Currently, the most common method for forecasting a person's PPGR is to look at the carbohydrate content of their meals. “People with type I diabetes determine how much insulin to inject based on the amount of carbs they're going to have in the meal,” says Segal. “That's the gold standard,” but carb content only weakly correlates with PPGR.
Alternatively, people could consult the glycemic index (GI), which puts a number on a food's effect on blood sugar. But the Weizmann team argues that these numbers are based on single foods, and don't reflect realistic meals with combinations of ingredients in varying amounts. “Ascribing a single PPGR to each food also assumes that the response is solely an intrinsic property of the food,” adds Segal. “But there are very striking differences between people's responses to identical meals.” Their genes, lifestyle choices, the bacteria in their guts, and even the meals they had recently eaten, all have an influence.
To account for these factors, students David Zeevi and Tal Korem subjected 800 non-diabetic volunteers to “the most comprehensive profiling we could.” Upon signing up, each participant filled out a questionnaire about their medical histories and dietary habits, and provided a stool sample so the team could analyze their gut microbes.
Then, for a week, they wore skin-mounted monitors that continuously measured their blood sugar, and used a mobile app to record exercise and sleep patterns, stressful events, and all their meals, down to the weights of every ingredient where possible. For their first bites of the day, they ate one of four standardized meals that the team provided. Beyond that, they ate their usual food.
Although people are often notoriously unreliable at documenting their meals, Segal says that his volunteers were unusually motivated. “We didn't pay them,” he says. “They joined because we explained that we'd be able to tell them which of the foods they normally eat spike their glucose levels. They came because they wanted to know and we said that if they didn't log properly, we wouldn't be able to tell them.”
The team found a huge amount of variation between the volunteers. The same food would cause huge sugar spikes in some people but tiny blips in others. The volunteers also differed substantially in the foods that triggered the sharpest spikes: Participant 445, for example, reacted strongly to bananas, while participant 644 spiked heavily post-cookies. “When people talk to their diabetic friends about foods that spike their glucose level, it's really different for everyone,” says Segal. “That's the intuition but, as far as I know, it's never been demonstrated quantitatively on this scale.”
Zeevi and Korem showed that these personal differences were influenced by familiar factors like age and body mass index, and also less familiar ones like gut microbes. They found several groups of bacteria, and families of bacterial genes, that were linked to stronger PPGRs.