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      Estimating the potential impact of the UK government’s sugar reduction programme on child and adult health: modelling study

      1 , 2 , , 1 , 1 , 3
      The BMJ
      BMJ Publishing Group Ltd.

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          To estimate the impact of the UK government’s sugar reduction programme on child and adult obesity, adult disease burden, and healthcare costs.


          Modelling study.


          Simulated scenario based on National Diet and Nutrition Survey waves 5 and 6, England.


          1508 survey respondents were used to model weight change among the population of England aged 4-80 years.

          Main outcome measures

          Calorie change, weight change, and body mass index change were estimated for children and adults. Impact on non-communicable disease incidence, quality adjusted life years, and healthcare costs were estimated for adults. Changes to disease burden were modelled with the PRIMEtime-CE Model, based on the 2014 population in England aged 18-80.


          If the sugar reduction programme was achieved in its entirety and resulted in the planned sugar reduction, then the calorie reduction was estimated to be 25 kcal/day (1 kcal=4.18 kJ=0.00418 MJ) for 4-10 year olds (95% confidence interval 23 to 26), 25 kcal/day (24 to 28) for 11-18 year olds, and 19 kcal/day (17 to 20) for adults. The reduction in obesity could represent 5.5% of the baseline obese population of 4-10 year olds, 2.2% of obese 11-18 year olds, and 5.5% of obese 19-80 year olds. A modelled 51 729 quality adjusted life years (95% uncertainty interval 45 768 to 57 242) were saved over 10 years, including 154 550 (132 623 to 174 604) cases of diabetes and relating to a net healthcare saving of £285.8m (€332.5m, $373.5m; £249.7m to £319.8m).


          The UK government’s sugar reduction programme could reduce the burden of obesity and obesity related disease, provided that reductions in sugar levels and portion sizes do not prompt unanticipated changes in eating patterns or product formulation.

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          Most cited references5

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          Dietary fructose reduces circulating insulin and leptin, attenuates postprandial suppression of ghrelin, and increases triglycerides in women.

          Previous studies indicate that leptin secretion is regulated by insulin-mediated glucose metabolism. Because fructose, unlike glucose, does not stimulate insulin secretion, we hypothesized that meals high in fructose would result in lower leptin concentrations than meals containing the same amount of glucose. Blood samples were collected every 30-60 min for 24 h from 12 normal-weight women on 2 randomized days during which the subjects consumed three meals containing 55, 30, and 15% of total kilocalories as carbohydrate, fat, and protein, respectively, with 30% of kilocalories as either a fructose-sweetened [high fructose (HFr)] or glucose-sweetened [high glucose (HGl)] beverage. Meals were isocaloric in the two treatments. Postprandial glycemic excursions were reduced by 66 +/- 12%, and insulin responses were 65 +/- 5% lower (both P < 0.001) during HFr consumption. The area under the curve for leptin during the first 12 h (-33 +/- 7%; P < 0.005), the entire 24 h (-21 +/- 8%; P < 0.02), and the diurnal amplitude (peak - nadir) (24 +/- 6%; P < 0.0025) were reduced on the HFr day compared with the HGl day. In addition, circulating levels of the orexigenic gastroenteric hormone, ghrelin, were suppressed by approximately 30% 1-2 h after ingestion of each HGl meal (P < 0.01), but postprandial suppression of ghrelin was significantly less pronounced after HFr meals (P < 0.05 vs. HGl). Consumption of HFr meals produced a rapid and prolonged elevation of plasma triglycerides compared with the HGl day (P < 0.005). Because insulin and leptin, and possibly ghrelin, function as key signals to the central nervous system in the long-term regulation of energy balance, decreases of circulating insulin and leptin and increased ghrelin concentrations, as demonstrated in this study, could lead to increased caloric intake and ultimately contribute to weight gain and obesity during chronic consumption of diets high in fructose.
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            The Preventable Risk Integrated ModEl and Its Use to Estimate the Health Impact of Public Health Policy Scenarios

            Noncommunicable disease (NCD) scenario models are an essential part of the public health toolkit, allowing for an estimate of the health impact of population-level interventions that are not amenable to assessment by standard epidemiological study designs (e.g., health-related food taxes and physical infrastructure projects) and extrapolating results from small samples to the whole population. The PRIME (Preventable Risk Integrated ModEl) is an openly available NCD scenario model that estimates the effect of population-level changes in diet, physical activity, and alcohol and tobacco consumption on NCD mortality. The structure and methods employed in the PRIME are described here in detail, including the development of open source code that will support a PRIME web application to be launched in 2015. This paper reviews scenario results from eleven papers that have used the PRIME, including estimates of the impact of achieving government recommendations for healthy diets, health-related food taxes and subsidies, and low-carbon diets. Future challenges for NCD scenario modelling, including the need for more comparisons between models and the improvement of future prediction of NCD rates, are also discussed.
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              Prediction of body weight changes caused by changes in energy balance.

              Experimental difficulties have so far restricted knowledge of the effects of energy imbalance on change in body weight. Direct measurement requires that the subjects are kept under dietary supervision for several months while the activity is being measured. The effects of energy balance can be calculated using a combination of fundamental principles and directly measurable data: the law of energy conservation (increase in combustible energy equals the difference between energy intake and energy expenditure); data on energy expenditure of fat and lean tissues; and data on the composition of added/removed tissue during weight change. We obtained an explicit differential equation describing the development of body weight over time, with energy intake and energy expenditure as control variables. Using this model it is possible to isolate and analyse the measured effects of parameters not included in the model, such as age or 'adaptivity' of the body.

                Author and article information

                Role: MRC DPhil student and honorary academic clinical fellow
                Role: academic visitor
                Role: associate professor
                The BMJ
                BMJ Publishing Group Ltd.
                17 April 2019
                : 365
                [1 ]Centre for Population Approaches to Non-Communicable Disease Prevention, Big Data Institute, University of Oxford, Headington, Oxford OX3 7FZ, UK
                [2 ]Centre for Primary Care, University of Manchester, Manchester, UK
                [3 ]National Institute of Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Headington, Oxford, UK
                Author notes
                Correspondence to: B Amies-Cull ben.amies-cull@ 123456dph.ox.ac.uk
                Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions

                This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/.




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