Abstract
BackgroundVariations in the pattern of molecular associations are observed during disease development. The comprehensive analysis of molecular association patterns and their changes in relation to different physiological conditions can yield insight into the biological basis of disease-specific phenotype variation.MethodologyHere, we introduce a formal statistical method for the differential analysis of molecular associations via network representation. We illustrate our approach with extensive data on lipoprotein subclasses measured by NMR spectroscopy in 4,406 individuals with normal fasting glucose, and 531 subjects with impaired fasting glucose (prediabetes). We estimate the pair-wise association between measures using shrinkage estimates of partial correlations and build the differential network based on this measure of association. We explore the topological properties of the inferred network to gain insight into important metabolic differences between individuals with normal fasting glucose and prediabetes.Conclusions/SignificanceDifferential networks provide new insights characterizing differences in biological states. Based on conventional statistical methods, few differences in concentration levels of lipoprotein subclasses were found between individuals with normal fasting glucose and individuals with prediabetes. By performing the differential analysis of networks, several characteristic changes in lipoprotein metabolism known to be related to diabetic dyslipidemias were identified. The results demonstrate the applicability of the new approach to identify key molecular changes inaccessible to standard approaches.
Highlights
With advances in the theory of complex networks [1] and its application to describe architectural features of molecular systems [2], network-based approaches have been increasingly used to capture underlying properties of biological systems
Due to a predominance of males in the impaired fasting glucose (IFG) group, we base our biological interpretation on the male data; the differential network for the female data is shown in the supplementary material
The initial inspection of the data using Mann-Whitney test showed no significant differences in concentration levels of the M = 60 lipoprotein subclass components between individual with normal fasting glucose and impaired fasting glucose at the Bonferroni corrected threshold pv0:01=M
Summary
With advances in the theory of complex networks [1] and its application to describe architectural features of molecular systems [2], network-based approaches have been increasingly used to capture underlying properties of biological systems. Network theory is of interest to identify variations between different physiological states as well as biological systems. Comprehensive assessment of molecular associations can yield disease-specific signatures providing a complementary tool to unravel the biological basis of phenotype variation in the process of disease development, such as the pathogenesis of type 2 diabetes mellitus (T2DM) [5]. The comprehensive analysis of molecular association patterns and their changes in relation to different physiological conditions can yield insight into the biological basis of disease-specific phenotype variation
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