Age and drug therapy are key prognostic factors for first clinical fracture in patients with primary osteoporosis.

UNLABELLED: We evaluated the characteristics of 1,142 women and men who attended Canadian osteoporosis clinics and had T-score < or = -2.0 and no prior fractures to determine the predictors of first clinical fracture. Greater age and failure to start osteoporosis drug treatment increased the risk of first clinical fracture. INTRODUCTION: Although risk factors for osteoporotic fractures are well-known, it is unclear which factors predict poor prognosis in patients with primary osteoporosis. The purpose of this study was to determine prognostic factors for first clinical fracture in patients with T-score < or = -2.0 and no previous clinical fracture. METHODS: We examined prospectively collected data from 1,142 patients aged 40 and over in the Canadian Database of Osteoporosis and Osteopenia. We used prognosis methodology and performed survival analysis to determine factors that increase the risk of first clinical fracture. RESULTS: Our inception cohort (mean age = 60.6 years, 91% females) had a cumulative fracture incidence of 5.1% (incidence rate: 2.53/100 person-years). Age and osteoporosis drug use predicted incident clinical fractures in multivariable regression analyses. The risk of first fracture increased by 3% per year. Failing to initiate osteoporosis treatment increased fracture risk by 2.4 times. In addition, low physical activity, high body mass index and low T-scores were found to predict fracture risk in certain patient subgroups using tree-structured survival analysis. These findings were robust and did not change with most sensitivity analyses. CONCLUSION: Age and osteoporosis drug treatment are the main prognostic predictors of first clinical fracture in patients with T-score < or = -2.0.

Mammographic density as a marker of susceptibility to breast cancer: a hypothesis.

We propose that radiological features of breast tissue provide an index of cumulative exposure to the current and past hormonal and reproductive events that influence breast cancer incidence. The changes in breast tissue that occur with ageing, and changes in the associated radiological features of the breast, are similar to the concept of "breast tissue ageing" proposed by Pike, and may explain features of the age-specific incidence of breast cancer, both within the population and between populations. These radiological features can be observed and measured, can be related directly to risk of breast cancer, and are likely to be of value in research into the etiology of breast cancer. Identification of the sources of variation in this radiological characteristic of the breast is likely to lead to a better understanding of the factors that cause breast cancer and to new approaches to prevention of the disease.

Modeling complex disease with demographic and environmental covariates and a candidate gene marker.

We randomly chose replicates 28 and 29 of the simulated data sets of Genetic Analysis Workshop 12 to model the dependence of affection status on covariates, quantitative traits, and genes using all living pedigree members. First we explored the relationship of affection status to demographic and environmental factors using logistic regression and the Cox proportional hazards models. In the second stage of our analyses the generalized transmission disequilibrium test (GTDT) was applied to nuclear families with at least two affected siblings to select single markers and high-risk alleles, which were tested in the population association analyses including all pedigree members. Multiple logistic regression models were fitted to investigate the joint contributions of genetic and nongenetic factors and a block-recursive modeling approach was adopted to study inherent hierarchical dependence structure in the data. We found that allele 2 on marker 35 of chromosome 6 is associated with higher risk compared with the other 3 alleles of this marker. In addition to this significant genetic effect, age at exam and four of the five quantitative traits (QT1, QT2, QT4, and QT5) had a significant association with the disease. Our results were obtained without knowledge of the true disease generating models.

A comparison of some allele-sharing based linkage analysis methods for detecting complex trait loci.

Using randomly selected sib pairs from a subset of the GAW11 simulated data in Problem 2, we compared the results of some linkage analysis methods based on allele sharing. One method was the Haseman-Elston test for a binary disease outcome (unaffected vs. mild or severe). The other methods, which analyzed the trinary ordered outcome unaffected/mild/severe were the Haseman-Elston test, an extended Haseman-Elston incorporating sib-pair sums, variance components analysis, and regression analysis. Our analysis was done without knowledge of the generating model.

Mammographic densities and risk of breast cancer among subjects with a family history of this disease.

BACKGROUND: A family history of breast cancer is known to increase risk of the disease, but other genetic and environmental factors that modify this risk are likely to exist. One of these factors is mammographic density, and we have sought evidence that it is associated with increased risk of breast cancer among women with a family history of breast cancer. METHODS: We used data from a nested case-control study based on the Canadian National Breast Screening Study (NBSS). From 354 case patients with incident breast cancer detected at least 12 months after entry into the NBSS and 354 matched control subjects, we analyzed subjects who were identified as having a family history of breast cancer according to one of three, nonmutually exclusive, criteria. We compared the mammographic densities of case patients and control subjects by radiologic and computer-assisted methods of measurement. RESULTS: After adjustment for other risk factors for breast cancer, the relative risks (RRs) between the most and least extensive categories of breast density were as follows: For at least one first-degree relative with breast cancer, RR = 11.14 (95% confidence interval [CI] = 1.54-80.39); for at least two affected first- or second-degree relatives, RR = 2.57 (95% CI = 0.23-28.22); for at least one first- or second-degree relative with breast cancer, RR = 5.43 (95% CI = 1.85-15.88). CONCLUSIONS: These results suggest that mammographic density may be strongly associated with risk of breast cancer among women with a family history of the disease. Because mammographic densities can be modified by dietary and hormonal interventions, the results suggest potential approaches to the prevention of breast cancer in women with a family history of breast cancer.

Reasoning about data with directed graphs.

This paper constructs graphical models for two important analysis problems to demonstrate how graphs can clearly represent complex relationships. A powerful property of graphical models called d-separation describes the statistical associations in a given graphical model. This allows the effects of unmeasured variables to be predicted, and suggests conditional analyses which can distinguish conjectured models. If an assumption called quasi-linearity is made, further conclusions can be drawn based on the structure of the graph and d-separation. Making the quasi-linearity assumption explicit also contributes to our understanding of which aspects of our causal intuition are based on linearity assumptions. The examples that we consider are the scientific interpretation of interventions and the evaluation of the validity of candidate surrogate endpoints for clinical trials. Intervention studies can be plagued by non-compliance with assigned treatment and ambiguity in interpreting results when treatment assignment manipulates multiple factors. We show graphically the conclusions that can be drawn under various assumptions. The topic of surrogate endpoints is addressed in causal terms and modelled graphically.

Modelling study quality in meta-analysis.

When summarizing studies in a meta-analysis, the methodological quality of the studies can vary widely. This variation is often described by assigning quality scores to studies. This paper presents a probability model for the effect of quality on a summary effect measure. The model motivates criteria used to assess quality, and can be used to incorporate quality into the calculation of a summary effect. We derive a number of summarization methods and compare them for simulated data and an actual set of studies.

Macronutrient intake and change in mammographic density at menopause: results from a randomized trial.

To examine the effects of dietary fat intake on breast cancer risk, we are conducting a randomized trial of dietary intervention in women with extensive areas of radiologically dense breast tissue on mammography, a risk factor for breast cancer. Early results show that after 2 years on a low-fat, high-carbohydrate diet there is a significant reduction in area of density, particularly in women going through menopause. In women who went through menopause during the 2-year follow-up, the mean decreases in area of density and percentage of density in the intervention group were 11.0 cm2 and 11.0%, respectively, whereas the control group decreased 4.5 cm2 and 5.2%. The purpose of this analysis was to determine whether changes in intake of specific macronutrients could account for the observed reduction in breast density in these women. Differences between 2-year and baseline values of macronutrients (averaged over 3 nonconsecutive days of food intake) were calculated. We examined the effect of dietary variables, adjusted for changes in total calorie intake and weight and for family history of breast cancer, on changes in area of density and percentage of density using linear regression. Reduction in total or saturated fat intake or cholesterol intake was significantly associated with decreased dense area (p < or = .004). The most significant dietary variable associated with reduction in percentage of density was reduction in dietary cholesterol intake (P = 0.001), although reducing saturated fat intake was of borderline significance (P = 0.05). The effect of the membership in the intervention and control groups on change in area of density or percentage of density was reduced by models that included changes in intake of any fat, or cholesterol, or carbohydrates. The observation of an effect of diet at menopause on breast density, a marker of increased risk of breast cancer, may be an indication that exposures at this time have an enhanced effect on subsequent risk.

Mammographic densities and breast cancer risk.

The radiological appearance of the female breast varies among individuals because of differences in the relative amounts and X-ray attenuation characteristics of fat and epithelial and stromal tissues. Fat is radiolucent and appears dark on a mammogram, and epithelium and stroma are radiodense and appear light. We review here the evidence that these variations, known as mammographic parenchymal patterns, are related to risk of breast cancer. Studies that used quantitative measurement to classify mammographic patterns have consistently found that women with dense tissue in more than 60-75% of the breast are at four to six times greater risk of breast cancer than those with no densities. These risk estimates are independent of the effects of other risk factors and have been shown to persist over at least 10 years of follow up. Estimates of attributable risk suggest that this risk factor may account for as many as 30% of breast cancer cases. Mammographically dense breast tissue is associated both with epithelial proliferation and with stromal fibrosis. The relationship between these histological features and risk of breast cancer may by explained by the known actions of growth factors that are thought to play important roles in breast development and carcinogenesis. Mammographically dense tissue differs from most other breast cancer risk factors in the strength of the associated relative and attributable risks for breast cancer, and because it can be changed by hormonal and dietary interventions. This risk factor may be most useful as a means of investigating the etiology of breast cancer and of testing hypotheses about potential preventive strategies.

The relationship of anthropometric measures to radiological features of the breast in premenopausal women.

We studied 273 premenopausal women recruited from mammography units who had different degrees of density of the breast parenchyma on mammography, in whom we measured height, weight and skinfold thicknesses. Mammograms were digitized to high spatial resolution by a scanning densitometer and images analysed to measure the area of dense tissue and the total area of the breast. Per cent density and the area of non-dense tissue were calculated from these measurements. We found that the mammographic measures had different associations with body size. Weight and the Quetelet index of obesity were strongly and positively associated with the area of non-dense tissue and with the total area of the breast, but less strongly and negatively correlated with the area of dense tissue. We also found a strong inverse relationship between the areas of radiologically dense and non-dense breast tissue. Statistical models containing anthropometric variables explained up to 8% of the variance in dense area, but explained up to 49% of the variance in non-dense area and 43% of variance in total area. These results suggest that aetiological studies in breast cancer that use mammographic density should consider dense and non-dense tissues separately. In addition to per cent density, methods should be examined that combine information from these two tissues.

Analysis of mammographic density and breast cancer risk from digitized mammograms.

To evaluate the association between mammographic density and breast cancer risk, a simple, observer-assisted technique called interactive thresholding was developed that allows reliable quantitative assessment of mammographic density with use of a computer workstation. Use of this technique helps confirm that mammographic density is one of the strongest risk factors for breast cancer and is present in a large proportion of breast cancer cases. The strong relationship between mammographic density and breast cancer risk suggests that the causes of breast cancer may be better understood by identifying the factors associated with mammographically dense tissue and determining how such tissue changes as these factors vary. Furthermore, because it can be modified, mammographic density may also be a good vehicle for the development and monitoring of potential preventive strategies. Areas of ongoing investigation include evaluating a potential genetic component of mammographic density by comparing density measurements in twins and understanding changes in density relative to age, menopausal status, exogenous hormone use, and exposure to environmental carcinogens. In addition, work is ongoing to establish measurements from imaging modalities other than mammography and to relate these measurements directly to breast cancer risk.

Breast cancer risk and measured mammographic density.

It has been well established that there is a positive correlation between the dense appearance of breast stroma and parenchyma on a mammogram and the risk of breast cancer. Subjective assessment by radiologists indicated relative risks on the order of 4 to 6 for the group of women whose mammograms showed a density of over 75% or more of the projected area compared to those with an absence of density. In order to obtain a more quantitative, continuous and reproducible means of estimating breast density, which is sensitive to small changes, we have developed quantitative methods for the analysis of mammographic density, which can be applied to digitized mammograms. These techniques have been validated in a nested case-control study on 708 women aged 40-59 years (on entry) who participated in a national mammographic screening study. An interactive image segmentation method and two completely automated techniques based on image texture and grey scale histogram measures have been developed and evaluated. While our methods all show statistically significant risk factors for dense breasts, the interactive method currently provides the highest risk values (relative risk 4.0, 95% confidence interval (CI) = 2.12-7.56) compared to a measure based on the shape of the image histogram (relative risk 3.35, 95% CI = 1.57-7.12) or the fractal dimension of the mammogram (relative risk 2.54, 95% CI = 1.14-5.68). All methods were highly consistent between images of the left and right breast and between the two standard views (cranio-caudal and medio-lateral oblique) of each breast, so that studies can be done by sampling only one of the four views per examination. There is a large number of factors in addition to breast density which affect the appearance of the mammogram. In particular, the assessment of density is made difficult where the breast is not uniformly compressed, e.g. at the periphery. We have designed and are currently evaluating an image processing algorithm that effectively corrects for this problem and have considered methods for controlling some of the variables of image acquisition in prospective studies. Measurements of breast density may be helpful in assigning risk groups to women. Such measurements might guide the frequency of mammographic screening, aid the study of breast cancer aetiology, and be useful in monitoring possible risk-modifying interventions. Using our techniques, we have been able to show that reduction of the proportion of fat in the diet can result in reductions of breast density, although the direct connection to risk has not yet been made. The relationship between breast density and hormone-related and genetic factors is also of great interest. It is often not possible or ethical to obtain mammograms on some groups of women for whom information on density would be very useful. This includes younger women as well as groups in which it would be desirable to obtain such information at frequent intervals. For this reason, we are exploring the use of imaging approaches such as ultrasound and magnetic resonance imaging, which do not require ionizing radiation, to make measurements analogous to those now being performed by using X-ray mammograms.

Mammographic densities and breast cancer risk.

Variations between individuals in the radiographic appearance, or mammographic pattern, of the female breast arise because of differences in the relative amounts and X-ray attenuation characteristics of fat and connective and epithelial tissue. Studies using quantitative methods of assessment have consistently shown these variations to be strongly related to risk of breast cancer. Individuals with extensive areas of radiologically dense breast tissue on the mammogram have been found to have a risk of breast cancer that is four to six times higher than women with little or no density. In this paper, we propose a model for the relationship of mammographic densities to risk of breast cancer. We propose that the risk of breast cancer associated with mammographically dense breast tissue is due to the combined effects of two processes: cell proliferation (mitogenesis), induced by growth factors and sex hormones and influenced by reproductive risk factors for breast cancer; and damage to the DNA of dividing cells (mutagenesis) by mutagens generated by lipid peroxidation. We review the evidence that each of these processes is associated with mammographic densities and propose further work that we believe should be done to clarify these relationships.

4-1BB costimulatory signals preferentially induce CD8+ T cell proliferation and lead to the amplification in vivo of cytotoxic T cell responses.

The 4-1BB receptor is an inducible type I membrane protein and member of the tumor necrosis factor receptor (TNFR) superfamily that is rapidly expressed on the surface of CD4+ and CD8+ T cells after antigen- or mitogen-induced activation. Cross-linking of 4-1BB and the T cell receptor (TCR) on activated T cells has been shown to deliver a costimulatory signal to T cells. Here, we expand upon previously published studies by demonstrating that CD8+ T cells when compared with CD4+ T cells are preferentially responsive to both early activation events and proliferative signals provided via the TCR and 4-1BB. In comparison, CD28-mediated costimulatory signals appear to function in a reciprocal manner to those induced through 4-1BB costimulation. In vivo examination of the effects of anti-4-1BB monoclonal antibodies (mAbs) on antigen-induced T cell activation have shown that the administration of epitope-specific anti-4-1BB mAbs amplified the generation of H-2d-specific cytotoxic T cells in a murine model of acute graft versus host disease (GVHD) and enhanced the rapidity of cardiac allograft or skin transplant rejection in mice. Cytokine analysis of in vitro activated CD4+ and CD8+ T cells revealed that anti-4-1BB costimulation markedly enhanced interferon-gamma production by CD8+ T cells and that anti-4-1BB mediated proliferation of CD8+ T cells appears to be IL-2 independent. The results of these studies suggest that regulatory signals delivered by the 4-1BB receptor play an important role in the regulation of cytotoxic T cells in cellular immune responses to antigen.

Automated analysis of mammographic densities and breast carcinoma risk.

BACKGROUND: There is considerable evidence that one of the strongest risk factors for breast carcinoma can be assessed from the mammographic appearance of the breast. However, the magnitude of the risk factor and the reliability of the prediction depend on the method of classification. Subjective classification requires specialized observer training and suffers from inter- and intraobserver variability. Furthermore, the categoric scales make it difficult to distinguish small differences in mammographic appearance. To address these limitations, automated analysis techniques that characterize mammographic density on a continuous scale have been considered, but as yet, these have been evaluated only for their ability to reproduce subjective classifications of mammographic parenchyma. METHODS: In this study, using a nested case-control design, the authors evaluated the direct association between breast carcinoma risk and quantitative image features derived from automated analysis of digitized film mammograms. Two parameters, one describing the distribution of breast tissue density as reflected by brightness of the mammogram (regional skewness) and the other characterizing texture (fractal dimension), were calculated for images from 708 subjects identified from the Canadian National Breast Screening Study. RESULTS: These parameters were evaluated for their ability to distinguish cases (those women who developed breast carcinoma) from controls. It was found that both the skewness and fractal parameters were significantly related to risk of developing breast carcinoma. CONCLUSIONS: Although the relative risk estimates were moderate (typically > 2.0) and less than those from subjective classification or for an interactive computer method the authors have previously described, they are comparable to other risk factors for the disease. The observer independence and reproducibility of the automated methods may facilitate their more widespread use.

Effects at two years of a low-fat, high-carbohydrate diet on radiologic features of the breast: results from a randomized trial. Canadian Diet and Breast Cancer Prevention Study Group.

BACKGROUND: The appearance of breast tissue on mammography varies according to its composition. Fat is radiolucent and appears dark on mammography, while stromal and epithelial tissue has greater optical density and appears light. Extensive areas of radiologically dense breast tissue seen on mammography are associated with an increased risk of breast cancer. PURPOSE: The purpose of the present study was to determine whether the adoption of a low-fat, high-carbohydrate diet for 2 years would reduce breast density. METHODS: Women with radiologic densities in more than 50% of the breast area on mammography were recruited and randomly allocated to an intervention group taught to reduce intake of dietary fat (mean, 21% of calories) and increase complex carbohydrate (mean, 61% of calories) or to a control group (mean, 32% of calories from fat and 50% of calories from carbohydrates). Mammographic images from 817 subjects were taken at baseline and compared with those taken 2 years after random allocation by use of a quantitative image analysis system, without knowledge of the dietary group of the subjects or of the sequence in which pairs of images had been taken. The effects of the intervention on the mammographic features of breast area, area of dense tissues in the breast, and the percent of the breast occupied by dense tissue were examined using t tests. Multiple regression was used to examine these effects while accounting for age at trial entry, weight change, and menopausal status. RESULTS: After 2 years, the total area of the breast was reduced by an average of 233.7 mm2 (2.4%) (95% confidence interval [CI] = 106.9-360.6) in the intervention group compared with an average increase of 26.3 mm2 (0.3%) (95% CI = -108.0-160.5) in the control group (P = .01). The area of density was reduced by 374.4 mm2 (6.1%) (95% CI = 235.1-513.8) in the intervention group compared with an average of 127.7 mm2 (2.1%) (95% CI = 8.6-246.7) in the control group (P = .01). Weight loss was associated with a reduction in breast area. The effect of the intervention on breast area was only marginally statistically significant after weight change, menopausal status, and age at trial entry were taken into account (P = .06). Greater weight loss and becoming postmenopausal were associated with statistically significant reductions in the area of density on the mammographic image at 2 years (P = .04 and P<.001, respectively). Age at entry into the trial was marginally significant in the same direction (P = .06). The effect of the intervention on area of density remained statistically significant after controlling for weight loss, age at entry, and menopausal status (P = .03). The change in the percentage of dense tissue in the mammographic image was not significantly different between the two groups (P = .71). CONCLUSIONS AND IMPLICATIONS: These results show that after 2 years, a low-fat, high-carbohydrate diet reduced the area of mammographic density, a radiographic feature of the breast that is a risk factor for breast cancer. Longer observation of a larger number of subjects will be required to determine whether these effects are associated with changes in risk of breast cancer.

Effects of a low-fat high-carbohydrate diet on plasma sex hormones in premenopausal women: results from a randomized controlled trial. Canadian Diet and Breast Cancer Prevention Study Group.

We are conducting a long-term randomized controlled trial to determine if intervention with a low-fat high-carbohydrate diet reduces breast cancer risk. The present study examines the effects of 2 years of dietary intervention on serum sex hormone levels in premenopausal women. Subjects with extensive mammographic densities were enrolled in a dietary intervention trial. The intervention involved intensive individual counselling aimed at reducing total dietary fat to 15% of calories. Control subjects received general advice about diet but were not counselled to change their fat intake. Serum sex hormone levels were measured in 220 premenopausal subjects at entry and 2 years after randomization. Two years after randomization oestradiol levels were 20% (70 pmol l(-1)) lower (P = 0.04) and progesterone levels were 35% (1.0 nmol l(-1)) lower (P = 0.004) and follicle-stimulating hormone (FSH) levels were 7% (1 IU) higher (P = 0.38) in the intervention group than in the control group. The FSH-oestradiol ratio was 13% higher in the intervention group (P = 0.18). Samples analysed accounting for the timing of the blood sample in relation to the menstrual cycle showed that, in the intervention group, oestradiol and progesterone levels were lower and FSH levels higher in subjects with blood samples taken more than 30 days after the last menstrual period. Because of the strong evidence linking ovarian hormonal activity to breast cancer risk, these results suggest that a low-fat high-carbohydrate diet may reduce risk of breast cancer by reducing exposure to ovarian hormones that are a stimulus to cell division in the breast.

Comparison of evidence for linkage from different analytic methods.

In a randomly chosen replicate of extended pedigrees from GAW10, we conducted robust multipoint genome scans for linkage using a dense marker map. For analysis of the quantitative traits, we selected sibships from the pedigrees, and for analysis of disease status, small families of affected relatives were selected. Lod-score likelihood analyses were conducted in the full pedigrees and in the affected relative families for selected regions. We located a flanking marker for MG1 on chromosome 5, and identified marker regions including MG2, MG4, and MG5 on chromosomes 8 and 9. The analytic methods were consistent for the major gene with a strong effect; false positive errors on chromosomes 1 and 10 could have been eliminated by requiring evidence from more than one method.

Explanatory analyses of randomized studies.

This paper considers randomized interventions which do not completely determine an intended determinant of response, and which may also manipulate additional, possibly unobserved, variables influencing response. The example we use throughout this paper is counseling for a low-fat diet for breast cancer prevention, where the intervention is counseling and dietary fat is hypothesized to reduce breast cancer risk. We use additive linear models to derive conditions and assumptions for considering fat to be the sole explanation of an observed treatment effect. A modified experimental design which supports stronger conclusions about causality is proposed.