Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands.
Am J Public Health. 2012 Apr;102(4):e3-10. doi: 10.2105/AJPH.2011.300596. Epub 2012 Feb 16.
We investigated the quality of 162 variables, focusing on the contribution of genetic markers, used solely or in combination with other characteristics, when predicting mortality.
In 5974 participants from the Rotterdam Study, followed for a median of 15.1 years, 7 groups of factors including age and gender, genetics, socioeconomics, lifestyle, physiological characteristics, prevalent diseases, and indicators of general health were related to all-cause mortality. Genetic variables were identified from 8 genome-wide association scans (n = 19,033) and literature review.
We observed 3174 deaths during follow-up. The fully adjusted model (C-statistic for 15-year follow-up [C15y] = 0.80; 95% confidence interval [CI] = 0.79, 0.81) predicted mortality well [corrected]. Most of the additional information apart from age and sex stemmed from physiological markers, prevalent diseases, and general health. Socioeconomic factors and lifestyle contributed meaningfully to mortality risk prediction with longer prediction horizon. Although specific genetic factors were independently associated with mortality, jointly they contributed little to mortality prediction (C(15y) = 0.56; 95% CI = 0.55, 0.57).
Mortality can be predicted reasonably well over a long period. Genetic factors independently predict mortality, but only modestly more than other risk indicators.
我们研究了 162 个变量的质量，重点关注遗传标记的贡献，这些标记单独或与其他特征结合使用时可预测死亡率。
在随访中位数为 15.1 年的 5974 名鹿特丹研究参与者中，年龄和性别、遗传学、社会经济状况、生活方式、生理特征、现患疾病以及一般健康状况指标等 7 组因素与全因死亡率相关。遗传变量是从 8 项全基因组关联扫描（n = 19033）和文献综述中确定的。
我们观察到随访期间发生了 3174 例死亡。完全调整模型（15 年随访的 C 统计量 [C15y] = 0.80；95%置信区间 [CI] = 0.79, 0.81）预测死亡率良好[已纠正]。除年龄和性别外，大多数额外信息来自生理标志物、现患疾病和一般健康状况。社会经济因素和生活方式对死亡率风险预测具有重要意义，预测时间更长。尽管特定的遗传因素与死亡率独立相关，但它们共同对死亡率预测的贡献很小（C15y = 0.56；95%CI = 0.55, 0.57）。
可以在较长时间内合理预测死亡率。遗传因素独立预测死亡率，但与其他风险指标相比，只有适度增加。
