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Health and economic benefits of reducing sugar intake in the USA, including effects via non-alcoholic fatty liver disease: a microsimulation model
  1. Rick A Vreman1,
  2. Alex J Goodell1,
  3. Luis A Rodriguez2,
  4. Travis C Porco2,3,
  5. Robert H Lustig1,4,
  6. James G Kahn1
  1. 1 Philip R. Lee Institute for Health Policy Studies, University of California San Francisco, California, USA
  2. 2 Department of Epidemiology & Biostatistics, University of California, San Francisco, California, USA
  3. 3 FI Proctor Foundation for Research in Ophthalmology, University of California, San Francisco, California, USA
  4. 4 Department of Pediatrics, University of California San Francisco, San Francisco, California, USA
  1. Correspondence to Rick A Vreman; r.a.vreman{at}


Objectives Excessive consumption of added sugars in the human diet has been associated with obesity, type 2 diabetes (T2D), coronary heart disease (CHD) and other elements of the metabolic syndrome. Recent studies have shown that non-alcoholic fatty liver disease (NAFLD) is a critical pathway to metabolic syndrome. This model assesses the health and economic benefits of interventions aimed at reducing intake of added sugars.

Methods Using data from US National Health Surveys and current literature, we simulated an open cohort, for the period 2015–2035. We constructed a microsimulation model with Markov chains for NAFLD (including steatosis, non-alcoholic steatohepatitis (NASH), cirrhosis and hepatocellular carcinoma (HCC)), body mass index, T2D and CHD. We assessed reductions in population disease prevalence, disease-attributable disability-adjusted life years (DALYs) and costs, with interventions that reduce added sugars consumption by either 20% or 50%.

Findings The model estimated that a 20% reduction in added sugars intake will reduce prevalence of hepatic steatosis, NASH, cirrhosis, HCC, obesity, T2D and CHD. Incidence of T2D and CHD would be expected to decrease by 19.9 (95% CI 12.8 to 27.0) and 9.4 (95% CI 3.1 to 15.8) cases per 100 000 people after 20 years, respectively. A 20% reduction in consumption is also projected to annually avert 0.767 million (M) DALYs (95% CI 0.757M to 0.777M) and a total of US$10.3 billion (B) (95% CI 10.2B to 10.4B) in discounted direct medical costs by 2035. These effects increased proportionally when added sugars intake were reduced by 50%.

Conclusions The decrease in incidence and prevalence of disease is similar to results in other models, but averted costs and DALYs were higher, mainly due to inclusion of NAFLD and CHD. The model suggests that efforts to reduce consumption of added sugars may result in significant public health and economic benefits.

  • added sugar
  • non-alcoholic fatty liver disease
  • type 2 diabetes
  • microsimulation
  • obesity
  • fructose

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  • Contributors RAV was involved in conceptualising the study, reviewing literature, conducting the modelling analysis, analysing the data and writing the manuscript. AJG was involved in conducting the modelling analysis and in editing the paper. LAR and RHL were involved in conceptualising the model, providing and structuring data inputs and editing the manuscript. TCP was involved in reviewing and revising the manuscript, checking statistical and mathematical assumptions and establishing overall validity of the model. JGK was involved in conceptualisation of the model, input data review, guiding the modelling process and providing a critical review of the manuscript. All authors read and approved the final manuscript.

  • Competing interests RHL has received author fees from Hudson Street Press regarding his authorship of 'Fat Chance: Beating the Odds Against Sugar, Processed Food, Obesity, and Disease', 'The Fat Chance Cookbook' and 'Sugar has 56 names: A Shopper's Guide'. He is also the unpaid chief science officer of the non-profit EatREAL.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement An online supplement will be made available containing comprehensive tables of used input data. The modelling code is available through github ( or can be accessed via the corresponding author.

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