The statistics do not support the view that there are big differences in sugar consumption between the fat and the thin. We need to define our enemy clearly in the battle against obesity.
The Sydney Morning Herald has announced a war on sugar. Its rationale is that we need to combat obesity with all its attendant ills. Good thinking. Sugar may appear to be easy pickings. Beware.
It is vital that individual sugar consumption not be cast as the behaviour that we must attack with all our might. That will be a waste of energy – no pun intended – and leave the real changes essential for reversing our current trend to a fatter, less healthy community untouched.
Just how critical is sugar to obesity? A study of 132 479 individuals in the UK, published in the International Journal of Epidemiology* in 2016, analysed their consumption of macronutrients – fat, protein, carbohydrate and sugar – and compared how much energy in the diet of obese versus non-obese individuals came from these food categories. This group was assembled for the UK Biobank genetic study and the current study made use of the comprehensive health data collected on all participants.
Anderson and Pell, the lead authors of the study from the University of Glasgow, made the point that in this study ‘dietary intake was self-reported outside the clinic, which may encourage more truthful reporting, and was collected using a 24 hour recall questionnaire which produce more accurate results than a food frequency questionnaire (the usual approach adopted in large-scale studies)’. Their general conclusion was ‘66.3% of men and 51.8% of women were overweight/obese.’
Anderson et al wrote: ‘Compared with [those participants with] normal BMI, obese participants had 11.5% higher total energy intake and 14.6%, 13.8%, 9.5% and 4.7% higher intake from fat, protein, starch and sugar, respectively.’ So while the fat folk were consuming more energy than the thin, the excess due to sugar intake between the two groups was quite small. ‘There is only a weak correlation between absolute energy derived from sugar and from fat. Therefore, targeting high sugar consumers will not necessarily target high consumers of fat and overall energy.’
They concluded that ‘fat is the largest contributor to overall energy. The proportion of energy from fat in the diet, but not sugar, is higher among overweight/obese individuals. Focusing public health messages on sugar may mislead on the need to reduce fat and overall energy consumption.’
Do these observations mean that we should not include sugar as needing attention in our approach to obesity? It cannot be said to be the main game. Unlike tobacco – a single and inessential commodity – there is no case to ban it completely, nor is such an approach desirable. A sugar tax would make all sugar-containing foods and drinks more expensive and hence less accessible to less affluent consumers and needs careful calibration against the criterion of equity. Also, Anderson et al warn of the tendency to substitute one source of energy for another and if this substitute is fat then we are no further ahead.
The power of the sugar industry – cane, corn and beet – is immense and it is far from squeaky clean when it comes to promoting a healthy diet. It is at the level of production and marketing that our attention needs to focus in encouraging a healthier approach to sugar.
Encouraging individuals to lobby for less sugar in processed foods and drinks will not be easy and blood will be spilt as that battle plays out. But it is there – and not by beating up individuals to reduce their individual consumption of sugar (desirable but neither necessary nor sufficient) – that our efforts should be applied.
It is interesting that in an international comparison of cost-effective ways of reducing obesity, McKinsey and Co, a consultancy, nominated reducing portion size as the best. Given the nearly 12% difference in total energy intake between the obese and non-obese participants in this study, reducing the size of meals we eat by 10% would seem a wise recommendation – without worrying too much about macronutrients such as sugar.
Stephen Leeder is an emeritus professor of public health and community medicine at the University of Sydney. *He is also editor of the International Journal of Epidemiology.
Stephen Leeder is an Emeritus Professor of public health and community medicine at the University of Sydney. Steve has 45 years of experience in epidemiological research, medical education reform and in mentoring young investigators and is currently Co-Editor-in-Chief of the International Journal of Epidemiology. He held the position of Chair of the Western Sydney Local Health District Board from 2011 until 2016 and was Editor-in-Chief of the Medical Journal of Australia from January 2013 until April 2015.
Comments
2 responses to “STEPHEN LEEDER: A little bit of sugar may (or may not) make the weight go down.”
Stephen, great to read you again, and every good wish. Good piece, too!
The data below includes the impact of 13 dietary risk factors. A diet high in sweetened beverages only accounts for 0.3% of the burden of disease, in comparison to the 7.2% of the burden of disease attributable to all dietary risk facts.
That is the reason that a tax just on sugar or sweetened beverages is simplistic and likely to have little impact.
Instead there should be a tax on food which accounts for more of the dietary risk factors than just one.
One way of doing it would be to modify the GST on food, so that foods with a low health star rating had a high 20% tax rate, those foods with a medium health star rating had a 10% tax rate, and for those foods with a high health star rating there would be no GST. The health star rating does not account for all the dietary risk factors in the list below, but using a health star rating based tax would be much more effective than a simplistic, populist tax on sugar.
Per cent of total DALYs in 2011 attributable to dietary risk factors
Diet low in fruit 2.0%
Diet low in vegetables 1.4%
Diet high in processed meat 1.4%
Diet low in nuts and seeds 1.4%
Diet low in whole grains 1.1%
Diet low in fibre 1.0%
Diet high in saturated fat 0.7%
Diet low in omega-3 fatty acids 0.3%
Diet high in sodium 0.3%
Diet high in sweetened beverages 0.3%
Diet high in red meat 0.2%
Diet low in milk 0.2%
Diet low in calcium 0.1%
Joint effect of all dietary factors 7.2%
Source: AIHW burden of disease analyses