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.”
There is a lot to be optimistic about regarding the new safe and highly effective vaccines, which are moving society closer toward the goal of close human contact once again.
To be clear, these vaccine candidates for COVID-19, both authorized and not yet authorized, are highly effective and safe. In fact, across all trials and sites, all six vaccines were 100% effective in preventing hospitalizations and death from COVID-19.
All Vaccines' Phase 3 Clinical Data
Complete protection against hospitalization and death from COVID-19 exhibited by all vaccines with phase 3 clinical trial data.
This astounding level of protection from SARS-CoV-2 from all vaccine candidates across multiple regions is likely due to robust T cell response from vaccination and will "defang" the virus from the concerns that led to COVID-19 restrictions initially: the ability of the virus to cause severe illness. This is a time of hope and optimism. After the devastating third surge of COVID-19 infections and deaths over the winter, we finally have an opportunity to stem the crisis – if only people readily accept the vaccines.
Amidst these incredible scientific advancements, however, public health officials and politicians have been pushing downright discouraging messaging. The ubiquitous talk of ongoing masks and distancing restrictions without any clear end in sight threatens to dampen uptake of the vaccines. It's imperative that we break down each concern and see if we can revitalize our public health messaging accordingly.
The first concern: we currently do not know if the vaccines block asymptomatic infection as well as symptomatic disease, since none of the phase 3 vaccine trials were set up to answer this question. However, there is biological plausibility that the antibodies and T-cell responses blocking symptomatic disease will also block asymptomatic infection in the nasal passages. IgG immunoglobulins (generated and measured by the vaccine trials) enter the nasal mucosa and systemic vaccinations generate IgA antibodies at mucosal surfaces. Monoclonal antibodies given to outpatients with COVID-19 hasten viral clearance from the airways.
Although it is prudent for those who are vaccinated to wear masks around the unvaccinated in case a slight risk of transmission remains, two fully vaccinated people can comfortably abandon masking around each other.
Moreover, data from the AztraZeneca trial (including in the phase 3 trial final results manuscript), where weekly self-swabbing was done by participants, and data from the Moderna trial, where a nasal swab was performed prior to the second dose, both showed risk reductions in asymptomatic infection with even a single dose. Finally, real-world data from a large Pfizer-based vaccine campaign in Israel shows a 50% reduction in infections (asymptomatic or symptomatic) after just the first dose.
Therefore, the likelihood of these vaccines blocking asymptomatic carriage, as well as symptomatic disease, is high. Although it is prudent for those who are vaccinated to wear masks around the unvaccinated in case a slight risk of transmission remains, two fully vaccinated people can comfortably abandon masking around each other. Moreover, as the percentage of vaccinated people increases, it will be increasingly untenable to impose restrictions on this group. Once herd immunity is reached, these restrictions can and should be abandoned altogether.
The second concern translating to "doom and gloom" messaging lately is around the identification of troubling new variants due to enhanced surveillance via viral sequencing. Four major variants circulating at this point (with others described in the past) are the B.1.1.7 variant ("UK variant"), B.1.351 ("South Africa variant), P.1. ("Brazil variant"), and the L452R variant identified in California. Although the UK variant is likely to be more transmissible, as is the South Africa variant, we have no reason to believe that masks, distancing and ventilation are ineffective against these variants.
Moreover, neutralizing antibody titers with the Pfizer and Moderna vaccines do not seem to be significantly reduced against the variants. Finally, although the Novavax 2-dose and Johnson and Johnson (J&J) 1-dose vaccines had lower rates of efficacy against moderate COVID-19 disease in South Africa, their efficacy against severe disease was impressively high. In fact J&J's vaccine still prevented 100% of hospitalizations and death from COVID-19. When combining both hospitalizations/deaths and severe symptoms managed at home, the J&J 1-dose vaccine was 85% protective across all three sites of the trial: the U.S., Latin America (including Brazil), and South Africa.
In South Africa, nearly all cases of COVID-19 (95%) were due to infection with the B.1.351 SARS-CoV-2 variant. Finally, since herd immunity does not rely on maximal immune responses among all individuals in a society, the Moderna/Pfizer/J&J vaccines are all likely to achieve that goal against variants. And thankfully, all of these vaccines can be easily modified to boost specifically against a new variant if needed (indeed, Moderna and Pfizer are already working on boosters against the prominent variants).
The third concern of some public health officials is that people will abandon all restrictions once vaccinated unless overly cautious messages are drilled into them. Indeed, the false idea that if you "give people an inch, they will take a mile" has been misinforming our messaging about mitigation since the beginning of the pandemic. For example, the very phrase "stay at home" with all of its non-applicability for essential workers and single individuals is stigmatizing and unrealistic for many. Instead, the message should have focused on how people can additively reduce their risks under different circumstances.
The public will be more inclined to trust health officials if those officials communicate with nuanced messages backed up by evidence, rather than with broad brushstrokes that shame. Therefore, we should be saying that "vaccinated people can be together with other vaccinated individuals without restrictions but must protect the unvaccinated with masks and distancing." And we can say "unvaccinated individuals should adhere to all current restrictions until vaccinated" without fear of misunderstandings. Indeed, this kind of layered advice has been communicated to people living with HIV and those without HIV for a long time (if you have HIV but partner does not, take these precautions; if both have HIV, you can do this, etc.).
Our heady progress in vaccine development, along with the incredible efficacy results of all of them, is unprecedented. However, we are at risk of undermining such progress if people balk at the vaccine because they don't believe it will make enough of a difference. One of the most critical messages we can deliver right now is that these vaccines will eventually free us from the restrictions of this pandemic. Let's use tiered messaging and clear communication to boost vaccine optimism and uptake, and get us to the goal of close human contact once again.
