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.”
Peanuts on a plate can be deadly for those with severe allergies, but an Israeli startup company wants to alleviate that fear.
People with life-threatening allergies live in constant fear of coming into contact with deadly allergens. Researchers estimate that about 32 million Americans have food allergies, with the most severe being milk, egg, peanut, tree nuts, wheat, soy, fish, and shellfish.
"It is important to understand that just several years ago, this would not have been possible."
Every three minutes, a food allergy reaction sends someone to the emergency room, and 200,000 people in the U.S. require emergency medical care each year for allergic reactions, according to Food Allergy Research and Education.
But what if there was a way you could easily detect if something you were about to eat contains any harmful allergens? Thanks to Israeli scientists, this will soon be the case — at least for peanuts. The team has been working to develop a handheld device called Allerguard, which analyzes the vapors in your meal and can detect allergens in 30 seconds.
Leapsmag spoke with the founder and CTO of Allerguard, Guy Ayal, about the groundbreaking technology, how it works, and when it will be available to purchase.
What prompted you to create this device? Do you have a personal connection with severe food allergies?
Guy Ayal: My eldest daughter's best friend suffers from a severe food allergy, and I experienced first-hand the effect it has on the person and their immediate surroundings. Most notable for me was the effect on the quality of life – the experience of living in constant fear. Everything we do at Allerguard is basically to alleviate some of that fear.
How exactly does the device work?
The device is built on two main pillars. The first is the nano-chemical stage, in which we developed specially attuned nanoparticles that selectively adhere only to the specific molecules that we are looking for. Those molecules, once bound to the nanoparticles, induce a change in their electrical behavior, which is measured and analyzed by the second main pillar -- highly advanced machine learning algorithms, which can surmise which molecules were collected, and thus whether or not peanuts (or in the future, other allergens) were detected.
It is important to understand that just several years ago, this would not have been possible, because both the nano-chemistry, and especially the entire world of machine learning, big data, and what is commonly known as AI only started to exist in the '90s, and reached applicability for handheld devices only in the past few years.
Where are you at in the development process and when will the device be available to consumers?
We have concluded the proof of concept and proof of capability phase, when we demonstrated successful detection of the minimal known clinical amount that may cause the slightest effect in the most severely allergic person – less than 1 mg of peanut (actually it is 0.7 mg). Over the next 18 months will be productization, qualification, and validation of our device, which should be ready to market in the latter half of 2021. The sensor will be available in the U.S., and after a year in Europe and Canada.
The Allerguard was made possible through recent advances in machine learning, big data, and AI.
(Courtesy)
How much will it cost?
Our target price is about $200 for the device, with a disposable SenseCard that will run for at least a full day and cost about $1. That card is for a specific allergen and will work for multiple scans in a day, not just one time.
[At a later stage, the company will have sensors for other allergens like tree nuts, eggs, and milk, and they'll develop a multi-SenseCard that works for a few allergens at once.]
Are there any other devices on the market that do something similar to Allerguard?
No other devices are even close to supplying the level of service that we promise. All known methods for allergen detection rely on sampling of the food, which is a viable solution for homogenous foodstuffs, such as a factory testing their raw ingredients, but not for something as heterogenous as an actual dish – especially not for solid allergens such as peanuts, treenuts, or sesame.
If there is a single peanut in your plate, and you sample from anywhere on that plate which is not where that peanut is located, you will find that your sample is perfectly clean – because it is. But the dish is not. That dish is a death trap for an allergic person. Allerguard is the only suggested solution that could indeed detect that peanut, no matter where in that plate it is hiding.
Anything else readers should know?
Our first-generation product will be for peanuts only. You have to understand, we are still a start-up company, and if we don't concentrate our limited resources to one specific goal, we will not be able to achieve anything at all. Once we are ready to market our first device, the peanut detector, we will be able to start the R&D for the 2nd product, which will be for another allergen – most likely tree nuts and/or sesame, but that will probably be in debate until we actually start it.
