Personalized nutrition apps could provide valuable data to people trying to eat healthier, though more research must be done to show effectiveness.
As a type 2 diabetic, Michael Snyder has long been interested in how blood sugar levels vary from one person to another in response to the same food, and whether a more personalized approach to nutrition could help tackle the rapidly cascading levels of diabetes and obesity in much of the western world.
Eight years ago, Snyder, who directs the Center for Genomics and Personalized Medicine at Stanford University, decided to put his theories to the test. In the 2000s continuous glucose monitoring, or CGM, had begun to revolutionize the lives of diabetics, both type 1 and type 2. Using spherical sensors which sit on the upper arm or abdomen – with tiny wires that pierce the skin – the technology allowed patients to gain real-time updates on their blood sugar levels, transmitted directly to their phone.
It gave Snyder an idea for his research at Stanford. Applying the same technology to a group of apparently healthy people, and looking for ‘spikes’ or sudden surges in blood sugar known as hyperglycemia, could provide a means of observing how their bodies reacted to an array of foods.
“We discovered that different foods spike people differently,” he says. “Some people spike to pasta, others to bread, others to bananas, and so on. It’s very personalized and our feeling was that building programs around these devices could be extremely powerful for better managing people’s glucose.”
Unbeknown to Snyder at the time, thousands of miles away, a group of Israeli scientists at the Weizmann Institute of Science were doing exactly the same experiments. In 2015, they published a landmark paper which used CGM to track the blood sugar levels of 800 people over several days, showing that the biological response to identical foods can vary wildly. Like Snyder, they theorized that giving people a greater understanding of their own glucose responses, so they spend more time in the normal range, may reduce the prevalence of type 2 diabetes.
The commercial potential of such apps is clear, but the underlying science continues to generate intriguing findings.
“At the moment 33 percent of the U.S. population is pre-diabetic, and 70 percent of those pre-diabetics will become diabetic,” says Snyder. “Those numbers are going up, so it’s pretty clear we need to do something about it.”
Fast forward to 2022,and both teams have converted their ideas into subscription-based dietary apps which use artificial intelligence to offer data-informed nutritional and lifestyle recommendations. Snyder’s spinoff, January AI, combines CGM information with heart rate, sleep, and activity data to advise on foods to avoid and the best times to exercise. DayTwo–a start-up which utilizes the findings of Weizmann Institute of Science–obtains microbiome information by sequencing stool samples, and combines this with blood glucose data to rate ‘good’ and ‘bad’ foods for a particular person.
“CGMs can be used to devise personalized diets,” says Eran Elinav, an immunology professor and microbiota researcher at the Weizmann Institute of Science in addition to serving as a scientific consultant for DayTwo. “However, this process can be cumbersome. Therefore, in our lab we created an algorithm, based on data acquired from a big cohort of people, which can accurately predict post-meal glucose responses on a personal basis.”
The commercial potential of such apps is clear. DayTwo, who market their product to corporate employers and health insurers rather than individual consumers, recently raised $37 million in funding. But the underlying science continues to generate intriguing findings.
Last year, Elinav and colleagues published a study on 225 individuals with pre-diabetes which found that they achieved better blood sugar control when they followed a personalized diet based on DayTwo’s recommendations, compared to a Mediterranean diet. The journal Cell just released a new paper from Snyder’s group which shows that different types of fibre benefit people in different ways.
“The idea is you hear different fibres are good for you,” says Snyder. “But if you look at fibres they’re all over the map—it’s like saying all animals are the same. The responses are very individual. For a lot of people [a type of fibre called] arabinoxylan clearly reduced cholesterol while the fibre inulin had no effect. But in some people, it was the complete opposite.”
Eight years ago, Stanford's Michael Snyder began studying how continuous glucose monitors could be used by patients to gain real-time updates on their blood sugar levels, transmitted directly to their phone.
The Snyder Lab, Stanford Medicine
Because of studies like these, interest in precision nutrition approaches has exploded in recent years. In January, the National Institutes of Health announced that they are spending $170 million on a five year, multi-center initiative which aims to develop algorithms based on a whole range of data sources from blood sugar to sleep, exercise, stress, microbiome and even genomic information which can help predict which diets are most suitable for a particular individual.
“There's so many different factors which influence what you put into your mouth but also what happens to different types of nutrients and how that ultimately affects your health, which means you can’t have a one-size-fits-all set of nutritional guidelines for everyone,” says Bruce Y. Lee, professor of health policy and management at the City University of New York Graduate School of Public Health.
With the falling costs of genomic sequencing, other precision nutrition clinical trials are choosing to look at whether our genomes alone can yield key information about what our diets should look like, an emerging field of research known as nutrigenomics.
The ASPIRE-DNA clinical trial at Imperial College London is aiming to see whether particular genetic variants can be used to classify individuals into two groups, those who are more glucose sensitive to fat and those who are more sensitive to carbohydrates. By following a tailored diet based on these sensitivities, the trial aims to see whether it can prevent people with pre-diabetes from developing the disease.
But while much hope is riding on these trials, even precision nutrition advocates caution that the field remains in the very earliest of stages. Lars-Oliver Klotz, professor of nutrigenomics at Friedrich-Schiller-University in Jena, Germany, says that while the overall goal is to identify means of avoiding nutrition-related diseases, genomic data alone is unlikely to be sufficient to prevent obesity and type 2 diabetes.
“Genome data is rather simple to acquire these days as sequencing techniques have dramatically advanced in recent years,” he says. “However, the predictive value of just genome sequencing is too low in the case of obesity and prediabetes.”
Others say that while genomic data can yield useful information in terms of how different people metabolize different types of fat and specific nutrients such as B vitamins, there is a need for more research before it can be utilized in an algorithm for making dietary recommendations.
“I think it’s a little early,” says Eileen Gibney, a professor at University College Dublin. “We’ve identified a limited number of gene-nutrient interactions so far, but we need more randomized control trials of people with different genetic profiles on the same diet, to see whether they respond differently, and if that can be explained by their genetic differences.”
Some start-ups have already come unstuck for promising too much, or pushing recommendations which are not based on scientifically rigorous trials. The world of precision nutrition apps was dubbed a ‘Wild West’ by some commentators after the founders of uBiome – a start-up which offered nutritional recommendations based on information obtained from sequencing stool samples –were charged with fraud last year. The weight-loss app Noom, which was valued at $3.7 billion in May 2021, has been criticized on Twitter by a number of users who claimed that its recommendations have led to them developed eating disorders.
With precision nutrition apps marketing their technology at healthy individuals, question marks have also been raised about the value which can be gained through non-diabetics monitoring their blood sugar through CGM. While some small studies have found that wearing a CGM can make overweight or obese individuals more motivated to exercise, there is still a lack of conclusive evidence showing that this translates to improved health.
However, independent researchers remain intrigued by the technology, and say that the wealth of data generated through such apps could be used to help further stratify the different types of people who become at risk of developing type 2 diabetes.
“CGM not only enables a longer sampling time for capturing glucose levels, but will also capture lifestyle factors,” says Robert Wagner, a diabetes researcher at University Hospital Düsseldorf. “It is probable that it can be used to identify many clusters of prediabetic metabolism and predict the risk of diabetes and its complications, but maybe also specific cardiometabolic risk constellations. However, we still don’t know which forms of diabetes can be prevented by such approaches and how feasible and long-lasting such self-feedback dietary modifications are.”
Snyder himself has now been wearing a CGM for eight years, and he credits the insights it provides with helping him to manage his own diabetes. “My CGM still gives me novel insights into what foods and behaviors affect my glucose levels,” he says.
He is now looking to run clinical trials with his group at Stanford to see whether following a precision nutrition approach based on CGM and microbiome data, combined with other health information, can be used to reverse signs of pre-diabetes. If it proves successful, January AI may look to incorporate microbiome data in future.
“Ultimately, what I want to do is be able take people’s poop samples, maybe a blood draw, and say, ‘Alright, based on these parameters, this is what I think is going to spike you,’ and then have a CGM to test that out,” he says. “Getting very predictive about this, so right from the get go, you can have people better manage their health and then use the glucose monitor to help follow that.”
A fierce champion fighter in action, representing the incredible power of the human immune system.
It's vacation time. You and your family visit a country where you've never been and, in fact, your parents or grandparents had never been. You find yourself hiking beside a beautiful lake. It's a gorgeous day. You dive in. You are not alone.
How can your T cells and B cells react to a pathogen they've never seen?
In the water swim parasites, perhaps a parasite called giardia. The invader slips in through your mouth or your urinary tract. This bug is entirely new to you, and there's more. It might be new to everyone you've ever met or come into contact with. The parasite may have evolved in this setting for hundreds of thousands of years so that it's different from any giardia bug you've ever come into contact with before or that thrives in the region where you live.
How can your T cells and B cells react to a pathogen they've never seen, never knew existed, and were never inoculated against, and that you, or your doctors, in all their wisdom, could never have foreseen?
This is the infinity problem.
For years, this was the greatest mystery in immunology.
As I reported An Elegant Defense -- my book about the science of the immune system told through the lives of scientists and medical patients -- I was repeatedly struck by the profundity of this question. It is hard to overstate: how can we survive in a world with such myriad possible threats?
Matt Richtel's new book about the science of the immune system, An Elegant Defense, was published this month.
To further underscore the quandary, the immune system has to neutralize threats without killing the rest of the body. If the immune system could just kill the rest of the body too, the solution to the problem would be easy. Nuke the whole party. That obviously won't work if we are to survive. So the immune system has to be specific to the threat while also leaving most of our organism largely alone.
"God had two options," Dr. Mark Brunvand told me. "He could turn us into ten-foot-tall pimples, or he could give us the power to fight 10 to the 12th power different pathogens." That's a trillion potential bad actors. Why pimples? Pimples are filled with white blood cells, which are rich with immune system cells. In short, you could be a gigantic immune system and nothing else, or you could have some kind of secret power that allowed you to have all the other attributes of a human being—brain, heart, organs, limbs—and still somehow magically be able to fight infinite pathogens.
Dr. Brunvand is a retired Denver oncologist, one of the many medical authorities in the book – from wizened T-cell innovator Dr. Jacques Miller, to the finder of fever, Dr. Charles Dinarello, to his eminence Dr. Anthony Fauci at the National Institutes of Health to newly minted Nobel-Prize winner Jim Allison.
In the case of Dr. Brunvand, the oncologist also is integral to one of the book's narratives, a remarkable story of a friend of mine named Jason. Four years ago, he suffered late, late stage cancer, with 15 pounds of lymphoma growing in his back, and his oncologist put him into hospice. Then Jason became one of the first people ever to take an immunotherapy drug for lymphoma and his tumors disappeared. Through Jason's story, and a handful of other fascinating tales, I showcase how the immune system works.
There are two options for creating such a powerful immune system: we could be pimples or have some other magical power.
Dr. Brunvand had posited to me that there were two options for creating such a powerful and multifaceted immune system: we could be pimples or have some other magical power. You're not a pimple. So what was the ultimate solution?
Over the years, there were a handful of well-intentioned, thoughtful theories, but they strained to account for the inexplicable ability of the body to respond to virtually anything. The theories were complex and suffered from that peculiar side effect of having terrible names—like "side-chain theory" and "template-instructive hypothesis."
This was the background when along came Susumu Tonegawa.
***
Tonegawa was born in 1939, in the Japanese port city of Nagoya, and was reared during the war. Lucky for him, his father was moved around in his job, and so Tonegawa grew up in smaller towns. Otherwise, he might've been in Nagoya on May 14,1944, when the United States sent nearly 550 B-29 bombers to take out key industrial sites there and destroyed huge swaths of the city.
Fifteen years later, in 1959, Tonegawa was a promising student when a professor in Kyoto told him that he should go to the United States because Japan lacked adequate graduate training in molecular biology. A clear, noteworthy phenomenon was taking shape: Immunology and its greatest discoveries were an international affair, discoveries made through cooperation among the world's best brains, national boundaries be damned.
Tonegawa wound up at the University of California at San Diego, at a lab in La Jolla, "the beautiful Southern California town near the Mexican border." There, in multicultural paradise, he received his PhD, studying in the lab of Masaki Hayashi and then moved to the lab of Renato Dulbecco. Dr. Dulbecco was born in Italy, got a medical degree, was recruited to serve in World War II, where he fought the French and then, when Italian fascism collapsed, joined the resistance and fought the Germans. (Eventually, he came to the United States and in 1975 won a Nobel Prize for using molecular biology to show how viruses can lead, in some cases, to tumor creation.)
In 1970, Tonegawa—now armed with a PhD—faced his own immigration conundrum. His visa was set to expire by the end of 1970, and he was forced to leave the country for two years before he could return. He found a job in Switzerland at the Basel Institute for Immunology.
***
Around this time, new technology had emerged that allowed scientists to isolate different segments of an organism's genetic material. The technology allowed segments to be "cut" and then compared to one another. A truism emerged: If a researcher took one organism's genome and cut precisely the same segment over and over again, the resulting fragment of genetic material would match each time.
When you jump in that lake in a foreign land, filled with alien bugs, your body, astonishingly, well might have a defender that recognizes the creature.
This might sound obvious, but it was key to defining the consistency of an organism's genetic structure.
Then Tonegawa found the anomaly.
He was cutting segments of genetic material from within B cells. He began by comparing the segments from immature B cells, meaning, immune system cells that were still developing. When he compared identical segments in these cells, they yielded, predictably, identical fragments of genetic material. That was consistent with all previous knowledge.
But when he compared the segments to identical regions in mature B cells, the result was entirely different. This was new, distinct from any other cell or organism that had been studied. The underlying genetic material had changed.
"It was a big revelation," said Ruslan Medzhitov, a Yale scholar. "What he found, and is currently known, is that the antibody-encoding genes are unlike all other normal genes."
The antibody-encoding genes are unlike all other normal genes.
Yes, I used italics. Your immune system's incredible capabilities begin from a remarkable twist of genetics. When your immune system takes shape, it scrambles itself into millions of different combinations, random mixtures and blends. It is a kind of genetic Big Bang that creates inside your body all kinds of defenders aimed at recognizing all kinds of alien life forms.
So when you jump in that lake in a foreign land, filled with alien bugs, your body, astonishingly, well might have a defender that recognizes the creature.
Light the fireworks and send down the streamers!
As Tonegawa explored further, he discovered a pattern that described the differences between immature B cells and mature ones. Each of them shared key genetic material with one major variance: In the immature B cell, that crucial genetic material was mixed in with, and separated by, a whole array of other genetic material.
As the B cell matured into a fully functioning immune system cell, much of the genetic material dropped out. And not just that: In each maturing B cell, different material dropped out. What had begun as a vast array of genetic coding sharpened into this particular, even unique, strand of genetic material.
***
This is complex stuff. But a pep talk: This section is as deep and important as any in describing the wonder of the human body. Dear reader, please soldier on!
***
Researchers, who, eventually, sought a handy way to define the nature of the genetic change to the material of genes, labeled the key genetic material in an antibody with three initials: V, D, and J.
The letter V stands for variable. The variable part of the genetic material is drawn from hundreds of genes.
D stands for diversity, which is drawn from a pool of dozens of different genes.
And J is drawn from another half dozen genes.
In an immature B cell, the strands of V, D, and J material are in separate groupings, and they are separated by a relatively massive distance. But as the cell matures, a single, random copy of V remains, along with a single each of D and J, and all the other intervening material drops out. As I began to grasp this, it helped me to picture a line of genetic material stretching many miles. Suddenly, three random pieces step forward, and the rest drops away.
The combination of these genetic slices, grouped and condensed into a single cell, creates, by the power of math, trillions of different and virtually unique genetic codes.
In anticipation of threats from the unfathomable, our defenses evolved as infinity machines.
Or if you prefer a different metaphor, the body has randomly made hundreds of millions of different keys, or antibodies. Each fits a lock that is located on a pathogen. Many of these antibodies are combined such that they are alien genetic material—at least to us—and their locks will never surface in the human body. Some may not exist in the entire universe. Our bodies have come stocked with keys to the rarest and even unimaginable locks, forms of evil the world has not yet seen, but someday might. In anticipation of threats from the unfathomable, our defenses evolved as infinity machines.
"The discoveries of Tonegawa explain the genetic background allowing the enormous richness of variation among antibodies," the Nobel Prize committee wrote in its award to him years later, in 1987. "Beyond deeper knowledge of the basic structure of the immune system these discoveries will have importance in improving immunological therapy of different kinds, such as, for instance, the enforcement of vaccinations and inhibition of reactions during transplantation. Another area of importance is those diseases where the immune defense of the individual now attacks the body's own tissues, the so-called autoimmune diseases."
Indeed, these revelations are part of a period of time it would be fair to call the era of immunology, stretching from the middle of the 20th century to the present. During that period, we've come from sheer ignorance of the most basic aspects of the immune system to now being able to tinker under the hood with monoclonal antibodies and other therapies. And we are, in many ways, just at the beginning.
