Evidence map›Paper›PMID 37276937›Full record

Trial reportThe Journal of nutrition2023

Metabolomic Profiling of an Ultraprocessed Dietary Pattern in a Domiciled Randomized Controlled Crossover Feeding Trial.

Lauren E O'Connor, Kevin D Hall, Kirsten A Herrick, Jill Reedy, Stephanie T Chung, Michael Stagliano, Amber B Courville, Rashmi Sinha, Neal D Freedman, Hyokyoung G Hong and 2 more

2 registry-linked trialsOpen access · greenAbstract readRandomized Controlled Trial
In one paragraph

Trial report in The Journal of nutrition, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 30 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 1 pooled it
9.6field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT03407053 nacompletednot on this map

Effect of Ultra Processed Versus Unprocessed Diets on Energy Intake

TypeinterventionalSponsorNational Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)Ran2018 to 2020Enrolled20ConditionsHealthy DietArmsUltra-processed diet, Unprocessed diet
NCT03878108 nacompletednot on this map

Effect of Low Fat Versus Low Carbohydrate Diets on Energy Metabolism

TypeinterventionalSponsorNational Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)Ran2019 to 2020Enrolled21ConditionsHealthy, Healthy VolunteersArmsLCHF diet, LFHC diet
3 · Its place in the literature

Who cites it

30 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Trial
  4. Trial
  5. Article
  6. Review
  7. Review
  8. Article
  9. Association of age-related macular degeneration with exposome related metabolomics.Metabolomics : Official journal of the Metabolomic Society · 2026
    Article
  10. Article
  11. Review
  12. Article
  13. High-dimensional partial linear model with trend filtering.Electronic journal of statistics · 2026
    Article
  14. Article
  15. Article
  16. The role of ultra-processed food in obesity.Nature reviews. Endocrinology · 2025
    Review
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 4 institutions in 1 country.

Lauren E O'ConnorFood Components and Health Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, USDA, Beltsville, MD, USA; Division of Cancer Control and Population Sciences, Risk Factor Assessment Branch, NCI, Bethesda, MD, USA.
Kevin D HallLaboratory of Biological Modeling, NIDDK, Bethesda, MD, USA.
Kirsten A HerrickDivision of Cancer Control and Population Sciences, Risk Factor Assessment Branch, NCI, Bethesda, MD, USA.
Jill ReedyDivision of Cancer Control and Population Sciences, Risk Factor Assessment Branch, NCI, Bethesda, MD, USA.
Stephanie T ChungDiabetes, Endocrinology, and Obesity Branch, NIDDK, Bethesda, MD, USA.
Michael StaglianoDiabetes, Endocrinology, and Obesity Branch, NIDDK, Bethesda, MD, USA.
Amber B CourvilleDiabetes, Endocrinology, and Obesity Branch, NIDDK, Bethesda, MD, USA.
Rashmi SinhaDivision of Cancer Epidemiology and Genetics, Metabolic Epidemiology Branch, NCI, Bethesda, MD, USA.
Neal D FreedmanDivision of Cancer Epidemiology and Genetics, Metabolic Epidemiology Branch, NCI, Bethesda, MD, USA.
Hyokyoung G HongDivision of Cancer Epidemiology and Genetics, Biostatistics Branch, NCI, Bethesda, MD, USA.
Paul S AlbertDivision of Cancer Epidemiology and Genetics, Biostatistics Branch, NCI, Bethesda, MD, USA.
Erikka LoftfieldDivision of Cancer Epidemiology and Genetics, Metabolic Epidemiology Branch, NCI, Bethesda, MD, USA. Electronic address: erikka.loftfield@nih.gov.
Division of Cancer Epidemiology and GeneticsNational Institute of Diabetes and Digestive and Kidney Diseases · USAgricultural Research Service · USNational Institutes of Health · US

Funding

Physical Activity, Energy Balance, and CancerZIACP010197 · NCI · DIVISION OF CANCER EPIDEMIOLOGY AND GENETICS · PI ALBANES, DEMETRIUS · 2009 to 2025
$17.5M
Modeling interactions of diet, exercise, metabolism, and weight change in humansZIADK013037 · NIDDK · NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES · PI DARCEY, VALERIE · 2009 to 2025
$10.8M
6 · The paper itself

Abstract

backgroundObjective markers of ultraprocessed foods (UPF) may improve the assessment of UPF intake and provide insight into how UPF influences health.

objectivesTo identify metabolites that differed between dietary patterns (DPs) high in or void of UPF according to Nova classification.

methodsIn a randomized, crossover, controlled-feeding trial (clinicaltrials.govNCT03407053), 20 domiciled healthy participants (mean ± standard deviation: age 31 ± 7 y, body mass index [kg/m

resultsAfter multiple comparisons correction, 257 out of 993 plasma and 606 out of 1279 24-h urine metabolites differed between UPF-DP and UN-DP. Overall, 21 known and 9 unknown metabolites differed between DPs across all time points and biospecimen types. Six metabolites were higher (4-hydroxy-L-glutamic acid, N-acetylaminooctanoic acid, 2-methoxyhydroquinone sulfate, 4-ethylphenylsulfate, 4-vinylphenol sulfate, and acesulfame) and 14 were lower following the UPF-DP; pimelic acid, was lower in plasma but higher in urine following the UPF-DP.

conclusionsConsuming a DP high in, compared with 1 void of, UPF has a measurable impact on the short-term human metabolome. Observed differential metabolites could serve as candidate biomarkers of UPF intake or metabolic response in larger samples with varying UPF-DPs. This trial was registered at clinicaltrials.gov as NCT03407053 and NCT03878108.

Indexed as

DietMetabolomicsAdultBody Mass IndexEnergy IntakeFast FoodsFoodFood HandlingHumansYoung Adultdietary assessmentdietary biomarkersdietary patternsfood processingmetabolomicsplasma metabolitesprocessed foodsUnited States adultsurine metabolites

Identifiers

PMID37276937
PMCPMC10447619
OpenAlexW4379209643

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.