All these measurements added up to millions of datapoints, which needed to be analysed with sophisticated machine learning techniques (a type of artificial intelligence) in order to spot patterns and make predictions.
The first thing we noticed was the wide variation in individual insulin, blood sugar and blood fat responses to the same meals, even for identical twins. For example, one twin might have healthy responses to eating carbohydrates but not fat, while the other twin is the opposite. Straight away, this tells us that we are all unique and that there is no perfect diet or correct way to eat that will work for everyone.
The observation that genetics only plays a minor role in determining how we respond to food also tells us that simple genetic tests claiming to determine the “right diet for your genes” are ineffective and misleading. Curiously, identical twins only shared around a third of the same gut microbe species, which may help to explain some of the variation in nutritional responses and also points towards an opportunity to improve health and weight by manipulating the microbiome.
We also discovered that the timing of meals affects nutritional responses in a personalised way. The same meal at breakfast caused a different nutritional response in some people when eaten for lunch. But in other people there was no difference, busting the myth that there are correct mealtimes that will work for all.
Another surprise was finding that the composition of meals in terms of calories, fat, carbohydrates, proteins and fibre (macronutrients or “macros”) also had a highly individualised effect on nutritional responses. Some people handle carbs better than fat, for example, while others have the opposite response. So prescriptive diets based on fixed calorie counts or macronutrient ratios are too simplistic and will not work for everyone.
However, despite the wide variability between participants, each person’s own responses to identical meals eaten at the same times on different days were remarkably consistent. This makes it possible to predict how someone might respond to any food based on knowledge of their underlying metabolism.
Intriguingly, we found that the levels of inflammatory molecules in the blood varied by up to tenfold, even in seemingly healthy people, and that a rise in these inflammation markers was linked to having unhealthy responses to fat.
We use the term “dietary inflammation” to refer to these unhealthy metabolic effects that are triggered after eating. Repeatedly experiencing dietary inflammation brought on by excessive blood sugar and fat responses is linked with an increased risk of conditions such as heart disease, type 2 diabetes, non-alcoholic fatty liver disease and obesity.
On a more positive note, our findings suggest that it might be possible to improve weight management and long-term health by eating in a more personalised way designed to avoid triggering unhealthy inflammatory responses after meals.
When it comes to weight, we’ve traditionally put a huge emphasis on factors we have no control over, especially genetics. The fact is, while genetics plays a role, many more important factors affect how our metabolism, weight and health. It’s time to move away from overly generalised guidelines, fad diets and one-size-fits-all plans and develop more personalised, scientific approaches to nutrition that understand and work together with our bodies, not against them.
This article is republished from The Conversation under a Creative Commons license. Read the original article.