A New Algorithm Could Provide Healthy Individualized Diet Plans

For the most part, people feel that they have a good idea of what foods are “healthy.” Particularly when it comes to sugar, we feel we have a general idea of how it affects us. However, a new study may change the way we look at sugar and the way it affects us a individuals. As it turns out, something that is “healthy” for you may not be so for the next person. New research shows significant differences in how different people react to the same foods. It also may provide a method to devise healthy, individualized diet plans for everyone.

This research could show that personalized diet plans may be the wave of the future. Instead of sticking to general guidelines of healthy eating as we now understand it, we may have totally different diet plans for each individual that are beneficial. This could explain why that trendy new diet you just started is working so well for all your friends but not for you.

Two researchers from the Wizeman Institute of Science, Eran Elinav and Eran Segal, recently published their findings in this breakthrough study. The study focused on blood sugar levels and whether the same food would cause different blood sugar reactions for different people. These findings could be very helpful for everyday people looking to become healthier. However, in a nation overrun with an obesity epidemic and growing statistics in diabetes, this information could have even greater impact.

The CDC reports that as of 2014, 29.1 million Americans have either Type 1 or Type 2 diabetes. That is 9.3 percent of the population. A different report by UCLA in 2016 shows that 37 percent of adults and almost one third of American young adults have prediabetic symptoms or are already diabetic. The same report showed that 70 percent of people with prediabetic symptoms end up developing diabetes.

The diabetes epidemic is even more distressing when combined with the even more prevalent issue of obesity. Over 68 percent of American adults are considered overweight or obese. Over 35 percent are considered obese, and 6.3 percent are extremely obese. These are undoubtedly very disturbing numbers, and American’s will welcome any effective methods to lower blood sugar.

The good news is that Elinav and Segal not only determined how foods affect individuals differently, they may have also devised a method of measuring and lowering sugars for these individuals. The algorithm they developed can be tailored for any person, so you may even be using it soon to help control your weight.

The Study

This extensive study could change the way Americans diet.

The study began a sample of 800 non-diabetic volunteers aged 18-70 and set out to examine their postprandial glucose responses, or PPGRs. Postprandial glucose means the amount of sugar in your blood after you eat. Your PPGR closely relates to weight gain/loss and other significant aspects of how your food affects your body, but it is generally only closely monitored for diabetics. It is currently calculated according to the amount of carbohydrates you take in, but Segal feels this is inexact.

“People with type I diabetes determine what proportion insulin to inject supported the quantity of carbs they’re getting to have within the meal,” says Segal. They believed they could find a more effective way of monitoring and hopefully improving PPGR.

In order to do this, they first put all participants through, “the most comprehensive profiling we could.” This included medical history, eating habits and several other factors that could affect the normal sugar levels of the volunteers.

Then each individual was connected to a continuous glucose monitor, or GCM, which could accurately calculate glucose levels after meals. They were also instructed to log their all of their meals, activities, sleep patterns and any stressful events into a mobile app. Participants also provided a stool sample to help measure microbial bacteria in the gut.

“During the connection week,” says Segal. “Participants were asked to follow their normal daily routine and dietary habits, apart from the primary meal of each day, which we provided together of 4 differing types of standardized meals, each consisting of fifty g of obtainable carbohydrates. This resulted during a total of 46,898 real-life meals with close-to or full nutritional values.”

The Results

The results they found were quite remarkable. They first compared the responses of people to the standardized breakfast meals. They found that their responses were mostly the same from day to day, but when compared to another individual’s response to the same meals, the differences were telling.

The same food would cause a small change in the blood glucose level of one individual while causing a huge spike of that in another. When measuring the results of the non-standardized meals, the researchers found similarly remarkable differences in how different individuals reacted to the same food.

“When people ask their diabetic friends about foods that spike their glucose level, it’s really different for everybody ,” says Segal. “That’s the intuition but, as far as I know, it’s never been demonstrated quantitatively on this scale.”

The differences in these reactions seemed to be caused by a number of factors including genetics, lifestyle, insulin sensitivity and gut microbes.

“There are profound differences between individuals — in some cases, individuals have opposite responses to at least one another,” Segal explained.

The Algorithm

Based on these factors and many (almost 150) more, the team developed an algorithm that they believed could predict the PPGR of individuals. This method would be different than the usual counting of carbohydrates, because it would take into account the individual’s personal characteristics. They first tested the algorithm using the results of the first 800 people and located that it had been very accurate in predicting PPGR. Then so as to validate the tactic further, they began a replacement study with 100 new participates. The goal was to ascertain if the algorithm could determine PPGR spikes before they were known. within the new study, the volunteers went through an equivalent evaluation and therefore the same testing methods were used. The results showed that the algorithm could predict PPGR right down to a 0.7 percent correlation. For those that don’t spend tons of your time within the lab , that’s pretty exact.

The researchers further tested the new method with a gaggle of 26 individuals. The goal here was to check the algorithm on foods that were typically considered healthy versus foods that were known to be unhealthy. The participants were then split into two groups; one was given a hotel plan developed using the algorithm, and therefore the other received an idea developed by two dietary experts. Both plans included foods that were typically considered “good” and “bad.”

“It wasn’t just salad a day ,” says Segal. “Some people got alcohol, chocolate, and ice-cream, carefully . These are items that you’d typically never find on a dietician’s recommendations.”

The differences between the 2 diets were very different and comparatively predictable. “On the bad diets, blood sugar really reached abnormal level but on the great diets, they normalized to healthy ranges,” said Segal.

The surprising aspect was again within the efficiency of the algorithm. In reference to the plan devised by the experts, the plan using the algorithm actually performed slightly better. While both plans were devised supported an individual’s glucose level from the previous week, the algorithm might be wont to predict PPGR for any meal. “It’s not constrained to recommending people meals that have already been measured,” says Segal. “You could recommend any meal.”


One of the most important takeaways from these studies addressed PPGR in reference to gut microbes. Examining the microbial bacteria in an individual’s stomach can go an extended thanks to predict how different foods will affect the person’s glucose levels. The study showed how certain gut microbes affect the food we eat and vice-versa.

The algorithm will definitely be the foremost beneficial results of the study. While old ways of predicting PPGR only involved the carbohydrates in food, the algorithm are often wont to include fat, fiber and a person’s individual body chemistry.

According to Jennie Brand-Miller, a nutrition expert at the University of Sydney, learning the relation between the microbial bacteria during a person’s stomach and PPGR is, “a game-changer. This drives home the medical relevance of high glucose levels within the so-called normal range.”

Of course, these were only short-term studies, and there’s still far more work to be done. the great news is that folks are lining up to urge that employment done. After the initial study was published and word got out, people began flocking to the Institute. Segal says he now features a roll of over 4,000 volunteers hoping to participate within the next study.

While the algorithm and studies are still in their scientific infancy, the potential benefits are clear. After further research has been done, implementation of this algorithm could help treat diabetes and prediabetic symptoms in ways we had never imagined.

Also, if they will successfully determine how certain foods affect certain people, you’ll finally quit that trendy diet that doesn’t seem to figure in favor of 1 specifically designed for you. Segal’s partner Eran Elinav says it’s , “Elinav said the work “really enlightened us on how inaccurate we all were about one among the foremost basic concepts of our existence, which is how we eat and the way we integrate nutrition into our lifestyle .”

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