AI and you: Is the promise of personalized nutrition apps worth the hype?
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.”
Your phone could show if a bridge is about to collapse
In summer 2017, Thomas Matarazzo, then a postdoctoral researcher at the Massachusetts Institute of Technology, landed in San Francisco with a colleague. They rented two cars, drove up to the Golden Gate bridge, timing it to the city’s rush hour, and rode over to the other side in heavy traffic. Once they reached the other end, they turned around and did it again. And again. And again.
“I drove over that bridge 100 times over five days, back and forth,” says Matarazzo, now an associate director of High-Performance Computing in the Center for Innovation in Engineering at the United States Military Academy, West Point. “It was surprisingly stressful, I never anticipated that. I had to maintain the speed of about 30 miles an hour when the speed limit is 45. I felt bad for everybody behind me.”
Matarazzo had to drive slowly because the quality of data they were collecting depended on it. The pair was designing and testing a new smartphone app that could gather data about the bridge’s structural integrity—a low-cost citizen-scientist alternative to the current industrial methods, which aren’t always possible, partly because they’re expensive and complex. In the era of aging infrastructure, when some bridges in the United States and other countries are structurally unsound to the point of collapsing, such an app could inform authorities about the need for urgent repairs, or at least prompt closing the most dangerous structures.
There are 619,588 bridges in the U.S., and some of them are very old. For example, the Benjamin Franklin Bridge connecting Philadelphia to Camden, N.J., is 96-years-old while the Brooklyn Bridge is 153. So it’s hardly surprising that many could use some upgrades. “In the U.S., a lot of them were built in the post-World War II period to accommodate the surge of motorization,” says Carlo Ratti, architect and engineer who directs the Senseable City Lab at Massachusetts Institute of Technology. “They are beginning to reach the end of their life.”
According to the 2022 American Road & Transportation Builders Association’s report, one in three U.S. bridges needs repair or replacement. The Department of Transportation (DOT) National Bridge Inventory (NBI) database reveals concerning numbers. Thirty-six percent of U.S. bridges need repair work and over 78,000 bridges should be replaced. More than 43,500 bridges are rated in poor condition and classified as “structurally deficient” – an alarming description. Yet, people drive over them 167.5 million times a day. The Pittsburgh bridge which collapsed in January this year—only hours before President Biden arrived to discuss the new infrastructure law—was on the “poor” rating list.
Assessing the structural integrity of a bridge is not an easy endeavor. Most of the time, these are visual inspections, Matarazzo explains. Engineers check cracks, rust and other signs of wear and tear. They also check for wildlife—birds which may build nests or even small animals that make homes inside the bridge structures, which can slowly chip at the structure. However, visual inspections may not tell the whole story. A more sophisticated and significantly more expensive inspection requires placing special sensors on the bridge that essentially listen to how the bridge vibrates.
“Some bridges can afford expensive sensors to do the job, but that comes at a very high cost—hundreds of thousands of dollars per bridge per year,” Ratti says.
We may think of bridges as immovable steel and concrete monoliths, but they naturally vibrate, oscillating slightly. That movement can be influenced by the traffic that passes over them, and even by wind. Bridges of different types vibrate differently—some have longer vibrational frequencies and others shorter ones. A good way to visualize this phenomenon is to place a ruler over the edge of a desk and flick it slightly. If the ruler protrudes far off the desk, it will vibrate slowly. But if you shorten the end that hangs off, it will vibrate much faster. It works similarly with bridges, except there are more factors at play, including not only the length, but also the design and the materials used.
The long suspension bridges such as the Golden Gate or Verrazano Narrows, which hang on a series of cables, are more flexible, and their vibration amplitudes are longer. The Golden Gate Bridge can vibrate at 0.106 Hertz, where one Hertz is one oscillation per second. “Think about standing on the bridge for about 10 seconds—that's how long it takes for it to move all the way up and all the way down in one oscillation,” Matarazzo says.
On the contrary, the concrete span bridges that rest on multiple columns like Brooklyn Bridge or Manhattan Bridge, are “stiffer” and have greater vibrational frequencies. A concrete bridge can have a frequency of 10 Hertz, moving 10 times in one second—like that shorter stretch of a ruler.
The special devices that can pick up and record these vibrations over time are called accelerometers. A network of these devices for each bridge can cost $20,000 to $50,000, and more—and require trained personnel to place them. The sensors also must stay on the bridge for some time to establish what’s a healthy vibrational baseline for a given bridge. Maintaining them adds to the cost. “Some bridges can afford expensive sensors to do the job, but that comes at a very high cost—hundreds of thousands of dollars per bridge per year,” Ratti says.
Making sense of the readouts they gather is another challenge, which requires a high level of technical expertise. “You generally need somebody, some type of expert capable of doing the analysis to translate that data into information,” says Matarazzo, which ticks up the price, so doing visual inspections often proves to be a more economical choice for state-level DOTs with tight budgets. “The existing systems work well, but have downsides,” Ratti says. The team thought the old method could use some modernizing.
Smartphones, which are carried by millions of people, contain dozens of sensors, including the accelerometers capable of picking up the bridges’ vibrations. That’s why Matarazzo and his colleague drove over the bridge 100 times—they were trying to pick up enough data. Timing it to rush hour supported that goal because traffic caused more “excitation,” Matarazzo explains. “Excitation is a big word we use when we talk about what drives the vibration,” he says. “When there's a lot of traffic, there's more excitation and more vibration.” They also collaborated with Uber, whose drivers made 72 trips across the bridge to gather data in different cars.
The next step was to clean the data from “noise”—various vibrations that weren’t relevant to the bridge but came from the cars themselves. “It could be jumps in speed, it could be potholes, it could be a bunch of other things," Matarazzo says. But as the team gathered more data, it became easier to tell the bridge vibrational frequencies from all others because the noises generated by cars, traffic and other things tend to “cancel out.”
The team specifically picked the Golden Gate bridge because the civil structural engineering community had studied it extensively over the years and collected a host of vibrational data, using traditional sensors. When the researchers compared their app-collected frequencies with those gathered by 240 accelerometers formerly placed on the Golden Gate, the results were the same—the data from the phones converged with that from the bridge’s sensors. The smartphone-collected data were just as good as those from industry devices.
The study authors estimate that officials could use crowdsourced data to make key improvements that would help new bridges to last about 14 years longer.
The team also tested their method on a different type of bridge—not a suspension one like the Golden Gate, but a concrete span bridge in Ciampino, Italy. There they compared 280 car trips over the bridge to the six sensors that had been placed on the bridge for seven months. The results were slightly less matching, but a larger volume of trips would fix the divergence, the researchers wrote in their study, titled Crowdsourcing bridge dynamic monitoring with smartphone vehicle trips, published last month in Nature Communications Engineering.
Although the smartphones proved effective, the app is not quite ready to be rolled out commercially for people to start using. “It is still a pilot version,” so there’s room for improvement, says Ratti, who co-authored the study. “But on a more optimistic note, it has really low barriers to entry—all you need is smartphones on cars—so that makes the system easy to reach a global audience.” And the study authors estimate that the use of crowdsourced data would result in a new bridge lasting about 14 years longer.
Matarazzo hopes that the app could be eventually accessible for your average citizen scientist to collect the data and supply it to their local transportation authorities. “I hope that this idea can spark a different type of relationship with infrastructure where people think about the data they're collecting as some type of contribution or investment into their communities,” he says. “So that they can help their own department of transportation, their own municipality to support that bridge and keep it maintained better, longer and safer.”
Lina Zeldovich has written about science, medicine and technology for Popular Science, Smithsonian, National Geographic, Scientific American, Reader’s Digest, the New York Times and other major national and international publications. A Columbia J-School alumna, she has won several awards for her stories, including the ASJA Crisis Coverage Award for Covid reporting, and has been a contributing editor at Nautilus Magazine. In 2021, Zeldovich released her first book, The Other Dark Matter, published by the University of Chicago Press, about the science and business of turning waste into wealth and health. You can find her on http://linazeldovich.com/ and @linazeldovich.
The Friday Five: Sugar could help catch cancer early
The Friday Five covers five stories in research that you may have missed this week. There are plenty of controversies and troubling ethical issues in science – and we get into many of them in our online magazine – but this news roundup focuses on scientific creativity and progress to give you a therapeutic dose of inspiration headed into the weekend.
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Here are the promising studies covered in this week's Friday Five:
- Catching cancer early could depend on sugar
- How to boost memory in a flash
- This is your brain on books
- A tiny sandwich cake could help the heart
- Meet the top banana for fighting Covid variants