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.”
Biochemist Longxing Cao is working with colleagues at the University of Washington on promising research to disable infectious coronavirus in a person's nose.
This may sounds like science fiction, but several research groups are already working on such trickster coronavirus drugs, with some candidates close to clinical trials and possibly even becoming available late this year. The teams began working on them when the pandemic arrived, and continued in lockdown.
When the pandemic first hit and the state of California issued a lockdown order on March 16, postdoctoral researchers Anum and Jeff Glasgow found themselves stuck at home with nothing to do. The two scientists who study bioengineering felt that they were well equipped to research molecular ways of disabling coronavirus's cell-penetrating spike protein, but they could no longer come to their labs at the University of California San Francisco.
"We were upset that no one put us in the game," says Anum Glasgow. "We have a lot of experience between us doing these types of projects so we wanted to contribute." But they still had computers so they began modeling the potential virus-disabling proteins in silico using Robetta, special software for designing and modeling protein structures, developed and maintained by University of Washington biochemist David Baker and his lab.
"We saw some imperfections in that lock and key and we created something better. We made a 10 times tighter adhesive."
The SARS-CoV-2 virus that causes Covid-19 uses its surface spike protein to bind on to a specific receptor on human cells called ACE2. Unfortunately for humans, the spike protein's molecular shape fits the ACE2 receptor like a well-cut key, making it very successful at breaking into our cells. But if one could design a molecular ACE2-mimic to "trick" the virus into latching onto it instead, the virus would no longer be able to enter cells. Scientists call such mimics receptor traps or inhibitors, or blockers. "It would block the adhesive part of the virus that binds to human cells," explains Jim Wells, professor of pharmaceutical chemistry at UCSF, whose lab took part in designing the ACE2-receptor mimic, working with the Glasgows and other colleagues.
The idea of disabling infectious or inflammatory agents by tricking them into binding to the targets' molecular look-alikes is something scientists have tried with other diseases. The anti-inflammatory drugs commonly used to treat autoimmune conditions, including rheumatoid arthritis, Crohn's disease and ulcerative colitis, rely on conceptually similar molecular mechanisms. Called TNF blockers, these drugs block the activity of the inflammatory cytokines, molecules that promote inflammation. "One of the biggest selling drugs in the world is a receptor trap," says Jeff Glasgow. "It acts as a receptor decoy. There's a TNF receptor that traps the cytokine."
In the recent past, scientists also pondered a similar look-alike approach to treating urinary tract infections, which are often caused by a pathogenic strain of Escherichia coli. An E. coli bacterium resembles a squid with protruding filaments equipped with proteins that can change their shape to form hooks, used to hang onto specific sugar molecules called ligands, which are present on the surface of the epithelial cells lining the urinary tract.
A recent study found that a sugar-like compound that's structurally similar to that ligand can play a similar trick on the E. Coli. When administered in in sufficient amounts, the compound hooks the bacteria on, which is then excreted out of the body with urine. The "trickster" method had been also tried against the HIV virus, but it wasn't very effective because HIV has a high mutation rate and multiple ways of entering human cells.
But the coronavirus spike protein's shape is more stable. And while it has a strong affinity for the ACE2 receptors, its natural binding to these receptors isn't perfect, which allowed the UCSF researchers to design a mimic with a better grip. "We saw some imperfections in that lock and key and we created something better," says Wells. "We made a 10 times tighter adhesive." The team demonstrated that their traps neutralized SARS-CoV-2 in lab experiments and published their study in the Proceedings of the National Academy of Sciences.
Baker, who is the director of the Institute for Protein Design at the University of Washington, was also devising ACE2 look-alikes with his team. Only unlike the UCSF team, they didn't perfect the virus-receptor lock and key combo, but instead designed their mimics from scratch. Using Robetta, they digitally modeled over two million proteins, zeroed-in on over 100,000 potential candidates and identified a handful with a strong promise of blocking SARS-CoV-2, testing them against the virus in human cells. Their design of the miniprotein inhibitors was published in the journal Science.
Biochemist David Baker, pictured in his lab at the University of Washington.
UW
The concept of the ACE2 receptor mimics is somewhat similar to the antibody plasma, but better, the teams explain. Antibodies don't always coat all of the virus's spike proteins and sometimes don't bind perfectly. By contrast, the ACE2 mimics directly compete with the virus's entry mechanism. ACE2 mimics are also easier and cheaper to make, researchers say.
Antibodies, which are long protein chains, must be grown inside mammalian cells, which is a slow and costly process. As drugs, antibody cocktails must be kept refrigerated. On the contrary, proteins that mimic ACE2 receptors are smaller and can be produced by bacteria easily and inexpensively. Designed to specs, these proteins don't need refrigeration and are easy to store. "We designed them to be very stable," says Baker. "Our computation design tries to come up with the stable proteins that have the desired functions."
That stability may allow the team to create an inhaler drug rather than an intravenous one, which would be another advantage over the antibody plasma, given via an IV. The team envisions people spraying the miniprotein solution into their nose, creating a protecting coating that would disable the inhaled virus. "The infection starts from your respiratory system, from your nose," explains Longxing Cao, the study's co-author—so a nasal spray would be a natural way to administer it. "So that you can have it like a layer, similar to a mask."
As the virus evolves, new variants are arising. But the teams think that their ACE2 protein mimics should work on the new variants too for several reasons. "Since the new SARS-CoV-2 variants still use ACE2 for their cell entry, they will likely still be susceptible to ACE2-based traps," Glasgow says.
Cao explains that their approach should work too because most of the mutations happen outside the ACE2 binding region. Plus, they are building multiple binders that can bind to an array of the coronavirus variants. "Our binder can still bind with most of the variants and we are trying to make one protein that could inhibit all the future escape variants," he says.
Baker and Cao hope that their miniproteins may be available to patients later this year. But besides getting the medicine out to patients, this approach will allow researchers to test the computer-modeled mimics end-to-end with an unprecedented speed. That would give humans a leg up in future pandemics or zoonotic disease outbreaks, which remain an increasingly pressing threat due to human activity and climate change.
"That's what we are focused on right now—understanding what we have learned from this pandemic to improve our design methods," says Baker. "So that we should be able to obtain binders like these very quickly when a new pandemic threat is identified."
