Evidence map›Paper›PMID 39997714›Full record

ArticleMetabolites2025

Clustering-Based Identification of BMI-Associated Metabolites with Mechanistic Insights from Network Analysis in Korean Men.

JooYong Park, Jihyun Kang, Ji-Yeoun Lee, Daehee Kang, Joo-Youn Cho, Ji-Yeob Choi

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In one paragraph

Article in Metabolites, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

JooYong ParkDepartment of Big Data Medical Convergence, Eulji University, Seongnam-si 13135, Republic of Korea.ORCID 0000-0002-6444-3754
Jihyun KangDepartment of Clinical Pharmacology and Therapeutics, College of Medicine and Hospital, Seoul National University, Seoul 03080, Republic of Korea.
Ji-Yeoun LeeDepartment of Big Data Medical Convergence, Eulji University, Seongnam-si 13135, Republic of Korea.
Daehee KangDepartment of Biomedical Sciences, Seoul National University Graduate School, Seoul 03080, Republic of Korea.
Joo-Youn ChoDepartment of Biomedical Sciences, Seoul National University Graduate School, Seoul 03080, Republic of Korea.ORCID 0000-0001-9270-8273
Ji-Yeob ChoiDepartment of Biomedical Sciences, Seoul National University Graduate School, Seoul 03080, Republic of Korea.

Funding

the National Research Foundation of Korea grants funded by the Korean government NRF-2018R1A2A3075397 and NRF-2022R1A2B5B01002471
6 · The paper itself

Abstract

backgroundEpidemiological studies using metabolomics often encounter challenges due to metabolite profiles being influenced by multiple modifiable behavioral factors, including regular exercise, smoking, drinking, and weight control. This study aimed to identify modifiable behavioral factors reflected in metabolites by clustering subjects based on their metabolite profiles. Networks of metabolites were constructed to visualize their relationships and the differences between clustering groups.

methodsSixty-four healthy men were included in this study. Information on regular exercise, smoking, and drinking was collected by questionnaires, and body mass index (BMI), an indicator of weight control, was calculated based on measured height and weight. Through targeted metabolomics, the concentrations of 149 metabolites were quantified. Subjects were clustered using the k-means method based on metabolite composition. Correlation-based networks were constructed for each cluster using Cytoscape software, followed by network analysis.

resultsThe subjects were divided into two clusters, with BMI identified as a distinguishing feature. Four lyso-phosphatidylcholines (PCs), six diacyl-PCs, and one acyl-alkyl-PC were positively associated with BMI. In the constructed network, acyl-alkyl-PCs exhibited the highest degrees, suggesting their central role in BMI-associated metabolic pathways.

conclusionsThese findings suggest that metabolites can reflect behavioral factors, with BMI exerting a significant influence on metabolite profiles, particularly through its associations with phosphatidylcholines.

Indexed as

clusteringmodifiable behaviornetwork analysistargeted metabolomics

Identifiers

PMID39997714
PMCPMC11857321

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