Eldridge Ronald C, Flanders W Dana, Bostick Roberd M, Fedirko Veronika, Gross Myron, Thyagarajan Bharat, Goodman Michael
a Department of Epidemiology, Rollins School of Public Health , Emory University , Atlanta , GA , USA.
b Department of Biostatistics and Bioinformatics, Rollins School of Public Health , Emory University , Atlanta , GA , USA.
Biomarkers. 2017 Sep;22(6):517-524. doi: 10.1080/1354750X.2017.1306752. Epub 2017 Mar 29.
Since oxidative stress involves a variety of cellular changes, no single biomarker can serve as a complete measure of this complex biological process. The analytic technique of structural equation modeling (SEM) provides a possible solution to this problem by modelling a latent (unobserved) variable constructed from the covariance of multiple biomarkers.
Using three pooled datasets, we modelled a latent oxidative stress variable from five biomarkers related to oxidative stress: F-isoprostanes (FIP), fluorescent oxidation products, mitochondrial DNA copy number, γ-tocopherol (Gtoc) and C-reactive protein (CRP, an inflammation marker closely linked to oxidative stress). We validated the latent variable by assessing its relation to pro- and anti-oxidant exposures.
FIP, Gtoc and CRP characterized the latent oxidative stress variable. Obesity, smoking, aspirin use and β-carotene were statistically significantly associated with oxidative stress in the theorized directions; the same exposures were weakly and inconsistently associated with the individual biomarkers.
Our results suggest that using SEM with latent variables decreases the biomarker-specific variability, and may produce a better measure of oxidative stress than do single variables. This methodology can be applied to similar areas of research in which a single biomarker is not sufficient to fully describe a complex biological phenomenon.
由于氧化应激涉及多种细胞变化，没有单一的生物标志物能够完整衡量这一复杂的生物学过程。结构方程模型（SEM）分析技术通过对由多个生物标志物的协方差构建的潜在（未观察到的）变量进行建模，为解决这一问题提供了一种可能的方法。
使用三个汇总数据集，我们从与氧化应激相关的五个生物标志物中构建了一个潜在氧化应激变量模型，这五个生物标志物分别是：F-异前列腺素（FIP）、荧光氧化产物、线粒体DNA拷贝数、γ-生育酚（Gtoc）和C反应蛋白（CRP，一种与氧化应激密切相关的炎症标志物）。我们通过评估其与抗氧化剂和促氧化剂暴露的关系来验证这个潜在变量。
FIP、Gtoc和CRP表征了潜在氧化应激变量。肥胖、吸烟、使用阿司匹林和β-胡萝卜素在理论方向上与氧化应激存在显著统计学关联；相同的暴露因素与单个生物标志物的关联较弱且不一致。
我们的结果表明，使用带有潜在变量的结构方程模型可降低生物标志物特异性变异性，并且可能比单一变量更能有效衡量氧化应激。这种方法可应用于类似的研究领域，即单一生物标志物不足以充分描述复杂生物学现象的领域。
