Scientists can detect junk food consumption in blood and urine tests

Using machine learning, scientists at the National Institutes of Health (NIH) identified hundreds of metabolites (molecules produced during metabolism) that correlated with processed food intake.

The team developed a "biomarker score" that predicts ultraprocessed food intake based on metabolite measurements in blood and urine, according to Erikka Loftfield, Ph.D., M.P.H., of the National Cancer Institute in Maryland.

The researchers drew baseline data from 718 older adults who provided urine and blood samples and reported their dietary habits over a 12-month period, as detailed in a press release from NIH.

Next, they conducted a small clinical trial of 20 adults. For two weeks, the group ate a diet high in ultraprocessed foods, and for another two weeks they ate a diet with no UPFs.

The findings were published in the journal PLOS Medicine.

Large-scale studies investigating the health risks of ultraprocessed foods often rely on self-reported dietary questionnaires, which can be prone to errors, per the NIH.

Loftfield added, "It was surprising to find that UPF-correlated metabolites are involved in numerous and diverse biological pathways, underscoring the complex impact of diet on the metabolome."

Ultraprocessed foods are defined as "ready-to-eat or ready-to-heat, industrially manufactured products, typically high in calories and low in essential nutrients," according to the NIH.

Since the current trial focused mainly on older adults, researchers say more research is needed across various age groups and diets.

"Metabolite scores should be evaluated and improved in populations with different diets and a wide range of UPF intake," Loftfield acknowledged.

This method could potentially be used in future research to link the consumption of processed foods with chronic diseases, according to the researchers.