the details of the war registry have been published elsewhere ( for details , see refs.1920 ) . briefly , from 1991 to july 2011 , the war registry enrolled participants in the breast clinic at columbia university medical center , who met at least one of the following criteria : one or more first - degree relative with premenopausal breast cancer ; two or more first - degree relatives with postmenopausal breast cancer ; known brca1 or brca2 deleterious mutation carrier ; a biopsy - proven history of lobular carcinoma in situ ; a biopsy - proven history of atypical ductal hyperplasia or atypical lobular hyperplasia . we extracted all patient data from medical chart reviews and a detailed epidemiological questionnaire completed at baseline enrollment , and all incident breast cancer cases were confirmed with the new york - presbyterian hospital tumor registry . as a woman ages , her breasts become less dense , and how might these changes over time affect cancer risk ? these authors tackled that question by analyzing mammographic data on women at high risk of breast cancer . they found that breast density decreased in women who did not develop cancer , but not in those who did , suggesting that monitoring changes in breast density could help identify women at greater risk of disease . of the 2670 women in the war cohort , 104 women were prospectively diagnosed with breast cancer by may , 2010 ( an average of 5 years after enrollment , range 115 years ) . of these 104 women , 85 had information from at least one prediagnostic film mammogram and 67 had information on at least two prediagnostic film mammograms ( we used only film mammograms for the analysis , repeated measures were all film ) . we individually matched these 85 cases on year of birth with cohort members free from cancer at the time of the case 's diagnosis ( the controls ) in a 1:1 ratio . in addition to individual matching on year of birth and time in study , we also frequency - matched cases and controls on age at the first pre - diagnostic mammogram , and time ( in years ) between first and second mammograms . we used incidence density sampling so controls were selected from the remaining cohort of women still being followed by the high risk registry . two readers independently assessed densities for the left breast blinded to cases  control status , first or second mammogram , and blinded to any prior medical history including history of benign breast disease ( bbd ) or exposure information , using a semiautomated computer - assisted technique with cumulus software,21 and their scores were averaged . we read breast density in batches , with the repeated mammograms for an individual randomized within the same batch and random quality repeats per batch to determine reliability . we reread any mammograms when the findings between readers differed by more than 15% . the within - batch reliability was 95% , and the between - batch reliability was 86% . we defined changes in percent density as the difference between percent density at first mammogram and percent density at second mammogram , for participants with two mammograms . because breast density measures are known to decline around the menopause , we examined whether our pairs were concordant on menopausal status at both their first and second mammograms ( i.e. , both member of pair premenopausal for both mammograms , postmenopausal for both mammograms , or changed their menopausal status between first and second mammograms ) . we found that for 75 of 85 pairs ( 88% ) the pairs were concordant on menopausal status . for 10 of 85 pairs ( 12% ) we ran sensitivity analyses excluding these 10 pairs and also additionally adjusted for menopausal status in the analysis to account for the discordance of these 10 pairs on menopausal status . we examined established breast cancer risk factors as potential confounders in our analyses ( including age at first mammogram , parity , family history , body mass index ( bmi ) , oral contraceptive use , alcohol use and menopausal status ) . we examined frequency statistics and univariable logistic regression to determine which risk factors were associated with case and control status and associated with breast density , to identify potential confounders . because women were individually matched by year of birth , we did not additionally adjust for age . we then conducted a multivariable conditional logistic regression to examine the relation between breast density , change in breast density , and case / control status , adjusted for age at first mammogram ( model 1 ) as well as for other identified confounders ( model 2 ) and additionally adjusted for baseline density , ( model 3 ) . additionally , we conducted multivariable linear regression using continuous change in percent density as the dependent variable , and time between first and second mammogram ( continuous ) as the independent variable , adjusted for age at first mammogram and other identified confounders . this is well known , but only for single measure of breast density . as a woman ages , her breasts become less dense , and how might these changes over time affect cancer risk ? these authors tackled that question by analyzing mammographic data on women at high risk of breast cancer . they found that breast density decreased in women who did not develop cancer , but not in those who did , suggesting that monitoring changes in breast density could help identify women at greater risk of disease . the median age at breast cancer diagnosis was 54.5 years , and cancer diagnosis occurred a median of 1.5 years after the second mammogram ( range , 6 months to 9.4 years ) . cases and controls did not differ significantly on baseline factors , including median age at first mammogram used for our study ( cases : 52.7-years - old , controls 51.5-years - old ) , median time between first and second mammogram ( cases 4.7 years , controls 4.6 years ) , or baseline percent density ( cases ' baseline percent density 29.0% , controls 29.2% ) . percent density decreased from time 1 to time 2 in both cases and controls , consistent with increasing age over time in the cohort ( mean percent density at time 2 : cases 25.5% , controls 23.7% ) . the study population was predominantly white ( 90% ) , had an average body mass index ( bmi ) of 25 , and about 50% of both cases and controls were postmenopausal at the time of first mammogram used for analysis . greater than 60% of both cases and controls had a personal history of bbd , and greater than 60% had a family history of breast cancer . distribution of demographic and density characteristics among cases and age - matched controls with mammographic data in the women at risk registry , columbia university medical center the age at diagnosis for the controls refers to the age of the controls when their matched case was diagnosed with cancer . age at first mammo is age at the first mammogram used for density readings in this analysis , not the age of the woman at her first ever mammogram . time since last birth is time between last birth and cancer diagnosis . for controls , time since last birth is time between last birth and the cancer diagnosis of their matched case . table 2 describes the findings of the logistic regression for percent density and change in percent density , using a conditional logistic regression to create three models : a model adjusted for age at first mammogram , a model additionally adjusted for age at first mammogram , parity , family history and menopausal status , and a model adjusted for those factors and additionally adjusted for baseline density reading . a > 5% decrease in percent density was inversely associated with breast cancer ( or = 0.56 , 95%ci 0.152.17 ) , while a > 5% increase in percent density was positively associated with breast cancer ( or = 2.55 , 95%ci 0.6310.26 ) ; however , these associations were not statistically significant , and a test for trend did not demonstrate significance . association between mammographic breast density and change in breast density and breast cancer risk , women at risk registry , columbia university medical center all models estimated using conditional logistic regression models , adjusted for age at first mammogram baseline density measures include dense area in cm at time 1 , and percent density time 1 . percent density at time 1 and time 2 cut - points were defined by tertiles of percent density for controls at first mammogram , rounded to nearest whole % . figure 1 illustrates the association between time between first and second mammograms and change in percent density , adjusted for age at first mammogram , parity , family history and baseline density . for the controls , as time between first and second mammograms increased , breast density decreased significantly (  = 1.62% per year in percent density p = 0.004 ) . for the cases , as time between first and second mammograms increased , breast density did not decrease (  = 0.29% per year in percent density , p = 0.63 ) ; in an ancova comparing the slopes of the regression lines , the slopes were significantly different for cases as compared with controls ( p = 0.009 ) . regression plot of change in % density by time between mammograms for cases and controls , women at risk registry . we found that , as calendar time between mammograms increased , mammographic density decreased , on average , for women that did not go on to be diagnosed with breast cancer ( controls ) , but not for high - risk women who developed breast cancer . few studies have prospectively examined changes in continuous measures of density.1113,22 although larger decreases ( 2025% ) in percent density reduced breast cancer risk in several studies11,22 ; another study found no association between change in percent density with breast cancer risk when examining changes in percent density by quartiles ; the range of percent density changes was relatively small in the study ( 10% to + 6.5% ) . in a more recent study , where changes in density were examined prospectively , a decrease in density was not associated with a change in breast cancer risk.13 we prospectively assessed breast cancer risk in relation to density on mammograms that occurred between 6 months and 9.5 years before diagnosis , in a population at higher risk of breast cancer , and found that there was , overall , an association with change in breast density in cases versus controls . although we observed an approximate two - fold association between higher breast density and breast cancer risk , our cohort is limited by the overall sample size , which decreased the statistical power to evaluate this association . we were also limited by incomplete data on exogenous hormone use from the medical record . tamoxifen , a chemopreventive agent , has been shown to decrease breast density,10,23 and a previous study indicated that a 10% or greater decrease in density was associated with a 63% reduction in breast cancer risk.23 however , the use of tamoxifen as a preventive agent was likely more limited given that the cohort was established in the mid-1990s . if controls , however , were more likely to use tamoxifen as compared to the cases this might explain the decrease in breast density over time relative to the cases . given that all women were part of a high risk registry , at a single institution follow - up care and prevention recommendations were likely more homogenous . if replicated in larger studies , our findings suggest that high - risk women who go on to develop breast cancer may have slower declines in breast density over time as compared to women who are not diagnosed with breast cancer . tracking of changes in mammographic density within individuals may be clinically possible , as automated calculations of mammographic density now exist for digital mammography.24 however , it is important to note that the declines observed in the women who did not go on to be diagnosed with cancer were modest , and therefore larger studies that replicate our findings are needed before clinical guidelines on breast density changes are used for risk monitoring . the promise , however , could mean that following women over time to observe within - individual changes in density may help target women at higher future risk , and allow them to engage in preventive measures , including chemoprevention , if they do not experience decreases in breast density over time .
high mammographic breast density is one of the strongest intermediate markers of breast cancer risk , and decreases in density over time have been associated with decreases in breast cancer risk . using repeated measures of mammographic density in a cohort of high - risk women , the women at risk ( war ) cohort at columbia university medical center ( n = 2670 ) 
 , we examined whether changes in prediagnostic mammographic density differed among 85 prospectively - ascertained breast cancer cases and 85 age - matched controls , using a nested case 
 control design . 
 median age at first mammogram was 51 years ( range , 2977 years ) , with a median of 4 years between first and second prediagnostic mammogram ( range , 115 years ) . using linear regression with change in percent density as the outcome 
 , we found that in women who did not go on to be diagnosed with breast cancer , change in percent density decreased as time between first and second mammogram increased (  = 1.62% per year , p = 0.004 ) . 
 however , in women who did go on to be diagnosed with breast cancer , there was no overall change in percent density associated with time between first and second mammogram (  = 0.29% per year , p = 0.61 ) ; the change over time was statistically significantly different between cases versus controls ( p < 0.009 ) . 
 if replicated in larger cohorts , these results suggest that within - individual changes in mammographic density as measured by percent density may be a useful biomarker of breast cancer risk .