Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 6120 Executive Plaza South, EPS 8049, Bethesda, MD 20892-7244, USA.
J Natl Cancer Inst. 2011 Jul 6;103(13):1037-48. doi: 10.1093/jnci/djr172. Epub 2011 Jun 24.
Although modifiable risk factors have been included in previous models that estimate or project breast cancer risk, there remains a need to estimate the effects of changes in modifiable risk factors on the absolute risk of breast cancer.
Using data from a case-control study of women in Italy (2569 case patients and 2588 control subjects studied from June 1, 1991, to April 1, 1994) and incidence and mortality data from the Florence Registries, we developed a model to predict the absolute risk of breast cancer that included five non-modifiable risk factors (reproductive characteristics, education, occupational activity, family history, and biopsy history) and three modifiable risk factors (alcohol consumption, leisure physical activity, and body mass index). The model was validated using independent data, and the percent risk reduction was calculated in high-risk subgroups identified by use of the Lorenz curve.
The model was reasonably well calibrated (ratio of expected to observed cancers = 1.10, 95% confidence interval [CI] = 0.96 to 1.26), but the discriminatory accuracy was modest. The absolute risk reduction from exposure modifications was nearly proportional to the risk before modifying the risk factors and increased with age and risk projection time span. Mean 20-year reductions in absolute risk among women aged 65 years were 1.6% (95% CI = 0.9% to 2.3%) in the entire population, 3.2% (95% CI = 1.8% to 4.8%) among women with a positive family history of breast cancer, and 4.1% (95% CI = 2.5% to 6.8%) among women who accounted for the highest 10% of the total population risk, as determined from the Lorenz curve.
These data give perspective on the potential reductions in absolute breast cancer risk from preventative strategies based on lifestyle changes. Our methods are also useful for calculating sample sizes required for trials to test lifestyle interventions.
尽管先前的模型已经纳入了可改变的风险因素来估计或预测乳腺癌风险，但仍需要评估可改变风险因素的变化对乳腺癌绝对风险的影响。
我们使用了意大利一项病例对照研究的数据（1991 年 6 月 1 日至 1994 年 4 月 1 日研究的 2569 例病例患者和 2588 例对照者）以及佛罗伦萨登记处的发病率和死亡率数据，开发了一个预测乳腺癌绝对风险的模型，该模型纳入了 5 个不可改变的风险因素（生殖特征、教育、职业活动、家族史和活检史）和 3 个可改变的风险因素（饮酒、休闲体力活动和体重指数）。我们使用独立数据验证了该模型，并使用洛伦兹曲线确定了高危亚组，计算了风险降低的百分比。
该模型的校准情况较好（预期癌症与观察癌症的比例=1.10，95%置信区间[CI]为 0.96 至 1.26），但区分准确性一般。暴露因素改变带来的绝对风险降低与改变风险因素前的风险大致成比例，并随年龄和风险预测时间跨度的增加而增加。在 65 岁的女性中，20 年的绝对风险平均降低 1.6%（95%CI=0.9%至 2.3%），在有乳腺癌阳性家族史的女性中降低 3.2%（95%CI=1.8%至 4.8%），在洛伦兹曲线确定的总人群风险最高的 10%女性中降低 4.1%（95%CI=2.5%至 6.8%）。
这些数据为基于生活方式改变的预防策略可能降低的绝对乳腺癌风险提供了视角。我们的方法还可用于计算测试生活方式干预的试验所需的样本量。
