Evidence map›Paper›PMID 40259489›Full record

ArticleDiabetes, obesity & metabolism2025

Ultra-processed food consumption among adults with prediabetes and diabetes, 2001-2018.

Zijing Guo, Amelia S Wallace, Mary R Rooney, Dan Wang, Casey M Rebholz, Eurídice Martínez Steele, Vanessa Garcia-Larsen, Julia A Wolfson, Mika Matsuzaki, Elizabeth Selvin and 1 more

Abstract read
In one paragraph

Article in Diabetes, obesity & metabolism, 2025. 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
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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

11 authors.

Zijing GuoWelch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA.ORCID https://orcid.org/0000-0003-0457-811X
Amelia S WallaceWelch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA.
Mary R RooneyWelch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA.
Dan WangWelch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA.
Casey M RebholzWelch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA.
Eurídice Martínez SteeleDepartment of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil.
Vanessa Garcia-LarsenDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Julia A WolfsonDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Mika MatsuzakiDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Elizabeth SelvinWelch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA.
Michael FangWelch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA.

Funding

CARDIOVASCULAR EPIDEMIOLOGY INSTITUTIONAL TRAININGT32HL007024 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI Elizabeth Selvin · 1985 to 2026
$17.5M
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
Systems Science Approaches for Reducing Youth Obesity DisparitiesK01HL165465 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI Mika Matsuzaki · 2023 to 2026
$683k
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
FAPESP 2023/16144-3NHLBI NIH HHS K01 HL165465NHLBI NIH HHS K24 HL152440NHLBI NIH HHS R01 HL153178NHLBI NIH HHS T32 HL007024NIDDK NIH HHS K01 DK138273
6 · The paper itself

Abstract

aimsThe American Diabetes Association recently recommended minimizing ultra-processed food consumption in persons with prediabetes or diabetes to reduce the risk of glycemic progression and clinical complications. We characterized trends in ultra-processed food consumption among these populations using nationally representative data. MATERIALS AND

methodsWe conducted serial cross-sectional analyses in adults ≥20 years with prediabetes or diabetes in the 2001-2018 National Health and Nutrition Examination Survey (NHANES). We defined prediabetes as HbA1c 5.7%-<6.5% and no self-reported diabetes diagnosis and diabetes as HbA1c ≥6.5% or self-reported diabetes diagnosis. We estimated the percent of total energy from ultra-processed food consumption (% kilocalories), as defined by Nova food classification system. We characterized the mean percent of total energy intake from ultra-processed food by diabetes status from 2001 to 2018 and assessed time trends with linear regression.

resultsWe included 16 024 adults (63.7% with prediabetes and 36.3% with diabetes). From 2001-2002 to 2017-2018, ultra-processed food consumption increased in adults with prediabetes (53.8 to 57.3% of kilocalories, p = 0.006) and diabetes (51.9 to 56.6% of kilocalories, p = 0.001). The proportion that consumed at least 75% of total kilocalories through ultra-processed food increased in persons with prediabetes (12.4 to 21.4%) and diabetes (8.9 to 20.5%). Younger adults (<45 years) and those without a college degree had the highest ultra-processed food consumption.

conclusionPatient education, nutritional interventions and nationwide policies are needed to reduce the growing consumption of ultra-processed food in persons with prediabetes and diabetes.

Indexed as

Diabetes MellitusFast FoodsPrediabetic StateAdultAgedCross-Sectional StudiesEnergy IntakeFemaleFood, ProcessedGlycated HemoglobinHumansMaleMiddle AgedNutrition SurveysUnited StatesYoung AdultGlycated Hemoglobinprediabetes, NHANES, trends in consumptiontype 2 diabetesultra‐processed food

Identifiers

PMID40259489
PMCPMC12331211

What Socratic 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 Socratic graph.