We eagerly await the official declaration and apology, but we aren’t holding our breaths. Despite repeated public awareness campaigns and official dietary recommendations, the obesity epidemic remains a persistent problem in the United States, with obesity-related conditions such as metabolic syndrome on the rise. One reason for this may be the lack of personalized dietary advice.
Interestingly, studies have shown that giving specific weight loss guidance along with an empathetic approach can be far more effective than simply instructing someone to “improve their diet.” Research in mice has also suggested that genetics may play a key role in determining which diet works best for an individual. If replicated in humans, such findings could demonstrate that precision dietetics may be far more effective than the standard “one-size-fits-all” approach.
Groundbreaking new research has now done just that. Drawing from a large twin study, scientists conducted a nutritional response study using applied machine learning algorithms to show that one size truly does not fit all when it comes to diet. In fact, the study found that even identical twins can respond differently to the same foods.
This research is part of the largest ongoing scientific study of its kind, conducted by researchers at King’s College London (KCL) in the United Kingdom and Massachusetts General Hospital in Boston, in collaboration with nutritional science company ZOE. The first results of this ongoing study were presented at the American Society of Nutrition conference in Baltimore, Maryland, and the American Diabetes Association conference in San Francisco, California.
Tim Spector, a professor of genetic epidemiology at KCL and scientific founder of ZOE, led the TwinsUK Study, which provided the foundation for the new project. In the TwinsUK study, Prof. Spector and his team examined 14,000 identical and non-identical twins to explore the causes of chronic conditions and distinguish between genetic and environmental factors.
Building on these findings, the large-scale PREDICT 1 study examined the biological responses of 1,100 participants to specific foods over 14 days, with roughly 60% of participants being twins. Researchers measured markers such as blood sugar levels, triglycerides, insulin resistance, physical activity, and gut microbiome health. Participants also logged their food intake, hunger levels, sleep, and exercise, while blood samples were collected throughout the study.
Prof. Spector explained that the study used a specially designed app to collect the most detailed dietary data ever obtained at this scale. The app combined dietary assessment technology with real-time support from nutritionists, ensuring high-quality data collection. Machine learning was then used to predict an individual’s personalized response to food, with accuracy improving as more people participated.
The results revealed significant variation in biological responses to identical meals, regardless of carbohydrate or fat content. Some participants experienced spikes in blood sugar and insulin levels, which are linked to weight gain and diabetes, while others had prolonged increases in triglycerides, which are associated with heart disease. Importantly, genes accounted for only a portion of these differences: less than 50% of blood sugar variation, less than 30% of insulin variation, and less than 20% of triglyceride variation.
Even identical twins shared only 37% of gut bacteria, slightly higher than the 35% shared between unrelated individuals. Despite having the same genes and similar environments, twins often had very different glucose responses to the same meals. Nutritional labels, such as fat, protein, and carbohydrate content, explained less than 40% of the variation in biological responses to food.
These findings suggest that individual differences in metabolism, gut microbiome composition, schedules, meal timing, and physical activity are just as important as the nutrient composition of food. “There’s a real shift happening in nutrition,” Prof. Spector said. “People are beginning to reject the idea that simply following general guidelines — five servings of vegetables, counting calories, reducing fat — will ensure lifelong health.”
The study highlights the lack of clarity around how specific food choices impact health and disease, as well as the absence of optimal individualized nutritional plans. “Our research shows for the first time how much our responses to food can be modified,” Prof. Spector explained. “It’s not all determined by genes or calorie content — we as individuals have the power to change how we respond to food and choose the foods that are best for us.”
For the remainder of the year, ZOE’s PREDICT study is expanding in collaboration with Stanford University and Massachusetts General Hospital. Researchers are enrolling 1,000 volunteers across the U.S. to participate from home. The goal is to collect an even broader dataset to better understand individual food responses. In 2020, ZOE plans to launch a home test and app to help people learn their unique responses to foods and optimize their metabolism.