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Abstract Details
Towards harnessing the value of organokine crosstalk to predict the risk for cardiovascular disease in non-alcoholic fatty liver disease
Metabolism. 2022 May;130:155179. doi: 10.1016/j.metabol.2022.155179.Epub 2022 Mar 10.
1Department of Internal Medicine, University Hospital, Knappschaftskrankenhaus, Ruhr University Bochum, 44892 Bochum, Germany; Section of Metabolic and Preventive Medicine, University Hospital, Knappschaftskrankenhaus, Ruhr University Bochum, 44892 Bochum, Germany.
2Department of Internal Medicine, University Hospital, Knappschaftskrankenhaus, Ruhr University Bochum, 44892 Bochum, Germany; Laboratory of Immunology and Molecular Biology, University Hospital, Knappschaftskrankenhaus, Ruhr University Bochum, 44892 Bochum, Germany.
3Department of Internal Medicine, University Hospital, Knappschaftskrankenhaus, Ruhr University Bochum, 44892 Bochum, Germany; Section of Cardiology and Internal Emergency Medicine, University Hospital, Knappschaftskrankenhaus, Ruhr University Bochum, 44892 Bochum, Germany.
4Department of Internal Medicine, University Hospital, Knappschaftskrankenhaus, Ruhr University Bochum, 44892 Bochum, Germany; Section of Hepatology and Gastroenterology, University Hospital, Knappschaftskrankenhaus, Ruhr University Bochum, 44892 Bochum, Germany. Electronic address: ali.canbay@rub.de.
Abstract
Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease. Importantly, NAFLD increases the risk for cardiovascular disease (CVD). A causal relationship has been substantiated. Given the pandemic proportions of NAFLD, a reliable scoring system for predicting the risk of NAFLD-associated CVD is an urgent medical need. We here review cumulative evidence suggesting that systemically released organokines - especially certain adipokines, hepatokines, and cardiokines - may serve this purpose. The underlying rationale is that these signalers directly communicate between white adipose tissue, liver, and heart as key players in the pathogenesis of NAFLD and resultant CVD events. Moreover, evidence suggests that these organ-specific cytokines are secreted in a biologically predetermined, cascade-like pattern. Consequently, upon pinpointing organokines of relevance, we sketch requirements to establish an algorithm predictive of the CVD risk in patients with NAFLD. Such an algorithm, as to be consolidated in the form of an applicable equation, may be improved continuously by machine learning. To the best of our knowledge, such an option has not yet been considered. Establishing and implementing a reliable algorithm for determining the NAFLD-associated CVD risk has the potential to save many NAFLD patients from life-threatening CVD events.