how scientists discovered that one size doesn’t fit everyone

If you ate too much during the holiday season, you may be thinking about a healthy eating plan for 2022. But as anyone who has ever dieted knows, there are tons of options. Right now, we are in the midst of a revolutionary time for understanding the human body, and so the question arises: Can new science tell us which diet is the best for weight loss?

Many diets have their origin in a system of rating foods for the effect they have on our blood sugar levels. This way of characterizing food came from research led by David Jenkins at the University of Toronto in 1981. They rated each type of food based on how much it raised blood sugar, using sugar as the benchmark, with a score of 100 Honey scored 87, sweet corn scored 59, tomato soup 38, and so on. Today, every edible thing imaginable has been analyzed in this way, and countless diet plans are based on this way of arranging food. In general, people who want to lose weight are advised to avoid foods that raise blood sugar levels.

But we’ve all come across someone who seems to maintain a healthy weight no matter how much cake, chocolate, or wine they consume. And this – the differences between us – is where vital progress is made, leading us to a new understanding of what the best diet plan really is.

In 2015, Eran Elinav and Eran Segal of the Weizmann Institute of Science in Israel conducted a fascinating study. They recruited 800 participants, and instead of measuring glucose a few times over the course of a few hours, as was done in 1981, each participant’s blood sugar was measured every five minutes for seven days using a small sensor called developed for people with diabetes. In addition, each participant answered a detailed medical questionnaire, was subjected to a variety of physical assessments, such as measurements of their height and hip circumference, and all had their stools analyzed for the types of bacteria they contained.

It turned out that glucose levels spiked exactly in line with previous research. But crucially, this was only the case on average. The variation from person to person was enormous.

For a particular food, some people’s glucose levels would rise dramatically, while others seemed to barely respond. This couldn’t be explained away as a random fluctuation, because the same person reacted the same way every time they ate that particular food. For example, for a middle-aged woman, her blood glucose spiked every time she ate tomatoes. Another person spiked particularly strongly after eating bananas.

Person holding tomatoes.
A woman’s blood glucose spiked every time she ate tomatoes.

Segal’s wife, Keren, was especially stunned. As a dietitian, she was trained to advise countless people about what to eat and what not to eat. Now her husband had proof that her nutritional advice may not always have been helpful. The fact that some people’s sugar levels spiked more in response to rice than to ice cream after eating was shocking to her. It dawned on her that she may have even pointed out some of her patients a type of food that, while moderately beneficial, was personally wrong for them.

A machine learning algorithm (a type of artificial intelligence) was used to figure out which factors should be taken into account to generate the most accurate prediction of a person’s glucose response after a meal. One factor was found to be by far the most important contributor: the types of bacteria found in their feces, which reflect their gut microbiome.

Beautiful complex

So what does this mean? It means there isn’t one best diet plan – everything is personal. What a healthy eating plan is depends on who eats it: their genetics, their lifestyle, their microbiome, maybe even the state of their immune system, their history of infections, and more. Each of them is extraordinarily complex on their own terms, and how they interact with each other even more so.

Our understanding of the details — what makes a diet work or not for a person — is still in its infancy. But in the near future, with the help of computer algorithms and big data analysis, we will certainly revolutionize the science of nutrition and nutrition.

When it becomes clear that personalized nutrition would have a huge impact on human health, the question arises: should analyzing a person’s blood and microbiome to create a personalized diet plan become part of routine, preventive health care, paid for by taxes? Indeed, where would we draw the line between a food product, a nutritional plan and a medicine? As any science matures, new policies must be developed. This will be especially important when it comes to such an essential part of our daily lives: what we eat and drink.

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