Book of note: “The Personalized Diet” by Eran Segal and Eran Elinav

I have blogged earlier about the book by neuroscientist Sandra Aamodt and have discussed there in passing the pioneering work by Weizmann Institute scientists Eran Segal and Eran Elinav on the individual microbiome (our “gut bacteria population”) and how it affects blood sugar levels. Now the duo has teamed up with editor Eve Adamson, and together they have put out a popularized book:

I am familiar with some of the original papers in top scientific journals—the book is of course much more readable, and the authors and editors have done a good job of presenting their work in lay language while preserving the broad strokes of their work.

The bottom line of their research is this: each of us carries a whole ecosystem of bacteria in our intestines, which help us digest and absorb food. The specific mix of bacteria varies between individuals, and hence so do our responses to different foods. While weight gain/loss is best seen as an outcome—one aspect of overall health—glycemic response, the changes in blood sugar levels after a meal (“postprandial glucose response”) are sufficiently rapid that they can be monitored in real time (e.g. with a continuous glucose monitor) and correlated with what the person ate (logged in a smartphone app). Doing this for thousands of people is a big-data project par excellence, and this is how computer scientist Segal teamed up with gastroenterologist Elinav.

But this isn’t where it ends. Gut bacteria populations can of course be obtained from stool samples, and subjected to analysis—another aspect of the massive big-data puzzle. Moreover, some of what they infer from the data can be checked in an animal model—for instance, certain gut bacteria can be administered to sterile mice and their weight gain (or lack thereof) in response to certain food mixtures tested on a much shorter time scale than would be possible in slow, relatively large, and long-lived mammals like us.

The duo brought different, complementary perspectives to the problem, not just scientifically but personally. Elinav always loved to take machines apart and see how they fit together (fittingly, he did his military service aboard a submarine), then became fascinated with living organisms. He ended up studying medicine, then specializing in internal medicine. During his residency, he was exposed to the human suffering caused by “metabolic syndrome” (the term given to the combination of severe obesity, adult-onset diabetes, fatty liver, hyperlipidemia, and the complications thereof). He realized that they spent all their time as doctors dealing with the consequences and complications rather than with the root cause.

Segal, on the other hand, was an avid long-distance runner in his spare time. He started experimenting with different nutritional approaches to improve his endurance as a runner, assisted in this pursuit by his wife, a clinical dietitian. As he dove deeper into this and observed diets of fellow runners, it became increasingly clear to him that there was no one-size-fits-all, and that recommendations that were held to be gospel truth (or Torah from Sinai, in our case) were, in fact, counterproductive for some. Why do some runners who eat dates before a run become energized and others exhausted? Who do some do best with carb-loading, and indeed thrive on high-carb diets, while others quickly pack on the pounds and suffer from low energy?

Segal was already involved in the computational study of the human genome at the time and then started reading about the emergent field of study of the microbiome. One thing led to another, a mutual acquaintance put Segal and Elinav in touch with each other, and together they embarked on the collaboration that eventually morphed into the personalized nutrition project.

One factor that facilitated their research was that rapid, reliable, and minimally-invasive blood glucose monitoring technology has become relatively inexpensive. And here some of their first surprises came. Anybody who has followed a Gary Taubes-type diet, or who is trying to manage diabetes, is aware of the ‘glycemic index’ (GI) of foods—the increase in blood sugar levels caused by eating a given amount of the food, compared to the same amount of pure glucose (for which GI=100 by definition). But how uniform are these values really?

Segal and Elinav found that the GI for some foods (e.g., bananas) differed very little between their test subjects (say, 60-65), while others (e.g., apples) were all over the place (40-90). Moreover, the variation was not random but correlated with the person.

One would expect glycemic response to go up more or less linearly with the amount of the food consumed was a given. They found that this is indeed true for smaller amounts, but at some point saturation sets in as the body manufactures more insulin, and the glucose response levels off. (This, of course, does not mean you can just eat ten times as much: the insulin will cause the excess energy to be stored as fat!)

More surprising, however, was that higher fat content in the meal on average caused a minor decrease in glycemic response. For a nontrivial number of their participants, eating toast with butter or olive oil actually did less glycemic harm than eating the toast on its own.

Now trying to keep blood sugar levels on a more even keel has two major benefits. In the short term, yo-yoing blood sugar levels lead to a reduction in energy, a feeling of exhaustion as the body pumps out insulin in response to a sugar spike and blood sugar dips. As for the long term: Segal and Elinav found across their sample that glycemic response after habitual meals is strongly correlated with BMI. Keeping blood sugar levels on a more even keel turns out to be a win-win on all counts.

And here’s the catch—”thanks” to our microbiome, glycemic response is highly individual. Segal himself ‘spikes’ after eating rice, while Elinav does not. One person spikes after ice cream, while another does not—and the same person who spikes after an evening snack of ice cream can safely have chocolate instead, go figure.

This addresses a seeming paradox. It’s not that diets don’t work—in fact, many do for some people, though long-term compliance can be an issue—it’s that there is no diet that will work for everyone, or even for most people.

So the next step, then, was to have a computer analyze the data for some of the participants in depth, and have it plan out a personalized diet that would keep blood sugar levels as steady as possible for that patient. Guess what? Yup, you guessed it.

Now some people might be discouraged by the idea of carrying around a blood sugar monitor for two weeks and carefully logging every meal (and physical activity). But once a large enough dataset has been established, and correlated to analyses of the gut flora composition in all the test persons, it becomes possible to predict glycemic responses to different foods with reasonable accuracy based on a bacterial population analysis of stool samples. A startup company named DayTwo is offering to do exactly that. [Full disclosure: I have no financial interest in DayTwo or in any of Drs. Segal and Elinav’s ventures.]

We are at the dawn of a major revolution in healthcare—a shift away from a paradigm of statistical averages to one of detailed monitoring of individual patients. Call it ‘personalized medicine’ or any other buzzword: it does seem poised to radically change healthcare and individual health outcomes for the better.


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