Our analysis of postmenopausal breast cancer in the PLCO study revealed interesting differences from the generally established epidemiology of breast cancer. Increasing age, parity, family history of breast cancer, and use of menopausal hormone therapy were all associated with breast cancer as expected. Associations with other key risk factors – age at menarche, age at menopause, and obesity – were slightly different from the previously published literature. These differences have at least two implications for ongoing and future research on breast cancer.
First, the magnitude of some risk factor associations may be changing. Published studies have reported consistent, linear risk relationships with older ages at menarche (e.g.: 5% decrease in risk per 1-year delay after age 12) and menopause (e.g.: 3% increase in risk per 1-year delay age at menopause)[8
]. In contrast, we observed a fairly uniform decreased risk with older ages at menarche and a non-significant increased risk only in the oldest age-at-menopause group. The larger numbers of older women in the PLCO cohort could explain the lower RRs. Alternatively, inaccurate recall, especially among older women [19
], or non-differential misclassification could be a factor, because our questionnaire collected only categorical data on these ages.
Body size is positively associated with postmenopausal breast cancer [20
]. In a pooled analysis of cohort studies, risk increased significantly by 7% per 4-kg/m2
BMI increase, 7% per 5-cm height increase, and 6% per 10-kg weight increase [7
]. In our analysis, all three factors increased the relative risks by 20%. Despite the large sample size, only the RRs for the highest categories were statistically significant; the BMI association was stronger than the height and weight associations.
We observed a higher RR for breast cancer for the combination of low parity and later age at first birth, whereas an earlier meta-analysis reported declining RRs with lower parity in women whose first birth occurred at older ages [21
]. Both that and our study included few multiparous women who first gave birth after age 35.
These changes could reflect underlying demographic changes. Mean age of menarche among U.S. females has declined in recent decades, whereas later age at natural menopause is more common than before [22
]. The prevalence of women who first give birth after age 35 is increasing, as is the prevalence of obesity [24
]. These factors are potentially related: obesity might spark early estrogen production and the onset of puberty, whereas parity and BMI are also associated with later age at menopause [25
]. Teasing apart the quantitative effect of these changes on risk factor associations, as well as the underlying biologic implications, may prove to be a challenge.
Changing distributions of risk factors will affect the use of risk prediction models that project individual probabilities of breast cancer and influence eligibility for clinical trials. The widely used Gail model [29
] relies on readily available medical information, such as age at first birth and age at menopause. Modified prediction models incorporate additional clinical information, such as breast density [30
]. If the relative risk measures that underlie the projection of absolute risks in these models are changing, then there is the potential for the models to lose some of their current calibratory and discriminatory ability. Continued assessment of model performance among newer population-based studies with diverse populations could address this and shed further light on the potential influence of changing demographics on the epidemiology of postmenopausal breast cancer.
The known risk factors for breast cancer account for perhaps only 50% of the population burden of breast cancer [31
]. A polygenic model of breast cancer hypothesizes that many genetic factors contribute individually small but collectively large effects that could explain the remaining 50% of the population attributable risk [32
]. Based on extensive results to date of candidate pathways, the overall effect of low-penetrance SNPs is minimal [33
]. The SNP-based associations that have emerged from marker-based scans have unknown function or functions unrelated to the hormonal pathways linked with breast cancer [34
]. Other important genetic markers with relevant functions might surface. Exploration of genetic factors is likely to be most fruitful if placed within the context of the known risk factors for breast cancer, both to see whether markers modify those risks or are independently associated with those risk factors [11
Whether known or future genetic markers can improve the performance of existing risk prediction models – or potential new models that incorporate the clinical heterogeneity of breast cancers [36
]-is uncertain. Readily available lifestyle or questionnaire-based information, such as reproductive history, will remain the cornerstone of risk prediction and stratification even if it becomes easy, inexpensive, and risk-free for large numbers of women to determine their genetic profile because the small-magnitude risk associations are unlikely to be useful for prediction [37
]. Changes in the underlying associations between those risk factors and breast cancer would not adversely affect such evaluation, but it would require continued surveillance of breast cancer epidemiology among contemporary populations, such as PLCO, both individually and within large-scale replication efforts [32
Our large sample size makes it unlikely that the deviations from expectation that we observed were due to chance. Overall exposure and endpoint data were likely good, but residual confounding might exist. The questionnaire lacked information on some risk factors, such as breastfeeding [8
], physical activity [39
], and alcohol use [40
]. For others – particularly menopausal hormone therapy [41
] – the baseline questionnaire did not allow us to differentiate the higher-risk estrogen-plus-progestin formulations from estrogen-only formulations. Compared with 2001 U.S. Census Bureau data on women aged 55–74 [42
], lower percentages of PLCO participants reported receiving some formal education beyond high school across all racial/ethnic groups: 58% vs. 53% for whites, 67% vs. 52% for African-Americans, and 57% vs. 52% for Asian/Pacific Islanders. We cannot rule out that other unmeasured factors may make our study population slightly different from the U.S. population on the whole. Our analysis covered a relatively short follow-up, and continued follow-up of the PLCO population may confirm both the validity and generalizability of our findings.