柳叶刀：新型工具或可帮助预测个体在未来5年的死亡风险

  近日，来自乌普萨拉大学(Uppsala University)的研究人员研究了人们在未来5年的死亡风险，他们通过利用英国生物样本库（UK Biobank）中大量的数据设计出了一种特殊的健康风险计算器，英国生物样本库中包含有大约50万人的数据，研究人员希望这种健康风险计算器可以应用于临床治疗、公众健康保障及研究中，相关研究刊登于国际杂志The Lancet上。
  研究者Erik Ingelsson教授表示，利用一些简单的问题，个体就可以预测其在5年内的死亡风险，而我们开发的新型风险计算器或许可以作为一种重要的工具来帮助研究者和医生们进行相关的个体死亡风险评估。
  英国生物样本库中包含了655个突变体的相关书库，而这些突变体是从大约50万年龄在40至70岁间的英国人中收集的；此前研究者进行了一系列小型研究来揭示一些风险因子和个体未来死亡率之间的关联；这项研究中，研究者利用计算机算法以较高的精度对所进行的调查问卷进行了组合，最后针对男性制定了13个问题，针对女性制定了11个问题；基于个体对问卷内容的回答就可以计算出个体的Ubble年龄（Ubble age），如果这个年龄比个体的实际年龄低就意味着其相比相同性别和年龄的个体死亡风险低，目前研究人员已经联合网络工程师和设计师开发了一种对结果可视化的工具，这种工具就可以计算出个体的Ubble年龄以及其未来5年的死亡风险。
  Andrea Ganna博士表示，本文研究仅仅是阐明了统计学的关联，实际上这并不是是否不同的变化会引发个体死亡的问题，因此我们应当避免对个体进行全面的评价，因为这些结果仅仅显示的是个体的平均风险以及预测结果。毫无置疑，本文研究开发的新工具或可帮助研究人员分析大量的数据材料，研究者希望后期对该工具进行一些改进以使其可以结合多种因素来更为精确地评估个体的死亡风险。（转化医学网360zhyx.com）
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转化医学网推荐的原文摘要：

5 year mortality predictors in 498 103 UK Biobank participants: a prospective population-based study
The Lancet    doi:10.1016/S0140-6736(15)60175-1
Andrea Ganna, PhD, Prof Erik Ingelsson, MD
Background
To our knowledge, a systematic comparison of predictors of mortality in middle-aged to elderly individuals has not yet been done. We investigated predictors of mortality in UK Biobank participants during a 5 year period. We aimed to investigate the associations between most of the available measurements and 5 year all-cause and cause-specific mortality, and to develop and validate a prediction score for 5 year mortality using only self-reported information.
Methods
Participants were enrolled in the UK Biobank from April, 2007, to July, 2010, from 21 assessment centres across England, Wales, and Scotland with standardised procedures. In this prospective population-based study, we assessed sex-specific associations of 655 measurements of demographics, health, and lifestyle with all-cause mortality and six cause-specific mortality categories in UK Biobank participants using the Cox proportional hazard model. We excluded variables that were missing in more than 80% of the participants and all cardiorespiratory fitness test measurements because summary data were not available. Validation of the prediction score was done in participants enrolled at the Scottish centres. UK life tables and census information were used to calibrate the score to the overall UK population.
Findings
About 500 000 participants were included in the UK Biobank. We excluded participants with more than 80% variables missing (n=746). Of 498 103 UK Biobank participants included (54% of whom were women) aged 37–73 years, 8532 (39% of whom were women) died during a median follow-up of 4·9 years (IQR 4·33–5·22). Self-reported health (C-index including age 0·74 [95% CI 0·73–0·75]) was the strongest predictor of all-cause mortality in men and a previous cancer diagnosis (0·73 [0·72–0·74]) was the strongest predictor of all-cause mortality in women. When excluding individuals with major diseases or disorders (Charlson comorbidity index >0; n=355 043), measures of smoking habits were the strongest predictors of all-cause mortality. The prognostic score including 13 self-reported predictors for men and 11 for women achieved good discrimination (0·80 [0·77–0·83] for men and 0·79 [0·76–0·83] for women) and significantly outperformed the Charlson comorbidity index (p<0·0001 in men and p=0·0007 in women). A dedicated website allows the interactive exploration of all results along with calculation of individual risk through an online questionnaire.
Interpretation
Measures that can simply be obtained by questionnaires and without physical examination were the strongest predictors of all-cause mortality in the UK Biobank population. The prediction score we have developed accurately predicts 5 year all-cause mortality and can be used by individuals to improve health awareness, and by health professionals and organisations to identify high-risk individuals and guide public policy.
Funding
Knut and Alice Wallenberg Foundation and the Swedish Research Council.