. The strong negative correlated between them will lead to underestimation of the effects on risk of either pathway if confounding is not controlled. (Cancer Epidemiol Biomarkers Prev 2006;15(11):2086–92)

Optimal Designs for Binary Data

Abstract This article is concerned with the problem of selecting the values of the explanatory variable in logistic regression to obtain likelihood-based confidence regions of minimum area. One way of finding analytic solutions is by replacing the log-likelihood by a quadratic surface around the maximum, approximating in this way the likelihood regions by ellipses. The optimal allocation for this local approximation is derived without unnecessary restrictions. Since the implementation of the optimal allocation requires accurate initial estimates, a two-stage procedure that uses the second stage to complement the first is recommended. An alternative approach is to deal directly with the likelihood regions. In this case, the identification of the globally optimal allocation calls for numerical integration and optimization. The results presented here attempt to strengthen those introduced by Abdelbasit and Plackett (1983), who derived optimal allocation for the local criterion under the restriction of symmet...

Mammographic Density and Breast Cancer Risk: Evaluation of a Novel Method of Measuring Breast Tissue Volumes

Background: Mammographic density has been found to be strongly associated with risk of breast cancer. We have assessed a novel method of assessing breast tissue that is fully automated, does not require an observer, and measures the volume, rather than the projected area, of the relevant tissues in digitized screen-film mammogram. Methods: Sixteen mammography machines in seven locations in Toronto were calibrated to allow the estimation of the proportion of radiologically dense (stromal and epithelial tissue) and nondense (fatty) tissue represented in each pixel of the mammographic image. This information was combined with a measurement of breast thickness to calculate the volumes of these tissues. Women with newly diagnosed breast cancer (cases) identified on these mammography machines during the years 2000 to 2003 were compared with other women of the same age who did not have breast cancer (controls). Results: Three hundred sixty-four cases and 656 controls were recruited, epidemiologic data were collected, screen-film mammograms were digitized and measured using both a computer-assisted thresholding method, and the new measure of the volume of density. After adjustment for other risk factors, the odds ratio for those in the 5th quintile compared with the 1st quintile was 1.98 (95% confidence interval, 1.3-3.1) for the volume measure and 1.86 (95% CI, 1.1-3.0) for the area measurement. After inclusion of the volume and area measures in a predictive model, the volume measure lost significance, whereas the area measure remained significant. Conclusions: Contrary to our expectations, measurement of the volume of breast tissue did not improve prediction of breast cancer risk. (Cancer Epidemiol Biomarkers Prev 2009;18(6):1754–62)

Family History, Mammographic Density, and Risk of Breast Cancer

Purpose: Mammographic density is a strong and highly heritable risk factor for breast cancer. The purpose of this study was to examine the extent to which mammographic density explains the association of family history of breast cancer with risk of the disease. Subjects and Methods: We carried out three nested case-control studies in screening programs that included in total 2,322 subjects (1,164 cases and 1,158 controls). We estimated the independent and combined associations of family history and percent mammographic density at baseline with subsequent breast cancer risk. Results: After adjustment for age and other risk factors, compared with women with no affected first-degree relatives, percent mammographic density was 3.1% greater for women with one affected first-degree relative, and 7.0% greater for women with two or more affected relatives (P = 0.001 for linear trend across family history categories). The odds ratios for breast cancer risk were 1.37 [95% confidence interval (95% CI), 1.10-1.72] for having one affected relative, and 2.45 (95% CI, 1.30-4.62) for having two or more affected relatives (P for trend = 0.0002). Adjustment for percent mammographic density reduced these odds ratios by 16% and 14%, respectively. Percent mammographic density explained 14% (95% CI, 4-39%) of the association of family history (at least one affected first-degree relative) with breast cancer risk. Conclusions: Percent mammographic density has features of an intermediate marker for breast cancer, and some of the genes that explain variation in percent mammographic density might be associated with familial risk of breast cancer. Cancer Epidemiol Biomarkers Prev; 19(2); 456–63

Mammographic Density as a Surrogate Marker for the Effects of Hormone Therapy on Risk of Breast Cancer

Background: Some types of hormone therapy increase both risk of breast cancer and mammographic density, a risk factor for the disease, suggesting that mammographic density may be a surrogate marker for the effects of hormones on risk of breast cancer. This research was undertaken to determine whether the effect of hormone therapy on breast cancer risk is mediated by its effect on mammographic density. Methods: Individually matched cases and controls from three nested case-control studies in breast screening populations were studied. Cases had developed invasive breast cancer at least 12 months after the initial screen. Information was collected on hormone use and other risk factors at the time of the baseline mammogram, and percent density was measured by a computer-assisted method. Results: There were 1,748 postmenopausal women, of whom 426 (24.4%) were using hormones at the time of their initial screening mammogram. Current use of hormone therapy was associated with an increased risk of breast cancer (odds ratio, 1.26; 95% confidence interval, 1.0-1.6) that was little changed by adjustment for percent density in the baseline mammogram (odds ratio, 1.19; 95% confidence interval, 0.9-1.5). Percent density in the baseline mammogram was among cases greater in current users of hormones that in never-users (difference = 5.0%, P < 0.001), but the difference was smaller and nonsignificant in controls (difference = 1.6%, P = 0.3). Conclusion: Although the effects of hormone therapy on mammographic density were greater in cases than controls, we did not find evidence that these effects were causally related to risk of breast cancer. (Cancer Epidemiol Biomarkers Prev 2006;15(5):961–6)

Mammographic density: a hormonally responsive risk factor for breast cancer

Mammographic density refers to radiologically dense breast tissue, and reflects variations in the tissue composition of the breast. It is positively associated with collagen and epithelial and non-epithelial cells, and negatively associated with fat. There is extensive evidence that mammographic density is a risk factor for breast cancer, independent of other risk factors, and is associated with large relative and attributable risks for the disease. The epidemiology of mammographic density, notably the inverse association with age, is consistent with it being a marker of susceptibility to breast cancer. Cumulative exposure to mammographic density may be an important determinant of the age-specific incidence of breast cancer in the population. All risk factors for breast cancer must ultimately exert their influence by an effect on the breast, and these findings suggest that, for at least some risk factors, this influence includes an effect on the number of cells and the quantity of collagen in the breast, which is reflected in differences in mammographic density. Many of the genetic and environmental factors that influence the risk of breast cancer affect the proliferative activity and quantity of stromal and epithelial tissue in the breast, and these effects are reflected in differences in mammographic density among women of the same age. Some of these influences include endogenous and exogenous hormones, and the menopause. A better understanding of the factors that influence the response of breast tissue to these hormonal exposures may lead to an improved understanding of the aetiology of mammographic density and of breast cancer.

A randomized trial of dietary intervention for breast cancer prevention.

Epidemiologic data and animal experiments suggest that dietary fat may influence risk of breast cancer. To determine whether intervention with a low-fat, high-carbohydrate diet would reduce breast cancer incidence in women at increased risk of the disease, we carried out a randomized controlled trial in Canada. We recruited 4,690 women with extensive mammographic density and randomized them to an intervention group or a comparison group. The intervention group received intensive dietary counseling to reduce fat intake to a target of 15% of calories and increase carbohydrate to 65% of calories. Dietary intakes were assessed throughout using food records. Subjects were followed for at least 7 years and for an average of 10 years. The main outcome was invasive breast cancer. Percentage of calories from fat in the intervention group decreased from 30% at baseline to 20% after randomization and remained 9% to 10% lower than the comparison group throughout. There were 118 invasive breast cancers in the intervention group and 102 in the comparison group [adjusted hazard ratio = 1.19 (95% CI: 0.91-1.55)]. Analysis of food records showed that fat intake at baseline and after randomization was not associated with total breast cancer incidence. Greater weight and lower carbohydrate intake at baseline and after randomization were associated with an increased risk of estrogen receptor (ER)-positive breast cancer. Our findings suggest that a sustained reduction in dietary fat intake did not reduce risk of breast cancer in women with extensive mammographic density. Weight and carbohydrate intakes were associated with risk of ER-positive breast cancer.