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.”
In 2013, the Human Brain Project set out to build a realistic computer model of the brain over ten years. Now, experts are reflecting on HBP's achievements with an eye toward the future.
Scholars have found that the project did help advance neuroscience more than some detractors initially expected, specifically in the area of brain simulations and virtual models. Using an interdisciplinary approach of combining technology, such as AI and digital simulations, with neuroscience, the HBP worked to gain a deeper understanding of the human brain’s complicated structure and functions, which in some cases led to novel treatments for brain disorders. Lastly, through online platforms, the HBP spearheaded a previously unmatched level of global neuroscience collaborations.
Simulating a human brain stirs up controversy
Right from the start, the project was plagued with controversy and condemnation. One of its prominent critics was Yves Fregnac, a professor in cognitive science at the Polytechnic Institute of Paris and research director at the French National Centre for Scientific Research. Fregnac argued in numerous articles that the HBP was overfunded based on proposals with unrealistic goals. “This new way of over-selling scientific targets, deeply aligned with what modern society expects from mega-sciences in the broad sense (big investment, big return), has been observed on several occasions in different scientific sub-fields,” he wrote in one of his articles, “before invading the field of brain sciences and neuromarketing.”
"A human brain model can simulate an experiment a million times for many different conditions, but the actual human experiment can be performed only once or a few times," said Viktor Jirsa, a professor at Aix-Marseille University.
Responding to such critiques, the HBP worked to restructure the effort in its early days with new leadership, organization, and goals that were more flexible and attainable. “The HBP got a more versatile, pluralistic approach,” said Viktor Jirsa, a professor at Aix-Marseille University and one of the HBP lead scientists. He believes that these changes fixed at least some of HBP’s issues. “The project has been on a very productive and scientifically fruitful course since then.”
After restructuring, the HBP became a European hub on brain research, with hundreds of scientists joining its growing network. The HBP created projects focused on various brain topics, from consciousness to neurodegenerative diseases. HBP scientists worked on complex subjects, such as mapping out the brain, combining neuroscience and robotics, and experimenting with neuromorphic computing, a computational technique inspired by the human brain structure and function—to name just a few.
Simulations advance knowledge and treatment options
In 2013, it seemed that bringing neuroscience into a digital age would be farfetched, but research within the HBP has made this achievable. The virtual maps and simulations various HBP teams create through brain imaging data make it easier for neuroscientists to understand brain developments and functions. The teams publish these models on the HBP’s EBRAINS online platform—one of the first to offer access to such data to neuroscientists worldwide via an open-source online site. “This digital infrastructure is backed by high-performance computers, with large datasets and various computational tools,” said Lucy Xiaolu Wang, an assistant professor in the Resource Economics Department at the University of Massachusetts Amherst, who studies the economics of the HBP. That means it can be used in place of many different types of human experimentation.
Jirsa’s team is one of many within the project that works on virtual brain models and brain simulations. Compiling patient data, Jirsa and his team can create digital simulations of different brain activities—and repeat these experiments many times, which isn’t often possible in surgeries on real brains. “A human brain model can simulate an experiment a million times for many different conditions,” Jirsa explained, “but the actual human experiment can be performed only once or a few times.” Using simulations also saves scientists and doctors time and money when looking at ways to diagnose and treat patients with brain disorders.
Compiling patient data, scientists can create digital simulations of different brain activities—and repeat these experiments many times.
The Human Brain Project
Simulations can help scientists get a full picture that otherwise is unattainable. “Another benefit is data completion,” added Jirsa, “in which incomplete data can be complemented by the model. In clinical settings, we can often measure only certain brain areas, but when linked to the brain model, we can enlarge the range of accessible brain regions and make better diagnostic predictions.”
With time, Jirsa’s team was able to move into patient-specific simulations. “We advanced from generic brain models to the ability to use a specific patient’s brain data, from measurements like MRI and others, to create individualized predictive models and simulations,” Jirsa explained. He and his team are working on this personalization technique to treat patients with epilepsy. According to the World Health Organization, about 50 million people worldwide suffer from epilepsy, a disorder that causes recurring seizures. While some epilepsy causes are known others remain an enigma, and many are hard to treat. For some patients whose epilepsy doesn’t respond to medications, removing part of the brain where seizures occur may be the only option. Understanding where in the patients’ brains seizures arise can give scientists a better idea of how to treat them and whether to use surgery versus medications.
“We apply such personalized models…to precisely identify where in a patient’s brain seizures emerge,” Jirsa explained. “This guides individual surgery decisions for patients for which surgery is the only treatment option.” He credits the HBP for the opportunity to develop this novel approach. “The personalization of our epilepsy models was only made possible by the Human Brain Project, in which all the necessary tools have been developed. Without the HBP, the technology would not be in clinical trials today.”
Personalized simulations can significantly advance treatments, predict the outcome of specific medical procedures and optimize them before actually treating patients. Jirsa is watching this happen firsthand in his ongoing research. “Our technology for creating personalized brain models is now used in a large clinical trial for epilepsy, funded by the French state, where we collaborate with clinicians in hospitals,” he explained. “We have also founded a spinoff company called VB Tech (Virtual Brain Technologies) to commercialize our personalized brain model technology and make it available to all patients.”
The Human Brain Project created a level of interconnectedness within the neuroscience research community that never existed before—a network not unlike the brain’s own.
Other experts believe it’s too soon to tell whether brain simulations could change epilepsy treatments. “The life cycle of developing treatments applicable to patients often runs over a decade,” Wang stated. “It is still too early to draw a clear link between HBP’s various project areas with patient care.” However, she admits that some studies built on the HBP-collected knowledge are already showing promise. “Researchers have used neuroscientific atlases and computational tools to develop activity-specific stimulation programs that enabled paraplegic patients to move again in a small-size clinical trial,” Wang said. Another intriguing study looked at simulations of Alzheimer’s in the brain to understand how it evolves over time.
Some challenges remain hard to overcome even with computer simulations. “The major challenge has always been the parameter explosion, which means that many different model parameters can lead to the same result,” Jirsa explained. An example of this parameter explosion could be two different types of neurodegenerative conditions, such as Parkinson’s and Huntington’s diseases. Both afflict the same area of the brain, the basal ganglia, which can affect movement, but are caused by two different underlying mechanisms. “We face the same situation in the living brain, in which a large range of diverse mechanisms can produce the same behavior,” Jirsa said. The simulations still have to overcome the same challenge.
Understanding where in the patients’ brains seizures arise can give scientists a better idea of how to treat them and whether to use surgery versus medications.
The Human Brain Project
A network not unlike the brain’s own
Though the HBP will be closing this year, its legacy continues in various studies, spin-off companies, and its online platform, EBRAINS. “The HBP is one of the earliest brain initiatives in the world, and the 10-year long-term goal has united many researchers to collaborate on brain sciences with advanced computational tools,” Wang said. “Beyond the many research articles and projects collaborated on during the HBP, the online neuroscience research infrastructure EBRAINS will be left as a legacy even after the project ends.”
Those who worked within the HBP see the end of this project as the next step in neuroscience research. “Neuroscience has come closer to very meaningful applications through the systematic link with new digital technologies and collaborative work,” Jirsa stated. “In that way, the project really had a pioneering role.” It also created a level of interconnectedness within the neuroscience research community that never existed before—a network not unlike the brain’s own. “Interconnectedness is an important advance and prerequisite for progress,” Jirsa said. “The neuroscience community has in the past been rather fragmented and this has dramatically changed in recent years thanks to the Human Brain Project.”
According to its website, by 2023 HBP’s network counted over 500 scientists from over 123 institutions and 16 different countries, creating one of the largest multi-national research groups in the world. Even though the project hasn’t produced the in-silico brain as Markram envisioned it, the HBP created a communal mind with immense potential. “It has challenged us to think beyond the boundaries of our own laboratories,” Jirsa said, “and enabled us to go much further together than we could have ever conceived going by ourselves.”
