Is Food Addictive?

The Neurosceptic at Discovermagazine.com took on that question in a recent blog post. The author discusses a recent article arguing that dopamine release in response to food is evidence of food-related addition.  Here are the problems with that thinking:

If you view addiction as essentially about reward (pleasure), surely that means that anything pleasurable could also be addictive? Or to put it another way, if you’re saying that addiction is the direct consequence of over-indulgence in a reward, then aren’t you saying that reward itself is ultimately what’s addictive?

and

If everything from food to friends to music are rewarding because they trigger dopamine release, then surely all of those things could be ‘addictive’. If ‘reward’ is essentially monolithic, and the various kinds of rewards differ only in how powerful they are, then everything’s addictive to a degree. The more fun, the more (potentially) addictive. The better something is, the worse it is.

This seems to me to be the logical conclusion of this approach to addiction. Let’s call this (very widespread) approach neuropuritanism.

The funny thing is that this idea – for all its medical, neurobiological, scientific language – actually undermines the concept of addiction as a ‘disease’ and reduces it to what amounts to a moral failing – it casts addiction as over-indulgence. The Sin of Gluttony, if you will.

Bloomberg's soda ban fizzles

That was the title of the editorial I just published at the New York Daily News. The editor added the subtitle "The mayor's paternalism knows no bounds."

Here are a few excerpts:​

Tuesday was set to be the last day to legally buy a large sugary soda in the Big Apple. Fortunately, a state judge stepped in late Monday to halt the ban — but not before the mayor’s attitude toward his fellow New Yorkers was exposed.

The leaders of a vibrant city that is home to some of the most diverse and creative people on Earth no longer have faith in the decision-making abilities of their fellow citizens. At least, that is, when it comes to food.

and

This schizophrenic paternalism results from an awkward attempt to walk a fine line between a liberal agenda that yields to, even celebrates, freedom of choice and expression when it comes to abortion, sex, speech and drugs — but stops far short when those same freedoms might benefit evil corporations.

It is an odd position that posits us so weak as to fall for anything offered by Ronald McDonald or Tony the Tiger yet so strong to know when to keep a baby alive or which truths to speak to power.

I even worked in a little economics:

And exactly how is it that New York City's leaders envision a large sugary soda ban actually benefitting the people who buy Pepsi and Coca-Cola? By removing an alternative some people previously preferred, the ban is simply making people pick a lesser desirable (and thus less satisfying) alternative. Moreover, those alternatives, whether it be fruit juice or beer, may not be any less calorie-dense.

What about soda taxes that may be coming down the pike? Most economists will tell you that making people pay higher prices is akin to reducing their income. Last I checked, no one — particularly not the lower-income people about whom most politicians profess to be concerned — is better off with less money.

In conclusion:

Better drink 'em while you still got 'em

The whole thing is online here.

Does Sugar Consumption Drive Diabetes?

A recent article in the journal PLoS ONE by the anti-sugar crusader Robert Lustig and three other co-authors has created quite a stir by purporting to show that increased sugar consumption causes diabetes.  In the paper, the authors hold up just shy of saying "cause" but that is the inference drawn by many in the media (see for example this story in Bloomberg among other places) who say things like:

Excessive sugar consumption may be the main driver of a global rise in diabetes,

Moreover, on Mark Bittman's NYT Blog, the author, Lustig, is cited as saying:​

This study is proof enough that sugar is toxic. Now it’s time to do something about it.

There is no way a study like this (comparing differences across countries) can firmly establish causation.  So, at a minimum the study indicates an interesting (and perhaps suggestive) correlation that might warrant a randomized control trial.  Nonetheless, I was intrigued and wanted to check out the evidence for myself.  

The evidence by Lustig and colleagues comes by linking data on diabetes prevalence rates across countries (which I was able to easily find online here) and data from the UN FAO on the availability of calories from different food stuffs in different countries (after a bit of digging, I was also able to find it online here - go the the "food balance sheets").  After a bit of effort, I downloaded both data sets for the most recent years available, merged them, and checked out the claims made in the paper.  

At first blush, I find very similar results to the ones reported in the paper.  Holding constant total calories available, a simple linear regression shows that for every 100 kcal increase in sugar availability, the prevalence of diabetes goes up by 1.3 percentage points (say from 8.5% (the sample mean) to 9.8%).  The estimated equation is:  ​

(% with diabetes)=​1.067+0.013*(per-capita available sugar kcal)+0.001*(per capita total available kcal)

My estimate is a little higher than the one reported in the paper probably because I'm not controlling for other factors (like GDP, kcal intake from meat, etc.) as the authors did.  Moreover, I'm using data on diabetes from 2012 whereas the authors used 2011 and older data (note: I use data from 174 countries in my estimates).  The only coefficient significant at the p=0.05 level in the above equation is the 0.013 estimate associated with sugar.   

So far so good - the correlation is confirmed.  

But let's get to the nitty gritty of the interpretation.  The data is at the country level.  So, what this implies is that a country that increases per-capita sugar availability by 100kcal will tend to have a 1.3 percentage point increase in the percent of the population with diabetes. 

But, we don't really care about countries per se.  We care about people.  There are a lot more people in some countries than others.  ​In the data set, the range is from a low of 0.00066 million adults to 980 million adults.  Shouldn't this factor into the analysis?  If we care about how many people in the world have diabetes, we'd better pay a lot more attention to China than to Luxembourg.  

We know from the mini-scandal associated with the claim that small schools outperform larger ones (see one account here)​ that outcomes from small schools (or small countries) tends to be a lot more variable (with more outliers) than data from large schools (or large countries).  That's just basic statistics.  

Intuitively, we should want a larger country to count more than a smaller one.  After all, there are many more people in larger countries - so if we want to think about the prevalence of diabetes in the world (rather than the average prevalence rate across countries)​, we'd want to calculate a weighted average, where larger countries get more weight (because they have more people).  The more people, the higher the weight.

Likewise, when we want to run analyses like the one above, we want to give more weight to countries with more people.  We can do this by running a weighted regression, where each country gets a weight proportional to it's population size.  This converts the equation to one about how countries differ to one about how individuals differ.  ​Stated differently, the weighted regression places the estimates at the level of the individual (picked at random from any country) rather than the level of the country (picked at random from a group of countries).

Here is the equation I get when I weight by a country's adult population:​

(% with diabetes)=0.692+0.002*(total available sugar kcal)+0.002*(total available kcal)

Now, the effect of sugar falls dramatically (and most importantly, it is no longer statistically significant at standard levels; the p-value is 0.074).  A 100 kcal increase in per-capita sugar availability only increases the % with diabetes by 0.2 (rather than 1.3 as previously estimated).  Moreover, total energy from all sources is now significant and roughly the same magnitude as sugar.  Thus, what matters in this framework is total kcal from any food source.  Moreover this regression suggests that a sugar calorie is roughly the same as any other calorie insofar as affecting diabetes.    ​

The paper at PLoS ONE says "regressions are population weighted."  But, I'm wondering that is indeed the case.  It could be true.  I don't have access to all their data and I'm not including all their controls.  

I'm happy to share the data and SAS code with anybody who cares to see it.​

​********

Addendum

​The nice thing about the web is that you get feedback.  Here's an update.  The source that reports diabetes prevalence actually reported three measures.  In the regressions above, I used national prevalence (total number with diabetes divided by total population).  However, as indicated at the data source here, they also report some sort of age adjusted measure that is likely more useful in comparing across countries that might have different mean ages.  

​When I use this "IGT comparative prevalence" measure, as they call it, then I get exactly the opposite of the results mentioned above.  When the data are NOT weighted, the sugar coefficient is only 0.0019 (p-value 0.27).  But, when the data ARE weighted by adult population, the sugar coefficient is 0.01277 (p-value < 0.001).  

So, there is an interesting mix of things going on here between the population, weighting, and age adjustment.  Just out of curiosity, and for some robustness checks, I did two things.  First, I re-ran the "preferred" model with population weighting using "IGT comparative prevalence" diabetes but included population as an explanatory variable. When I do this, sugar is no longer statistically significant (the estimate is 0.00242 with a p-value of 0.107), but population is (the estimate suggests larger populations have lower diabetes prevalence).  I can't quite figure out what is going on here but there has to be something weird going on in the sense that the model is  weighting by population and the dependent variable (and independent variables) are per-capita (i.e., are divided by population), that might be producing some unexpected results.    

Second, I ran a quantile regression to see how the results hold up at the median (rather than the mean, which is more sensitive to outliers), I find that (using IGT comparative prevalence and adult population as a weight with only sugar and total calories as explanatory vars) the sugar effect, at the median, is 0.0148 but the 95% confidence interval is (-0.0191, 0.0217) when using the SAS default rank method of calculating standard errors.  The 95% confidence interval changes to (0.0041, 0.0254) when using an alternative resampling method.  So, whether the median effect is statistically significant depends on which method of calculating standard errors is used.

Here is the plot of the "sugar effect" at each quantile.  The first shows the 95% confidence intervals determined by the resampling method and the second uses the SAS default (I have to admit that I'm not sure which method is preferred in this case). 

The (Not So) Extraordinary Science of Addictive Junk Food

​The New York Times Magazine ran a feature story this weekend by Michael Moss entitled The Extraordinary Science of Addictive Junk Food.  There is really so much that could be said about this piece (and probably the forthcoming book by Moss), but for now, I'll just leave you with the letter I sent to the editors of the NYT:

Michael Moss’s over-wrought piece on the “Science” of addictive junk food misses some key facts.  Around the time the executives of Big Food were in their clandestine meeting, regular folk were voluntarily cutting back.  CDC data reveals that the average weight of 40-49 year old women fell 0.2 lbs over the last ten years.  In the last four years, the average weight of men in this age range went down 1.7 lbs (women’s weight fell by 3.3 lbs).  It seems that the addictions cooked up by nefarious food scientists are waning.  Or maybe they weren’t addictive at all.  I gave up regular Dr. Pepper in 2002 when my pants began fitting too snugly, and I can’t recall any withdrawal symptoms.  If Big Food isn’t in their lab trying to create new tasty treats I want to try again and again, I’m not sure why they exist.​

Does the Food Stamp Program Subsidize Obesity?

At his NYT blog, Mark Bittman says that beneficiaries of SNAP (otherwise known as food stamps) shouldn't be allowed to pay for sodas.  Here is Bittman:

What’s to be done? How to improve the quality of calories purchased by SNAP recipients? The answer is easy: Make sure that SNAP dollars are spent on nutritious food.

This could happen in two ways: first, remove the subsidy for sugar-sweetened beverages, since no one without a share in the profits can argue that the substance plays a constructive role in any diet. . . . 

Simultaneously, make it easier to buy real food; several cities, including New York, have programs that double the value of food stamps when used for purchases at farmers markets. The next step is to similarly increase the spending power of food stamps when they’re used to buy fruits, vegetables, legumes and whole grains, not just in farmers markets but in supermarkets – indeed, everywhere people buy food.

On one level, I'm sympathetic to Bittman's argument.  Why should my tax dollars be used to subsidize bad behavior?  But, if we are going to accept that argument, where do we stop?  As a professor, I indirectly get a paycheck from the State of Oklahoma.  What is to stop someone like Bittman one day proposing that the federal or state government dictating where and how I can spend the portion of my paycheck that comes from taxpayers?     

Aside from this "slippery slope" argument, it is useful to consider the broader debate in economics on whether foods stamps, in general, cause obesity.  Unless a person (before receiving food stamps) is constrained in the food they buy, a standard economic model suggests that food stamps simply act as an income transfer.  Stated differently, under the aforementioned situation, a person shouldn't treat food stamps any differently than a monthly cash gift.  There are lots of empirical papers studying whether this true and the evidence is a bit mixed but I think its safe to say that a big part of the effect of food stamps (if not the total effect) is simply an income effect.  

This is useful to consider this perspective because - if we are going to give food stamps out of some feeling of charity or benevolence - we have to realize that restricting their use may not have the intended effect.  Money is fungible.  If you can't use food stamps to buy sodas, you'll use them to buy more of something else - freeing up money to buy soda.  

I also think it isn't particularly helpful to claim that food stamps are "subsidizing" obesity simply because sodas can be bought directly with food-stamp dollars.  Food stamp recipients are choosing to use their dollars (and food stamp coupons/debit cards) to buy sodas.  Food stamps are only "subsidizing" obesity to the extent that extra income is subsidizing obesity.  It isn't food stamps per se that are causing soda consumption - it is people's preferences for sodas that are leading to soda consumption.