Evidence map›Paper›PMID 37036868›Full record

ArticlePloS one2023

The association between body image perception and metabolic syndrome in normal-weight adults.

Jieun Shin, Sungjung Kwak

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2023. 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
0.4field-weighted citation impact, top 31% of its field
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, 2 citations in OpenAlex.

  1. 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

2 authors at 2 institutions in 1 country.

Jieun ShinDepartment of Biomedical Informatics, College of medicine, Konyang University, Daejeon, Republic of Korea.ORCID 0000-0002-6192-2610
Sungjung KwakDepartment of Nursing, Howon University, Gunsan, Republic of Korea.ORCID 0000-0002-4092-3948
Howon University · KRKonyang University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimThis study analyzed the association between metabolic syndrome and its components according to body image perception in normal-weight adults.

backgroundManagement of chronic diseases that are the main cause of death in Korea is essential. The representative cause of the increase in these chronic diseases is metabolic syndrome, and preventing it is essential for chronic disease management.

methodsFor data analysis, this study used data from Korea National Health and Nutritional Examination Survey (KNHANES-VII), 2016-2019. A total of 6479 survey respondents with normal BMI, aged 19 to 64 years old, were finally selected for analysis. Analysis was performed separately for men and women because gender-dependent differences were observed in the self-perceived body image types (underestimation, congruence, and overestimation groups). The incidence and risk of metabolic syndrome-related indicators according to body image perception were analyzed.

resultsAs a result, it was found that men tending toward underestimation had lower prevalence and risk of metabolic syndrome and women tending toward overestimation had higher prevalence and risk of metabolic syndrome.

conclusionThis suggests that even individuals with the same BMI can have effects on the prevalence and risk of metabolic syndrome depending on their body image perception. This allows the conclusion that subjective body image perception can function as a supplementary predictor of metabolic syndrome.

Indexed as

Body ImageMetabolic SyndromeAdultBody Mass IndexBody WeightFemaleHumansMaleMiddle AgedNutrition SurveysRepublic of KoreaYoung Adult

Identifiers

PMID37036868
PMCPMC10085018
OpenAlexW4363645522

What Socratic holds

Textmetadata
LicenceCC BY
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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.