Network medicine and type 2 diabetes mellitus: insights into disease mechanism and guide to precision medicine
Understanding the genomic basis of type 2 diabetes mellitus is a major challenge. Simple genome-wide association studies (GWAS) have identified ~250 loci that link to the phenotype; however, the great majority have tiny effect size of uncertain mechanistic significance. Polygenic risk score strategies do nothing more than integrate these statistical association into a single scalar parameter, again offering limited mechanistic insight. The new discipline of network medicine offers an approach by which to provide useful mechanistic information from GWAS and other omic data sets. To understand disease in the network context requires using a predefined comprehensive network—in our case the protein–protein interaction network, or interactome—as a template upon which to map loci from GWAS or other data sources. These loci have been shown to cluster in a subnetwork in the interactome (as is the case for most diseases), exploration of which identifies novel pathways that regulate disease pathogenesis and uncovers novel targets for therapeutic intervention. Such an approach is essential for utilizing the growing pool of omic data in a mechanistically rational way as we move increasingly towards precision medicine for this highly prevalent disorder.
KeywordsSystems biology Network medicine Complex systems Genomics Interactome Metabolism
The author thank Ms. Stephanie Tribuna for expert technical assistance. This work was supported in part by National Institutes of Health grants HL61795, HG007690, GM107618, and HL119145; and by American Heart Association grant D007382.
Compliance with ethical standards
Conflict of interest
The author is a scientific cofounder of Scipher Medicine, Inc., which uses molecular network approaches for precision diagnostics and therapeutics.
This article does not contain any studies with human participants or animals performed by the author.
- 4.A. Mahajan, D. Taliun, M. Thurner, N.R. Robertson, J.M. Torres, N.W. Rayner, A.J. Payne, V. Steinthorsdottir, R.A. Scott, N. Grarup, J.P. Cook, E.M. Schmidt, M. Wuttke, C. Sarnowski, R. Magi, J. Nano, C. Gieger, S. Trompet, C. Lecoeur, M.H. Preuss, B.P. Prins, X. Guo, L.F. Bielak, J.E. Below, D.W. Bowden, J.C. Chambers, Y.J. Kim, M.C.Y. Ng, L.E. Petty, X. Sim, W. Zhang, A.J. Bennett, J. Bork-Jensen, C.M. Brummett, M. Canouil, K.U. Ec Kardt, K. Fischer, S.L.R. Kardia, F. Kronenberg, K. Lall, C.T. Liu, A.E. Locke, J. Luan, I. Ntalla, V. Nylander, S. Schonherr, C. Schurmann, L. Yengo, E.P. Bottinger, I. Brandslund, C. Christensen, G. Dedoussis, J.C. Florez, I. Ford, O.H. Franco, T.M. Frayling, V. Giedraitis, S. Hackinger, A.T. Hattersley, C. Herder, M.A. Ikram, M. Ingelsson, M.E. Jorgensen, T. Jorgensen, J. Kriebel, J. Kuusisto, S. Ligthart, C.M. Lindgren, A. Linneberg, V. Lyssenko, V. Mamakou, T. Meitinger, K.L. Mohlke, A.D. Morris, G. Nadkarni, J.S. Pankow, Al Peters, N. Sattar, A. Stancakova, K. Strauch, K.D. Taylor, B. Thorand, G. Thorleifsson, U. Thorsteinsdottir, J. Tuomilehto, D.R. Witte, J. Dupuis, P.A. Peyser, E. Zeggini, R.J.F. Loos, P. Froguel, E. Ingelsson, L. Lind, L. Groop, M. Laakso, F.S. Collins, J.W. Jukema, C.N.A. Palmer, H. Grallert, A. Metspalu, A. Dehghan, A. Kottgen, G.R. Abecasis, J.B. Meigs, J.I. Rotter, J. Marchini, O. Pedersen, T. Hansen, C. Langenberg, N.J. Wareham, K. Stefansson, A.L. Gloyn, A.P. Morris, M. Boehnke, M.I. McCarthy, Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenomic maps. Nat. Genet. 50, 1505–1513 (2018)CrossRefGoogle Scholar
- 6.A.V. Khera, M. Chaffin, K.G. Aragam, M.E. Haas, C. Roselli, S.H. Choi, P. Natarajan, E.S. Lander, S.A. Lubitz, P.T. Ellinor, S. Kathiresan, Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to mongenic diseases. Nat. Genet. 50, 1219–1224 (2018)CrossRefGoogle Scholar
- 8.M.O. Goodarzi, T. Nagpal, P. Greer, J. Cui, Y.I. Chen, X. Guo, J.S. Pankow, J.I. Rotter, S. Alkaade, S.T. Amann, J. Baillie, P.A. Banks, R.E. Brand, D.L. Conwell, G.A. Cote, C.E. Forsmark, T.B. Gardner, A. Gelrud, N. Guda, J. LaRusch, M.D. Lewis, M.E. Money, T. Muniraj, G.I. Papachristou, J. Romagnuolo, B.S. Sandhu, S. Sherman, V.K. Singh, C.M. Wilcox, S.J. Pandol, W.G. Park, D.K. Andersen, M.D. Bellin, P.A. Hart, D. Yadav, D.C. Whitcomb, Consortium for the Study of Chronic Pancreatitis, Diabetes, and Pancreatic Cancer (CPDPC). Genetic risk score in diabetes associated with chronic pancreatitis versus type 2 diabetes mellitus. Clin. Transl. Gastroenterol. 10(7), e00057 (2019).CrossRefGoogle Scholar
- 15.E. Kim, P.J. Caraballo, M.R. Castro, D.S. Pieczkiewicz, G.J. Simon, Towards more accessible precision medicine: building a more transferable machine learning model to support prognostic decisions for micro- and macrovascular complications of type 2 diabetes mellitus. J. Med. Syst. 43, 185 (2019)CrossRefGoogle Scholar
- 16.S.M. Schüssler-Fiorenza Rose, K. Contrepois, K.J. Moneghetti, W. Zhou, T. Mishra, S. Mataraso, O. Dagan-Rosenfeld, A.B. Ganz, J. Dunn, D. Hornburg, S. Rego, D. Perelman, S. Ahadi, M.R. Sailani, Y. Zhou, S.R. Leopold, J. Chen, M. Ashland, J.W. Christle, M. Avina, P. Limcaoco, C. Ruiz, M. Tan, A.J. Butte, G.M. Weinstock, G.M. Slavich, E. Sodergren, T.L. McLaughlin, F. Haddad, M.P. Snyder, A longitudinal big data approach for precision health. Nat. Med. 25, 792–804 (2019)CrossRefGoogle Scholar