A new mathematical model to quantify and characterize the response to pro- and anti-oxidants of the copper-induced oxidation of LDL assay. A tool for examination of potential preventive compounds and clinical risk prediction

Abstract

The copper-induced oxidation of low density lipoprotein (LDL) particles represents a particularly suitable method for studying, both in vitro and in vivo, the effects of a number of alimentary, environmental and lifestyle factors on the mechanism of a biochemical process involved in many oxidative stress-related diseases, such as atherosclerosis, which affects around 50% of the occidental people. The available data about the role of pro- and anti-oxidants in LDL dynamics are abundant, but often, only semi-quantitative conclusions can be achieved. The lack of detailed algebraic models, the concomitant difficulties for assessing the statistical consistence of the results and designing definite experiments, and a certain shortage in characterizing criteria are some of the causes hindering the advances in a field which is per se very complex. The approach we propose describes, in an algebraically explicit way, the complete and generally biphasic time-course of the diene formation – as well as the peroxide profile – in LDL particles, including the separate or joint effects of any series of pro- and anti-oxidant concentrations. The model was tested using experimental results from other authors under different conditions, and very accurate and statistically consistent fittings were obtained in all the cases. Results, which enabled rigorous and detailed comparisons between LDL responses to oxidation modifiers, suggest an alternative criteria for the prediction of risks of occurrence of stress diseases. Thus, the basis of more complex multivariable models is provided. In all experimental data used, the calculated parameters were always statistically significant (Student's t-test, α=0.05), the equations were consistent (Fisher's F-test) and the goodness of fit coefficient of determination was higher than 0.99.