Biomarkers Found in blood and urine may predict Ultraprocessed Food Intake, suggests study

This finding is based on a large-scale metabolomics analysis employing data collected from the Interactive Diet and Activity Tracking in AARP (IDATA) Study, which monitored dietary intake among older U.S. adults for 12 months. Since over half of the total American's daily caloric contribution is provided by UPF, determining objective biomarkers of UPF consumption is necessary to further nutritional science and public health policy. The results of the study establish that poly-metabolite scores groupings of particular metabolites are highly associated with UPF intake, offering a valid, quantitative tool for estimating dietary exposure.

A total of 718 adults between 50–74 years were selected from an original group of 1,082 IDATA participants who had donated blood and urine and filled out one to six 24-hour dietary recalls (ASA-24s) in one year. With the aid of ultra-high-performance liquid chromatography with tandem mass spectrometry, scientists quantified more than 1,000 metabolites in serum and 24-hour urine. UPF consumption was classified according to the Nova system and average daily UPF consumption expressed as a proportion of total energy consumed.

To examine the data, researchers used Partial Spearman correlations to investigate correlations between a single metabolite and UPF consumption. They subsequently applied Least Absolute Shrinkage and Selection Operator (LASSO) regression to construct predictive poly-metabolite scores statistical models with chosen metabolites used as biomarkers for UPF consumption.

To confirm these results, investigators conducted a post-hoc analysis based on data from a controlled randomized crossover feeding study of 20 adults (ages 18–50, BMI >18.5 kg/m²). These subjects cycled through diets providing 80% UPF and 0% UPF, each for two weeks under supervised care at the NIH Clinical Center.

Key Findings

• Mean UPF intake among the IDATA participants was 50% of total energy.

• UPF consumption was strongly associated with 191 of 952 serum and 293 of 1,044 urine metabolites (FDR-adjusted P < 0.01).

Major categories of metabolites were:

• Lipid-associated: 56 (serum), 22 (urine)

• Amino acid-associated: 33 (serum), 61 (urine)

• Carbohydrates: 4 (serum), 8 (urine)

• Xenobiotics: 33 (serum), 70 (urine)

• Cofactors/vitamins: 9 (serum), 12 (urine)

• Peptides: 7 (serum), 6 (urine)

• Nucleotides: 7 (serum), 10 (urine)

• LASSO regression identified 28 serum and 33 urine metabolites as markers of UPF consumption.

Significant overlapping metabolites were:

• (S)C(S)S-S-Methylcysteine sulfoxide (rs = −0.23 serum, −0.19 urine)

• N2,N5-diacetylornithine (rs = −0.27 serum, −0.26 urine)

• Pentoic acid (rs = −0.30 serum, −0.32 urine)

• N6-carboxymethyllysine (rs = 0.15 serum, 0.20 urine)

This research concludes that poly-metabolite scores from blood and urine are valid ultra-processed food predictors of intake in older adults, with great potential to enhance the accuracy of dietary assessment in epidemiological research. Subsequent research should seek to calibrate and validate these scores in more varied and younger groups, opening the doors for their application in large-scale health surveillance and intervention programs.

Reference:

Abar, L., Steele, E. M., Lee, S. K., Kahle, L., Moore, S. C., Watts, E., O’Connell, C. P., Matthews, C. E., Herrick, K. A., Hall, K. D., O’Connor, L. E., Freedman, N. D., Sinha, R., Hong, H. G., & Loftfield, E. (2025). Identification and validation of poly-metabolite scores for diets high in ultra-processed food: An observational study and post-hoc randomized controlled crossover-feeding trial. PLoS Medicine, 22(5), e1004560. https://doi.org/10.1371/journal.pmed.1004560