Evidence map›Paper›PMID 39881624›Full record

ArticlePublic health nutrition2025

Eighty-five percent of menu items from the six highest selling fast-food restaurants in the USA are ultra-processed.

Anthony J Basile, Nereus K Noshirwani, Karen L Sweazea

Abstract read
In one paragraph

Article in Public health nutrition, 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. African American Food Environments and Anti-Inflammatory Intake in Pregnancy.International journal of environmental research and public health · 2026
    Article
  2. 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

3 authors.

Anthony J BasileSchool of Life Sciences, Arizona State University, Tempe, AZ, USA.ORCID 0000-0001-5742-7468
Nereus K NoshirwaniSchool of Life Sciences, Arizona State University, Tempe, AZ, USA.
Karen L SweazeaSchool of Life Sciences, Arizona State University, Tempe, AZ, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveWhile fast-food is typically considered highly processed, an analysis to demonstrate this has yet to be conducted. Therefore, the objective of this research was to examine the menu items and ingredients from six fast-food restaurant menus using the NOVA classification.

designCross-sectional study.

settingData were collected from the top six highest selling US restaurants, per each food category, identified using the Quick Service and Fast Casual Restaurants (QSR) 2020 report.

participantsA total of 740 menu items were identified and classified according to their degree of processing based on ingredient lists using the NOVA classification: minimally processed (MPF), culinary processed ingredient (CPF), processed (PRF) or ultra-processed (UPF). In addition, individual ingredients that appeared on at least three menus were classified into NOVA groups, and the twenty most common ingredients were identified based on frequency of appearance in ingredient lists.

resultsAcross all menus, 85 % (range: 70-94 %) of items were UPF with only 11 % (range: 6-25 %) being MPF (

conclusionsThese findings show that the vast majority of menu items from major fast-food restaurants are UPF, and there are few options for MPF. Fast-food companies should consider reformulation or the addition of MPF to the menu to increase healthful food options for their patrons.

Indexed as

Fast FoodsFood HandlingFood IngredientsRestaurantsCross-Sectional StudiesHumansUnited StatesFood IngredientsFast-foodFood processingQuick serviceRestaurantUltra-processed foods

Identifiers

PMID39881624
PMCPMC11983999

What OpenQuestion holds

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LicenceCC BY
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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.