Literature DB >> 29664676

A mathematical analysis of adaptations to the metabolic fate of fructose in essential fructosuria subjects.

Richard J Allen1, Cynthia J Musante1.   

Abstract

Fructose is a major component of Western diets and is implicated in the pathogenesis of obesity and type 2 diabetes. In response to an oral challenge, the majority of fructose is cleared during "first-pass" liver metabolism, primarily via phosphorylation by ketohexokinase (KHK). A rare benign genetic deficiency in KHK, called essential fructosuria (EF), leads to altered fructose metabolism. The only reported symptom of EF is the appearance of fructose in the urine following either oral or intravenous fructose administration. Here we develop and use a mathematical model to investigate the adaptations to altered fructose metabolism in people with EF. First, the model is calibrated to fit available data in normal healthy subjects. Then, to mathematically represent EF subjects, we systematically implement metabolic adaptations such that model simulations match available data for this phenotype. We hypothesize that these modifications represent the major metabolic adaptations present in these subjects. This modeling approach suggests that several other aspects of fructose metabolism, beyond hepatic KHK deficiency, are altered and contribute to the etiology of this benign condition. Specifically, we predict that fructose absorption into the portal vein is altered, peripheral metabolism is slowed, renal reabsorption of fructose is mostly ablated, and alternate pathways for hepatic metabolism of fructose are upregulated. Moreover, these findings have implications for drug discovery and development, suggesting that the therapeutic targeting of fructose metabolism could lead to unexpected metabolic adaptations, potentially due to a physiological response to high-fructose conditions.

Entities:  

Keywords:  essential fructosuria; fructose metabolism; mathematical modeling

Mesh:

Substances:

Year:  2018        PMID: 29664676     DOI: 10.1152/ajpendo.00317.2017

Source DB:  PubMed          Journal:  Am J Physiol Endocrinol Metab        ISSN: 0193-1849            Impact factor:   4.310


  5 in total

Review 1.  Translational Quantitative Systems Pharmacology in Drug Development: from Current Landscape to Good Practices.

Authors:  Jane P F Bai; Justin C Earp; Venkateswaran C Pillai
Journal:  AAPS J       Date:  2019-06-03       Impact factor: 4.009

2.  Ketohexokinase inhibition improves NASH by reducing fructose-induced steatosis and fibrogenesis.

Authors:  Emma L Shepherd; Raquel Saborano; Ellie Northall; Kae Matsuda; Hitomi Ogino; Hiroaki Yashiro; Jason Pickens; Ryan E Feaver; Banumathi K Cole; Stephen A Hoang; Mark J Lawson; Matthew Olson; Robert A Figler; John E Reardon; Nobuhiro Nishigaki; Brian R Wamhoff; Ulrich L Günther; Gideon Hirschfield; Derek M Erion; Patricia F Lalor
Journal:  JHEP Rep       Date:  2020-11-20

3.  A Quantitative Systems Pharmacology Perspective on the Importance of Parameter Identifiability.

Authors:  Anna Sher; Steven A Niederer; Gary R Mirams; Anna Kirpichnikova; Richard Allen; Pras Pathmanathan; David J Gavaghan; Piet H van der Graaf; Denis Noble
Journal:  Bull Math Biol       Date:  2022-02-07       Impact factor: 1.758

4.  A Quantitative Systems Pharmacology Model of Liver Lipid Metabolism for Investigation of Non-Alcoholic Fatty Liver Disease.

Authors:  Theodore R Rieger; Richard J Allen; Cynthia J Musante
Journal:  Front Pharmacol       Date:  2022-07-19       Impact factor: 5.988

5.  Prevalence and cardiometabolic correlates of ketohexokinase gene variants among UK Biobank participants.

Authors:  Joseph A Johnston; David R Nelson; Pallav Bhatnagar; Sarah E Curtis; Yu Chen; James G MacKrell
Journal:  PLoS One       Date:  2021-02-23       Impact factor: 3.240

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.