Cornell Food and Brand Lab: an Update

After mentioning the embattled Brian Wansink and the Cornell Food and Brand Lab in my post last week, a friend passed along the most recent update on this story. Interestingly it appears Buzzfeed is the news outlet doing the most reporting on this story as it continues to develop.

A quick review:
Brian Wansink is the head of the Cornell Food and Brand Lab, which publishes all sorts of interesting research about the psychology behind eating and how we process information on health. Even if you’ve never heard his name, you may have heard about his work….studies like “people eat more soup if their bowl refills so they don’t know how much they’ve eaten”  or “kids eat more vegetables when they have fun names” tend to be from his lab.

About a year ago, he published a blog post where he praised one of his grad students for taking a data set that didn’t really show much and turning it in to 5 publishable papers.  This turned in to an enormous scandal as many people quickly pointed out that a feat like that almost certainly involved lots of data tricks that would make the papers results very likely to be false.  As the scrutiny went up, things got worse as now people were pouring over his previous work.

Not only did this throw Wansink’s work in to question, but a lot of people (myself included) who had used his work in their work now had to figure out whether or not to retract or update what they had written. Ugh.

So where are we now?
Well as I mentioned, Buzzfeed has been making sure this doesn’t drop. In September, they reported that the aforementioned “veggie with fun names” study had a lot of problems. Worse yet, Wansink couldn’t produce the data when asked.   What was incredibly concerning is that this particular paper is part of a program Wansink was piloting for school lunches. With his work under scrutiny, over $20 million in research and training grants may have gone towards strategies that may not actually be effective. To be clear, the “fun veggie name study” wasn’t the only part of this program, but it’s definitely not encouraging to find out that parts of it are so shaky.

To make things even worse, they are now reporting that several of his papers that allegedly were done on three different topics in three different years sent to three different sample populations show the exact same number of survey respondents: 770. Those papers are being reviewed.

Finally, the report he has a 4th paper being retracted, this one on WWII veterans and cooking habits. An interview with the researcher who helped highlight the issues with the paper is up here at Retraction Watch, and some of the problems with the paper are pretty amazing. When asked where he first noted problems, he said: “First there is the claim that only 80% of people who saw heavy, repeated combat during WW2 were male.”  Yeah, that seems a little off. Wansink has responded to the Buzzfeed report to say that this was due to a spreadsheet error.

Overall, the implications of this are going to be felt for a while. While only 4 papers have been retracted so far, Buzzfeed reports that 8 more have planned corrections, and over 50 are being looked at. With such a prolific lab and results that are used in so many places, this story could go on for years. I appreciate the journalists keeping up on this story as it’s an incredibly important cautionary tale for members of the scientific community and the public alike.

Food Insecurity: A Semester in Review

I mentioned a few months back that I was working on my capstone project for my degree this semester. I’ve mostly finished it up (just adjusting some formatting), so I thought it would be a good moment to post on my project and some of my findings. Since I have to present this all in a week or two, it’s a good moment to gather my thoughts as well.

The American Time Use Survey is a national survey carried out by the Bureau of Labor Statistics that surveys Americans about how they spend their time. From 2014-2016 they administered a survey module that asked specifically about health status and behaviors. They make the questionnaire and data files publicly available here.

What interested me about this data set is that they asked specifically about food insecurity….i.e. “Which of the following statements best describes the amount of food eaten in your household in the last 30 days – enough food to eat, sometimes not enough to eat, or often not enough to eat?” Based on that data, I was able to compare those who were food secure (those who said “I had enough food to eat”) vs the food insecure (those who said they “sometimes” or “frequently” did not have enough to eat.

This is an interesting comparison to make, because there’s some evidence that in the US these two groups don’t always look like what you’d expect. Previous work has found that people who report they are food insecure actually tend to weigh more than those who are food secure. I broke my research down in to three categories:

  1. Confirmation of BMI differences
  2. Comparison of health habits between food secure and food insecure people
  3. Correlation of specific behaviors with BMI within the food insecure group

Here’s what I found:

Confirmation of BMI differences:
Yes, the paradox is true for this data set. Those who were “sometimes” or “frequently” food insecure were almost 2 BMI points heavier than those who were food secure…around 10-15 pounds for most height ranges. Level of food insecurity didn’t seem to matter, and the effect persisted even after controlling for public assistance and income.

Interestingly, my professor asked me if the BMI difference was due more to food insecure people being shorter (indicating a possible nutritional deficiency) or from being heavier, and it turns out it’s both. The food insecure group was about an inch shorter and 8 lbs heavier than the food secure group.

Differences in health behaviors or status:
Given my sample size (over 20,000), most of the questions they asked ended up having statistically significant differences. The ones that seemed to be both practically and statistically significant were:

  1. Health status People who were food insecure were WAY more likely to say they were in poor health. This isn’t terribly surprising since disability would impact people’s assessment of their health status and ability to work/earn a living.
  2. Shopping habits While most people from both groups did their grocery shopping at grocery stores, food insecure people were more likely to use other stores like “supercenters” (i.e. Walmart or Target) and convenience stores or “other” types of stores. Food secure people were more likely to use places like Costco or Sam’s Club. Unsurprisingly, people who were food insecure were much more likely to say they selected their stores based on the prices. My brother had asked specifically up front if “food deserts” were an issue, so I did note that the two groups answered “location” was a factor in their shopping at equal rates.
  3. Soda consumption Food insecure people were much more likely to have drank soda in the last 7 days (50% vs 38%) and much less likely to say it was a diet soda (40% vs 21.5%) than the food secure group.
  4. Exercise Food insecure people were much less likely to have exercised in the last 7 days (50.5%) than food secure people were (63.9%). Given the health status ranking, this doesn’t seem surprising.
  5. Food shopping/preparation Food insecure people were much more likely to be the primary food shopper and preparer. This makes sense when you consider that food insecurity is a self reported metric. If you’re the one looking at the bills, you’re probably more likely to feel insecure than if you’re not. Other researchers have noted that many food stamp recipients will also cut their own intake to make sure their children have enough food.

Yes, I have confidence intervals for all of these, but I’m sparing you.

BMI correlation within the food insecure group:
Taking just the group that said they were food insecure, I then took a look at which factors were most associated with higher BMIs. These were:

  1. Time spent eating Interestingly, increased time spent eating was actually associated with lower BMIs. This may indicate that people who can plan regular meal times might be healthier than those eating while doing other things (the survey asked about both).
  2. Drinking beverages other than water Those who regularly drank beverages other than water were heavier than those who didn’t
  3. Lack of exercise No shock here
  4. Poor health The worse the self assessed health, the higher the BMI. It’s hard to tease out the correlation/causation here. Are people in bad health due to an obesity related illness (like diabetes) or are they obese because they have an issue that makes it hard for them to move (like a back injury)? Regardless, this correlation was QUITE strong: people in “excellent” health had BMIs almost 5 points lower than those in “poor” health.
  5. Being the primary shopper I’m not clear on why this association exists, but primary shoppers were 2 BMI points heavier than those that shared shopping duties.
  6. Public assistance  Those who were food insecure AND received public assistance were heavier than those who were just food insecure.

It should be noted that I did nothing to establish causality here, everything reported is just an association. Additionally, it’s interesting to note a few things that didn’t show up here: fast food consumption, shopping locations and snacking all didn’t make much of a difference.

While none of this is definitive, I thought it was an interesting exploration in to the topic. I have like 30 pages of this stuff, so I can definitely clarify anything I didn’t go in to. Now to put my presentation together and be done with this!


Eating Season

Happy almost Thanksgiving! Please enjoy this bit of trivia I recently stumbled on about American food consumption patterns during this time of year! It’s from the book “Devoured: From Chicken Wings to Kale Smoothies – How What We Eat Defines Who We Are” by Sophie Egan.

From page 173:

A few paragraphs later, she goes a bit more in depth about what happens to shopping habits (note: she quotes the embattled Cornell Food and Brand lab, but since their data matches another groups data on this, I’m guessing it’s pretty solid):

I had no idea that “eating season” had gone so far outside the bounds of what I think of as the holiday season. Kinda makes you wonder if this is all just being driven by winter and the holidays are just an excuse.

On a related note, my capstone project is done/accepted with no edits and I will probably be putting up some highlights about my research in to food insecurity and health habits on Sunday.

Happy Thanksgiving!

Blood Sugar Model Magik?

An interesting new-to-me study came on my radar this week “Personalized Nutrition by Prediction of Glycemic Responses” published by Zeevi et al in 2015. Now, if you’ve ever had the unfortunate experience of talking about food with me in real life, you probably know I am big on  quantifying things and particularly obsessed with blood sugar numbers. The blood sugar numbers thing started when I was pregnant with my son and got gestational diabetes. 4 months of sticking yourself with a needle a couple of times a day will do that to a person.

Given that a diagnosis of gestational diabetes is correlated with a much higher risk of an eventual Type 2 diabetes diagnosis, I’ve been pretty interested in what effects blood sugar numbers. One of those things is the post-prandial glucose response (PPGR) or basically how high your blood sugar numbers go after you eat a meal. Unsurprisingly, chronically high numbers after meals tend to correlate with overall elevated blood sugar and diabetes risk. To try and help people manage this response the glycemic index was created, which attempted to measure what an “average” glucose response to particular foods. This sounds pretty good, but the effects of using this as a basis for food choices in non-diabetics have been kind of mixed. While it appears that eating all high glycemic index foods (aka refined carbs) is bad, it’s not clear that parsing things out further is very helpful.

There are a lot of theories about why glycemic index may not work that well: measurement issues (it measures an area under a curve without taking in to account the height of the spike), the quantities of food eaten (watermelon has a high glycemic index, but it’s hard to eat too much of it calorie-wise), or the effects of mixing foods with each other (the values were determined by having people eat just one food at a time). Zeevi et al had yet another theory: maybe the problem was taking the “average” response. Given that averages can often hide important information about the population they’re describing, they wondered if individual variability was mucking about with the accuracy of the numbers.

To test this theory, they recruited 800 people, got a bunch of information about them, and hooked them up to a continuous glucose monitor and had them log what they ate. They discovered that while some foods caused a similar reaction in everyone (white bread for example), some foods actually produced really different responses (pizza or bananas for example). They then used factors like BMI, activity level, gut microbiome data to build a model that they hoped would predict who would react to what food.

To give this study some real teeth, they then took the model they built and applied it to 100 new study participants. This is really good because it means they tested if they overfit their model….i.e. tailored it too closely to the original group to get an exaggerated correlation number. They showed that their model worked just as well on the new group as the old group (r=.68 vs r=.70). To take it a step further, they recruited 26 more people, got their data, then feed them a diet predicted to be either “good” or “bad” for them.  They found overall that eating the “good” diet helped keep blood sugar in check as compared to just regular carbohydrate counting.

The Atlantic did a nice write up of the study here, but a few interesting/amusing things I wanted to note:

  1. Compliance was high Nutrition research has been plagued by self reporting bias and low compliance to various diets, but apparently that wasn’t a problem in this study. The researchers found that by emphasizing to people what the immediate benefit to them would be (a personalized list of “good” and “bad” foods, people got extremely motivated to be honest. Not sure how this could be used in other studies, but it was interesting.
  2. They were actually able to double blind the study Another chronic issue with nutrition research is the inability to blind people to what they’re eating. However, since people didn’t know what their “good” foods were, it actually was possible to do some of that for this study. For example, some people were shocked to find that their “good” diet had included ice cream or chocolate.
  3. Carbohydrates  and fat content were correlated with PPGR, but not at the same level for everyone At least for glucose issues, it turns out the role of macronutrients was more pronounced in some people than others. This has some interesting implications for broad nutrition recommendations.
  4. Further research confirmed the issues with glycemic index  In the Atlantic article, some glycemic index proponents were cranky because this study only compared itself to carb counting, not the glycemic index. Last year some Tufts researchers decided to focus just on the glycemic index response and found that inter-person variability was high enough that they didn’t recommend using it.
  5. The long term effects remain to be seen It’s good to note that the nutritional intervention portion of this study was just one week, so it’s not yet clear if this information will be helpful in the long run. On the one hand, it seems like personalized information could be really helpful to people…it’s probably easier to avoid cookies if you know you can still have ice cream. On the other hand, we don’t yet know how stable these numbers are. If you cut out cookies entirely but keep ice cream in your diet, will your body react to it the same way in two years?

That last question, along with “how does this work in the real world” is where the researchers are going next. They want to see if people getting personalized information are less likely to develop diabetes over the long term. I can really see this going either way. Will people get bored and revert to old eating patterns? Will they overdo it on foods they believe are “safe”? Or will finding out you can allow some junk food increase compliance and avoid disease? As you can imagine, they are having no trouble recruiting people. 4,000 people (in Israel) are already on their waiting list, begging to sign up for future studies. I’m sure we’ll hear more about this in the years to come.

Personally, I’m fascinated by the whole concept. I read about this study in Robb Wolf’s new book “Wired to Eat“, in which he proposes a way people can test their own tolerance for various carbohydrates at home. Essentially you follow a low to moderate carbohydrate paleo (no dairy, no legumes, no grain) plan for 30 days, then test your blood glucose response to a single source of carbohydrates every day for 7 days. I plan on doing this and will probably post the results here. Not sure what I’ll do with the results, but like I said, I’m a sucker for data experiments like this.

Soda bans and research misapplications

When I first read about Mayor Bloomberg’s proposed soda restrictions for NYC, I immediately thought of this post where I mentioned the utter failure of removing vending machines from schools.  Thus, I was extremely skeptical that this ban would work at all, and it seemed quite an intrusion in to private business for what I saw as an untested theory.

To be honest, I didn’t put much more thought in to it.  I saw the studies about people eating more from large containers floating around, but I dismissed on the basis that (like with the vending machine theory) they were skipping a crucial step.  Even if this ban got people to drink less soda, that doesn’t actually prove it would reduce obesity.  You have to prove all the steps in the series to prove the conclusion.

A few days ago, the authors of the “bigger containers cause people to eat more” study published their own rebuttal to the ban.  In an excellent example of the clash of politics and research, they claim that to apply their work on portion sizes in this manor is a misreading of the body of their work.  They highlight that the larger containers study was done by assigning portion sizes at random, to subjects who had no expectations as to what they would be getting.  In their words, the ban is a problem because (highlight mine):

Banning larger sizes is a visible and controversial idea. If it fails, no one will trust that the next big — and perhaps better — idea will work, because “Look what happened in New York City.” It poisons the water for ideas that may have more potential.

Second, 150 years of research in food economics tells us that people get what they want. Someone who buys a 32-ounce soft drink wants a 32-ounce soft drink. He or she will go to a place that offers fountain refills, or buy two. If the people who want them don’t have much money, they might cut back on fruits or vegetables or a bit of their family meal budget.

In essence, by removing the random element and forcibly replacing what people want with something the don’t, you frequently will have the worst possible effect: rebellion.

Mindless eating can be a problem, but rebellious eating is even worse.

When the researchers you’re trying to use to back yourself up start protesting your policies, you know you got it all wrong.

Food Deserts and Big City Living

The Assistant Village Idiot did a good post on a new report on the prevalence of “food deserts” and if this was the crisis it’s been reported to be.

While I will point out that the study refuting the idea of food deserts uses self reported data for height, weight and eating habits (check out my previous post on this issue), I was glad to see someone take this issue on.  Food deserts reporting has always fascinated me, mostly because I lived in the middle of the Boston area (albeit in different locations) for about 9 years.   The food desert idea always sort of baffled me, and when I took a look at the USDAs food desert locator, I notice that the only part of Boston proper or the close suburbs that qualifies as a food desert is…..Logan Airport.

I currently live in a suburb that is near 2 food deserts, so checking those out was interesting as well.  One is actually a small peninsula, and I happen to know you have to drive by a grocery store to get on the main route out there.  The other is next to the docks.

For cities, this data gets complicated by the fact that many very small grocers sell all sorts of produce in small spaces that wouldn’t make the list.  For rural areas, personal gardens are not counted.  I also liked that the article pointed out that some people researching this have done grocery stores/1000 people, a metric which make cities look bleak.  That’s a classic case of needing to review why you actually want the data.  A busy grocery store is not a lack of a grocery store.  Additionally, I have never seen one of these surveys that added in farmer’s markets or grocery store delivery services.  While not always the cheapest option, delivery services allowed me (when I was a broke college student) to buy in bulk and save money other ways.  They run about $7 ($5 when I was in college), when a train ride to and from the store was $4 round trip, and a taxi would have been at least $10 (not counting ghost taxis that exist almost exclusively in front of city grocery stores and help you with your groceries for around $5).

Overall, I’m sure access is an issue for some people, I just balk when people who don’t live in the middle of cities on a limited budget like I did try to tell me what it’s like.  I DO think that before we flip out about an issue, doing research as to how much access really affects obesity is key.  The number of regulations and reforms that get pushed without any data proving their relevance staggers me, and I’m glad to see someone questioning the wisdom in this case.