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.”
Miniature lab-grown models of human organs have major advantages over standard animal testing.
But he wasn't the only one to follow this train of thought. In the year since the pandemic began, many researchers have been using organoids to study how the coronavirus infects human cells, and find potential treatments. Beumer's pivot represents a remarkable and fast-emerging paradigm shift in how drugs and diseases will be studied in the coming decades. With future pandemics likely to be more frequent and deadlier, such a shift is necessary to reduce the average clinical development time of 5.9 years for antiviral agents.
Part of that shift means developing models that replicate human biology in the lab. Animal models, which are the current standard in biomedical research, fail to do so—96% of drugs that pass animal testing, for example, fail to make it to market. Injecting potentially toxic drugs into living creatures, before eventually slaughtering them, also raises ethical concerns for some. Organoids, on the other hand, respond to infectious diseases, or potential treatments, in a way that is relevant to humans, in addition to being slaughter-free.
Human intestinal organoids infected with SARS-CoV-2 (white).
Credit: Joep Beumer/Clevers group/Hubrecht Institute
Urgency Sparked Momentum
Though brain organoids were previously used to study the Zika virus during the 2015-16 epidemic, it wasn't until COVID-19 that the field really started to change. "The organoid field has advanced a lot in the last year. The speed at which it happened is crazy," says Shuibing Chen, an associate professor at Weill Cornell Medicine in New York. She adds that many federal and private funding agencies have now seen the benefits of organoids, and are starting to appreciate their potential in the biomedical field.
Last summer, the Organo-Strat (OS) network—a German network that uses human organoid models to study COVID-19's effects—received 3.2 million euros in funding from the German government. "When the pandemic started, we became aware that we didn't have the right models to immediately investigate the effects of the virus," says Andreas Hocke, professor of infectious diseases at the Charité Universitätsmedizin in Berlin, Germany, and coordinator of the OS network. Hocke explained that while the World Health Organization's animal models showed an "overlap of symptoms'' with humans, there was "no clear reflection" of the same disease.
"The network functions as a way of connecting organoid experts with infectious disease experts across Germany," Hocke continues. "Having organoid models on demand means we can understand how a virus infects human cells from the first moment it's isolated." Overall, OS aims to create infrastructure that could be applied to future pandemics. There are 28 sub-projects involved in the network, covering a wide assortment of individual organoids.
Cost, however, remains an obstacle to scaling up, says Chen. She says there is also a limit to what we can learn from organoids, given that they only represent a single organ. "We can add drugs to organoids to see how the cells respond, but these tests don't tell us anything about drug metabolism, for example," she explains.
A Related "Leaps" in Progress
One way to solve this issue is to use an organ-on-a-chip system. These are miniature chips containing a variety of human cells, as well as small channels along which functions like blood or air flow can be recreated. This allows scientists to perform more complex experiments, like studying drug metabolism, while producing results that are relevant to humans.
An organ-on-a-chip system.
Credit: Fraunhofer IGB
Such systems are also able to elicit an immune response. The FDA has even entered into an agreement with Wyss Institute spinoff Emulate to use their lung-on-a-chip system to test COVID-19 vaccines. Representing multiple organs in one system is also possible. Berlin-based TissUse are aiming to make a so-called 'human on a chip' system commercially available. But TissUse senior scientist Ilka Maschmeyer warns that there is a limit to how far the technology can go. "The system will not think or feel, so it wouldn't be possible to test for illnesses affecting these abilities," she says.
Some challenges also remain in the usability of organs-on-a-chip. "Specialized training is required to use them as they are so complex," says Peter Loskill, assistant professor and head of the organ-on-a-chip group at the University of Tübingen, Germany. Hocke agrees with this. "Cell culture scientists would easily understand how to use organoids in a lab, but when using a chip, you need additional biotechnology knowledge," he says.
One major advantage of both technologies is the possibility of personalized medicine: Cells can be taken from a patient and put onto a chip, for example, to test their individual response to a treatment. Loskill also says there are other uses outside of the biomedical field, such as cosmetic and chemical testing.
"Although these technologies offer a lot of possibilities, they need time to develop," Loskill continues. He stresses, however, that it's not just the technology that needs to change. "There's a lot of conservative thinking in biomedical research that says this is how we've always done things. To really study human biology means approaching research questions in a completely new way."
Even so, he thinks that the pandemic marked a shift in people's thinking—no one cared how the results were found, as long as it was done quickly. But Loskill adds that it's important to balance promise, potential, and expectations when it comes to these new models. "Maybe in 15 years' time we will have a limited number of animal models in comparison to now, but the timescale depends on many factors," he says.
Beumer, now a post-doc, was eventually allowed to return to the lab to develop his coronavirus model, and found working on it to be an eye-opening experience. He saw first-hand how his research could have an impact on something that was affecting the entire human race, as well as the pressure that comes with studying potential treatments. Though he doesn't see a future for himself in infectious diseases, he hopes to stick with organoids. "I've now gotten really excited about the prospect of using organoids for drug discovery," he says.
The coronavirus pandemic has slowed society down in many respects, but it has flung biomedical research into the future—from mRNA vaccines to healthcare models based on human biology. It may be difficult to fully eradicate animal models, but over the coming years, organoids and organs-on-a-chip may become the standard for the sake of efficacy -- and ethics.