Dataset Information

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.

ABSTRACT:

BACKGOUND: Ultra-processed food (UPF) accounts for a majority of calories consumed in the United States, but the impact on human health remains unclear. We aimed to identify poly-metabolite scores in blood and urine that are predictive of UPF intake.

METHODS AND FINDINGS: Of the 1082 Interactive Diet and Activity Tracking in AARP (IDATA) Study (clinicaltrials.gov ID NCT03268577) participants, aged 50-74 years, who provided biospecimen consent, n=718 with serially collected blood and urine and one to six 24-hour dietary recalls (ASA-24s), collected over 12-months, met eligibility criteria and were included in the metabolomics analysis. Ultra-high performance liquid chromatography with tandem mass spectrometry was used to measure >1000 serum and urine metabolites. Average daily UPF intake was estimated as percentage energy according to the Nova system. Partial Spearman correlations and Least Absolute Shrinkage and Selection Operator (LASSO) regression were used to estimate UPF-metabolite correlations and build poly-metabolite scores of UPF intake, respectively. Scores were tested in a previously conducted randomized, controlled, crossover-feeding trial (clinicaltrials.gov ID NCT03407053) of 20 subjects who were admitted to the NIH Clinical Center and randomized to consume ad libitum diets that were 80% or 0% energy from UPF for 2 weeks immediately followed by the alternate diet for 2 weeks; eligible subjects were between 18-50 years old with a body mass index of >18.5 kg/m2 and weight-stable. IDATA participants were 51% female, and 97% completed 4 ASA-24s. Mean intake was 50% energy from UPF. UPF intake was correlated with 191 (of 952) serum and 293 (of 1044) 24-hour urine metabolites (FDR-corrected P-value < 0.01), including lipid (n=56 serum, n=22 24-hour urine), amino acid (n=33, 61), carbohydrate (n=4, 8), xenobiotic (n=33, 70), cofactor and vitamin (n= 9, 12), peptide (n=7, 6), and nucleotide (n=7, 10) metabolites. Using LASSO regression, 28 serum and 33 24-hour urine metabolites were selected as predictors of UPF intake; biospecimen-specific scores were calculated as a linear combination of selected metabolites. Overlapping metabolites included (S)C(S)S-S-methyl cysteine sulfoxide (rs= -0.19, -0.23), N2-N5-diacetylornithine (rs= -0.26, -0.27), pentoic acid (rs= -0.28, -0.31), and N6-carboxymethyllysine (rs=0.15, 0.21). Within the cross-over feeding trial, the poly-metabolite scores differed, within individual, between UPF diet phases (P-value for paired t-test <0.001). IDATA Study participants were older US adults whose diets may not be reflective of other populations.

CONCLUSIONS: Poly-metabolite scores, developed in IDATA participants with varying diets, are predictive of UPF intake and could advance epidemiological research on UPF and health. Poly-metabolite scores should be evaluated and iteratively improved in populations with a wide range of UPF intake.

INSTRUMENT(S): Liquid Chromatography MS - - - Q Exactive

PROVIDER: MTBLS12705 | MetaboLights | 2025-07-12

REPOSITORIES: MetaboLights

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	a_MTBLS12705_22463_METHOD1_POS_1_metabolite_profiling_mass_spectrometry.txt	Txt
	a_MTBLS12705_22463_METHOD2_POS_2_metabolite_profiling_mass_spectrometry.txt	Txt
	a_MTBLS12705_22463_METHOD3_NEG_1_metabolite_profiling_mass_spectrometry.txt	Txt
	a_MTBLS12705_22463_METHOD4_NEG_2_metabolite_profiling_mass_spectrometry.txt	Txt
	a_MTBLS12705_22464_METHOD1_POS_1_metabolite_profiling_mass_spectrometry.txt	Txt

Items per page:

1 - 5 of 12

Publications

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.

Abar Leila L Steele Eurídice Martínez EM Lee Sang Kyu SK Kahle Lisa L Moore Steven C SC Watts Eleanor E O'Connell Caitlin P CP Matthews Charles E CE Herrick Kirsten A KA Hall Kevin D KD O'Connor Lauren E LE Freedman Neal D ND Sinha Rashmi R Hong Hyokyoung G HG Loftfield Erikka E

PLoS medicine 20250520 5

<h4>Background</h4>Ultra-processed food (UPF) accounts for a majority of calories consumed in the United States, but the impact on human health remains unclear. We aimed to identify poly-metabolite scores in blood and urine that are predictive of UPF intake.<h4>Methods and findings</h4>Of the 1,082 Interactive Diet and Activity Tracking in AARP (IDATA) Study (clinicaltrials.gov ID NCT03268577) participants, aged 50-74 years, who provided biospecimen consent, n = 718 with serially collected blood ...[more]

PMID: 40392756

Similar Datasets

Project description:The gut microbiome refers to the complex community of microorganisms (bacteria, fungi, archaea and viruses) present within our gut. The composition and complexity of gut microbiota communities are crucial for maintaining human health. Humans depend on the gut microbiota to undertake essential metabolic functions and for effective immune system operations, such as protection against pathogens and maintaining gut integrity. Richer diversity in these microbiota communities has been associated with better health. A key modulator of intestinal microbial colonies is food. Dietary bioactives, also referred to as phytochemicals (non-nutrient food components) present in fruit and vegetables, can modulate metabolic processes, promoting better health; however, the interaction of bioactives with microbial communities is not yet fully understood. We have conducted a 2x2 randomised crossover study to estimate the effect of diets rich in dietary bioactives on gut microbial diversity and health markers in 20 healthy participants. Participants consumed a diet of high in bioactives (HB) rich foods (containing polyphenols, Sulphur (S) metabolites and carotenoids) vs a low in bioactives (LB) diet for 2 wk, with an in-between washout period of 4 wk. The primary aim was to examine the effect of a 2-wk diet high in bioactives rich foods on gut microbial diversity compared to a low in bioactives diet. The secondary aim was to assess a high vs a low in bioactives diet on markers of cardiovascular health, inflammation and post-prandial glucose. We hypothesized that consuming a high in bioactives diet would increase microbial diversity compared to a low in bioactives diet.METHODS: Each volunteer completed 4 study visit days, 1 at the start and 1 at the end of each intervention phase. Dietary intake, including bioactives, was recorded through the Libro app linked to Nutritics. We recorded diets during a 7-d period (baseline habitual diet) followed by a 14-d intervention period per arm. On each study day, volunteers provided a 24 h urine sample collected in the 24 h preceding their visit and a stool sample. Urine and stool samples were utilized for both untargeted and targeted metabolomics. Microbial composition was assessed through whole-genome shotgun sequencing carried out by GeneWiz, performed using Illumina HiSeq 2500 platform with paired-end read length 2 x 125 bp. For vascular health, total HDL/LDL cholesterol and triglycerides, along with inflammatory markers such as hs-CRP, were analyzed with an at-home finger-prick blood sample collection kit (Medichecks). Participants also carried out an oral glucose tolerance test; glucose measurements were provided through the Freestyle Libre continuous glucose monitor (Abbott Laboratories) over a 2 h period following ingestion of 113 mL of polycal (75 g anhydrous glucose). Targeted metabolomics is reported in the current study <a href='https://www.ebi.ac.uk/metabolights/MTBLS7290' rel='noopener noreferrer' target='_blank'>MTBLS7290</a>.Untargeted metabolomics is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS8100' rel='noopener noreferrer' target='_blank'>MTBLS8100</a>. Linked cross omic data sets:Whole genome sequencing data associated with this study are available in the European Nucleotide Archive (ENA): accession number <a href='https://www.ebi.ac.uk/ena/browser/view/PRJEB55607' rel='noopener noreferrer' target='_blank'>PRJEB55607</a>.Metagenomic data associated with this study are available from MGnify: accession number <a href='https://www.ebi.ac.uk/metagenomics/studies/MGYS00006076' rel='noopener noreferrer' target='_blank'>MGYS00006076</a>.

Project description:The gut microbiome refers to the complex community of microorganisms (bacteria, fungi, archaea and viruses) present within our gut. The composition and complexity of gut microbiota communities are crucial for maintaining human health. Humans depend on the gut microbiota to undertake essential metabolic functions and for effective immune system operations, such as protection against pathogens and maintaining gut integrity. Richer diversity in these microbiota communities has been associated with better health. A key modulator of intestinal microbial colonies is food. Dietary bioactives, also referred to as phytochemicals (non-nutrient food components) present in fruit and vegetables, can modulate metabolic processes, promoting better health; however, the interaction of bioactives with microbial communities is not yet fully understood. We have conducted a 2x2 randomised crossover study to estimate the effect of diets rich in dietary bioactives on gut microbial diversity and health markers in 20 healthy participants. Participants consumed a diet of high bioactive (HB) rich foods (containing polyphenols, Sulphur (S) metabolites and carotenoids) vs a low bioactive (LB) diet for 2 wk, with an in-between washout period of 4 wk. The primary aim was to examine the effect of a 2-wk diet high in bioactives rich foods on gut microbial diversity compared to a low bioactive diet. The secondary aim was to assess a high vs low bioactive diet on markers of cardiovascular health, inflammation and post-prandial glucose. We hypothesized that consuming a high bioactive diet would increase microbial diversity compared to a low bioactive diet.METHODS: Each volunteer completed 4 study visit days, 1 at the start and 1 at the end of each intervention phase. Dietary intake, including bioactives, was recorded through the Libro app linked to Nutritics. We recorded diets during a 7-d period (baseline habitual diet) followed by a 14-d intervention period per arm. On each study day, volunteers provided a 24 h urine sample collected in the 24 h preceding their visit and a stool sample. Urine and stool samples were utilized for both untargeted and targeted metabolomics. Microbial composition was assessed through whole-genome shotgun sequencing carried out by GeneWiz, performed using Illumina HiSeq 2500 platform with paired-end read length 2 x 125 bp. For vascular health, total HDL/LDL cholesterol and triglycerides, along with inflammatory markers such as hs-CRP, were analyzed with an at-home finger-prick blood sample collection kit (Medichecks). Participants also carried out an oral glucose tolerance test; glucose measurements were provided through the Freestyle Libre continuous glucose monitor (Abbott Laboratories) over a 2 h period following ingestion of 113 mL of polycal (75 g anhydrous glucose). Untargeted metabolomics is reported in the current study <a href='https://www.ebi.ac.uk/metabolights/MTBLS8100' rel='noopener noreferrer' target='_blank'>MTBLS8100</a>.Targeted metabolomics is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS7290' rel='noopener noreferrer' target='_blank'>MTBLS7290</a>. Linked cross omic data sets:Whole genome sequencing data associated with this study are available in the European Nucleotide Archive (ENA): accession number <a href='https://www.ebi.ac.uk/ena/browser/view/PRJEB55607' rel='noopener noreferrer' target='_blank'>PRJEB55607</a>.Metagenomic data associated with this study are available from MGnify: accession number <a href='https://www.ebi.ac.uk/metagenomics/studies/MGYS00006076' rel='noopener noreferrer' target='_blank'>MGYS00006076</a>.

Dataset Information

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.

Dataset's files

Publications

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.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets