Improving Mortality Prediction Using Biosocial Surveys
Improving Mortality Prediction Using Biosocial Surveys
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DOI：
10.1093/aje/kwn389
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发表时间：
2009-03-15
影响因子：
5
通讯作者：
Weinstein, Maxine
中科院分区：
文献类型：
--
作者：
Goldman, Noreen;Glei, Dana A.;Weinstein, Maxine
作者使用的数据来自对933名54岁或以上成年人的全国代表性调查（平均年龄= 66.2岁;标准差，8.0），以探讨是否通过使用3组生物标志物改善老年人的死亡率预测：1）标准心血管和代谢危险因素; 2）疾病进展标志物;和3）非临床（神经内分泌和免疫）标志物。他们还评估了这些生物标志物在多大程度上解释了女性在生存方面的优势。2000年至2006年期间死亡概率的逻辑回归模型估计（162人死亡;平均随访时间= 5.8年）显示，包含3组标记物中的每一组，（分别为P = 0.024，P = 0.002和P = 0.003）与调整人口统计学特征，吸烟，和基线健康状况。疾病进展标志物组和非临床标志物组均比标准风险因素提供更高的区分力。大多数男性死亡率过高是由于男性比女性更容易吸烟，但与疾病进展或炎症相关的3种标志物（白蛋白，中性粒细胞和白细胞介素-6）中的每一种都解释了超过10%的男性死亡率。
The authors used data from a nationally representative survey of 933 adults aged 54 years or older (mean age = 66.2 years; standard deviation, 8.0) in Taiwan to explore whether mortality prediction at older ages is improved by the use of 3 clusters of biomarkers: 1) standard cardiovascular and metabolic risk factors; 2) markers of disease progression; and 3) nonclinical (neuroendocrine and immune) markers. They also evaluated the extent to which these biomarkers account for the female advantage in survival. Estimates from logistic regression models of the probability of dying between 2000 and 2006 (162 deaths; mean length of follow-up = 5.8 years) showed that inclusion of each of the 3 sets of markers significantly (P = 0.024, P = 0.002, and P = 0.003, respectively) improved discriminatory power in comparison with a base model that adjusted for demographic characteristics, smoking, and baseline health status. The set of disease progression markers and the set of nonclinical markers each provided more discriminatory power than standard risk factors. Most of the excess male mortality resulted from the men being more likely than women to smoke, but each of 3 markers related to disease progression or inflammation (albumin, neutrophils, and interleukin-6) explained more than 10% of excess male mortality.