4.4 Article Proceedings Paper

From sugar to liver fat and public health: systems biology driven studies in understanding non-alcoholic fatty liver disease pathogenesis

Journal

PROCEEDINGS OF THE NUTRITION SOCIETY
Volume 78, Issue 3, Pages 290-304

Publisher

CAMBRIDGE UNIV PRESS
DOI: 10.1017/S0029665119000570

Keywords

Non-alcoholic fatty liver disease; Obesity; Sugar; Stratified medicine; Personalised nutrition; Genome-scale metabolic networks

Funding

  1. UK Biotechnology and Biological Sciences Research Council [BB/J014451/1, BB/I008195/1]
  2. University of Surrey
  3. University of Leeds
  4. BBSRC [BB/I008195/1] Funding Source: UKRI

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Non-alcoholic fatty liver disease (NAFLD) is now a major public health concern with an estimated prevalence of 25-30% of adults in many countries. Strongly associated with obesity and the metabolic syndrome, the pathogenesis of NAFLD is dependent on complex interactions between genetic and environmental factors that are not completely understood. Weight loss through diet and lifestyle modification underpins clinical management; however, the roles of individual dietary nutrients (e.g. saturated and n-3 fatty acids; fructose, vitamin D, vitamin E) in the pathogenesis or treatment of NAFLD are only partially understood. Systems biology offers valuable interdisciplinary methods that are arguably ideal for application to the studying of chronic diseases such as NAFLD, and the roles of nutrition and diet in their molecular pathogenesis. Although present in silico models are incomplete, computational tools are rapidly evolving and human metabolism can now be simulated at the genome scale. This paper will review NAFLD and its pathogenesis, including the roles of genetics and nutrition in the development and progression of disease. In addition, the paper introduces the concept of systems biology and reviews recent work utilising genome-scale metabolic networks and developing multi-scale models of liver metabolism relevant to NAFLD. A future is envisioned where individual genetic, proteomic and metabolomic information can be integrated computationally with clinical data, yielding mechanistic insight into the pathogenesis of chronic diseases such as NAFLD, and informing personalised nutrition and stratified medicine approaches for improving prognosis.

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