Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, 70210 Kuopio, Finland.
Department of Medicine, Kuopio University Hospital, 70200 Kuopio, Finland.
Int J Mol Sci. 2024 Oct 30;25(21):11636. doi: 10.3390/ijms252111636.
Reliable predictors of long-term all-cause mortality are needed for middle-aged and older populations. Previous metabolomics mortality studies have limitations: a low number of participants and metabolites measured, measurements mainly using nuclear magnetic spectroscopy, and the use only of conventional statistical methods. To overcome these challenges, we applied liquid chromatography-tandem mass spectrometry and measured >1000 metabolites in the METSIM study including 10,197 men. We applied the machine learning approach together with conventional statistical methods to identify metabolites associated with all-cause mortality. The three independent machine learning methods (logistic regression, XGBoost, and Welch's -test) identified 32 metabolites having the most impactful associations with all-cause mortality (25 increasing and 7 decreasing the risk). From these metabolites, 20 were novel and encompassed various metabolic pathways, impacting the cardiovascular, renal, respiratory, endocrine, and central nervous systems. In the Cox regression analyses (hazard ratios and their 95% confidence intervals), clinical and laboratory risk factors increased the risk of all-cause mortality by 1.76 (1.60-1.94), the 25 metabolites by 1.89 (1.68-2.12), and clinical and laboratory risk factors combined with the 25 metabolites by 2.00 (1.81-2.22). In our study, the main causes of death were cancers (28%) and cardiovascular diseases (25%). We did not identify any metabolites associated with cancer but found 13 metabolites associated with an increased risk of cardiovascular diseases. Our study reports several novel metabolites associated with an increased risk of mortality and shows that these 25 metabolites improved the prediction of all-cause mortality beyond and above clinical and laboratory measurements.
需要可靠的预测因子来预测中年和老年人群的长期全因死亡率。以前的代谢组学死亡率研究存在局限性：参与者和测量的代谢物数量较少，主要使用磁共振光谱测量，仅使用常规统计方法。为了克服这些挑战，我们在 METSIM 研究中应用了液相色谱-串联质谱法，测量了包括 10197 名男性在内的>1000 种代谢物。我们应用机器学习方法结合常规统计方法来识别与全因死亡率相关的代谢物。三种独立的机器学习方法（逻辑回归、XGBoost 和 Welch's -检验）确定了 32 种与全因死亡率相关性最强的代谢物（25 种增加死亡率，7 种降低死亡率）。这些代谢物中有 20 种是新的，涵盖了各种代谢途径，影响心血管、肾脏、呼吸、内分泌和中枢神经系统。在 Cox 回归分析（风险比及其 95%置信区间）中，临床和实验室危险因素使全因死亡率的风险增加了 1.76（1.60-1.94），25 种代谢物使风险增加了 1.89（1.68-2.12），临床和实验室危险因素与 25 种代谢物共同使风险增加了 2.00（1.81-2.22）。在我们的研究中，主要死亡原因是癌症（28%）和心血管疾病（25%）。我们没有发现任何与癌症相关的代谢物，但发现了 13 种与心血管疾病风险增加相关的代谢物。我们的研究报告了几种与死亡率增加相关的新型代谢物，并表明这 25 种代谢物在临床和实验室测量的基础上进一步提高了全因死亡率的预测能力。
