(PO08-36-22) Plasma and Urine Metabolomic Response to an Ultra-Processed Dietary Pattern: A Biomarker Discovery Analysis in a Domiciled Randomized Controlled Crossover Feeding Trial

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Presenting Author(s)

LO

Lauren E. O'Connor, PhD, MPH
– Research Nutritionist, Beltsville Human Nutrition Research Center, ARS, USDA, Beltsville, Maryland, United States

Co-Author(s)

Kevin D. Hall, PhD
– Senior Investigator, National Institutes of Health, Bethesda, Maryland, United States
KH

Kirsten A. Herrick, PhD, MSc
– Program Director, National Cancer Institute, ROCKVILLE, Maryland, United States
JR

Jill Reedy, PhD, MPH, RDN
– Branch Chief, National Cancer Institute, Bethesda, Maryland, United States
SC

Stephanie T. Chung, MBBS
– National Institute of Diabetes and Digestive and Kidney Diseases, Besthesda, Maryland, United States
MS

Michael Stagliano, CRNP
– National Institutes of Health, Bethesda, Maryland, United States
AC

Amber Courville, PhD, RD
– National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, Maryland, United States
RS

Rashmi Sinha
– NIH/NCI
EL

Erikka Loftfield
– NIH/NCI

Disclosure(s):

Lauren E. O'Connor, PhD, MPH: No relevant financial relationship(s) with ineligible companies to disclose.

Jill Reedy, PhD, MPH, RDN: No relevant financial relationship(s) with ineligible companies to disclose.

Objectives: To identify metabolomic markers that differed between dietary patterns (DP) that are either high in or void of ultra-processed foods (UPFs) according to NOVA .

Methods: A secondary analysis of a randomized, crossover, controlled feeding trial in which 20 domiciled, healthy participants (mean ± SD: 31 ± 7 years, BMI 22 ± 11.6, 50% female) consumed a UPF-DP (80% UPFs) and an unprocessed DP (UN-DP; 0% UPFs) for two weeks with no washout. DPs were matched for energy, macronutrients, total fiber, total sugar, and sodium; presented at 200% of energy requirements; and consumed ad libitum. Metabolite levels were measured in EDTA plasma at the end of each DP (wk 2) and in 24-hr and spot urine at wk 1 and 2, using untargeted liquid chromatography with high resolution/tandem mass spectrometry and annotated using Metabolon’s reference library and authentic standards. Metabolites (n=1000 plasma, n=1272 24-hr urine, and n=1281 spot urine) with < 80% missing data and coefficients of variation < 30% were assigned minimum detected values, scaled to median of 1, and log2-transformed. Linear mixed models in SAS identified metabolites that differed between UPF-DP and UN-DP adjusted for trial, DP sequence, timepoint, and body weight changes, with a subject-specific random intercept and Benjamini-Hochberg multiple comparison correction.

Results: For plasma, 183 metabolites differed between UPF-DP and UN-DP at wk 2. For 24-hr urine, 461 metabolites differed between UPF-DP and UN-DP at wk 1 and 2, 68 of which also differed at wk 1 and 2 for spot urine. Twenty metabolites consistently differed between UPF-DP and UN-DP at each timepoint and for each sample type. The sub pathways for these 20 metabolites included glutamate metabolism (n=1 metabolite); ascorbate and aldarate metabolism (n=1); benzoate metabolism (n=2); methionine, cysteine, SAM and taurine metabolism (n=2); secondary bile acid metabolism (n=2); fatty acid dicarboxylate (n=1); and plant-food components (n=2); 9 could not be annotated.

Conclusions: We identified exogenous and endogenous metabolites, representing a range of metabolic pathways, that consistently differed between a UPF-DP and UN-DP. These candidate biomarkers of UPF intake require investigation in larger samples with dietary data sufficient for NOVA classification.

Funding Sources: NCI, NIDDK