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.

3 thoughts on “Blood Sugar Model Magik?

  1. I track my blood-sugars twice a day for 30 days twice a year, leading up to my appointment. I was shocked to see that wine didn’t affect my blood-sugar that much, but a small bag of cheetos sends me off the charts. Fair trade! Doing any sort of 30-day major diet restriction to test subsequent responses just ain’t gonna happen, but it would be valuable to know specific rather than general tradeoffs. No corn, potatoes in moderation, wheat and rice I can flex more than you’d think? Yeah, I can do that.

    Liked by 1 person

Comments are closed.