基于代谢学的机器学习来预测死亡率:揭示多系统对健康的影响
Anniina Oravilahti1, Jagadish Vangipurapu1, Markku Laakso1,2
1Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, 70210 Kuopio, Finland.
International journal of molecular sciences
|November 9, 2024
概括
新的代谢标志物显著改善了对男性长期死亡率的预测. 这项研究确定了32种具有影响力的代谢物,包括20种新型代谢物,增强了传统因素之外的风险评估.
科学领域:
- 代谢学 代谢学 代谢学
- 生物标志物 生物标志物
- 死亡率预测的预测
背景情况:
- 在中年和老年人群中,对长期全因死亡率的可靠预测因素至关重要.
- 以前的代谢学研究受到小样本大小,有限的代谢物测量和传统统计方法的限制.
研究的目的:
- 使用先进技术识别与全因死亡相关的新型代谢物.
- 评估已识别的代谢物对死亡风险的预测价值.
主要方法:
- 利用液体染色学-双重质谱测量测量了来自METSIM研究的10,197名男性的1000多种代谢物.
- 应用了三种机器学习方法 (逻辑回归,XGBoost,Welch的t-test) 与传统的统计数据一起.
- 进行了考克斯回归分析,以评估危险比率和置信区间.
主要成果:
- 确定了32种与所有原因死亡率相关的有影响力的代谢物 (25种增加,7种降低风险),包括20种跨越各种途径的新型代谢物.
- 这些25种代谢物改善了超出临床和实验室风险因素的全因死亡率预测 (HR1.89对1.76).
- 发现13种与心血管疾病死亡风险增加相关的代谢物,但没有癌症死亡.
结论:
- 发现的新型代谢物显著提高了男性全因死亡率的预测.
- 代谢学,特别是机器学习,提供了一种强大的方法来发现死亡生物标志物.
- 这些发现对心血管疾病风险分层和理解死亡途径有意义.
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