A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals
A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals
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DOI：
10.1038/s41467-019-11311-9
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发表时间：
2019-08-20
影响因子：
16.6
通讯作者：
Slagboom, P. Eline
中科院分区：
文献类型：
--
作者：
Deelen, Joris;Kettunen, Johannes;Slagboom, P. Eline
预测长期死亡风险需要收集临床数据，这通常很麻烦。因此，我们使用一个标准化的代谢组学平台来确定44，168人（基线年龄18-109岁）循环中长期死亡率的代谢预测因子，其中5512人在随访期间死亡。我们应用一个逐步的（向前向后）程序的基础上荟萃分析的结果，并确定14个循环生物标志物独立与全因死亡率。总体而言，这些关联在男性和女性以及不同年龄层中是相似的。我们随后发现，基于包含已识别生物标志物和性别的模型（C-统计量分别为0.837和0.830）的5年和10年死亡率预测准确性优于包含传统死亡率风险因素的模型（C-统计量分别为0.772和0.790）。在临床研究中，使用确定的代谢谱作为死亡率或替代终点的预测因子需要进一步研究。
Predicting longer-term mortality risk requires collection of clinical data, which is often cumbersome. Therefore, we use a well-standardized metabolomics platform to identify metabolic predictors of long-term mortality in the circulation of 44,168 individuals (age at baseline 18-109), of whom 5512 died during follow-up. We apply a stepwise (forward-backward) procedure based on meta-analysis results and identify 14 circulating biomarkers independently associating with all-cause mortality. Overall, these associations are similar in men and women and across different age strata. We subsequently show that the prediction accuracy of 5- and 10-year mortality based on a model containing the identified biomarkers and sex (C-statistic = 0.837 and 0.830, respectively) is better than that of a model containing conventional risk factors for mortality (C-statistic = 0.772 and 0.790, respectively). The use of the identified metabolic profile as a predictor of mortality or surrogate endpoint in clinical studies needs further investigation.