Evidence map›Paper›PMID 40447663›Full record

ArticleScientific reports2025

Effect of visceral fat on onset of metabolic syndrome.

Hiroto Bushita, Naoki Ozato, Kenta Mori, Hiromitsu Kawada, Yoshihisa Katsuragi, Noriko Osaki, Tatsuya Mikami, Ken Itoh, Koichi Murashita, Shigeyuki Nakaji and 1 more

Erratum issuedAbstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Hiroto BushitaDepartment of Medical Data Intelligence, Research Center for Health-Medical Data Science, Hirosaki University Graduate School of Medicine, Aomori, Japan.
Naoki OzatoHuman Health Care Products Research Laboratories, Kao Corporation, Tokyo, Japan.
Kenta MoriHuman Health Care Products Research Laboratories, Kao Corporation, Tokyo, Japan.
Hiromitsu KawadaHuman Health Care Products Research Laboratories, Kao Corporation, Tokyo, Japan.
Yoshihisa KatsuragiDepartment of Active Life Promotion Sciences, Hirosaki University Graduate School of Medicine, Aomori, Japan.
Noriko OsakiHuman Health Care Products Research Laboratories, Kao Corporation, Tokyo, Japan.
Tatsuya MikamiDepartment of Preemptive Medicine, Innovation Center for Health Promotion, Hirosaki University Graduate School of Medicine, Aomori, Japan.
Ken ItohDepartment of Stress Response Science, Biomedical Research Center, Hirosaki University Graduate School of Medicine, Aomori, Japan.
Koichi MurashitaResearch Institute of Health Innovation, Hirosaki University, Aomori, Japan.
Shigeyuki NakajiDepartment of Social Medicine, Hirosaki University Graduate School of Medicine, Aomori, Japan.
Yoshinori TamadaDepartment of Medical Data Intelligence, Research Center for Health-Medical Data Science, Hirosaki University Graduate School of Medicine, Aomori, Japan. y.tamada@hirosaki-u.ac.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study analysed the effects of visceral fat on metabolic syndrome (MetS) and developed an algorithm to predict its onset using health examination data from the Iwaki Health Promotion Project in Japan. The dataset included 213 cases of MetS onset within three years and 1320 non-onset cases. The data was split into training and test sets with an 8:2 ratio. In the training set, the MetS onset group had significantly higher visceral fat area than the non-onset group (p < 0.00001). A cut-off value of 82 cm2 for the visceral fat area was determined, with an AUC of 0.86. Additionally, a machine learning algorithm utilizing seven non-invasive factors, including visceral fat, achieved high accuracy with a five-fold cross-validation AUC of 0.90 in the training set and 0.88 in the test set. Visceral fat was identified as the main factor, as supported by the SHAP value. In conclusion, this study found visceral fat to be crucial in predicting the onset of MetS, and a high-accuracy onset prediction algorithm based on non-invasive parameters, including visceral fat, was developed.

Indexed as

Intra-Abdominal FatMetabolic SyndromeAdultAgedAlgorithmsFemaleHumansJapanMachine LearningMaleMiddle AgedEarly detectionMachine learningMetabolic syndrome (MetS)Onset prediction modelsSHapley additive exPlanations (SHAP)Visceral fat

Identifiers

PMID40447663
PMCPMC12125335

What Socratic holds

Textmetadata
LicenceCC BY
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.