Evidence map›Paper›PMID 41617090›Full record

ArticleThe American journal of clinical nutrition2026

Comparison of food processing classification frameworks in the NHANES 2017-2018.

Shutong Du, Jiaqi Yang, Valerie K Sullivan, Jiahuan Helen He, Michael Fang, Lawrence J Appel, Casey M Rebholz

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in The American journal of clinical nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

7 authors.

Shutong DuWelch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, United States; Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States.
Jiaqi YangWelch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, United States; Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States.
Valerie K SullivanWelch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, United States; Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States.
Jiahuan Helen HeWelch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, United States; Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States.
Michael FangWelch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, United States; Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States.
Lawrence J AppelWelch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, United States; Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States; Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Casey M RebholzWelch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, United States; Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States; Division of Nephrology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States. Electronic address: crebhol1@jhu.edu.

Funding

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
NHLBI NIH HHS R01 HL153178
6 · The paper itself

Abstract

backgroundFood processing has become an important focus in nutrition science due to its link to adverse health outcomes. Evaluating different classification systems provides valuable insights into how food processing is defined and its implications for nutrition research, cardiometabolic health, and food policy.

objectivesWe aim to compare the NOVA, International Agency for Research on Cancer (IARC), International Food Information Council (IFIC), and University of North Carolina classification systems by evaluating their characteristics, reliability, and associations with nutrient intake and cardiometabolic risk factors.

methodsUsing data from 4392 adult participants in the 2017 to 2018 NHANES cycle, we categorized 4605 food and beverage items according to 4 classification systems and harmonized processing levels into 3 categories: minimally processed, processed, and highly processed/formulated foods. Sociodemographic characteristics, nutrient profiles, and associations with cardiometabolic risk factors were analyzed using linear regression models that accounted for survey weights.

resultsThe mean age of the population was 47 y, 52% were female, and 63% were white. Inter-rater agreement between 2 coders ranged from 68% to 86%. IARC classified the largest proportion of food and beverage items as highly processed/formulated, whereas IFIC classified the lowest. Across all systems, the top contributors to highly processed/formulated food intake were grain products; meat, poultry, fish, and mixtures; and sugars, sweets, and beverages. Greater intake of highly processed/formulated foods was consistently associated with younger age, White and Black race, lower education, and lower intake of protein, fiber, and several micronutrients. All systems linked greater highly processed/formulated food consumption to higher body mass index and elevated levels of log-transformed high-sensitivity C-reactive protein.

conclusionsDespite differences in classification criteria across systems, associations with nutrients, sociodemographic, and cardiometabolic parameters were consistent. A standardized classification system is needed to ensure a unified understanding of ultraprocessed food and its health implications.

Indexed as

Food HandlingNutrition SurveysAdultCardiometabolic Risk FactorsFemaleFood, ProcessedHumansMaleMiddle AgedUnited Statesdiet and nutritionfood classification systemsInternational Agency for Research on CancerInternational Food Information CouncilNHANESNOVA classificationprocessed foodsultraprocessed foods

Identifiers

PMID41617090
PMCPMC12975295

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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.