Network medicine: an approach to complex kidney disease phenotypes

Scientific reductionism has been the basis of disease classification and understanding for more than a century. However, the reductionist approach of characterizing diseases from a limited set of clinical observations and laboratory evaluations has proven insufficient in the face of an exponential growth in data generated from transcriptomics, proteomics, metabolomics and deep phenotyping. A new systematic method is necessary to organize these datasets and build new definitions of what constitutes a disease that incorporates both biological and environmental factors to more precisely describe the ever-growing complexity of phenotypes and their underlying molecular determinants. Network medicine provides such a conceptual framework to bridge these vast quantities of data while providing an individualized understanding of disease. The modern application of network medicine principles is yielding new insights into the pathobiology of chronic kidney diseases and renovascular disorders by expanding the understanding of pathogenic mediators, novel biomarkers and new options for renal therapeutics. These efforts affirm network medicine as a robust paradigm for elucidating new advances in the diagnosis and treatment of kidney disorders. In this Review, the authors describe biological networks, discuss the properties that make these networks ideal for understanding how diseases arise from complex interactions of molecular and cellular systems, and explore how network medicine can be used to improve understanding of kidney and renovascular diseases. Key points Network medicine applies the principles of network theory to disease diagnostics and therapeutics to provide a novel understanding of disease. Biological networks can be constructed from several different biomolecules, representing simple to complex inter-relationships between these entities. The network basis of disease is the disease module or subnetwork. Kidney diseases are well-positioned for exploitation by network medicine approaches to achieve a more personalized and precise approach to treatment.