血浆金属组学揭示了潜在的生物标志物和洞察元素与急性心肌梗死的矛盾关联

S. Lim, Hiranya Dayal, S. J. Seah, Renke Tan, Z. E. Low, A. K. Laserna, S. H. Tan, M. Chan, S. F. Y. Li
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引用次数: 1

摘要

急性心肌梗死(AMI)是世界范围内死亡率和发病率的主要原因。使用经过验证且高效的ICP-MS/MS-based工作流程,在一项包括101名AMI患者和66名年龄匹配的健康对照者的研究中,共分析了30个金属学特征。金属学特征包括12种必需元素(Ca、Co、Cu、Fe、K、Mg、Mn、Na、P、S、Se、Zn), 8种非必需/有毒元素(Al、As、Ba、Cd、Cr、Ni、Rb、Sr、U、V), 10种临床相关元素对产物/比值(Ca/Mg、CaxP、Cu/Se、Cu/Zn、Fe/Cu、P/Mg、Na/K、Zn/Se)。初步的线性回归与特征选择证实吸烟状态是非必需/有毒元素的主要决定因素,并揭示了潜在的作用途径。对协变量进行调整的单变量评估揭示了Cu、Fe和P与AMI的矛盾关系,同时也证实了Se的心脏保护作用。此外,铜和硒除了作为危险因素外,还可能参与AMI发病/干预的反应机制,这一点通过另外2个时间点(1- 6个月随访)的纵向数据分析得到了证实。最后,基于单变量测试和多变量分类模型,确定了元素对比率测量的潜在更敏感的标记(例如,Cu/Se, Fe/Cu)。总的来说,基于金属学的生物标志物可能对AMI的预测有实用价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Plasma metallomics reveals potential biomarkers and insights into the ambivalent associations of elements with acute myocardial infarction
Acute myocardial infarction (AMI) is a leading cause of mortality and morbidity worldwide. Using a validated and efficient ICP-MS/MS-based workflow, a total of 30 metallomic features were profiled in a study comprising 101 AMI patients and 66 age-matched healthy controls. The metallomic features include 12 essential elements (Ca, Co, Cu, Fe, K, Mg, Mn, Na, P, S, Se, Zn), 8 non-essential/toxic elements (Al, As, Ba, Cd, Cr, Ni, Rb, Sr, U, V), and 10 clinically relevant element-pair product/ratios (Ca/Mg, CaxP, Cu/Se, Cu/Zn, Fe/Cu, P/Mg, Na/K, Zn/Se). Preliminary linear regression with feature selection confirmed smoking status as a predominant determinant for the non-essential/toxic elements, and revealed potential routes of action. Univariate assessments with adjustments for covariates revealed insights into the ambivalent relationships of Cu, Fe, and P with AMI, while also confirming cardioprotective associations of Se. Also, beyond their roles as risk factors, Cu and Se may be involved in the response mechanism in AMI onset/intervention, as demonstrated via longitudinal data analysis with 2 additional time-points (1-/6-month follow-up). Finally, based on both univariate tests and multivariate classification modelling, potentially more sensitive markers measured as element-pair ratios were identified (e.g., Cu/Se, Fe/Cu). Overall, metallomics-based biomarkers may have utility for AMI prediction.
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