The complex nature of the mechanisms behind cardiovascular diseases prevents the detection of latent early risk conditions. Network representations are ideally suited to investigate the complex interconnections between the individual components of a biological system that underlies complex diseases. Here, we investigate the patterns of correlations of an array of 29 metabolites identified and quantified in the plasma of 864 healthy blood donors and use a systems biology approach to define metabolite probabilistic networks specific for low and high latent cardiovascular risk. To this task, we developed the PCLRC method, based on the likelihood of correlation and methods from information theory and combined them with resampling techniques and standard data analysis tools like INDSCAL. Our results show that plasma metabolite networks can be defined that associate with latent cardiovascular disease risk. The analysis of the networks supports our previous finding of a possible association between cardiovascular risk and impaired mitochondrial activity and highlights posttranslational modifications (glycosilation and oxidation) of lipoproteins as a possible target-mechanism for early detection of latent cardiovascular risk. Reference: E. Saccenti et al, " Probabilistic networks of blood metabolites in healthy subjects as indicators of latent cardiovascular risk " .