Magnetic Resonance Center (CERM), University of Florence , Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy.
Department of Experimental and Clinical Medicine, University of Florence , Largo Brambilla 3, 50134 Florence, Italy.
J Proteome Res. 2018 Jan 5;17(1):97-107. doi: 10.1021/acs.jproteome.7b00404. Epub 2017 Nov 21.
In the era of precision medicine, the analysis of simple information like sex and age can increase the potential to better diagnose and treat conditions that occur more frequently in one of the two sexes, present sex-specific symptoms and outcomes, or are characteristic of a specific age group. We present here a study of the association networks constructed from an array of 22 plasma metabolites measured on a cohort of 844 healthy blood donors. Through differential network analysis we show that specific association networks can be associated with sex and age: Different connectivity patterns were observed, suggesting sex-related variability in several metabolic pathways (branched-chain amino acids, ketone bodies, and propanoate metabolism). Reduction in metabolite hub connectivity was also found to be associated with age in both sex groups. Network analysis was complemented with standard univariate and multivariate statistical analysis that revealed age- and sex-specific metabolic signatures. Our results demonstrate that the characterization of metabolite-metabolite association networks is a promising and powerful tool to investigate the human phenotype at a molecular level.
在精准医学时代,对性别和年龄等简单信息的分析可以提高诊断和治疗更频繁发生在两性中的一种性别、具有特定性别症状和结果的疾病的潜力,或者是特定年龄组的特征。我们在这里介绍了一项对 844 名健康献血者队列中测量的 22 种血浆代谢物进行的关联网络分析研究。通过差异网络分析,我们表明特定的关联网络可以与性别和年龄相关联:观察到不同的连接模式,表明几种代谢途径(支链氨基酸、酮体和丙酸盐代谢)存在性别相关的可变性。在两个性别组中,还发现代谢物枢纽连接的减少与年龄有关。网络分析补充了标准的单变量和多变量统计分析,揭示了年龄和性别特异性的代谢特征。我们的结果表明,代谢物-代谢物关联网络的特征描述是在分子水平上研究人类表型的一种很有前途和强大的工具。