Evidence map›Paper›PMID 41577034›Full record

Trial reportThe Journal of nutrition2026

Chemical Analysis of Controlled Diets High in and Free of Ultraprocessed Foods and Proof-of-Concept Findings: Reducing Ultraprocessed Food Consumption May Lower Diabetes Risk in Midlife Adults.

Bailey T Capra, Summer Hudson, Katherine M Phillips, Elaina L Marinik, Eleni Laskaridou, Aubrey L Johnson, Evan A Wilson, Mengyi Dong, Lawrence A David, Nolan Ives and 6 more

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in The Journal of nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
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

16 authors.

Bailey T CapraDepartment of Human Nutrition, Foods, and Exercise, Virginia Tech, Blacksburg, VA, United States; Translational Obesity Research Interdisciplinary Graduate Education Program, Virginia Tech Graduate School, Blacksburg, VA, United States. Electronic address: baileytc@vt.edu.
Summer HudsonDepartment of Human Nutrition, Foods, and Exercise, Virginia Tech, Blacksburg, VA, United States.
Katherine M PhillipsBiochemistry Department, Virginia Tech, Blacksburg, VA, United States.
Elaina L MarinikDepartment of Human Nutrition, Foods, and Exercise, Virginia Tech, Blacksburg, VA, United States; Translational Obesity Research Interdisciplinary Graduate Education Program, Virginia Tech Graduate School, Blacksburg, VA, United States.
Eleni LaskaridouDepartment of Human Nutrition, Foods, and Exercise, Virginia Tech, Blacksburg, VA, United States; Translational Obesity Research Interdisciplinary Graduate Education Program, Virginia Tech Graduate School, Blacksburg, VA, United States.
Aubrey L JohnsonDepartment of Human Nutrition, Foods, and Exercise, Virginia Tech, Blacksburg, VA, United States; Translational Obesity Research Interdisciplinary Graduate Education Program, Virginia Tech Graduate School, Blacksburg, VA, United States.
Evan A WilsonDepartment of Human Nutrition, Foods, and Exercise, Virginia Tech, Blacksburg, VA, United States; Translational Obesity Research Interdisciplinary Graduate Education Program, Virginia Tech Graduate School, Blacksburg, VA, United States.
Mengyi DongDepartment of Molecular Genetics & Microbiology, Duke University School of Medicine, Durham, NC, United States; Duke Microbiome Center, Duke University, Durham, NC.
Lawrence A DavidDepartment of Molecular Genetics & Microbiology, Duke University School of Medicine, Durham, NC, United States; Duke Microbiome Center, Duke University, Durham, NC.
Nolan IvesDepartment of Molecular Genetics & Microbiology, Duke University School of Medicine, Durham, NC, United States; Duke Microbiome Center, Duke University, Durham, NC.
Jordan TroobDepartment of Molecular Genetics & Microbiology, Duke University School of Medicine, Durham, NC, United States; Duke Microbiome Center, Duke University, Durham, NC.
Jun ZengDepartment of Molecular Genetics & Microbiology, Duke University School of Medicine, Durham, NC, United States; Duke Microbiome Center, Duke University, Durham, NC.
Jyoti T SavlaTranslational Obesity Research Interdisciplinary Graduate Education Program, Virginia Tech Graduate School, Blacksburg, VA, United States; Center for Gerontology, College of Liberal Arts and Human Sciences, Virginia Tech, Blacksburg, VA, United States.
Valisa E HedrickDepartment of Human Nutrition, Foods, and Exercise, Virginia Tech, Blacksburg, VA, United States; Translational Obesity Research Interdisciplinary Graduate Education Program, Virginia Tech Graduate School, Blacksburg, VA, United States.
Kevin P DavyDepartment of Human Nutrition, Foods, and Exercise, Virginia Tech, Blacksburg, VA, United States; Translational Obesity Research Interdisciplinary Graduate Education Program, Virginia Tech Graduate School, Blacksburg, VA, United States.
Brenda M DavyDepartment of Human Nutrition, Foods, and Exercise, Virginia Tech, Blacksburg, VA, United States; Translational Obesity Research Interdisciplinary Graduate Education Program, Virginia Tech Graduate School, Blacksburg, VA, United States.

Funding

Vascular Consequences of Ultra-Processed Foods in Middle-Aged AdultsR21AG075930 · NIA · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI DAVY, KEVIN P · 2022 to 2023
$455k
Ultra-processed food consumption, gut microbiota, and glucose homeostasis in mid-life adultsR21AG077143 · NIA · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI DAVY, BRENDA M · 2022 to 2023
$455k
NIA NIH HHS R21 AG075930NIA NIH HHS R21 AG077143
6 · The paper itself

Abstract

backgroundUltraprocessed food (UPF) intake is associated with increased type 2 diabetes (T2D) risk. No controlled feeding trial has investigated UPF exposure and T2D risk or performed chemical analysis of UPF study diets.

objectivesTo design and chemically validate nutritionally matched high- and non-UPF diets and to examine the effects of a 6-wk high-UPF diet on T2D risk in midlife adults.

methodsHigh-UPF (81% UPF energy) and non-UPF (0% UPF energy) diets were designed and chemically analyzed to validate energy, macro- and micronutrients, and other dietary components. Plant-based ingredients in the diet were also assessed by FoodSeq. After a 2-wk standardized lead-in diet (59% UPF), 18 adults aged 40 to 65 y were randomly assigned to a eucaloric high-UPF or non-UPF diet for 6 wk. Insulin sensitivity and 24-h glycemic control were measured at baseline and post intervention. Serum global metabolomic profiles were evaluated.

resultsThe high-UPF and non-UPF diets were well-matched and consistent with planned energy and nutrient targets. FoodSeq indicated that the high-UPF diet showed more frequent detection of UPF ingredients, such as guar (gum) and corn (corn starch and corn meal). There were no changes in Matsuda Index or HOMA-IR in the pilot trial, however glucose AUC (high-UPF: 13431 ± 3914 mg·min/dL to 13656 ± 4005 mg·min/dL; non-UPF: 15349 ± 4068 mg·min/dL to 14,221 ± 3722 mg·min/dL, P = 0.054; ES = 0.52) and mean amplitude of glycemic excursions (MAGE) (high-UPF: 37.6 ± 10.1 mg/dL to 40.2 ± 7.3 mg/dL; non-UPF: 44.5 ± 11.0 mg/dL to 39.3 ± 9.5mg/dL, P = 0.055; ES = 0.51) tended to worsen in the high-UPF vs non-UPF groups. In the non-UPF group, reductions in the food contact chemical 2,4-ditert-butylphenol and in the thermal food processing by-product N6-carboxymethyllysine were observed.

conclusionsFindings provide preliminary evidence that reducing UPF may reduce T2D risk. Large-scale trials are warranted to evaluate causal effects.

Indexed as

Diabetes Mellitus, Type 2DietFood HandlingFood, ProcessedAdultAgedBlood GlucoseFemaleHumansInsulin ResistanceMaleMiddle AgedProof of Concept StudyBlood Glucosegut dysbiosisinflammationinsulin resistancetype 2 diabetesultraprocessed food

Identifiers

PMID41577034
PMCPMC12951216

What OpenQuestion holds

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Registered trials

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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.