Blood-based metabolic signature outperforms standard method for predicting diet, disease risk: The study found that machine learning techniques improved diet prediction by 10-20%

When it comes to studying food and diet, it’s difficult to know what people are eating — let alone their risk of disease caused by what they eat.

Doctors and researchers usually ask people to fill out a long-from food frequency questionnaire that estimates caloric intake, food groups and nutrients. That relies on a person’s memory and may not provide the most accurate picture.

However, a research team led by a Michigan Medicine cardiologist have found a method using molecular profiling and machine learning to develop blood-based dietary signatures that more accurately predict both diet and the risk of cardiovascular disease and type 2 diabetes. The results are published in European Heart Journal.

“Diet is not one dimensional; it’s constantly changing, and the ways we traditionally assess it are not perfect,” said senior author Venkatesh Murthy, M.D., Ph.D., a cardiologist at the University of Michigan Health Frankel Cardiovascular Center and an associate professor of cardiology at U-M Medical School.

“We need tools that are more reliable and precise while also being easy to use for everyone. Using metabolite signatures and data science, we can improve our understanding of how much people are actually taking in, as well as what risks they may incur for cardiometabolic disease that affect millions of Americans,” Murthy said.

Researchers followed more than 2,200 white and Black adults in the Coronary Artery Risk Development in Young Adults study, using blood samples and food surveys to determine metabolite signatures of diet and subsequent disease risk over 25 years. Through a machine learning model, investigators were able to create a blood-based dietary signature that more accurately predicts a person’s entire diet over 19 food groups by 10-20%.

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