title: A differential network approach to exploring differences between biological states: an application to prediabetes.
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.
  
  
  METHODS
  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
  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.
authors: Epidemiology and Biostatistics, Imperial College London, London, United Kingdom.
authors: Beatriz
authors: B
authors: Valcárcel
authors: Peter
authors: P
authors: Würtz
authors: Nafisa-Katrin
authors: NK
authors: Seich al Basatena
authors: Taru
authors: T
authors: Tukiainen
authors: Antti J
authors: AJ
authors: Kangas
authors: Pasi
authors: P
authors: Soininen
authors: Marjo-Riitta
authors: MR
authors: Järvelin
authors: Mika
authors: M
authors: Ala-Korpela
authors: Timothy M
authors: TM
authors: Ebbels
authors: Maria
authors: M
authors: de Iorio
mesh_terms: D001786:Blood Glucose / Q000737:chemistry; D015331:Cohort Studies; D003920:Diabetes Mellitus / Q000378:metabolism*; D050171:Dyslipidemias / Q000235:genetics; D005260:Female; D005387:Finland; D053263:Gene Regulatory Networks; D020022:Genetic Predisposition to Disease / Q000235:genetics; D014644:Genetic Variation; D006801:Humans; D008074:Lipoproteins / Q000737:chemistry; D008075:Lipoproteins, HDL / Q000737:chemistry; D009682:Magnetic Resonance Spectroscopy / Q000379:methods; D008297:Male; D008954:Models, Biological; D008957:Models, Genetic; D015233:Models, Statistical; D010641:Phenotype; D049490:Systems Biology
chemical_list: D001786:Blood Glucose; D008074:Lipoproteins; D008075:Lipoproteins, HDL