Evidence map›Paper›PMID 39001935›Full record

ArticleDiabetologia2024

Ultra-processed food consumption and risk of diabetes: results from a population-based prospective cohort.

Shutong Du, Valerie K Sullivan, Michael Fang, Lawrence J Appel, Elizabeth Selvin, Casey M Rebholz

Abstract read
In one paragraph

Article in Diabetologia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Not all ultra-processed foods are created equal: a review.BMJ nutrition, prevention & health · 2026
    Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. 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

6 authors.

Shutong DuWelch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA.ORCID http://orcid.org/0000-0001-8072-1075
Valerie K SullivanWelch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA.ORCID http://orcid.org/0000-0002-2321-0188
Michael FangWelch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA.ORCID http://orcid.org/0000-0003-2849-1780
Lawrence J AppelWelch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA.ORCID http://orcid.org/0000-0002-0673-6823
Elizabeth SelvinWelch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA.ORCID http://orcid.org/0000-0001-6923-7151
Casey M RebholzWelch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA. crebhol1@jhu.edu.ORCID http://orcid.org/0000-0002-5442-8745

Funding

THE ATHEROSCLEROSIS RISK IN COMMUNITIES (ARIC) STUDY - COORDINATING CENTER - TASK AREA B.2 AND B.375N92022D00001 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI COUPER, DAVID · 2022 to 2025
$13.7M
THE ATHEROSCLEROSIS RISK IN COMMUNITIES (ARIC) STUDY - FIELD CENTER - TASK ORDER 01, TASK AREA A75N92022D00003 · NHLBI · UNIVERSITY OF MINNESOTA · PI LUTSEY, PAMELA · 2022 to 2025
$5.1M
THE ATHEROSCLEROSIS RISK IN COMMUNITIES (ARIC) STUDY - FIELD CENTER - TASK ORDER 01, TASK AREA A75N92022D00005 · NHLBI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI WAGENKNECHT, LYNNE · 2022 to 2025
$5.0M
THE ATHEROSCLEROSIS RISK IN COMMUNITIES (ARIC) STUDY - FIELD CENTER - TASK ORDER 01, TASK AREA A75N92022D00004 · NHLBI · UNIVERSITY OF MISSISSIPPI MED CTR · PI WINDHAM, BEVERLY GWEN · 2022 to 2025
$4.8M
THE ATHEROSCLEROSIS RISK IN COMMUNITIES (ARIC) STUDY - FIELD CENTER - TASK ORDER 01, TASK AREA A75N92022D00002 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI CORESH, JOSEF · 2022 to 2025
$4.7M
Discovery, Replication, and Validation of Biomarkers of the DASH Diet and HypertensionR01HL153178 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI REBHOLZ, CASEY MARIE · 2021 to 2024
$3.1M
Research and Mentoring in the Clinical Epidemiology of Cardiovascular Disease and Type 2 DiabetesK24HL152440 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI SELVIN, ELIZABETH · 2020 to 2024
$612k
Disparities in the management and prognosis of type 1 diabetesK01DK138273 · NIDDK · JOHNS HOPKINS UNIVERSITY · PI Michael Fang · 2024 to 2026
$470k
NHLBI NIH HHS 75N92022D00001NHLBI NIH HHS 75N92022D00002NHLBI NIH HHS 75N92022D00003NHLBI NIH HHS 75N92022D00004NHLBI NIH HHS 75N92022D00005NHLBI NIH HHS K24 HL152440NHLBI NIH HHS R01 HL153178NIDDK NIH HHS K01 DK138273
6 · The paper itself

Abstract

aims/hypothesisUnderstanding the impact of the overall construct of ultra-processed foods on diabetes risk can inform dietary approaches to diabetes prevention. In this study, we aimed to evaluate the association between ultra-processed food consumption and risk of diabetes in a community-based cohort of middle-aged adults in the USA. We hypothesised that a higher intake of ultra-processed foods is associated with a higher risk of incident diabetes.

methodsThe study included 13,172 participants without diabetes at baseline (1987-1989) in the Atherosclerosis Risk in Communities (ARIC) study. Dietary intake was assessed with a 66-item semiquantitative food frequency questionnaire, and foods were categorised by processing level using the Nova classification system. Ultra-processed food was analysed categorically (quartiles of energy-adjusted intake) and continuously (per one additional serving/day). We used Cox regression to evaluate the association of ultra-processed food intake with risk of diabetes with adjustment for sociodemographic characteristics, total energy intake, health behaviours and clinical factors.

resultsOver a median follow-up of 21 years, there were 4539 cases of incident diabetes. Participants in the highest quartile of ultra-processed food intake (8.4 servings/day on average) had a significantly higher risk of diabetes (HR 1.13; 95% CI 1.03, 1.23) compared with participants in the lowest quartile of intake after adjustment for sociodemographic, lifestyle and clinical factors. Each additional serving of ultra-processed food consumed daily was associated with a 2% higher risk of diabetes (HR 1.02; 95% CI 1.00, 1.04). Highest quartile consumption of certain ultra-processed food groups, including sugar- and artificially sweetened beverages, ultra-processed meats and sugary snacks, was associated with a 29%, 21% and 16% higher risk of diabetes, respectively, compared with the lowest quartile. CONCLUSIONS/

interpretationWe found that a higher intake of ultra-processed food was associated with higher risk of incident diabetes, particularly sugar- and artificially sweetened beverages, ultra-processed meats and sugary snacks. Our findings suggest interventions reducing ultra-processed food consumption and specific food groups may be an effective strategy for diabetes prevention.

Indexed as

Fast FoodsCohort StudiesDiabetes MellitusDietEnergy IntakeFemaleFood HandlingFood, ProcessedHumansMaleMiddle AgedProspective StudiesRisk FactorsARIC studyDiabetes preventionDiet and nutritionNova classificationSugar-sweetened beveragesUltra-processed food

Identifiers

PMID39001935
PMCPMC11559431

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.