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      The implications of human metabolic network topology for disease comorbidity.

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          Abstract

          Most diseases are the consequence of the breakdown of cellular processes, but the relationships among genetic/epigenetic defects, the molecular interaction networks underlying them, and the disease phenotypes remain poorly understood. To gain insights into such relationships, here we constructed a bipartite human disease association network in which nodes are diseases and two diseases are linked if mutated enzymes associated with them catalyze adjacent metabolic reactions. We find that connected disease pairs display higher correlated reaction flux rate, corresponding enzyme-encoding gene coexpression, and higher comorbidity than those that have no metabolic link between them. Furthermore, the more connected a disease is to other diseases, the higher is its prevalence and associated mortality rate. The network topology-based approach also helps to uncover potential mechanisms that contribute to their shared pathophysiology. Thus, the structure and modeled function of the human metabolic network can provide insights into disease comorbidity, with potentially important consequences for disease diagnosis and prevention.

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          Author and article information

          Journal
          Proc Natl Acad Sci U S A
          Proceedings of the National Academy of Sciences of the United States of America
          Proceedings of the National Academy of Sciences
          1091-6490
          0027-8424
          Jul 22 2008
          : 105
          : 29
          Affiliations
          [1 ] Center for Complex Network Research and Department of Physics, Biology, and Computer Science, Northeastern University, Boston, MA 02115, USA.
          Article
          0802208105
          10.1073/pnas.0802208105
          2481357
          18599447
          367faf29-1780-4ec3-acbd-14458add18f1
          History

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