A differential network approach to exploring differences between biological states: an application to prediabetes.

TitleA differential network approach to exploring differences between biological states: an application to prediabetes.
Publication TypeJournal Article
Year of Publication2011
AuthorsValcárcel B, Würtz P, Seich al Basatena N-K, Tukiainen T, Kangas AJ, Soininen P, Järvelin M-R, Ala-Korpela M, Ebbels TM, De Iorio M
JournalPLoS One
Volume6
Issue9
Paginatione24702
Date Published2011
ISSN1932-6203
KeywordsBlood Glucose, Cohort Studies, Diabetes Mellitus, Dyslipidemias, Female, Finland, Gene Regulatory Networks, Genetic Predisposition to Disease, Genetic Variation, Humans, Lipoproteins, Lipoproteins, HDL, Magnetic Resonance Spectroscopy, Male, Models, Biological, Models, Genetic, Models, Statistical, Phenotype, Systems Biology
Abstract

BACKGROUND: Variations 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.

METHODOLOGY: Here, 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/SIGNIFICANCE: Differential 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.

DOI10.1371/journal.pone.0024702
Alternate JournalPLoS ONE
PubMed ID21980352