Evidence map›Paper›PMID 42163955›Full record

ReviewFrontiers in nutrition2026

The collapse of the food matrix: how ultra-processed foods impact satiety and metabolism by altering physical structure beyond nutrient composition.

Xinna Wang, Hang Chen, Yan Xu, Qiaoli Xu, Hongtao Cui, Fang Liu

Abstract readReview
In one paragraph

Review in Frontiers in nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Xinna WangDepartment of Encephalopathy, Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, China.
Hang ChenDepartment of Pediatrics, The First Affiliated Hospital, Henan University of Chinese Medicine, Zhengzhou, Henan, China.
Yan XuDepartment of Pediatrics, The First Affiliated Hospital, Henan University of Chinese Medicine, Zhengzhou, Henan, China.
Qiaoli XuDepartment of Encephalopathy, Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, China.
Hongtao CuiDepartment of Pediatrics, Chongqing Traditional Chinese Medicine Hospital, Chongqing, China.
Fang LiuThe First Affiliated Hospital of Chongqing University of Chinese Medicine, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The global consumption of ultra-processed foods (UPFs) is strongly associated with the prevalence of non-communicable diseases (NCDs). This link has traditionally been attributed to their poor nutritional profiles. However, evidence shows that even when nutrient-matched, UPFs promote excess energy intake and weight gain, suggesting a pathogenic mechanism beyond their chemical composition. This review proposes a central conceptual framework: the core threat of UPFs to health may originate profoundly from the industrial collapse of their physical "food matrix." While evidence-informed, this framework remains a conceptual proposition requiring further causal validation. We hypothesize that this structural disintegration triggers a proposed top-down cascade of dysregulation. In the oral phase, a soft matrix accelerates eating rates by reducing chewing requirements, thereby weakening early satiety signals. In the gastrointestinal tract, the excessively rapid absorption of nutrients suppresses the secretion of distal gut satiety hormones, such as glucagon-like peptide-1 (GLP-1) and peptide YY (PYY). This supraphysiological nutrient flux imposes a significant challenge on core metabolic organs, driving insulin resistance and hepatic

Indexed as

food matrixmetabolismphysical structuresatietyultra-processed foods

Identifiers

PMID42163955
PMCPMC13185557

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.