Probabilistic Networks of Blood Metabolites in Healthy Subjects As Indicators of Latent Cardiovascular Risk

Abstract

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. We adapted methods based on the likelihood of correlation and methods from information theory and combined them with resampling techniques. 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 post-translational modifications (glycosilation and oxidation) of lipoproteins as a possible target-mechanism for early detection of latent cardiovascular risk.

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Multi-Level Integration of Environmentally Perturbed Internal Phenotypes Reveals Key Points of Connectivity between Them

“…We further validated the observed correlations by comparing them with a null model obtained by randomly permuting the data along the samples (Eguíluz et al, 2005; Saccenti et al, 2015). In the randomly permuted samples we expect all inferred associations to be spurious, as the permutation process destroys any possible correlation between the variables.…”

“…For each pair of integrated datasets a null model of the association networks was constructed using a strategy based on random permutations of measured values (Saccenti et al, 2015). Measured data-points were randomly permuted over samples before data integration to obtain randomized datasets that still retained the same value distribution for each variable.…”